Radiation control method and device applied to mobile C-shaped arm, equipment and medium

Abstract

The invention relates to a radiation control method and device applied to a mobile C-shaped arm, equipment and a medium, and the method comprises the steps: obtaining the radiation data of each direction of a to-be-detected region corresponding to a skeleton instrument, and determining the radiation intensity of the to-be-detected region according to the radiation data of each direction; under the condition that the radiation intensity exceeds a preset threshold value, according to a difference value between the radiation intensity and the preset threshold value, obtaining an adjustment parameter through a trained neural network PID model; and adjusting operation parameters of the skeleton instrument according to the adjustment parameters. The C-shaped arm skeleton instrument has the effect of improving the anti-radiation performance and safety of the C-shaped arm skeleton instrument.

Description

Technical Field

[0001] The present invention relates to the technical field of bone instruments, and in particular to a radiation control method, device, equipment and medium applied to a mobile C-arm. Background Art

[0002] At present, among medical imaging devices, mobile C-arms are widely used in the medical field, especially in operating rooms and other places to provide high-quality X-ray images. However, mobile C-arms generate radiation during operation, posing a potential threat to medical staff and patients. If the device fails to effectively control the radiation dose during operation, it will not only cause additional harm to the patient, but also pose a potential health risk to the operating doctor. Since the mobile C-arm bone instrument has poor calculation and processing effect on the radiation intensity within the sensing range during use, the radiation control effect is poor.

[0003] The above-mentioned existing technical solutions have the following defects: when the mobile C-arm bone instrument is in use, the calculation and processing of the radiation intensity within the sensing range is poor, resulting in poor radiation control effect, and thus failing to provide timely radiation warning and protection for medical staff and patients, so there is room for improvement. Summary of the invention

[0004] In order to improve the radiation protection performance and safety of a C-arm bone instrument, the present application provides a radiation control method, device, equipment and medium applied to a mobile C-arm.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A radiation control method applied to a mobile C-arm, the radiation control method applied to a mobile C-arm comprising: obtaining radiation data of each orientation of a to-be-detected area corresponding to the bone instrument, and determining the radiation intensity of the to-be-detected area according to the radiation data of each orientation; When the radiation intensity exceeds a preset threshold, an adjustment parameter is obtained through a trained neural network PID model according to a difference between the radiation intensity and the preset threshold; According to the adjustment parameters, the operating parameters of the skeleton instrument are adjusted.

[0006] By adopting the above technical solution, when the skeleton instrument is in working state, the radiation data of the area to be detected can be obtained through the radiometer, and the radiometer is placed in various directions in the area to be detected, and the radiation data of various directions within the range of the area to be detected are detected and recorded, which is helpful to analyze the radiation data of the skeleton instrument, detect whether there is any abnormal radiation data, improve the safety of personnel when using the mobile C-arm skeleton instrument, and help doctors to diagnose and treat patients more accurately; when the radiation data of different directions in the area to be detected is detected, the various radiation data are statistically analyzed, and the radiation data are weighted averaged to obtain the average radiation data within the range of the area to be detected as the radiation intensity data of the area to be detected, which helps doctors to evaluate the radiation level received by the patient and judge whether there is a risk of excessive radiation. By determining the radiation intensity, medical staff can take necessary measures, such as adjusting the treatment plan or taking protective measures, to ensure the safety and health of the patient. At the same time, this also helps to monitor and record the radiation exposure of the patient, and provide a reference for future treatment and prevention; based on When the radiation intensity exceeds the preset threshold, the adjustment parameters are obtained by the trained neural network PID model. This method can realize automatic adjustment of the radiation intensity. When the radiation intensity exceeds the set threshold, the trained neural network PID model will calculate the parameter value that needs to be adjusted according to the difference between the radiation intensity and the preset threshold, so as to adjust the system in time to make the radiation intensity return to a safe range. This method can improve the stability and safety of radiation equipment and ensure that patients and medical staff are protected from adverse effects. At the same time, by training the neural network PID model to obtain the adjustment parameters, the system can also be adjusted more accurately and efficiently for different situations. According to the previously obtained adjustment parameters, the operating parameters of the bone instrument are adjusted, including changing the working mode of the bone instrument, setting the scanning range or adjusting the radiation dose, etc. By adjusting parameters such as voltage data and current data, the operation of the bone instrument can be optimized, its performance can be improved, and the safety of patients and medical staff can be ensured at the same time. Corresponding adjustments can be made according to specific circumstances and needs to meet different detection requirements and treatment needs.

[0007] In a preferred example, the present application can be further configured as follows: the step of obtaining radiation data of each position of the to-be-detected area corresponding to the skeleton instrument includes: Acquire range data of the area to be detected, wherein the range data of the area to be detected includes area boundary data and measurement point position data; and acquire radiation data of each orientation of the area to be detected by a radiometer according to the range data of the area to be detected.

[0008] By adopting the above technical solution, in the process of obtaining the range data of the area to be detected, the range data of the area to be detected includes two parts: area boundary data and measurement point position data. The area boundary data is used to determine the specific range and shape of the area to be detected, and the measurement point position data refers to the specific position coordinates that need to be measured or detected in the area to be detected. These positions are usually determined in advance to ensure comprehensive coverage and accurate measurement of the entire area. The acquisition of these data can be used as a preparatory step for subsequent detection or measurement work to ensure a comprehensive understanding and accurate measurement of the area to be detected; according to the range data of the area to be detected, the radiation data of the area to be detected is obtained by a radiometer. First, according to the area boundary information provided in the range data of the area to be detected, the specific range and position of the area to be detected are determined. Then, the radiometer is used to measure in the area to be detected to obtain radiation data of different directions of the area. These data may include information such as radiation intensity and radiation dose, which is convenient for evaluating the radiation level in the area to be detected. By detecting the radiation data of the area to be detected, detailed data on the radiation level of the area to be detected can be obtained, which is convenient for subsequent abnormal analysis and processing of radiation intensity.

[0009] In a preferred example, the present application may be further configured as follows: determining the radiation intensity of the to-be-detected area according to the radiation data in each orientation includes: The radiation data of each direction is calculated one by one through the weighted average formula: Calculate and obtain the average radiation intensity of the radiation data in each direction, and use the average radiation intensity as the radiation intensity of the area to be detected, where x i is the value of each data point, w i is the weight value of the corresponding data point, Σ represents the sum of all data points, and C is the average radiation intensity.

[0010] By adopting the above technical solution, according to the given radiation data, the value x of each data point is i And the corresponding weight value w i Multiply them, then add up all these product values, and finally divide them by the sum of all weight values ​​to get the average radiation intensity C as the radiation intensity of the area to be detected. The weight value can be set according to the importance of different data points or other factors. The purpose of using the weighted average calculation formula is to obtain a comprehensive assessment of the radiation intensity of the entire area to be detected by weighted averaging the radiation data, so as to more accurately understand the radiation level of the area.

[0011] In a preferred example, the present application may be further configured as follows: before obtaining the adjustment parameters through the trained neural network PID model, training and generating the trained neural network PID model specifically includes: Acquire abnormal radiation intensity data, perform difference calculation between the abnormal radiation intensity data and the preset threshold value to obtain the abnormal radiation difference, and obtain the corresponding standard adjustment parameters, and use the abnormal radiation difference and the standard adjustment parameters as a training data set; use the training data set to train the neural network PID model to obtain the trained neural network PID model.

[0012] By adopting the above technical solution, abnormal radiation intensity data is obtained, that is, data that is abnormal compared with a preset threshold value. Then, the difference between these abnormal radiation intensity data and the preset threshold value is calculated to obtain the abnormal radiation difference. Then, according to the abnormal radiation difference and the preset standard adjustment parameter, the parameter value used to adjust the abnormal situation can be obtained. Finally, these abnormal radiation difference and standard adjustment parameters are used as training data sets to be used for training models or algorithms to identify and handle similar abnormal situations, which helps the system to realize automatic detection and adjustment of abnormal radiation data and improve the stability and reliability of the system; the prepared training data set containing abnormal radiation difference and standard adjustment parameters is input into the neural network PID model, and the weights and parameters of the neural network are continuously adjusted through the training algorithm and optimization method so that it can learn the relationship between the abnormal situation and the adjustment parameters. After a large number of training iterations, the model gradually improves its performance and finally obtains a trained neural network PID model. This trained model can be used for real-time abnormal radiation intensity data analysis and adjustment, helping the system to automatically deal with radiation abnormalities and improve system stability and safety.

[0013] In a preferred example, the present application can be further configured as follows: when the radiation intensity exceeds a preset threshold, obtaining the adjustment parameter through a trained neural network PID model according to the difference between the radiation intensity and the preset threshold includes: When the radiation intensity exceeds the preset threshold, calculating the difference between the radiation intensity and the preset threshold to obtain a radiation intensity difference; The radiation intensity differences are input one by one into the trained neural network PID model for comparison and analysis, and the adjustment parameters are obtained through analysis.

[0014] By adopting the above technical solution, when the radiation intensity exceeds the preset threshold, by calculating the difference between the radiation intensity and the preset threshold, first, in the case where the radiation intensity exceeds the preset threshold, the actually measured radiation intensity will be compared with the preset threshold, and then, by calculating the difference between the two, the difference in radiation intensity can be obtained, which reflects the degree to which the actual radiation intensity exceeds the set threshold. This process can be used to evaluate the abnormal degree of the radiation level so as to further take necessary measures to adjust or process it; the radiation intensity difference is input into the trained neural network PID model one by one, and compared and analyzed to obtain the required adjustment parameters. First, according to the radiation intensity difference calculation described above, these differences are used as inputs one by one and transmitted to the trained neural network PID model. Then, the model will compare and analyze the radiation intensity difference according to the relationship learned during the previous training to determine the appropriate adjustment parameters. These adjustment parameters can help the system adjust the operating parameters of the radiation equipment so that the radiation intensity returns to a safe range and ensures the safety of patients and medical staff. Through this process, the system can realize automatic control and adjustment of real-time radiation intensity and improve the stability and work efficiency of the system.

[0015] In a preferred example, the present application may be further configured as follows: before obtaining the adjustment parameter by using the trained neural network PID model according to the difference between the radiation intensity and the preset threshold, the radiation control method applied to the mobile C-arm further includes: When the radiation intensity exceeds the preset threshold, generating a warning signal; The fault cause analysis is performed on the warning signal to obtain operation suggestion information, and the information is transmitted to the user input terminal.

[0016] By adopting the above technical solution, when the actual radiation intensity monitored exceeds the preset safety threshold, the system will automatically send out a warning signal to remind relevant personnel or systems to carry out emergency processing. The warning signal can take various forms, such as visual warning lights, sound alarms, text message notifications, etc., to remind operators or supervisors to take timely measures to reduce radiation risks and protect personnel health and safety. The generation of warning signals can effectively reduce the occurrence of radiation accidents, timely discover and solve radiation abnormalities, and ensure the safety of work and production environments; the warning signal is analyzed for the cause of the fault, and operation suggestion information is obtained, and this information is transmitted to the user input terminal. Once the system generates a warning signal, the monitoring system that receives the signal will analyze the cause of the fault to determine the specific cause of the warning. According to the analysis results, the system will generate corresponding operation suggestion information, including how to solve the problem, what emergency measures to take, etc. These operation suggestion information will be transmitted to the user input terminal for operators or relevant personnel to view and refer to. Through this process, users can understand the root cause of the problem in a timely manner and take corresponding measures to deal with potential radiation risks and ensure the safety of equipment and environment.

[0017] The second object of the invention is achieved by the following technical solutions: A radiation control device applied to a mobile C-arm, the radiation control device applied to the mobile C-arm comprising: a radiation intensity acquisition module, used to acquire radiation data of each orientation of a to-be-detected area corresponding to the bone instrument, and determine the radiation intensity of the to-be-detected area according to the radiation data of each orientation; A module for obtaining adjustment parameters, used for obtaining adjustment parameters through a trained neural network PID model according to a difference between the radiation intensity and the preset threshold value when the radiation intensity exceeds the preset threshold value; The adjustment module is used to adjust the operating parameters of the skeleton instrument according to the adjustment parameters.

[0018] By adopting the above technical solution, when the skeleton instrument is in working state, the radiation data of the area to be detected can be obtained through the radiometer, and the radiometer is placed in various directions in the area to be detected, and the radiation data within the area to be detected is detected and recorded, which is helpful to analyze the radiation data of the skeleton instrument, detect whether there is any abnormal radiation data, improve the safety of personnel when using the mobile C-arm skeleton instrument, and help doctors to make more accurate diagnosis and treatment of patients; when the radiation data in different directions in the area to be detected is detected, the various radiation data are statistically analyzed, and the radiation data are weighted averaged to obtain the average radiation data within the area to be detected as the radiation intensity data of the area to be detected, which is helpful for doctors to evaluate the radiation level of the patient and determine whether there is a risk of excessive radiation. By determining the radiation intensity, medical staff can take necessary measures, such as adjusting the treatment plan or taking protective measures, to ensure the safety and health of the patient. At the same time, this also helps to monitor and record the radiation exposure of the patient, and provide a reference for future treatment and prevention; according to the radiation When the radiation intensity exceeds the preset threshold, the adjustment parameters are obtained by the trained neural network PID model. This method can realize automatic adjustment of the radiation intensity. When the radiation intensity exceeds the set threshold, the trained neural network PID model will calculate the parameter value that needs to be adjusted according to the difference between the radiation intensity and the preset threshold, so as to adjust the system in time to make the radiation intensity return to a safe range. This method can improve the stability and safety of radiation equipment and ensure that patients and medical staff are protected from adverse effects. At the same time, by training the neural network PID model to obtain the adjustment parameters, the system can also be adjusted more accurately and efficiently for different situations. According to the previously obtained adjustment parameters, the operating parameters of the bone instrument are adjusted, including changing the working mode of the bone instrument, setting the scanning range or adjusting the radiation dose, etc. By adjusting parameters such as voltage data and current data, the operation of the bone instrument can be optimized, its performance can be improved, and the safety of patients and medical staff can be ensured at the same time. Corresponding adjustments can be made according to specific circumstances and needs to meet different detection requirements and treatment needs.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned radiation control method applied to a mobile C-arm when executing the computer program.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned radiation control method applied to a mobile C-arm.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. When the bone instrument is in working state, the radiation data of the area to be detected can be obtained through the radiometer. The radiometer is placed in various positions in the area to be detected, and the radiation data within the area to be detected is detected and recorded, which is helpful for analyzing the radiation data of the bone instrument and detecting whether there is any abnormal radiation data, improving the safety of personnel when using the mobile C-arm bone instrument, and helping doctors to make more accurate diagnosis and treatment of patients; when the radiation data of different positions in the area to be detected is detected, the various radiation data are statistically analyzed, and the radiation data are weighted averaged to obtain the average radiation data within the area to be detected as the radiation data of the area to be detected. Intensity data can help doctors assess the radiation level to which patients are exposed and determine whether there is a risk of excessive radiation. By determining the radiation intensity, medical staff can take necessary measures, such as adjusting treatment plans or taking protective measures, to ensure the safety and health of patients. At the same time, this also helps to monitor and record patients' radiation exposure, providing a reference for future treatment and prevention. When the radiation intensity exceeds the preset threshold, the adjustment parameters are obtained through the trained neural network PID model. This method can realize automatic adjustment of the radiation intensity. When the radiation intensity exceeds the set threshold, the trained neural network PID model will adjust the radiation intensity according to the difference between the radiation intensity and the preset threshold. To calculate the parameter values ​​that need to be adjusted, so as to adjust the system in time and bring the radiation intensity back to a safe range. This method can improve the stability and safety of radiation equipment and ensure that patients and medical staff are protected from adverse effects. At the same time, by training the neural network PID model to obtain the adjustment parameters, the system can also be adjusted more accurately and efficiently in different situations. According to the previously obtained adjustment parameters, the operating parameters of the skeleton instrument are adjusted, including changing the working mode of the skeleton instrument, setting the scanning range or adjusting the radiation dose. By adjusting parameters such as voltage data and current data, the operation of the skeleton instrument can be optimized, its performance can be improved, and at the same time, it can ensure safety for patients and medical staff. Safety, can be adjusted accordingly according to specific circumstances and needs to meet different detection requirements and treatment needs; 2. In the process of obtaining the range data of the area to be detected, the range data of the area to be detected includes two parts: area boundary data and measurement point location data. The area boundary data is used to determine the specific range and shape of the area to be detected, and the measurement point location data refers to the specific location coordinates that need to be measured or detected in the area to be detected. These locations are usually determined in advance to ensure full coverage and accurate measurement of the entire area. The acquisition of these data can be used as a preparatory step for subsequent detection or measurement work to ensure a comprehensive understanding and accurate measurement of the area to be detected;According to the range data of the area to be detected, the radiation data of the area to be detected is obtained by using a radiometer. First, according to the area boundary information provided in the range data of the area to be detected, the specific range and position of the area to be detected are determined. Then, the radiometer is used to measure in the area to be detected to obtain radiation data of different directions of the area. These data may include information such as radiation intensity and radiation dose, which is convenient for evaluating the radiation level in the area to be detected. By detecting the radiation data of the area to be detected, detailed data on the radiation level of the area to be detected can be obtained, which is convenient for subsequent analysis and processing of abnormal radiation intensity; 3. Acquire abnormal radiation intensity data, that is, data that is abnormal compared with a preset threshold value, and then perform difference calculation on these abnormal radiation intensity data and the preset threshold value to obtain abnormal radiation difference. Then, according to this abnormal radiation difference and the preset standard adjustment parameter, the parameter value used to adjust the abnormal situation can be obtained. Finally, these abnormal radiation difference and standard adjustment parameters are used as training data sets to be used for training models or algorithms to identify and handle similar abnormal situations, which helps the system to realize automatic detection and adjustment of abnormal radiation data and improve the stability and reliability of the system. The prepared training data set containing abnormal radiation difference and standard adjustment parameters is input into the neural network PID model. Through the training algorithm and optimization method, the weights and parameters of the neural network are continuously adjusted so that it can learn the relationship between abnormal situations and adjustment parameters. After a large number of training iterations, the model gradually improves its performance, and finally a trained neural network PID model is obtained. This trained model can be used for real-time abnormal radiation intensity data analysis and adjustment, helping the system to automatically respond to radiation abnormalities and improve system stability and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a radiation control method applied to a mobile C-arm in an embodiment of the present application; Figure 2 is a flowchart for implementing step S10 in a radiation control method applied to a mobile C-arm in an embodiment of the present application; Figure 3 is another implementation flow chart of step S10 in the radiation control method applied to a mobile C-arm in one embodiment of the present application; Figure 4 is another implementation flow chart of S30 in the radiation control method applied to a mobile C-arm in one embodiment of the present application; Figure 5 is a flowchart for implementing step S30 in a radiation control method applied to a mobile C-arm in an embodiment of the present application; Figure 6is another implementation flow chart of the radiation control method applied to a mobile C-arm in one embodiment of the present application; Figure 7 It is a principle block diagram of a radiation control device applied to a mobile C-arm in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a radiation control method applied to a mobile C-arm, which specifically includes the following steps: S10: Acquire radiation data of each direction of the to-be-detected area corresponding to the bone instrument, and determine the radiation intensity of the to-be-detected area according to the radiation data of each direction.

[0025] In this embodiment, the radiation data of each orientation of the area to be detected refers to the radiation data of each position in the radiation area of ​​the C-arm bone instrument to be detected.

[0026] Specifically, when the bone analyzer is in working condition, the radiation data of the area to be detected can be obtained through the radiometer. The radiometer is placed at various positions in the area to be detected, and the radiation data within the area to be detected is detected and recorded. The various radiation data are statistically analyzed and weighted averaged to obtain the average radiation data within the area to be detected. This is used as the radiation intensity data of the area to be detected, which helps doctors evaluate the radiation level received by patients and determine whether there is a risk of excessive radiation.

[0027] S20: When the radiation intensity exceeds a preset threshold, an adjustment parameter is obtained through a trained neural network PID model according to a difference between the radiation intensity and the preset threshold.

[0028] In this embodiment, the radiation intensity refers to the radiation intensity in the detection area of ​​the mobile C-arm bone instrument. Specifically, when the radiation intensity exceeds a preset threshold, the adjustment parameter is obtained by using a trained neural network PID model. This method can realize automatic adjustment of the radiation intensity. When the radiation intensity exceeds the set threshold, the trained neural network PID model will calculate the parameter value that needs to be adjusted according to the difference between the radiation intensity and the preset threshold, so as to adjust the system in time to bring the radiation intensity back to a safe range. This method can improve the stability and safety of radiation equipment and ensure that patients and medical staff are protected from adverse effects. At the same time, by training the neural network PID model to obtain the adjustment parameter, the system can also be adjusted more accurately and efficiently in different situations.

[0029] S30: Adjust the operating parameters of the skeleton analyzer according to the adjustment parameters.

[0030] In this embodiment, the operating parameters of the skeleton instrument refer to the operating voltage data, current data and exposure time of the mobile C-arm skeleton instrument.

[0031] Specifically, according to the adjustment parameters obtained previously, the operating parameters of the skeleton instrument are adjusted, including changing the working mode of the skeleton instrument, setting the scanning range or adjusting the radiation dose, etc. By adjusting parameters such as voltage data and current data, the operation of the skeleton instrument can be optimized and its performance can be improved while ensuring the safety of patients and medical staff. Corresponding adjustments can be made according to specific circumstances and needs to meet different detection requirements and treatment needs.

[0032] When the skeleton instrument is in working state, the radiation data of the area to be detected can be obtained through the radiometer. The radiometer is placed in various positions in the area to be detected, and the radiation data within the area to be detected is detected and recorded, which is helpful for analyzing the radiation data of the skeleton instrument and detecting whether there is any abnormal radiation data, thereby improving the safety of personnel when using the mobile C-arm skeleton instrument and helping doctors to make more accurate diagnosis and treatment of patients. When the radiation data in different positions in the area to be detected is detected, the various radiation data are statistically analyzed and weighted averaged to obtain the average radiation data within the area to be detected as the radiation intensity data of the area to be detected, which is helpful for doctors to evaluate the radiation level received by the patient and determine whether there is a risk of excessive radiation. By determining the radiation intensity, medical staff can take necessary measures, such as adjusting the treatment plan or taking protective measures, to ensure the safety and health of the patient. At the same time, this also helps to monitor and record the patient's radiation exposure and provide a reference for future treatment and prevention. Under the preset threshold, the adjustment parameters are obtained by the trained neural network PID model. This method can realize automatic adjustment of the radiation intensity. When the radiation intensity exceeds the set threshold, the trained neural network PID model will calculate the parameter value that needs to be adjusted according to the difference between the radiation intensity and the preset threshold, so as to adjust the system in time to make the radiation intensity return to a safe range. This method can improve the stability and safety of radiation equipment and ensure that patients and medical staff are protected from adverse effects. At the same time, by training the neural network PID model to obtain the adjustment parameters, the system can also be adjusted more accurately and efficiently under different circumstances. According to the previously obtained adjustment parameters, the operating parameters of the bone instrument are adjusted, including changing the working mode of the bone instrument, setting the scanning range or adjusting the radiation dose, etc. By adjusting parameters such as voltage data and current data, the operation of the bone instrument can be optimized and its performance can be improved, while ensuring the safety of patients and medical staff. Corresponding adjustments can be made according to specific circumstances and needs to meet different detection requirements and treatment needs.

[0033] In one embodiment, if Figure 2 As shown, in step S10, the radiation data of each direction of the to-be-detected area corresponding to the skeleton instrument is obtained, which specifically includes: S11: Acquire the range data of the area to be detected, where the range data of the area to be detected includes area boundary data and measurement point position data.

[0034] In this embodiment, the area boundary data refers to the boundary coordinates or boundary information of the area to be detected. The measurement point position data refers to the specific position coordinates that need to be measured or detected in the area to be detected.

[0035] Specifically, in the process of obtaining the range data of the area to be detected, the range data of the area to be detected includes two parts: area boundary data and measurement point position data. The area boundary data is used to determine the specific range and shape of the area to be detected, and the measurement point position data refers to the specific position coordinates that need to be measured or detected in the area to be detected. These positions are usually determined in advance to ensure comprehensive coverage and accurate measurement of the entire area. The acquisition of these data can be used as a preparatory step for subsequent detection or measurement work to ensure a comprehensive understanding and accurate measurement of the area to be detected.

[0036] S12: According to the range data of the area to be detected, the radiation data of the area to be detected is obtained by a radiometer.

[0037] Specifically, based on the range data of the area to be detected, the radiation data of the area to be detected is obtained by a radiometer. First, based on the area boundary information provided in the range data of the area to be detected, the specific range and position of the area to be detected are determined. Then, the radiometer is used to measure in the area to be detected to obtain radiation data from different directions of the area. These data may include information such as radiation intensity and radiation dose, which is convenient for evaluating the radiation level in the area to be detected. By detecting the radiation data of the area to be detected, detailed data on the radiation level of the area to be detected can be obtained, which is convenient for subsequent analysis and processing of abnormal radiation intensity.

[0038] In one embodiment, if Figure 3 As shown, in step S10, the radiation intensity of the area to be detected is determined according to the radiation data in each direction, which specifically includes: S21: The radiation data of each direction is calculated one by one through the weighted average formula: Calculate and obtain the average radiation intensity of the radiation data in each direction, and use the average radiation intensity as the radiation intensity of the area to be detected, where x i is the value of each data point, w i is the weight value of the corresponding data point, Σ represents the sum of all data points, and C is the average radiation intensity.

[0039] Specifically, according to the given radiation data, the value x of each data point is i And the corresponding weight value w i Multiply them, then add up all these product values, and finally divide them by the sum of all weight values ​​to get the average radiation intensity C as the radiation intensity of the area to be detected. The weight value can be set according to the importance of different data points or other factors. The purpose of using the weighted average calculation formula is to obtain a comprehensive assessment of the radiation intensity of the entire area to be detected by weighted averaging the radiation data, so as to more accurately understand the radiation level of the area.

[0040] In one embodiment, if Figure 4 As shown, before step S30, that is, before obtaining the adjustment parameter through the trained neural network PID model according to the difference between the radiation intensity and the preset threshold, training and generating the trained neural network PID model specifically includes: S301: Acquire abnormal radiation intensity data, perform difference calculation between the abnormal radiation intensity data and a preset threshold value to obtain an abnormal radiation difference, and obtain corresponding standard adjustment parameters, and use the abnormal radiation difference and the standard adjustment parameters as a training data set.

[0041] In this embodiment, abnormal radiation intensity data refers to data of radiation intensity exceeding a preset radiation threshold. The training data set refers to a data set for training the initial data model.

[0042] Specifically, abnormal radiation intensity data is obtained, that is, data that is abnormal when compared with a preset threshold value, and then the difference between these abnormal radiation intensity data and the preset threshold value is calculated to obtain the abnormal radiation difference. Then, based on the abnormal radiation difference and the preset standard adjustment parameters, the parameter value for adjusting the abnormal situation can be obtained. Finally, these abnormal radiation difference and standard adjustment parameters are used as training data sets for training models or algorithms to identify and handle similar abnormal situations, which helps the system to realize automatic detection and adjustment of abnormal radiation data and improve the stability and reliability of the system.

[0043] S302: Using the training data set to train the neural network PID model to obtain a trained neural network PID model.

[0044] Specifically, a prepared training data set containing abnormal radiation differences and standard adjustment parameters is input into the neural network PID model. Through training algorithms and optimization methods, the weights and parameters of the neural network are continuously adjusted so that it can learn the relationship between abnormal conditions and adjustment parameters. After a large number of training iterations, the model gradually improves its performance and eventually obtains a trained neural network PID model. This trained model can be used for real-time abnormal radiation intensity data analysis and adjustment, helping the system to automatically respond to radiation abnormalities and improve system stability and safety.

[0045] In one embodiment, if Figure 5 As shown, in step S30, when the radiation intensity exceeds the preset threshold, the adjustment parameters are obtained by the trained neural network PID model according to the difference between the radiation intensity and the preset threshold, which specifically includes: S31: When the radiation intensity exceeds a preset threshold, a difference between the radiation intensity and the preset threshold is calculated to obtain a radiation intensity difference.

[0046] Specifically, when the radiation intensity exceeds the preset threshold, the difference between the radiation intensity and the preset threshold is calculated. First, when the radiation intensity exceeds the preset threshold, the actually measured radiation intensity is compared with the preset threshold. Then, by calculating the difference between the two, the difference in radiation intensity can be obtained. This difference reflects the extent to which the actual radiation intensity exceeds the set threshold. This process can be used to evaluate the degree of abnormality in the radiation level so that further necessary measures can be taken to adjust or process it.

[0047] S32: Input the radiation intensity differences one by one into the trained neural network PID model for comparison and analysis, and obtain the adjustment parameters through analysis.

[0048] Specifically, the radiation intensity differences are input one by one into the trained neural network PID model for comparison and analysis to obtain the required adjustment parameters. First, according to the radiation intensity difference calculation described above, these differences are used as input one by one and transmitted to the trained neural network PID model. Then, the model will compare and analyze the radiation intensity differences based on the relationship learned during previous training to determine the appropriate adjustment parameters. These adjustment parameters can help the system adjust the operating parameters of the radiation equipment to bring the radiation intensity back to a safe range and ensure the safety of patients and medical staff. Through this process, the system can achieve automatic control and adjustment of real-time radiation intensity and improve the stability and work efficiency of the system.

[0049] In one embodiment, if Figure 6 As shown, the radiation control method applied to the mobile C-arm also includes: S51: When the radiation intensity exceeds a preset threshold, a warning signal is generated.

[0050] Specifically, when the actual radiation intensity monitored exceeds the preset safety threshold, the system will automatically issue a warning signal to remind relevant personnel or systems to take emergency measures. The warning signal can take various forms, such as visual warning lights, sound alarms, text message notifications, etc., to remind operators or supervisors to take timely measures to reduce radiation risks and ensure the health and safety of personnel. The generation of warning signals can effectively reduce the occurrence of radiation accidents, promptly detect and resolve radiation abnormalities, and ensure the safety of the work and production environment.

[0051] S52: Analyze the cause of the fault on the warning signal, obtain operation suggestion information, and transmit it to the user input terminal.

[0052] In this embodiment, the operation suggestion information refers to suggestion information for operating the system.

[0053] Specifically, the fault cause analysis of the warning signal is performed to obtain operational suggestion information, and this information is transmitted to the user input terminal. Once the system generates a warning signal, the monitoring system that receives the signal will perform a fault cause analysis to determine the specific cause of the warning. Based on the analysis results, the system will generate corresponding operational suggestion information, including how to solve the problem, what emergency measures to take, etc. These operational suggestion information will be transmitted to the user input terminal for the operator or relevant personnel to view and refer to. Through this process, users can understand the root cause of the problem in a timely manner and take appropriate measures to deal with potential radiation risks and ensure the safety of equipment and the environment.

[0054] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0055] In one embodiment, a radiation control device applied to a mobile C-arm is provided, and the radiation control device applied to the mobile C-arm corresponds one-to-one with the radiation control method applied to the mobile C-arm in the above embodiment. Figure 7 As shown, the radiation control device applied to the mobile C-arm includes a radiation intensity acquisition module, an adjustment parameter acquisition module and an adjustment module. The detailed description of each functional module is as follows: A radiation intensity acquisition module is used to acquire radiation data of each direction of the to-be-detected area corresponding to the bone instrument, and determine the radiation intensity of the to-be-detected area according to the radiation data of each direction; The module for obtaining adjustment parameters is used to obtain the adjustment parameters through the trained neural network PID model according to the difference between the radiation intensity and the preset threshold value when the radiation intensity exceeds the preset threshold value; The adjustment module is used to adjust the operating parameters of the skeleton instrument according to the adjustment parameters.

[0056] Optionally, the radiation acquisition module includes: The submodule for obtaining area range data is used to obtain the area range data to be detected, and the area range data to be detected includes area boundary data and measurement point location data; The radiation detection submodule is used to obtain radiation data of the area to be detected through a radiometer according to the range data of the area to be detected.

[0057] Optionally, the radiation intensity acquisition module includes: The calculation submodule is used to calculate the radiation data of each direction one by one through the weighted average calculation formula: Calculate and obtain the average radiation intensity of the radiation data in each direction, and use the average radiation intensity as the radiation intensity of the area to be detected, where x i is the value of each data point, wi is the weight value of the corresponding data point, Σ represents the sum of all data points, and C is the average radiation intensity.

[0058] Optionally, the radiation control device applied to the mobile C-arm further includes: The training data acquisition module is used to acquire abnormal radiation intensity data, perform difference calculation between the abnormal radiation intensity data and a preset threshold value, obtain the abnormal radiation difference, and obtain the corresponding standard adjustment parameters, and use the abnormal radiation difference and the standard adjustment parameters as a training data set; The training model module is used to train the neural network PID model using the training data set to obtain a trained neural network PID model.

[0059] Optionally, the module for obtaining adjustment parameters includes: The difference calculation submodule is used to calculate the difference between the radiation intensity and the preset threshold value when the radiation intensity exceeds the preset threshold value, so as to obtain the radiation intensity difference value; The parameter submodule is used to input the radiation intensity differences one by one into the trained neural network PID model for comparison and analysis, and obtain the adjustment parameters through analysis.

[0060] Optionally, the radiation control device applied to the mobile C-arm further includes: An early warning module is used to generate an early warning signal when the radiation intensity exceeds a preset threshold; The display module is used to analyze the cause of the fault of the warning signal, obtain the operation suggestion information, and transmit it to the user input terminal.

[0061] For the specific definition of the radiation control device applied to the mobile C-arm, please refer to the definition of the radiation control method applied to the mobile C-arm above, which will not be repeated here. The various modules in the above-mentioned radiation control device applied to the mobile C-arm can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0062] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a radiation control method applied to a mobile C-arm is implemented.

[0063] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtain radiation data of each direction of the area to be detected corresponding to the bone instrument, and determine the radiation intensity of the area to be detected according to the radiation data of each direction; When the radiation intensity exceeds the preset threshold, the adjustment parameters are obtained through the trained neural network PID model according to the difference between the radiation intensity and the preset threshold; Adjust the operating parameters of the skeleton analyzer according to the adjustment parameters.

[0064] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtain radiation data of each direction of the area to be detected corresponding to the bone instrument, and determine the radiation intensity of the area to be detected according to the radiation data of each direction; When the radiation intensity exceeds the preset threshold, the adjustment parameters are obtained through the trained neural network PID model according to the difference between the radiation intensity and the preset threshold; Adjust the operating parameters of the skeleton analyzer according to the adjustment parameters.

[0065] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0066] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0067] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A radiation control method applied to a mobile C-arm, characterized in that: The radiation control method applied to the mobile C-arm includes: Acquire radiation data of each orientation of the area to be detected corresponding to the bone instrument, and determine the radiation intensity of the area to be detected according to the radiation data of each orientation; When the radiation intensity exceeds a preset threshold, an adjustment parameter is obtained through a trained neural network PID model according to a difference between the radiation intensity and the preset threshold; According to the adjustment parameters, the operating parameters of the skeleton instrument are adjusted.

2. The radiation control method for a mobile C-arm according to claim 1, characterized in that: The step of obtaining radiation data of each position of the to-be-detected area corresponding to the skeleton instrument comprises: Acquire range data of the area to be detected, wherein the range data of the area to be detected includes area boundary data and measurement point position data; and acquire radiation data of each orientation of the area to be detected by a radiometer according to the range data of the area to be detected.

3. The radiation control method for a mobile C-arm according to claim 2, characterized in that: Determining the radiation intensity of the area to be detected according to the radiation data in each direction includes: The radiation data of each direction is calculated one by one through the weighted average formula: Calculate and obtain the average radiation intensity of the radiation data in each direction, and use the average radiation intensity as the radiation intensity of the area to be detected, where x i is the value of each data point, w i is the weight value of the corresponding data point, Σ represents the sum of all data points, and C is the average radiation intensity.

4. The radiation control method for a mobile C-arm according to claim 1, characterized in that: Before obtaining the adjustment parameters through the trained neural network PID model, training and generating the trained neural network PID model specifically includes: Acquire abnormal radiation intensity data, perform difference calculation between the abnormal radiation intensity data and the preset threshold value to obtain the abnormal radiation difference, and obtain the corresponding standard adjustment parameters, and use the abnormal radiation difference and the standard adjustment parameters as a training data set; use the training data set to train the neural network PID model to obtain the trained neural network PID model.

5. The radiation control method for a mobile C-arm according to claim 1, characterized in that: When the radiation intensity exceeds the preset threshold, obtaining the adjustment parameter through the trained neural network PID model according to the difference between the radiation intensity and the preset threshold includes: When the radiation intensity exceeds the preset threshold, calculating the difference between the radiation intensity and the preset threshold to obtain a radiation intensity difference; The radiation intensity differences are input one by one into the trained neural network PID model for comparison and analysis, and the adjustment parameters are obtained through analysis.

6. The radiation control method for a mobile C-arm according to claim 1, characterized in that: Before obtaining the adjustment parameter through the trained neural network PID model according to the difference between the radiation intensity and the preset threshold, the radiation control method applied to the mobile C-arm further includes: When the radiation intensity exceeds the preset threshold, generating a warning signal; The fault cause analysis is performed on the warning signal to obtain operation suggestion information, and the information is transmitted to the user input terminal.

7. A radiation control device for a mobile C-arm, characterized in that: The radiation control device applied to the mobile C-arm comprises: A radiation intensity acquisition module is used to acquire radiation data of each orientation of the to-be-detected area corresponding to the bone instrument, and determine the radiation intensity of the to-be-detected area according to the radiation data of each orientation; A module for obtaining adjustment parameters, used for obtaining adjustment parameters through a trained neural network PID model according to a difference between the radiation intensity and the preset threshold value when the radiation intensity exceeds the preset threshold value; The adjustment module is used to adjust the operating parameters of the skeleton instrument according to the adjustment parameters.

8. The radiation control device for a mobile C-arm according to claim 7, characterized in that: The radiation control device applied to the mobile C-arm also includes: An early warning module, configured to generate an early warning signal when the radiation intensity exceeds the preset threshold; The display module is used to analyze the cause of the fault of the warning signal, obtain operation suggestion information, and transmit it to the user input terminal.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the radiation control method applied to a mobile C-arm as claimed in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the radiation control method applied to a mobile C-arm as claimed in any one of claims 1 to 6 are implemented.

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