Ambient environment radiation influence early warning system of proton treatment system

Abstract

The invention relates to the technical field of radiation early warning, in particular to an ambient radiation influence early warning system for a proton treatment system. The system comprises a radiation monitoring data acquisition module, a radiation space distribution generation module, a radiation risk level determination module, a radiation leakage analysis module, a risk information dynamic notification module and a treatment optimization suggestion module. According to the invention, through real-time monitoring and data integration of the radiation monitoring environment, the response speed and accuracy of radiation safety early warning are improved, and a detailed radiation distribution diagram can be constructed by using spatial data and radiation intensity information, so that risk assessment is more visual and easy to understand; the accurate assessment of the radiation risk allows specific and targeted protection measures to be taken, the potential leakage points are classified according to the radiation level deviation, the risk information is dynamically reported, it is ensured that the therapeutic personnel can receive key safety alarms in time, the therapeutic process is optimized, and the radiation risk of the patient is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of radiation warning, and particularly to a radiation impact warning system for the environment around a proton therapy system. Background Art

[0002] The technical field of radiation warning focuses on developing systems and methods for detecting and warning of harmful radiation levels. It aims to monitor the radiation intensity in the environment in real time to ensure the safety of personnel and equipment. The field covers various technologies from radiation detectors to complex network warnings, including sensors, software, and communication technologies for measuring, analyzing, and responding to radiation. It usually includes a threshold setting function that can automatically issue an alarm when the radiation level exceeds a preset safety value, so as to take corresponding protective measures. In addition, the system can be applied to fields such as nuclear power plants, medical radiotherapy, industrial inspection, and scientific research, where the demand for radiation safety management is particularly urgent.

[0003] Among them, a radiation impact warning system for the environment around a proton therapy system is a warning system specifically designed to monitor the radiation level in the environment around a proton therapy device. The main purpose of the system is to ensure the safety of personnel and visitors inside a medical facility and to ensure that the radiation level does not exceed legal and health safety standards. The system deploys radiation detection sensors at key positions to monitor the change of radiation level in real time, and when an abnormally high radiation level is detected, it reminds relevant personnel to take measures through an audio and visual alarm mechanism. In addition, the system also helps the safe operation and maintenance management of proton therapy devices and is an important safety enhancement tool in the field of medical radiotherapy.

[0004] Traditional warning systems focus on general radiation monitoring and simple threshold warnings, lacking the analysis of the spatial distribution and temporal variation of radiation intensity, which limits the ability of the warning system in accurately locating the dynamic changes of radiation sources and specific positions. There is a lack of detailed spatial distribution maps and precise division of risk levels. When radiation mutations occur, insufficient information is provided, resulting in untimely or overly general protective responses and being unable to effectively handle specific risks within a specific area. For example, in the absence of precise risk level division, once a radiation leak occurs, it may trigger unnecessary large-scale evacuations, disrupt normal medical activities, affect the treatment process of patients, and may lead to adverse consequences, indicating that the existing technology has obvious deficiencies in rapid and accurate risk assessment and response in actual operation. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and to propose a radiation impact warning system for the environment around a proton therapy system.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A radiation impact early warning system for the environment around a proton therapy system, the system comprising:

[0008] The radiation monitoring data acquisition module, based on the proton therapy environment, monitors the radiation intensity in real time. According to the radiation intensity of the monitor and the detector position data, numerical sorting and grouping are performed in the order of monitoring points to obtain a radiation detection set;

[0009] The radiation spatial distribution generation module, based on the radiation detection set, extracts the radiation intensity and spatial coordinate data in the set, constructs a radiation distribution map within a two-dimensional space range to obtain radiation distribution information;

[0010] The radiation risk level determination module, based on the radiation distribution information, marks the grid points exceeding the threshold as high-risk areas, and performs classification and collection according to the positions and risk level classifications of the risk points to obtain risk level annotation data;

[0011] The radiation leakage analysis module, based on the risk level annotation data, compares the intensity values of the high-risk points with the normal radiation data point by point, combines the leakage point source position data to determine the leakage category, classifies and collects the high-risk area data to obtain a radiation leakage classification result;

[0012] The risk information dynamic notification module, based on the radiation leakage classification result, combines the regional impact range and risk category, formulates corresponding early warning notification content, and sends it to the target receiving end through a communication device to notify the treatment personnel to obtain leakage risk notification information;

[0013] The treatment optimization suggestion module, based on the leakage risk information notification, evaluates the health risks of patients, adjusts the subsequent proton therapy dose value, and plans the proton therapy interval to obtain optimized treatment suggestions.

[0014] Optionally, the radiation detection set includes monitoring point position information, radiation intensity values, and timestamps; the radiation distribution information includes grid coordinate points, grid intensity values, and a two-dimensional space range; the risk level annotation data includes high-risk area coordinates, risk level classifications, and the number of high-risk points; the radiation leakage classification result is specifically the leakage point source position, abnormal intensity value, and high-risk area category; the leakage risk notification information includes risk area coordinates, leakage type classifications, and regional impact ranges; the optimized treatment suggestions include dose adjustment parameters, treatment interval values, and equipment operation status parameters.

[0015] Optionally, the radiation monitoring data acquisition module includes:

[0016] Based on the proton therapy environment, the radiation intensity extraction sub-module uses the pre-installed radiation detectors at the monitoring points to collect the radiation intensity data recorded by each detector, extracts the intensity values and corresponding monitoring positions of each detection point, and obtains the radiation intensity information of the detection points;

[0017] Based on the radiation intensity information of the detection points, the monitoring point collection sub-module calls the monitoring positions and radiation intensity value data of each detection point, classifies each group of monitoring point positions and values by region, and organizes them in a set form to obtain the monitoring point position intensity set;

[0018] Based on the monitoring point position intensity set, the timestamp grouping and sorting sub-module extracts the timestamp information of the monitoring points, collects and sorts the monitoring point data within the same time period, and groups and sorts the intensity values and position information corresponding to the monitoring points in chronological order to obtain the radiation detection set.

[0019] Optionally, the radiation spatial distribution generation module includes:

[0020] Based on the radiation detection set, the spatial grid construction sub-module extracts the spatial coordinate values of the monitoring points, divides the regional grid according to the coordinate range, and combines the grid rules to construct a grid for the monitoring area to obtain a spatial grid model;

[0021] Based on the spatial grid model, the intensity value distribution calculation sub-module extracts the spatial coordinates of the grid points and the corresponding radiation intensity values according to the time series grouped intensity data, and calculates the distribution relationship of the radiation intensity values point by point according to the distribution between the grid points to obtain the intensity value spatial distribution data;

[0022] Based on the intensity value spatial distribution data, the two-dimensional image generation sub-module extracts the radiation intensity values and the corresponding grid coordinate points, and constructs a visual two-dimensional distribution map of the radiation intensity values of the grid points according to the two-dimensional coordinate layout rules to obtain the radiation distribution information.

[0023] Optionally, the radiation risk level determination module includes:

[0024] Based on the radiation distribution information, the risk threshold comparison sub-module extracts the radiation intensity values in the grid point data, compares them point by point with the preset risk level threshold, and identifies the grid point data exceeding the threshold to obtain the risk over-threshold grid point data;

[0025] Based on the risk over-threshold grid point data, the high-risk point marking sub-module extracts the grid point coordinate values and intensity values exceeding the risk threshold, marks the grid points, and defines them as high-risk points to obtain the high-risk point marking information;

[0026] The risk level aggregation sub-module evaluates the risk level of high-risk points based on the high-risk point marking information according to the radiation intensity value of the high-risk points, and classifies and aggregates the high-risk points according to the preset risk level classification criteria to obtain risk level annotation data.

[0027] Optionally, the formula for evaluating the risk level of high-risk points is:

[0028]

[0029] Where R i represents the risk level value of the i-th grid point, S i represents the radiation intensity value of the i-th grid point, represents the average value of the radiation intensity values of all grid points, σ S represents the standard deviation of the radiation intensity values of all grid points, T i represents the time dimension marking value of the i-th grid point, T max represents the maximum value of the time dimension marking values among all grid points, and w1 and w2 are weighting coefficients.

[0030] Optionally, the radiation leakage analysis module includes:

[0031] The intensity deviation calculation sub-module extracts the radiation intensity values of the grid points in the high-risk area and the corresponding leakage point source location data based on the risk level annotation data, compares the radiation intensity values of the grid points with the average value of the normal radiation data point by point, calculates the deviation amplitude between the current radiation intensity value and the normal radiation average value, and obtains the radiation intensity deviation data;

[0032] The leakage type classification sub-module classifies the radiation intensity deviation values in the high-risk area based on the radiation intensity deviation data and combines the spatial position of the leakage point source. By analyzing the deviation characteristics of multiple areas and the types of leakage point sources, it determines the leakage category of each area and obtains the leakage type judgment information;

[0033] The high-risk area annotation sub-module extracts the radiation intensity value, spatial range, and corresponding leakage category information of the high-risk area based on the leakage type judgment information, and annotates the information item by item to the high-risk area grid to obtain the radiation leakage classification result.

[0034] Optionally, the risk information dynamic notification module includes:

[0035] The leakage information extraction sub-module extracts the location data, risk level data, and leakage point source data of multiple high-risk areas based on the radiation leakage classification result, aggregates each extracted data item by item, and obtains the high-risk area information set;

[0036] Based on the high-risk area information set, the dynamic notification content generation sub-module extracts the area location, risk level, and leakage point source data, combines the preset area influence range and risk category analysis criteria, matches and correlates the data, generates the content format required for the notification according to the preset rules, collects the notification information, adds the area location and risk classification description, and generates the dynamic notification content for high-risk areas;

[0037] Based on the dynamic notification content of high-risk areas, the area risk data matching sub-module matches the high-risk area data in the notification content with the preset target receiver information, sends it to the target receiver through a communication device, notifies the treatment personnel, and the communication device includes mobile phones and computers, generating leakage risk notification information.

[0038] Optionally, the treatment optimization suggestion module includes:

[0039] Based on the leakage risk notification information, the parameter joint adjustment sub-module extracts the radiation intensity data, area range data, and patient exposure duration data, combines the patient exposure situation and the preset health risk assessment criteria, evaluates the health risk of radiation to the patient, and generates a health risk assessment result;

[0040] Based on the health risk assessment result, the dose update calculation sub-module adjusts the dose value of the subsequent proton therapy according to the patient exposure health risk level data and combines the patient's current treatment dose parameters to obtain the treatment dose update parameter;

[0041] Based on the treatment dose update parameter, the treatment interval optimization sub-module combines the patient's treatment time planning data, and according to the radiation exposure impact and treatment effect, re-plans the time interval of proton therapy to obtain an optimized treatment suggestion.

[0042] Optionally, the formula for estimating the health risk of radiation to the patient is:

[0043]

[0044] where H r represents the health risk index of the patient, D represents the cumulative radiation dose value in the leakage area, D safe represents the set safe radiation dose threshold, T e represents the cumulative exposure duration of the patient in the high-radiation area, T max represents the maximum exposure duration in the leakage area, R H represents the health risk level value of the radiation leakage area, R H,max represents the preset maximum health risk level value, and V1, V2, and V3 are the weighting coefficients of the radiation dose deviation, exposure duration impact, and health risk level weight respectively.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] In the present invention, through the real-time monitoring and data integration of the radiation monitoring environment, the response speed and accuracy of radiation safety early warning are improved. By using the detectors deployed around the proton therapy equipment, the radiation intensity and its changes can be accurately captured, enabling any abnormal radiation level to be quickly identified and responded to. By using spatial data and radiation intensity information, a detailed radiation distribution map can be constructed, making the risk assessment more intuitive and easy to understand. The precise assessment of radiation risk allows for specific and targeted protective measures, improving the safety of personnel and equipment. High-risk areas can be identified, and potential leakage points can be classified according to the deviation of radiation levels, promoting effective crisis management. By dynamically notifying risk information, it is ensured that the treatment personnel can receive critical safety alerts in a timely manner, optimizing the treatment process and reducing the radiation risk to patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0048] Figure 1 is the system flow chart of the present invention;

[0049] Figure 2 is the schematic diagram of the system framework of the present invention;

[0050] Figure 3 is the flow chart of the radiation monitoring data acquisition module of the present invention;

[0051] Figure 4 is the flow chart of the radiation spatial distribution generation module of the present invention;

[0052] Figure 5 is the flow chart of the radiation risk level determination module of the present invention;

[0053] Figure 6 is the flow chart of the radiation leakage analysis module of the present invention;

[0054] Figure 7 is the flow chart of the risk information dynamic notification module of the present invention;

[0055] Figure 8 is the flow chart of the treatment optimization suggestion module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The following describes the technical solutions in the present invention in conjunction with the drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0058] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0059] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] See also Figure 1 , a proton therapy system surrounding environment radiation impact early warning system, the system comprises:

[0062] The radiation monitoring data acquisition module is based on the proton therapy environment and uses radiation detectors pre-installed at the monitoring points to monitor the radiation intensity in real time. Based on the radiation intensity and detector position data of the monitor, the data of each monitoring point is aggregated by timestamp. Combined with the timestamp, the location information and intensity value corresponding to each set of data are extracted. The values are sorted and grouped according to the order of the monitoring points to obtain the radiation detection set.

[0063] The radiation spatial distribution generation module extracts the radiation intensity and spatial coordinate data from the radiation detection set, performs grid calculation of the radiation intensity value according to the coordinate points, establishes the intensity value distribution relationship between the grid points, and constructs a radiation distribution map within the two-dimensional space based on the grid distribution to obtain radiation distribution information;

[0064] The radiation risk level determination module extracts the radiation intensity value from the grid data based on the radiation distribution information, compares it point by point with the preset risk level threshold, marks the grid points that exceed the threshold as high-risk areas, and classifies the risk points according to their location and risk level to obtain risk level annotation data;

[0065] The radiation leakage analysis module extracts the radiation intensity and leakage source location of the labeled medium and high-risk grids based on the risk level labeled data. It compares the intensity value of the high-risk point with the normal radiation data point by point, calculates the deviation between the current intensity value and the normal radiation mean, and determines the leakage category based on the leakage source location data. It classifies and collects the data of the high-risk area and labels the corresponding leakage type to obtain the radiation leakage classification result.

[0066] The risk information dynamic notification module extracts the location, risk level, and leakage source data of high-risk areas from the classification results based on the radiation leakage classification results. It then formulates corresponding early warning notification content based on the regional impact range and risk category. The module sends the notification to the target receiving end via communication equipment, including mobile phones and computers, to notify the treatment personnel.

[0067] The treatment optimization recommendation module assesses the patient's health risks based on leakage risk information notifications, radiation intensity, regional scope, and patient exposure time. Based on the health risks, it adjusts subsequent proton therapy dose values and plans proton therapy intervals to obtain optimized treatment recommendations.

[0068] The radiation detection set includes monitoring point location information, radiation intensity values and timestamps; the radiation distribution information includes grid coordinate points, grid intensity values and two-dimensional spatial range; the risk level annotation data includes high-risk area coordinates, risk level classification and the number of high-risk points; the radiation leakage classification results are specifically the leakage point source location, abnormal intensity value and high-risk area category; the leakage risk notification information includes risk area coordinates, leakage type classification and regional impact range; the optimized treatment recommendations include dose adjustment parameters, treatment interval values and equipment operating status parameters.

[0069] See also Figure 2 and Figure 3 ,The radiation monitoring data acquisition module includes a radiation intensity extraction submodule, a monitoring point collection submodule, and a time stamp grouping submodule;

[0070] The radiation intensity extraction submodule is based on the proton therapy environment and uses radiation detectors pre-installed at monitoring points to collect radiation intensity data recorded by each detector, extract the intensity value of each detection point and the corresponding monitoring position, and obtain the radiation intensity information of the detection point;

[0071] Based on the recorded content of the radiation detector in the proton therapy environment, the radiation intensity data collected by the detectors pre-installed at each monitoring point is read item by item. The output data of the detectors is parsed according to the time series. During the parsing process, the real-time radiation intensity value of each monitoring point needs to be extracted, and the value is compared with the preset geographical location information of the detector to confirm the geographical location to which the detector data belongs. The parsing steps include data verification, denoising processing, and abnormal data elimination. In the verification stage, it is necessary to judge the physical range of each data point, and determine the validity of the data by checking whether it is within the effective measurement range of the device (for example, the common radiation intensity range is 0.1 - 10 mSv). After removing the data outside the range, the sliding window average method is used to remove the random noise from the continuously recorded data of the detection points. Combining the remaining data with the calibration position information of the detector, the radiation intensity data of each monitoring point is generated, and the radiation intensity values matching the geographical locations of each monitoring point are output. These intensity values are summarized into the radiation intensity information table of the detection points through the calibration results. The radiation intensity information of the detection points includes the real-time radiation intensity value of each detection point and its physical position.

[0072] Based on the radiation intensity information of the detection points, the monitoring position and radiation intensity value data of each detection point are called, and the positions and values of each group of monitoring points are classified by region and organized in a set form to obtain the monitoring point position intensity set;

[0073] Based on the radiation intensity information of the detection points, the monitoring positions and radiation intensity value data of the monitoring points are classified by region. The geographical location information of all detection points is imported into the regional division model. The regional division model needs to partition all monitoring points based on the preset grid division standard. Each regional division is based on physical distance. By checking the coordinate range of the grid points, the detection points are divided into different regions according to their coordinates. At the same time, the intensity values of the detection points within each region are extracted. After classification, the data is sorted according to the arrangement order of the detection points within the region, and finally organized in a set form to output the monitoring point position intensity set. The set contains the monitoring positions and corresponding radiation intensity values of all monitoring points within each region, and each set is stored in units of regions for subsequent data grouping and analysis.

[0074] Based on the monitoring point position intensity set, the timestamp grouping and sorting sub-module extracts the timestamp information of the monitoring points, collects and sorts the monitoring point data within the same time period, and groups and organizes the intensity values and position information corresponding to the monitoring points in chronological order to obtain the radiation detection set;

[0075] Based on the timestamp information in the set of monitoring point position intensities, it is necessary to group and organize the monitoring point data for each time period. During the data organization process, it is necessary to first extract the timestamps recorded by each detection point and check whether their recording intervals meet the specified time range. For example, for real-time monitoring data, the time interval is usually set within 1 minute. Timestamp data outside the range needs to be grouped and classified. For the classified timestamp data, by comparing it with the detection point positions and radiation intensity data in the corresponding area, all detection point data within the same time period is integrated into a single group. After grouping, it is rearranged in chronological order to ensure that the data can completely present the trend of radiation intensity changing over time, and it is organized into a radiation detection set. The radiation detection set contains the intensity values and position information of all monitoring points in each time period arranged in chronological order, which is used for subsequent spatial distribution analysis.

[0076] Please refer to Figure 2 and Figure 4 , the radiation spatial distribution generation module includes a spatial grid construction sub-module, an intensity value distribution calculation sub-module, and a two-dimensional image generation sub-module;

[0077] Based on the radiation detection set, the spatial grid construction sub-module extracts the spatial coordinate values of the monitoring points, divides the regional grid according to the coordinate range, and combines the grid rules to construct a grid for the monitoring area to obtain a spatial grid model;

[0078] Based on the radiation detection set, extract the spatial coordinate values of the monitoring points for grid division, extract the geospatial coordinate values of each monitoring point, and eliminate abnormal points whose coordinate deviations exceed the preset range by checking the consistency between the calibrated coordinates and the actual measured coordinates of the monitoring points. Subsequently, adopt an equidistant division method, determine the total regional range of grid division according to the maximum and minimum values of the monitoring point coordinate range, divide the monitoring area into several small areas according to the target standard unit grid, and the unique identifier of each grid is composed of its lower left corner coordinates. Divide the monitoring point data within each grid, classify each monitoring point into the corresponding grid according to its coordinate position, and at the same time check whether there are empty grids or abnormal grids (i.e., grids without monitoring point data), mark the empty grids and eliminate the abnormal points to obtain a complete spatial grid model. The spatial grid model contains the coordinate range of each grid cell and the list of monitoring point data it contains.

[0079] Based on the spatial grid model, the intensity value distribution calculation sub-module groups the intensity data according to the time series, extracts the spatial coordinates of the grid points and the corresponding radiation intensity values, and calculates the distribution relationship of the radiation intensity values point by point according to the distribution between the grid points to obtain the intensity value spatial distribution data;

[0080] Based on the spatial grid model, it is necessary to calculate the distribution relationship of the radiation intensity value point by point according to the time series grouped intensity data, extract the spatial coordinates of each grid point and the radiation intensity data of the time series group, check the integrity of each time series data and eliminate missing values, and then classify the intensity data in each grid according to the time point. The radiation intensity data of the same time period is assigned to the grid point, and the average radiation intensity value of each grid point is calculated point by point. At the same time, combined with the spatial interpolation method, the gradient of the spatial distribution is calculated based on the distance and radiation intensity difference between adjacent grid points, and the gradient data is checked for anomalies (that is, the gradient exceeds the set safety threshold range). For points exceeding the threshold, the distance weight algorithm is used to readjust their intensity distribution values, and the corrected intensity data is output as intensity value spatial distribution data. The intensity value spatial distribution data contains the average intensity value and spatial gradient information of each grid point.

[0081] The two-dimensional image generation submodule extracts the radiation intensity value and the corresponding grid coordinate point based on the intensity value spatial distribution data, and constructs a visual two-dimensional distribution map of the grid point radiation intensity value according to the two-dimensional coordinate layout rules to obtain the radiation distribution information;

[0082] Based on the spatial distribution data of intensity values, the radiation intensity value of each grid point and its corresponding grid coordinate point are extracted. By checking the integrity of the spatial distribution data, it is ensured that each grid point contains complete intensity value and coordinate information. The intensity value of the missing grid point is filled by interpolation. According to the two-dimensional coordinate layout rules, the coordinate value of each grid point is mapped to the two-dimensional plane. At the same time, the radiation intensity value is converted into a color gradient value through color mapping. The color range usually ranges from blue to red to represent the increase of radiation intensity. The generated two-dimensional distribution map is verified to ensure that the color distribution is consistent with the actual intensity data. Finally, the complete radiation distribution information is output through a visualization tool. The radiation distribution information includes the spatial coordinates of each grid point and the visualized intensity value image.

[0083] See also Figure 2 and Figure 5 ,The radiation risk level determination module includes a risk threshold comparison ,submodule, a high risk point marking submodule, and a risk level aggregation ,submodule;

[0084] The risk threshold comparison submodule extracts the radiation intensity value from the grid point data based on the radiation distribution information, compares it with the preset risk level threshold point by point, identifies the grid point data that exceeds the threshold, and obtains the risk exceeding threshold grid point data;

[0085] Based on the radiation distribution information, extract the radiation intensity values in the grid point data and compare them point by point with the preset risk level thresholds. Extract the radiation intensity values of each grid point, ensuring that the measurement data of each intensity value is complete and within a reasonable range (for example, within the typical detection range between 0.1 mSv and 10 mSv). Mark and eliminate the outliers, and then call the preset risk level thresholds to obtain the threshold criteria for each grid point from the database. The threshold criteria are usually set according to the radiation safety requirements of the specific environment. By comparing the radiation intensity value of each grid point with the corresponding threshold point by point, filter out the grid point data that exceeds the threshold, mark the intensity values of the grid points that exceed the threshold as high-risk intensity values, and record their corresponding grid point coordinates at the same time. Summarize all the grid point data that exceeds the threshold to generate risk over-threshold grid point data containing the grid point intensity values and the corresponding spatial coordinates. The risk over-threshold grid point data is used for the subsequent marking and analysis of high-risk points.

[0086] The high-risk point marking sub-module extracts the grid point coordinate values and intensity values that exceed the risk threshold based on the risk over-threshold grid point data, marks the grid points, defines them as high-risk points, and obtains the high-risk point marking information.

[0087] Based on the risk over-threshold grid point data, it is necessary to extract the grid point coordinate values and intensity values that exceed the risk threshold and mark them. Extract the radiation intensity values and grid point spatial coordinates in the over-threshold grid point data, ensuring that each data point contains complete intensity value and spatial coordinate information. For the grid points with missing intensity values, use the weighted average of the data of adjacent grid points in the area to fill them. Correct the grid point data with missing coordinates by referring to the original records. Then verify all the over-threshold grid point data to ensure that the intensity values do exceed the threshold and the markings are correct. By comparing the radiation intensity value of the grid point with the preset high-risk standard, mark the grid points that exceed the high-risk threshold as high-risk points, and record their coordinates and radiation intensity values at the same time. Organize the marking information and output it to generate high-risk point marking information containing the grid point intensity values, spatial coordinates, and high-risk markings. The high-risk point marking information provides the basic data for the subsequent risk level aggregation.

[0088] The risk level aggregation sub-module evaluates the risk level of the high-risk points based on the high-risk point marking information, and classifies and aggregates the high-risk points according to the preset risk level classification criteria to obtain the risk level annotation data.

[0089] The formula for evaluating the risk level of high-risk points is:

[0090]

[0091] Among them, R i represents the risk level value of the i-th grid point, Si Represents the radiation intensity value of the \(i\)-th grid point, represents the average value of the radiation intensity values of all grid points, \(\sigma\) S represents the standard deviation of the radiation intensity values of all grid points, \(T\) i represents the time dimension marking value of the \(i\)-th grid point, \(T\) max represents the maximum value of the time dimension marking values among all grid points, and \(w_1\) and \(w_2\) are weighting coefficients.

[0092] Meaning and acquisition method of parameters:

[0093] \(S\) i : The radiation intensity value of the \(i\)-th grid point. It is obtained by real-time monitoring with a radiation detector installed at this grid point, and the unit is millisievert (mSv).

[0094] The average value of the radiation intensity values of all grid points. It is calculated by adding up the radiation intensity values of all grid points and then dividing by the total number \(N\) of grid points, and the unit is millisievert (mSv).

[0095] \(\sigma\) S : The standard deviation of the radiation intensity values of all grid points. By calculating the degree of dispersion of the radiation intensity values, it reflects the deviation degree of the radiation intensity of each grid point from the average value, and the unit is millisievert (mSv).

[0096] \(T\) i : The cumulative exposure duration of the \(i\)-th grid point. It is obtained from the time data recorded by the radiation detector, and the unit is hour (h).

[0097] \(T\) max : The maximum value of the cumulative exposure durations among all grid points. By comparing the \(T\) i values of all grid points and selecting the maximum value among them, the unit is hour (h).

[0098] \(w_1\) and \(w_2\): Weighting coefficients of the risk level aggregation algorithm. They are used to adjust the influence degrees of the radiation intensity deviation and the time dimension on the risk level, and the specific values are set according to the actual situation.

[0099] Calculation example:

[0100] Suppose there are the following data: Total number of grid points \(N = 5\); Radiation intensity values \(S\) of each grid point i (unit: mSv): \(S_1 = 2.5\), \(S_2 = 3.0\), \(S_3 = 2.8\), \(S_4 = 3.2\), \(S_5 = 2.9\); Cumulative exposure durations \(T\) of each grid point i (unit: h): \(T_1 = 10\), \(T_2 = 15\), \(T_3 = 12\), \(T_4 = 18\), \(T_5 = 14\); Weighting coefficients: \(w_1 = 0.6\), \(w_2 = 0.4\);

[0101] Calculate the average radiation intensity value

[0102]

[0103] Calculate the standard deviation σ of the radiation intensity value S :

[0104]

[0105] Determine the maximum cumulative exposure duration T max :

[0106] T max = max(T1, T2, T3, T4, T5)= 18h;

[0107] Calculate the risk level value R1 of the first grid point (i = 1):

[0108] Calculate the normalized value of the radiation intensity deviation:

[0109]

[0110] Calculate the logarithm of the time ratio:

[0111]

[0112] Apply the weighting coefficient and sum:

[0113]

[0114] Round to obtain the risk level value R1:

[0115]

[0116] Through the above calculations, the risk level value of the first grid point is obtained as 2. This indicates that the radiation risk at this grid point is at the second level in the preset risk level classification standard, suggesting that corresponding protective measures need to be taken.

[0117] Please refer to Figure 2 and Figure 6 , the radiation leakage analysis module includes an intensity deviation calculation sub-module, a leakage type classification sub-module, and a high-risk area marking sub-module;

[0118] Based on the risk level marking data, the intensity deviation calculation sub-module extracts the radiation intensity values of the grid points in the high-risk area and the corresponding leakage point source location data, compares the radiation intensity values of the grid points with the average value of the normal radiation data point by point, calculates the deviation amplitude between the current radiation intensity value and the normal radiation average value, and obtains the radiation intensity deviation data;

[0119] Based on the risk - level - labeled data, extract the radiation intensity values and the leakage point - source location data of the grid points in the high - risk area. Retrieve the radiation intensity values of each grid point from the database, and conduct point - by - point comparison in combination with the spatial location data of the leakage point - source to check the integrity of the grid - point radiation intensity data. Eliminate or complete the missing or abnormal values. For the completion operation, interpolation methods of the radiation intensity data of surrounding grid points can be used for correction. For example, the inverse - distance - weighted interpolation method can be used to assign higher weights to the grid - point data closer to the leakage point - source, and calculate the effective intensity value of each grid point. Then, compare the radiation intensity value of each grid point with the average value of the normal radiation data. The normal radiation data can be calculated from the data of the uncontaminated reference points in the monitoring area, and its average value is obtained according to the time series. Subtract the normal radiation average value from the current radiation intensity value of each grid point to calculate its deviation amplitude, ensuring that all calculations are carried out in a unified unit. At the same time, re - sort all the grid - point data according to the spatial coordinates, and output the radiation intensity deviation data containing the radiation intensity deviations of each grid point. The radiation intensity deviation data is used for the subsequent classification analysis of the leakage type.

[0120] The leakage - type classification sub - module classifies the radiation intensity deviation values in the high - risk area based on the radiation intensity deviation data and in combination with the spatial location of the leakage point - source. By analyzing the deviation characteristics of multiple areas and the types of leakage point - sources, determine the leakage category of each area to obtain the leakage - type judgment information.

[0121] Based on the radiation intensity deviation data, it is necessary to classify and analyze the radiation intensity deviation values in the high - risk area in combination with the spatial location of the leakage point - source. Extract the radiation intensity deviation values of each grid point and the corresponding spatial coordinate information in the high - risk area, conduct zoning processing on the spatial coordinate information, divide each high - risk area into multiple sub - areas, and divide the grid - point data in each sub - area into three layers: near, middle, and far according to the distance from the leakage point - source. Subsequently, analyze the distribution characteristics of the radiation intensity deviation values of each layer of grid points. By calculating the average value and standard deviation of the radiation intensity deviation values of each layer, judge the attenuation law of the radiation intensity with distance, and in combination with the type of the leakage point - source, such as equipment failure, natural attenuation, pipeline rupture, etc., conduct feature classification on each sub - area. The classification method can be based on empirical rules. For example, if the radiation intensity deviation is significantly higher in the near area than in other areas and shows no regular attenuation, it can be determined as a local equipment failure. If the radiation intensity decays exponentially with distance, it may be a natural leakage. After confirming the classification accuracy, generate the leakage - category information of each sub - area, and output the leakage - type judgment information containing the radiation intensity deviation values and their corresponding leakage categories of each high - risk area.

[0122] The high - risk - area labeling sub - module extracts the radiation intensity value, spatial range, and the corresponding leakage - category information of the high - risk area based on the leakage - type judgment information, and labels the information item by item into the high - risk - area grid to obtain the radiation - leakage classification result.

[0123] Based on the leakage type judgment information, it is necessary to extract the radiation intensity value, spatial range and corresponding leakage category information of the high-risk area, annotate the high-risk area grids one by one, extract the radiation intensity value data in each high-risk area from the leakage type judgment information, assign the intensity value to the corresponding grid according to the spatial coordinates of the grid point, check the integrity of the spatial coordinates, ensure that each grid point can match the leakage category information, regroup the high-risk areas according to the spatial range, each group contains an independent high-risk area, and classify the grid points in the area according to the leakage category as the classification standard, and perform labeling operations on the classified grid point data, including labeling the leakage category, radiation intensity value and spatial coordinate range. After the labeling operation is completed, the data will be output in the order of grid point arrangement to generate a radiation leakage classification result including the grid point radiation intensity value, spatial range and leakage category. The classification result can be used for subsequent risk assessment and regional management.

[0124] See also Figure 2 and Figure 7 ,The risk information dynamic notification module includes a leakage information extraction submodule, a dynamic notification content generation submodule, and a regional risk data matching submodule;

[0125] The leakage information extraction submodule extracts the location data, risk level data, and leakage point source data of multiple high-risk areas based on the radiation leakage classification results, and aggregates each extracted data item to obtain a high-risk area information set;

[0126] Based on the radiation leakage classification results, the location data, risk level data and leakage point source data of multiple high-risk areas are extracted, and the extracted data are collected item by item. The specific location of each high-risk area is extracted from the radiation leakage classification results, including its center point coordinates and boundary range information. Then the risk level data of each high-risk area is extracted. According to the risk level recorded in the classification results, all areas are uniformly numbered to ensure that each risk level is uniquely and clearly labeled. Then, the leakage point source data corresponding to the high-risk area is extracted. The leakage point source information includes leakage type (such as equipment failure, natural leakage), leakage point spatial location and leakage point intensity. The data is collected one by one according to the regional location, and the correlation between the leakage point and the high-risk area is verified. By calculating the spatial distance from the leakage point to the center point of the high-risk area, the data correlation is ensured to be correct. A high-risk area information set containing the high-risk area location, risk level and leakage point source information is obtained. The set contains key data of all high-risk areas and provides complete input for subsequent processing.

[0127] The dynamic notification content generation submodule extracts regional location, risk level, and leakage source data based on the high-risk area information collection. It then matches and correlates the data based on the preset regional impact scope and risk category analysis standards, generates the required notification content format according to preset rules, aggregates the notification information, and adds regional location and risk classification descriptions to generate dynamic notification content for high-risk areas.

[0128] Based on the high-risk area information set, the regional location, risk level, and leakage source data are extracted. Combined with the preset regional impact range and risk category analysis standards, the data is matched and associated, and the content format required for the notification is generated according to the preset rules. The regional impact range standard is called, and the radius of the impact range is calculated based on the center point and boundary range information of each high-risk area. The location coordinates of each high-risk area are mapped with the impact range to generate regional impact range data. Subsequently, the risk category analysis standard is called to analyze the correspondence between the risk level of the high-risk area and the leakage source data. For example, the type of leakage source (such as equipment failure or external contamination) is matched with the pattern of radiation intensity distribution in the area to determine the risk category of the area. The matched data is aggregated into the initial content of the notification information, and the content is organized according to the preset notification format, including recording the regional location in latitude and longitude format, converting the risk level into a more readable description, and adding a description of the risk category. The complete high-risk area dynamic notification content is generated. The notification content includes a detailed description of the location, risk level, and leakage source of each high-risk area, and provides structured input data for subsequent notification operations.

[0129] The regional risk data matching submodule matches the high-risk area data in the dynamic notification content of the high-risk area with the preset target receiving end information, sends it to the target receiving end through communication equipment, including mobile phones and computers, and notifies the treatment staff to generate leakage risk notification information;

[0130] Based on the content of the dynamic notification of high-risk areas, match the high-risk area data in the notification content with the preset target recipient information, and send it to the target recipient through communication devices to notify the treatment personnel. First, extract the key data of each high-risk area in the dynamic notification content, including the area location, risk level, and leakage category description. Subsequently, call the target recipient information library to classify and match the recipient information. For example, match the recipient with device operation permissions to the device failure notification corresponding to the leakage point source, and match the high-risk area information related to the patient's health to the treatment personnel. Generate customized notification content according to the information type of each recipient, generate notification messages in the corresponding format according to the device type (such as mobile phone or computer) of the recipient for the matched high-risk area data. After verifying the integrity of the message, send it through communication devices, record the sending status and receiving situation of each message, and generate leakage risk notification information containing all notification content and its sending status. The notification information provides a detailed basic record for subsequent tracking and handling.

[0131] Please refer to Figure 2 and Figure 8 , the treatment optimization suggestion module includes a parameter joint adjustment sub-module, a dose update calculation sub-module, and a treatment interval optimization sub-module;

[0132] The parameter joint adjustment sub-module extracts radiation intensity data, regional range data, and patient exposure duration data based on the leakage risk notification information, combines the patient exposure situation and the preset health risk assessment criteria, evaluates the health risk of radiation to the patient, and generates a health risk assessment result;

[0133] The formula for estimating the health risk of radiation to the patient is:

[0134]

[0135] where, H r represents the health risk index of the patient, D represents the cumulative radiation dose value in the leakage area, D safe represents the set safety radiation dose threshold, T e represents the cumulative exposure duration of the patient in the high-radiation area, T max represents the maximum exposure duration in the leakage area, R H represents the health risk level value of the radiation leakage area, R H,max represents the preset maximum health risk level value, and V1, V2, and V3 are the weighting coefficients of the radiation dose deviation, exposure duration impact, and health risk level weight, respectively.

[0136] Meaning and acquisition method of parameters:

[0137] D: Cumulative radiation dose value, representing the cumulative radiation dose of the patient during exposure in the leakage area, with the unit of millisievert (mSv), which is obtained by accumulating the real-time dose data recorded by the radiation detector installed in the leakage area.

[0138] D safe : Safety radiation dose threshold, which is the safety radiation dose limit set according to international radiation protection standards (such as the International Atomic Energy Agency IAEA or the International Commission on Radiological Protection ICRP), used to determine whether it exceeds the safe range, with the unit of millisievert (mSv), and is directly retrieved from international standards or national standard documents. For example, the annual dose limit for the general public by ICRP is 1 mSv, and the annual dose limit for occupational exposed persons is 20 mSv.

[0139] T e : Cumulative exposure duration, representing the total exposure time of the patient in the high-radiation area, with the unit of hour (h), which is obtained by the patient's trajectory tracking record system, combining the timestamp data, and statistically calculating and accumulating the time periods when the patient enters and leaves the high-radiation area.

[0140] T max : Maximum exposure duration, which is the maximum value of the exposure duration among all patients in the leakage area, with the unit of hour (h), and is obtained by comparing the exposure duration data recorded for all patients' trajectories.

[0141] R H : Health risk level value, representing the health risk level of the radiation leakage area, calculated based on the risk assessment model, used to quantify the radiation risk level of the leakage area, with the unit of dimensionless, and is directly extracted from the leakage risk notification information, usually statistically obtained according to the grid-based risk assessment results in the area.

[0142] R H,max : Maximum health risk level value, which is the preset highest health risk level, used to normalize the risk levels of different areas, with the unit of dimensionless, and is set according to the risk level classification standard. For example, the risk level is usually divided into levels 1 - 5, and the maximum risk level is 5.

[0143] V1, V2, and V3: Weighting coefficients, which are respectively used to adjust the influence weights of radiation dose deviation, exposure duration, and health risk level on the health risk index, and are set according to expert experience or statistical analysis, combined with the specific scenario requirements. For example, the optimal weights are selected through the weight optimization model.

[0144] Calculation example:

[0145] Set the following data: D = 15 mSv: The cumulative radiation dose of the patient, calculated by accumulating the detector data; D safe = 10 mSv: Safety radiation dose threshold, set according to international radiation protection standards; Te = 8 h: Patient exposure duration, obtained by statistical analysis of trajectory records; T max = 12 h: Maximum exposure duration, obtained by statistical analysis of all patient data; R H = 3: Health risk level value, obtained by the leakage area risk assessment model; R H,max = 5: Maximum health risk level, the preset highest level; Weight coefficients: V1 = 0.6, V2 = 0.3, V3 = 0.1.

[0146] Calculate the normalized value of the radiation dose deviation:

[0147]

[0148] Calculate the normalized square root value of the exposure duration:

[0149]

[0150] Calculate the normalized value of the health risk level:

[0151]

[0152] Apply the weighting coefficients and sum:

[0153] Substitute the calculation result into the formula

[0154] 0.6·0.5 + 0.3·0.816 + 0.1·0.6 = 0.3 + 0.2448 + 0.06 = 0.6048;

[0155] Use the ceiling operation

[0156] H r = 1;

[0157] The calculated patient health risk index is H r = 1, indicating that the patient's health risk is at the lowest level and no immediate additional measures are required.

[0158] Based on the health risk assessment results, the dose update calculation sub-module adjusts the dose value of the subsequent proton therapy according to the patient exposure health risk level data in combination with the patient's current treatment dose parameters to obtain the treatment dose update parameters;

[0159] Based on the results of the health risk assessment, according to the health risk level data of the patient's exposure, combined with the current treatment dose parameters of the patient, the dose value of the subsequent proton therapy is adjusted. First, extract the risk level information in the health risk assessment results, and sequentially match the corresponding treatment dose adjustment ranges according to the mild, moderate, and severe risk levels. For example, the mild risk corresponds to 95%-100% of the current treatment dose, the moderate risk corresponds to 90%-95%, and the severe risk corresponds to 85%-90%. Extract the current treatment dose parameters of the patient, and match and calculate the dose parameters with the adjustment ranges corresponding to the risk levels. For example, calculate the new treatment dose value through a weighted method. When performing the weighted calculation, combine the severity of the patient's condition and the treatment tolerance ability, set the disease weight to 0.6, and the tolerance weight to 0.4. Adjust the current dose value of the patient according to the risk level, and output the treatment dose update parameters. The treatment dose update parameters provide input data for the optimization of the subsequent treatment time interval.

[0160] Based on the treatment dose update parameters, combined with the patient's treatment time planning data, according to the radiation exposure impact and treatment effect, re-plan the time interval of proton therapy to obtain an optimized treatment recommendation;

[0161] Based on the treatment dose update parameters, it is necessary to combine the patient's treatment time planning data and re-plan the time interval of proton therapy according to the radiation exposure impact and treatment effect. Extract the latest dose value in the treatment dose update parameters and compare it with the patient's treatment time planning data to check whether the current treatment plan can meet the latest dose requirements. Subsequently, extract the radiation exposure data of the patient during the treatment interval, including the exposure time, radiation intensity, and cumulative dose. Integrate the data into a single cumulative radiation dose curve according to the time series and analyze it in combination with the change trend of the patient's treatment effect. Calculate the patient's treatment tolerance level and recovery rate during the analysis process. Through the comprehensive consideration of the patient's cumulative radiation dose, treatment tolerance level, and treatment effect changes, dynamically adjust the treatment time interval and output the optimized treatment time interval. The treatment interval data and the treatment dose update parameters together constitute the optimized treatment recommendation, providing a basis for adjusting the subsequent proton therapy plan.

[0162] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0163] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or a similar expression means any combination of these items, including any combination of single item(s) or plural item(s). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0164] It should be understood that in various embodiments of the present invention, the magnitude of the sequence numbers of the above - mentioned processes does not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0165] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0166] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0167] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0168] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] In addition, in each embodiment of the present invention, each functional unit can be integrated in a processing unit, can exist physically alone for each unit, or two or more units can be integrated in one unit.

[0170] If the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0171] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A warning system for the radiation impact on the surrounding environment of a proton therapy system, characterized in that, The system comprises: The radiation monitoring data acquisition module monitors the radiation intensity in real time based on the proton therapy environment. According to the radiation intensity of the monitor and the detector position data, the module sorts and groups the values in the order of the monitoring points to obtain the radiation detection set. The radiation spatial distribution generation module extracts the radiation intensity and spatial coordinate data in the radiation detection set based on the radiation detection set, constructs a radiation distribution map within a two-dimensional space, and obtains radiation distribution information; The radiation risk level determination module marks grid points exceeding a threshold as high-risk areas based on the radiation distribution information, and classifies the risk points according to their locations and risk levels to obtain risk level annotation data; The radiation leakage analysis module compares the intensity values of high-risk points with normal radiation data point by point based on the risk level labeled data, determines the leakage category in combination with the leakage point source location data, classifies and aggregates the high-risk area data, and obtains the radiation leakage classification result; The risk information dynamic notification module formulates corresponding early warning notification content based on the radiation leakage classification results, combined with the regional impact range and risk category, and sends it to the target receiving end through the communication equipment to notify the treatment personnel and obtain the leakage risk notification information; The treatment optimization recommendation module assesses the patient's health risk based on the leakage risk information notification, adjusts subsequent proton therapy dose values, and plans proton therapy intervals to obtain optimized treatment recommendations.

2. The environmental radiation impact warning system around the proton therapy system according to claim 1, wherein, The radiation detection set includes monitoring point location information, radiation intensity values and timestamps; the radiation distribution information includes grid coordinate points, grid intensity values and two-dimensional spatial range; the risk level annotation data includes high-risk area coordinates, risk level classification and the number of high-risk points; the radiation leakage classification results are specifically the leakage point source location, abnormal intensity value and high-risk area category; the leakage risk notification information includes risk area coordinates, leakage type classification and regional impact range; the optimized treatment recommendations include dose adjustment parameters, treatment interval values and equipment operating status parameters.

3. The radiation impact early warning system for the environment around the proton therapy system according to claim 1, wherein The radiation monitoring data acquisition module includes: The radiation intensity extraction submodule is based on the proton therapy environment and uses radiation detectors pre-installed at monitoring points to collect radiation intensity data recorded by each detector, extract the intensity value of each detection point and the corresponding monitoring position, and obtain the radiation intensity information of the detection point; The monitoring point location aggregation submodule calls the monitoring location and radiation intensity numerical data of each detection point based on the radiation intensity information of the detection point, classifies each group of monitoring point locations and values by region, and organizes them into a set form to obtain a monitoring point location intensity set; The timestamp grouping submodule extracts the timestamp information of the monitoring points based on the monitoring point location intensity set, aggregates and sorts the monitoring point data within the same time period, groups and organizes the intensity values and location information corresponding to the monitoring points in chronological order, and obtains a radiation detection set.

4. The radiation impact early warning system for the surrounding environment of the proton therapy system according to claim 1, wherein, The radiation spatial distribution generation module includes: The spatial grid construction sub-module extracts the spatial coordinate values of the monitoring points based on the radiation detection set, divides the regional grid according to the coordinate range, and combines the grid rules to construct a grid for the monitoring area to obtain a spatial grid model; The intensity value distribution calculation sub-module, based on the spatial grid model, groups the intensity data according to the time series, extracts the spatial coordinates of the grid points and the corresponding radiation intensity values, and calculates the distribution relationship of the radiation intensity values point by point according to the distribution between the grid points to obtain the spatial distribution data of the intensity values; The two-dimensional image generation sub-module, based on the spatial distribution data of the intensity values, extracts the radiation intensity values and the corresponding grid coordinate points, and constructs a visual two-dimensional distribution map of the radiation intensity values of the grid points according to the two-dimensional coordinate layout rules to obtain the radiation distribution information.

5. The radiation impact early warning system for the environment around the proton therapy system according to claim 1, characterized in that, The radiation risk level determination module includes: The risk threshold comparison sub-module extracts the radiation intensity values in the grid point data based on the radiation distribution information, compares them with the preset risk level thresholds point by point, and identifies the grid point data exceeding the threshold to obtain the risk over-threshold grid point data; The high-risk point marking sub-module extracts the grid point coordinate values and intensity values exceeding the risk threshold based on the risk over-threshold grid point data, marks the grid points and defines them as high-risk points to obtain the high-risk point marking information; The risk level aggregation sub-module evaluates the risk levels of the high-risk points based on the high-risk point marking information according to the radiation intensity values of the high-risk points, and classifies and aggregates the high-risk points according to the preset risk level classification criteria to obtain the risk level annotation data.

6. The radiation impact early warning system for the environment around the proton therapy system according to claim 5, characterized in that, The formula for evaluating the risk level of the high-risk points is: Among them, R i represents the risk level value of the i-th grid point, S i represents the radiation intensity value of the i-th grid point, represents the average value of the radiation intensity values of all grid points, σ S represents the standard deviation of the radiation intensity values of all grid points, T i represents the time dimension marking value of the i-th grid point, T max represents the maximum value of the time dimension marking values among all grid points, and w1 and w2 are weighting coefficients.

7. The environmental radiation impact warning system around the proton therapy system according to claim 1, characterized in that, The radiation leakage analysis module includes: The intensity deviation calculation sub-module extracts the radiation intensity values of the grid points in the high-risk area and the corresponding leakage point source location data based on the risk level annotation data, compares the radiation intensity values of the grid points with the average value of the normal radiation data point by point, and calculates the deviation amplitude between the current radiation intensity value and the normal radiation average value to obtain the radiation intensity deviation data; The leakage type classification sub-module classifies the radiation intensity deviation values in the high-risk area based on the radiation intensity deviation data and combines the spatial positions of the leakage point sources. By analyzing the deviation characteristics of multiple areas and the types of leakage point sources, it determines the leakage category of each area to obtain the leakage type judgment information; The high-risk area annotation sub-module extracts the radiation intensity values, spatial ranges and corresponding leakage category information of the high-risk areas based on the leakage type judgment information, and annotates the information item by item to the high-risk area grid to obtain the radiation leakage classification result.

8. The warning system for radiation impact on the surrounding environment of the proton therapy system according to claim 1, characterized in that, The risk information dynamic notification module includes: The leakage information extraction sub-module extracts the location data, risk level data and leakage point source data of multiple high-risk areas based on the radiation leakage classification result, and aggregates each extracted data item by item to obtain the high-risk area information set; The dynamic notification content generation sub-module extracts the regional location, risk level, and leakage point source data based on the high-risk area information set, matches and correlates the data in combination with the preset regional influence range and risk category analysis criteria, generates the content format required for the notification according to the preset rules, collects the notification information, and adds the regional location and risk classification description to generate the dynamic notification content for high-risk areas. The regional risk data matching sub-module matches the high-risk area data in the notification content with the preset target receiver information based on the dynamic notification content of the high-risk area, sends it to the target receiver through a communication device, notifies the treatment personnel, and the communication device includes mobile phones and computers, generating leakage risk notification information.

9. The warning system for the radiation impact of the environment around the proton therapy system according to claim 1, wherein The treatment optimization suggestion module includes: The parameter joint adjustment sub-module extracts the radiation intensity data, regional range data, and patient exposure duration data based on the leakage risk notification information, evaluates the health risk of radiation to the patient in combination with the patient's exposure situation and the preset health risk assessment criteria, and generates a health risk assessment result. The dose update calculation sub-module adjusts the dose value of the subsequent proton therapy based on the health risk assessment result, the patient exposure health risk level data, and the current treatment dose parameters of the patient to obtain the treatment dose update parameter. The treatment interval optimization sub-module re-plans the time interval of proton therapy based on the treatment dose update parameter, the patient's treatment time planning data, and the radiation exposure impact and treatment effect to obtain an optimized treatment suggestion.

10. The warning system for the radiation impact on the surrounding environment of the proton therapy system according to claim 9, wherein The formula for estimating the health risk of radiation to the patient is: Among them, H r represents the health risk index of the patient, D represents the cumulative radiation dose value in the leakage area, D safe represents the set safe radiation dose threshold, T e represents the cumulative exposure duration of the patient in the high-radiation area, T max represents the maximum value of the exposure duration in the leakage area, R H represents the health risk level value of the radiation leakage area, R H,max represents the preset maximum health risk level value, and V1, V2, and V3 are the weighting coefficients of the radiation dose deviation, exposure duration impact, and health risk level weight, respectively.

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