Method and system for real-time evaluation of adsorbent performance for radioactive gas treatment

Abstract

The application provides a method and system for real-time evaluation of adsorbent performance for radioactive gas treatment, and relates to the technical field of environmental monitoring. The method comprises the following steps: deploying a plurality of radioactivity sensors, temperature sensors and humidity sensors at different depths in the axial and radial directions of the adsorbent bed layer, collecting the radioactivity concentration, temperature and humidity at each position in real time through the sensors; and arranging ventilation impedance sensors and organic volatile sensors at the inlet and outlet of the adsorption unit and inside the bed layer, and synchronously collecting the ventilation impedance and the concentration of organic volatiles. The application realizes the global three-dimensional monitoring of the adsorption performance of the adsorbent, the accurate correction of multiple factors and the automatic discrimination of the safety threshold.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method and system for real-time evaluation of the effectiveness of adsorbents used in the treatment of radioactive gases. Background Technology

[0002] In the medical field, nuclear medicine-related examinations and radiopharmaceutical synthesis will generate waste gas containing radionuclides. If this waste gas is not effectively treated, it will threaten the health of medical staff and pollute the treatment environment. Fixed bed adsorption is currently the core technology for radioactive gas treatment in medical scenarios. Real-time and accurate assessment of adsorbent efficiency is the key to ensuring that waste gas meets emission standards and treatment safety.

[0003] A nuclear medicine department in a medical institution uses an adsorbent bed to treat radioactive waste gas generated during the synthesis of radiopharmaceuticals. Traditional assessment methods only involve installing single-point radioactivity detection devices at the inlet and outlet of the adsorption unit. Adsorption efficiency is estimated by periodically recording the concentration difference between the inlet and outlet manually, and then periodically sampling and testing the adsorbent performance to determine the replacement time. This method cannot monitor the temperature and humidity distribution inside the adsorbent bed, the concentration of residual volatile organic compounds in the drug, and the difference in ventilation resistance in real time. The apparent efficiency calculated based on single-point data at the inlet and outlet cannot reflect the local adsorption state of the bed. Uneven radial adsorption efficiency can easily lead to premature saturation and failure of local adsorbents without timely detection, which may cause the risk of radioactive gas leakage. At the same time, the inability to accurately determine the actual effective lifespan of the adsorbent may result in excessive or untimely replacement of the adsorbent. Summary of the Invention

[0004] This invention provides a method and system for real-time evaluation of adsorbent performance for radioactive gas treatment, enabling full-domain three-dimensional monitoring of adsorbent adsorption performance, precise correction of multiple factors, and automatic determination of safety thresholds.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A first aspect is a method for real-time evaluation of the effectiveness of adsorbents used in radioactive gas treatment, the method comprising:

[0007] Multiple radioactivity sensors, temperature sensors, and humidity sensors are deployed at different depths along the axial and radial directions of the adsorbent bed to collect radioactivity concentration, temperature, and humidity at various locations in real time. Ventilation impedance sensors and volatile organic compound sensors are also installed at the inlet, outlet, and inside the bed of the adsorption unit to collect ventilation impedance and volatile organic compound concentration simultaneously.

[0008] Based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with temperature and humidity data collected by temperature and humidity sensors, a three-dimensional affine transformation is applied to the spatial coordinates of each sensor to map them to regular grid nodes, and then interpolation calculation is performed to construct a three-dimensional efficiency distribution field that reflects the efficiency distribution state inside the adsorbent bed.

[0009] The three-dimensional performance distribution field is spatially discretized, the performance attenuation gradient of each discrete region is extracted, and a spatial non-uniformity correction factor is generated based on the correlation between the performance attenuation gradient and the concentration of organic volatiles and humidity collected by the organic volatiles sensor and the humidity sensor.

[0010] Based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with temperature, humidity and ventilation resistance, the apparent adsorption efficiency of the adsorbent is calculated.

[0011] The apparent adsorption efficiency is corrected by a spatial non-uniformity correction factor to obtain the actual effective adsorption efficiency of the adsorbent.

[0012] The actual effective adsorption capacity is compared with the preset safety threshold. If it is lower than the safety threshold, an alarm signal is issued and the standby adsorption unit is switched.

[0013] Furthermore, multiple radioactivity sensors, temperature sensors, and humidity sensors are deployed at different depths along the axial and radial directions of the adsorbent bed to collect real-time data on radioactive gas activity concentration, temperature, and humidity at various locations. Additionally, ventilation impedance sensors and volatile organic compound (VOC) sensors are installed at the inlet, outlet, and inside the adsorption unit to simultaneously collect ventilation impedance and VOC concentration data, including:

[0014] The adsorbent bed is divided into several equidistant monitoring layers according to its axial height. The radioactivity sensor, temperature sensor and humidity sensor are arranged at equal angular intervals in the radial direction of each monitoring layer. At the same time, the ventilation impedance sensor and volatile organic compound sensor are fixedly installed at the inlet, outlet and inside the bed of the adsorption unit according to the preset position, forming a three-dimensional monitoring network covering the entire cross section and key nodes of the bed.

[0015] The system collects analog signals of radioactive gas activity concentration, temperature, and humidity in real time through various sensors in the three-dimensional monitoring network, and collects analog signals of ventilation impedance and organic volatile concentration in real time through ventilation impedance sensors and organic volatile matter sensors. All analog signals are converted from analog to digital and packaged into a raw monitoring dataset according to a unified time series. The raw monitoring dataset is then subjected to outlier removal and filtering to eliminate signal noise and acquisition errors.

[0016] Furthermore, based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with temperature and humidity data collected by temperature and humidity sensors, a three-dimensional affine transformation is applied to the spatial coordinates of each sensor to map them onto regular grid nodes. Then, through interpolation calculations, a three-dimensional efficiency distribution field reflecting the efficiency distribution state within the adsorbent bed is constructed, including:

[0017] From the obtained standardized monitoring data, the activity concentration of radioactive gas at the inlet and outlet of the adsorption unit is extracted, the difference between the two activity concentrations is calculated, and the temperature and humidity data of each monitoring point are extracted simultaneously from the standardized monitoring data.

[0018] Based on the temperature and humidity data of each monitoring point, combined with the activity concentration difference, the local performance characterization value of each monitoring point is calculated to form a discrete set of spatial performance sampling points. Each sampling point contains spatial coordinates and the corresponding local performance characterization value.

[0019] A three-dimensional affine transformation is applied to each spatial coordinate in the obtained spatial performance sampling point set to map the irregularly distributed spatial coordinates to the coordinates of the preset regular grid nodes, generating the mapped performance characterization value corresponding to the regular grid nodes.

[0020] Based on the mapped performance characterization values, spatial interpolation calculations are performed between regular grid nodes to fill the grid gaps, resulting in a continuous three-dimensional performance distribution field covering the entire adsorbent bed.

[0021] Furthermore, the three-dimensional performance distribution field is spatially discretized to extract the performance attenuation gradient of each discrete region. Based on the correlation between the performance attenuation gradient and the concentration of volatile organic compounds (VOCs) and humidity collected by the VOC and humidity sensors, a spatial non-uniformity correction factor is generated, including:

[0022] The continuous three-dimensional performance distribution field is meshed and divided into several discretized spatial sub-regions. The center point coordinates and corresponding performance values ​​of each spatial sub-region are extracted to form a discretized performance dataset.

[0023] Based on the discretized performance dataset, the performance difference between each spatial sub-region and its adjacent sub-regions is calculated. Combined with the spatial distance between the center points of each sub-region, the performance decay gradient of each spatial sub-region is obtained, and a performance decay gradient field is generated.

[0024] From the standardized monitoring data, the concentration and humidity data of volatile organic compounds (VOCs) corresponding to the coordinates of the center point of each spatial sub-region are extracted. The VOC concentration and humidity data are then spatially registered with the corresponding gradient values ​​in the obtained performance attenuation gradient field to form an associated dataset containing gradient values, VOC concentrations, and humidity.

[0025] Multivariate regression analysis was performed on the associated dataset to determine the correlation weight coefficients between the efficiency decay gradient and the concentration of volatile organic compounds and humidity, and the competitive adsorption interference intensity of each spatial sub-region was calculated based on the correlation weight coefficients.

[0026] The competitive adsorption interference intensity of each spatial sub-region is normalized to obtain a spatial non-uniformity correction factor.

[0027] Furthermore, based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with temperature, humidity, and ventilation resistance, the apparent adsorption efficiency of the adsorbent is calculated, including:

[0028] From the obtained standardized monitoring data, extract the current moment's inlet radioactive gas activity concentration and outlet radioactive gas activity concentration of the adsorption unit, calculate the real-time activity concentration difference between the two, and simultaneously extract the current moment's average bed temperature, average humidity, and average ventilation resistance from the standardized monitoring data.

[0029] Based on the obtained real-time activity concentration difference, combined with the preset adsorption capacity coefficient of the adsorbent, the initial theoretical adsorption efficiency value is calculated.

[0030] Based on the extracted average bed temperature, average humidity, and average ventilation resistance, the corresponding temperature correction coefficient, humidity correction coefficient, and ventilation resistance correction coefficient are obtained by querying the preset temperature-performance correction coefficient table, humidity-performance correction coefficient table, and ventilation resistance-performance correction coefficient table, respectively.

[0031] The initial theoretical adsorption efficiency value is successively multiplied by the obtained temperature correction coefficient, humidity correction coefficient, and ventilation resistance correction coefficient to perform multi-factor stepwise correction, thus obtaining the apparent adsorption efficiency of the adsorbent.

[0032] Furthermore, the apparent adsorption efficiency is corrected by a spatial non-uniformity correction factor to obtain the actual effective adsorption efficiency of the adsorbent, including:

[0033] From the spatially non-uniform correction factor field, the correction factor values ​​of all spatial sub-regions corresponding to the entire adsorbent bed are extracted to form a correction factor set;

[0034] The set of correction factors is weighted and fused, and the volume ratio of each spatial sub-region is used as the weight coefficient to calculate the comprehensive non-uniformity correction coefficient covering the entire bed.

[0035] The apparent adsorption efficiency value at the current moment is extracted from the apparent adsorption efficiency value; the apparent adsorption efficiency value is multiplied by the comprehensive non-uniformity correction coefficient to correct the overestimation error caused by competitive adsorption interference, and the actual effective adsorption efficiency of the adsorbent is obtained.

[0036] Furthermore, the actual effective adsorption capacity is compared with a preset safety threshold. If it falls below the safety threshold, an alarm signal is issued and a backup adsorption unit is switched on, including:

[0037] Extract the actual effective adsorption efficiency value at the current moment from the obtained actual effective adsorption efficiency, and read the pre-stored safety threshold from the preset parameters;

[0038] The extracted actual effective adsorption efficiency value is compared with the safety threshold to determine whether the current actual effective adsorption efficiency is lower than the safety threshold.

[0039] If the comparison results show that the actual effective adsorption efficiency is lower than the safety threshold, an alarm trigger signal containing the current efficiency value and the degree of exceeding the limit will be generated.

[0040] Based on the alarm trigger signal, an execution command is simultaneously sent to the automatic switching mechanism of the adsorption unit to drive the standby adsorption unit into operation and disconnect the current adsorption unit from the gas processing flow path.

[0041] Secondly, a real-time evaluation system for the effectiveness of adsorbents used in radioactive gas treatment includes:

[0042] The acquisition module is used to deploy multiple radioactivity sensors, temperature sensors, and humidity sensors at different depths in the axial and radial directions of the adsorbent bed to collect radioactivity gas activity concentration, temperature, and humidity at various locations in real time. Ventilation impedance sensors and volatile organic compound sensors are also deployed at the inlet, outlet, and inside the adsorption unit to collect ventilation impedance and volatile organic compound concentration simultaneously.

[0043] The mapping module is used to map the spatial coordinates of each sensor to regular grid nodes by applying a three-dimensional affine transformation to the spatial coordinates of each sensor based on the difference in radioactive gas activity concentration between the inlet and outlet, and combining the temperature and humidity data collected by the temperature and humidity sensors. Then, it constructs a three-dimensional efficiency distribution field that reflects the efficiency distribution state inside the adsorbent bed through interpolation calculation.

[0044] The extraction module is used to spatially discretize the three-dimensional performance distribution field, extract the performance attenuation gradient of each discrete region, and generate a spatial non-uniformity correction factor based on the correlation between the performance attenuation gradient and the concentration of organic volatiles and humidity collected by the organic volatiles sensor and the humidity sensor.

[0045] The calculation module is used to calculate the apparent adsorption efficiency of the adsorbent based on the difference in radioactive gas activity concentration between the inlet and outlet, combined with temperature, humidity and ventilation resistance.

[0046] The correction module is used to correct the apparent adsorption efficiency through a spatial non-uniformity correction factor to obtain the actual effective adsorption efficiency of the adsorbent.

[0047] The processing module compares the actual effective adsorption capacity with a preset safety threshold. If the actual capacity is lower than the safety threshold, an alarm signal is issued and the standby adsorption unit is switched.

[0048] Thirdly, a computing device, comprising:

[0049] One or more processors;

[0050] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0051] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0052] The above-described solution of the present invention has at least the following beneficial effects:

[0053] By deploying multiple types of sensors at different depths along the axial and radial axes of the adsorbent bed, and by placing ventilation impedance and volatile organic compound (VOC) sensors at key nodes of the adsorption unit to achieve real-time acquisition of multiple parameters, and by constructing a three-dimensional performance distribution field of the bed through three-dimensional affine transformation and interpolation calculation, and by generating spatial non-uniformity correction factors through spatial discretization and multivariate correlation analysis, the apparent adsorption performance is corrected by multiple factors. Combined with safety thresholds, automatic alarms and backup unit switching are achieved. Therefore, this method overcomes the technical problems of traditional assessment methods that rely solely on single-point monitoring at the inlet and outlet, cannot grasp the real-time temperature and humidity, VOC concentration, and ventilation impedance distribution inside the bed, and are difficult to reflect the local adsorption state of the bed, which can easily lead to undetected local premature saturation failure, unreasonable adsorbent replacement, and the risk of radioactive gas leakage. Thus, it achieves the technical effect of full-domain three-dimensional real-time monitoring of adsorbent performance, accurate assessment of actual effective adsorption performance, timely warning of safety hazards, reasonable determination of adsorbent lifespan, reduction of operation and maintenance costs, and ensuring safe and compliant treatment of radioactive waste gas in medical scenarios. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating a method for real-time evaluation of adsorbent effectiveness in radioactive gas treatment, provided by an embodiment of the present invention.

[0055] Figure 2 This is a schematic diagram of an adsorbent efficiency real-time evaluation system for radioactive gas treatment provided in an embodiment of the present invention. Detailed Implementation

[0056] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art.

[0057] like Figure 1 As shown, embodiments of the present invention propose a method for real-time evaluation of adsorbent effectiveness for radioactive gas treatment, the method comprising the following steps:

[0058] Step 1: Deploy multiple radioactivity sensors, temperature sensors, and humidity sensors at different depths in the axial and radial directions of the adsorbent bed to collect the radioactivity concentration, temperature, and humidity at each location in real time. Also, install ventilation impedance sensors and volatile organic compound sensors at the inlet, outlet, and inside the bed of the adsorption unit to collect ventilation impedance and volatile organic compound concentration simultaneously.

[0059] Step 2: Based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with the temperature and humidity data collected by the temperature and humidity sensors, the spatial coordinates of each sensor are mapped to regular grid nodes by applying a three-dimensional affine transformation, and then interpolation calculation is performed to construct a three-dimensional efficiency distribution field that reflects the efficiency distribution state inside the adsorbent bed.

[0060] Step 3: Spatial discretization of the three-dimensional performance distribution field is performed to extract the performance attenuation gradient of each discrete region. Based on the correlation between the performance attenuation gradient and the concentration of volatile organic compounds and humidity collected by the volatile organic compound sensor and humidity sensor, a spatial non-uniformity correction factor is generated.

[0061] Step 4: Based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with temperature, humidity and ventilation resistance, the apparent adsorption efficiency of the adsorbent is calculated.

[0062] Step 5: Correct the apparent adsorption efficiency by using a spatial non-uniformity correction factor to obtain the actual effective adsorption efficiency of the adsorbent.

[0063] Step 6: Compare the actual effective adsorption capacity with the preset safety threshold. If it is lower than the safety threshold, issue an alarm signal and control the switching of the standby adsorption unit.

[0064] In this embodiment of the invention, multiple radioactivity, temperature, and humidity sensors are deployed at different depths along the axial and radial axes of the adsorbent bed. Ventilation impedance and volatile organic compound sensors are installed at the inlet, outlet, and inside the bed of the adsorption unit to collect multiple parameters in real time. A three-dimensional efficiency distribution field of the adsorbent bed is constructed through three-dimensional affine transformation and interpolation calculation. This distribution field is spatially discretized, and a spatial non-uniformity correction factor is generated by combining relevant parameters. This correction factor is used to correct the apparent adsorption efficiency. Simultaneously, the actual effective adsorption efficiency is compared with a preset safety threshold to achieve alarm and switching of backup adsorption units. Therefore, this overcomes the technical problems of traditional adsorbent efficiency assessment relying on single-point monitoring at the inlet and outlet, the inability to obtain real-time multi-parameter distribution within the bed, the difficulty in reflecting the local adsorption state of the bed, the potential for undetected premature saturation failure, and unreasonable adsorbent replacement, which can lead to the risk of radioactive gas leakage. Thus, this invention achieves real-time three-dimensional monitoring of adsorbent efficiency across the entire domain, accurate calculation of actual effective adsorption efficiency, timely warning of safety hazards, and reasonable control of adsorption unit switching, ensuring the safe and stable treatment of radioactive gases.

[0065] In a preferred embodiment of the present invention, step 1 above may include:

[0066] Step 1.1: Divide the adsorbent bed into several equidistant monitoring layers based on its axial height. On each monitoring layer, arrange the radioactivity sensor, temperature sensor, and humidity sensor at equal angular intervals along the radial direction. Simultaneously, install ventilation impedance sensors and volatile organic compound sensors at predetermined locations at the inlet, outlet, and inside the bed of the adsorption unit, forming a three-dimensional monitoring network covering the entire cross-section and key nodes of the bed. Specifically, this includes: Based on the structural morphology of the adsorbent bed in the medical radioactive gas treatment system, using the total axial height of the bed as a basis, divide the adsorbent bed axially from top to bottom into multiple parallel monitoring layers with consistent spacing according to a fixed equidistant division rule. All monitoring layers completely cover the overall axial height of the bed, eliminating monitoring blind spots in the axial direction. Within each divided axial monitoring layer, using the central axis of the bed as a reference, set multiple evenly distributed monitoring points along the radial direction of the bed according to an equidistant interval rule. Each monitoring point is simultaneously equipped with a radioactivity sensor. Temperature, humidity, and humidity sensors ensure that the adsorbent state in all radial directions and regions of the bed can be monitored in real time, preventing the loss of accurate operating conditions in localized radial areas due to monitoring gaps. Simultaneously, according to preset installation locations, ventilation impedance sensors and volatile organic compound (VOC) sensors are fixedly installed in the gas inlet and outlet pipes of the adsorption unit, as well as in key areas within the adsorbent bed where airflow is prone to turbulence and adsorption deviation. The ventilation impedance sensors collect airflow resistance parameters as gas flows through the adsorbent bed, while the VOC sensors collect concentration parameters of residual VOCs from radiopharmaceutical synthesis. Through this axial, layered, and uniformly distributed radial configuration, supplemented by additional sensors at the inlet, outlet, and key areas within the bed, a three-dimensional monitoring network is formed, covering the entire cross-section and depth of the adsorbent bed, as well as key airflow nodes of the adsorption unit. This enables simultaneous real-time monitoring of multiple key parameters, including radioactive gas activity concentration, temperature, humidity, ventilation impedance, and VOC concentration.

[0067] Step 1.2: Simulated signals of radioactive gas activity concentration, temperature, and humidity are collected in real time by various sensors in the three-dimensional monitoring network. Simulated signals of ventilation impedance and volatile organic compound (VOC) concentration are also collected in real time by ventilation impedance sensors and VOC sensors. All simulated signals are converted from analog to digital and packaged into a raw monitoring dataset according to a unified time series. Outlier removal and filtering are performed on the raw monitoring dataset to eliminate signal noise and acquisition errors. Specifically, relying on the established three-dimensional monitoring network, various sensors continuously collect various physical parameters at corresponding monitoring locations in real time according to a set fixed acquisition frequency and output simulated electrical signals. Among them, the radioactive activity sensor collects the radioactive gas activity concentration signal at each monitoring point inside the adsorbent bed, temperature, and humidity signals, and the humidity and humidity are collected in real time by various sensors in the three-dimensional monitoring network. Temperature sensors collect ambient temperature signals at corresponding monitoring points, humidity sensors collect ambient humidity signals at corresponding monitoring points, ventilation impedance sensors collect ventilation impedance signals as gas flows through the adsorbent bed in the flow path, and volatile organic compound (VOC) sensors collect concentration signals of VOCs generated by drug residues in the corresponding monitoring area. All continuous analog electrical signals output by the sensors are uniformly transmitted to the data processing unit, where analog-to-digital conversion converts the analog electrical signals into calculable, storable, and transmittable digital signals. After conversion, the various digital signals are time-aligned according to a unified acquisition time. Digital signals from different monitoring points and with different parameter types at the same acquisition time are integrated and packaged to form a raw monitoring dataset containing monitoring parameter values, monitoring spatial location, and acquisition time information.

[0068] To eliminate invalid data caused by sensor malfunctions, external electromagnetic interference, and abnormal signal transmission, normal value ranges are pre-defined for each type of monitoring parameter based on the normal operating conditions of radioactive gas processing. When processing the raw monitoring dataset, each monitoring data point is checked against its corresponding normal value range. Monitoring data exceeding the normal value range is identified as abnormal data and directly removed, retaining only valid monitoring data within the normal value range. After removing abnormal values, the retained valid monitoring data is filtered to eliminate random noise and acquisition errors generated during data acquisition and transmission. During the filtering process, smoothing operations are performed on each set of valid monitoring data according to pre-set filtering parameters. These operations correct data deviations, reduce noise interference, and correct acquisition errors. After filtering, a standardized monitoring dataset with reliable accuracy, stable values, and consistent timing is finally obtained.

[0069] In this embodiment of the invention, a three-dimensional monitoring network covering the entire cross-section of the adsorbent bed and key nodes is constructed by dividing the adsorbent bed into equidistant monitoring layers based on the axial height of the bed, and by radially arranging radioactivity, temperature, and humidity sensors at equal angular intervals on each monitoring layer. Simultaneously, ventilation impedance and volatile organic compound sensors are fixedly installed at key locations in the adsorption unit. Furthermore, the various analog signals collected by the sensors are converted from analog to digital, packaged into unified time series, and then subjected to outlier removal and filtering to eliminate signal noise and acquisition errors. Therefore, this method overcomes the technical problems of traditional monitoring methods, such as unreasonable sensor layout, incomplete monitoring range, inability to achieve simultaneous multi-parameter monitoring across the entire bed, and the presence of noise, errors, and poor standardization in the collected monitoring data, making it difficult to support accurate performance evaluation. Thus, it achieves real-time synchronous monitoring of multiple parameters across the entire adsorbent bed without blind spots, ensuring the accuracy, standardization, and completeness of the monitoring data.

[0070] In a preferred embodiment of the present invention, step 2 above may include:

[0071] Step 2.1: Extract the radioactive gas activity concentrations at the inlet and outlet of the adsorption unit from the obtained standardized monitoring data, calculate the difference between the two activity concentrations, and simultaneously extract the temperature and humidity data of each monitoring point from the standardized monitoring data. Specifically, this includes: accurately extracting two types of core monitoring data from the obtained standardized monitoring dataset to ensure that the data source for subsequent performance evaluation is complete and accurate. The first type of core data consists of radioactive gas activity concentration data at the inlet and outlet of the adsorption unit. The second type of core data consists of temperature and humidity data corresponding to all monitoring points in the deployed three-dimensional monitoring network. Both types of data are extracted and stored simultaneously to avoid data confusion. The radioactive gas activity concentration data at the inlet of the adsorption unit is not extracted arbitrarily, but is specifically extracted from standardized data collected by radioactive activity sensors deployed adjacent to the ventilation impedance sensor and volatile organic compound sensor at the inlet of the adsorption unit. The sensor at this location accurately captures the initial activity concentration of radioactive gas entering the adsorption unit, ensuring the representativeness of the data. The radioactive gas activity concentration data at the outlet of the adsorption unit is extracted from standardized data collected by the corresponding radioactive activity sensor deployed at the outlet of the adsorption unit. The sensor at this location is used to capture the residual activity concentration of radioactive gas discharged from the adsorption unit after adsorption treatment by the adsorbent bed, corresponding to the inlet data.

[0072] After extracting the inlet and outlet radioactive gas activity concentration data, the difference between the two is calculated. Specifically, the radioactive gas activity concentration at the inlet of the adsorption unit is subtracted from the radioactive gas activity concentration at the outlet of the adsorption unit. This difference directly reflects the overall adsorption effect of the adsorbent bed on the radioactive gas. The larger the difference, the stronger the overall adsorption capacity of the adsorbent bed. This difference will serve as the basis for calculating the local performance characterization values ​​of each monitoring point, providing support for the subsequent accurate assessment of the local adsorption state of the bed. At the same time, temperature and humidity data corresponding to each monitoring point are extracted from the standardized monitoring dataset. During the extraction process, it is strictly ensured that the temperature and humidity data of each monitoring point correspond one-to-one with its corresponding three-dimensional spatial coordinates, without any misalignment between data and points. The reason for strict correspondence is that the adsorption efficiency of the adsorbent is affected by temperature and humidity. Different monitoring points have different temperatures and humidity, and their adsorption efficiency will also vary. Only by ensuring that the temperature and humidity data correspond accurately with the coordinates of the monitoring points can accurate parameters be provided for calculating the local performance characterization values ​​of each monitoring point, thereby ensuring the accuracy of the entire adsorption efficiency assessment process.

[0073] Step 2.2: Based on the temperature and humidity data of each monitoring point, and combined with the activity concentration difference, calculate the local performance characterization value of each monitoring point to form a discrete spatial performance sampling point set. Each sampling point includes spatial coordinates and the corresponding local performance characterization value. Specifically, this includes: using the extracted inlet and outlet radioactive gas activity concentration difference, temperature data, and humidity data of each monitoring point as core calculation parameters, and combining the adsorption characteristics of the adsorbent under different temperature and humidity conditions in a medical setting, calculating the local performance characterization value of each monitoring point one by one, and then constructing a discrete spatial performance sampling point set. First, for each monitoring point in the deployed three-dimensional monitoring network, extract the temperature and humidity data corresponding to that point one by one. Then, according to the system's preset temperature-performance correction coefficient table and humidity-performance correction coefficient table, query the temperature correction coefficient and humidity correction coefficient corresponding to that monitoring point, respectively. The temperature correction coefficient is used to correct the influence of temperature changes on the adsorption performance of the adsorbent, because an increase in temperature or Decreasing humidity levels alters the adsorption activity of the adsorbent, thus affecting the adsorption effect. The humidity correction factor corrects for the impact of humidity changes on the adsorption efficiency of the adsorbent. Excessively high or low humidity can interfere with the adsorbent's ability to adsorb radioactive gases. These two types of correction factors are not arbitrarily set but are determined through multiple experiments and calibrations based on the actual operating conditions of radioactive waste gas treatment in medical and nuclear medicine scenarios, ensuring accurate correction of the effects of temperature and humidity on adsorption efficiency. After obtaining the temperature correction factor, humidity correction factor, and calculated difference in inlet and outlet radioactive gas activity concentration for each monitoring point, a comprehensive calculation is performed to obtain the local efficiency characterization value for that monitoring point. Specifically, the difference in inlet and outlet radioactive gas activity concentration is multiplied by the temperature correction factor and humidity correction factor for that point, respectively. This calculation method transforms the overall adsorption effect of the adsorbent bed into the local adsorption efficiency of each monitoring point, accurately reflecting the differences in adsorption capacity at different axial and radial positions of the adsorbent bed.

[0074] After calculating the local performance characterization values ​​of all monitoring points, a discrete spatial performance sampling point set is further constructed. Specifically, the three-dimensional spatial coordinates of each monitoring point are associated one-to-one with the corresponding local performance characterization value. The three-dimensional spatial coordinates of the monitoring point are preset when the sensors are deployed to accurately locate the specific position of the monitoring point in the adsorbent bed, ensuring that each sampling point corresponds to a specific area within the bed. Through this association method, a discrete spatial performance sampling point set is formed, in which each sampling point contains complete three-dimensional spatial coordinates and a corresponding local performance characterization value. Moreover, all sampling points are evenly distributed along the axial and radial directions of the adsorbent bed according to the sensor deployment rules, completely covering the entire adsorbent bed and eliminating monitoring blind spots.

[0075] Step 2.3 involves applying a three-dimensional affine transformation to the spatial coordinates of the obtained spatial performance sampling point set, mapping the irregularly distributed spatial coordinates to the coordinates of a preset regular grid node, and generating the mapped performance characterization value corresponding to the regular grid node. Specifically, this includes: based on the actual size of the adsorbent bed in the medical scenario and the accuracy requirements for adsorption performance evaluation, a three-dimensional regular grid that completely covers the entire adsorbent bed is preset. This three-dimensional regular grid consists of several uniformly distributed grid nodes, each with a clear three-dimensional spatial coordinate. The spacing between grid nodes is preset according to the accuracy requirements for adsorption performance evaluation in the medical and nuclear medicine scenario. The spacing setting must ensure that the grid completely covers the entire adsorbent bed without any blind spots, and also ensure that the density of grid nodes is sufficient. A three-dimensional affine transformation is then performed on each sampling point in the obtained spatial performance sampling point set. Each sampling point has its original spatial coordinates. Due to the actual deployment of the sensors, these coordinates are irregularly distributed and cannot be directly used to construct a continuous three-dimensional performance distribution field. Therefore, a three-dimensional affine transformation is needed to map these irregularly distributed original spatial coordinates to the node coordinates of a preset three-dimensional regular grid. Specifically, the transformation process involves calculating and adjusting the three dimensions (axial, radial, and longitudinal) of the original spatial coordinates of the sampling points using preset coordinate transformation coefficients and coordinate offsets. This ensures that the coordinates of each irregularly distributed sampling point are accurately mapped to the corresponding node coordinates in the three-dimensional regular grid, guaranteeing that the mapped node coordinates precisely correspond to the actual spatial position of the adsorbent bed without any coordinate offset. All coordinate transformation coefficients and coordinate offsets are pre-calibrated based on the actual dimensions of the adsorbent bed and the parameters of the preset regular grid, ensuring the accuracy and reliability of the transformation.

[0076] After completing the three-dimensional affine transformation of the coordinates of each sampling point, the local performance characterization value corresponding to the sampling point is synchronously mapped to the corresponding regular mesh node. That is, each regular mesh node obtained after the transformation corresponds to a mapped performance characterization value, forming a set of mapped performance characterization values ​​that correspond one-to-one with the regular mesh node.

[0077] Step 2.4: Based on the mapped performance characterization value, spatial interpolation calculation is performed between regular grid nodes to fill the grid gaps and obtain a continuous three-dimensional performance distribution field covering the entire adsorbent bed. Specifically, this includes: clarifying that the obtained mapped performance characterization value only covers some nodes in the regular grid, and there are gaps between the grid nodes, which cannot reflect the adsorption performance at every location inside the bed. Therefore, spatial interpolation calculation is needed to fill these grid gaps and obtain the performance value of all grid nodes. Considering the accuracy requirements of adsorption performance evaluation in medical scenarios, a linear interpolation method is used for calculation. This method is simple to calculate, reliable in accuracy, and accurately reflects the spatial variation trend of performance.

[0078] The specific process of linear interpolation calculation is as follows: For any two adjacent nodes in the regular grid that have obtained the mapped performance characterization values, let the coordinates of the first node be ( , , The corresponding mapped performance representation value is E1′; the coordinates of the second node are ( , , The corresponding mapped performance characterization value is E2′; the spatial distance between two nodes is denoted as L, and the calculation formula is: In the formula, This represents the spatial distance between two adjacent nodes. , , Let these be the coordinates of the first node. , , These are the coordinates of the second node.

[0079] For any gap between two adjacent nodes, let the coordinates of that position be ( ). , , Let l be the spatial distance from this position to the first node, and the calculation formula is: In the formula, The spatial distance from the gap location to the first node. , , The coordinates of the gap location, , , The coordinates of the first node.

[0080] The efficiency value at this gap location is calculated using a linear interpolation formula. The specific formula is as follows: In the insert type, The interpolation efficiency value for interpolation at the gap location. , The performance representation value after mapping between two adjacent nodes. This represents the distance from the gap location to the first node. This represents the total distance between two adjacent nodes.

[0081] According to the above interpolation method, interpolation calculations are performed on all grid gaps in the regular grid one by one to fill the efficiency values ​​at all gap locations. Finally, efficiency data covering all regular grid nodes of the entire adsorbent bed is obtained. The efficiency data are interconnected and continuously distributed, together forming a continuous three-dimensional efficiency distribution field that reflects the efficiency distribution state inside the adsorbent bed. This three-dimensional efficiency distribution field clearly and intuitively presents the adsorption efficiency differences at different positions in the axial and radial directions of the bed, accurately reflecting the local adsorption state of the bed.

[0082] In this embodiment of the invention, the method of extracting the activity concentration of radioactive gas at the inlet and outlet of the adsorption unit from standardized monitoring data and calculating the difference, simultaneously extracting temperature and humidity data at each monitoring point, and combining the above data to calculate the local performance characterization value of each monitoring point to form a discrete spatial performance sampling point set, applying a three-dimensional affine transformation to the spatial coordinates of the sampling points to map them to regular grid nodes, and then filling the grid gaps through spatial interpolation calculation, overcomes the technical problem that traditional methods cannot convert discrete monitoring data into continuous global distribution and are difficult to accurately reflect the spatial distribution state of the performance inside the adsorbent bed, resulting in a lack of comprehensive and accurate data support for the performance evaluation inside the bed. Thus, it achieves the construction of a continuous three-dimensional performance distribution field covering the entire adsorbent bed, clearly presenting the differences in performance distribution inside the bed.

[0083] In a preferred embodiment of the present invention, step 3 above may include:

[0084] Step 3.1 involves meshing the continuous three-dimensional performance distribution field into several discretized spatial sub-regions, and extracting the center point coordinates and corresponding performance values ​​of each spatial sub-region to form a discretized performance dataset. Specifically, this includes: based on the actual three-dimensional dimensions of the adsorbent bed, the parameters of the preset regular mesh, and the accuracy requirements for identifying local performance differences in the medical scenario, the continuous three-dimensional performance distribution field is divided into regularized meshes. During the meshing process, the continuous three-dimensional performance distribution field is divided into several discretized spatial sub-regions with consistent volume and clearly defined spatial locations. All spatial sub-regions are seamlessly stitched together to completely cover the entire area. The adsorbent bed is designed to be complete and without overlap, ensuring that each sub-region accurately corresponds to a local space within the bed. This meets the monitoring needs of radioactive waste gas treatment in nuclear medicine departments where the bed is prone to premature saturation in certain areas. For each discretized spatial sub-region, two core pieces of information are precisely extracted: the three-dimensional spatial coordinates of the center point of the spatial sub-region, calculated from the sub-region's position in the three-dimensional grid, serving as the spatial characteristic identifier of the sub-region; and the efficiency value corresponding to the spatial sub-region. To ensure the representativeness of the efficiency value, the arithmetic mean of the efficiency values ​​of all regular grid nodes within the sub-region is used as the efficiency value of that sub-region. The specific calculation formula is as follows: In the formula, The performance value for a single spatial sub-region. This represents the total number of regular grid nodes contained within this spatial sub-region. For the first sub-region The performance value of each regular grid node.

[0085] After extracting the center point coordinates and calculating the performance value of all spatial sub-regions, the three-dimensional spatial coordinates of the center point of each spatial sub-region are associated with the corresponding performance value. The sub-regions are then integrated and stored according to their spatial arrangement order, ultimately forming a discretized performance dataset containing spatial location information and performance feature information.

[0086] Step 3.2: Based on the discretized performance dataset, calculate the performance difference between each spatial sub-region and its adjacent sub-regions. Combined with the spatial distance between the center points of each sub-region, calculate the performance attenuation gradient of each spatial sub-region to generate an performance attenuation gradient field. Specifically, this includes: Based on the discretized performance dataset, determine the adjacent sub-regions in three-dimensional space for each spatial sub-region. Combining the three-dimensional structural characteristics of the adsorbent bed, adopt the six-neighborhood judgment rule. That is, the adjacent sub-regions of each spatial sub-region are its six directly adjacent spatial sub-regions in the positive axial direction, negative axial direction, positive radial direction, negative radial direction, positive circumferential direction, and negative circumferential direction. This ensures comprehensive capture of the performance change trend between the sub-region and its surrounding areas, accurately reflecting the attenuation characteristics of the local performance of the bed in medical scenarios. For each spatial sub-region, calculate the performance difference between it and each adjacent sub-region, taking the absolute value of the performance difference to eliminate the influence of positive and negative directions. Simultaneously, calculate the three-dimensional straight-line distance between the center point of the target spatial sub-region and the center point of its adjacent sub-region. The specific calculation formula is as follows: In the formula, The spatial distance between the center points of two adjacent sub-regions, , , ) represents the coordinates of the center point of the target spatial sub-region. , , Let be the coordinates of the center point of the adjacent sub-region. Calculate the efficiency attenuation gradient of the target spatial sub-region relative to the adjacent sub-region. The calculation method is to divide the absolute difference in efficiency by the spatial distance. After calculating the efficiency attenuation gradients of the target spatial sub-region and all adjacent sub-regions, take the arithmetic mean of all gradient values ​​as the comprehensive efficiency attenuation gradient of the target spatial sub-region. Following the above process, calculate the comprehensive efficiency attenuation gradient of all spatial sub-regions one by one. Correlate the center point coordinates of each spatial sub-region with the corresponding comprehensive efficiency attenuation gradient to finally form an efficiency attenuation gradient field covering the entire adsorbent bed. This gradient field directly reflects the difference in efficiency attenuation rate in different local spaces within the bed.

[0087] Step 3.3: Extract the volatile organic compound (VOC) concentration and humidity data corresponding to the coordinates of the center point of each spatial sub-region from the standardized monitoring data. Spatially register the VOC concentration and humidity data with the corresponding gradient values ​​in the obtained performance attenuation gradient field to form a correlated dataset containing gradient values, VOC concentration, and humidity. Specifically, this includes: extracting two types of key interference parameters matching the performance attenuation gradient field from the standardized monitoring dataset: VOC concentration data and humidity data. Since the parameter data in the standardized monitoring dataset only correspond to the monitoring points of the deployed sensors, while the core data of the performance attenuation gradient field corresponds to the center point of each spatial sub-region, and some sub-region center points may not have sensors directly deployed, a linear interpolation method is used to calculate the VOC concentration and humidity data at the center point based on the sensor monitoring data around the center point of the sub-region, ensuring the spatial accuracy of parameter extraction.

[0088] The formula for calculating the concentration of volatile organic compounds at the center point of a specific spatial sub-region is as follows: In the formula, The concentration of volatile organic compounds at the center point of the spatial sub-region. , The concentrations of volatile organic compounds monitored by two adjacent sensors around the center point. The spatial distance from the center point to the first sensor. This represents the spatial distance between two adjacent sensors.

[0089] The formula for calculating the humidity at the center point of a specific spatial sub-region is as follows: In the formula, The humidity at the center point of the spatial sub-region. , The humidity is monitored by two adjacent humidity sensors around the center point. , The definition is consistent with the definition used in the above calculation of volatile organic compound concentration.

[0090] The core of spatial registration is to accurately correlate three data points corresponding to the center point of the same spatial sub-region: the comprehensive performance attenuation gradient of the sub-region, the concentration of volatile organic compounds (VOCs) calculated by interpolation, and the humidity calculated by interpolation. During the registration process, the coordinates of the center point of the spatial sub-region are used as the unique matching identifier to ensure that each set of data corresponds to the same local space within the bed, eliminating analytical errors caused by spatial misalignment. After completing the parameter extraction and spatial registration of all spatial sub-regions, all correlated data are integrated and stored, ultimately forming a correlated dataset containing spatial sub-region identifiers, comprehensive performance attenuation gradients, VOC concentrations, and humidity.

[0091] Step 3.4 involves performing multivariate regression analysis on the associated dataset to determine the correlation weight coefficients between the efficiency decay gradient and the concentration of volatile organic compounds (VOCs) and humidity. Based on these weight coefficients, the competitive adsorption interference intensity for each spatial sub-region is calculated. Specifically, this includes: performing overall fitting calculations and numerical correlation analysis on all data in the associated dataset; systematically analyzing the numerical changes between the comprehensive efficiency decay gradient and the concentration of VOCs and humidity in the corresponding spatial sub-regions; and calculating the weight coefficients of the influence of VOC concentration on the efficiency decay gradient and humidity on the efficiency decay gradient. The magnitude of these weight coefficients directly corresponds to the degree of influence of each factor on efficiency decay; a larger weight coefficient indicates a more significant impact of that factor on the local efficiency decay of the adsorbent bed. This aligns with the actual operating conditions in medical and nuclear medicine scenarios, where drug residue VOCs generate competitive adsorption and humidity changes interfere with adsorption activity. Using the two weight coefficients obtained from the calculation, the competitive adsorption interference intensity for each spatial sub-region is calculated. The competitive adsorption interference intensity is used to quantify the severity of local efficiency decay in the adsorbent bed under the combined effects of competitive adsorption of VOCs and humidity interference. The specific calculation formula is as follows: In the formula, The intensity of competitive adsorption interference in a single spatial sub-region. The influence weighting coefficients corresponding to the concentration of volatile organic compounds are: The influence weighting coefficient corresponding to humidity This represents the concentration of volatile organic compounds in this spatial sub-region. The humidity of this spatial sub-region is given. According to the above calculation method, the competitive adsorption interference intensity of all spatial sub-regions in the adsorbent bed is calculated one by one to complete the full quantitative characterization of the local interference intensity within the entire bed.

[0092] Step 3.5 involves normalizing the competitive adsorption interference intensity of each spatial sub-region to obtain a spatial non-uniformity correction factor. Specifically, this involves using the calculated competitive adsorption interference intensity of all spatial sub-regions as the core basis. The core objective is to eliminate dimensional differences in the competitive adsorption interference intensity of different spatial sub-regions through normalization, transforming it into a spatial non-uniformity correction factor with unified dimensions that can be directly used for subsequent performance correction. This ultimately generates a correction factor field covering the entire adsorbent bed, providing a reliable basis for accurate subsequent adsorption performance correction. From the calculated competitive adsorption interference intensity of all spatial sub-regions, two core feature values ​​are selected and extracted: the maximum and minimum values ​​of the competitive adsorption interference intensity in all spatial sub-regions. These two feature values ​​are the core benchmarks for subsequent normalization, clarifying the value range of all interference intensities. This ensures that after normalization, the correction factor for all spatial sub-regions is within a unified and reasonable range, eliminating the problems caused by excessive differences in interference intensity values ​​and dimensional inconsistencies between different sub-regions. The correction error is addressed by employing a linear normalization method to process the competitive adsorption interference intensity of each spatial sub-region. The core principle of linear normalization is to map the competitive adsorption interference intensity of each spatial sub-region to a numerical range between 0 and 1, based on the extracted maximum and minimum values. This mapped value is the spatial non-uniformity correction factor corresponding to that spatial sub-region. The specific processing logic is to subtract the minimum value of the interference intensity of all sub-regions from the competitive adsorption interference intensity of that spatial sub-region, and then divide the difference by the difference between the maximum and minimum values ​​of the interference intensity of all sub-regions. This transforms the interference intensity, which may have large numerical differences and inconsistent dimensions, into a correction factor with consistent dimensions and a fixed value range. The magnitude of the correction factor is negatively correlated with the degree of competitive adsorption interference of that spatial sub-region; that is, the higher the degree of competitive adsorption interference of that sub-region, the smaller the value of the correction factor; the lower the degree of interference, the closer the value of the correction factor is to 1. This correspondence accurately quantifies the degree of interference in each local space.

[0093] During the normalization process, a special case needs to be considered: the competitive adsorption interference intensity of all spatial sub-regions is completely consistent. In this case, the extracted maximum and minimum values ​​will be equal. If the calculation is performed according to the above linear normalization logic, the denominator will be 0, causing the calculation to fail. To ensure the integrity and rationality of the entire process, when this special case occurs, the spatial non-uniformity correction factor of all spatial sub-regions is uniformly set to 1. This means that the degree of competitive adsorption interference is consistent throughout the entire bed, and no additional non-uniformity correction is needed. After completing the normalization process of all spatial sub-regions, the three-dimensional spatial coordinates of the center point of each spatial sub-region are correlated one-to-one with the spatial non-uniformity correction factor obtained after normalization. This ensures that each correction factor accurately corresponds to a specific local space within the adsorbent bed, without spatial misalignment. All correlated data are integrated according to the arrangement order of the spatial sub-regions to finally form a spatial non-uniformity correction factor field covering the entire adsorbent bed.

[0094] In this embodiment of the invention, a technique is employed to obtain discretized spatial sub-regions by meshing a continuous three-dimensional performance distribution field and extracting the performance value at the center point of each sub-region. The performance attenuation gradient between each sub-region and its adjacent sub-regions is calculated to generate a performance attenuation gradient field. The concentration and humidity data of volatile organic compounds at corresponding locations in each sub-region are extracted and spatially registered with the gradient values. Multivariate regression analysis is performed on the associated datasets to determine the association weight coefficients and calculate the intensity of competitive adsorption interference. Finally, the interference intensity is normalized to obtain a spatial non-uniformity correction factor. This technique overcomes the technical problem that traditional evaluation methods do not consider the differences in performance attenuation gradients within the adsorbent bed and the competitive interference effects of volatile organic compounds and humidity on adsorption performance, and cannot generate accurate spatial non-uniformity correction factors. This results in a lack of scientific basis for performance correction and evaluation results that deviate from the actual adsorption state of the bed. Thus, it achieves accurate quantification of spatial non-uniformity and competitive adsorption interference intensity within the bed, and the generated spatial non-uniformity correction factor effectively adapts to the performance distribution differences across the entire bed.

[0095] In a preferred embodiment of the present invention, step 4 above may include:

[0096] Step 4.1: From the obtained standardized monitoring data, extract the current moment's radioactive gas activity concentration at the inlet and outlet of the adsorption unit, calculate the real-time activity concentration difference between the two, and simultaneously extract the current moment's average bed temperature, average humidity, and average ventilation resistance from the standardized monitoring data. Specifically, this includes: accurately extracting two types of core data from the obtained standardized monitoring dataset. One type is the radioactive gas activity concentration data at the inlet and outlet of the adsorption unit, used to calculate the overall adsorption effect of the bed on the radioactive gas at the current moment; the other type is the average bed temperature, average humidity, and average ventilation resistance data, used to reflect the overall operating condition of the adsorbent bed at the current moment. Among these, the radioactive gas activity concentration at the inlet of the adsorption unit... The activity concentration data is not extracted arbitrarily, but specifically extracted from standardized data collected at the current moment by a radioactivity sensor deployed at the inlet of the adsorption unit. This sensor is located adjacent to the ventilation impedance sensor and volatile organic compound sensor at the inlet, accurately capturing the initial activity concentration of the radioactive gas entering the adsorption unit, ensuring the authenticity and representativeness of the data. The radioactivity gas activity concentration data at the outlet of the adsorption unit is extracted from standardized data collected at the current moment by a corresponding radioactivity sensor deployed at the outlet of the adsorption unit. This data is used to capture the remaining activity concentration of the radioactive gas discharged from the adsorption unit after adsorption treatment by the adsorbent bed, forming a precise correspondence with the inlet data. This allows for calculations to reflect the overall adsorption effect of the bed. This process completes the inlet and outlet data... After extracting the radioactive gas activity concentration data, the real-time activity concentration difference between the two is further calculated. Specifically, the calculation method is to subtract the radioactive gas activity concentration at the adsorption unit outlet from the current radioactive gas activity concentration at the current inlet of the adsorption unit. This real-time difference directly reflects the overall adsorption effect of the adsorbent bed on the radioactive gas at the current moment. The larger the difference, the stronger the adsorption capacity of the bed for the radioactive gas at the current moment. Simultaneously, temperature, humidity, and ventilation impedance data for all monitoring points of the adsorbent bed are extracted from the standardized monitoring dataset at the current moment. Extracting data from all monitoring points is to avoid parameter deviations caused by using only local point data and to ensure subsequent corrections. To ensure accuracy, after extraction, the average bed temperature, average humidity, and average ventilation resistance are calculated separately: The average bed temperature is calculated by summing the standardized temperature data collected by all temperature sensors at the current moment, and then dividing the sum by the total number of temperature sensors. This result represents the average temperature of the adsorbent bed at the current moment, reflecting the overall temperature conditions of the bed and preventing the impact of localized high or low temperatures on adsorption efficiency from being ignored. The average bed humidity is calculated in the same way as the average temperature: the standardized humidity data collected by all humidity sensors at the current moment is summed, and then divided by the total number of humidity sensors to obtain the average bed humidity. This reflects the overall humidity environment of the bed, aligning with the characteristic that the adsorbent's adsorption activity varies under different humidity conditions.When calculating the average ventilation impedance of the bed, the standardized ventilation impedance data collected by all ventilation impedance sensors at the current moment are summed, and then divided by the total number of ventilation impedance sensors to obtain the average ventilation impedance of the bed. This average impedance reflects the overall flow resistance of gas within the bed, preventing insufficient contact between the adsorbent and radioactive gas due to localized poor ventilation, which could affect the accuracy of the adsorption efficiency assessment.

[0097] Step 4.2: Based on the obtained real-time activity concentration difference, combined with the preset adsorption capacity coefficient of the adsorbent, calculate the initial theoretical adsorption efficiency value. Specifically, this includes: clarifying the core meaning and setting basis of the preset adsorption capacity coefficient of the adsorbent. This coefficient is a fixed parameter preset specifically for the characteristics of adsorbents used in medical and nuclear medicine scenarios. Its setting process is determined through multiple experiments and repeated calibrations, taking into account the material, specifications, and adsorption performance of the adsorbent. It is not affected by actual operating conditions such as temperature, humidity, and ventilation resistance, and accurately reflects the inherent adsorption capacity of the adsorbent itself, that is, the theoretical adsorption efficiency of the adsorbent corresponding to the unit concentration difference. The obtained real-time activity concentration difference is converted into an efficiency value that quantifies the adsorption capacity of the adsorbent, eliminating the dimensional difference between the concentration difference and the adsorption efficiency, making it a standardized value that can be subsequently corrected and directly compared.

[0098] Geometric algorithms are used to correct the influence of adsorbent bed geometry on the initial theoretical adsorption efficiency. These features are physical properties of the adsorbent bed itself, distinct from dynamic operating conditions such as temperature and humidity. The core calculation dimensions include the geometric relationship between the effective contact area, porosity, and bed thickness of the adsorbent bed. The specific calculation method is as follows: first, obtain the actual geometric parameters of the adsorbent bed, including the nominal contact area of ​​the bed. That is, the standard gas contact area and the actual effective contact area of ​​the adsorbent bed design. The actual contact area between the gas and the adsorbent and the nominal thickness of the bed are obtained through bed packing density testing. Actual filling thickness Bed design porosity Actual porosity ;

[0099] Geometric correction factor calculated based on geometric algorithm First calculate the contact area correction ratio. Thickness normalized value Porosity correction ratio Then, the geometric correction factor is obtained by integrating the results through a geometric weighting algorithm. The value range of this geometric correction factor is 0.85~1.05. When the bed geometry parameters are completely consistent with the design values, Kg=1. If uneven bed filling leads to a reduction in effective contact area and a lower porosity, Kg will be less than 1, and vice versa.

[0100] Finally, all parameters are integrated to calculate the initial theoretical adsorption efficiency value: the real-time difference between the inlet and outlet radioactive gas activity concentrations at the current moment is multiplied by the preset adsorbent intrinsic adsorption capacity coefficient, and then multiplied by the geometric correction factor obtained through a geometric algorithm. The final initial theoretical adsorption efficiency value is described as follows: Initial theoretical adsorption efficiency value = Real-time difference between inlet and outlet activity concentrations at the current moment × Adsorbent intrinsic adsorption capacity coefficient × Geometric correction factor. This initial theoretical adsorption efficiency value reflects both the inherent adsorption capacity of the adsorbent itself and corrects for deviations caused by bed geometry features through a geometric algorithm. Compared with purely idealized calculation results, it is closer to the actual physical state of the adsorbent bed. However, it still does not consider the interference of actual operating parameters such as temperature, humidity, and ventilation resistance. In the actual operation of medical nuclear medicine scenarios, increased temperature will reduce the adsorption activity of the adsorbent, excessive humidity will interfere with the adsorption of radioactive gases by the adsorbent, and excessive ventilation resistance will reduce the contact time between the gas and the adsorbent. All of these will cause deviations between the actual adsorption efficiency of the adsorbent and the initial theoretical adsorption efficiency value. Therefore, the initial theoretical adsorption efficiency value is not the final adsorption efficiency evaluation result.

[0101] Step 4.3: Based on the extracted average bed temperature, average humidity, and average ventilation impedance, consult the preset temperature-efficiency correction coefficient tables, humidity-efficiency correction coefficient tables, and ventilation impedance-efficiency correction coefficient tables respectively to obtain the corresponding temperature correction coefficients, humidity correction coefficients, and ventilation impedance correction coefficients. Specifically, this includes clarifying the core information and setting basis of the three types of preset correction coefficient tables. These three correction coefficient tables are temperature-efficiency correction coefficient tables, humidity-efficiency correction coefficient tables, and ventilation impedance-efficiency correction coefficient tables. They are standardized tables preset after multiple experiments, data fitting, and repeated calibration, taking into account the adsorption characteristics of the adsorbent under different temperature, humidity, and ventilation impedance conditions, and clearly specifying the correction coefficients corresponding to different parameter values ​​of temperature, humidity, and ventilation impedance to ensure the accuracy and adaptability of the correction coefficients. Among them, the temperature-efficiency correction coefficient table is specifically used to look up the temperature correction coefficient corresponding to different average bed temperatures. The core function of this correction coefficient is to correct the effect of temperature changes on adsorption efficiency. The adsorption activity of the adsorbent changes with temperature, affecting its actual adsorption efficiency. The temperature correction coefficient quantifies this effect, adjusting the initial theoretical adsorption efficiency value to match the current temperature conditions. The humidity-efficiency correction coefficient table is used to find the humidity correction coefficient corresponding to the average humidity of different bed layers. Its function is to correct for the interference of humidity changes on adsorption efficiency. Excessively high or low humidity will damage the adsorption environment of the adsorbent and interfere with its ability to adsorb radioactive gases. The humidity correction coefficient quantifies the degree of this interference, ensuring the accuracy of subsequent corrections. The ventilation resistance-efficiency correction coefficient table is used to find the ventilation resistance correction coefficient corresponding to the average ventilation resistance of different bed layers. Its function is to correct for the impact of ventilation resistance changes on adsorption efficiency. Changes in ventilation resistance directly affect the gas flow rate within the adsorbent bed, thus affecting the contact time between the adsorbent and the radioactive gas. A longer contact time results in better adsorption, and vice versa. The ventilation resistance correction coefficient quantifies this effect, ensuring that the corrected efficiency value matches the actual ventilation conditions of the bed.

[0102] The specific correction factor lookup process strictly adheres to the principles of parameter correspondence and precise matching. The calculated average bed temperature is precisely substituted into the temperature-performance correction factor table, and the temperature correction factor matching the current average bed temperature is retrieved based on the corresponding temperature range or specific value in the table. Similarly, the calculated average bed humidity is substituted into the humidity-performance correction factor table to retrieve the corresponding humidity correction factor. Finally, the calculated average bed ventilation resistance is substituted into the ventilation resistance-performance correction factor table to retrieve the corresponding ventilation resistance correction factor.

[0103] During the query process, a special case needs to be considered: if the calculated average bed temperature, average humidity, and average ventilation resistance values ​​are not completely consistent with the standard values ​​in the correction coefficient table, for example, the standard values ​​are 25℃ and 30℃, while the current average temperature is 27℃, then linear interpolation is used to calculate the corresponding correction coefficient to ensure that the correction coefficient accurately matches the actual working conditions of the current bed and avoids inaccurate subsequent corrections due to parameter deviations. The core logic of linear interpolation is to calculate the correction coefficient corresponding to the current parameter based on the correction coefficients corresponding to the two standard parameters that are closest to the current parameter.

[0104] Step 4.4 involves multiplying the initial theoretical adsorption efficiency value sequentially by the obtained temperature correction coefficient, humidity correction coefficient, and ventilation resistance correction coefficient to perform a multi-factor stepwise correction, thereby obtaining the apparent adsorption efficiency of the adsorbent. Specifically, the initial theoretical adsorption efficiency value only reflects the inherent adsorption capacity of the adsorbent and does not consider the interference of any actual operating parameters. However, in actual operation in medical nuclear medicine scenarios, temperature, humidity, and ventilation resistance all affect the actual adsorption efficiency of the adsorbent to varying degrees. Directly using the initial theoretical adsorption efficiency value as the evaluation result would lead to distortion and fail to reflect the actual adsorption state of the bed. Therefore, a stepwise correction method is needed to eliminate the interference of these three factors sequentially. Each correction step retains the effect of the previous step, gradually approaching the actual adsorption efficiency of the adsorbent. This ensures that the final apparent adsorption efficiency reflects both the inherent adsorption capacity of the adsorbent and the actual operating conditions of the bed. The correction process uses a stepwise multiplicative correction method, specifically divided into three consecutive correction steps to eliminate the interference of temperature, humidity, and ventilation resistance. Each correction step has a clear logic and purpose, as detailed below:

[0105] Temperature correction eliminates the impact of temperature changes on adsorption efficiency. Specifically, the initial theoretical adsorption efficiency value is multiplied by a temperature correction factor to adjust the initial theoretical adsorption efficiency value to a value that fits the current average temperature conditions of the bed. This quantifies the effect of temperature on adsorption efficiency. For example, when an increase in temperature leads to a decrease in adsorption activity, the temperature correction factor will be less than 1. Multiplying this by the initial theoretical adsorption efficiency value will result in a temperature-corrected efficiency value that is lower than the initial theoretical value, thus better reflecting the actual adsorption state. Conversely, when the temperature is within the optimal activity range of the adsorbent, the temperature correction factor is close to 1, and the corrected efficiency value is basically consistent with the initial theoretical value.

[0106] Humidity correction eliminates the interference of humidity changes on adsorption efficiency. Specifically, it involves multiplying the temperature-corrected efficiency value obtained in the first step by the humidity correction coefficient to obtain the temperature- and humidity-corrected efficiency value. While retaining the effect of temperature correction, it further eliminates humidity interference. For example, when humidity is too high and interferes with adsorption, the humidity correction coefficient will be less than 1. Multiplying this by the temperature-corrected efficiency value further adjusts the resulting temperature- and humidity-corrected efficiency value, making it more closely reflective of actual operating conditions. If the humidity is within a suitable range, the humidity correction coefficient is close to 1, and the corrected efficiency value essentially remains the same as the temperature-corrected result.

[0107] Ventilation impedance correction eliminates the impact of changes in ventilation impedance on adsorption efficiency. Specifically, the efficiency value after temperature and humidity correction obtained in the second step is multiplied by the obtained ventilation impedance correction coefficient to obtain the final apparent adsorption efficiency of the adsorbent. While retaining the effects of temperature and humidity correction, the influence of ventilation impedance is eliminated. For example, when the ventilation impedance is too high, resulting in slow gas flow and excessive contact time between the adsorbent and the gas, the ventilation impedance correction coefficient will be adjusted appropriately to ensure that the corrected efficiency value reflects the actual adsorption effect under this condition. If the ventilation impedance is within a reasonable range, the ventilation impedance correction coefficient is close to 1, and the final apparent adsorption efficiency is basically consistent with the efficiency value after temperature and humidity correction.

[0108] After all the correction calculations are completed, the obtained value is the apparent adsorption efficiency of the adsorbent at the current moment. This efficiency value is a preliminary quantification of the overall adsorption state of the adsorbent bed. It not only reflects the inherent adsorption capacity of the adsorbent itself, but also fully considers the actual operating conditions of the current bed, and more realistically reflects the adsorption effect of the adsorbent bed on radioactive gases at the current moment.

[0109] In this embodiment of the invention, the difference in activity concentration of radioactive gas at the inlet and outlet of the adsorption unit, the average temperature of the bed, the average humidity, and the average ventilation resistance are extracted to calculate the initial theoretical adsorption efficiency value. The initial theoretical adsorption efficiency value is then corrected step by step by temperature, humidity, and ventilation resistance by querying a preset multi-factor correction coefficient table. This overcomes the technical problem of traditional evaluation methods that simply calculate the inlet and outlet concentrations without comprehensively considering the influence of actual operating conditions on the adsorption effect, resulting in distorted apparent adsorption efficiency calculations and failure to reflect the adsorption capacity under real operating conditions. Thus, it achieves accurate calculation of apparent adsorption efficiency that conforms to the actual operating conditions.

[0110] In a preferred embodiment of the present invention, step 5 above may include:

[0111] Step 5.1: Extract correction factor values ​​for all spatial sub-regions corresponding to the entire adsorbent bed from the spatial non-uniformity correction factor field to form a correction factor set. Specifically, this includes: clarifying the core characteristics of the spatial non-uniformity correction factor field, which is generated through a series of processes and covers the entire adsorbent bed. Each spatial sub-region corresponds to a unique spatial non-uniformity correction factor, which accurately quantifies the degree of competitive adsorption interference in the corresponding spatial sub-region, including interference caused by competitive adsorption of volatile organic compounds and local humidity differences. The higher the degree of interference, the smaller the correction factor value, and vice versa. Then, conduct correction factor extraction. From the spatial non-uniformity correction factor field, extract the spatial non-uniformity correction factor value corresponding to each spatial sub-region one by one according to the spatial arrangement order of the adsorbent bed. During the extraction process, strictly ensure that the correction factor for each spatial sub-region is accurately extracted without omission, duplication, or misalignment. Ensure that the extracted correction factors completely cover all local spaces of the entire adsorbent bed, avoiding inaccurate calculation of the subsequent comprehensive correction coefficient due to the omission of correction factors for some sub-regions, thus preventing the inability to fully eliminate the influence of local competitive adsorption interference in the bed.

[0112] After extraction, all extracted spatial non-uniformity correction factor values ​​are uniformly organized and stored in an orderly manner, forming a set containing correction factors for all spatial sub-regions of the entire bed, i.e., the correction factor set. Each value in this set corresponds to a specific spatial sub-region within the bed, comprehensively reflecting the degree of competitive adsorption interference in different local spaces of the entire bed.

[0113] Step 5.2 involves weighted fusion of the correction factor set, using the volume proportion of each spatial sub-region as the weight coefficient to calculate the comprehensive non-uniformity correction coefficient covering the entire bed. Specifically, this includes clarifying the core logic of weighted fusion and the basis for setting the weight coefficients. Since the adsorbent bed is divided into several spatial sub-regions, the volumes of different spatial sub-regions vary. The larger the volume of a sub-region, the greater its impact on the adsorption efficiency of the entire bed, and the more significant its corresponding competitive adsorption interference is on the overall bed efficiency assessment. Therefore, the volume proportion of each spatial sub-region is used as the weight coefficient for weighted fusion to ensure that the comprehensive non-uniformity correction coefficient is more in line with the actual situation of the bed and to avoid the underestimation of the impact of larger, more interfering sub-regions due to the use of equal weights for all sub-regions, thus affecting the correction accuracy.

[0114] Calculate the volume percentage of each spatial sub-region, and calculate the actual volume of each spatial sub-region. This volume is calculated based on the sub-region size set during mesh generation and the actual size of the adsorbent bed, ensuring accurate volume calculation for each sub-region. Calculate the total volume of the entire adsorbent bed, which is the sum of the volumes of all spatial sub-regions. Divide the volume of each spatial sub-region by the total volume of the entire adsorbent bed to obtain the volume percentage of that spatial sub-region. This percentage is the weighting coefficient of the correction factor for that sub-region. The specific calculation formula is as follows: In the formula, This represents the volume percentage of a single spatial sub-region. For the volume of a single spatial subregion, This represents the total volume of the entire adsorbent bed.

[0115] A weighted fusion calculation was performed to obtain the comprehensive non-uniformity correction coefficient. Specifically, the correction factor value for each spatial sub-region in the correction factor set was multiplied by the corresponding volume percentage (weighting coefficient) of that sub-region to obtain the weighted correction factor for each sub-region. Then, the weighted correction factors of all sub-regions were summed. The summation result is the comprehensive non-uniformity correction coefficient covering the entire adsorbent bed. This coefficient comprehensively considers the degree of competitive adsorption interference in all sub-regions and also takes into account the influence of different sub-region volumes on the entire bed. The specific calculation formula is as follows: In the formula, This is the overall non-uniformity correction factor for the entire bed. This is a correction factor for the spatial non-uniformity of a single spatial sub-region. This represents the volume percentage of a single spatial sub-region. The total number of spatial sub-regions into which the adsorbent bed is divided is calculated. After the calculation is completed, the comprehensive non-uniformity correction coefficient is checked to ensure that its value is between 0 and 1. If abnormal values ​​are found, the sub-region volume calculation, volume ratio calculation and weighted summation process are checked in time to ensure the accuracy of the comprehensive non-uniformity correction coefficient.

[0116] Step 5.3: Extract the apparent adsorption efficiency value at the current moment from the apparent adsorption efficiency; multiply the apparent adsorption efficiency value with the comprehensive non-uniformity correction coefficient to correct the overestimation error caused by competitive adsorption interference, and obtain the actual effective adsorption efficiency of the adsorbent. Specifically, this includes: extracting the apparent adsorption efficiency value at the current moment. From the final obtained apparent adsorption efficiency of the adsorbent, the apparent adsorption efficiency value at the current moment is accurately extracted. This apparent adsorption efficiency value has eliminated the interference of three types of operating condition parameters: temperature, humidity, and ventilation resistance. It reflects the overall adsorption state of the adsorbent bed, but it has not yet considered the local competitive adsorption interference of the bed, the non-uniform interference caused by the competitive adsorption of volatile organic compounds and the local humidity difference. Therefore, there is a certain overestimation error in efficiency, which cannot truly reflect the actual situation of local adsorption non-uniformity of the bed. This is also one of the core problems that traditional evaluation methods have not solved; carry out the correction work of apparent adsorption efficiency to eliminate the overestimation error caused by competitive adsorption interference. The core logic of the correction is to multiply the apparent adsorption efficiency value at the current moment by the calculated comprehensive non-uniformity correction coefficient, quantify the overall competitive adsorption interference of the entire bed into the apparent adsorption efficiency, eliminate the overestimation of efficiency caused by local competitive adsorption interference, and make the corrected efficiency value truly reflect the actual adsorption capacity of the adsorbent bed.

[0117] The comprehensive non-uniformity correction coefficient quantifies the overall competitive adsorption interference level of the entire bed. When the local competitive adsorption interference is strong, the comprehensive non-uniformity correction coefficient will be less than 1. Multiplying the apparent adsorption efficiency value by this coefficient will result in an actual effective adsorption efficiency value that is lower than the apparent adsorption efficiency value, thus eliminating the overestimation error. When the competitive adsorption interference is weak and uniformly distributed throughout the bed, the comprehensive non-uniformity correction coefficient will be close to 1. The actual effective adsorption efficiency value is basically consistent with the apparent adsorption efficiency value, which closely matches the actual adsorption state of the bed. After the correction calculation is completed, the obtained value is the actual effective adsorption efficiency of the adsorbent at the current moment. This efficiency value is a precise quantification of the actual adsorption capacity of the adsorbent bed.

[0118] In this embodiment of the invention, correction factor values ​​of all spatial sub-regions of the adsorbent bed are extracted, and the correction factors are weighted and fused with the volume ratio of each sub-region as the weight to obtain a comprehensive non-uniformity correction coefficient. This coefficient is then used to multiply and correct the apparent adsorption efficiency. This overcomes the technical problem of overestimation error caused by competitive adsorption interference and spatial non-uniformity, which fails to truly reflect the actual adsorption capacity of the adsorbent. Thus, it achieves the elimination of evaluation bias and the acquisition of accurate and reliable actual effective adsorption efficiency.

[0119] In a preferred embodiment of the present invention, step 6 above may include:

[0120] Step 6.1: Extract the actual effective adsorption efficiency value at the current moment from the obtained actual effective adsorption efficiency, and read the pre-stored safety threshold from the preset parameters. Specifically, this includes: extracting the actual effective adsorption efficiency value at the current moment. This accurately reflects the actual processing capacity of the adsorbent bed for radioactive gas at the current moment and is the core basis for determining whether the adsorbent has failed and needs to be replaced. During the extraction process, it is strictly ensured that the extracted efficiency value accurately corresponds to the current moment, avoiding extraction errors. Historical or erroneous data is used to ensure the accuracy of subsequent comparisons and judgments, aligning with the core needs of real-time monitoring and response in medical scenarios. A preset safety threshold is read, set at 80%, or 0.8. This value is determined based on strict adherence to the radioactive waste gas treatment standards for medical and nuclear medicine scenarios, the adsorption performance limits of the activated carbon adsorbent used, and the radioactive waste gas emission limit of ≤0.5μSv / h in the treatment environment. It was determined after more than 50 simulation tests and 3 months of clinical testing and calibration, and is used to define whether the adsorbent bed continues to meet the minimum efficiency standard for radioactive waste gas treatment.

[0121] Step 6.2: Compare the extracted actual effective adsorption efficiency value with the safety threshold to determine whether the current actual effective adsorption efficiency is lower than the safety threshold. Specifically: If the current actual effective adsorption efficiency value is greater than or equal to 80% of the safety threshold, it means that the current adsorption capacity of the adsorbent bed meets the requirements for radioactive waste gas treatment, and neither the overall bed nor any local area has reached saturation failure, so there is no need to trigger an alarm or switchover action; If the current actual effective adsorption efficiency value is less than 80% of the safety threshold, it means that the current actual adsorption capacity of the adsorbent bed can no longer meet the requirements for radioactive waste gas treatment, and the bed may have experienced premature saturation failure in some areas. If not dealt with in time, it may easily lead to radioactive gas leakage, in which case an alarm or switchover action needs to be triggered. The system continuously alarms and automatically switches between values, performing specific numerical comparisons. It automatically retrieves the current actual effective adsorption efficiency value and the 80% safety threshold, accurately comparing the two values. During the comparison process, it ensures that the numerical accuracy is uniformly consistent as a percentage, retaining one decimal place to avoid judgment errors due to precision deviations. For example, if the current actual effective adsorption efficiency value is 75.5%, the system uses a built-in numerical comparison algorithm to accurately determine that 75.5% < 80%, confirming that the current adsorbent bed efficiency is insufficient; if the current actual effective adsorption efficiency value is 82.3%, it determines that 82.3% ≥ 80%, confirming that the bed efficiency meets the standard. At the same time, it records the comparison results, including the current actual effective adsorption efficiency value and the specific value of the 80% safety threshold.

[0122] Step 6.3: If the comparison result indicates that the actual effective adsorption efficiency is lower than the safety threshold, an alarm trigger signal containing the current efficiency value and the degree of exceedance is generated. Specifically, the preconditions for alarm triggering are clarified: the alarm signal generation action is only triggered when the numerical comparison result is that the current actual effective adsorption efficiency value is less than 80% of the safety threshold; if the comparison result is that the current actual effective adsorption efficiency value is greater than or equal to 80% of the safety threshold, no alarm signal is generated, the overall equipment maintains normal operation, the adsorption unit continues to process radioactive waste gas normally, and the degree of exceedance of adsorption efficiency is calculated. This degree of exceedance is used to quantify the severity of the current actual effective adsorption efficiency value being lower than the 80% safety threshold, providing a reference for staff to judge the urgency of the fault and subsequent handling. The specific calculation method is as follows: subtract the current actual effective adsorption efficiency value from the preset 80% safety threshold to obtain the difference, then divide the difference by the 80% safety threshold, and finally multiply the calculation result by 100%. The percentage value obtained is the degree of exceedance of the current actual effective adsorption efficiency. The greater the degree of exceedance, the more severe the insufficient efficiency of the adsorbent bed. The higher the likelihood of local saturation failure, the greater the risk of radioactive gas leakage. For example, if the actual effective adsorption efficiency is 75.5% at the current moment, the exceedance rate is 5.625% according to the above calculation method; if the actual effective adsorption efficiency is 60% at the current moment, the exceedance rate is 25%. At this point, the bed efficiency is severely insufficient, and the risk of leakage is extremely high. After calculating the exceedance rate, an alarm trigger signal is generated. This alarm trigger signal contains two core key pieces of information: the actual effective adsorption efficiency value at the current moment and the calculated exceedance rate. This ensures that staff can quickly grasp the specific situation of the adsorbent efficiency anomaly. At the same time, the alarm trigger signal also includes auxiliary information such as the trigger time and the number of the current adsorption unit, which facilitates staff to trace the anomaly and locate the faulty unit. After the signal is generated, the alarm trigger signal is simultaneously sent to two terminals: one is the staff's monitoring terminal, including the duty room display and mobile reminder devices, to remind staff to pay attention and handle the situation in a timely manner; the other is the automatic switching mechanism control terminal of the adsorption unit, to trigger subsequent automatic switching actions and ensure timely response.

[0123] Step 6.4: Based on the alarm trigger signal, an execution command is simultaneously sent to the automatic switching mechanism of the adsorption unit to drive the standby adsorption unit into operation and disconnect the current adsorption unit from the gas treatment flow path. Specifically, this includes: clarifying the core purpose and principle of automatic switching. The core purpose of automatic switching is to quickly activate the standby adsorption unit when the current adsorption unit is ineffective or at risk of leakage, ensuring that radioactive waste gas is continuously and stably treated, and avoiding the direct emission of untreated waste gas, which could threaten the health of medical staff and pollute the treatment environment. The switching principle is seamless connection and safety and reliability, that is, the startup of the standby adsorption unit and the disconnection of the current adsorption unit are carried out simultaneously to ensure that the gas treatment flow path is not interrupted, while avoiding gas leakage during the switching process. The automatic switching mechanism receives the alarm trigger signal. After receiving the generated alarm trigger signal, the control terminal of the automatic switching mechanism of the adsorption unit automatically analyzes the core information in the signal to confirm the adsorption unit number that needs to be switched and the reason for triggering the switch. Then, the automatic switching program is started, and a specific execution command is sent to the automatic switching mechanism. This command includes two core action commands: the command to drive the standby adsorption unit into operation and the command to disconnect the current adsorption unit from the gas treatment flow path.

[0124] The specific switching action involves two aspects. First, the automatic switching mechanism activates the relevant equipment of the backup adsorption unit according to the instructions, including the inlet valve, outlet valve, and adsorbent bed airflow stabilization device, ensuring that the backup adsorption unit quickly enters working status within three seconds and immediately takes over the treatment of radioactive waste gas. The backup adsorption unit is filled with brand-new columnar activated carbon, whose initial actual effective adsorption efficiency is far higher than the safety threshold of 80%, fully meeting the compliant treatment requirements of radioactive waste gas in the nuclear medicine department. Second, the automatic switching mechanism simultaneously closes the inlet and outlet valves of the current adsorption unit, completely cutting off the current adsorption unit from the gas treatment flow path. The closing action is completed within two seconds, preventing the current adsorption unit with insufficient efficiency from continuing to treat waste gas, and preventing radioactive waste gas from leaking from the valve interface of the current unit. After the switch is completed, the automatic switching mechanism sends out a switch completion signal, and the switch result is simultaneously pushed to the staff monitoring terminal, including the switch completion time, the backup unit being put into operation, and the current unit being cut off from the flow path, reminding the staff to process the cut-off adsorption unit within two hours, including replacing the adsorbent and checking the valve sealing. At the same time, the actual effective adsorption efficiency of the backup adsorption unit is monitored in real time, and the next round of efficiency evaluation and threshold comparison process is initiated to ensure that the treatment of radioactive waste gas remains in a safe and stable state.

[0125] In this embodiment of the invention, by extracting the actual effective adsorption efficiency value and comparing it with a preset safety threshold in real time, an alarm trigger signal is generated when the efficiency is lower than the threshold. At the same time, the automatic switching mechanism is driven to put the backup adsorption unit into operation and switch out the currently failed adsorption unit. Therefore, the technical problems of traditional assessment methods, such as delayed safety judgment, inability to automatically alarm and automatically switch adsorption units, which can easily lead to radioactive gas leakage and untimely safety protection response, are overcome. Thus, the invention achieves the technical effect of real-time automatic judgment and closed-loop control of the safety status of adsorbent efficiency, ensuring the continuous, stable and safe operation of the radioactive gas treatment system, and effectively avoiding the risk of radioactive waste gas leakage.

[0126] like Figure 2 As shown, embodiments of the present invention also provide a real-time evaluation system for adsorbent efficiency in radioactive gas treatment, comprising:

[0127] The acquisition module is used to deploy multiple radioactivity sensors, temperature sensors, and humidity sensors at different depths in the axial and radial directions of the adsorbent bed to collect radioactivity gas activity concentration, temperature, and humidity at various locations in real time. Ventilation impedance sensors and volatile organic compound sensors are also deployed at the inlet, outlet, and inside the adsorption unit to collect ventilation impedance and volatile organic compound concentration simultaneously.

[0128] The mapping module is used to map the spatial coordinates of each sensor to regular grid nodes by applying a three-dimensional affine transformation to the spatial coordinates of each sensor based on the difference in radioactive gas activity concentration between the inlet and outlet, and combining the temperature and humidity data collected by the temperature and humidity sensors. Then, it constructs a three-dimensional efficiency distribution field that reflects the efficiency distribution state inside the adsorbent bed through interpolation calculation.

[0129] The extraction module is used to spatially discretize the three-dimensional performance distribution field, extract the performance attenuation gradient of each discrete region, and generate a spatial non-uniformity correction factor based on the correlation between the performance attenuation gradient and the concentration of organic volatiles and humidity collected by the organic volatiles sensor and the humidity sensor.

[0130] The calculation module is used to calculate the apparent adsorption efficiency of the adsorbent based on the difference in radioactive gas activity concentration between the inlet and outlet, combined with temperature, humidity and ventilation resistance.

[0131] The correction module is used to correct the apparent adsorption efficiency through a spatial non-uniformity correction factor to obtain the actual effective adsorption efficiency of the adsorbent.

[0132] The processing module compares the actual effective adsorption capacity with a preset safety threshold. If the actual capacity is lower than the safety threshold, an alarm signal is issued and the standby adsorption unit is switched.

[0133] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0134] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0135] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0136] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time evaluation of adsorbent effectiveness for radioactive gas treatment, characterized in that, The method includes: Multiple radioactivity sensors, temperature sensors, and humidity sensors are deployed at different depths along the axial and radial directions of the adsorbent bed to collect real-time data on radioactive gas activity concentration, temperature, and humidity at various locations. Furthermore, ventilation impedance sensors and volatile organic compound (VOC) sensors are installed at the inlet, outlet, and inside the adsorption unit to simultaneously collect ventilation impedance and VOC concentration data, including: The adsorbent bed is divided into several equidistant monitoring layers according to its axial height. The radioactivity sensor, temperature sensor and humidity sensor are arranged at equal angular intervals in the radial direction of each monitoring layer. At the same time, the ventilation impedance sensor and volatile organic compound sensor are fixedly installed at the inlet, outlet and inside the bed of the adsorption unit according to the preset position, forming a three-dimensional monitoring network covering the entire cross section and key nodes of the bed. The analog signals of radioactive gas activity concentration, temperature, and humidity are collected in real time by various sensors in the three-dimensional monitoring network, and the analog signals of ventilation impedance and organic volatile concentration are collected in real time by ventilation impedance sensors and organic volatile matter sensors. All analog signals are converted from analog to digital and packaged into a raw monitoring dataset according to a unified time series. Outlier removal and filtering are performed on the raw monitoring dataset to eliminate signal noise and acquisition errors. Based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with temperature and humidity data collected by temperature and humidity sensors, a three-dimensional affine transformation is applied to the spatial coordinates of each sensor to map them to regular grid nodes, and then interpolation calculation is performed to construct a three-dimensional efficiency distribution field that reflects the efficiency distribution state inside the adsorbent bed. The three-dimensional performance distribution field is spatially discretized, the performance attenuation gradient of each discrete region is extracted, and a spatial non-uniformity correction factor is generated based on the correlation between the performance attenuation gradient and the concentration of organic volatiles and humidity collected by the organic volatiles sensor and the humidity sensor. Based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with temperature, humidity and ventilation resistance, the apparent adsorption efficiency of the adsorbent is calculated. The apparent adsorption efficiency is corrected by a spatial non-uniformity correction factor to obtain the actual effective adsorption efficiency of the adsorbent. The actual effective adsorption capacity is compared with the preset safety threshold. If it is lower than the safety threshold, an alarm signal is issued and the standby adsorption unit is switched.

2. The method for real-time evaluation of adsorbent efficiency for radioactive gas treatment according to claim 1, characterized in that, Based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with temperature and humidity data collected by temperature and humidity sensors, a three-dimensional affine transformation is applied to the spatial coordinates of each sensor to map them onto regular grid nodes. Then, interpolation calculations are performed to construct a three-dimensional efficiency distribution field reflecting the efficiency distribution state within the adsorbent bed, including: From the obtained standardized monitoring data, the activity concentration of radioactive gas at the inlet and outlet of the adsorption unit is extracted, the difference between the two activity concentrations is calculated, and the temperature and humidity data of each monitoring point are extracted simultaneously from the standardized monitoring data. Based on the temperature and humidity data of each monitoring point, combined with the activity concentration difference, the local performance characterization value of each monitoring point is calculated to form a discrete set of spatial performance sampling points. Each sampling point contains spatial coordinates and the corresponding local performance characterization value. A three-dimensional affine transformation is applied to each spatial coordinate in the obtained spatial performance sampling point set to map the irregularly distributed spatial coordinates to the coordinates of the preset regular grid nodes, generating the mapped performance characterization value corresponding to the regular grid nodes. Based on the mapped performance characterization values, spatial interpolation calculations are performed between regular grid nodes to fill the grid gaps, resulting in a continuous three-dimensional performance distribution field covering the entire adsorbent bed.

3. The method for real-time evaluation of adsorbent efficiency for radioactive gas treatment according to claim 2, characterized in that, The three-dimensional performance distribution field is spatially discretized, and the performance attenuation gradient of each discrete region is extracted. Based on the correlation between the performance attenuation gradient and the concentration of volatile organic compounds (VOCs) and humidity collected by the VOC and humidity sensors, a spatial non-uniformity correction factor is generated, including: The continuous three-dimensional performance distribution field is meshed and divided into several discretized spatial sub-regions. The center point coordinates and corresponding performance values ​​of each spatial sub-region are extracted to form a discretized performance dataset. Based on the discretized performance dataset, the performance difference between each spatial sub-region and its adjacent sub-regions is calculated. Combined with the spatial distance between the center points of each sub-region, the performance decay gradient of each spatial sub-region is obtained, and a performance decay gradient field is generated. From the standardized monitoring data, the concentration and humidity data of volatile organic compounds (VOCs) corresponding to the coordinates of the center point of each spatial sub-region are extracted. The VOC concentration and humidity data are then spatially registered with the corresponding gradient values ​​in the obtained performance attenuation gradient field to form an associated dataset containing gradient values, VOC concentrations, and humidity. Multivariate regression analysis was performed on the associated dataset to determine the correlation weight coefficients between the efficiency decay gradient and the concentration of volatile organic compounds and humidity, and the competitive adsorption interference intensity of each spatial sub-region was calculated based on the correlation weight coefficients. The competitive adsorption interference intensity of each spatial sub-region is normalized to obtain a spatial non-uniformity correction factor.

4. The method for real-time evaluation of adsorbent efficiency for radioactive gas treatment according to claim 3, characterized in that, Based on the difference in radioactive gas activity concentration between the inlet and outlet, and combined with temperature, humidity, and ventilation resistance, the apparent adsorption efficiency of the adsorbent is calculated, including: From the obtained standardized monitoring data, extract the current moment's inlet radioactive gas activity concentration and outlet radioactive gas activity concentration of the adsorption unit, calculate the real-time activity concentration difference between the two, and simultaneously extract the current moment's average bed temperature, average humidity, and average ventilation resistance from the standardized monitoring data. Based on the obtained real-time activity concentration difference, combined with the preset adsorption capacity coefficient of the adsorbent, the initial theoretical adsorption efficiency value is calculated. Based on the extracted average bed temperature, average humidity, and average ventilation resistance, the corresponding temperature correction coefficient, humidity correction coefficient, and ventilation resistance correction coefficient are obtained by querying the preset temperature-performance correction coefficient table, humidity-performance correction coefficient table, and ventilation resistance-performance correction coefficient table, respectively. The initial theoretical adsorption efficiency value is successively multiplied by the obtained temperature correction coefficient, humidity correction coefficient, and ventilation resistance correction coefficient to perform multi-factor stepwise correction, thus obtaining the apparent adsorption efficiency of the adsorbent.

5. The method for real-time evaluation of adsorbent efficiency for radioactive gas treatment according to claim 4, characterized in that, The apparent adsorption efficiency is corrected by a spatial non-uniformity correction factor to obtain the actual effective adsorption efficiency of the adsorbent, including: From the spatially non-uniform correction factor field, the correction factor values ​​of all spatial sub-regions corresponding to the entire adsorbent bed are extracted to form a correction factor set; The set of correction factors is weighted and fused, and the volume ratio of each spatial sub-region is used as the weight coefficient to calculate the comprehensive non-uniformity correction coefficient covering the entire bed. The apparent adsorption efficiency value at the current moment is extracted from the apparent adsorption efficiency value; the apparent adsorption efficiency value is multiplied by the comprehensive non-uniformity correction coefficient to correct the overestimation error caused by competitive adsorption interference, and the actual effective adsorption efficiency of the adsorbent is obtained.

6. The method for real-time evaluation of adsorbent efficiency for radioactive gas treatment according to claim 5, characterized in that, The actual effective adsorption capacity is compared with a preset safety threshold. If it falls below the safety threshold, an alarm signal is issued and a backup adsorption unit is switched on. This includes: Extract the actual effective adsorption efficiency value at the current moment from the obtained actual effective adsorption efficiency, and read the pre-stored safety threshold from the preset parameters; The extracted actual effective adsorption efficiency value is compared with the safety threshold to determine whether the current actual effective adsorption efficiency is lower than the safety threshold. If the comparison results show that the actual effective adsorption efficiency is lower than the safety threshold, an alarm trigger signal containing the current efficiency value and the degree of exceeding the limit will be generated. Based on the alarm trigger signal, an execution command is simultaneously sent to the automatic switching mechanism of the adsorption unit to drive the standby adsorption unit into operation and disconnect the current adsorption unit from the gas processing flow path.

7. A real-time evaluation system for the adsorbent efficiency in radioactive gas treatment, the system implementing the method as described in any one of claims 1 to 6, characterized in that, include: The acquisition module is used to deploy multiple radioactivity sensors, temperature sensors, and humidity sensors at different depths in the axial and radial directions of the adsorbent bed, and to collect the radioactivity concentration, temperature, and humidity at each location in real time through the sensors; Ventilation impedance sensors and volatile organic compound sensors are installed at the inlet, outlet, and inside the bed of the adsorption unit to simultaneously collect ventilation impedance and volatile organic compound concentration. The mapping module is used to map the spatial coordinates of each sensor to regular grid nodes by applying a three-dimensional affine transformation to the spatial coordinates of each sensor based on the difference in radioactive gas activity concentration between the inlet and outlet, and combining the temperature and humidity data collected by the temperature and humidity sensors. Then, it constructs a three-dimensional efficiency distribution field that reflects the efficiency distribution state inside the adsorbent bed through interpolation calculation. The extraction module is used to spatially discretize the three-dimensional performance distribution field, extract the performance attenuation gradient of each discrete region, and generate a spatial non-uniformity correction factor based on the correlation between the performance attenuation gradient and the concentration of organic volatiles and humidity collected by the organic volatiles sensor and the humidity sensor. The calculation module is used to calculate the apparent adsorption efficiency of the adsorbent based on the difference in radioactive gas activity concentration between the inlet and outlet, combined with temperature, humidity and ventilation resistance. The correction module is used to correct the apparent adsorption efficiency through a spatial non-uniformity correction factor to obtain the actual effective adsorption efficiency of the adsorbent. The processing module compares the actual effective adsorption capacity with a preset safety threshold. If the actual capacity is lower than the safety threshold, an alarm signal is issued and the standby adsorption unit is switched.

8. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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