Intelligent control system and control method for hospital radioactive wastewater decay tank

Abstract

The invention relates to the technical field of wastewater treatment, and particularly discloses an intelligent control system and a control method for a hospital radioactive wastewater decay pool, which are characterized in that wastewater flow and radioactive substance concentration data in the decay pool are acquired in real time, the decay state of wastewater is identified, and whether the wastewater enters a serious decay state or a slight decay state is judged; in a serious attenuation state, generating an early warning signal by calculating the concentration difference of different areas in the decay pool; in a slight attenuation state, a future decay trend is predicted through historical data analysis and real-time monitoring, control parameters such as temperature and flow in a decay pool are adjusted according to a prediction result so as to optimize the attenuation process of radioactive substances in wastewater, and the method further comprises training and application of a machine learning model. And judging the decay state of a decay pool in the future by utilizing a support vector machine model through the comprehensive feature vector of the wastewater flow blocking index and the radioactive substance concentration abnormal index, and carrying out early warning and emergency response in advance.

Description

Technical Field

[0001] The invention relates to the technical field of wastewater treatment, and in particular to an intelligent control system and a control method for a hospital radioactive wastewater decay pool. Background Art

[0002] With the development of the medical industry, the discharge of radioactive wastewater from hospitals has gradually attracted widespread attention from the society. Radioactive wastewater usually comes from wastewater used in medical activities such as radioactive treatment and diagnosis in hospitals, which contains various types of radioactive substances. If not handled properly, these wastewaters will not only pollute the environment, but may also pose a serious threat to public health. Therefore, it is crucial to manage the treatment process of radioactive wastewater in hospitals efficiently and stably. Traditional radioactive wastewater treatment usually relies on manual operation and experience, which makes it difficult to achieve accurate real-time monitoring and adjustment, and easily leads to instability and inefficiency in the treatment process, and cannot ensure environmental safety and efficient wastewater treatment.

[0003] The prior art has the following deficiencies: Existing technologies have certain limitations in real-time monitoring and control of wastewater flow and radioactive material concentration, making it difficult to achieve a dynamic and accurate wastewater treatment process. For example, the fluctuations in wastewater flow and changes in radioactive material concentrations have not been effectively monitored and analyzed in real time, resulting in the inability to optimize the treatment effect in the decay pool. In addition, the traditional system also has errors in predicting the attenuation trend of wastewater, and fails to promptly detect possible abnormal conditions in the decay pool, resulting in insufficient wastewater treatment and increased risks of environmental pollution. Therefore, existing technologies urgently need to innovate in intelligent monitoring, data prediction, and automatic control to improve the safety and efficiency of hospital radioactive wastewater treatment. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent control system and control method for a hospital radioactive wastewater decay pool to solve the above-mentioned problems.

[0005] The purpose of the present invention can be achieved by the following technical solutions: An intelligent control method for a hospital radioactive wastewater decay pool comprises the following steps: S1: Real-time collection of wastewater flow and radioactive material concentration data in the hospital's radioactive wastewater decay pool, including the radioactivity intensity of beta rays, gamma rays and alpha rays; S2: Analyze the collected wastewater flow data and radioactive substance concentration data, calculate the attenuation degree of radioactive substances in the wastewater, identify the decay state of the wastewater, and determine whether it has entered a severe attenuation state or a slight attenuation state; S3: If the wastewater decay state is severe decay, the concentration difference of each area in the decay pool is calculated by comparing the concentration of radioactive substances in different areas in the decay pool, and an early warning signal is generated according to the difference; S4: If the wastewater decay state is slight decay, the wastewater decay degree in the future period is predicted through historical data analysis and real-time monitoring of wastewater flow and radioactive substance concentration in the decay pool to obtain the future decay trend; S5: According to the predicted decay trend, the control parameters in the decay pool, including temperature and flow rate, are automatically adjusted to optimize the decay process of radioactive substances in the wastewater.

[0006] As a further solution of the present invention: the identification of the decay state of wastewater specifically includes: Obtain wastewater flow data within the monitoring period, and calculate the wastewater flow barrier index based on the fluctuation degree of the wastewater flow, which is used to evaluate the impact of the wastewater flow on the treatment efficiency of the wastewater decay pool; Obtain radioactive material concentration data within the monitoring period, calculate the degree of change in radioactive material concentration in the decay pool, and calculate the radioactive material concentration anomaly index to evaluate the impact of radioactive material concentration on the wastewater decay pool treatment efficiency; The wastewater flow barrier index and the radioactive substance concentration anomaly index are normalized and calculated to obtain the wastewater attenuation index, which is used to identify the decay state of the wastewater.

[0007] As a further solution of the present invention: the process of obtaining the wastewater flow barrier index is: Obtain wastewater flow time series data from the monitoring period, and perform multi-level decomposition on the wastewater flow time series data from the monitoring period, decomposing it into low-frequency components and high-frequency components of different scales; For each level of high-frequency component decomposed, the fluctuation energy of each level is calculated, and the calculation expression is: ;in, Indicates Level quantity, Indicates coefficients, Indicates The fluctuation energy of the magnitude component, Indicates The first high frequency component coefficients, Indicates the total number of data points in the monitoring period; The fluctuation energies at each level are normalized to obtain the normalized values ​​of the fluctuation energies at each level. The wastewater flow barrier index is obtained by calculating the ratio of the normalized energy of the highest level high-frequency component to the normalized energy of the lowest level high-frequency component.

[0008] As a further solution of the present invention: the process of obtaining the abnormal index of radioactive substance concentration is as follows: Acquire the radioactive material concentration data within the monitoring period, divide the data into a three-dimensional data structure according to time points and spatial coordinates, and represent it as a tensor; perform low-rank tensor decomposition on the concentration data tensor, decomposing it into a low-rank tensor and a sparse tensor, wherein the low-rank tensor represents the normal mode of the concentration data; the sparse tensor represents the abnormal component of the concentration data, capturing the abnormal part in the concentration change; The abnormal concentration contribution at each time point is extracted from the sparse tensor, and the absolute sum of the abnormal values ​​of all spatial coordinates in the tensor is summed to obtain the time point The abnormal concentration contribution of The average concentration change during the monitoring period is calculated by calculating the mean of the abnormal contributions of all time points. The concentration change is then compared with the overall mean of the concentration data to obtain the radioactive material concentration anomaly index.

[0009] As a further solution of the present invention: the determination of whether the state has entered a severe attenuation state or a mild attenuation state specifically includes: Determine whether the attenuation index of the wastewater is greater than or equal to a preset threshold. If so, it is recorded as a severe attenuation state; if not, it is recorded as a slight attenuation state.

[0010] As a further solution of the present invention: the generating of the warning signal specifically includes: Obtain radioactive material concentration data for multiple areas of the same area in the decay pool during the monitoring period; calculate the dynamic time-warped distance between the concentration series of any two areas; The concentration difference coefficient of the decay pool is obtained by calculating the ratio of the mean to the variance of the dynamic time warping distance in different areas of the decay pool; Based on the comparison between the calculated concentration difference coefficient and the preset threshold, if the concentration difference coefficient is greater than or equal to the preset threshold, an early warning signal is triggered.

[0011] As a further solution of the present invention: the prediction of the wastewater attenuation degree within a future period of time to obtain the future decay trend specifically includes: The wastewater flow barrier index and the radioactive substance concentration anomaly index of the wastewater in the decay pool are obtained, and the wastewater flow barrier index and the radioactive substance concentration anomaly index are constructed into a comprehensive feature vector as the input of the machine learning model. The model is trained through historical data, and the attenuation index is output according to the trained model to judge the attenuation degree in the future monitoring period. The machine learning model is a support vector machine.

[0012] As a further solution of the present invention: the construction process of the machine learning model is: The wastewater flow barrier index and the radioactive material concentration abnormality index are combined to form a comprehensive feature vector. The support vector machine model is trained using historical data. The input feature is the comprehensive feature vector of each monitoring period, corresponding to the decay state of the decay pool during the monitoring period. The label value is 0 or 1, where 0 indicates a slight decay state and 1 indicates a severe decay state. The training process is as follows: Extract the wastewater flow barrier index and radioactive material concentration abnormality index of each period from historical data, and construct a training set. Each sample in the training set consists of a comprehensive feature vector and a label. The support vector machine model is trained using historical data to classify the state of the decay pool by maximizing the interval between categories. The support vector machine model will automatically adjust the hyperparameters during the training process to minimize the classification error. The standard loss function of the support vector machine is used for training, the model is cross-validated, and the prediction accuracy is improved by adjusting the hyperparameters.

[0013] As a further solution of the present invention: the control parameters in the decay pool, including temperature and flow rate, are automatically adjusted according to the predicted decay trend to optimize the decay process of radioactive substances in wastewater, specifically including: Temperature control: If the decay pool is predicted to be in a slight decay state, the system will increase the temperature in the decay pool to accelerate the decay reaction of the radioactive material; if it is in a severe decay state, the reaction speed will be controlled by lowering the temperature; Flow control: According to the fluctuation of wastewater flow and the current treatment status of the decay pool, the flow is adjusted to ensure that the wastewater stays in the pool longer so that the radioactive substances can be fully decayed. For flow fluctuations, the stability of the flow is increased to ensure decay efficiency.

[0014] An intelligent control system for a hospital radioactive wastewater decay pool, comprising: A data acquisition module, which collects wastewater flow and radioactive material concentration data in the hospital's radioactive wastewater decay pool in real time, including the radioactive intensity of beta rays, gamma rays and alpha rays; A decay state identification module, which analyzes the collected wastewater flow data and radioactive substance concentration data, calculates the decay degree of the radioactive substances in the wastewater, identifies the decay state of the wastewater, and determines whether it has entered a severe decay state or a slight decay state; An early warning module, wherein if the wastewater decay state is severe decay, the early warning module calculates the concentration difference of each area in the decay pool by comparing the concentration of radioactive substances in different areas in the decay pool, and generates an early warning signal according to the difference; A decay prediction module, wherein if the wastewater decay state is slight decay, the decay prediction module predicts the degree of wastewater decay in the future through historical data analysis and real-time monitoring of wastewater flow and radioactive substance concentration in the decay pool to obtain future decay trends; The decay control adjustment module automatically adjusts the control parameters in the decay pool, including temperature and flow rate, according to the predicted decay trend, so as to optimize the decay process of the radioactive substances in the wastewater.

[0015] Beneficial effects of the present invention: (1) The present invention realizes accurate evaluation and dynamic monitoring of the wastewater decay process in the decay pool by collecting wastewater flow and radioactive material concentration data in real time and combining advanced data analysis techniques such as low-rank tensor decomposition and dynamic time warping algorithm. Specifically, low-rank tensor decomposition technology can efficiently extract normal and abnormal modes from high-dimensional concentration data, thereby identifying abnormal fluctuations in radioactive material concentration and timely reflecting the degree of wastewater decay. The dynamic time warping algorithm captures subtle time series differences by comparing concentration changes in different areas of the decay pool, ensuring high-precision diagnosis of the wastewater decay process. In addition, the present invention adopts a data-driven intelligent control method to automatically adjust the control parameters of the decay pool, such as temperature and flow, based on real-time monitoring data and prediction results to optimize the decay process of radioactive materials in the wastewater. Through this innovative intelligent regulation, the stability and efficiency of the wastewater decay treatment process can be significantly improved, the safety, environmental protection and efficiency of hospital radioactive wastewater treatment can be guaranteed, and the risk of treatment failure or environmental pollution can be avoided.

[0016] (2) The present invention realizes accurate monitoring and optimized management of hospital radioactive wastewater decay pools by real-time collection and analysis of wastewater flow and radioactive material concentration data, combined with advanced prediction algorithms and intelligent control systems. The system can dynamically adjust key control parameters in the decay pool, such as temperature and flow, to ensure that the decay process of radioactive materials in the wastewater achieves the optimal effect. When the operating state of the decay pool is close to severe decay, the system can quickly issue an early warning signal and automatically activate the emergency response mechanism when potential risks occur through advance prediction and continuous monitoring. This not only avoids the spread of radioactive contamination that may be caused by the failure of decay pool treatment, but also effectively protects the environment and public safety. Combined with the innovative solution of the present invention, the system's intelligent control capability and automated emergency response greatly improve the safety, reliability and operating efficiency of wastewater treatment, and ensure the stability and controllability of the hospital radioactive wastewater treatment process in a complex environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1It is a flowchart of the specific steps of an intelligent control method for a hospital radioactive wastewater decay pool of the present invention; Figure 2 The present invention is a flowchart of an intelligent control system for a hospital radioactive wastewater decay pool. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, the present invention is an intelligent control method for a hospital radioactive wastewater decay pool, comprising the following steps: S1: Real-time collection of wastewater flow and radioactive material concentration data in the hospital's radioactive wastewater decay pool, including the radioactivity intensity of beta rays, gamma rays and alpha rays; S2: Analyze the collected wastewater flow data and radioactive substance concentration data, calculate the attenuation degree of radioactive substances in the wastewater, identify the decay state of the wastewater, and determine whether it has entered a severe attenuation state or a slight attenuation state; S3: If the wastewater decay state is severe decay, the concentration difference of each area in the decay pool is calculated by comparing the concentration of radioactive substances in different areas in the decay pool, and an early warning signal is generated according to the difference; S4: If the wastewater decay state is slight decay, the wastewater decay degree in the future period is predicted through historical data analysis and real-time monitoring of wastewater flow and radioactive substance concentration in the decay pool to obtain the future decay trend; S5: According to the predicted decay trend, the control parameters in the decay pool, including temperature and flow rate, are automatically adjusted to optimize the decay process of radioactive substances in the wastewater.

[0021] In S1, the wastewater flow and radioactive material concentration data in the hospital's radioactive wastewater decay pool are collected in real time, including the radioactivity intensity of beta rays, gamma rays and alpha rays, including: Real-time collection of wastewater flow data in the hospital's radioactive wastewater decay pool is accomplished through a high-precision flow meter. The flow meter uses sensors to monitor changes in wastewater flow in real time and accurately records the volume of wastewater flowing through the decay pool per unit time. These flow data provide the necessary basic information for subsequent treatment effect evaluation and control strategy adjustment of the decay pool.

[0022] Real-time data on the concentration of radioactive materials in the decay pool, including the radioactive intensity of beta rays, gamma rays and alpha rays, is collected using highly sensitive radiation detectors. The intensity of beta rays and alpha rays is usually obtained through scintillation detectors, while the intensity of gamma rays is accurately measured using a gamma spectrometer. These detectors can provide real-time feedback on the intensity of various types of rays in the decay pool, thereby providing key data for subsequent analysis and dynamic regulation of the decay pool.

[0023] In S2, the collected wastewater flow data and radioactive substance concentration data are analyzed to calculate the attenuation degree of radioactive substances in the wastewater, identify the decay state of the wastewater, and determine whether it has entered a severe attenuation state or a slight attenuation state, including: Obtain wastewater flow data within the monitoring period, and calculate the wastewater flow barrier index based on the fluctuation degree of the wastewater flow, which is used to evaluate the impact of the wastewater flow on the treatment efficiency of the wastewater decay pool; Obtain radioactive material concentration data within the monitoring period, calculate the degree of change in radioactive material concentration in the decay pool, and calculate the radioactive material concentration anomaly index to evaluate the impact of radioactive material concentration on the wastewater decay pool treatment efficiency; The wastewater flow barrier index and the radioactive substance concentration abnormality index are normalized and calculated to obtain the wastewater attenuation index, which is used to identify the decay state of the wastewater; Determine whether the attenuation index of the wastewater is greater than or equal to a preset threshold value, if so, it is recorded as a severe attenuation state, if not, it is recorded as a slight attenuation state; The process of obtaining the wastewater flow barrier index is as follows: Obtain wastewater flow time series data from the monitoring period, obtain wastewater flow time series data from the monitoring period, perform multi-level decomposition, decompose it into low-frequency components and high-frequency components of different scales, and use the decomposed high-frequency components to characterize the fluctuation characteristics of wastewater flow; For the high-frequency components decomposed at each level, the fluctuation energy of each level is calculated to quantify the intensity of wastewater flow fluctuation. The calculation expression is: ;in, Indicates Level quantity, Indicates coefficients, Indicates The fluctuation energy of the magnitude component, Indicates The first high frequency component coefficients, Indicates the total number of data points in the monitoring period; Normalize the fluctuation energy at each level to get the relative contribution rate of fluctuations at each level. The calculation expression is: ;in, Indicates The normalized value of the level component fluctuation energy, It represents the total number of layers of Haar wavelet decomposition. The wastewater flow barrier index is calculated based on the ratio of the normalized energy of the highest-level high-frequency component to the normalized energy of the lowest-level high-frequency component.

[0024] The process of obtaining the abnormal index of radioactive substance concentration is as follows: Acquire the radioactive material concentration data within the monitoring period, divide the data into a three-dimensional data structure according to time points and spatial coordinates, and represent it as a tensor; perform low-rank tensor decomposition on the concentration data tensor, decomposing it into a low-rank tensor and a sparse tensor, wherein the low-rank tensor represents the normal mode of the concentration data; the sparse tensor represents the abnormal component of the concentration data, capturing the abnormal part in the concentration change, and the decomposition is based on sparse regularization, constraining the abnormal component to minimize its deviation from the concentration data; The abnormal concentration contribution at each time point is extracted from the sparse tensor, and the absolute sum of the abnormal values ​​of all spatial coordinates in the tensor is summed to obtain the time point The abnormal concentration contribution of is calculated as: ;in, Indicates time point and location The outliers on represents the spatial coordinates, It represents the number of decay pools divided horizontally. It represents the number of decay pools divided along the longitudinal direction. Indicates time point The abnormal concentration contribution of By calculating the abnormal contribution of all time points The average concentration change degree during the monitoring period is calculated, and the concentration change degree is calculated by the ratio of the overall mean of the concentration data to obtain the abnormal concentration index of radioactive substances.

[0025] The calculation expression of the attenuation index is: ; In the formula, represents the wastewater flow barrier index, Indicates the abnormal concentration index of radioactive substances. and represents the preset scale factor, and and are greater than 0, represents the decay index; It should be noted that: since excessive or insufficient wastewater flow may lead to insufficient decay pool treatment or too long retention time, the wastewater flow barrier index affects the wastewater decay pool treatment efficiency, and the larger the value of the wastewater flow barrier index, the greater the degree of influence of the wastewater flow on the decay pool treatment efficiency, and the larger the value of the radioactive substance concentration abnormality index, the greater the degree of influence of the corresponding radioactive substance concentration on the decay pool treatment efficiency.

[0026] In S3, if the wastewater decay state is severe decay, the concentration difference of each area in the decay pool is calculated by comparing the concentration of radioactive substances in different areas in the decay pool, and an early warning signal is generated according to the difference; Obtain radioactive material concentration data for multiple areas of the same area in the decay pool during the monitoring period; calculate the dynamic time warping distance between the concentration series of any two areas. The dynamic time warping metric can effectively identify the concentration differences caused by different fluctuations occurring at different time points by comparing the change patterns of the time series. The dynamic time warping algorithm recursively calculates the shortest distance between time series to ensure that all possible alignments are considered, thereby capturing the differences in concentration changes; The concentration difference coefficient of the decay pool is obtained by calculating the ratio of the mean to the variance of the dynamic time warping distance in different areas of the decay pool; Based on the comparison between the calculated concentration difference coefficient and the preset threshold, if the concentration difference coefficient is greater than or equal to the preset threshold, an early warning signal is triggered.

[0027] In S4, if the wastewater decay state is slight decay, the wastewater decay degree in the future period is predicted through historical data analysis and real-time monitoring of wastewater flow and radioactive substance concentration in the decay pool, and the future decay trend is obtained, including: Obtain the wastewater flow barrier index and the radioactive substance concentration anomaly index of the wastewater in the decay pool, construct the wastewater flow barrier index and the radioactive substance concentration anomaly index into a comprehensive feature vector as the input of the machine learning model, train the model through historical data, and output the attenuation index according to the trained model to judge the attenuation degree in the future monitoring period. The machine learning model is a support vector machine, which specifically includes: The wastewater flow barrier index and the radioactive material concentration abnormality index are combined to form a comprehensive feature vector. The support vector machine model is trained using historical data. The input feature is the comprehensive feature vector of each monitoring period, corresponding to the decay state of the decay pool during the monitoring period. The label value is 0 or 1, where 0 indicates a slight decay state and 1 indicates a severe decay state. The training process is as follows: The wastewater flow barrier index and radioactive material concentration abnormality index of each period are extracted from the historical data to construct a training set. Each sample in the training set consists of a comprehensive feature vector and a label; Use historical data to train the support vector machine model to classify the state of the decay pool by maximizing the interval between categories. The support vector machine model will automatically adjust its hyperparameters during the training process to minimize the classification error. Use the standard loss function of the support vector machine for training to optimize the classification ability of the model. Cross-validate the model to ensure the generalization ability of the model on new data, and improve the prediction accuracy by adjusting hyperparameters (such as penalty parameter C and kernel function parameters). After the training is completed, the trained support vector machine model is used to predict the attenuation state in the future monitoring period.

[0028] In S5, according to the predicted decay trend, the control parameters in the decay pool, including temperature and flow, are automatically adjusted to optimize the decay process of radioactive substances in the wastewater, including: According to the predicted decay index, it is determined whether the decay pool is in a severe decay state or a slight decay state, and the key control parameters (such as temperature and flow) in the decay pool are automatically adjusted to optimize the decay process of radioactive substances in the wastewater, including: Temperature control: If the decay pool is predicted to be in a state of slight decay, the system can moderately increase the temperature in the decay pool to accelerate the decay reaction of the radioactive material; if it is in a state of severe decay, the reaction speed is controlled by lowering the temperature to avoid excessive decay; Flow control: According to the fluctuation of wastewater flow and the current treatment status of the decay pool, the flow is adjusted to ensure that the wastewater stays in the pool long enough to fully decay the radioactive substances. In the case of large flow fluctuations, the flow stability is increased to ensure decay efficiency; By using sensors to monitor various indicators in the decay pool in real time, including temperature, flow rate and radioactive material concentration, the system can dynamically adjust the control strategy and optimize the operating efficiency of the decay pool through closed-loop feedback. Each adjustment process is corrected based on real-time data to ensure the optimal effect of radioactive material attenuation in wastewater.

[0029] During the decay state prediction process of the decay pool, if the prediction results indicate that the decay pool is about to reach a critical state of severe decay, the system will automatically issue an early warning signal and trigger an emergency response mechanism based on the early warning to quickly restore the stability of the decay pool by increasing the temperature or flow rate, thereby avoiding potential contamination spread or treatment failure.

[0030] See also Figure 2 As shown, an intelligent control system for a hospital radioactive wastewater decay pool includes: A data acquisition module, which collects wastewater flow and radioactive material concentration data in the hospital's radioactive wastewater decay pool in real time, including the radioactive intensity of beta rays, gamma rays and alpha rays; A decay state identification module, which analyzes the collected wastewater flow data and radioactive substance concentration data, calculates the decay degree of the radioactive substances in the wastewater, identifies the decay state of the wastewater, and determines whether it has entered a severe decay state or a slight decay state; An early warning module, wherein if the wastewater decay state is severe decay, the early warning module calculates the concentration difference of each area in the decay pool by comparing the concentration of radioactive substances in different areas in the decay pool, and generates an early warning signal according to the difference; A decay prediction module, wherein if the wastewater decay state is slight decay, the decay prediction module predicts the degree of wastewater decay in the future through historical data analysis and real-time monitoring of wastewater flow and radioactive substance concentration in the decay pool to obtain future decay trends; The decay control adjustment module automatically adjusts the control parameters in the decay pool, including temperature and flow rate, according to the predicted decay trend, so as to optimize the decay process of the radioactive substances in the wastewater.

[0031] The working principle of the present invention is to realize accurate monitoring and regulation of the wastewater decay process by real-time collection of wastewater flow and radioactive material concentration data, combined with advanced data analysis and machine learning technology. In the specific implementation process, firstly, the wastewater flow and radioactive material concentration data in the decay pool, including the intensity of beta rays, gamma rays and alpha rays, are collected in real time by high-precision flowmeters and radiation detectors. Subsequently, the wastewater flow barrier index and the radioactive material concentration anomaly index are calculated by multi-level decomposition and low-rank tensor decomposition and other methods. These two indexes are used to evaluate the influence of wastewater flow and concentration on the treatment efficiency of the decay pool, and the decay index is calculated accordingly to determine whether the decay pool is in a severe decay state or a slight decay state. In the severe decay state of the decay pool, the concentration difference coefficient of different areas in the pool is calculated by the dynamic time warping algorithm, and an early warning signal is generated according to the degree of difference; in the slight decay state, the future decay trend is predicted based on historical data and real-time monitoring data, and the control parameters of the decay pool, including temperature and flow, are adjusted to optimize the decay process of radioactive substances in the wastewater. Temperature control can be appropriately increased or decreased according to the decay state to accelerate or slow down the decay reaction, while flow control ensures that the wastewater stays in the decay pool for a sufficient time to ensure decay efficiency. In addition, the system also has the ability to automatically issue warnings and trigger emergency response mechanisms based on the predicted decay state to quickly restore stability in the pool and prevent potential contamination spread or treatment failure. Through the closed-loop feedback mechanism, the system can adjust the control strategy in real time to ensure the optimal treatment effect of radioactive wastewater.

[0032] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0033] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0034] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0035] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0036] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent control method for a hospital radioactive wastewater decay pool, characterized in that: The following steps are involved: S1: Real-time collection of wastewater flow and radioactive material concentration data in the hospital's radioactive wastewater decay pool, including the radioactivity intensity of beta rays, gamma rays and alpha rays; S2: Analyze the collected wastewater flow data and radioactive substance concentration data, calculate the attenuation degree of radioactive substances in the wastewater, identify the decay state of the wastewater, and determine whether it has entered a severe attenuation state or a slight attenuation state; S3: If the wastewater decay state is severe decay, the concentration difference of each area in the decay pool is calculated by comparing the concentration of radioactive substances in different areas in the decay pool, and an early warning signal is generated according to the difference; S4: If the wastewater decay state is slight decay, the wastewater decay degree in the future period is predicted through historical data analysis and real-time monitoring of wastewater flow and radioactive substance concentration in the decay pool to obtain the future decay trend; S5: According to the predicted decay trend, the control parameters in the decay pool, including temperature and flow rate, are automatically adjusted to optimize the decay process of radioactive substances in the wastewater.

2. The intelligent control method for a hospital radioactive wastewater decay pool according to claim 1 is characterized in that: The identification of the decay state of the wastewater specifically includes: Obtain wastewater flow data within the monitoring period, and calculate the wastewater flow barrier index based on the fluctuation degree of wastewater flow, which is used to evaluate the impact of wastewater flow on the treatment efficiency of the wastewater decay pool; Obtain radioactive material concentration data within the monitoring period, calculate the degree of change in radioactive material concentration in the decay pool, and calculate the radioactive material concentration anomaly index to evaluate the impact of radioactive material concentration on the wastewater decay pool treatment efficiency; The wastewater flow barrier index and the radioactive substance concentration anomaly index are normalized and calculated to obtain the wastewater attenuation index, which is used to identify the decay state of the wastewater.

3. The intelligent control method for hospital radioactive wastewater decay pool according to claim 2 is characterized in that: The process of obtaining the wastewater flow barrier index is as follows: Obtain wastewater flow time series data from the monitoring period, and perform multi-level decomposition on the wastewater flow time series data from the monitoring period, decomposing it into low-frequency components and high-frequency components of different scales; For each level of high-frequency component decomposed, the fluctuation energy of each level is calculated, and the calculation expression is: ;in, Indicates Level quantity, Indicates coefficients, Indicates The fluctuation energy of the magnitude component, Indicates The first high frequency component coefficients, Indicates the total number of data points in the monitoring period; The fluctuation energies at each level are normalized to obtain the normalized values ​​of the fluctuation energies at each level. The wastewater flow barrier index is obtained by calculating the ratio of the normalized energy of the highest level high-frequency component to the normalized energy of the lowest level high-frequency component.

4. The intelligent control method for hospital radioactive wastewater decay pool according to claim 2 is characterized in that: The process of obtaining the abnormal index of radioactive substance concentration is as follows: Acquire the radioactive material concentration data within the monitoring period, divide the data into a three-dimensional data structure according to time points and spatial coordinates, and represent it as a tensor; perform low-rank tensor decomposition on the concentration data tensor, decomposing it into a low-rank tensor and a sparse tensor, wherein the low-rank tensor represents the normal mode of the concentration data; the sparse tensor represents the abnormal component of the concentration data, capturing the abnormal part in the concentration change; The abnormal concentration contribution at each time point is extracted from the sparse tensor, and the absolute sum of the abnormal values ​​of all spatial coordinates in the tensor is summed to obtain the time point The abnormal concentration contribution of The average concentration change during the monitoring period is calculated by calculating the mean of the abnormal contributions of all time points. The concentration change is then compared with the overall mean of the concentration data to obtain the radioactive material concentration anomaly index.

5. The intelligent control method for hospital radioactive wastewater decay pool according to claim 1 is characterized in that: The determining whether the state has entered a severe attenuation state or a mild attenuation state specifically includes: Determine whether the attenuation index of the wastewater is greater than or equal to a preset threshold. If so, it is recorded as a severe attenuation state; if not, it is recorded as a slight attenuation state.

6. The intelligent control method for hospital radioactive wastewater decay pool according to claim 1 is characterized in that: The generating of the early warning signal specifically includes: Obtain radioactive material concentration data for multiple areas of the same area in the decay pool during the monitoring period; calculate the dynamic time-warped distance between the concentration series of any two areas; The concentration difference coefficient of the decay pool is obtained by calculating the ratio of the mean to the variance of the dynamic time warping distance in different areas of the decay pool; Based on the comparison between the calculated concentration difference coefficient and the preset threshold, if the concentration difference coefficient is greater than or equal to the preset threshold, an early warning signal is triggered.

7. The intelligent control method for hospital radioactive wastewater decay pool according to claim 1 is characterized in that: The prediction of the wastewater attenuation degree in the future period of time and the future decay trend are obtained, specifically including: The wastewater flow barrier index and the radioactive substance concentration anomaly index of the wastewater in the decay pool are obtained, and the wastewater flow barrier index and the radioactive substance concentration anomaly index are constructed into a comprehensive feature vector as the input of the machine learning model. The model is trained through historical data, and the attenuation index is output according to the trained model to judge the attenuation degree in the future monitoring period. The machine learning model is a support vector machine.

8. The intelligent control method for hospital radioactive wastewater decay pool according to claim 7 is characterized in that: The construction process of the machine learning model is as follows: The wastewater flow barrier index and the radioactive material concentration abnormality index are combined to form a comprehensive feature vector. The support vector machine model is trained using historical data. The input feature is the comprehensive feature vector of each monitoring period, corresponding to the decay state of the decay pool during the monitoring period. The label value is 0 or 1, where 0 indicates a slight decay state and 1 indicates a severe decay state. The training process is as follows: Extract the wastewater flow barrier index and radioactive material concentration abnormality index of each period from historical data, and construct a training set. Each sample in the training set consists of a comprehensive feature vector and a label. The support vector machine model is trained using historical data to classify the state of the decay pool by maximizing the interval between categories. The support vector machine model will automatically adjust the hyperparameters during the training process to minimize the classification error. The standard loss function of the support vector machine is used for training, the model is cross-validated, and the prediction accuracy is improved by adjusting the hyperparameters.

9. The intelligent control method for hospital radioactive wastewater decay pool according to claim 1 is characterized in that: The control parameters in the decay pool, including temperature and flow rate, are automatically adjusted according to the predicted decay trend to optimize the decay process of radioactive substances in the wastewater, specifically including: Temperature control: If the decay pool is predicted to be in a slight decay state, the system will increase the temperature in the decay pool to accelerate the decay reaction of the radioactive material; if it is in a severe decay state, the reaction speed will be controlled by lowering the temperature; Flow control: According to the fluctuation of wastewater flow and the current treatment status of the decay pool, the flow is adjusted to ensure that the wastewater stays in the pool longer so that the radioactive substances can be fully decayed. For flow fluctuations, the stability of the flow is increased to ensure decay efficiency.

10. An intelligent control system for a hospital radioactive wastewater decay pool, characterized in that: A method for intelligently controlling a hospital radioactive wastewater decay pool as claimed in any one of claims 1 to 9, comprising: A data acquisition module, which collects wastewater flow and radioactive material concentration data in the hospital's radioactive wastewater decay pool in real time, including the radioactive intensity of beta rays, gamma rays and alpha rays; A decay state identification module, which analyzes the collected wastewater flow data and radioactive substance concentration data, calculates the decay degree of the radioactive substances in the wastewater, identifies the decay state of the wastewater, and determines whether it has entered a severe decay state or a slight decay state; An early warning module, wherein if the wastewater decay state is severe decay, the early warning module calculates the concentration difference of each area in the decay pool by comparing the concentration of radioactive substances in different areas in the decay pool, and generates an early warning signal according to the difference; A decay prediction module, if the wastewater decay state is slight decay, the decay prediction module predicts the degree of wastewater decay in the future through historical data analysis and real-time monitoring of wastewater flow and radioactive substance concentration in the decay pool to obtain future decay trends; The decay control adjustment module automatically adjusts the control parameters in the decay pool, including temperature and flow rate, according to the predicted decay trend, so as to optimize the decay process of the radioactive substances in the wastewater.

Images