An intelligent electron accelerator for industrial irradiation

Abstract

The present invention discloses an intelligent electron accelerator for industrial irradiation, comprising a conveying unit for conveying a material to be irradiated into an irradiation room, a pre-irradiation detection unit for performing pre-irradiation detection on the material and recording initial parameters of the material, a calibration unit for adjusting calibration parameters of the accelerator based on the pre-irradiation detection result, an accelerator unit for starting the accelerator after the calibration parameters are adjusted to accurately irradiate the material, and a maintenance calibration unit for recalibrating parameters after maintenance to ensure that the accuracy deviation of the accelerator is within an allowable range. The intelligent electron accelerator for industrial irradiation solves the problem of easy errors in accurate operation in the prior art, especially in complex parameter settings, due to improper human operation or insufficient system detection accuracy, resulting in deviations in the energy and distribution of the electron beam. Such deviations in accuracy not only affect the effect of the irradiation process, but also have an adverse effect on the quality of the final product and even cause production safety hazards.

Description

Technical Field

[0001] The present invention relates to the field of nuclear technology applications, and in particular to an intelligent electron accelerator for industrial irradiation. Background Art

[0002] Electron accelerators are a common device in industrial irradiation, widely used in applications such as material modification, food disinfection, and sterilization of medical supplies. These devices accelerate electrons to form a high-energy electron beam, which then processes the target substance to achieve the desired modification, sterilization, or disinfection effect. With the advancement of industrial automation and intelligent technologies, intelligent electron accelerators are being introduced to precisely control the stability and accuracy of the electron beam, improving production efficiency and ensuring product quality.

[0003] Intelligent electron accelerator systems currently on the market typically use automated control technology, which can automatically adjust and optimize the distribution of electron beam energy according to set parameters. Through intelligent systems, operators can simplify operating procedures, reduce manual intervention, and improve the operating efficiency and stability of equipment during the production process. However, despite the many advantages of this technology, there are still some problems in practical applications.

[0004] First of all, after a long period of operation, the equipment needs regular maintenance and parameter calibration. During this process, the calibration operation is prone to errors, especially in complex parameter settings. Due to improper human operation or insufficient system detection accuracy, the energy and distribution of the electron beam deviate. This deviation in accuracy will not only affect the effect of the irradiation process, but also have an adverse effect on the quality of the final product, and even cause production safety hazards. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent electron accelerator for industrial irradiation to solve the problem that errors are prone to occur in the accuracy of the existing technology, especially in complex parameter settings. Due to improper human operation or insufficient system detection accuracy, the energy and distribution of the electron beam will deviate. Such accuracy deviation will not only affect the effect of the irradiation process, but also have an adverse effect on the quality of the final product and even cause production safety hazards.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent electron accelerator for industrial irradiation, comprising:

[0007] A conveying unit, used for conveying the material to be irradiated into the irradiation chamber;

[0008] The pre-irradiation detection unit connected to the conveying unit is used to perform pre-irradiation detection on the material and record the initial parameters of the material.

[0009] Specifically, by collecting data on the initial parameters of the material and the corresponding irradiation effects, a multivariate linear regression model capable of predicting irradiation effects is trained based on historical data. Based on the initial parameters of the material, the model is used to predict whether the effect after irradiation meets the standards. The initial parameters of the material are then transmitted to the accelerator unit to assist in subsequent irradiation control.

[0010] A calibration unit connected to the pre-irradiation detection unit is used to adjust the calibration parameters of the accelerator based on the pre-irradiation detection results. Specifically, the calibration unit initializes the state estimation and error covariance matrix of the Kalman filter, uses the results of the pre-irradiation detection unit as observation data, uses the prediction formula of the Kalman filter to estimate the current calibration parameters based on the previous state, and uses the Kalman gain to update the calibration parameters based on the difference between the measured value and the predicted value;

[0011] The accelerator unit connected to the calibration unit is used to start the accelerator after the calibration parameters are adjusted to accurately irradiate the material;

[0012] The maintenance calibration unit connected to the accelerator unit is used to recalibrate the parameters after maintenance to ensure that the accuracy deviation of the accelerator is within the allowable range. Specifically, the value range of the calibration parameters of the accelerator is set according to the initial state after maintenance, and a set of random calibration parameters is generated as the initial population. The calibration accuracy of each set of calibration parameters is evaluated by the fitness function. The excellent parameter combination is selected according to the fitness, and crossover and mutation are performed to generate a new parameter combination. After multiple iterations, the calibration parameter combination with the highest accuracy is finally obtained.

[0013] Preferably, the conveying unit conveys the material to be irradiated into the irradiation chamber, comprising:

[0014] Create a map of the conveyor unit's work area, set the material transfer starting and destination points, and transfer the material from the loading point to the irradiation chamber. Apply the A-star algorithm based on heuristic search to optimize the material transfer path using the following evaluation function:

[0015] ,

[0016] in, Representation node The total cost, From the starting point to the node The actual cost, Represents a slave node Heuristic estimated cost to the target node, Represents a node in a path search.

[0017] Preferably, the pre-irradiation detection unit performs pre-irradiation detection on the material and records the initial parameters of the material, further comprising:

[0018] The specific formula for multivariate linear regression is:

[0019] ,

[0020] in, Indicates the expected irradiation results of the material, Indicates different initial parameters of the material during pre-irradiation testing. represents the regression coefficient to be trained, represents the error term.

[0021] Preferably, the calibration unit adjusts the calibration parameters of the accelerator based on the pre-irradiation detection result and further includes:

[0022] The prediction steps of the Kalman filter are:

[0023] , ;

[0024] in, Indicates the The estimated value of the predicted state at time t, Indicates the The updated state estimate at time t, represents the state transition matrix, represents the control input matrix, represents the control input value, represents the prediction error covariance matrix, represents the updated error covariance matrix, represents the process noise covariance matrix;

[0025] The formula for the update step is:

[0026] ,

[0027] ,

[0028] ,

[0029] in, represents the Kalman gain, represents the prediction error covariance matrix, Indicates the The measured value at the moment, represents the observation matrix, represents the measurement noise covariance matrix, Indicates the The updated state estimate at time t, Indicates the The estimated value of the predicted state at time t, Represents the identity matrix.

[0030] Preferably, the accelerator unit starts the accelerator after the calibration parameters are adjusted to accurately irradiate the material, including:

[0031] According to the calibration results, the target electron beam energy and distribution of the accelerator are set, and the electron beam energy and distribution of the accelerator are monitored in real time. By comparing the target value and the actual value, the error signal is obtained. , according to the error value, the PID control algorithm is applied to adjust the electron beam energy of the accelerator. The specific formula is:

[0032] ,

[0033] in, Indicates the control signal, Indicates the error value, Represents proportional, integral, and differential coefficients.

[0034] Preferably, the calibration unit further includes:

[0035] Obtaining initial parameters for pre-irradiation testing;

[0036] Analyze the deviation between initial parameters and historical standard parameters;

[0037] Based on the deviation, an adjustment amount for the calibration parameter is calculated;

[0038] Adjust the calibration parameters of the accelerator;

[0039] The adjustment amount of the calculated calibration parameter is specifically:

[0040] Calculate the deviation D between the initial parameters P of the pre-irradiation test and the historical standard parameters S, using the formula D = |P-S|;

[0041] Where P represents the initial parameters of the pre-irradiation test, S represents the historical standard parameters, and D represents the deviation;

[0042] Determine whether the deviation D exceeds a predetermined threshold T;

[0043] If the deviation D is greater than the threshold T, the calibration parameters need to be adjusted; otherwise, the calibration parameters do not need to be adjusted.

[0044] Preferably, if the deviation D is greater than the threshold T, the calibration parameters need to be adjusted, including:

[0045] If D>T, proceed to the next step;

[0046] Calculate the adjustment amount A of the calibration parameter using the formula A=k×D;

[0047] Where k represents the proportional coefficient and D represents the deviation;

[0048] Based on the calculated adjustment amount A, the calibration parameters of the accelerometer are updated.

[0049] Preferably, updating the calibration parameters of the accelerator based on the calculated adjustment amount A includes:

[0050] Get the current calibration parameter C;

[0051] Calculate the new calibration parameter N using the formula N=C+A;

[0052] Where C represents the current calibration parameter and A represents the adjustment amount;

[0053] Based on the calculated new calibration parameter N, the calibration parameters of the accelerator are updated.

[0054] Preferably, updating the calibration parameters of the accelerator based on the calculated new calibration parameter N includes:

[0055] After each update of the calibration parameters, record the new calibration parameters N and the current time T;

[0056] Determine whether the time interval ΔT between two updates is less than the predetermined time threshold t, the formula is ΔT <t;

[0057] Where ΔT represents the actual time interval between two updates, and t represents the predetermined time threshold;

[0058] If ΔT is less than t, continue monitoring, otherwise complete the update process.

[0059] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0060] The intelligent electron accelerator for industrial irradiation transmits the material to be irradiated into the irradiation room through the transmission unit, the pre-irradiation detection unit performs pre-irradiation detection on the material and records the initial parameters of the material, the calibration unit adjusts the calibration parameters of the accelerator based on the pre-irradiation detection results, and the accelerator unit starts the accelerator after the calibration parameters are adjusted to accurately irradiate the material. The maintenance calibration unit is used to calibrate the parameters again after maintenance to ensure that the accuracy deviation of the accelerator is within the allowable range. The energy and distribution of the electron beam can be automatically detected and corrected, avoiding the accuracy deviation caused by human operation, improving the stability and safety of the equipment, effectively reducing the maintenance frequency and ensuring the quality consistency of the product, significantly reducing the need for manual intervention, improving production efficiency, and reducing safety hazards caused by human operation errors. It solves the problem that errors are prone to occur in the existing technology in accurate operation, especially in complex parameter settings. Due to improper human operation or insufficient system detection accuracy, the energy and distribution of the electron beam deviate. This accuracy deviation not only affects the effect of the irradiation process, but also has an adverse effect on the quality of the final product and even causes production safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of the connection of each unit of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0063] like Figure 1 As shown, an intelligent electron accelerator for industrial irradiation includes:

[0064] A conveying unit, used for conveying the material to be irradiated into the irradiation chamber;

[0065] The pre-irradiation detection unit connected to the conveying unit is used to perform pre-irradiation detection on the material and record the initial parameters of the material.

[0066] Specifically, by collecting data on the initial parameters of the material and the corresponding irradiation effects, a multivariate linear regression model capable of predicting irradiation effects is trained based on historical data. Based on the initial parameters of the material, the model is used to predict whether the effect after irradiation meets the standards. The initial parameters of the material are then transmitted to the accelerator unit to assist in subsequent irradiation control.

[0067] A calibration unit connected to the pre-irradiation detection unit is used to adjust the calibration parameters of the accelerator based on the pre-irradiation detection results. Specifically, the calibration unit initializes the state estimation and error covariance matrix of the Kalman filter, uses the results of the pre-irradiation detection unit as observation data, uses the prediction formula of the Kalman filter to estimate the current calibration parameters based on the previous state, and uses the Kalman gain to update the calibration parameters based on the difference between the measured value and the predicted value;

[0068] The accelerator unit connected to the calibration unit is used to start the accelerator after the calibration parameters are adjusted to accurately irradiate the material;

[0069] The maintenance calibration unit connected to the accelerator unit is used to recalibrate the parameters after maintenance to ensure that the accuracy deviation of the accelerator is within the allowable range. Specifically, the value range of the calibration parameters of the accelerator is set according to the initial state after maintenance, and a set of random calibration parameters is generated as the initial population. The calibration accuracy of each set of calibration parameters is evaluated by the fitness function. The excellent parameter combination is selected according to the fitness, and crossover and mutation are performed to generate a new parameter combination. After multiple iterations, the calibration parameter combination with the highest accuracy is finally obtained.

[0070] After the material is delivered to the irradiation chamber via a conveyor unit, the pre-irradiation detection unit checks the material's initial parameters and uses a multivariate linear regression model to predict whether the irradiation effect meets the required standards. If the prediction indicates that the irradiation effect may not meet the requirements, the system promptly adjusts the accelerator's operating parameters. Based on the pre-irradiation detection, the calibration unit uses Kalman filtering technology to update the accelerator's calibration parameters, ensuring precise accelerator control. After the irradiation is completed, during equipment maintenance, the maintenance calibration unit will optimize the calibration parameters through genetic algorithms to ensure the long-term accuracy of the accelerator. The irradiation effect of the material is predicted through pre-irradiation detection and linear regression models, and the effect can be evaluated before irradiation, which greatly improves the accuracy and reliability of irradiation control and reduces the production of defective products. The calibration unit adopts the Kalman filter algorithm, which can adjust the calibration parameters of the accelerator according to real-time data to ensure that the accelerator always maintains high precision under different materials and operating conditions. The maintenance calibration unit optimizes the calibration parameters through genetic algorithms, avoiding the errors of manual calibration and improving the irradiation accuracy after maintenance. The automated calibration process is efficient and reliable. The entire system is highly intelligent and can automatically adjust the irradiation parameters according to the characteristics of different materials, improve production efficiency and reduce the burden on operators.

[0071] The conveying unit transports the material to be irradiated into the irradiation room and includes:

[0072] Create a map of the conveyor unit's work area, set the material transfer starting and destination points, and transfer the material from the loading point to the irradiation chamber. Apply the A-star algorithm based on heuristic search to optimize the material transfer path using the following evaluation function:

[0073] ,

[0074] in, Representation node The total cost, From the starting point to the node The actual cost, Represents a slave node Heuristic estimated cost to the target node, Represents a node in a path search.

[0075] In this embodiment, the transport unit first models the material transport area of ​​the entire factory, generates a map, and sets the starting and destination points of the materials. The transport system uses the A-star algorithm to search for paths. The A-star algorithm combines the actual path cost and heuristic estimation to plan the material transport path to the optimal one. The algorithm calculates the evaluation function at each step. To evaluate each path node, ensure that the material can be delivered to the irradiation room safely and quickly along the optimal path. By using the A-star algorithm, the system can plan the optimal material delivery path in real time to ensure that the material is delivered to the destination in the shortest time and with the lowest energy consumption in a complex factory environment. The A-star algorithm can dynamically avoid obstacles and congestion in the working area, thereby improving delivery efficiency and reducing delays in material delivery. Through precise evaluation function calculation and sensor data feedback, the delivery system can achieve precise control of the material delivery process and reduce errors and deviations caused by improper path selection. This embodiment is highly intelligent, can autonomously plan paths, and make adjustments according to real-time conditions during the delivery process, reducing human intervention and reducing the workload of operators.

[0076] The pre-irradiation detection unit performs pre-irradiation detection on the material and records the initial parameters of the material, including:

[0077] The specific formula for multivariate linear regression is:

[0078] ,

[0079] in, Indicates the expected irradiation results of the material, Indicates different initial parameters of the material during pre-irradiation testing. represents the regression coefficient to be trained, represents the error term.

[0080] The pre-irradiation detection unit of the present invention predicts the irradiation effect of the material through a multivariate linear regression model. By collecting a large amount of historical data, the model can identify the relationship between the initial parameters of the material and its irradiation effect. The model uses the regression coefficient to predict the irradiation effect of the material, so that it can be evaluated whether the material meets the irradiation requirements before the irradiation operation. The data after the pre-irradiation detection will be transmitted to the accelerator unit to help further optimize the irradiation parameters. The multivariate linear regression model can more accurately predict the irradiation effect of the material through training with historical data, reducing the uncertainty in the experiment. The pre-irradiation detection unit can obtain the initial parameters of the material in real time and quickly predict the irradiation effect, so that the entire system can operate efficiently. Through real-time prediction results, the system can adjust the irradiation parameters of the accelerator before the actual irradiation operation, avoiding the occurrence of substandard irradiation, improving production efficiency and product quality. The model can accumulate more data and retrain over time, thereby gradually improving the system's prediction ability, enabling it to cope with changes in different materials and environments.

[0081] The calibration unit adjusts the calibration parameters of the accelerator based on the pre-irradiation detection results and further includes:

[0082] The prediction steps of the Kalman filter are:

[0083] , ;

[0084] in, Indicates the The estimated value of the predicted state at time t, Indicates the The updated state estimate at time t, represents the state transition matrix, represents the control input matrix, represents the control input value, represents the prediction error covariance matrix, represents the updated error covariance matrix, represents the process noise covariance matrix;

[0085] The formula for the update step is:

[0086] ,

[0087] ,

[0088] ,

[0089] in, represents the Kalman gain, represents the prediction error covariance matrix, Indicates the The measured value at the moment, represents the observation matrix, represents the measurement noise covariance matrix, Indicates the The updated state estimate at time t, Indicates the The estimated value of the predicted state at time t, Represents the identity matrix.

[0090] In this embodiment, a Kalman filter is used to adjust the calibration parameters of the accelerator in real time. First, the system predicts the state of the accelerator based on a preset state transition equation to obtain the predicted calibration parameters and error covariance matrix. Subsequently, the system obtains the actual observation data of the material through the pre-irradiation detection unit and adjusts the predicted calibration parameters in combination with the Kalman gain. The updated calibration parameters ensure the accuracy of the accelerator, so that the irradiation effect can meet the expected standards. The Kalman filter can dynamically adjust the calibration parameters of the accelerator in the presence of noise, providing high accuracy and ensuring the stability of the accelerator during the irradiation process. The algorithm has real-time adjustment capabilities and can quickly update the calibration parameters of the accelerator based on the pre-irradiation detection results, avoiding the lag in traditional calibration methods. The Kalman filter can handle random noise and uncertainty in the system, and can still maintain a good calibration effect in more complex and unstable environments, reducing the impact of errors on the irradiation results. The calibration parameters can be continuously updated according to real-time observation data to adapt to changes in different materials or environments, thereby improving the flexibility of the system.

[0091] The accelerator unit starts the accelerator after adjusting the calibration parameters to accurately irradiate the material including:

[0092] According to the calibration results, the target electron beam energy and distribution of the accelerator are set, and the electron beam energy and distribution of the accelerator are monitored in real time. By comparing the target value and the actual value, the error signal is obtained. , according to the error value, the PID control algorithm is applied to adjust the electron beam energy of the accelerator. The specific formula is:

[0093] ,

[0094] in, Indicates the control signal, Indicates the error value, Represents proportional, integral, and differential coefficients.

[0095] This embodiment uses a PID control algorithm to dynamically adjust the electron beam energy of the accelerator in real time. The electron beam energy output by the accelerator unit is compared with the target value through real-time monitoring to generate an error signal. The PID controller calculates the adjustment amount based on the error signal, and dynamically adjusts the control parameters of the accelerator through the synergistic effect of the proportional, integral, and differential parts to ensure the accurate and stable output of the electron beam energy. Through the PID control algorithm, the accelerator can accurately adjust the output electron beam energy according to the real-time error to ensure that the irradiation effect meets the predetermined standard. The PID control algorithm can quickly respond to changes in the error and make advance corrections through the differential term, making the accelerator's adjustment of the target energy faster and more accurate. The integral term in the PID control can accumulate error information, eliminate steady-state errors, and ensure that the electron beam energy of the accelerator is stable and does not deviate from the target value during long-term operation. The PID control has good anti-interference ability and can adapt to noise and interference in the environment or system, so that the accelerator maintains stable output under different working conditions.

[0096] The calibration unit also includes:

[0097] Obtaining initial parameters for pre-irradiation testing;

[0098] Analyze the deviation between initial parameters and historical standard parameters;

[0099] Based on the deviation, an adjustment amount for the calibration parameter is calculated;

[0100] Adjust the calibration parameters of the accelerator;

[0101] The adjustment amount of the calculated calibration parameter is specifically:

[0102] Calculate the deviation D between the initial parameters P of the pre-irradiation test and the historical standard parameters S, using the formula D = |P-S|;

[0103] Where P represents the initial parameters of the pre-irradiation test, S represents the historical standard parameters, and D represents the deviation;

[0104] Determine whether the deviation D exceeds a predetermined threshold T;

[0105] If the deviation D is greater than the threshold T, the calibration parameters need to be adjusted; otherwise, the calibration parameters do not need to be adjusted.

[0106] If the deviation D is greater than the threshold T, the calibration parameters that need to be adjusted include:

[0107] If D>T, proceed to the next step;

[0108] Calculate the adjustment amount A of the calibration parameter using the formula A=k×D;

[0109] Where k represents the proportional coefficient and D represents the deviation;

[0110] Based on the calculated adjustment amount A, the calibration parameters of the accelerometer are updated;

[0111] Based on the calculated adjustment amount A, updating the calibration parameters of the accelerometer includes:

[0112] Get the current calibration parameter C;

[0113] Calculate the new calibration parameter N using the formula N=C+A;

[0114] Where C represents the current calibration parameter and A represents the adjustment amount;

[0115] Based on the calculated new calibration parameter N, the calibration parameters of the accelerator are updated;

[0116] Based on the calculated new calibration parameter N, updating the calibration parameters of the accelerator includes:

[0117] After each update of the calibration parameters, record the new calibration parameters N and the current time T;

[0118] Determine whether the time interval ΔT between two updates is less than the predetermined time threshold t, the formula is ΔT <t;

[0119] Where ΔT represents the actual time interval between two updates, and t represents the predetermined time threshold;

[0120] If ΔT is less than t, continue monitoring, otherwise complete the update process.

[0121] The calibration unit analyzes the initial parameters obtained from the pre-irradiation test and calculates the deviation between them and the historical standard parameters. When the deviation exceeds the predetermined threshold, the system calculates the adjustment amount of the calibration parameters based on the size of the deviation and dynamically updates the calibration parameters of the accelerator to ensure that it maintains the optimal irradiation state. The calibration parameters are recorded after each update, and the adjustment frequency is further optimized through time interval monitoring. By analyzing the initial parameters of the material in real time and calculating the deviation, the calibration parameters of the accelerator can be accurately adjusted to ensure that the irradiation effect reaches the optimal state. The calibration parameters can be automatically adjusted according to the deviation, reducing manual intervention and improving the degree of automation of the production process. The adjustment process of the calibration parameters is monitored at time intervals to ensure that the accelerator maintains the optimal calibration state in a short period of time, avoiding system instability caused by excessively frequent adjustments. By referring to the historical standard parameters, the system can perform personalized calibration according to the characteristics of different materials, improving applicability and flexibility.

[0122] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent electron accelerator for industrial irradiation, characterized in that: include: A conveying unit, used for conveying the material to be irradiated into the irradiation chamber; The pre-irradiation detection unit connected to the conveying unit is used to perform pre-irradiation detection on the material and record the initial parameters of the material. Specifically, by collecting data on the initial parameters of the material and the corresponding irradiation effects, a multivariate linear regression model capable of predicting irradiation effects is trained based on historical data. Based on the initial parameters of the material, the model is used to predict whether the effect after irradiation meets the standards. The initial parameters of the material are then transmitted to the accelerator unit to assist in subsequent irradiation control. A calibration unit connected to the pre-irradiation detection unit is used to adjust the calibration parameters of the accelerator based on the pre-irradiation detection results. Specifically, the calibration unit initializes the state estimation and error covariance matrix of the Kalman filter, uses the results of the pre-irradiation detection unit as observation data, uses the prediction formula of the Kalman filter to estimate the current calibration parameters based on the previous state, and uses the Kalman gain to update the calibration parameters based on the difference between the measured value and the predicted value; The accelerator unit connected to the calibration unit is used to start the accelerator after the calibration parameters are adjusted to accurately irradiate the material; The maintenance calibration unit, connected to the accelerator unit, is used to recalibrate parameters after maintenance to ensure that the accelerator's accuracy deviation is within the allowable range. Specifically, the unit sets the value range of the accelerator's calibration parameters based on the initial state after maintenance, generates a set of random calibration parameters as the initial population, evaluates the calibration accuracy of each set of calibration parameters using the fitness function, selects the best parameter combination based on the fitness, performs crossover and mutation, generates new parameter combinations, and finally obtains the calibration parameter combination with the highest accuracy through multiple iterations. The calibration unit further comprises: Obtaining initial parameters for pre-irradiation testing; Analyze the deviation between initial parameters and historical standard parameters; Based on the deviation, an adjustment amount for the calibration parameter is calculated; Adjust the calibration parameters of the accelerator; The adjustment amount of the calculated calibration parameter is specifically: Calculate the deviation D between the initial parameters P of the pre-irradiation test and the historical standard parameters S, using the formula D = |P-S|; Where P represents the initial parameters of the pre-irradiation test, S represents the historical standard parameters, and D represents the deviation; Determine whether the deviation D exceeds a predetermined threshold T; If the deviation D is greater than the threshold T, the calibration parameters need to be adjusted; otherwise, the calibration parameters do not need to be adjusted; If the deviation D is greater than the threshold T, the calibration parameters need to be adjusted, including: If D>T, proceed to the next step; Calculate the adjustment amount A of the calibration parameter using the formula A=k×D; Where k represents the proportional coefficient and D represents the deviation; Based on the calculated adjustment amount A, the calibration parameters of the accelerometer are updated; The updating of the calibration parameters of the accelerator based on the calculated adjustment amount A includes: Get the current calibration parameter C; Calculate the new calibration parameter N using the formula N=C+A; Where C represents the current calibration parameter and A represents the adjustment amount; Based on the calculated new calibration parameter N, the calibration parameters of the accelerator are updated; After each update of the calibration parameters, record the new calibration parameters N and the current time T; Determine whether the time interval ΔT between two updates is less than the predetermined time threshold t, the formula is ΔT <t; Where ΔT represents the actual time interval between two updates, and t represents the predetermined time threshold; If ΔT is less than t, continue monitoring, otherwise complete the update process.

2. The intelligent electron accelerator for industrial irradiation according to claim 1, characterized in that: The conveying unit conveys the material to be irradiated into the irradiation chamber, comprising: Create a map of the conveyor unit's work area, set the material transfer starting and destination points, and transfer the material from the loading point to the irradiation chamber. Apply the A-star algorithm based on heuristic search to optimize the material transfer path using the following evaluation function: f(n)=g(n)+h(n), Among them, f(n) represents the total cost of node n, g(n) represents the total cost from the starting point to the node The actual cost of h(n) is the cost of the slave node. The heuristic estimated cost to the target node, where n represents a node in the path search.

3. The intelligent electron accelerator for industrial irradiation according to claim 1, characterized in that: The pre-irradiation detection unit performs pre-irradiation detection on the material and records the initial parameters of the material, and further includes: The specific formula for multivariate linear regression is: y=β0+β1x1+β2x2+…+βnxn+ε, Where y represents the expected irradiation result of the material, x1, x2, …, xn represent different initial parameters of the material during pre-irradiation testing, β1, β2, …, βn represent the regression coefficients to be trained, and ε represents the error term.

4. The intelligent electron accelerator for industrial irradiation according to claim 1, characterized in that: The calibration unit adjusting the calibration parameters of the accelerator based on the pre-irradiation detection result further includes: The prediction steps of the Kalman filter are: ， ； in, represents the estimated value of the predicted state at the kth moment, Indicates the The updated state estimate at time t, A represents the state transfer matrix, B represents the control input matrix, represents the control input value, represents the prediction error covariance matrix, represents the updated error covariance matrix, Q represents the process noise covariance matrix; The formula for the update step is: ， ， ， in, represents the Kalman gain, represents the prediction error covariance matrix, represents the measurement value at the kth moment, H represents the observation matrix, R represents the measurement noise covariance matrix, Indicates the The updated state estimate at time t, Indicates the The predicted state estimate at time t, I represents the identity matrix.

5. The intelligent electron accelerator for industrial irradiation according to claim 1, characterized in that: The accelerator unit starts the accelerator after adjusting the calibration parameters, and accurately irradiates the material, including: According to the calibration results, the target electron beam energy and distribution of the accelerator are set, and the electron beam energy and distribution of the accelerator are monitored in real time. By comparing the target value and the actual value, the error signal e(t) is obtained. According to the error value, the PID control algorithm is applied to adjust the electron beam energy of the accelerator. The specific formula is: ， Among them, u(t) represents the control signal, e(t) represents the error value, Kp, Ki, Kd represent the proportional, integral, and differential coefficients.

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