Proton accelerator cooling control system for boron neutron capture therapy system

Abstract

The present application discloses a proton accelerator cooling control system for a boron neutron capture therapy system, which relates to the medical field of proton accelerators. The system includes a treatment parameter acquisition unit for receiving treatment plan information; a plurality of cooling units, each cooling unit is configured with a controller for controlling the flow rate and temperature of the coolant; a sensor unit is used to monitor heat load information, generate temperature, flow rate, pressure and other data, and control the configuration parameters of the sensor based on the treatment plan information; a control unit generates and dynamically updates a heat influence matrix to describe the thermal interference relationship between the cooling units. Through a multi-objective optimization algorithm, the control unit can simultaneously optimize the self-heating effect and the total heat influence value of each cooling unit to ensure that all cooling units operate within a safe temperature range. The present invention has the advantages of precise control, dynamic response and global thermal management, adapts to the cooling requirements of proton accelerators under different operating conditions, and improves the overall efficiency and stability of the system.

Description

Technical Field

[0001] The present application relates to the field of proton accelerator medicine, and in particular to a proton accelerator cooling control system for a boron neutron capture therapy system. Background Art

[0002] With the widespread application of proton accelerators in tumor radiotherapy, their high-precision and high-energy characteristics make the radiation dose control of tumor tissue more accurate, effectively reducing the damage to surrounding healthy tissues. However, in the process of generating and transmitting high-energy proton beams, the equipment will generate a lot of heat. If this heat cannot be dissipated in a timely and effective manner, it may not only cause the proton accelerator to overheat, affecting the accuracy of treatment, but also pose a potential risk to patient safety. Therefore, the cooling system of the proton accelerator is particularly important in radiotherapy equipment. Existing proton accelerator cooling systems usually adopt a coolant circulation method to use the coolant to take away the heat generated during the operation of the equipment. However, with the increase in treatment doses and the complexity of the accelerator structure, the traditional cooling system often cannot effectively deal with local overheating due to the lack of precise control of the key heat source area, resulting in low efficiency of coolant utilization, affecting the treatment effect and patient safety.

[0003] For example, the Chinese patent with the authorization announcement number CN113616938B discloses a compact electron linear accelerator system for FLASH radiotherapy, which includes an electron gun, a resonant acceleration cavity system, a power source system and a cooling system. The cooling system is configured to take away the heat generated by the high frequency of the resonant acceleration cavity system, so that the radio frequency superconducting cavity remains in a superconducting state, thereby ensuring stable acceleration of the electron beam during the treatment process.

[0004] However, the above methods still have shortcomings in practical applications. During radiotherapy, due to the high peak current intensity of the electron beam, the thermal load of the resonant acceleration cavity will change dynamically. The traditional cooling system does not respond quickly enough to deal with this rapidly changing thermal load, which may cause local overheating and fail to control the temperature within a safe range in time, affecting the accuracy of the radiation dose and the patient's treatment effect. In addition, the existing cooling system mainly dissipates heat in local areas and lacks global thermal management capabilities for the entire system. In the resonant acceleration cavity, the heat distribution is uneven, and the formation of local hot spots may affect the operating stability of the radiotherapy system and the safety of the patient.

[0005] Therefore, there is an urgent need for a proton accelerator cooling control system that can provide precise and efficient cooling according to the dynamic changes of heat load during radiotherapy, so as to improve the stability and treatment effect of radiotherapy equipment and ensure the safety of patients. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present application discloses a proton accelerator cooling control system for a boron neutron capture therapy system, comprising:

[0007] A treatment parameter acquisition unit, used to receive treatment plan information of boron neutron capture therapy, wherein the treatment plan information includes: treatment dose, irradiation position, and treatment time;

[0008] A plurality of cooling units are deployed in a first region of a proton accelerator, each of the cooling units is configured with a controller for controlling a flow rate and a temperature of a coolant; wherein the controller controls the cooling unit based on a cooling parameter; the first region includes: a critical heat source location and a target heat load region;

[0009] A sensor unit is deployed in the first area of ​​the proton accelerator; the sensor unit is used to determine the heat load information of the first area of ​​the proton accelerator; wherein the sensor types in the sensor unit include: a temperature sensor, a flow rate sensor, and a pressure sensor; the sensor unit is also used to control the configuration parameters of various sensors based on the treatment plan information;

[0010] A control unit is used to automatically control the cooling parameters of each cooling unit through the controller of each cooling unit based on the heat load information, and the cooling parameters include the flow rate and temperature of the coolant.

[0011] As an optional implementation manner, the control unit is further used for:

[0012] Based on the temperature information, flow rate information, pressure information collected by the sensor unit, and the heat conduction relationship between the cooling units, a heat influence matrix is ​​generated; wherein the heat influence matrix is ​​used to describe the degree of thermal interference between the cooling units.

[0013] As an optional implementation, the heat impact matrix is ​​dynamically updated based on the heat load information.

[0014] As an optional implementation, generating a heat impact matrix includes:

[0015] Combining the temperature information with the relative position relationship of each cooling unit to determine the temperature gradient between each cooling unit;

[0016] Determining a flow path coefficient between each of the cooling units based on the flow rate information and the pressure information;

[0017] Based on the temperature gradient, flow path coefficient, and relative position between the cooling units, the heat conduction relationship between the cooling units is determined to generate the heat influence matrix.

[0018] As an optional implementation, the heat conduction relationship is determined based on the following mathematical expression:

[0019]

[0020] in, is the heat transfer coefficient of the coolant, represents the heat conduction relationship between cooling unit i and cooling unit j, represents the temperature gradient between cooling unit i and cooling unit j, represents the relative position relationship between cooling unit i and cooling unit j, represents the flow path coefficient between cooling unit i and cooling unit j.

[0021] As an optional implementation, the heat impact matrix is ​​a two-dimensional matrix;

[0022] The rows and columns of the heat influence matrix correspond to each cooling unit, and the element values ​​in the heat influence matrix are It represents the heat conduction relationship between cooling unit i and cooling unit j, that is, the degree of thermal influence of cooling unit i on cooling unit j;

[0023] Wherein, when i=j, the diagonal elements of the heat influence matrix are represents the self-heating effect of the cooling unit i.

[0024] As an optional implementation manner, the control unit is further used for:

[0025] Based on the heat impact matrix, determining the self-heating effect and the total heat impact value of each cooling unit;

[0026] Set the self-heating effect threshold and the total heat impact threshold;

[0027] A joint optimization algorithm is used to simultaneously optimize the self-heating effect and the total thermal impact value of each cooling unit.

[0028] As an optional implementation, the joint optimization algorithm includes:

[0029] The objective function is defined as:

[0030]

[0031] Where n represents the total number of cooling units, i represents the index of the cooling unit, and in the range of i=1 to n, i represents a different cooling unit; and are the weights of self-heating effect and total thermal impact value respectively; is the self-heating effect threshold; is the total heat impact threshold; is the self-heating effect of the cooling unit i; is the total thermal effect of the cooling unit i; is the normalized representation of the self-heating effect of the cooling unit i; is the normalized representation of the total thermal impact value of the cooling unit i.

[0032] As an optional implementation, a multi-objective optimization algorithm is used to optimize the cooling parameter set. Perform iterative optimization, the multi-objective optimization algorithm includes:

[0033] Generate an initial set of cooling parameters ,in, Including the coolant flow rate and temperature of each cooling unit i;

[0034] Evaluating the fitness of the current cooling parameter set based on the objective function to generate the fitness evaluation information;

[0035] Based on the fitness evaluation information, a set of Pareto solutions is generated by a Pareto optimization method;

[0036] Update the cooling parameter set by iteration , in response to the self-heating effect and total heat impact values ​​of all cooling units being lower than their respective thresholds, or when the maximum number of iterations is reached, the cooling parameter set is output .

[0037] As an optional implementation manner, the control unit is further configured to: , the coolant flow rate and temperature are controlled by the controller of each cooling unit.

[0038] Compared with the prior art, the beneficial effects of the present invention are: by deploying multiple cooling units at the key heat source positions of the proton accelerator and combining sensor units to monitor the heat load information in real time, the system can dynamically adjust the cooling parameters of each cooling unit based on the heat load information, thereby achieving precise temperature control of each key heat source and avoiding local overheating.

[0039] By constructing a heat impact matrix and combining it with a multi-objective optimization algorithm, the system can simultaneously optimize the self-heating effect and total heat impact value of each cooling unit, realize global heat management of the cooling system, ensure that thermal interference between cooling units is minimized, and improve the overall efficiency of the system.

[0040] In addition, by acquiring treatment plan information of boron neutron capture therapy, the configuration parameters of each sensor are dynamically adjusted to improve the quality of data acquired by the sensor unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of the system structure of a proton accelerator cooling control system for a boron neutron capture therapy system provided in this application;

[0042] Figure 2 A schematic diagram of the cooling unit structure provided in this application;

[0043] Figure 3 A schematic diagram of the structure of a sensor unit provided in this application;

[0044] Figure 4 A flow chart of a method for generating a heat impact matrix provided in this application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0046] See also Figures 1 to 3 , Figure 1 This is a schematic diagram of the system structure of a proton accelerator cooling control system for a boron neutron capture therapy system provided in this application. The system includes:

[0047] The treatment parameter acquisition unit 40 is used to receive treatment plan information of boron neutron capture therapy, wherein the treatment plan information includes: treatment dose, irradiation position, and treatment time;

[0048] A plurality of cooling units 10 are deployed in a first region of a proton accelerator, each of the cooling units 10 is provided with a controller 11 for controlling a flow rate and a temperature of a coolant; wherein the controller 11 controls the cooling unit 10 based on cooling parameters; the first region includes: a critical heat source location and a target heat load region;

[0049] A sensor unit 20 is deployed in the first area of ​​the proton accelerator; the sensor unit 20 is used to determine the heat load information of the first area of ​​the proton accelerator; wherein the sensor types in the sensor unit 20 include: a temperature sensor 21, a flow rate sensor 22, and a pressure sensor 23; the sensor unit 20 is also used to control the configuration parameters of various sensors based on the treatment plan information;

[0050] The control unit 30 is used to automatically control the cooling parameters of each cooling unit 11 through the controller of each cooling unit 11 based on the heat load information, and the cooling parameters include the flow rate and temperature of the coolant.

[0051] In a specific implementation, the cooling system is composed of a plurality of cooling units 10, which are arranged in the first area of ​​the proton accelerator. The first area includes key heat source locations and target heat load areas. For example, key heat source locations include: magnet coils, acceleration cavities, etc. in the proton accelerator. These parts will generate a lot of heat during the acceleration process, and effective cooling management is required to ensure the stable operation of the equipment. In addition, the locations prone to heat generation can also be recorded based on the historical operation of the proton accelerator and marked as target heat load areas.

[0052] Each cooling unit 10 is equipped with a controller 11 for adjusting the flow rate and temperature of the coolant. The controller 11 can adjust the parameters of the coolant in real time according to the actual needs of the cooling unit 10 to ensure the optimization of the cooling effect. In this way, the cooling unit 10 can accurately control the temperature of the target area to avoid equipment failure or performance degradation due to overheating.

[0053] The treatment parameter acquisition unit 40 is used to receive the treatment plan information of the boron neutron capture therapy, and the treatment plan information includes patient-specific treatment data, such as treatment dose, irradiation position, treatment time, etc. The treatment parameter acquisition unit 40 can be connected to the treatment plan system through wired or wireless communication to obtain the latest treatment plan information in real time.

[0054] In a specific implementation, the treatment plan information received by the treatment parameter acquisition unit 40 includes: treatment dose information: energy distribution and dose requirements of the proton beam in the patient's body; irradiation position information: the spatial position and range of the target area that the proton beam needs to irradiate; treatment time information: the start time, duration and end time of each treatment, etc.

[0055] By acquiring the above information, the treatment parameter acquisition unit 40 provides important reference data for the sensor unit 20, assisting it in more accurately determining the thermal load information of the proton accelerator.

[0056] In order to monitor the operating status and cooling effect of the cooling unit 10, sensor units 20 are provided in the system. These sensor units 20 are arranged in the first area and are responsible for detecting the heat load information of the area. The sensor units 20 include a temperature sensor 21, a flow rate sensor 22 and a pressure sensor 23, which are respectively used to monitor the temperature change of the cooling unit 10 and its surrounding environment, the flow rate of the coolant and the pressure condition. These sensors collect data in real time and transmit the data to the control unit 30 of the system.

[0057] In addition, the sensor unit 20 performs a comprehensive analysis on the monitoring data and controls specific configuration parameters of each sensor based on the treatment plan information received from the treatment parameter acquisition unit 40 .

[0058] Specifically, the sensor unit 20 combines the treatment plan information with the real-time monitoring data to more accurately determine the thermal load information of the first region of the proton accelerator.

[0059] In a specific implementation, based on the treatment dose information, the sensor unit 20 can predict the trend of thermal load changes of the proton accelerator within a specific time period. If a higher proton beam energy output is required at a certain stage in the treatment plan, the sensor unit 20 can predict in advance that the temperature of the corresponding area may increase in order to control the configuration parameters of the sensor. Combined with the irradiation position information, the sensor unit 20 can identify the areas where the proton beam will focus on the action, and focus on monitoring the temperature, flow rate and pressure of these key areas. Based on the treatment time information, the sensor unit 20 can increase the monitoring frequency before the start of treatment or at certain key treatment time points to ensure that subtle changes in temperature and pressure are captured during the treatment process, thereby improving the accuracy of the thermal load information.

[0060] In this way, the sensor unit 20 not only relies on real-time monitoring data, but also combines information from the treatment plan to more comprehensively and accurately determine the thermal load information of the proton accelerator. This provides a more reliable data basis for the control unit 30, enabling it to more effectively adjust the cooling parameters of the cooling unit 10.

[0061] The control unit 30 is the core component of the entire cooling system. It analyzes and determines the cooling effect information of the first area in the proton accelerator based on the heat load information collected by the sensor unit 20. The control unit 30 can adjust the cooling parameters (such as the flow rate and temperature of the coolant) of each cooling unit 10 based on this information to ensure that the heat load area is fully cooled. Through this closed-loop control method, the cooling system can maintain an efficient and stable working state under various operating conditions.

[0062] The cooling system of the present invention can effectively cope with the large amount of heat generated during the operation of the proton accelerator through this distributed, multi-point monitoring and real-time control method, thereby ensuring the safety and performance of the equipment.

[0063] As an optional embodiment, the control unit 30 is also used to: generate a thermal influence matrix based on the temperature information, flow rate information, pressure information collected by the sensor unit 20, and the heat conduction relationship between the cooling units 10; wherein the thermal influence matrix is ​​used to describe the degree of thermal interference between the cooling units 10.

[0064] In a specific implementation, the control unit 30 is further used to generate a heat influence matrix. The heat influence matrix is ​​generated based on the temperature information, flow rate information, pressure information collected by the sensor unit 20 and the heat conduction relationship between the cooling units 10.

[0065] The main function of the heat influence matrix is ​​to describe the degree of thermal interference between the cooling units 10. Specifically, each element value of the heat influence matrix reflects the thermal influence of one cooling unit on another cooling unit. By analyzing this matrix, the control unit 30 can understand the thermal effect of each cooling unit 10 in the entire cooling system, including how it is affected by other units and its heat conduction effect on other units.

[0066] In a specific implementation, the process of generating the heat influence matrix is ​​as follows: First, the control unit 30 obtains the temperature, flow rate and pressure data of each cooling unit 10 provided by the sensor unit 20. These data not only reflect the state of the cooling unit 10 itself, but also include the heat exchange information between it and the adjacent units. Subsequently, the control unit 30 calculates the heat conduction relationship between each cooling unit 10 based on these data using a pre-set algorithm, thereby forming a heat influence matrix.

[0067] The heat impact matrix provides a global perspective for the control unit 30, allowing the system to consider the potential impact on other units when adjusting a single cooling unit 10. This global, matrix-based management approach improves the overall coordination and stability of the cooling system.

[0068] By generating and utilizing the heat influence matrix, the cooling system can more accurately manage the heat load in the proton accelerator, ensuring that the temperature of each critical area is always within a safe and controllable range, thereby ensuring the long-term stable operation of the equipment.

[0069] As an optional implementation, the heat impact matrix is ​​dynamically updated based on the heat load information.

[0070] In a specific implementation, the heat influence matrix is ​​not only used to describe the thermal interference relationship between the cooling units 10, but also has the ability to be dynamically updated. The control unit 30 will automatically update the heat influence matrix regularly or under preset conditions based on the real-time collected heat load information. This dynamic update process ensures that the cooling system can adapt to changes in the proton accelerator under different operating conditions.

[0071] The dynamic update mechanism relies on the real-time data provided by the sensor unit 20. These data include the current temperature of each cooling unit 10, the flow rate of the coolant, the system pressure, etc. When the working state of the proton accelerator changes (such as load increase, ambient temperature change, or coolant flow rate adjustment), these changes will be reflected in the real-time data of the sensor.

[0072] After receiving the updated heat load information, the control unit 30 recalculates the heat conduction relationship between the cooling units 10, thereby updating the heat influence matrix. This process includes re-evaluating the temperature gradient, flow path coefficient, and relative position of the cooling units 10. By dynamically updating the heat influence matrix, the control unit 30 can maintain precise control of the entire cooling system, ensuring cooling efficiency and safety even when the system load changes significantly.

[0073] This dynamic update mechanism enables the cooling system to not only adapt to the current heat load, but also foresee and respond to possible changes in the future, thereby effectively improving the reliability and flexibility of the system. By continuously updating the thermal impact matrix, the cooling system can always operate under optimal conditions, reducing the risk of overheating caused by changes in heat load and ensuring the long-term stable operation of the proton accelerator.

[0074] See also Figure 4 , Figure 4 This is a flow chart of a method for generating a heat impact matrix provided in the present application. As an optional implementation, the generation of the heat impact matrix includes steps S101 to S103, wherein:

[0075] S101, combining the temperature information with the relative position relationship of each cooling unit 10 to determine the temperature gradient between each cooling unit 10;

[0076] S102, determining a flow path coefficient between each of the cooling units 10 based on the flow rate information and the pressure information;

[0077] S103, based on the temperature gradient, flow path coefficient, and relative position between the cooling units 10, determine the heat conduction relationship between the cooling units 10, and generate the heat influence matrix.

[0078] In a specific implementation, in the process of generating the heat impact matrix, the control unit 30 comprehensively considers key factors such as temperature information, relative position relationship between cooling units 10, flow rate information and pressure information.

[0079] First, the control unit 30 obtains the temperature information of each cooling unit 10 from the sensor unit 20. This information is used to determine the temperature gradient between the cooling units 10, that is, the temperature difference between different cooling units 10. The temperature gradient is a key parameter affecting the efficiency of heat conduction. The greater the temperature difference, the higher the potential for heat transfer.

[0080] Secondly, the control unit 30 uses the flow rate information and pressure information provided by the sensor to determine the flow path coefficient of the coolant between the cooling units 10. The flow path coefficient reflects the flow resistance and path length of the coolant when it flows through different cooling units 10. Different flow paths will lead to differences in the heat transfer efficiency of the coolant, so the flow path coefficient is an important factor affecting the heat conduction relationship.

[0081] Finally, the control unit 30 combines the temperature gradient, the flow path coefficient, and the relative position relationship between the cooling units 10 to calculate the heat conduction relationship between the cooling units 10. These heat conduction relationships reflect the heat exchange intensity between the cooling units 10 and are represented in the heat influence matrix in matrix form.

[0082] In a specific implementation, the relative position is determined based on the physical layout and spatial distribution of the cooling unit 10 inside the proton accelerator.

[0083] The physical position of each cooling unit 10 in the proton accelerator can be represented by three-dimensional coordinates. When installed, the precise position (eg, XYZ coordinates) of the cooling unit 10 is determined by design drawings or a sensor recording system and stored in a database of the control unit 30.

[0084] The control unit 30 calculates the distance between any two cooling units 10 according to these three-dimensional coordinates, usually using the Euclidean distance formula.

[0085] The relative position refers not only to the linear distance between the two cooling units 10, but also to their relative positions on the coolant flow path. If the two cooling units 10 are located at different positions in the same cooling circuit, and the coolant flows from unit i to unit j, the heat carried by the coolant will affect the heat load of unit j. The relative position relationship will also affect the direction and intensity of heat conduction, especially in a complex cooling circuit, where this spatial relationship determines the path and efficiency of heat transfer.

[0086] The calculated relative position value directly affects the value of the element in the heat influence matrix. Cooling units that are closer have a greater influence on each other's heat conduction, which is reflected in the matrix as a higher heat conduction relationship coefficient. Cooling units that are farther away have a weaker influence on heat conduction, and the matrix element value is relatively low.

[0087] Through these steps, the control unit 30 can generate a heat influence matrix that fully reflects the heat conduction characteristics of the cooling system. This matrix not only takes into account the temperature difference of the cooling units 10, but also integrates the flow characteristics and spatial layout of the coolant, thereby accurately describing the thermal interference between the cooling units 10.

[0088] This matrix generation method based on multi-factor analysis enables the cooling system to manage heat load more accurately, achieve efficient cooling of key areas of the proton accelerator, and ensure stable and safe operation of the equipment.

[0089] As an optional implementation, the heat conduction relationship is determined based on the following mathematical expression:

[0090]

[0091] in, is the heat transfer coefficient of the coolant, represents the heat conduction relationship between cooling unit i and cooling unit j, represents the temperature gradient between cooling unit i and cooling unit j, represents the flow path length between cooling unit i and cooling unit j, represents the flow path coefficient between cooling unit i and cooling unit j.

[0092] In a specific implementation, the determination of the heat conduction relationship is based on a mathematical model that comprehensively considers the physical properties of the coolant, the temperature gradient between the cooling units, the relative position relationship, and the flow path coefficient of the coolant.

[0093] That is, the degree of heat influence transferred from cooling unit i to cooling unit j through the coolant. This value is directly reflected in the heat influence matrix and is used to quantify the intensity of heat exchange between two cooling units. A higher value indicates a stronger influence of heat conduction.

[0094] Depends on the physical properties of the coolant, such as thermal conductivity, density, specific heat capacity, etc. This coefficient reflects the efficiency of the coolant in conducting heat. Different types of coolants (such as water, ethylene glycol, liquid helium) will have different The specific thermal conductivity value of the coolant can be obtained by consulting the manual and experimental measurement, which will not be described here. It indicates the change in the temperature gradient of the material when the heat per unit area passes through a unit length per unit time. The unit of thermal conductivity is Watt / meter·Kelvin (W / m·K). The larger the thermal conductivity, the better the thermal conductivity of the material, that is, the faster the heat can be conducted in the material.

[0095] The calculation method is The temperature gradient is the driving force of heat conduction. The greater the temperature difference, the faster the heat transfer. Specifically, the temperature of cooling unit i and cooling unit j can be measured by the sensor unit 20. and get.

[0096] It is understandable that if Greater than , then heat will be transferred from cooling unit i to cooling unit j, and vice versa.

[0097] Represents the length of the flow path between cooling unit i and cooling unit j. That is, the actual flow path length that the coolant passes through from cooling unit i to cooling unit j. It can be directly measured or calculated through the fluid dynamics model or the geometric structure of the cooling system. In the specific calculation, factors such as the path, elbows, and pipeline length of the coolant in the pipeline need to be considered.

[0098] Longer flow paths increase the resistance to heat transfer, thus reducing conduction efficiency. As the path length gets larger, The smaller.

[0099] For example, inside the proton accelerator, cooling unit i and cooling unit j are connected by a straight pipe, and the total length of the pipe is , the length is 50 cm. If there are no other obstacles or path resistances to the flow of coolant in the pipe, in this case, the flow path length is the actual length of the pipe:

[0100] Exemplarily, cooling unit i and cooling unit j are connected by a complex pipe system, which includes several short-distance pipes and an elbow. The coolant flow path is as follows: starting from cooling unit i, it flows through a 20 cm straight pipe. Passing through a 90-degree elbow (each elbow is equivalent to the resistance of a 2 cm straight pipe). Then it flows through a 15 cm straight pipe to reach cooling unit j. It is known that the lengths of the straight pipe sections are 20 cm and 15 cm respectively. The equivalent length of the elbow is 2 cm.

[0101] Therefore, the actual flow path length of the coolant from cooling unit i to cooling unit j is 37 cm.

[0102] It is understandable that for complex piping systems, actual measurements can be performed experimentally to determine the equivalent straight path of a non-straight pipe. In addition, the flow path length between each cooling unit can be obtained through computer simulation calculations. Once a proton accelerator is built, the physical structure will not be easily changed, so the parameter flow path length will not be changed.

[0103] is the flow path coefficient of the coolant flowing from cooling unit i to cooling unit j, which takes into account the flow rate of the coolant, the flow resistance and the geometry of the path. The flow path with higher flow rate and smaller path resistance, The larger the value, the higher the heat transfer efficiency.

[0104] Flow rate It refers to the average velocity of the coolant when it flows from cooling unit i to cooling unit j. The flow rate can be directly measured by the flow rate sensor 22, or calculated based on the flow rate Q and the pipe cross-sectional area A:

[0105]

[0106] in, is the coolant flow rate flowing through path i to j, is the cross-sectional area of ​​the pipe corresponding to the path.

[0107] resistance It is the flow resistance caused by factors such as friction on the inner wall of the pipe, elbows, valves, etc. when the coolant flows through path i to j.

[0108] The resistance can be calculated using the Darcy-Weisbach equation, which describes the pressure drop for fluid flow in a pipe:

[0109]

[0110] where f is the friction factor (depending on the roughness of the pipe and the Reynolds number of the fluid), is the flow path length, is the density of the coolant. is the flow rate and D is the inside diameter of the pipe.

[0111] In some applications, the flow resistance can also be obtained through known empirical formulas or tables in piping design manuals.

[0112] The flow path coefficient It can be expressed as:

[0113]

[0114] This formula expresses the ratio of flow rate to resistance: High and resistance When the flow path coefficient is small, It is also larger, indicating that the coolant is more efficient in transferring heat along this path.

[0115] In this way, the control unit 30 can calculate the heat conduction relationship between the cooling units 10 , and fill it into the heat influence matrix. This mathematical model enables the cooling system to dynamically adjust and optimize the heat conduction path between cooling units to improve the overall cooling efficiency and stability of the system.

[0116] As an optional implementation, the heat impact matrix is ​​a two-dimensional matrix;

[0117] The rows and columns of the heat influence matrix correspond to each cooling unit, and the element values ​​in the heat influence matrix are Indicates the degree of thermal influence of cooling unit i on cooling unit j;

[0118] Wherein, when i=j, the diagonal elements of the heat influence matrix are represents the self-heating effect of the cooling unit i.

[0119] In a specific implementation, the rows and columns of the heat impact matrix H correspond to the cooling units in the system. The self-heating effect refers to the situation where the temperature of cooling unit i rises due to the heat generated by its own heat source (such as electronic devices or heat-generating components).

[0120] The heat influence matrix H provides a comprehensive perspective, and the control unit 30 can use the matrix to analyze the heat conduction relationship between the cooling units 10 in the cooling system. By analyzing the element values ​​in the matrix, it can be understood which cooling units 10 have more significant heat conduction and which cooling units 10 need special attention for their self-heating effects.

[0121] The control unit 30 can make real-time adjustments and optimizations based on the heat impact matrix H. For example, if the self-heating effect of a cooling unit i If the heat transfer relationship between the two cooling units is found to be too high, the system may need to increase the coolant flow rate to that unit or reduce the coolant temperature. Too strong, and the system may need to adjust the flow path or add additional insulation.

[0122] Since the heat load of the cooling unit 10 and the flow state of the coolant may change during the operation of the cooling system, the heat impact matrix H needs to be updated regularly. The control unit 30 recalculates the values ​​of each element based on the latest sensor data (such as temperature, flow rate, pressure, etc.) and updates the matrix. This dynamic update mechanism ensures that the cooling system can always operate in the optimal state.

[0123] By constructing and applying the heat influence matrix H, the cooling system of the present invention can more accurately manage and optimize the heat load in the proton accelerator, ensuring that each cooling unit 10 operates within a safe temperature range while improving the overall efficiency and stability of the system.

[0124] As an optional embodiment, the control unit 30 is also used to determine the self-heating effect and the total thermal impact value of each cooling unit 10 based on the thermal impact matrix; set the self-heating effect threshold and the total thermal impact threshold; and use a joint optimization algorithm to simultaneously optimize the self-heating effect and the total thermal impact value of each cooling unit 10.

[0125] In a specific implementation, the control unit 30 calculates the self-heating effect and the total heat impact value of each cooling unit 10 based on the heat impact matrix, and then uses this information to optimize the operating parameters of the cooling system.

[0126] The control unit 30 sets a safety threshold for the self-heating effect. If the self-heating effect of a cooling unit 10 exceeds this threshold, it indicates that the unit may be overheated, and the system needs to make corresponding cooling parameter adjustments, such as increasing the coolant flow rate or reducing the coolant temperature, to reduce the self-heating effect of the unit.

[0127] Total Heat Impact Value It is the sum of the heat conduction effects of all other cooling units 10 on cooling unit i, and the calculation formula is:

[0128]

[0129] Among them, the total heat impact value Describes the overall heat load status of cooling unit i, including the heat it receives from other units.

[0130] The control unit 30 also sets a threshold value of the total thermal impact value , to monitor the overall heat load of each cooling unit 10. If the total heat impact value of a cooling unit 10 If this threshold is exceeded, it means that the cooling load of the unit may be too large and its thermal load needs to be reduced by optimizing the cooling parameters of the system.

[0131] In order to ensure that the self-heating effect and total heat impact value of each cooling unit 10 are within the safe range, the control unit 30 uses a joint optimization algorithm to globally optimize the parameters of the cooling system. The joint optimization algorithm will consider both the self-heating effect and the total heat impact value, adjust the flow rate, temperature and other parameters of the coolant, and find an optimal solution that can meet the needs of all cooling units.

[0132] The control unit 30 will gradually optimize the parameters of the cooling system in an iterative manner to ensure that the system can stabilize in the optimal state after the self-heating effect and the total heat impact value of all cooling units 10 are lower than the set threshold. If new hot spots or excessive heat loads are found during the optimization process, the system will automatically adjust and continue to optimize until the expected cooling effect is achieved.

[0133] In some cases, the control unit 30 can also adjust the parameters of the cooling system in advance based on the prediction model to prevent potential overheating problems. For example, when it is predicted that the proton accelerator is about to enter a high-load operation stage, the system can increase the flow rate of the coolant and reduce the coolant temperature in advance to ensure the stability of operation.

[0134] In this way, the cooling system of the present invention can achieve precise thermal management of each cooling unit in the proton accelerator, ensure that all key components operate within a safe temperature range, and can adapt to changes in thermal load under various operating conditions to maintain the efficiency and stability of the system.

[0135] As an optional implementation, the joint optimization algorithm includes:

[0136] The objective function is defined as:

[0137]

[0138] Where n represents the total number of cooling units, i represents the index of the cooling unit, and in the range of i=1 to n, i represents a different cooling unit; and are the weights of self-heating effect and total thermal impact value respectively; is the self-heating effect threshold; is the total heat impact threshold; is the self-heating effect of the cooling unit i; is the total thermal effect of the cooling unit i; is the normalized representation of the self-heating effect of the cooling unit i; is the normalized representation of the total thermal impact value of the cooling unit i.

[0139] The objective function takes into account both the self-heating effect and the total heat impact value of each cooling unit 10, and amplifies those situations that are close to or exceed the threshold through the square term, so that the optimization algorithm pays more attention to and prioritizes solving these problems. By continuously adjusting the parameters of the cooling system, the goal is to minimize the value of the objective function, that is, the self-heating effect and the total heat impact value of each cooling unit 10 are as close to or lower than the set safety threshold as possible.

[0140] As an optional implementation, a multi-objective optimization algorithm is used to optimize the cooling parameter set. Perform iterative optimization, the multi-objective optimization algorithm includes: generating an initial cooling parameter set ,in, Including the coolant flow rate and temperature of each cooling unit i; evaluating the fitness of the current cooling parameter set based on the objective function to generate the fitness evaluation information; generating a set of Pareto solutions through the Pareto optimization method based on the fitness evaluation information; updating the cooling parameter set through iteration , in response to the self-heating effect and total heat impact values ​​of all cooling units being lower than their respective thresholds, or when the maximum number of iterations is reached, the cooling parameter set is output .

[0141] In a specific implementation, the cooling parameter set of each cooling unit i is This includes the unit's coolant flow rate, temperature, and other control variables that may affect the cooling effect. The selection of the initial cooling parameter set can be based on the system's design specifications, historical operating data, or determined by random generation.

[0142] Before the optimization process begins, the system sets the initial parameters of all cooling units. These initial conditions provide a starting point for subsequent iterative optimization, ensuring that the system can adjust parameters within the preset range.

[0143] In each iteration, the control unit 30 calculates the fitness of the current cooling parameter set based on the objective function defined above. The fitness evaluation is completed by substituting the self-heating effect and the total heat impact value of each cooling unit into the objective function. The lower the fitness value, the closer the current parameter set is to the optimal state in meeting the cooling demand.

[0144] Through the fitness evaluation, the control unit 30 generates fitness evaluation information reflecting the cooling effect of the current cooling parameter set.

[0145] Based on the fitness evaluation information, the control unit 30 applies the Pareto optimization method to generate a set of Pareto solutions. These solutions represent the best compromise between the self-heating effect and the total thermal impact value. Pareto optimization allows the system to find a balance between multiple objectives, ensuring that other objectives are optimized as much as possible without sacrificing one objective.

[0146] From the generated Pareto solution set, the control unit 30 will select an optimal solution that minimizes the value of the objective function while satisfying the cooling requirements of each cooling unit 10. This optimal solution will be used to update the current cooling parameter set.

[0147] The control unit 30 adjusts the cooling parameter set according to the selected Pareto optimal solution. Iterative updates are performed. In each iteration, the cooling parameters are adjusted in the direction of optimization, so that the cooling effect of the system gradually approaches the optimal one.

[0148] When the self-heating effect and the total heat impact value of all cooling units 10 are lower than the set safety threshold, the system will consider that the optimization goal has been achieved and the iterative process ends.

[0149] If the optimization goal cannot be fully achieved within the set maximum number of iterations, the system will output the cooling parameter set of the current iteration and record the cooling effect at this time.

[0150] When the termination condition is met, the control unit 30 will output the final optimized cooling parameter set. , and apply these parameters to the cooling system. Through this process, the system ensures that the self-heating effect and the total heat impact value of each cooling unit 10 are within a safe range and the entire system operates in an optimal state.

[0151] In addition, the optimization process is not only applicable to the current system configuration, but can also be adjusted according to the different operating states of the proton accelerator. For example, in different operating modes (such as high energy mode, low energy mode), the system can dynamically re-evaluate and optimize the cooling parameter set.

[0152] If a new cooling unit 10 is added to the system or the cooling circuit layout is changed, the control unit 30 can adapt to these changes by regenerating and optimizing the cooling parameter set to ensure the overall cooling effect of the system.

[0153] Among them, Pareto optimization is a multi-objective optimization method used to find a set of "optimal" solutions when optimizing multiple conflicting objectives at the same time. These solutions are called Pareto optimal solutions or Pareto frontiers. In Pareto optimization, there is no solution that can improve all objectives without compromising other objectives.

[0154] In this way, the cooling system of the present invention can achieve precise control of each cooling unit 10 in the proton accelerator under complex and changeable operating conditions, ensuring efficient and safe operation of the system.

[0155] As an optional implementation manner, the control unit 30 is further configured to: The flow rate and temperature of the coolant are controlled by the controller 11 of each cooling unit 10.

[0156] 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.

[0157] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0158] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0159] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0160] The preferred embodiments of the present invention disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details in detail, nor do they limit the present application to specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can understand and use the present application well. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. A proton accelerator cooling control system for a boron neutron capture therapy system, characterized in that: include: A treatment parameter acquisition unit, used to receive treatment plan information of boron neutron capture therapy, wherein the treatment plan information includes: treatment dose, irradiation position, and treatment time; A plurality of cooling units are deployed in a first region of a proton accelerator, each of the cooling units is configured with a controller for controlling a flow rate and a temperature of a coolant; wherein the controller controls the cooling unit based on a cooling parameter; the first region includes: a critical heat source location and a target heat load region; A sensor unit is deployed in the first area of ​​the proton accelerator; the sensor unit is used to determine the heat load information of the first area of ​​the proton accelerator; wherein the sensor types in the sensor unit include: a temperature sensor, a flow rate sensor, and a pressure sensor; the sensor unit is also used to control the configuration parameters of various sensors based on the treatment plan information; A control unit, configured to automatically control cooling parameters of each cooling unit through a controller of each cooling unit based on the heat load information, wherein the cooling parameters include a coolant flow rate and a temperature; The control unit is also used for: Based on the temperature information, flow rate information, pressure information collected by the sensor unit, and the heat conduction relationship between the cooling units, a heat influence matrix is ​​generated; wherein the heat influence matrix is ​​used to describe the degree of thermal interference between the cooling units; The heat impact matrix is ​​dynamically updated based on the heat load information; Generating a heat impact matrix comprises: Combining the temperature information with the relative position relationship of each cooling unit to determine the temperature gradient between each cooling unit; Determining a flow path coefficient between each of the cooling units based on the flow rate information and the pressure information; Based on the temperature gradient, flow path coefficient, and relative position between the cooling units, the heat conduction relationship between the cooling units is determined to generate the heat influence matrix.

2. The system according to claim 1, characterized in that The heat transfer relationship is determined based on the following mathematical expression: Where α is the heat transfer coefficient of the coolant, H ij represents the heat transfer relationship between cooling unit i and cooling unit j, ΔT ij represents the temperature gradient between cooling unit i and cooling unit j, L ij represents the relative position relationship between cooling unit i and cooling unit j, F ij represents the flow path coefficient between cooling unit i and cooling unit j.

3. The system according to claim 2, characterized in that The heat impact matrix is ​​a two-dimensional matrix; The rows and columns of the heat influence matrix correspond to each cooling unit, and the element value H in the heat influence matrix ij It represents the heat conduction relationship between cooling unit i and cooling unit j, that is, the degree of thermal influence of cooling unit i on cooling unit j; When i=j, the diagonal element H of the heat influence matrix is ii represents the self-heating effect of the cooling unit i.

4. The system according to claim 3, characterized in that The control unit is further used for: Based on the heat impact matrix, determining the self-heating effect and the total heat impact value of each cooling unit; Set the self-heating effect threshold and the total heat impact threshold; A joint optimization algorithm is used to simultaneously optimize the self-heating effect and the total thermal impact value of each cooling unit.

5. The system according to claim 4, characterized in that The joint optimization algorithm includes: The objective function is defined as: Where n represents the total number of cooling units, i represents the index of the cooling unit, and in the range of i = 1 to n, i represents different cooling units; w1 and w2 are the weights of the self-heating effect and the total heat impact value, respectively; H self is the self-heating effect threshold; H total is the total heat impact threshold; H ii is the self-heating effect of the cooling unit i; H i total is the total thermal effect of the cooling unit i; is the normalized representation of the self-heating effect of the cooling unit i; is the normalized representation of the total thermal impact value of the cooling unit i.

6. The system according to claim 5, characterized in that The cooling parameter set P is optimized using a multi-objective optimization algorithm. i Perform iterative optimization, the multi-objective optimization algorithm includes: Generate initial cooling parameter set P i , where P i Including the coolant flow rate and temperature of each cooling unit i; Evaluating the fitness of the current cooling parameter set based on the objective function to generate the fitness evaluation information; Based on the fitness evaluation information, a set of Pareto solutions is generated by a Pareto optimization method; By iteratively updating the cooling parameter set P i , in response to the self-heating effect and total heat impact value of all cooling units being lower than their respective thresholds, or reaching the maximum number of iterations, the cooling parameter set P is output i .

7. The system according to claim 6, characterized in that The control unit is further configured to: i , the coolant flow rate and temperature are controlled by the controller of each cooling unit.

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