Beam monitoring method for particle beam therapy system, beam monitoring program, information processing system, and particle beam system

Abstract

To adjust a beam orbit or a beam position of a charged particle beam in an accelerator.SOLUTION: The present disclosure relates to a beam monitoring method 100 for a particle beam therapy system 160. The particle beam therapy system 160 comprises an accelerator 170. The beam monitoring method 100 is executed by an information processing system 101. The beam monitoring method 100 includes a beam position information acquisition step 102 and a parameter acquisition step 103. The beam position information acquisition step 102 is the step of acquiring information of beam positions in beam monitors 181-184. The parameter acquisition step 103 is the step of acquiring parameters, which may be used for adjusting a beam orbit of a circulating charged particle beam 171 based on the acquired information of the beam positions, to be set to electromagnets 191-194.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a technique for monitoring a charged particle beam in a particle therapy system that irradiates a diseased area with a charged particle beam for treatment. [Background technology]

[0002] A particle beam therapy system typically includes at least a charged particle beam generator, an accelerator, and an irradiation device. The charged particle beam generator injects the generated charged particle beam into the accelerator. The accelerator (e.g., a synchrotron) imparts a desired amount of energy and properties to the charged particle beam injected from the charged particle beam generator. The accelerator then emits the charged particle beam with the desired amount of energy and properties. The irradiation device irradiates the charged particle beam extracted from the accelerator onto the affected area to provide treatment. An example of a prior art particle beam therapy system is disclosed in Patent Document 1. In the prior art disclosed in Patent Document 1, a position monitor or a beam position monitor measures the position of a charged particle beam extracted from an accelerator. Also, in the prior art disclosed in Patent Document 1, various electromagnets (e.g., steering electromagnets) adjust the position of the charged particle beam extracted from the accelerator. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 5707524 Summary of the Invention [Problem to be solved by the invention]

[0004] A particle beam therapy system is intended to irradiate a target area with a charged particle beam having a desired energy amount and properties. If the charged particle beam deviates from its desired beam trajectory (design beam trajectory) or from its desired beam position within the accelerator, the energy amount and properties of the charged particle beam will also deviate from those desired. In fact, due to various causes, the charged particle beam may deviate from its desired beam trajectory (design beam trajectory) or from its desired beam position within the accelerator. If the energy or properties of the charged particle beam deviate significantly from the desired value and fall outside the normal range, operators may be forced to shut down the particle therapy system or accelerator, which may require them to adjust the accelerator and change the treatment or operational schedule. Furthermore, if the charged particle beam deviates from the desired beam trajectory (design beam trajectory) or the desired beam position within the accelerator, the efficiency of extracting the charged particle beam from the accelerator may also decrease, which may result in an extension of the treatment time using a particle therapy system. Here, if the charged particle beam deviates from the desired beam orbit (design beam orbit) or the desired beam position within the accelerator, the energy amount and properties of the charged particle beam may deviate from the desired ones. However, the prior art disclosed in Patent Document 1, which adjusts the position of the charged particle beam by controlling the excitation current amount of various electromagnets (e.g., steering electromagnets) for the charged particle beam extracted from the accelerator, does not allow the energy amount and properties of the charged particle beam to approach the desired state. Furthermore, in the prior art disclosed in Patent Document 1, the position of the charged particle beam is adjusted after it has been extracted from the accelerator, and therefore, it is not possible to improve the efficiency of extracting the charged particle beam from the accelerator. To address deviations of the charged particle beam in the accelerator from the desired beam trajectory (design beam trajectory) or the desired beam position, the accelerator operator may adjust parameters related to accelerator control (e.g., parameters of the electromagnets in the accelerator). However, manually adjusting all of the parameters related to accelerator control places a burden on the accelerator operator.

[0005] In light of the above, one object of the present disclosure is to adjust the beam orbit or beam position of a charged particle beam in an accelerator. Another object of the present disclosure is to assist an accelerator operator or the like when adjusting the beam orbit or beam position of a charged particle beam in an accelerator. [Means for solving the problem]

[0006] In order to achieve at least one of the above objects, the present disclosure may have the following features, for example. One aspect of the present disclosure is a beam monitoring method for a particle beam therapy system. The particle beam therapy system includes an accelerator for accelerating a charged particle beam. The accelerator includes a circulation path for circulating the charged particle beam, a plurality of beam monitors installed in sequence along the circulation path, and a plurality of electromagnets installed in sequence along the circulation path. The beam monitor acquires beam position information, which is the position of the circulating charged particle beam when the circulating charged particle beam passes near the beam monitor. The electromagnet adjusts the beam trajectory of the circulating charged particle beam when the circulating charged particle beam passes near the electromagnet. The beam monitoring method is performed by an information processing system. The beam monitoring method includes a beam position information acquisition step and a parameter acquisition step. The beam position information acquisition step is a step of acquiring information on the beam position in each of the beam monitors. The parameter acquisition step is a step of acquiring parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired in the beam position information acquisition step. [Effects of the Invention]

[0007] As described above, the present disclosure can adjust the beam trajectory or beam position of a charged particle beam in an accelerator, and can also assist an accelerator operator or the like in adjusting the beam trajectory or beam position of a charged particle beam. Therefore, the present disclosure can provide a desired energy amount and properties of a charged particle beam. The present disclosure can also improve the availability and efficient operation of a particle beam therapy system. Furthermore, the present disclosure can reduce the burden on operators, etc.

[0008] A program or information processing system that achieves the same processing as the beam monitoring method described above can also achieve the same effects as the beam monitoring method described above. Furthermore, if it is in the form of a program, costs can be reduced in many cases. Programs also make it easier to make design changes to the processing. Other features that the present disclosure may have and the effects corresponding to those features will be disclosed in this specification, claims, or drawings. [Brief explanation of the drawings]

[0009] [Figure 1] 1 illustrates a functional configuration of an embodiment of the present disclosure. [Figure 2] 1 illustrates a system configuration according to an embodiment of the present disclosure. [Figure 3] The beam position (BP) and deviation explanation are shown. [Figure 4] Indicates one record in the database (DB). [Figure 5] The probable cause vector is shown. [Figure 6] 1 illustrates a computer architecture for implementing embodiments of the present disclosure. [Figure 7] The process of cause estimation, parameter adjustment (creation), and warm-up operation determination is shown. [Figure 8] This shows the rule-based cause estimation process. [Figure 9] This shows the process of inferring the cause by searching the database. [Figure 10] The input and output of the cause estimation model are shown. [Figure 11] This demonstrates machine learning of a cause inference model. [Figure 12] 1 shows a cause estimation process using a cause estimation model. [Figure 13] This shows the parameter adjustment (creation) process using COD correction. [Figure 14] This shows the parameter adjustment (creation) process by DB search. [Figure 15] The input and output of the parameter acquisition model are shown. [Figure 16] 1 shows machine learning of a parameter acquisition model. [Figure 17] Parameter adjustment (creation) processing using a parameter acquisition model is shown. [Figure 18] The process of creating a parameter set (three-dimensional array) is shown below. [Figure 19] A display screen showing parameters and estimated causes is shown. [Figure 20] 10 shows a warm-up operation determination process based on cause estimation. [Figure 21] This shows the warm-up operation determination process by DB search. [Figure 22] The input and output of the warm-up operation judgment model are shown. [Figure 23] This shows the machine learning of the warm-up operation judgment model. [Figure 24] 10 shows a warm-up operation determination process using a warm-up operation determination model. [Figure 25] 10 shows the adjustment time determination process. [Figure 26] The relationship between the estimated BP time series information and the parameter adjustment timing is shown. [Figure 27] The process of generating a regression analysis model for estimating BP time series information is shown below. [Figure 28] The estimation process of BP time series information using a regression analysis model is shown below. [Figure 29] The input and output of the BP time series estimation model are shown. [Figure 30] We demonstrate machine learning of the BP time series estimation model. [Figure 31] The estimation process of BP time series information using a BP time series estimation model is shown. [Figure 32] Illustrates remote monitoring. DETAILED DESCRIPTION OF THE INVENTION

[0010] Embodiments of the present disclosure will be described in detail below with reference to the drawings. Note that the embodiments described below do not limit the disclosure according to the claims, and not all of the elements and combinations thereof described in the embodiments are necessarily essential to the solutions of the present disclosure. The following description and drawings are examples for explaining the present disclosure, and appropriate omissions and simplifications have been made for clarity of explanation. The present disclosure can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. The position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc., in order to facilitate understanding of the invention. Therefore, the present disclosure is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings. Each of the systems, devices, or units disclosed herein may be integrated into a single piece of hardware, or may be divided into multiple parts that work together to perform their functions. Several systems, devices, or units may be integrated into hardware. Each of the systems, devices, or units may be realized by having a computer execute software (a program) (as in FIG. 6). Some of the functions of the system, device, or unit may be realized by hardware (for example, hardwired logic or FPGA (Field Programmable Gate Array)), and the remaining functions may be realized by executing software (a program). All of the functions of each of the systems, devices, or units may be realized by hardware. Some or all of the processing steps shown in the flowcharts and the like described in this disclosure may be realized by hardware. The program of the present disclosure may be included in the general concept of software that encompasses software that cooperates with hardware resources to create a specific information processing device or its operating method according to the intended use. In other words, the program of the present disclosure is not limited to a specific type or form of program. Also, the program may be initially recorded in a compressed format. The same reference numbers used in multiple drawings indicate similar items. In the drawings showing the flowcharts described below, rectangular boxes indicate processing steps, and diamond-shaped boxes indicate conditional branching steps. In the drawings showing the flowcharts, "step" is abbreviated as "S." Furthermore, the screen aspects shown in the drawings described below are examples and are not limited to these.

[0011] 1. Functional configuration of an embodiment of the present disclosure 1 shows a functional configuration of an embodiment of the present disclosure. Note that not all of the functional configurations shown in FIG. 1 are essential. 1 shows a beam monitoring method 100 according to the present disclosure, an information processing system 101 that executes the beam monitoring method 100, and a particle beam therapy system 160 that is the target of monitoring by the beam monitoring method 100. Here, the particle beam therapy system 160 and the information processing system 101 together are referred to as a particle beam system 105. The particle beam therapy system 160 includes an accelerator 170 for accelerating a charged particle beam 171 (hereinafter, the charged particle beam may be referred to as a "beam"). Although not shown in FIG. 1 , the particle beam therapy system 160 also includes a charged particle beam generator that injects the beam 171 into the accelerator 170, and an irradiation device (irradiation system) that irradiates the affected area with the beam 171 extracted from the accelerator 170. The type of charged particles constituting the beam 171 is not limited, and may be, for example, hydrogen, helium, or carbon. The particle beam therapy system 160 may also include a beam transport system that transports the beam 171 extracted from the accelerator 170 to the irradiation device (irradiation system). The accelerator 170 includes a circular path 172 for circulating the beam 171. In Fig. 1, the circular path 172 is shown as being made up of four straight path segments and four curved path segments, but the shape of the circular path 172 is not limited to the shape shown in Fig. 1. The accelerator 170 includes a plurality of electromagnets 191-194 that are installed in sequence along the circular path 172, and a plurality of beam monitors or beam position monitors (hereinafter, the beam monitors or beam position monitors may be referred to as "BPMs") 181-184 that are installed in sequence along the circular path 172. In FIG. 1, the accelerator 170 is shown equipped with four electromagnets and four BPMs, but the number of electromagnets and the number of BPMs in the accelerator 170 are not limited to four. The BPMs 181-184 acquire information on the beam position (hereinafter, the beam position may be referred to as "BP") of the charged particle beam 171 when the beam 171 traveling around the circuit path 172 passes near the BPMs 181-184. The BP in a BPM may be the position of the beam 171 in a coordinate system that intersects with the traveling direction of the circuit path 172 when the circulating beam 171 passes near the BPMs 181-184. In other words, the BP can be treated as the coordinate of a point in a one-dimensional coordinate system (in the distance direction from the center of the plane described by the circuit path 172) or as the coordinate of a point in a two-dimensional coordinate system (consisting of the distance direction and the perpendicular direction (up and down)). For example, the BPMs 181-184 include several electrodes close to the circuit path 172. When the beam 171 passes near these electrodes, a potential is applied to the electrodes. Since the potential of each electrode is determined according to BP, the BP can be determined by measuring the potential of each electrode. In this way, the BPMs 181 to 184 are non-destructive monitors and do not damage the circulating beam 171. In the accelerator 170, the BP acquired by the BPM can be handled as one-dimensional or two-dimensional point coordinates, which simplifies data handling. In addition to the distance component and vertical (up and down) component, BP may also include a component in the traveling direction of the circular path 172. If information on such components and time information are used together, it is expected that auxiliary information on the speed of the beam 171 can be obtained. The electromagnets 191 to 194 adjust the beam orbit 173 when the circulating beam 171 passes near the electromagnets 191 to 194. The electromagnets 191 to 194 apply a magnetic field having an excitation amount B to the circulating path 172 according to the magnitude of the current (current amount I) applied. The larger the excitation amount B, the greater the force acting on the circulating beam 171. In FIG. 1, electromagnets 191 to 194 are bending electromagnets. Note that the electromagnets having a steering function for adjusting the beam trajectory 173 and BP are not limited to bending electromagnets. For example, such electromagnets may be other types of electromagnets, such as steering electromagnets applied to linear path portions. For example, the bending electromagnets may be specialized for the function of deflecting the beam trajectory 173, and a steering electromagnet provided in the linear path portion may perform the steering function. In this way, the configuration of the electromagnets can be flexibly determined. The beam monitoring method 100 executed by the information processing system 101 includes a BP information acquisition step 102 and a parameter acquisition step 103. The BP information acquisition step 102 acquires information on the BPs in the respective BPMs 181-184. The parameter acquisition step 103 acquires parameters to be set for each of the electromagnets 191 to 194, which can be used to adjust the beam orbit 173 of the circulating beam 171, based on the BP information acquired in the BP information acquisition step 102. Here, the parameter acquisition method may be a calculation method, a method of obtaining the parameters by searching a database (hereinafter, the database may be referred to as a "DB"), a method of obtaining the parameters from the output of a trained model, or any other method.

[0012] The beam monitoring method 100 according to the present disclosure has the above-described functional configuration, and therefore can provide the effects shown in the above-described [Effects of the Invention].

[0013] 2. System Configuration of the Embodiment of the Present Disclosure FIG. 2 shows an information processing system 101 (or an information processing system 101 that is one aspect of the present disclosure) that executes a beam monitoring method 100, which is one aspect of the present disclosure, and a system configuration for realizing a particle beam therapy system 160 that is the subject of monitoring by the beam monitoring method 100 (or the information processing system 101).

[0014] The particle therapy system 160 includes an accelerator 170, an accelerator control device 240, a data accumulation server 250, a room temperature sensor 281, a cooling water temperature sensor 282, an installation surface position sensor 283, an outside air temperature sensor 284, and a sensor data collection device 280. Of the components included in the particle therapy system 160, at least the accelerator 170, the room temperature sensor 281, and the installation surface position sensor 283 are located in a building 270 that has a room in which the accelerator 170 is installed. The accelerator control device 240, the data accumulation server 250, and the sensor data collection device 280 may be located within the building 270 or in a location different from the building 270. The accelerator 170 is similar to the description of FIG. 1 , so a description of the accelerator 170 will be omitted here. The accelerator controller 240 controls the accelerator 170. To control the accelerator 170, the accelerator controller 240 selects parameters (e.g., a parameter for the amount of current I of the electromagnets 191 to 194 and a parameter related to the mode of warm-up operation or cooling operation) and applies them to the accelerator 170. To select these parameters and apply them to the accelerator 170, the accelerator controller 240 may be provided with an interface that can be handled by an operator or the like. The accelerator controller 240 includes a parameter storage device 241. The parameter storage device 241 stores parameter sets that can be applied to the accelerator 170. The accelerator controller 240 may select appropriate parameters from the parameter sets depending on, for example, the amount of energy to be imparted to the beam 171 and apply them to the accelerator 170. The parameter sets to be applied to the electromagnets 191 to 194 will be explained in more detail later with reference to FIG. 18. The accelerator control device 240 also collects various logs of the accelerator 170. The accelerator control device 240 collects, for example, BP information obtained by the BPMs 181-184, electromagnet information consisting of parameter information on the current amount I set in the electromagnets 191-194 or the excitation amount B of the electromagnets 191-194, and system information logs consisting of the date or time associated with each piece of information, the elapsed time since the start-up or restart of the particle beam therapy system 160, and the elapsed time since the construction of the building 270. The accelerator control device 240 includes a log temporary storage device 242. The log temporary storage device 242 temporarily stores the logs of the BP information, electromagnet information, and system information collected by the accelerator control device 240. The accelerator control device 240 transmits the BP information, electromagnet information, and system information temporarily stored in the log temporary storage device 242 to a data storage server 250. The data storage server 250 accumulates the BP information, electromagnet information, and system information transmitted from the accelerator control device 240. The sensor data collection device 280 collects measurement values ​​of various sensors related to the accelerator 170. Among the various sensors, a room temperature sensor 281 measures the room temperature of the room in which the accelerator 170 is housed. A cooling water temperature sensor 282 measures the temperature of the cooling water used in the accelerator 170. An installation surface position sensor 283 measures the inclination and shape of the floor on which the accelerator 170 is installed. An outside air temperature sensor 284 measures the temperature of the outside air surrounding the building 270.

[0015] The information processing system 101 includes a beam position information acquisition unit (BP information acquisition unit) 211, an electromagnet information acquisition unit 212, a sensor data acquisition unit 213, a system information acquisition unit 214, a cause estimation unit 221, a parameter acquisition calculation unit 222, a warm-up operation determination unit 223 (as will be shown later as a modified example, a determination may be made as to whether not only a warm-up operation but also a cooling operation is performed), an adjustment timing determination unit 224, a display output control unit 231, and a parameter setting unit 232. (In FIG. 2 and FIG. 32 described later, the "unit" portion is omitted.) Of these, the BP information acquisition unit 211 can execute the BP information acquisition step 102 in FIG. 1. Furthermore, the parameter acquisition calculation unit 222 can execute the parameter acquisition step 103 in FIG. 1. The BP information acquisition unit 211 acquires BP information from the data accumulation server 250. Furthermore, based on the acquired BP information, the BP information acquisition unit 211 determines whether or not the deviation of the beam trajectory 173 from the desired beam trajectory (design beam trajectory) falls outside the range in which adjustment is not required. FIG. 3 illustrates the beam position (BP) X and deviation. In FIG. 3, the horizontal axis indicates coordinates along the circular path 172 of the accelerator 170, and the vertical axis indicates the beam position (BP) X for each BPM 181-184. Deviation ΔX 301 indicates the difference between the beam position (BP) X and a normal value indicating the designed beam orbit. For convenience, the upper side in FIG. 3 indicates an area outside the designed beam orbit when viewed from the center of the circular path 172 (the center of the accelerator 170). A no-adjustment range 302 is set with a certain width so as to sandwich the normal value indicating the designed beam orbit. If the BP (or deviation ΔX 301) for all BPMs 181-184 falls within the no-adjustment range 302, the parameters (parameter set) for controlling the accelerator do not need to be adjusted. On the other hand, when the BP in any of the BPMs 181 to 184 deviates from the adjustment-free range 302 and falls within the adjustment-required range 303, a process for estimating the cause of the deviation or a process for adjusting (creating) parameters (parameter set) for controlling the accelerator may be performed. Note that when the BP in any of the BPMs 181 to 184 significantly deviates from the normal value and falls within the alarm generation range 304, the information processing system 101 or the accelerator control device 240 may notify an operator or the like of an alarm, or may stop the particle therapy system 160 including the accelerator 170. The electromagnet information acquisition unit 212 acquires electromagnet information from the data accumulation server 250. The sensor data acquisition unit 213 acquires measurement values ​​of various sensors from the sensor data collection device 280. If the sensor data acquisition unit 213 can acquire measurement values ​​directly from the various sensors, the sensor data collection device 280 may not be necessary. The system information acquisition unit 214 acquires system information from the data accumulation server 250. The electromagnet information acquisition unit 212, the sensor data acquisition unit 213, and the system information acquisition unit 214 may be collectively referred to as the condition information acquisition unit 210. The various pieces of information acquired by the condition information acquisition unit 210 may also be collectively referred to as condition information. The cause estimation unit 221, the parameter acquisition calculation unit 222, and the warm-up operation determination unit 223 may all operate when the deviation of the beam trajectory 173 from the desired beam trajectory (design beam trajectory) deviates from the adjustment-free range 302. The cause estimation unit 221 estimates the cause of the deviation of the beam trajectory 173 deviating from the adjustment-free range 302. The parameter acquisition calculation unit 222 acquires or calculates parameters (parameter sets) for controlling the accelerator 170. The parameters here may be, for example, parameters for the amount of current I to be set in the electromagnets 191 to 194. The warm-up operation determination unit 223 determines whether or not a warm-up operation is being performed and the type of the warm-up operation, and determines parameters related to the warm-up operation. As will be shown later as a modified example, a determination may be made as to whether or not a cooling operation is being performed in addition to a warm-up operation. It is expected that a change in the temperature of the accelerator (for example, the temperature of an electromagnet used in the accelerator) will affect the beam trajectory of the accelerator. To address this, the particle therapy system 160 is configured to be able to perform a warm-up operation or a cooling operation (including a temporary suspension of the particle therapy system 160). The adjustment timing determination unit 224 determines the timing for adjusting parameters related to the control of the accelerator 170 in a future period (estimated period, subsequent period). Note that the cause estimation unit 221, the parameter acquisition calculation unit 222, the warm-up operation determination unit 223, and the adjustment timing determination unit 224 may be collectively referred to as the various calculation units 220. The display output control unit 231 controls some kind of display device or output device to display or output information indicating the results output by the various calculation units 220 to an operator or the like of the accelerator 170. The parameter setting unit 232, in response to an instruction from an operator or the like of the accelerator 170, transfers and stores the parameters (parameter set) acquired by the parameter acquisition calculation unit 222, or parameters (parameter set) further modified by the operator or the like, to the parameter storage device 241, thereby setting the parameters (parameter set) in the accelerator control device 240. Alternatively, the parameter setting unit 232, in response to an instruction from an operator or the like of the accelerator 170, sets the parameters determined by the warm-up operation determination unit 223, or parameters further modified by the operator or the like, in the accelerator control device 240. Note that the parameter setting unit 232 may set the parameters (parameter set) acquired by the parameter acquisition calculation unit 222 or parameters determined by the warm-up operation determination unit 223 in the accelerator control device 240 without waiting for an instruction from the operator or the like of the accelerator 170.

[0016] The database (DB) 260 can provide the information processing system 101 with a group of records relating to the past operation history of the accelerator 170, machines of the same or similar type as the accelerator 170 that are installed in other locations, and synchrotrons in general. It is expected that the number of accelerators used in particle beam therapy systems will be greater than that of accelerators used for other purposes. In other words, accelerators used in particle beam therapy systems can easily accumulate records relating to their past operation history, making it possible to realize a comprehensive DB 260. Here, the DB 260 may be built into the information processing system 101, may be connected to the information processing system 101 via an intranet, or may be remotely connected to the information processing system 101 via the Internet. Fig. 4 shows each of the components (items) included in each record of DB260. As shown in Fig. 4, each record 401 of DB260 may have 14 components (items). Assuming that component numbers (item numbers) are represented by numbers in brackets [ ], each component (item) may include the following information for each component number (item number). Note that a record of DB260 may include components (items) that do not exist among the following components (items), and may also include components (items) that are different from the following components (items). [1] The temperature of the outside air surrounding building 270. This information can be used to indicate the season and time of day. [2] Room temperature in the room housing Accelerator 170. [3] The date or time associated with the information contained in this record. Of this information, the date information indicates the season. Note that both date and time information may be included, or only date or time information may be included, or time zone information may be included instead of time. [4] The elapsed time after startup or restart of the particle therapy system 160. For example, if the elapsed time is shorter than a predetermined time, the information on the elapsed time indicates the temperature of the accelerator 170 or the cooling water for the accelerator 170. [5] The temperature of the cooling water for the accelerator 170 (cooling water temperature). [6] Time elapsed since building 270 was constructed. [7] The position of the floor surface on which the accelerator 170 is installed (the position of the accelerator installation surface). Although the causal relationship or correlation between this information and the operation of the accelerator 170 is not necessarily clear, there is a possibility that some causal relationship or correlation exists. [8] Parameters for controlling the accelerator 170 that were applied to each electromagnet (accelerator parameters before adjustment, for example, current I or excitation B). [9] BP information measured every BPM (unadjusted BP), or deviation of BP from normal values.

[10] A probable cause vector indicating the cause of deviation when BP or deviation is outside the adjustment-free range. (See Figure 5 below.)

[11] Whether or not any adjustments were made to the parameters for controlling the accelerator 170 on that date or time.

[12] When the parameters are adjusted, the parameters for controlling the accelerator 170 after adjustment are applied to each electromagnet.

[13] Whether or not the warm-up operation was performed on that date or time. (As will be shown later as a modified example, if a cooling operation is possible, the component (item) of

[13] may include information on whether or not the cooling operation was performed on that date or time.)

[14] The remaining time of the warm-up operation, if performed. It may also be the time period of the warm-up operation. (As will be shown later as a modified example, if a cooling operation can be performed, the component (item) of

[14] may include information on the remaining time of the cooling operation, if performed, or the time period of the cooling operation.) In the following, the information of the components (items) shown in FIG. 4 or the information corresponding to the components (items) (not limited to the information contained in the records of DB260) may be indicated by the numbers in brackets [ ].

[0017] The estimated cause vector, which is the tenth component of the record in DB 260, is also output by cause estimation unit 221 as an estimated result of the cause of beam trajectory 173 (BP, deviation 301) deviating from adjustment-free range 302. 5 shows a probable cause vector. The probable cause vector 501 may have seven components (items). Component numbers (item numbers) are represented by numbers with <> brackets, and for each component number (item number), when the information of each component (item) is information that means a set, it may have the following meaning. When the information of each component (item) is information that means a reset, it may have no meaning. Note that the probable cause vector 501 may include components (items) that do not exist among the following components (items), and may also include components (items) that are different from the following components (items). <1> It is estimated that the temperature of the outside air surrounding building 270 (outside air temperature) is one of the causes. <2> It is believed that the room temperature in the room housing the accelerator 170 is one of the causes. <3> The date or time is assumed to be included in the cause. <4> The cause is presumed to include the time elapsed since the start-up or restart of the particle therapy system 160. <5> It is estimated that the temperature of the cooling water for the accelerator 170 (cooling water temperature) is one of the causes. <6> It is estimated that the cause may be the time that has passed since the construction of Building 270. <7> It is presumed that the cause includes the position of the floor surface on which the accelerator 170 is installed (the position of the accelerator installation surface). In the following, the information of the components (items) shown in FIG. 5 or the information corresponding to the components (items) (not limited to the information contained in the records of DB260) may be indicated by the numbers in <> above.

[0018] 3. Computer Architecture for Implementing Embodiments of the Present Disclosure FIG. 6 illustrates a computer architecture for implementing an embodiment of the present disclosure. To realize the information processing system 101, an information processing device (e.g., a processor, a CPU) 601, a storage device (e.g., a memory) 602, a non-volatile recording medium (e.g., a non-volatile memory, a non-volatile disk device) 603, an external recording medium drive (e.g., a disk drive) 604, a display or output device (e.g., a display, a printer) 606, an input device (e.g., a mouse, a keyboard, an image capture device, a sensor) 607, a communication device (e.g., a communication device for wired communication, a communication device for wireless communication; it may be a network interface device (NIC) that controls communication with other systems, devices, or servers according to a predetermined protocol) 608, and some or all of an external input / output port 609 may be interconnected by an interconnection unit (e.g., a bus, a crossbar switch) 610. The non-volatile recording medium 603 may store a program 620a (e.g., a beam monitoring program in the present disclosure) and various information. As the various information, the non-volatile recording medium 603 may store, for example, various tables 621 or various information 622. Alternatively, some or all of the above-described program or various information may be acquired (accessed) from outside the device shown in FIG. 6 . The external recording medium drive 604 can be connected to an external recording medium (e.g., a portable recording disk (such as a DVD), an IC card, or an SD card) 605. The program 620a (e.g., a beam monitoring program in the present disclosure) and the above-described various information may be transferred and stored from the external recording medium 605 to the non-volatile recording medium 603 or the storage device 602. In addition, the program 620a (e.g., the beam monitoring program in the present disclosure) and the various information described above may be provided via the communication device 608, the external input / output port 609, or the input device 607 and stored in the non-volatile recording medium 603 or the storage device 602. In order for the architecture of FIG. 6 to function as the information processing system 101, or each unit (functional unit) or part of each unit within the information processing system 101 (to execute one or a series of processes (steps)), the program 620a may be loaded into the storage device 602 (for example, from the non-volatile recording medium 603). The loaded program is indicated by 620b in FIG. 6. The information processing device 601 may then execute the program 620b (using various information present in the non-volatile recording medium 603, etc., as necessary). Execution of the program 620b realizes the functions of the information processing system 101, or each unit (functional unit) or part of each unit within the information processing system 101 (to execute one or a series of processes (steps)). At this time, various buffers 623 temporarily formed in the storage device 602 may also be used as appropriate.

[0019] 4. Processing of the embodiments of the present disclosure In the following, among the processes of the embodiment of the present disclosure, first, the processes mainly performed by the cause estimation unit 221, the parameter acquisition calculation unit 222, and the warm-up operation determination unit 223 will be collectively described. After that, the processes mainly performed by the adjustment timing determination unit 224 will be described. Note that it is not essential to perform all of the processes described below, and a beam monitoring method, beam monitoring program, or information processing system may be provided that performs only part of the processes described below.

[0020] 4-1. Cause estimation, parameter acquisition, and warm-up operation decision processing Among the processes that can be realized by the embodiments of the present disclosure, a series of processes that are performed when the BP (i.e., beam trajectory 173) in BPM181-184 deviates from the adjustment-free range 302, such as estimating the cause of the deviation, adjusting (creating) parameters to bring the BP closer to the normal value (i.e., bring the beam trajectory 173 closer to the designed beam trajectory), or determining whether or not warm-up operation is occurring and its state, are described in Figures 7 to 24. Fig. 7 shows a main flowchart of the processes for cause estimation, parameter adjustment (creation), and warm-up operation determination. Figs. 8 to 12 further explain the cause estimation process in step 704 in Fig. 7. Figs. 13 to 18 further explain the parameter adjustment (creation) process in step 707 in Fig. 7. Fig. 19 shows an example of a display screen resulting from the display output process in step 707 or step 708 in Fig. 7. Figs. 20 to 24 further explain the warm-up operation determination process in step 709 in Fig. 7.

[0021] 4.1.1. Determining whether or not there is deviation in the beam trajectory or BP In step 701 of FIG. 7, the BP information acquisition unit 211 acquires BP information for each BPM 181-184 from the data storage server 250. The BP information is acquired by the BPMs 181-184 observing the beam 171. The BP information is temporarily stored in the log temporary storage device 242 from the BPMs 181-184. The BP information is then transferred from the log temporary storage device 242 to the data storage server 250 and stored therein. The BP information accumulated in the data storage server 250 is acquired by the BP information acquisition unit 211. Note that as long as the BP information acquisition unit 211 can acquire the BP information, a modified example may be adopted in which one or both of the log temporary storage device 242 and the data storage server 250 are not present and the number of hops required to acquire the BP information is reduced. This step 701 may be a beam position information acquisition step. Furthermore, the BP information may be the coordinates themselves in a coordinate system (e.g., a two-dimensional coordinate system or a one-dimensional coordinate system) that intersects with the direction of travel of the circular path 172, or may be a deviation 301 from a normal value that indicates the designed beam trajectory, as shown in Figure 3. 7, the BP information acquisition unit 211 determines whether the BP information acquired in step 701 deviates from the adjustment-needing range 302. For example, as shown in FIG. 3, after defining the adjustment-needing range 302 for deviation 301, the BP information acquisition unit 211 may determine whether deviation 301 is within the adjustment-needing range 302 for each of BPMs 181 to 184. Here, as shown in FIG. 3, after defining the adjustment-needing range 303 and the alarm generation range 304 in the range deviating from the adjustment-needing range 302, the BP information acquisition unit 211 may determine that the determination result in step 702 in FIG. 7 is affirmative and that processing from step 703 onward should be performed if deviation 301 is within the adjustment-needing range 303 for any of BPMs 181 to 184 and is not within the alarm generation range 304 for any of BPMs 181 to 184. Furthermore, when deviation 301 for any of BPMs 181 to 184 falls within alarm generation range 304, BP information acquisition unit 211 may notify an alarm to an operator of accelerator 170 or the like to urge them to stop accelerator 170. (In this case, the information processing system 101 may not perform the processes from step 703 onward in FIG. 7, and accelerator control device 240 may perform processes to ensure the safety of accelerator 170.) Alternatively, alarm generation range 304 in FIG. 3 may not be set. In step 702, if the BP information acquisition unit 211 determines that the BP information is within the adjustment-free range 302 for all BPMs 181 to 184, control is returned to step 701, and BP information is again acquired for each of the BPMs 181 to 184. In step 702, if the BP information acquisition unit 211 determines that the BP information is outside the adjustment-free range 302 for any of the BPMs 181 to 184, control is transferred to step 703. In step 703 of FIG. 7, information on the BP, electromagnet, sensor, or system that is obtainable and determined to be used in the processing in the subsequent steps is obtained. Here, the BP information acquisition unit 211 operates to acquire BP information, the electromagnet information acquisition unit 212 operates to acquire electromagnet information (information on the current amount I or excitation amount B, which are parameters set in the electromagnets 191 to 194), the sensor data acquisition unit 213 operates to acquire sensor information (measurements of the room temperature sensor 281, the cooling water temperature sensor 282, the installation surface position sensor 283, or the outside air temperature sensor 284), and the system information acquisition unit 214 operates to acquire system information (information on the date or time, the elapsed time since the start-up or restart of the particle beam therapy system 160, or the elapsed time since the construction of the building 270). Here, as shown in FIG. 2, with regard to the BP, electromagnet, or system information, each piece of information may be acquired via the log temporary storage device 242 and the data accumulation server 250. 2, the sensor information may be acquired via the sensor data collecting device 280. Alternatively, each piece of information may be acquired without going through any of the log temporary storage device 242, the data accumulation server 250, or the sensor data collecting device 280 shown in FIG. In step 703, after acquiring information that is determined to be obtainable and to be used in the processing of subsequent steps from among the information on the BP, electromagnet, sensor, or system, the information processing system 101 can transfer control to the group of steps 704 to 708 (processing of cause estimation and parameter adjustment (creation)) or the group of steps 709 to 711 (processing of warm-up operation determination). The information processing system 101 may sequentially execute the process of cause estimation and parameter adjustment (creation) and the process of warm-up operation determination, or may execute these processes of cause estimation and parameter adjustment (creation) and the process of warm-up operation determination simultaneously in parallel. This step 703 may be a beam position information acquisition step, or a condition information acquisition step. Based on the condition information acquired in this condition information acquisition step and the BP information for each BPM acquired in the beam position information acquisition step, parameters to be set for each electromagnet are acquired in the subsequent step 706 (parameter acquisition step), and appropriate values ​​are set as the parameters to be set for each electromagnet. Furthermore, based on the condition information acquired in the condition information acquisition step and the BP information for each BPM acquired in the beam position information acquisition step, in the subsequent step 709 (warm-up operation determination step), if a determination is made as to whether or not warm-up operation is being performed and the nature of the warm-up operation (for example, the remaining time of the warm-up operation (the duration of the warm-up operation from the current point in time)), appropriate parameters for the warm-up operation are set.

[0022] 4.1.2. Estimation of the cause of beam orbit or BP deviation (A) 7, the cause estimation unit 221 estimates the cause of the deviation of the BP information from the adjustment-free range 302 in any of the BPMs 181 to 184. The cause estimation unit 221 performs the cause estimation process using each piece of information obtained in step 703. Below, three methods will be described as examples of the cause estimation process executed by the cause estimation unit 221. The three methods are a rule-based method, a DB search method, and a cause estimation model method. The cause estimation unit 221 may execute any one of these three methods. Alternatively, the cause estimation unit 221 may selectively execute any one of these three methods depending on whether a predetermined condition is satisfied. Alternatively, the cause estimation unit 221 may ensemble (for example, take a majority vote) the cause estimation results obtained by each of these three methods to obtain a final cause estimation result. Furthermore, the cause estimation unit 221 may perform cause estimation using a method other than these three methods. This step 704 may be considered as a cause estimation step. This cause estimation step estimates the cause of the deviation of the beam orbit 173 (BP information) of the circulating beam 171 from the adjustment-free range 302, so that the information processing system 101 can provide an operator or the like with information for making decisions when controlling the accelerator 170.

[0023] 4.1.2.1. Rule-based cause estimation (A-1) Fig. 8 shows a flowchart of rule-based cause estimation processing that can be performed by the cause estimation unit 221 in step 704 of Fig. 7. In the flowchart of Fig. 8, the cause estimation unit 221 may obtain the cause estimation result as an estimated cause vector in the format shown in Fig. 5. In step 801 of FIG. 8, the cause estimation unit 221 calculates all components (items) of the estimated cause vector (for example, <1> ~ <7> ) is the reset value. 8, the cause estimation unit 221 checks the positional relationship between the designed beam orbit and the beam orbit 173 for the information on the BP acquired in step 701 and determined to deviate from the adjustment-free range 302 in step 702, and determines whether the beam orbit 173 (or deviation 301) is located on the same side as the designed beam orbit (or normal value) for all of the BPMs 181 to 184. In other words, the cause estimation unit 221 determines whether the beam orbit 173 is deviated outward from the designed beam orbit (as viewed from the center of the accelerator 170) for all of the BPMs 181 to 184, or whether the beam orbit 173 is deviated inward from the designed beam orbit (as viewed from the center of the accelerator 170) for all of the BPMs 181 to 184. If the determination result in step 802 is positive, the cause estimation unit 221 shifts control to step 803. If the determination result in step 802 is positive, that is, if the beam trajectory 173 deviates outward from the designed beam trajectory (as viewed from the center of the accelerator 170) in any of the BPMs 181 to 184, or if the beam trajectory 173 deviates inward from the designed beam trajectory (as viewed from the center of the accelerator 170) in any of the BPMs 181 to 184, then, based on experience, it is often safe to estimate that the cause of the deviation of the beam trajectory 173 is due to some temperature related to the accelerator 170. Of the components (items) of the estimated cause vector in the format shown in FIG. 5, <1> and <2> and <3> and <4> and <5> The components (items) above may be responsible for some temperature-related issues with the accelerator 170. On the other hand, if the determination result in step 802 is negative, the cause estimation unit 221 shifts control to step 813. If the determination result in step 802 is negative, the deviation (distortion) of the beam trajectory 173 from the designed beam trajectory will be complex, and it is empirically estimated that the deviation of the beam trajectory 173 is caused by something other than the temperature of the accelerator 170. This step 802 may be regarded as a trajectory comparison step. If it is expected that such a trajectory comparison step can automate to some extent the adjustment (creation) of a parameter (set) for correcting the deviation of the beam trajectory 173 from the design beam trajectory, the adjustment (creation) of the parameter (set) can be performed in the parameter acquisition step indicated by step 706. 1 and 2, it is possible to determine whether the beam trajectory 173 (or deviation 301) is located on the same side of the designed beam trajectory (or normal value) for only each of the BPMs included in a pair of BPMs located at opposite positions (for example, a pair of BPMs 181 and 183).Simplifying the process in this way can reduce the processing load of step 802, for example, when the accelerator has a large number of BPMs. 8, the cause estimation unit 221 determines whether the outside temperature information (measured value of the outside temperature sensor 284) acquired in step 703 is characteristic. For example, in the case where the accelerator 170 is intended to be operated in a standard climate, if the outside temperature information is higher than a predetermined first (high temperature) threshold or if the outside temperature information is lower than a predetermined second (low temperature) threshold, the cause estimation unit 221 determines that the outside temperature information is characteristic. Step 804 is executed only when the outside temperature information is determined to be characteristic. In step 804 of FIG. 8, the cause estimation unit 221 calculates the estimated cause vector in the format shown in FIG. <1> The components (items) of are set to the values. 8, the cause estimation unit 221 determines whether the room temperature information (measurement value of the room temperature sensor 281) acquired in step 703 is characteristic. For example, in the case where the accelerator 170 is intended to be operated at a standard room temperature, if the room temperature information is higher than a predetermined third (high temperature) threshold or if the room temperature information is lower than a predetermined fourth (low temperature) threshold, the cause estimation unit 221 determines that the room temperature information is characteristic. Step 806 is executed only if the room temperature information is determined to be characteristic. In step 806 of FIG. 8, the cause estimation unit 221 calculates the estimated cause vector in the format shown in FIG. <2> The components (items) of are set to the values. 8, the cause estimation unit 221 determines whether the date or time information (the date or time information linked to the BP information, electromagnet information, and sensor information) acquired in step 703 is characteristic. For example, if the accelerator 170 is assumed to operate in a standard climate or time zone, the cause estimation unit 221 determines that the date or time information is characteristic if the date information belongs to summer or winter, or if the time information indicates several hours around noon in summer or nighttime in winter. Step 808 is executed only if the date or time information is determined to be characteristic. In step 808 of FIG. 8, the cause estimation unit 221 calculates the estimated cause vector in the format shown in FIG. <3> The components (items) of are set to the values. 8, the cause estimation unit 221 determines whether the time elapsed since the start-up or restart of the particle therapy system 160 acquired in step 703 is characteristic. Empirically, it is estimated that the time elapsed since the start-up or restart of the particle therapy system 160 is related to the temperature of each component of the accelerator 170, including the cooling water. For example, it is estimated that the temperature of each component of the accelerator 170, including the cooling water, remains low shortly after the start-up or restart of the particle therapy system 160. Therefore, if the information about the elapsed time since the start-up or restart of the particle therapy system 160 indicates a short time after the start-up or restart of the particle therapy system 160 (the elapsed time is smaller than a predetermined fifth (time) threshold), the cause estimation unit 221 determines that the information about the elapsed time since the start-up or restart of the particle therapy system 160 is characteristic. On the other hand, when the time elapsed since the start-up or restart of the particle therapy system 160 is too long, the temperature of each component of the accelerator 170, including the cooling water, may become higher than expected. Therefore, if the information on the time elapsed since the start-up or restart of the particle therapy system 160 indicates that the time elapsed since the start-up or restart of the particle therapy system 160 is too long (the elapsed time is greater than a predetermined sixth (time) threshold), the cause estimation unit 221 may determine that the information on the time elapsed since the start-up or restart of the particle therapy system 160 is characteristic. Step 810 is executed only if the information on the elapsed time after the start-up or restart of the particle therapy system 160 is determined to be characteristic. In step 810 of FIG. 8, the cause estimation unit 221 calculates the estimated cause vector in the format shown in FIG. <4> The components (items) of the estimated cause vector are set as the values. <4> The component (item) may be configured to be able to distinguish between the time elapsed since the start-up or restart of the particle therapy system 160 being less than a fifth (time) threshold and the time elapsed since the start-up or restart of the particle therapy system 160 being greater than a sixth (time) threshold. 8, the cause estimation unit 221 determines whether the coolant temperature information (the measured value of the coolant temperature sensor 282) acquired in step 703 is characteristic. For example, in a case where the accelerator 170 is intended to be operated using coolant at a standard temperature, if the coolant temperature information is lower than a predetermined seventh (low temperature) threshold, the cause estimation unit 221 determines that the coolant temperature information is characteristic. Incidentally, when the coolant time information is higher than the predetermined eighth (high temperature) threshold, the cause estimation unit 221 may also determine that the coolant temperature information is characteristic. Step 812 is executed only if the coolant temperature information is determined to be characteristic. In step 812 of FIG. 8, the cause estimation unit 221 calculates the estimated cause vector in the format shown in FIG. <5> The components (items) of the estimated cause vector are set as the values. <5> The component (item) may be configured to be able to distinguish between coolant temperature information being lower than a predetermined seventh (low temperature) threshold and coolant temperature information being higher than a predetermined eighth (high temperature) threshold. 8, the cause estimation unit 221 determines whether the time elapsed since the building 270 was constructed, which was acquired in step 703, is characteristic. Although the causal relationship or correlation between the time elapsed since the building 270 was constructed and the operation of the accelerator 170 is not necessarily clear, for example, if the time elapsed since the building 270 was constructed is longer than a predetermined ninth (time) threshold, the cause estimation unit 221 may determine that the time elapsed since the building 270 was constructed is characteristic. Step 814 is executed only when it is determined that the time elapsed since the building 270 was constructed is characteristic. In step 814 of FIG. 8, the cause estimation unit 221 calculates the estimated cause vector in the format shown in FIG. <6> The components (items) of are set to the values. 8, the cause estimation unit 221 determines whether the position of the floor surface on which the accelerator 170 is installed (the value measured by the installation surface position sensor 283) acquired in step 703 is characteristic. Although the causal relationship or correlation between the position of the floor surface on which the accelerator 170 is installed and the operation of the accelerator 170 is not necessarily clear, for example, if the deviation of the plane of the floor on which the accelerator 170 is installed from a plane perpendicular to the gravity vector is greater than a predetermined tenth (deviation) threshold due to the geographical characteristics of the location where the accelerator 170 is installed or crustal movement due to an earthquake, the cause estimation unit 221 may determine that the position of the floor surface on which the accelerator 170 is installed is characteristic. Step 816 is executed only if it is determined that the position of the floor surface on which the accelerator 170 is installed is characteristic. In step 816 of FIG. 8, the cause estimation unit 221 calculates the estimated cause vector in the format shown in FIG. <7> The components (items) of are set to the values. If rules that serve as criteria for judgment are determined in advance, the rule-based cause estimation process shown in FIG. 8 can be easily implemented.

[0024] 4.1.2.2. Estimation of the cause by database search (A-2) Fig. 9 shows a flowchart of the cause estimation process by DB search that can be performed by the cause estimation unit 221 in step 704 in Fig. 7. In the flowchart of Fig. 8, the cause estimation unit 221 obtains the cause estimation result in the form of the estimated cause vector shown in Fig. 5. 9, the cause estimation unit 221 compares the component (item) information in each record 401 in the format shown in FIG. 4 from the database (DB) 260 with the component (item) information obtained from the information on the BP, electromagnet, sensor, and system acquired in step 701 or step 703. The components (items) compared here may be, for example, some or all of the nine components (items) [1] to [9] among the components (items) shown in FIG. 4. Furthermore, the cause estimation unit 221 may perform comparison processing on components (items) other than the components (items) listed above. 9, the cause estimation unit 221 selects, from among the records 401 compared in step 901, a record that has a high similarity to the information on BP, electromagnet, sensor, and system (information linked to the deviation of the beam trajectory 173 (BP, deviation 301) from the adjustment-free range 302) acquired in step 701 or step 703. For example, the cause estimation unit 221 may score the difference obtained in the comparison process in step 901 for each of the nine components (items) described above, thereby obtaining a score representing the similarity for each record, and select the record with the highest score in step 902. 9, the cause estimation unit 221 extracts information (estimated cause vector in the format shown in FIG. 5) of the component (item)

[10] , which is the component (item) included in the record selected in step 902, and may use the extracted estimated cause vector as the estimated cause vector corresponding to each piece of information on the BP, electromagnet, sensor, and system (information linked to the deviation of the beam trajectory 173 from the adjustment-free range 302) acquired in step 701 or step 703. This estimated cause vector becomes the result of the cause estimation in FIG. 9. The above-described cause estimation process using a database search can relatively easily realize cause estimation using information on the accelerator's past operation history.

[0025] 4.1.2.3. Cause estimation using a cause estimation model (A-3) 10 shows inputs and outputs of the cause estimation model 1000 when the cause estimation unit 221 executes the cause estimation model 1000 to perform the cause estimation process in step 704 of Fig. 7. The cause estimation unit 221 realizes the cause estimation model 1000 based on a program and a group of model parameters for the cause estimation model 1000. Note that when realizing the cause estimation model 1000, part or all of it may be realized by hardware rather than by program control. The cause estimation model 1000 receives as input the information on the BP, electromagnet, sensor, and system (information linked to the deviation of the beam trajectory 173 from the adjustment-free range 302) acquired in step 701 or step 703, and outputs an estimated cause vector. In FIG. 10, the inputs to the cause estimation model 1000 can be BP information (information corresponding to [9] in FIG. 4), electromagnet information (information corresponding to [8] in FIG. 4), sensor information (information corresponding to [1], [2], [5], and [7] in FIG. 4), and system information (information corresponding to [3], [4], and [6] in FIG. 4). The output of the cause estimation model 1000 can be an estimated cause vector (information corresponding to

[10] in FIG. 4, or information in the format shown in FIG. 5 ( <1> ~ <7> There may be information 11 shows a flowchart of machine learning for training model parameters to construct the cause estimation model 1000. This machine learning may be executed by the information processing system 101 or by some other system than the information processing system 101. In step 1101 of FIG. 11, the system that performs machine learning reads one record from among the records held in the DB 260 (records in the format shown in FIG. 4) that are to be used for machine learning. In step 1102 of FIG. 11, the system performing machine learning inputs some or all of the nine components (items) consisting of [1] to [9] of the components (items) contained in the record read in step 1101 to the cause estimation model 1000. In step 1103 of FIG. 11, the system that executes machine learning obtains an output from the cause estimation model 1000 in accordance with the input of each component (item) to the cause estimation model 1000 performed in step 1102. Here, the output from the cause estimation model 1000 is an estimated cause vector (information corresponding to

[10] in FIG. 4, or information in the format shown in FIG. 5 ( <1> ~ <7> The information is in the form of In step 1104 of FIG. 11, the system that executes machine learning uses the information (presumed cause vector) of the component (item)

[10] among the components (items) of the record read in step 1101 as training data. 11, the system that performs machine learning treats the difference between the estimated cause vector obtained in step 1103 and the estimated cause vector that is the training data obtained in step 1104 as an error. Then, the system that performs machine learning adjusts (trains) the model parameters of the cause estimation model 1000 by an error backpropagation method or the like using the error. 11, the system that performs machine learning determines whether all of the records contained in the record set to be used for machine learning have been read out from the records held by DB 260. If the result of the determination shows that there are records that have not yet been read out in step 1101 to be used for machine learning, the system that performs machine learning returns control to step 1101, determines the next record to be read out from among the records that have not yet been read out in step 1101 to be used for machine learning, and reads that record. If the result of the determination shows that all of the records to be used for machine learning have been read out in step 1101, the machine learning processing of the cause estimation model 1000 is complete. FIG. 12 shows a flowchart of the cause estimation process when the cause estimation unit 221 executes the cause estimation model 1000 when performing the cause estimation process in step 704 of FIG. In step 1201 of FIG. 12, the cause estimation unit 221 inputs information corresponding to some or all of the nine components (items) [1] to [9] (represented by the components (items) shown in FIG. 4) from the information on BP, electromagnet, sensor, and system acquired in step 701 or step 703 (information linked to the deviation of the beam trajectory 173 (BP, deviation 301) from the adjustment-free range 302) into the cause estimation model 1000. In step 1202 of FIG. 12, the cause estimation unit 221 outputs an estimated cause vector (information corresponding to

[10] in FIG. 4 or information in the format shown in FIG. 5 ( <1> ~ <7> This estimated cause vector is the result of the cause estimation shown in FIG. The cause inference process using a model trained by machine learning as described above can realize cause inference that fully reflects the accelerator's past operating history information.

[0026] 4.1.2.4. Determine whether parameter adjustment (creation) processing is feasible based on the results of the cause estimation processing 7, the cause estimation unit 221 estimates the cause of the deviation of the BP information from the adjustment-free range 302 in any of the BPMs 181 to 184. Then, in step 705, the cause estimation unit 221 determines whether any of the estimated causes obtained as a result of the cause estimation is a cause derived from the temperature of the accelerator 170. For example, among the components (items) of the estimated cause vector in the format shown in FIG. <1> ~ <5> These five components (items) may be treated as temperature-related causes. If the result of this determination is positive (all of the estimated causes are temperature-related causes related to the accelerator 170), the information processing system 101 transfers control to step 706. If the result of the determination is negative, the information processing system 101 transfers control to step 708. 8. That is, in step 705, the cause estimation unit 221 may perform the same determination process as in step 802 of Fig. 8. That is, in step 705, the cause estimation unit 221 may check the positional relationship between the designed beam orbit and the beam orbit 173 for the information on the BP acquired in step 701 and determined to deviate from the adjustment-free range 302 in step 702, and determine whether the beam orbit 173 (or deviation 301) is located on the same side of the designed beam orbit (or normal value) in all of the BPMs 181 to 184. That is, the cause estimation unit 221 may determine whether the beam orbit 173 is deviated outward from the designed beam orbit (as viewed from the center of the accelerator 170) in all of the BPMs 181 to 184, or whether the beam orbit 173 is deviated inward from the designed beam orbit (as viewed from the center of the accelerator 170) in all of the BPMs 181 to 184. If the determination result in step 705 is positive, that is, if the beam trajectory 173 deviates outward from the designed beam trajectory (as viewed from the center of the accelerator 170) in any of the BPMs 181 to 184, or if the beam trajectory 173 deviates inward from the designed beam trajectory (as viewed from the center of the accelerator 170) in any of the BPMs 181 to 184, the information processing system 101 may transfer control to step 706. On the other hand, if the determination result in step 705 is negative, the information processing system 101 may transfer control to step 708. This step 705 may be regarded as a trajectory comparison step. If it is expected that such a trajectory comparison step will enable some degree of automation in adjusting (creating) parameters (sets) for correcting deviations of the beam trajectory 173 from the design beam trajectory, the parameter (set) adjustment (creation) process can be performed in the parameter acquisition step shown in step 706 (and the parameter display output step shown in step 707 can be performed). Alternatively, in step 705, the cause estimation unit 221 may perform processing that further simplifies the determination processing of step 802 in Fig. 8. Specifically, in step 705, the cause estimation unit 221 may determine whether the beam trajectory 173 (or deviation 301) is located on the same side of the designed beam trajectory (or normal value) for any BPM included in a pair of BPMs (e.g., a pair of BPMs 181 and 183) that are located opposite each other among the BPMs 181 to 184. In this case, the BPM 181 may be the first beam monitor, and the BPM 183 may be the second beam monitor. Also, the electromagnet 194 may be the first bending electromagnet, the electromagnet 191 may be the second bending electromagnet, the electromagnet 192 may be the third bending electromagnet, and the electromagnet 193 may be the fourth bending electromagnet. By simplifying the process in this way, it is possible to reduce the processing load of step 802 when the accelerator has a large number of BPMs, for example. If the determination result in step 705 is positive, the deviation 301 will have the same sign (uniformly positive or uniformly negative) for all BPMs 181-184 (or for all BPMs 181 and 183 located at corresponding positions), and the degree of complexity of the deviation of beam trajectory 173 from the design beam trajectory will tend to be low. In such a case, for example, if bending electromagnets also have a steering function for adjusting the magnetic field to adjust beam trajectory 173 are used as electromagnets 191-194, most of the deviation of beam trajectory 173 can be corrected by adjusting the deflection component of the magnetic field components (deflection component and steering component) generated by the bending electromagnets. Here, since the deflection component is easier to adjust than the steering component, the process of adjusting the electromagnet parameters is more easily realized. Furthermore, since there are many cases in which the determination result in step 705 is positive, records of those cases are likely to be accumulated as past operation history. Therefore, it can be said that performing the parameter set adjustment (creation) process in step 706 for cases in which the determination result in step 705 is positive is quite easy to achieve, for example, by directly utilizing the database (DB) 260 that has accumulated records of those cases, or by using a model trained by machine learning using the records of the DB 260.

[0027] 4.1.3. Adjusting (creating) parameter sets (B) 7, the parameter acquisition calculation unit 222 adjusts (creates) a set of parameters (for example, parameters of the current amount I or excitation amount B used to control each of the electromagnets 191-194) used to control the accelerator 170 in order to bring the BP information in each of the BPMs 181-184 closer to a normal value (bringing the absolute value of the deviation 301 closer to zero, bringing the beam trajectory 173 closer to the designed beam trajectory). Alternatively, the parameter acquisition calculation unit 222 calculates a set of parameters. Below, three methods will be described as examples of the process of adjusting (creating) a parameter set executed by the parameter acquisition calculation unit 222. The three methods are a method using closed orbit distortion (COD) correction, a method using a database search, and a method using a parameter acquisition model. The parameter acquisition calculation unit 222 may execute any one of these three methods. Alternatively, the parameter acquisition calculation unit 222 may selectively execute any one of these three methods depending on whether a predetermined condition is satisfied. For example, when the parameter acquisition calculation unit 222 is capable of executing the COD correction method and the DB search method, if a useful record for adjusting (creating) a parameter set is found in the DB 260, the parameter acquisition calculation unit 222 may perform the parameter set adjustment (creation) process using the DB search method, and if no useful record is found, the parameter set may be adjusted (created) using the COD correction method. Alternatively, if the deviation 301 to be corrected is relatively small, the COD correction method may be prioritized. Alternatively, the parameter acquisition calculation unit 222 may ensemble (e.g., average) the results of adjusting (creating) a parameter set obtained by each of these three methods to obtain a final cause estimation result. Furthermore, the parameter acquisition calculation unit 222 may adjust (create) a parameter set using a method other than these three methods. This step 706 may be a parameter acquisition step. Such a parameter acquisition step makes it possible to automate the adjustment (creation) of parameters (sets) for bringing the beam trajectory 173 in the accelerator 170 closer to the designed beam trajectory. In the following, after explaining the three methods, we will also explain a method for creating a parameter set when the parameter set is expressed as a three-dimensional array (a method that can be used in common for the three methods above).

[0028] 4.1.3.1. Adjustment (creation) of parameter set by COD correction (B-1) FIG. 13 shows a flowchart of the process of adjusting (creating) a parameter set by COD correction, which can be performed by the parameter acquisition calculation unit 222 in step 706 of FIG. 13, the parameter acquisition calculation unit 222 acquires (calculates) the deviation ΔX of the BP from the normal value indicating the design beam trajectory as information on the BP for each BPM. This deviation ΔX is shown as 301 in FIG. 13, the parameter acquisition calculation unit 222 acquires an adjustment amount ΔB of the excitation amount for each of the electromagnets 191 to 194 from the deviation ΔX for each of the BPMs 181 to 184 based on the COD correction equation. When the beam positions (BP) for each of the BPMs 181 to 184 are X_181, X_182, X_183, and X_184, and the excitation amounts for each of the electromagnets 191 to 194 are B_191, B_192, B_193, and B_194, the vector of the beam position (BP) X [X_181, X_182, X_183, and X_184] and the vector of the excitation amount B [B_191, B_192, B_193, and B_194] are related by some function F. In other words, it can be expressed as [X_181, X_182, X_183, X_184] = F([B_191, B_192, B_193, B_194]). Therefore, by using the function F (or the inverse function of the function F), the parameter acquisition calculation unit 222 can calculate the adjustment amount ΔB of the excitation amount for each of the electromagnets 191 to 194 from the deviation ΔX for each of the BPMs 181 to 184. Alternatively, if ΔX and ΔB can be considered to be infinitesimal, a linear mapping relationship may be established between the deviation vector [ΔX_181, ΔX_182, ΔX_183, ΔX_184] related to [X_181, X_182, X_183, X_184] and the adjustment vector [ΔB_191, ΔB_192, ΔB_193, ΔB_194] related to [B_191, B_192, B_193, B_194]. In other words, if a matrix representing the linear mapping is P, the deviation vector (or the deviation vector multiplied by (-1)) may be expressed as the product of the matrix P and the adjustment vector. In this case, the parameter acquisition calculation unit 222 can calculate the adjustment amount ΔB of the excitation amount for each of the electromagnets 191 to 194 from the deviation ΔX for each of the BPMs 181 to 184 by using the matrix P (or the inverse matrix of the matrix P). 13, the parameter acquisition calculation unit 222 acquires (calculates) an adjustment amount ΔI of the amount of current for each of the electromagnets 191 to 194 from the adjustment amount ΔB of the amount of excitation for each of the electromagnets 191 to 194 obtained in step 1302. The parameter acquisition calculation unit 222 can use a relationship (relational expression) between the amount of current I and the amount of excitation B for each of the electromagnets 191 to 194 that has been obtained in advance. In step 1304 of Fig. 13, the parameter acquisition calculation unit 222 creates an adjusted parameter set from the adjustment amount ΔI of the current amount for each electromagnet obtained in step 1303. For example, the parameter set is a three-dimensional array of parameters. The creation of a parameter set that is a three-dimensional array will be explained later in "4.1.3.4. Creating a parameter set as a three-dimensional array." The above-described process of adjusting (creating) parameters (sets) through COD correction can achieve adjustment (creation) of parameters (sets) even in a situation where there is little accumulated operational history information.

[0029] 4.1.3.2. Adjustment (creation) of parameter set by DB search (B-2) FIG. 14 shows a flowchart of the parameter set adjustment (creation) process by DB search that can be performed by the parameter acquisition calculation unit 222 in step 706 of FIG. In step 1401 of Fig. 14, the parameter acquisition calculation unit 222 compares the component (item) information in each record 401 in the format shown in Fig. 4 from the database (DB) 260 with the component (item) information obtained from the information on the BP, electromagnet, sensor, and system acquired in step 701 or step 703. The components (items) compared here may be, for example, information corresponding to some or all of the nine components (items) consisting of [1] to [9] among the components (items) shown in Fig. 4. Furthermore, the parameter acquisition calculation unit 222 may perform comparison processing on components (items) other than the components (items) listed above. 14, the parameter acquisition calculation unit 222 selects a record that has a high similarity with the information on BP, electromagnet, sensor, and system (information linked to the deviation of the beam trajectory 173 (BP, deviation 301) from the adjustment-free range 302) acquired in step 701 or step 703 from among the records 401 that were compared in step 1401. For example, the parameter acquisition calculation unit 222 may score the difference obtained in the comparison process in step 1401 for each of the nine components (items) described above, thereby obtaining a score representing the similarity for each record, and may select the record with the highest score in step 1402. In step 1403 of FIG. 14, the parameter acquisition calculation unit 222 creates an adjusted parameter set based on the information of the component (item)

[12] included in the record selected in step 1402. For example, the parameter acquisition calculation unit 222 may set the difference between the parameter indicated by the component (item)

[12] included in the selected record and the parameter as the electromagnet information acquired in step 703 as the adjustment amount ΔI of the current amount for each electromagnet, and then create an adjusted parameter set from this adjustment amount ΔI of the current amount for each electromagnet. For example, the parameter set is a three-dimensional array of parameters. The creation of a parameter set that is a three-dimensional array will be described later in "4.1.3.4. Creating a parameter set as a three-dimensional array." The parameter set adjustment (creation) process by DB search as described above can relatively easily realize the adjustment (creation) of a parameter set using the accelerator's past operation history information.

[0030] 4.1.3.3. Adjustment (creation) of parameter set using parameter acquisition model (B-3) 15 shows inputs and outputs of the parameter acquisition model 1500 when the parameter acquisition calculation unit 222 executes the parameter acquisition model 1500 to perform parameter set adjustment (creation) processing in step 706 in Fig. 7. The parameter acquisition calculation unit 222 realizes the parameter acquisition model 1500 based on a program and a group of model parameters for the parameter acquisition model 1500. Note that when realizing the parameter acquisition model 1500, part or all of it may be realized by hardware rather than by program control. The parameter acquisition model 1500 receives as input the information on the BP, electromagnet, sensor, and system acquired in step 701 or step 703 (information associated with the deviation of the beam trajectory 173 (BP, deviation 301) from the adjustment-free range 302), and outputs adjusted parameters (electromagnet information). In FIG. 15, the inputs to the parameter acquisition model 1500 can include BP information (information corresponding to [9] in FIG. 4), electromagnet information (information corresponding to [8] in FIG. 4), sensor information (information corresponding to [1], [2], [5], and [7] in FIG. 4), and system information (information corresponding to [3], [4], and [6] in FIG. 4). The output of the parameter acquisition model 1500 can include adjusted parameters (electromagnet information) (information corresponding to

[12] in FIG. 4). 16 shows a flowchart of machine learning for training model parameters to construct the parameter acquisition model 1500. This machine learning may be performed by the information processing system 101 or by some other system than the information processing system 101. In step 1601 of FIG. 16, the system that performs machine learning reads one record from among the records held in the DB 260 (records in the format shown in FIG. 4) that are to be used for machine learning. In step 1602 of Figure 16, the system performing machine learning inputs some or all of the nine components (items) consisting of [1] to [9] from the components (items) contained in the record read in step 1601 to the parameter acquisition model 1500. 16, the system that executes machine learning obtains an output from the parameter acquisition model 1500 in accordance with the input of each component (item) to the parameter acquisition model 1500 performed in step 1602. Here, the output from the parameter acquisition model 1500 is in the form of adjusted parameters (electromagnet information) (information corresponding to

[12] in FIG. 4). In step 1604 of FIG. 16, the system that executes machine learning uses the information of the component (item)

[12] among the components (items) of the record read in step 1601 as training data. 16, the system that performs machine learning treats, as an error, the difference between the adjusted parameters (electromagnet information) obtained in step 1603 and the adjusted parameters (electromagnet information) that are the teacher data obtained in step 1604. Then, the system that performs machine learning adjusts (trains) the model parameters of the parameter acquisition model 1500 by, for example, backpropagation using the error. 16, the system that performs machine learning determines whether all of the records contained in the record set to be used for machine learning have been read out of the records held by DB 260. If the result of the determination shows that there are records that have not yet been read out in step 1601 to be used for machine learning, the system that performs machine learning returns control to step 1601, determines the next record to be read out from among the records that have not yet been read out in step 1601 to be used for machine learning, and reads that record. If the result of the determination shows that all of the records to be used for machine learning have been read out in step 1601, the machine learning processing of parameter acquisition model 1500 is complete. FIG. 17 shows a flowchart of the parameter set adjustment (acquisition) process when the parameter acquisition calculation unit 222 executes the parameter set adjustment (creation) process in step 704 of FIG. 7 by executing the parameter acquisition model 1500. In step 1701 of Figure 17, the parameter acquisition calculation unit 222 inputs information corresponding to some or all of the nine components (items) consisting of [1] to [9] from the information on BP, electromagnet, sensor, and system acquired in step 701 or step 703 (information linked to the beam trajectory 173 (BP, deviation 301) deviating from the adjustment-free range 302) into the parameter acquisition model 1500. In step 1702 in FIG. 17, the parameter acquisition calculation unit 222 obtains adjusted parameters (electromagnet information for each electromagnet) (information corresponding to

[12] in FIG. 4) as output from the parameter acquisition model 1500. In step 1703 of FIG. 17, the parameter acquisition calculation unit 222 creates an adjusted parameter set based on the adjusted parameters (electromagnet information for each electromagnet) (information corresponding to

[12] in FIG. 4) obtained from the parameter acquisition model 1500 in step 1702. For example, the parameter acquisition calculation unit 222 may set the difference between the adjusted parameters (electromagnet information for each electromagnet) (information corresponding to

[12] in FIG. 4) obtained from the parameter acquisition model 1500 and the parameters as the electromagnet information obtained in step 703 as an adjustment amount ΔI of the current amount for each electromagnet, and then create an adjusted parameter set from this adjustment amount ΔI of the current amount for each electromagnet. For example, the parameter set is a three-dimensional array of parameters. The creation of a parameter set that is a three-dimensional array will be described later in "4-1-3-4. Creation of a parameter set as a three-dimensional array." The parameter set adjustment (creation) process using a model trained by machine learning as described above can achieve the adjustment (creation) of a parameter set that fully reflects the accelerator's past operating history information.

[0031] 1.3.4 Creating a Parameter Set as a Three-Dimensional Array In step 1304 of FIG. 13, step 1403 of FIG. 14, and step 1703 of FIG. 17, when the parameter acquisition calculation unit 222 creates a parameter set that is a three-dimensional array from the adjustment amount ΔI of the current amount for each electromagnet, the parameter acquisition calculation unit 222 may execute the processing of the flowchart shown in FIG. 18. The parameter set, which is a three-dimensional array, has target energy amount V, time T, and electromagnet position C as array indices. In other words, the target value of the energy amount that beam 171 obtains in the accelerator is set to V, and the parameter of the current amount I to be applied to the electromagnet located at electromagnet position C at time T is set to one element I(V, T, C) of the three-dimensional array. 18 is a flowchart showing how the parameter acquisition calculation unit 222 sequentially obtains the elements I(V, T, C) of the parameter set, which is a three-dimensional array as described above. To briefly explain the process in FIG. 18, I(V, T, C) is sequentially obtained by a triple loop process using V, T, and C as indexes, respectively. 18, the parameter acquisition calculation unit 222 initializes the indices of the elements of the three-dimensional array to be recalculated based on the adjustment amount ΔI of the current amount for each electromagnet. For example, if the range of possible values ​​of the indexes is from V_S to V_E for the target energy amount V, from T_S to T_E for the time T, and from C_S to C_E for the electromagnet position C, then in step 1801 the parameter acquisition calculation unit 222 initializes the indices V, T, and C of the elements of the three-dimensional array to be recalculated to V_S, T_S, and C_S. 18, the parameter acquisition calculation unit 222 recalculates the parameters stored in element I(V, T, C) of the three-dimensional array according to the adjustment amount ΔI of the current amount for each electromagnet. In other words, the parameter acquisition calculation unit 222 recalculates the parameter of the current amount I to be applied to the electromagnet at electromagnet position C at time T when the target energy amount V is set. Note that at this time, the parameter acquisition calculation unit 222 may use the current amount I before recalculation and the adjustment amount ΔI of the current amount to determine the current amount I after recalculation. 18, the parameter acquisition calculation unit 222 determines whether or not the electromagnet position C is the last electromagnet position C_E among the indexes of the elements of the three-dimensional array to be recalculated. If the determination result of step 1803 is affirmative, the parameter acquisition calculation unit 222 transfers control to step 1805. If the determination result of step 1803 is negative, the parameter acquisition calculation unit 222 transfers control to step 1804. 18, the parameter acquisition calculation unit 222 updates the electromagnet position C among the indices of the elements of the three-dimensional array to be recalculated. For example, if the difference in electromagnet position is ΔC, the parameter acquisition calculation unit 222 adds ΔC to the current value of C to obtain a new value of C. Then, the parameter acquisition calculation unit 222 transfers control to step 1802, and recalculates the parameter for the current amount I using the new value of C. 18, the parameter acquisition calculation unit 222 determines whether the time T is the last time T_E among the indices of the elements of the three-dimensional array to be recalculated. If the determination result of step 1805 is affirmative, the parameter acquisition calculation unit 222 transfers control to step 1807. If the determination result of step 1805 is negative, the parameter acquisition calculation unit 222 transfers control to step 1806. 18, the parameter acquisition calculation unit 222 updates the time T in the index of the element of the three-dimensional array to be recalculated. For example, if the time difference is ΔT, the parameter acquisition calculation unit 222 adds ΔT to the current value of T to obtain a new value of T. The parameter acquisition calculation unit 222 also resets the value of the electromagnet position C to its initial value, C_S. Then, the parameter acquisition calculation unit 222 transfers control to step 1802, where it recalculates the parameter of the current amount I using the new value of T and the initialized value of C. 18, the parameter acquisition calculation unit 222 determines whether the target energy amount V is the last target energy amount V_E among the indexes of the elements of the three-dimensional array to be recalculated. If the determination result of step 1807 is positive, the parameter acquisition calculation unit 222 has completed recalculation for each element of the three-dimensional array. If the determination result of step 1807 is negative, the parameter acquisition calculation unit 222 transfers control to step 1808. 18, the parameter acquisition calculation unit 222 updates the target energy amount V among the indexes of the elements of the three-dimensional array to be recalculated. For example, if the difference in the target energy amount is ΔV, the parameter acquisition calculation unit 222 adds ΔV to the current value of V to obtain a new value of V. The parameter acquisition calculation unit 222 also returns the value of time T to its initial value, T_S, and the value of electromagnet position C to its initial value, C_S. Then, the parameter acquisition calculation unit 222 transfers control to step 1802, where it recalculates the parameter of the current amount I using the new value of V and the initialized values ​​of T and C. 18, the parameter acquisition calculation unit 222 sequentially processes a triple loop using indexes V, T, and C, with V as the index of the outermost loop and C as the index of the innermost loop. Here, the form of the triple loop may be determined appropriately. Furthermore, the parameter acquisition calculation unit 222 may simultaneously process the triple loop using a technique such as loop unrolling.

[0032] 4.1.4. Display output of estimated cause and adjusted parameter set In step 706 of FIG. 7, after the parameter acquisition calculation unit 222 creates an adjusted parameter set, in step 707 the display output control unit 231 controls to display or output the cause estimated in step 704 and the adjusted parameter set created in step 706. FIG. 19 shows a display screen displayed on some display device (for example, the display or output device 606 shown in FIG. 6) under the control of the display output control unit 231. In step 707, the display screen 1900 may show a chart showing the most recent BP or deviation 301 for each BPM (in the example of FIG. 19, the chart in the upper left of the display screen 1900) and a chart showing the most recent excitation amount B or current amount I for each electromagnet (in the example of FIG. 19, the chart in the upper right of the display screen 1900). These charts may accurately show information on BP, deviation 301, excitation amount B, and current amount I. Alternatively, these charts may be displayed in some simplified form so that the characteristics of the information on BP, deviation 301, excitation amount B, and current amount I are visually easy for an operator or the like to understand. In step 707, the display screen 1900 may have an unadjusted parameter set file output icon 1901. When the unadjusted parameter set file output icon 1901 is clicked with a mouse or the like, a file (e.g., a CSV file) indicating the unadjusted parameter set (e.g., the parameter set in the form of a three-dimensional array described with reference to FIG. 18) is output. The operator or the like can separately view the file using appropriate software. In step 707, the display screen 1900 may have an estimated cause display field 1903. The estimated cause display field 1903 may display the cause estimated in step 704. The estimated cause display field 1903 may display, for example, whether a value is set or reset for each component (item) of the estimated cause vector 501 shown in FIG. 5. Here, the display may show the value of each component (item) itself, or may show the estimated cause in words. Instead of or together with the estimated cause display field 1903, an estimated cause output icon may be present. When the estimated cause output icon is clicked with a mouse or the like, information contained in the estimated cause vector may be output in the form of a file. In step 707, the display screen 1900 may display a chart (in the example of FIG. 19, the chart in the lower right of the display screen 1900) showing the parameters of the excitation amount B or the current amount I for each electromagnet created in step 706. The display screen 1900 may also display a chart (in the example of FIG. 19, the chart in the lower left of the display screen 1900) showing the BP or deviation 301 for each BPM that is expected when the parameters of the excitation amount B or the current amount I for each electromagnet created in step 706 are applied. These charts may accurately show the information on BP, deviation 301, excitation amount B, and current amount I. Alternatively, these charts may be displayed in some simplified form so that the characteristics of the information on BP, deviation 301, excitation amount B, and current amount I are visually easy for an operator or the like to understand. In step 707, the display screen 1900 may have an adjusted parameter set file output icon 1902. When the adjusted parameter set file output icon 1902 is clicked with a mouse or the like, a file (e.g., a CSV file) indicating the adjusted parameter set (e.g., the parameter set in the three-dimensional array described in FIG. 18) is output. The operator or the like can separately view the file using appropriate software. The operator or the like can also further modify the parameters included in the file. This step 707 may be a parameter display output step. In step 708 of FIG. 7, the display output control unit 231 controls to display or output the cause estimated in step 704. In step 708 of FIG. 7, under the control of the display output control unit 231, some or all of the following may be displayed on the display screen: a chart showing the most recent BP or deviation 301 for each BPM (in the example of FIG. 19, the chart in the upper left of the display screen 1900), a chart showing the most recent excitation amount B or current amount I for each electromagnet (in the example of FIG. 19, the chart in the upper right of the display screen 1900), a pre-adjustment parameter set file output icon 1901, and an estimated cause display column 1903. 7, the deviation of the beam trajectory 173 from the design beam trajectory is often complex. Therefore, in this case, the information processing system 101 may only present the cause estimated in step 704 and data indicating the current situation to an operator, etc. After this, it is expected that a person with specialized knowledge of the accelerator 170 will manually adjust the file (e.g., a CSV file) indicating the pre-adjustment parameter set. If the display output control unit 231 controls the display screen 1900 shown in FIG. 19 to display or output the cause estimated in step 704 and the adjusted parameter (set) obtained in step 706, the estimated cause and the adjusted parameter (set) can be communicated to the operator of the accelerator 170 in an easy-to-understand manner. Furthermore, the display output control unit 231 can provide the parameter sets before and after adjustment in the form of a file if necessary to the operator of the accelerator 170. Therefore, the operator of the accelerator 170 can set the parameter set obtained in the form of a file as is in the accelerator control device 240, or can further modify the parameter set obtained in the form of a file and then set the modified parameter set in the accelerator control device 240.

[0033] 4.1.5. Decision on warm-up operation (C) In step 709 of FIG. 7, the warm-up operation determination unit 223 determines whether to perform warm-up operation in the accelerator 170, and if warm-up operation is to be performed, determines the mode of warm-up operation, based on the information on the BP, electromagnet, sensor, or system acquired in step 701 or step 703. Furthermore, the same method as the determination regarding warm-up operation described below can be used to determine whether to perform cooling operation in the accelerator 170, as well as to determine the mode of cooling operation if cooling operation is performed, which will be shown later as a modified example. Below, three methods will be described as examples of the warm-up operation determination process executed by the warm-up operation determination unit 223. The three methods are a cause estimation method, a DB search method, and a method using a warm-up operation determination model. The warm-up operation determination unit 223 may execute any one of these three methods. Alternatively, the warm-up operation determination unit 223 may selectively execute any one of these three methods depending on whether a predetermined condition is satisfied. Alternatively, the warm-up operation determination unit 223 may ensemble (for example, by majority vote or by averaging) the determination results of whether or not a warm-up operation is to be performed and the determination results of the mode of the warm-up operation obtained by each of these three methods to obtain a final determination result regarding the warm-up operation. Furthermore, the warm-up operation determination unit 223 may make a determination regarding the warm-up operation using a method other than these three methods. This step 709 may be a warm-up operation determination step. Furthermore, in the warm-up operation determination process described below, the warm-up operation determination unit 223 determines the remaining time of the warm-up operation (duration from the present time) as the mode of the warm-up operation when the warm-up operation is performed. Alternatively, the warm-up operation determination unit 223 may determine the time period in which the warm-up operation is performed as the mode of the warm-up operation when the warm-up operation is performed. In this case, in the record held by the database (DB) 260 (or each piece of information before being stored in the DB 260), the component (item)

[14] among the components (items) shown in Fig. 4 may have information on the time period in which the warm-up operation was performed. In this modified example, in which the time period for performing the warm-up operation is determined, it is expected that the warm-up operation will be performed appropriately, for example, during a time period when the temperature is lower.

[0034] 4.1.5.1. Determining whether to warm up the engine based on cause estimation (C-1) FIG. 20 shows a flowchart of the warm-up operation determination based on cause estimation that can be performed by the warm-up operation determination unit 223 in step 709 of FIG. In step 2001 of FIG. 20, the warm-up operation determination unit 223 estimates the cause of the deviation of the BP information from the adjustment-unnecessary range 302 in any of the BPMs 181 to 184. The mode of the cause estimation process may be the same as that already described for the cause estimation unit 221. Alternatively, in step 2001 of FIG. 20, the warm-up operation determination unit 223 may request the cause estimation process from the cause estimation unit 221 and receive the estimation result from the cause estimation unit 221. Alternatively, in step 2001 of FIG. 20, the warm-up operation determination unit 223 may use the result of the cause estimation process performed by the cause estimation unit 221 in step 704. In step 2002 of Fig. 20, the warm-up operation determination unit 223 determines whether or not the estimated causes in the estimation result obtained in step 2001 are caused by the cooling water temperature of the accelerator 170. For example, in each component (item) included in the estimated cause vector in the format shown in Fig. 5, <4> and <5> These two components (items) may be derived from the coolant temperature of the accelerator 170. More specifically, in step 2002 of FIG. 20 , the warm-up operation determination unit 223 may make the determination result of step 2002 positive if it is determined that the coolant temperature is lower than a predetermined temperature threshold or that the elapsed time since the start-up or restart of the particle beam therapy system 160 is shorter than a predetermined time threshold. If the determination result of step 2002 is positive, the warm-up operation determination unit 223 shifts control to step 2003. If the determination result of step 2002 is negative, the warm-up operation determination unit 223 shifts control to step 2004. In step 2003 of FIG. 20, the warm-up operation determination unit 223 determines that a warm-up operation is required as a result of the warm-up operation determination process, and appropriately determines the remaining time of the warm-up operation (duration from the present time). In step 2004 of FIG. 20, the warm-up operation determination unit 223 determines that no warm-up operation is to be performed as a result of the warm-up operation determination process. By using the warm-up operation judgment process based on cause estimation as shown in Figure 20, if it is determined that an event for which warm-up operation is an effective measure, such as low cooling water used in the accelerator 170, is included in the cause of the beam trajectory 173 (BP and deviation 301 from normal values) deviating from the adjustment-free range 302 as viewed from the design beam trajectory, the information processing system 101 can recommend to an operator or the like to perform warm-up operation, or set the accelerator control device 240 to automatically execute warm-up operation.

[0035] 4.1.5.2. Determining whether to warm up the engine by searching the database (C-2) FIG. 21 shows a flowchart of the warm-up operation determination by DB search that can be performed by the warm-up operation determination unit 223 in step 709 of FIG. In step 2101 of Fig. 21, the warm-up operation determination unit 223 compares the component (item) information in each record 401 in the format shown in Fig. 4 from the database (DB) 260 with the component (item) information obtained from the information on the BP, electromagnet, sensor, and system acquired in step 701 or step 703. The components (items) compared here may be, for example, information corresponding to some or all of the nine components (items) consisting of [1] to [9] among the components (items) shown in Fig. 4. Furthermore, the warm-up operation determination unit 223 may perform comparison processing on components (items) other than the components (items) listed above. 21, the warm-up operation determination unit 223 selects, from among the records 401 compared in step 2101, a record that has a high similarity to the information on BP, electromagnet, sensor, and system (information linked to the deviation of the beam trajectory 173 (BP, deviation 301) from the adjustment-free range 302) acquired in step 701 or step 703. For example, the warm-up operation determination unit 223 may score the difference obtained in the comparison process in step 2101 for each of the nine components (items) described above, thereby obtaining a score for the similarity of each record, and may select the record with the highest score in step 2102. In step 2103 of FIG. 21, the warm-up operation determination unit 223 determines whether or not warm-up operation is being performed and the type of warm-up operation (for example, the remaining time of warm-up operation (duration from the current point in time)) as a result of the warm-up operation determination process based on the two components (items)

[13] and

[14] contained in the record selected in step 2102. The above-described process of determining whether to perform warm-up operation by searching the database makes it relatively easy to determine whether to perform warm-up operation by utilizing the accelerator's past operation history information.

[0036] 4.1.5.3. Warm-up operation judgment using a warm-up operation judgment model (C-3) 22 shows inputs and outputs of the warm-up operation determination model 2200 when the warm-up operation determination unit 223 executes the warm-up operation determination model 2200 when performing the warm-up operation determination process in step 709 in Fig. 7. The warm-up operation determination unit 223 realizes the warm-up operation determination model 2200 based on a program and a group of model parameters for the warm-up operation determination model 2200. Note that when realizing the warm-up operation determination model 2200, part or all of it may be realized by hardware rather than by program control. The warm-up operation judgment model 2200 receives as input the information on the BP, electromagnet, sensor, and system (information linked to the deviation of the beam trajectory 173 (BP, deviation 301) from the adjustment-free range 302) acquired in step 701 or step 703, and outputs the presence or absence of warm-up operation and the state of warm-up operation (for example, the remaining time of warm-up operation (duration from the current point)) as a result of the warm-up operation judgment process. In Figure 22, the inputs to the warm-up operation judgment model 2200 can include BP information (information corresponding to [9] in Figure 4), electromagnet information (information corresponding to [8] in Figure 4), sensor information (information corresponding to [1], [2], [5], and [7] in Figure 4), and system information (information corresponding to [3], [4], and [6] in Figure 4). The output of the parameter acquisition model 1500 may include whether or not warm-up operation is being performed (information corresponding to

[13] in Figure 4) and the state of warm-up operation (e.g., the remaining time of warm-up operation (duration from the current point in time)) (information corresponding to

[14] in Figure 4). 23 shows a flowchart of machine learning for training model parameters to construct the warm-up operation judgment model 2200. This machine learning may be executed by the information processing system 101 or may be executed by some system other than the information processing system 101. In step 2301 of FIG. 23, the system that performs machine learning reads one record from among the records held in DB 260 (records in the format shown in FIG. 4) that are to be used for machine learning. In step 2302 of Figure 23, the system performing machine learning inputs some or all of the nine components (items) consisting of [1] to [9] of the components (items) contained in the record read in step 2301 into the warm-up operation judgment model 2200. In step 2303 of Fig. 23, the system that executes machine learning obtains output from the warm-up operation judgment model 2200 according to the input of each component (item) to the warm-up operation judgment model 2200 performed in step 2302. Here, the output from the warm-up operation judgment model 2200 is in the form of the presence or absence of warm-up operation (information corresponding to

[13] in Fig. 4) and the state of warm-up operation (for example, the remaining time of warm-up operation (duration from the current point in time)) (information corresponding to

[14] in Fig. 4) as a result of the warm-up operation judgment process. In step 2304 of Figure 23, the system that performs machine learning uses the information on two components (items)

[13] and

[14] of the components (items) contained in the record read in step 2301 as training data. 23, the system that performs machine learning treats as an error the difference between the presence or absence of warm-up operation and the state of warm-up operation (e.g., the remaining time of warm-up operation) obtained in step 2303 and the presence or absence of warm-up operation and the state of warm-up operation (e.g., the remaining time of warm-up operation) that is the teacher data obtained in step 2304. Then, the system that performs machine learning adjusts (trains) the model parameters of the warm-up operation judgment model 2200 by, for example, backpropagation using the error. 23, the system that performs machine learning determines whether all of the records contained in the record set to be used for machine learning have been read out from the records held by DB 260. If the determination result shows that there are records that have not yet been read out in step 2301 to be used for machine learning, the system that performs machine learning returns control to step 2301, determines the next record to be read out from among the records that have not yet been read out in step 2301 to be used for machine learning, and reads that record. If the determination result shows that all of the records to be used for machine learning have been read out in step 2301, the machine learning processing of the warm-up operation determination model 2200 is complete. FIG. 24 shows a flowchart of the warm-up operation determination process in the case where the warm-up operation determination unit 223 executes the warm-up operation determination model 2200 when performing the warm-up operation determination process in step 709 of FIG. In step 2401 of Figure 24, the warm-up operation judgment unit 223 inputs information corresponding to some or all of the nine components (items) consisting of [1] to [9] from the information on BP, electromagnet, sensor, and system obtained in step 701 or step 703 (information linked to the beam trajectory 173 (BP, deviation 301) deviating from the adjustment-free range 302) into the warm-up operation judgment model 2200. In step 2402 of FIG. 24, the warm-up operation judgment unit 223 obtains, as output from the warm-up operation judgment model 2200, the presence or absence of warm-up operation (information corresponding to

[13] in FIG. 4) and the state of warm-up operation (e.g., remaining time of warm-up operation) (information corresponding to

[14] in FIG. 4). The warm-up operation judgment process using a model trained by machine learning as described above can realize a warm-up operation judgment that fully reflects the accelerator's past operating history information.

[0037] 4-1-5-4. Display output of warm-up operation judgment result 7, the warm-up operation determination unit 223 determines whether or not it has been determined that a warm-up operation should be performed (warm-up operation is performed) as a result of the warm-up operation determination process in step 709. If the determination result in step 710 is positive, the warm-up operation determination unit 223 shifts control to step 711. 7, the display output control unit 231 controls to display or output recommended information (recommendation information) regarding the warm-up operation, which is the result of the warm-up operation determination process in step 709, including a message indicating that the warm-up operation should be performed (that the warm-up operation is performed) and the state of the warm-up operation (for example, the remaining time for the warm-up operation). Here, when the display output control unit 231 controls to display or output the recommended information (recommendation information) regarding the warm-up operation in step 711, it may control to also display the recommended information (recommendation information) regarding the warm-up operation on the display screen 1900 of FIG. 19. Alternatively, when the display output control unit 231 controls to display or output the recommended information (recommendation information) regarding the warm-up operation in step 711, it may control to display or output the recommended information (recommendation information) regarding the warm-up operation on a display screen different from the display screen 1900 of FIG. If the display output control unit 231 controls to display or output the above-mentioned recommended information (recommended information) regarding the warm-up operation, the recommended warm-up operation for the accelerator 170 can be communicated to the operator of the accelerator 170 in an easy-to-understand manner. Furthermore, the display output control unit 231 may provide the recommended information (recommended information) regarding warm-up operation in the form of a file if required by an operator or the like of the accelerator 170. In this way, the operator or the like of the accelerator 170 can set the recommended content (recommended information) regarding warm-up operation obtained in the form of a file as is in the accelerator control device 240, or can further modify the recommended content (recommended information) regarding warm-up operation obtained in the form of a file and then set the modified recommended content (recommended information) regarding warm-up operation in the accelerator control device 240.

[0038] 4.1.6.Setting up the accelerator control device 7, in step 712, which is after step 707 or step 711, the parameter setting unit 232 determines whether or not an instruction has been received from the operator or the like to set the content displayed or output in step 707 or step 711 (parameter set for the electromagnet or warm-up operation parameters), or content further modified by the operator or the like, in the accelerator control device 240. If the determination result in step 712 is affirmative, the parameter setting unit 232 shifts control to step 713. 7, the parameter setting unit 232 sets the content (parameter set for the electromagnet or warm-up operation parameters) designated by an operator or the like as the setting target in the accelerator control device 240. For example, if a parameter set for an electromagnet is to be set, the parameter setting unit 232 stores the parameter set for the electromagnet that is the setting target in the parameter storage device 241. For example, if a parameter for warm-up operation (whether or not to perform warm-up operation and the mode of warm-up operation (e.g., the remaining time of warm-up operation (duration from the current point in time))) is to be set, the parameter setting unit 232 sets the warm-up operation parameters that are the setting target in the accelerator control device 240, and causes the accelerator 170 to perform the warm-up operation according to the set content. Step 713, which is performed after the determination result of step 712 is affirmative, may be a parameter post-instruction setting step. In this way, by allowing the operator or the like to confirm and examine the displayed or output contents and further modify the parameters as necessary, the operator or the like who has specialized knowledge about the accelerator 170 can be involved in the setting process for the accelerator control device 240, thereby making the control of the accelerator 170 more appropriate. In addition, in response to the selection of the target energy amount V to be given to the beam 171 by an operator or the like, the accelerator controller 240 selects a group of elements corresponding to the selected target energy amount V from the parameter set. Then, as time T progresses, the accelerator controller 240 sequentially applies the parameters of the elements of I[V, T, C] (for example, the parameter for the current amount I) to the electromagnet at the electromagnet position C. In this way, the parameter set is stored in the parameter storage device 241 (under the control of the parameter setting unit 232 in the information processing system 101), which can be accessed relatively quickly from the accelerator control device 240, and the accelerator control device 240 then applies the parameters stored in the parameter storage device 241 to the accelerator 170 as appropriate. Therefore, even when a setting change such as a change in the target energy amount V is made in the accelerator control device 240, or when the time T is initialized to initialize the acceleration process of the beam 171, the accelerator control device 240 can quickly apply the parameters to the accelerator 170.

[0039] 4.2. Processing for determining when to adjust parameters Among the processes that can be realized by the embodiments of the present disclosure, a process (adjustment time determination process) for determining the time (parameter adjustment time) to adjust (create) parameters for controlling the accelerator 170 in order to bring the beam orbit 173 closer to the design beam orbit (bring the BP or deviation 301 closer to a normal value) in the future is described in Figures 25 to 31. It is assumed that the following adjustment time determination process is also applied to the accelerator 170 similar to the accelerator 170 to which the cause estimation process, parameter adjustment (creation) process, and warm-up operation determination process already described have been applied. In the case of an accelerator having a plurality of BPMs 181 to 184 like the accelerator 170, the adjustment time determination process can be performed taking into account distortion of the beam orbit 173. However, the following adjustment time determination process is sufficient if it can handle the time series information of BP information (hereinafter referred to as "BP time series information"). Therefore, it is not necessary for an accelerator to have multiple BPMs, and the adjustment time determination process can also be applied to an accelerator that has only one BPM. In this way, the adjustment time determination process can be applied to a wide range of accelerators. Furthermore, in the adjustment time determination process described below, similarly to the cause estimation process, parameter adjustment (creation) process, and warm-up operation determination process described above, information corresponding to some or all of the components (items) [1] to [9] among the components (items) shown in FIG. 4 is used as input information in the process. In addition, in the adjustment time determination process described below, past information on the component (item)

[11] , "whether or not a parameter was adjusted," may also be used as input information in the process. By using such information indicating past parameter adjustment times in the adjustment time determination process, the adjustment time determination process can be realized, for example, such that "if the previous parameter adjustment was on a day in midsummer and the current date and outside temperature are in autumn, it is determined that the time to perform the next parameter adjustment is when autumn turns to winter." Fig. 25 shows the main flowchart of the adjustment timing determination process. Figs. 27 to 31 further explain the process of estimating time-series information of BP information for a future period, which is performed in step 2501 of Fig. 25.

[0040] 4.2.1. Estimation of time series information of BP information for future periods (BP time series information) (D) In step 2501 of FIG. 25, the adjustment timing determination unit 224 estimates time-series information of BP information (BP time-series information) for each of the BPMs 181 to 184 in a future period (hereinafter sometimes referred to as an estimated period or a subsequent period). Two methods will be described below as examples of the BP time-series information estimation process executed by the adjustment timing determination unit 224. The two methods are a method using a regression analysis model and a method using a BP time-series estimation model. The adjustment time determination unit 224 may execute either of these two methods. Alternatively, the adjustment time determination unit 224 may selectively execute either of these two methods depending on whether a predetermined condition is satisfied. Alternatively, the adjustment time determination unit 224 may ensemble (e.g., average) the estimation results of the BP time series information obtained by each of these two methods to obtain a final estimation result of the BP time series information. FIG. 26 explains the technical background of how the adjustment timing determination unit 224 estimates BP time-series information in step 2501 when determining the parameter adjustment timing for a future period (estimated period, subsequent period). 26 , the adjustment time determination unit 224 estimates BP time series information 2601 for a future period (estimated period, subsequent period), whereby the adjustment time determination unit 224 can determine the time when the BP information (or deviation 301) for each time T included in the BP time series information 2601 will deviate from the adjustment-unnecessary range 302. The adjustment time determination unit 224 can set the determined time as the parameter adjustment time 2603.

[0041] 4.2.1.1 Estimation of BP time series information using regression analysis model (D-1) Fig. 27 is a flowchart showing the process of generating a regression analysis model when the adjustment timing determination unit 224 performs the process of estimating BP time-series information using a regression analysis model in step 2501 of Fig. 25. The process of generating a regression analysis model shown in Fig. 27 may be executed by the information processing system 101 or by a system different from the information processing system 101. In step 2701 of Figure 27, the system that performs the process of generating the regression analysis model obtains time series information for each piece of information on BP, electromagnetics, sensors, and systems from each record 401 in the database (DB) 260 (or from the data storage server 250 or sensor data collection device 280, etc., if the most recent information that has not yet been stored in the DB 260 is also to be used). This step 2701 may be a step of acquiring beam position time-series information. Also, step 2701 may be a step of acquiring condition time-series information. In step 2702 of FIG. 27, the system performing the regression analysis model generation process generates a regression analysis model using the time-series information of the BP, electromagnetic, sensor, and system information acquired in step 2701. Here, the system performing the regression analysis model generation process uses information corresponding to some or all of the components (items) [1] to [8] of the components (items) included in record 401 in the format shown in FIG. 4 as explanatory variables in the regression analysis model. The system performing the regression analysis model generation process may also use the elapsed time since parameter adjustment that has already been performed, which is derived from component

[11] of the components (items) included in record 401 in the format shown in FIG. 4, as an explanatory variable in the regression analysis model. The system performing the regression analysis model generation process uses information corresponding to component [9] of the components (items) included in record 401 in the format shown in FIG. 4 as the objective variable of the regression analysis model. FIG. 28 shows a flowchart of the estimation process when the adjustment timing determination unit 224 performs the estimation process of BP time-series information using a regression analysis model in step 2501 of FIG. 28, the adjustment time determination unit 224 may obtain information corresponding to the component (item)

[11] among the components (items) in the format shown in FIG. 4 from each record 401 in the DB 260 (or from the data storage server 250, the sensor data collection device 280, or the like, if the most recent information not yet stored in the DB 260 is also used). Then, based on the information corresponding to the component (item)

[11] obtained above, the adjustment time determination unit 224 may determine information about the time (date or time) when the parameters for controlling the accelerator 170 (for example, the parameter for the amount of current I for each of the electromagnets 191-194) were last adjusted. The information about the time (date or time) when the last adjustment was made may be used as an explanatory variable of the regression analysis model. This step 2801 may be an adjustment time information acquisition step. 28, the adjustment time determination unit 224 may provisionally determine information corresponding to some or all of the components (items) [1] to [8] for the period after the present time 2602 in FIG. 26 (future period, estimated period, subsequent period), and then use the provisionally determined variables as explanatory variables of the regression analysis model. In this way, the adjustment time determination unit 224 obtains information corresponding to the component (item) [9] for the period after the present time 2602 in FIG. 26 (future period, estimated period, subsequent period) as the objective variable of the regression analysis model, and uses this information as the estimation result of the BP time-series information. In the example of FIG. 28, the adjustment time determination unit 224 stores, as the estimation result of the BP time-series information, element ΔX(T,M) of a two-dimensional array related to the deviation 301 between the BP information and normal value, where T is the time included in the period after the present time 2602 and M is the BPM position. The adjustment timing determination unit 224 may, as necessary, change the provisional determination of information corresponding to each component (item) [1] to [8] above for the period (future period, estimated period, subsequent period) after the present 2602 in Fig. 26, and redo the estimation of the BP time-series information. Each step will be explained below. 28, the adjustment timing determination unit 224 initializes the time T among the indexes (T, M) of the two-dimensional array of the deviation 301 to be estimated. For example, the present 2602 or the time immediately following the present 2602 is set to T_N, and the time T, which is the index, is set to T_N. 28, the adjustment timing determination unit 224 obtains the elapsed time from the time of the previous adjustment (date or time) to time T, which is an index of the two-dimensional array, based on information about the time when the parameters for controlling the accelerator 170 (e.g., the parameters for the amount of current I for each of the electromagnets 191-194) obtained in step 2801 were last adjusted. This elapsed time may be used as one of the explanatory variables of the regression analysis model. Note that it is not essential to use this elapsed time as an explanatory variable of the regression analysis model, and therefore the estimation process of BP time-series information using the regression analysis model may be performed without using this elapsed time. In step 2804 of FIG. 28, the adjustment timing determination unit 224 may provisionally determine information corresponding to some or all of the components (items) [1] to [8] above for time T, which is an index of the two-dimensional array, and use the provisionally determined information as an explanatory variable of the regression analysis model. In step 2805 of FIG. 28, the adjustment timing determination unit 224 obtains, as the output (objective variable) from the regression analysis model, BP information (pre-adjustment BP) measured for each BPM, which corresponds to the component [9] above, or information on the deviation of BP from the normal value. This step 2805 may be a beam position time series information estimation step. In step 2806 of FIG. 28, the adjustment timing determination unit 224 sets (stores) a two-dimensional array ΔX(T,M) based on the BP information or deviation at time T obtained in step 2805, with M as the BPM position. 28, the adjustment time determination unit 224 determines whether the time T, which is the index of the two-dimensional array, has reached the end of the period (future period, estimated period, subsequent period) for which BP time-series information is to be estimated. If the determination result of step 2807 is positive, the adjustment time determination unit 224 transfers control to step 2809. If the determination result of step 2807 is negative, the adjustment time determination unit 224 transfers control to step 2808. 28, the adjustment time determination unit 224 updates the time T, which is an index of the two-dimensional array. For example, when the time difference is ΔT, the adjustment time determination unit 224 adds the difference ΔT to the current time T to obtain the new time T. After step 2808, the adjustment time determination unit 224 returns control to step 2803 and continues calculating the element ΔX(T,M) of the two-dimensional array for the new time T. 28, the adjustment time determination unit 224 outputs a two-dimensional array ΔX(T,M) spanning the period (future period, estimation period, subsequent period) for which the BP time-series information is to be estimated. After step 2809, the adjustment time determination unit 224 transfers control to step 2810. In step 2810 of Figure 28, the adjustment time determination unit 224 determines whether to redo the estimation process of BP time series information using new provisional determinations as the contents of the provisional determinations of information corresponding to some or all of the components (items) [1] to [8] above over the period to be estimated for the BP time series information (future period, estimation period, subsequent period). If the determination result in step 2810 is positive, the adjustment time determination unit 224 returns control to step 2802 and continues calculating the element ΔX(T, M) of the two-dimensional array using the new provisional determinations. If the determination result in step 2810 is negative, the estimation process of BP time series information using the regression analysis model shown in Figure 28 is completed. Note that the provisional determination of information corresponding to some or all of the components (items) [1] to [8] above, which is performed in step 2804 of FIG. 28, may be performed using various methods. For example, the adjustment time determination unit 224 may perform the provisional determination using the average value of information from the same period over the past several years for the period for which the BP time-series information is estimated (future period, estimation period, subsequent period). Alternatively, the adjustment time determination unit 224 may set provisional determination of information corresponding to the components (items) [1] to [8] above, which indicates a critical situation in the accelerator 170, for the period for which the BP time-series information is estimated (future period, estimation period, subsequent period). 28 shows that the adjustment time determination unit 224 performs sequential processing for time T. However, the adjustment time determination unit 224 may also perform processing using a regression analysis model simultaneously for time T. The above-described estimation process of BP time-series information using a regression analysis model makes it relatively easy to verify the relationship between explanatory variables and the target variable, allowing operators to obtain estimated results of BP time-series information while grasping the correlation between variables.

[0042] 4.2.1.2. Estimation of BP time series information using a BP time series estimation model (D-2) Fig. 29 shows the input and output of the BP time series estimation model 2900 when the adjustment time determination unit 224 executes the BP time series estimation model 2900 when performing the adjustment time determination process in step 2501 in Fig. 25. The adjustment time determination unit 224 realizes the BP time series estimation model 2900 based on a program and a group of model parameters for the BP time series estimation model 2900. Note that when realizing the BP time series estimation model 2900, part or all of it may be realized by hardware rather than by program control. The BP time series estimation model 2900 receives as input to the model information on the parameter adjustment timing in the period preceding the present 2602 (hereinafter, this period may be referred to as the preceding period, and abbreviated as [preceding] in FIG. 29) (information corresponding to the component (item)

[11] in FIG. 4), BP information for the preceding period (information corresponding to [9] in FIG. 4), electromagnet information for the preceding period (parameters for controlling the electromagnets for each of the electromagnets 191 to 194 (for example, parameters for the amount of current I and the amount of excitation B)) (information corresponding to [8] in FIG. 4), and sensor information for the preceding period (information corresponding to [9] in FIG. 4). 4), system information for the preceding period (information corresponding to [3], [4], and [6] in FIG. 4), (provisionally determined) electromagnet information for the period following the present 2602 (the period for which BP time series information is estimated; hereinafter, this may be referred to as the subsequent period; abbreviated as [subsequent] in FIG. 29), (provisionally determined) sensor information for the subsequent period (information corresponding to [1], [2], [5], and [7] in FIG. 4), and (provisionally determined) system information for the subsequent period (information corresponding to [3], [4], and [6] in FIG. 4) may be included. BP time series estimation model 2900 also has BP information (BP time series information) for the subsequent period (information corresponding to [9] in FIG. 4) as an output from the model. FIG. 30 shows a flowchart of machine learning for training model parameters to construct the BP time series estimation model 2900. This machine learning may be performed by the information processing system 101, or may be performed by some system other than the information processing system 101. In the flowchart of machine learning for the BP time series estimation model 2900 shown in FIG. 30, the system that performs machine learning selects two consecutive periods in the past, treats the two periods as a pair of a preceding period and a following period, and selects a set of records corresponding to the preceding period and a set of records corresponding to the following period as sets of records to be used for machine learning. The system that performs machine learning then repeats the selection of two consecutive periods in the past a predetermined number of times, repeating machine learning for the BP time series estimation model 2900. Each step is described below. In step 3001 of Figure 30, the system performing machine learning reads out, from the records held by DB260 (records in the format shown in Figure 4), a set of records corresponding to the preceding period and a set of records corresponding to the following period as record sets to be used for machine learning. In step 3002 of Figure 30, the system performing machine learning inputs some or all of the 10 components (items) consisting of [1] to [9] and

[11] from the components (items) contained in the record group corresponding to the preceding period read in step 3001 to the BP time series estimation model 2900. In addition, in step 3002 of Figure 30, the system performing machine learning inputs some or all of the eight components (items) consisting of [1] to [8] of the components (items) contained in the record group corresponding to the subsequent period read in step 3001 to the BP time series estimation model 2900. 30, the system performing machine learning obtains output from the BP time series estimation model 2900 in accordance with the input of each component (item) to the BP time series estimation model 2900 performed in step 3002. Here, the output from the BP time series estimation model 2900 is in the form of BP information (pre-adjusted BP) measured for each BPM in the subsequent period, or BP deviation from the normal value (information corresponding to [9] in FIG. 4), as a result of the estimation process of the BP time series information. In step 3004 of Figure 30, the system performing machine learning uses the information on component (item) [9] of the components (items) contained in the record group corresponding to the subsequent period read in step 3001 as training data. 30, the system performing machine learning treats as an error the difference between the BP information measured for each BPM in the subsequent period (pre-adjusted BP) or the information on the deviation of BP from normal values ​​obtained in step 3003, and the BP information measured for each BPM (pre-adjusted BP) or the information on the deviation of BP from normal values, which is the training data obtained in step 3004. The system performing machine learning then adjusts (trains) the model parameters of the BP time series estimation model 2900 using the error backpropagation method or the like using the error. 30 , the system that executes machine learning determines whether the operation of selecting two consecutive periods in the past for records held in the DB 260, treating the two periods as a pair of a preceding period and a following period, and then selecting a set of records corresponding to the preceding period and a set of records corresponding to the following period as sets of records to be used in machine learning has been executed for all planned pairs. If the determination result in step 3006 is negative, the system that executes machine learning transfers control to step 3001, where machine learning is executed by the BP time series estimation model 2900 for the new pair of the preceding period and the following period. If the determination result in step 3006 is positive, the machine learning process of the BP time series estimation model 2900 is completed. FIG. 31 shows a flowchart of the adjustment time determination process in the case where the adjustment time determination unit 224 executes the BP time series estimation model 2900 when performing the adjustment time determination process in step 2501 of FIG. 31, the adjustment time determination unit 224 acquires information (time-series information) corresponding to some or all of the 10 components (items) consisting of [1] to [9] and

[11] from the information on the BP, electromagnet, sensor, and system for the period preceding the present 2602 (hereinafter, sometimes referred to as the preceding period). Note that the adjustment time determination unit 224 may acquire this information from the DB 260, the data accumulation server 250, the sensor data collection device 280, or the like. This step 3101 may be a step of acquiring beam position time-series information, a step of acquiring condition time-series information, or a step of acquiring adjustment timing information. In step 3102 of FIG. 31, the adjustment timing determination unit 224 inputs the information (time series information) corresponding to some or all of the components (items) [1] to [9] and

[11] of the preceding period, which is the information acquired in step 3101, into the BP time series estimation model 2900. In step 3103 of FIG. 31, the adjustment timing determination unit 224 provisionally determines some or all of the information (time series information) corresponding to the components (items) [1] to [8] above for each time T in the period following the present 2602 (hereinafter sometimes referred to as the subsequent period), and inputs the provisionally determined information (time series information) to the BP time series estimation model 2900. 31, the adjustment time determination unit 224 obtains information (time series information) of the components (items) in [9] above for each time T in the subsequent period as the output of the BP time series estimation model 2900. That is, the adjustment time determination unit 224 obtains, as the output of the BP time series estimation model 2900, information (time series information) corresponding to BP information measured for each BPM (pre-adjustment BP) or information (time series information) of BP deviation from the normal value. This step 3104 may be a beam position time series information estimation step. 31, the adjustment time determination unit 224 sets each of the elements ΔX(T,M) of a two-dimensional array that stores estimated values ​​of BP time series information (for example, estimated values ​​of deviation 301), where T is the time and M is the BPM position, based on the information (time series information) of the components (items) of the above [9] for the subsequent period obtained in step 3104. Then, the adjustment time determination unit 224 outputs each of the elements ΔX(T,M) of the two-dimensional array as a result of the estimation process of the BP time series information. 31, the adjustment timing determination unit 224 determines whether to perform the estimation process for BP time series information using new provisional determinations as the contents of the provisional determinations made in step 3103 (the contents of [1] to [8] above for the subsequent period). If the determination result in step 3106 is positive, the adjustment timing determination unit 224 transfers control to step 3103 and restarts the estimation process for BP time series information using the new provisional determinations. If the determination result in step 3106 is negative, the estimation process for BP time series information using the BP time series estimation model 2900 is completed. The provisional determination of information corresponding to each of the components (items) [1] to [8] above, which is performed in step 3103 of Figure 31, may be performed using various methods. For example, the adjustment time determination unit 224 may perform the provisional determination using the average value of information from the same period over the past several years for the period (subsequent period) for which the BP time-series information is estimated. Alternatively, the adjustment time determination unit 224 may set provisional determination of information corresponding to each of the components (items) [1] to [8] above, which indicates a critical situation in the accelerator 170, for the period (subsequent period) for which the BP time-series information is estimated. The estimation process for BP time series information using a model trained by machine learning as described above can realize estimation that fully reflects the accelerator's past operating history information.

[0043] 4.2.2. Determining the timing of parameter adjustment using estimated BP time series information In step 2501 of FIG. 25, the adjustment timing determination unit 224 performs an estimation process of BP time series information for the period from the present 2602 onwards (future period, estimated period, subsequent period), and then in step 2502, the adjustment timing determination unit 224 determines the time (parameter adjustment time) to adjust parameters for controlling the accelerator 170 (for example, parameters for the current amount I and excitation amount B for each of the electromagnets 191 to 194) for the period from the present 2602 onwards (future period, estimated period, subsequent period). The adjustment timing determination unit 224 may determine, as the parameter adjustment timing 2603, the time T at which the BP information or deviation 301 of any one of the BPMs 181 to 184 in the estimated BP time-series information 2601 deviates from the adjustment-free range 302. 26, the estimated BP time-series information 2601 may be expected to include seasonal fluctuations, time-of-day fluctuations, etc. Therefore, the adjustment timing determination unit 224 may calculate time-series information (trend value) indicating the trend of the estimated BP time-series information 2601, and determine the time T at which the trend value deviates from the adjustment-free range 302 as the parameter adjustment timing 2603. Note that methods for calculating the trend value include a method using a regression line or regression curve, and a method using a moving average line. This step 2502 may be an adjustment timing determination step.

[0044] 4.2.3. Display and output of estimated BP time series information and parameter adjustment timing In step 2503 of FIG. 25, the display output control unit 231 may perform control so that the BP time-series information obtained in step 2501 or the parameter adjustment timing obtained in step 2502 is displayed or output. For example, in step 2503 of Fig. 25, the display output control unit 231 may control the display or output device 606 to display or output the BP time-series information or the parameter adjustment time in the manner shown in Fig. 26. Alternatively, or in addition to this, the display output control unit 231 may control the BP time-series information or the parameter adjustment time to be provided (displayed or output) to an operator or the like as numerical information, date information, or time information. In this way, the information processing system 101 acquires already-existing BP time-series information, estimates BP time-series information for a period after the present 2602 (future period, estimated period, subsequent period) based on the acquired BP time-series information, determines a parameter adjustment time 2603 based on the estimated BP time-series information, and controls to display or output the determination result. Alternatively, the information processing system 101 acquires already-existing BP time-series information, condition time-series information, or adjustment time information, determines a parameter adjustment time 2603 for a period after the present 2602 (future period, estimated period, subsequent period) based on the acquired BP time-series information, condition time-series information, or adjustment time information, and controls to display or output the determination result. Therefore, the information processing system 101 can present an appropriate parameter adjustment time 2603 to an operator, etc. This makes it easier for the operator, etc., to make plans for parameter adjustments to control the accelerator 170.

[0045] 5. Other (variations) The present disclosure is not limited to the above-described embodiments and includes various modifications. Part of the configurations and processes of the embodiments may be replaced with the configurations and processes of other conceivable embodiments. The configurations and processes of other conceivable embodiments may be added to the configurations and processes of the embodiments.

[0046] For example, the present disclosure may include the following modified embodiments.

[0047] (α) Selective Execution In the flowchart of FIG. 7 in the above embodiment, when the BP (or deviation 301) for each BPM deviates from the adjustment-unnecessary range 302, a cause estimation process in step 704, a parameter set adjustment (creation) process in step 706 (if the judgment result in step 705 is positive), and a warm-up operation judgment process in step 709 are executed. However, some of these processes may not be performed. For example, the parameter set adjustment (creation) process of step 706 may be performed without performing the cause estimation process of step 704. This modification can simplify the processing performed by the information processing system 101 in cases where it is not necessary to place emphasis on the cause of the deviation of the BP (or deviation 301) for each BPM from the adjustment-free range 302. Furthermore, in a case where the cause estimation process of step 704 is not executed, a conditional branching process similar to step 802 in Fig. 8 may be performed instead of step 705 as a conditional determination as to whether step 706 is executed. That is, the parameter set adjustment (creation) process of step 706 may be executed when the BPs (or beam trajectories 173) in all BPMs are on the same side as seen from the design beam trajectory (when seen from the center of the accelerator 170, all of the beam trajectories 173 are on the outer side or on the inner side of the design beam trajectory) (or, to simplify the conditional determination, in the case of the accelerator 170 as shown in Fig. 1 or 2, the determination may be made as to when the BPs (or beam trajectories 173) in the BPMs 181 and 183, which are positioned opposite each other, are on the same side as seen from the design beam trajectory (all on the outer side or all on the inner side).). This modification enables the parameter set adjustment (creation) process to be performed only in cases where it is relatively easy to address the deviation of the beam orbit 173 (for example, cases where it is likely that the deviation of the beam orbit 173 from the design beam orbit can be corrected by simply adjusting the deflection component of the magnetic field components (deflection component and steering component) that a deflection electromagnet with a steering function applies to the circular path 172). In other words, it is possible to simplify the parameter set adjustment (creation) process performed by the information processing system 101. Also, for example, the parameter set adjustment (creation) process of step 706 may be executed, but the warm-up operation determination process of step 711 (or the cooling operation determination process described later) may not be executed. Conversely, the parameter set adjustment (creation) process of step 706 may not be executed, but the warm-up operation determination process of step 711 (or the cooling operation determination process described later) may be executed. This modification simplifies the processing performed by the information processing system 101 in cases where it is sufficient to perform either parameter set adjustment (creation) or warm-up operation determination (or cooling operation determination, described below).

[0048] (β) Selective use of information (components, items) In the above embodiment, information on the 14 components (items) shown in FIG. 4 and causes of the 7 components (items) shown in FIG. 5 are handled. However, it is not necessary to use all of the information and causes of these components (items) to perform the cause estimation process, parameter adjustment (creation) process, warm-up operation determination process, and adjustment timing determination process. Only some of the information and causes of these components (items) may be used. In addition, the cause estimation process, parameter adjustment (creation) process, warm-up operation determination process, and adjustment timing determination process may be performed using information and causes other than the components (items) discussed in the above embodiment. In this way, the present disclosure makes it possible to flexibly define how information is handled so that various processes can be performed according to the circumstances of each accelerator.

[0049] (γ) Utilizing information from previous adjustments of the parameter In the above description of the embodiment, it is assumed that information on the time when the parameters for controlling the accelerator 170 were previously adjusted and information on the time elapsed since the parameters for controlling the accelerator 170 were most recently adjusted, which is derived from the information indicated by the component (item)

[11] shown in Figure 4, may be used in the adjustment timing determination process (for example, the process shown in [Figures 25] to [Figures 31]). Information on the time when the parameters for controlling the accelerator 170 were previously adjusted and information on the time elapsed since the parameters for controlling the accelerator 170 were most recently adjusted, which is derived from the information indicated by the component (item)

[11] shown in Fig. 4, may also be used in each of the cause estimation process (e.g., the process shown in Figs. 8 to 12), the parameter adjustment (creation) process (e.g., the process shown in Figs. 13 to 18), and the warm-up operation determination process (e.g., the process shown in Figs. 20 to 24, including the cooling operation determination process in a modified example shown later). When used in this way, the information indicated by the component

[11] shown in Fig. 4 may be treated as one of the condition information. In this way, the present disclosure can perform processing that further incorporates information on the accelerator's operating history in each of the cause estimation processing, parameter adjustment (creation) processing, and warm-up operation judgment processing (including the cooling operation judgment processing in the modified example shown later).

[0050] (δ) Relaxation of the criteria in the orbit comparison step In the above embodiment, in step 802, which may be considered an orbit comparison step, the cause estimation unit 221 examines the positional relationship between the design beam orbit and the beam orbit 173, and determines whether the beam orbit 173 is located outside the design beam orbit (as viewed from the center of the accelerator 170) for any of the BPMs 181 to 184 (or a pair of BPMs at opposite positions), or whether the beam orbit 173 is located inside the design beam orbit (as viewed from the center of the accelerator 170) for any of the BPMs 181 to 184 (or a pair of BPMs at opposite positions). Furthermore, in the above description of the embodiment and the above description of (α), it is also indicated that instead of the determination process shown as step 704, a determination process similar to step 802 can be performed. It is expected that it will be possible to automate to some extent the adjustment (creation) of parameters (set) for correcting deviation of beam trajectory 173 from the design beam trajectory, whether the determination result of step 802 is positive, whether the determination result of step 704 is positive, or whether the determination result of a determination process in a modified example in which a determination process similar to step 802 is performed instead of the determination process shown as step 704. Therefore, if the determination result of the above-described determination process is positive, in step 706, which may be considered a parameter acquisition step, the parameter acquisition calculation unit 222 performs processing for adjusting (creating) parameters (set), and in step 707, which may be considered a parameter display output step, the display output control unit 231 controls to display or output the adjusted parameters (set). Here, the parameter acquisition step and the parameter display output step may be performed when it is expected that the adjustment (creation) of parameters (sets) for correcting the deviation of the beam trajectory 173 from the design beam trajectory can be automated to a certain extent, and therefore the criteria for the judgment result in the above judgment process to be positive may be relaxed. For example, the judgment result in the above-mentioned judgment process may be positive (the parameter acquisition step and the parameter display output step may be allowed to be executed) not only when the strict condition that the position (deviation) of the beam orbit 173 from the design beam orbit (as viewed from the center of the accelerator 170) is satisfied in all BPMs targeted for checking the positional relationship between the design beam orbit and the beam orbit 173 is on the same side (all outside or all inside) but also when the relaxed condition that the position (deviation) of the beam orbit 173 from the design beam orbit (as viewed from the center of the accelerator 170) is satisfied in a predetermined percentage of BPMs (for example, 75% of BPMs) among the BPMs targeted for checking the positional relationship between the design beam orbit and the beam orbit 173 is on the same side. Alternatively, for example, not only when the above-mentioned strict conditions are satisfied, but also when a relaxed condition is satisfied that even if there are BPMs where the position (deviation) of the beam trajectory 173 from the design beam trajectory (seen from the center of the accelerator 170) is on a different side, the absolute value of the deviation 301 at the BPM where the deviation to one side is observed is equal to or less than a threshold value (for example, within the adjustment-free range 302), the judgment result in the above-mentioned judgment process may be deemed positive (the parameter acquisition step and the parameter display output step may be allowed to be executed). As described above, for the above-mentioned judgment process, it is possible to set rational conditions that are expected to enable a certain degree of automation in the adjustment (creation) of parameters (sets) for correcting deviations of the beam trajectory 173 from the design beam trajectory, thereby increasing the opportunities for adjusting (creating) parameters (sets). Depending on the operational circumstances of the accelerator 170, the conditions for allowing the parameter acquisition step and the parameter display output step to be executed may be set to be stricter, contrary to the above.

[0051] (ε) Decision regarding cooling operation (cooling operation decision process) (modification of C) As shown in "4.1.5. Determination regarding warm-up operation (C)," in the above embodiment, the warm-up operation determination unit 223 determines whether to perform warm-up operation in the accelerator 170, and if warm-up operation is performed, determines what form the warm-up operation should take. As already pointed out, determinations regarding not only warm-up operation but also cool-down operation may be performed for the particle therapy system 160 (e.g., accelerator 170). It is expected that changes in the temperature of the accelerator (e.g., the temperature of the electromagnets used in the accelerator) will affect the beam trajectory of the accelerator. Here, not only a temperature lower than expected but also a temperature higher than expected can cause operational problems for the accelerator. To address this, the particle therapy system 160 may be configured to be able to perform warm-up operation or cool-down operation (including temporary suspension of the particle therapy system 160). In a modified example of determining whether or not to perform cooling operation in the accelerator 170, and determining the mode of cooling operation if cooling operation is performed, processing similar to the processing regarding warm-up operation shown in "4.1.5. Determination regarding warm-up operation (C)" may be performed regarding cooling operation.

[0052] Specifically, in the modified example, a cooling operation determination unit may be provided as a functional unit included in the information processing system 101, or in the modified example, the warm-up operation determination unit 223 may also perform processing for determination regarding the cooling operation. When the warm-up operation determination unit 223 also performs processing for determination regarding the cooling operation, this warm-up operation determination unit 223 may be called a "warm-up operation / cooling operation determination unit." 7. In addition, the cooling operation determination unit or the warm-up operation / cooling operation determination unit may execute steps similar to the steps related to the warm-up operation shown as steps 709 and 710 in Fig. 7. When both a determination related to the warm-up operation and a determination related to the cooling operation are made in step 709, step 709 may be called a "warm-up operation / cooling operation determination step." When a determination regarding the cooling operation is made by a method similar to that shown in "4-1-5-1. Determination of warm-up operation based on cause estimation (C-1)," in a step corresponding to step 2002, the cooling operation determination unit or the warm-up operation / cooling operation determination unit may make the determination result of the step corresponding to step 2002 affirmative if it determines that the coolant temperature is higher than a predetermined temperature threshold or that the elapsed time since the start-up or restart of the particle beam therapy system 160 is longer than a predetermined time threshold. If the determination result of the step corresponding to step 2002 is affirmative, in a step corresponding to step 2003, the cooling operation determination unit or the warm-up operation / cooling operation determination unit may determine that the cooling operation is enabled as a result of the cooling operation determination process, and may appropriately determine the mode of the cooling operation (for example, the remaining time of the cooling operation (the duration from the current time) or the time zone of the cooling operation). When a determination regarding cooling operation is made using a method similar to that shown in "4.1.5.2. Determination of warm-up operation by DB search (C-2)" or "4.1.5.3. Determination of warm-up operation using warm-up operation determination model (C-3)", the component (item)

[13] included in record 401 shown in Figure 4 may have information on whether or not cooling operation has been performed (in addition to or instead of information on whether or not warm-up operation has been performed), and the component (item)

[14] may have information on the nature of the cooling operation (for example, the remaining time of the cooling operation (the duration from the date and time related to the record) or the time zone of the cooling operation) (if cooling operation has been performed). In addition, instead of determining whether or not a cooling operation is being performed and the nature of the cooling operation, the cooling operation determination unit or the warm-up operation / cooling operation determination unit may determine whether or not a temporary suspension of the operation of the particle beam therapy system 160 or the accelerator 170 has been performed and the nature of the temporary suspension (for example, the length of time the temporary suspension will continue or the time period during which the temporary suspension will be implemented). As a variation of the above, the information processing system 101 may make a determination regarding both warm-up operation and cooling operation, or may make a determination regarding cooling operation but not regarding warm-up operation. According to the above modification, even when the temperature of the accelerator (for example, the temperature of the electromagnets used in the accelerator) is higher than expected, it is possible to take appropriate measures.

[0053] (ζ) Automatic setting 7 in the above embodiment, in step 712, the parameter setting unit 232 determines whether or not an instruction has been received from an operator or the like to set the content (parameter set for the electromagnet or warm-up operation parameters) displayed or output in step 707 or step 711, or content further modified by the operator or the like, in the accelerator control device 240. In other words, a clear instruction from the operator or the like is a condition for setting the accelerator control device 240. However, part or all of the setting process for the accelerator controller 240 may be executed automatically without waiting for a clear instruction from an operator or the like. For example, the parameter setting unit 232 may automatically set the adjusted parameter set created by the parameter set adjustment (creation) process of step 706 to the accelerator control device 240 (or store it in the parameter storage device 241) without requiring step 712. Alternatively, the parameter setting unit 232 may automatically set the warm-up operation parameters indicating the mode of warm-up operation determined by the warm-up operation determination process of step 711 to the accelerator control device 240 without requiring step 712. In such a modified example, the step of the setting process for the accelerator control device 240 performed by the parameter setting unit 232 may be an automatic parameter setting step. This type of modification allows for quick setting of the accelerator control device 240 in cases where the parameter settings for the adjusted electromagnet or the recommended warm-up parameters do not need to be confirmed by an operator or the like.

[0054] (η) Remote Monitoring In the above embodiment, steps related to display output, namely, steps 707, 708, and 711 in Fig. 7 and step 2503 in Fig. 25, are executed by the display output control unit 231 included in the information processing system 101. In addition, step 712 related to receiving instructions from an operator or the like is executed by the parameter setting unit 232 included in the information processing system 101. The information processing system 101 itself may be located in a local location close to the accelerator control device 240, or may be located in a remote location from the accelerator control device 240. However, if the information processing system 101 is located in a local location close to the accelerator control device 240, then both the steps related to display output and the steps related to receiving instructions from an operator, etc. will be performed in a local location close to the accelerator control device 240. However, even if the information processing system 101 is located (local) close to the accelerator control device 240, a system located remotely from the accelerator control device 240 may be involved in some way in some or all of the steps related to display output and the steps related to receiving instructions from an operator, etc.

[0055] Figure 32 shows a modified example in which the information processing system 101 is located (local) close to the accelerator control device 240, and a system located remotely from the accelerator control device 240 is somehow involved in some or all of the steps related to display output and the steps related to receiving instructions from an operator, etc. 32, the information processing system 101 has a communication control unit 3203. Furthermore, a remote (remote location) system 3200 is capable of communicating with the information processing system 101 via a network 3201. The remote (remote location) system 3200 has a remote display output control unit 3231 and a remote setting instruction unit 3232. When steps corresponding to steps 707, 708, and 711 in FIG. 7 and step 2503 in FIG. 25, which are steps related to display output, are to be executed, the communication control unit 3203 in the information processing system 101 transmits information to be displayed to the remote (remote location) system 3200 via the network 3201. In the remote (remote location) system 3200 that has received the information, the remote display output control unit 3231 controls some kind of display device or output device to display or output the information. Furthermore, the remote setting instruction unit 3232 in the remote (remote location) system 3200 accepts instructions (such as parameter settings for the electromagnet after adjustment or instructions to set warm-up operation parameters in the accelerator control device 240) from an operator or the like who has come into contact with the information. The remote setting instruction unit 3232 transmits the contents of the accepted instructions to the information processing system 101 via the network 3201. In the information processing system 101 that has received the instruction, the parameter setting unit 232 performs setting processing on the accelerator control device 240 in accordance with the instruction. Furthermore, the parameter set for the adjusted electromagnet and the warm-up operation parameters, which are controlled to be displayed or output by the remote display output control unit 3231, may be further modified by the remote (remote location) system 3200 (or an operator thereof), and the remote setting instruction unit 3232 may then receive an instruction to set the modified parameter (set) in the accelerator control device 240. In such a modified example, the processing step performed by the communication control unit 3203 may be a communication control step. Also, in such a modified example, the setting processing step performed by the parameter setting unit 232 for the accelerator control device 240 may be a parameter remote instruction post-setting step. With this modification, even if the information processing system 101 is located (local) close to the accelerator control device 240, it is possible to monitor the accelerator 170 and give setting instructions to the accelerator control device 240 at a location (remote) remote from the accelerator control device 240. This reduces the number of operators and the like to be placed at the locations (local) where each accelerator 170 is installed, and allows for centralized management at a remote location (remote), thereby enabling effective use of human resources such as operators. In the above-described modified example, the remote (remote location) system 3200 may have the remote display output control unit 3231 but may not have the remote setting instruction unit 3232. In other words, the remote (remote location) system 3200 may control the display and output of the results of each process, but may not instruct the accelerator control device 240 to set parameters. In this case, the information processing system 101 may instruct the accelerator control device 240 to set parameters, or the parameters may be set automatically in the accelerator control device 240. In this way, the relationship between the accelerator control device 240, the information processing system 101, and the remote system 3200 can be flexibly determined.

[0056] (θ) Tuning model parameters for multiple records in machine learning In the above embodiment, in steps 1105 and 1106 of Figure 11, steps 1605 and 1606 of Figure 16, steps 2305 and 2306 of Figure 23, and steps 3005 and 3006 of Figure 30, the error (Loss function) is calculated for each record used in machine learning of various models (in Figure 30, for each group of records included in one pair of preceding and succeeding periods), and then the model parameters are adjusted. However, in a modified example related to machine learning, the model parameters may be adjusted after calculating the overall error (Loss function) for the group of records to be treated collectively (the group of records corresponding to the group of pairs in Figure 30) in multiple record units (multiple pairs of the preceding and following periods in Figure 30) or in all record units used in machine learning (all pairs of the preceding and following periods in Figure 30). The above-described modified example can reduce the frequency of adjusting the model parameters and can perform learning processing in accordance with the standard characteristics exhibited by the set of records used in machine learning.

[0057] The technical matters shown in the above-described embodiments of the present disclosure and the modified examples of the embodiments can be combined as appropriate as long as no technical contradiction occurs. (Appendix 1) 1. A method for beam monitoring in a particle therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; the beam monitoring method is executed by an information processing system; The beam monitoring method includes: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; A beam monitoring method comprising a parameter acquisition step of acquiring parameters to be set for each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step. (Appendix 2) A beam monitoring method according to (Supplementary Note 1), a parameter acquisition step for calculating the parameters to be set for each electromagnet so that the beam orbit of the charged particle beam during orbit approaches the design beam orbit by calculating a closed orbit distortion (COD) correction that adjusts the amount of magnetic field excitation applied to the orbital path by the electromagnet based on the deviation between the beam position information in each beam monitor and a normal value corresponding to the design beam orbit. (Appendix 3) A beam monitoring method according to (Supplementary Note 1), The beam monitoring method includes a condition information acquisition step of acquiring, as condition information, at least one piece of information among an outside air temperature, a room temperature of a room containing the accelerator, a date or time, an amount of time elapsed since startup or restart of the particle beam therapy system, a temperature of cooling water, an amount of time elapsed since construction of a building containing the accelerator, a position of an installation surface of the accelerator, parameters set in the electromagnet, and information on a time when parameters to be set in the electromagnet were previously adjusted; A beam monitoring method, wherein the parameter acquisition step is a step of acquiring parameters to be set in the electromagnet based on information on the beam position in each of the beam monitors or the acquired condition information. (Appendix 4) A beam monitoring method according to (Supplementary Note 1), the accelerator includes, as the electromagnet, a plurality of bending electromagnets arranged in order along the circular path; The beam monitoring method includes an orbit comparison step of examining a positional relationship between a beam orbit of the charged particle beam orbiting near each of the beam monitors and a design beam orbit; a parameter acquisition step being executed when the orbit comparison step determines that the beam orbit of the circulating charged particle beam near any of the beam monitors is located outside the designed beam orbit, or that the beam orbit of the circulating charged particle beam near any of the beam monitors is located inside the designed beam orbit. (Appendix 5) A beam monitoring method according to (Supplementary Note 1), the accelerator includes, as the electromagnets, a first bending electromagnet, a second bending electromagnet, a third bending electromagnet, and a fourth bending electromagnet that are installed in this order along the circular path; the accelerator includes a first beam monitor serving as the beam monitor, between the first bending electromagnet and the second bending electromagnet; the accelerator includes a second beam monitor as the beam monitor, between the third bending electromagnet and the fourth bending electromagnet, the beam monitoring method includes an orbit comparison step of comparing information on the beam position in the first beam monitor with a normal value corresponding to a designed beam orbit, and comparing information on the beam position in the second beam monitor with a normal value corresponding to the designed beam orbit, the parameter acquisition step is executed when it is found by the trajectory comparison step that the beam trajectory of the circulating charged particle beam is located outside the designed beam trajectory both near the first beam monitor and near the second beam monitor, or that the beam trajectory of the circulating charged particle beam is located inside the designed beam trajectory both near the first beam monitor and near the second beam monitor, the beam monitoring method further comprising a parameter display output step of displaying or outputting the parameters acquired in the parameter acquisition step when it is found by the orbit comparison step that the beam orbit of the circulating charged particle beam near both the first beam monitor and the second beam monitor is located outside the designed beam orbit, or that the beam orbit of the circulating charged particle beam near both the first beam monitor and the second beam monitor is located inside the designed beam orbit. (Appendix 6) A beam monitoring method according to (Supplementary Note 1), a condition information acquisition step of acquiring, as condition information, at least one piece of information among an outside air temperature, a room temperature of a room containing the accelerator, a date or time, an amount of time elapsed since the start-up or restart of the particle beam therapy system, a temperature of cooling water, an amount of time elapsed since the construction of a building containing the accelerator, a position of an installation surface of the accelerator, parameters set in the electromagnet, and information on a time when parameters to be set in the electromagnet were adjusted in the past; a cause estimation step of estimating, when a deviation of the beam orbit of the circulating charged particle beam from a normal value corresponding to a design beam orbit in any of the beam monitors falls outside a range in which adjustment is not required, the cause of the beam orbit of the circulating charged particle beam falling outside the range in which adjustment is not required, based on the beam position information in each of the beam monitors or the acquired condition information. (Appendix 7) A beam monitoring method according to (Supplementary Note 6), a warm-up / cooling operation determination step for obtaining a determination result that it is desirable to perform a warm-up operation or a cooling operation, instead of or in addition to obtaining the parameters by the parameter acquisition step, when the causes estimated in the cause estimation step include either the elapsed time after startup or restart of the particle beam therapy system or the temperature of the cooling water. (Appendix 8) A beam monitoring method according to (Supplementary Note 1), a condition information acquisition step of acquiring, as condition information, at least one piece of information among an outside air temperature, a room temperature of a room containing the accelerator, a date or time, an amount of time elapsed since the start-up or restart of the particle beam therapy system, a temperature of cooling water, an amount of time elapsed since the construction of a building containing the accelerator, a position of an installation surface of the accelerator, parameters set in the electromagnet, and information on a time when parameters to be set in the electromagnet were adjusted in the past; A beam monitoring method comprising a warm-up operation / cooling operation determination step that determines whether warm-up operation or cooling operation is necessary, or the type of warm-up operation or cooling operation to be performed, based on information on the beam position in each of the beam monitors or the acquired condition information. (Appendix 9) A beam monitoring method according to (Supplementary Note 1), a beam position time series information acquisition step of acquiring time series information of the beam position in the beam monitor; a beam position time series information estimation step of estimating time series information of the beam position information in a future period based on the acquired time series information of the beam position information; A beam monitoring method comprising an adjustment timing determination step of determining when to adjust parameters to be set for the electromagnet based on time series information of estimated beam position information for a future period. (Appendix 10) A beam monitoring method according to (Supplementary Note 1), a beam position time series information acquisition step of acquiring time series information of the beam position in the beam monitor; a condition time-series information acquisition step of acquiring, as condition time-series information, time-series information consisting of at least one of information on the outside air temperature, the room temperature of the room in which the accelerator is housed, the date or time, the elapsed time since the start-up or restart of the particle beam therapy system, the temperature of the cooling water, the elapsed time since the construction of the building in which the accelerator is housed, the position of the installation surface of the accelerator, and parameters set in the electromagnet; an adjustment time information acquisition step of acquiring information on when parameters to be set to the electromagnet were previously adjusted as adjustment time information; A beam monitoring method comprising an adjustment timing determination step of determining when to adjust parameters to be set in the electromagnet based on the acquired time series information of the beam position, the acquired condition time series information, and the acquired adjustment timing information. (Appendix 11) A beam monitoring method according to (Supplementary Note 1), A beam monitoring method comprising an automatic parameter setting step of transferring and storing the acquired parameters in a storage device for an accelerator control device that controls the accelerator without receiving an input indicating a setting instruction for the parameters acquired by the parameter acquisition step. (Appendix 12) A beam monitoring method according to (Supplementary Note 1), a parameter display output step of displaying or outputting the parameters acquired by the parameter acquisition step; A beam monitoring method comprising a parameter instruction post-setting step of transferring and storing the acquired parameters or further modified parameters in a storage device for an accelerator control device that controls the accelerator, on the condition that an input indicating a setting instruction regarding the displayed or output parameters is received. (Appendix 13) A beam monitoring method according to (Supplementary Note 1), a communication control step of transmitting the parameters acquired in the parameter acquisition step to a system at a remote location from the building in which the accelerator is installed, so that the system displays or outputs the parameters; A beam monitoring method comprising a parameter remote instruction post-setting step of transferring and storing the parameters transmitted by the communication control step or further modified parameters in a storage device for an accelerator control device that controls the accelerator, on the condition that a setting instruction regarding the parameters has been received from the remote system. (Appendix 14) A beam monitoring program for an information processing system that monitors a particle beam therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The beam monitoring program is configured to: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; a beam monitoring program for executing a parameter acquisition step for acquiring parameters to be set for each of the electromagnets, which can be used to adjust the beam orbit of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step. (Appendix 15) An information processing system for monitoring a particle beam therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The information processing system includes: a beam position information acquisition unit that acquires information on the beam position in each of the beam monitors; a parameter acquisition calculation unit that acquires parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition unit; Information processing system. (Appendix 16) 1. A particle beam system comprising: the particle beam system includes a particle beam therapy system and an information processing system that monitors the particle beam therapy system; the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The information processing system includes: a beam position information acquisition unit that acquires information on the beam position in each of the beam monitors; a parameter acquisition calculation unit that acquires parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition unit; Particle beam systems.

Claims

1. 1. A method for beam monitoring in a particle therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the accelerator includes, as the electromagnet, a plurality of bending electromagnets arranged in order along the circular path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; the beam monitoring method is executed by an information processing system; The beam monitoring method includes: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; an orbit comparison step of examining a positional relationship between a beam orbit of the charged particle beam orbiting near each of the beam monitors and a design beam orbit; a parameter acquisition step of acquiring parameters to be set in each of the electromagnets, which can be used to adjust a beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step; a parameter acquisition step being executed when the orbit comparison step determines that the beam orbit of the circulating charged particle beam near any of the beam monitors is located outside the designed beam orbit, or that the beam orbit of the circulating charged particle beam near any of the beam monitors is located inside the designed beam orbit.

2. 1. A method for beam monitoring in a particle therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the accelerator includes, as the electromagnets, a first bending electromagnet, a second bending electromagnet, a third bending electromagnet, and a fourth bending electromagnet that are installed in this order along the circular path; the accelerator includes a first beam monitor as the beam monitor, between the first bending electromagnet and the second bending electromagnet, the accelerator includes a second beam monitor as the beam monitor, between the third bending electromagnet and the fourth bending electromagnet, the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; the beam monitoring method is executed by an information processing system; The beam monitoring method includes: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; an orbit comparison step of comparing the beam position information in the first beam monitor with a normal value corresponding to a designed beam orbit, and comparing the beam position information in the second beam monitor with a normal value corresponding to the designed beam orbit; a parameter acquisition step of acquiring parameters to be set in each of the electromagnets, which can be used to adjust a beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step; the parameter acquisition step is executed when it is found by the trajectory comparison step that the beam trajectory of the circulating charged particle beam is located outside the designed beam trajectory both in the vicinity of the first beam monitor and the vicinity of the second beam monitor, or that the beam trajectory of the circulating charged particle beam is located inside the designed beam trajectory both in the vicinity of the first beam monitor and the vicinity of the second beam monitor, the beam monitoring method further comprising a parameter display output step of displaying or outputting the parameters acquired in the parameter acquisition step when it is found by the orbit comparison step that the beam orbit of the circulating charged particle beam is located outside the designed beam orbit both in the vicinity of the first beam monitor and the vicinity of the second beam monitor, or that the beam orbit of the circulating charged particle beam is located inside the designed beam orbit both in the vicinity of the first beam monitor and the vicinity of the second beam monitor.

3. 1. A method for beam monitoring in a particle therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; the beam monitoring method is executed by an information processing system; The beam monitoring method includes: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; a condition information acquisition step of acquiring, as condition information, at least one piece of information among an outside air temperature, a room temperature of a room containing the accelerator, a date or time, an amount of time elapsed since the start-up or restart of the particle beam therapy system, a temperature of cooling water, an amount of time elapsed since the construction of a building containing the accelerator, a position of an installation surface of the accelerator, parameters set in the electromagnet, and information on a time when parameters to be set in the electromagnet were previously adjusted; a cause estimation step of estimating, when a deviation of the beam orbit of the circulating charged particle beam from a normal value corresponding to a design beam orbit in any of the beam monitors falls outside a range in which adjustment is not required, a cause of the beam orbit of the circulating charged particle beam falling outside a range in which adjustment is not required, based on the beam position information in each of the beam monitors or the acquired condition information; A beam monitoring method comprising a parameter acquisition step of acquiring parameters to be set for each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step.

4. A beam monitoring method according to claim 3, comprising: a warm-up / cooling operation determination step for obtaining a determination result that it is desirable to perform a warm-up operation or a cooling operation, instead of or in addition to obtaining the parameters by the parameter acquisition step, when the causes estimated in the cause estimation step include either the elapsed time after startup or restart of the particle beam therapy system or the temperature of the cooling water.

5. 1. A method for beam monitoring in a particle therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; the beam monitoring method is executed by an information processing system; The beam monitoring method includes: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; a condition information acquisition step of acquiring, as condition information, at least one piece of information among an outside air temperature, a room temperature of a room containing the accelerator, a date or time, an amount of time elapsed since the start-up or restart of the particle beam therapy system, a temperature of cooling water, an amount of time elapsed since the construction of a building containing the accelerator, a position of an installation surface of the accelerator, parameters set in the electromagnet, and information on a time when parameters to be set in the electromagnet were previously adjusted; a parameter acquisition step of acquiring parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step; A beam monitoring method comprising a warm-up operation / cooling operation determination step that determines whether warm-up operation or cooling operation is necessary, or the type of warm-up operation or cooling operation to be performed, based on the beam position information in each of the beam monitors or the acquired condition information.

6. 1. A method for beam monitoring in a particle therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; the beam monitoring method is executed by an information processing system; The beam monitoring method includes: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; a parameter acquisition step of acquiring parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step; a beam position time series information acquisition step of acquiring time series information of the beam position in the beam monitor; a beam position time series information estimation step of estimating time series information of the beam position information in a future period based on the acquired time series information of the beam position information; A beam monitoring method comprising an adjustment timing determination step of determining when to adjust parameters to be set for the electromagnet based on time series information of estimated beam position information for a future period.

7. 1. A method for beam monitoring in a particle therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; the beam monitoring method is executed by an information processing system; The beam monitoring method includes: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; a parameter acquisition step of acquiring parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step; a beam position time series information acquisition step of acquiring time series information of the beam position in the beam monitor; a condition time-series information acquisition step of acquiring, as condition time-series information, time-series information consisting of at least one of information on the outside air temperature, the room temperature of the room in which the accelerator is housed, the date or time, the elapsed time since the start-up or restart of the particle beam therapy system, the temperature of the cooling water, the elapsed time since the construction of the building in which the accelerator is housed, the position of the installation surface of the accelerator, and parameters set in the electromagnet; an adjustment time information acquisition step of acquiring information on when parameters to be set to the electromagnet were previously adjusted as adjustment time information; A beam monitoring method comprising an adjustment timing determination step of determining when to adjust parameters to be set in the electromagnet based on the acquired time series information of the beam position, the acquired condition time series information, and the acquired adjustment timing information.

8. A beam monitoring program for an information processing system that monitors a particle beam therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The beam monitoring program is configured to: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; a parameter acquisition step of acquiring parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step; a beam position time series information acquisition step of acquiring time series information of the beam position in the beam monitor; a beam position time series information estimation step of estimating time series information of the beam position information in a future period based on the acquired time series information of the beam position information; A beam monitoring program for executing an adjustment timing determination step for determining when to adjust parameters to be set on the electromagnet based on time series information of estimated beam position information for a future period.

9. A beam monitoring program for an information processing system that monitors a particle beam therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The beam monitoring program is configured to: a beam position information acquisition step of acquiring information on the beam position in each of the beam monitors; a parameter acquisition step of acquiring parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition step; a beam position time series information acquisition step of acquiring time series information of the beam position in the beam monitor; a condition time-series information acquisition step of acquiring, as condition time-series information, time-series information consisting of at least one of information on the outside air temperature, the room temperature of the room in which the accelerator is housed, the date or time, the elapsed time since the start-up or restart of the particle beam therapy system, the temperature of the cooling water, the elapsed time since the construction of the building in which the accelerator is housed, the position of the installation surface of the accelerator, and parameters set in the electromagnet; an adjustment time information acquisition step of acquiring information on when parameters to be set to the electromagnet were previously adjusted as adjustment time information; a beam monitoring program for executing an adjustment timing determination step for determining when to adjust parameters to be set in the electromagnet based on the acquired time series information of the beam position, the acquired condition time series information, and the acquired adjustment timing information.

10. An information processing system for monitoring a particle beam therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The information processing system includes: a beam position information acquisition unit that acquires information on the beam position in each of the beam monitors; a condition information acquiring unit that acquires, as condition information, at least one piece of information among an outside air temperature, a room temperature of a room that houses the accelerator, a date or time, an amount of time that has elapsed since the start-up or restart of the particle beam therapy system, a temperature of cooling water, an amount of time that has elapsed since the construction of a building that houses the accelerator, a position of an installation surface of the accelerator, parameters that have been set in the electromagnet, and information on a time when parameters to be set in the electromagnet were adjusted in the past; a cause estimation unit that, when a deviation of the beam orbit of the circulating charged particle beam from a normal value corresponding to a design beam orbit in any of the beam monitors falls outside a range in which adjustment of the beam orbit of the circulating charged particle beam is not required, estimates a cause of the beam orbit of the circulating charged particle beam falling outside a range in which adjustment is not required based on the beam position information in each of the beam monitors or the acquired condition information; an information processing system comprising: a parameter acquisition calculation unit that acquires parameters to be set for each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition unit.

11. An information processing system for monitoring a particle beam therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The information processing system includes: a beam position information acquisition unit that acquires information on the beam position in each of the beam monitors; a condition information acquiring unit that acquires, as condition information, at least one piece of information among an outside air temperature, a room temperature of a room that houses the accelerator, a date or time, an amount of time that has elapsed since the start-up or restart of the particle beam therapy system, a temperature of cooling water, an amount of time that has elapsed since the construction of a building that houses the accelerator, a position of an installation surface of the accelerator, parameters that have been set in the electromagnet, and information on a time when parameters to be set in the electromagnet were adjusted in the past; a parameter acquisition calculation unit that acquires parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition unit; and An information processing system comprising a warm-up operation / cooling operation determination unit that determines whether warm-up operation or cooling operation is necessary, or the type of warm-up operation or cooling operation to be performed, based on the beam position information in each of the beam monitors or the acquired condition information.

12. An information processing system for monitoring a particle beam therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The information processing system includes: a beam position information acquisition unit that acquires information on the beam position in each of the beam monitors; a parameter acquisition calculation unit that acquires parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition unit; and An adjustment time determination unit, acquiring time-series information of the beam position in the beam monitor; estimating time series information of the beam position information in a future period based on the acquired time series information of the beam position information; an adjustment timing determination unit that determines a timing for adjusting parameters to be set in the electromagnet based on estimated time-series information of the beam position in a future period.

13. An information processing system for monitoring a particle beam therapy system, comprising: the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The information processing system includes: a beam position information acquisition unit that acquires information on the beam position in each of the beam monitors; a parameter acquisition calculation unit that acquires parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition unit; and An adjustment time determination unit, acquiring time-series information of the beam position in the beam monitor; acquire, as condition time-series information, time-series information consisting of at least one of the following information: outside air temperature, room temperature of the room in which the accelerator is housed, date or time, elapsed time since startup or restart of the particle beam therapy system, temperature of cooling water, elapsed time since construction of the building in which the accelerator is housed, position of the installation surface of the accelerator, and parameters set in the electromagnet; acquiring information on when the parameters to be set in the electromagnet were adjusted in the past as adjustment time information; an adjustment timing determination unit that determines when to adjust parameters to be set in the electromagnet based on the acquired time series information of the beam position, the acquired condition time series information, and the acquired adjustment timing information.

14. 1. A particle beam system comprising: the particle beam system includes a particle beam therapy system and an information processing system that monitors the particle beam therapy system; the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The information processing system includes: a beam position information acquisition unit that acquires information on the beam position in each of the beam monitors; a parameter acquisition calculation unit that acquires parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition unit; and An adjustment time determination unit, acquiring time-series information of the beam position in the beam monitor; estimating time series information of the beam position information in a future period based on the acquired time series information of the beam position information; a particle beam system comprising the adjustment timing determination unit that determines a timing for adjusting parameters to be set in the electromagnet based on estimated time series information of information on the beam position in a future period.

15. 1. A particle beam system comprising: the particle beam system includes a particle beam therapy system and an information processing system that monitors the particle beam therapy system; the particle beam therapy system includes an accelerator for accelerating a charged particle beam; the accelerator comprises a circulation path for circulating the charged particle beam, a plurality of beam monitors arranged in sequence along the circulation path, and a plurality of electromagnets arranged in sequence along the circulation path; the beam monitor acquires beam position information, which is the position of the charged particle beam when the circulating charged particle beam passes near the beam monitor; the electromagnet adjusts the beam trajectory of the circulating charged particle beam when the charged particle beam passes near the electromagnet; The information processing system includes: a beam position information acquisition unit that acquires information on the beam position in each of the beam monitors; a parameter acquisition calculation unit that acquires parameters to be set in each of the electromagnets, which can be used to adjust the beam trajectory of the circulating charged particle beam, based on the beam position information acquired by the beam position information acquisition unit; and An adjustment time determination unit, acquiring time-series information of the beam position in the beam monitor; acquire, as condition time-series information, time-series information consisting of at least one of the following information: outside air temperature, room temperature of the room in which the accelerator is housed, date or time, elapsed time since startup or restart of the particle beam therapy system, temperature of cooling water, elapsed time since construction of the building in which the accelerator is housed, position of the installation surface of the accelerator, and parameters set in the electromagnet; acquiring information on when the parameters to be set in the electromagnet were adjusted in the past as adjustment time information; a particle beam system comprising: the adjustment time determination unit that determines when to adjust parameters to be set in the electromagnet based on the acquired time series information of the beam position information, the acquired condition time series information, and the acquired adjustment time information.

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