Multi-source high-energy particle detection data fusion method and device

Abstract

The embodiment of the specification provides a multi-source high-energy particle detection data fusion method and device, wherein the multi-source high-energy particle detection data fusion method comprises the following steps: obtaining initial detection data, performing geomagnetic coordinate conversion based on the initial detection data to determine a coordinate conversion result; determining a baseline model based on the coordinate conversion result, determining a fluctuation model based on the baseline model; establishing a data calibration model based on the baseline model and the fluctuation model; determining calibration data based on the data calibration model, determining a calibration coefficient based on the calibration data; performing data conversion based on the calibration coefficient and the data calibration model to determine fusion data. By increasing the calibration of fluctuation data at the data calibration level, the fluctuation detection data can be retained in the data fusion aspect, and the method has a wider application scenario.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of particle detection technology, and in particular to a multi-source high-energy particle detection data fusion method. Background Art

[0002] High-energy particles in Earth orbit are affected by the Earth's magnetic field and solar activity, resulting in a dynamic, high-energy particle environment distribution near Earth with multiple overlapping periods of variation. Different on-orbit particle detection data will also be affected by these factors. Excluding differences in the design and hardware of each detector, the data detected at different spatial locations and in different space environments will still be affected by the environment and spatial location, resulting in certain differences. According to current technology, data fusion calibration of high-energy particle detection data is mainly based on the following process:

[0003] 1. Based on the geomagnetic adiabatic invariant relationship, data comparison and calibration are performed in geomagnetic coordinates: Since high-energy particles in Earth orbit are affected by the Earth's magnetic field, based on the geomagnetic adiabatic invariant relationship, it can be assumed that the particle distribution obeys the same rules in the geomagnetic BL coordinate system. Therefore, even if the detectors on satellites with different longitudes, latitudes, and altitudes in real space are located, their detection data can be calibrated if the B / B0 and L values ​​of their geomagnetic coordinate systems are relatively consistent;

[0004] 2. The calibration data needs to be cleaned. Due to the design of each detector, the background noise may vary to a certain extent. The data needs to be cleaned before calibration.

[0005] 3. The specific calibration is to select basically consistent BL positions in geomagnetic space according to the BL coordinate system results of the cleaned detection data, and perform linear regression on the data of two different satellite detectors to obtain the slope coefficient, R2, p value and other parameters that characterize the calibration effect.

[0006] The above methods have the following problems:

[0007] 1. In the traditional calibration process, only the intrinsic geomagnetic field is generally considered. Therefore, for satellites with higher orbital altitudes or satellites passing through the Earth's polar regions at higher latitudes, their magnetic field B values ​​are more heavily influenced by the external magnetic field. Traditional calibration will discard data with excessively fluctuating BL values ​​and only use the data with relatively stable BL values.

[0008] 2. In the traditional calibration process, data fluctuations are eliminated during data cleaning, resulting in the lack of environmental disturbance data in the calibration and fusion results.

[0009] 3. The traditional calibration process usually only considers data during a period of calm or a certain event (such as a solar proton event, a high-energy electron storm event), and generally does not consider long-term processes.

[0010] Therefore, a better solution is urgently needed. SUMMARY

[0011] Therefore, the embodiments of the present specification provide a multi-source high-energy particle detection data fusion method. One or more embodiments of the present specification also relate to a multi-source high-energy particle detection data fusion device, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects in the prior art.

[0012] According to a first aspect of the embodiments of the present specification, a multi-source high-energy particle detection data fusion method is provided, comprising:

[0013] Obtaining initial detection data, performing geomagnetic coordinate conversion based on the initial detection data, and determining a coordinate conversion result;

[0014] Determining a baseline model based on the coordinate conversion result, and determining a fluctuation model based on the baseline model;

[0015] Establishing a data calibration model based on the baseline model and the fluctuation model;

[0016] Determining calibration data based on the data calibration model, and determining a calibration coefficient based on the calibration data;

[0017] Performing data conversion based on the calibration coefficient and the data calibration model, and determining fusion data.

[0018] In one possible implementation, performing geomagnetic coordinate conversion based on the initial detection data to determine the coordinate conversion result comprises:

[0019] Determining an internal source magnetic field and an external source magnetic field, and determining a mapping relationship based on the internal source magnetic field and the external source magnetic field;

[0020] Determining the coordinate conversion result based on the mapping relationship.

[0021] In one possible implementation, determining the baseline model based on the coordinate conversion result comprises:

[0022] Determining baseline data based on the coordinate conversion result;

[0023] Constructing a regular model corresponding to each energy parameter based on the baseline data;

[0024] Performing weighted superposition based on the regular model to determine the baseline model.

[0025] In one possible implementation, determining the fluctuation model based on the baseline model comprises:

[0026] determine fluctuation data based on the baseline model;

[0027] perform data model construction based on the fluctuation data and the machine learning model, and determine a fluctuation model.

[0028] In a possible implementation, the data calibration model is established based on the baseline model and the fluctuation model, and includes:

[0029] superimpose the baseline model and the fluctuation model to obtain the data calibration model.

[0030] In a possible implementation, the calibration coefficient is determined based on the calibration data, and includes:

[0031] determine a calibration object based on the calibration data;

[0032] determine the calibration coefficient based on the calibration object through linear regression.

[0033] In a possible implementation, the initial detection data includes detection data corresponding to at least two proton detectors;

[0034] Correspondingly, the data is converted based on the calibration coefficient and the data calibration model to determine fusion data, and includes:

[0035] determine a first calibration coefficient corresponding to a first detector in the proton detector and a first data calibration model, convert detection data of the first detector based on the first calibration coefficient and the first data calibration model, and determine first detection data;

[0036] determine a second calibration coefficient corresponding to a second detector in the proton detector and a second data calibration model, convert detection data of the second detector based on the second calibration coefficient and the second data calibration model, and determine second detection data;

[0037] determine fusion data based on the first detection data and the second detection data.

[0038] According to a second aspect of an embodiment of the present specification, a multi-source high-energy particle detection data fusion device is provided, and includes:

[0039] The coordinate conversion module is configured to obtain initial detection data, perform geomagnetic coordinate conversion based on the initial detection data, and determine a coordinate conversion result.

[0040] The fluctuation model module is configured to determine a baseline model based on the coordinate conversion result, and determine a fluctuation model based on the baseline model.

[0041] The calibration model module is configured to establish a data calibration model based on the baseline model and the fluctuation model.

[0042] A calibration coefficient module is configured to determine calibration data based on a data calibration model and to determine a calibration coefficient based on the calibration data;

[0043] The data fusion module is configured to perform data conversion based on the calibration coefficient and the data calibration model to determine the fused data.

[0044] According to a third aspect of an embodiment of this specification, a computing device is provided, including:

[0045] memory and processor;

[0046] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-source high-energy particle detection data fusion method are implemented.

[0047] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions. When the instructions are executed by a processor, the steps of the multi-source high-energy particle detection data fusion method are implemented.

[0048] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned multi-source high-energy particle detection data fusion method.

[0049] The embodiments of this specification provide a multi-source high-energy particle detection data fusion method and device, wherein the multi-source high-energy particle detection data fusion method includes: obtaining initial detection data, performing geomagnetic coordinate conversion based on the initial detection data, and determining the coordinate conversion result; determining a baseline model based on the coordinate conversion result, and determining a fluctuation model based on the baseline model; establishing a data calibration model based on the baseline model and the fluctuation model; determining calibration data based on the data calibration model, and determining a calibration coefficient based on the calibration data; performing data conversion based on the calibration coefficient and the data calibration model, and determining fused data. By adding calibration of fluctuation data at the data calibration level, the fluctuating detection data can be retained in data fusion, which has a wider range of application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a multi-source high-energy particle detection data fusion method provided by one embodiment of this specification;

[0051] Figure 2 This is a schematic structural diagram of a multi-source high-energy particle detection data fusion device provided by one embodiment of this specification;

[0052] Figure 3 This is a structural block diagram of a computing device provided by one embodiment of this specification. DETAILED DESCRIPTION

[0053] The following description sets forth many specific details to facilitate a thorough understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0054] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a," "an," and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0055] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0056] In this specification, a multi-source high-energy particle detection data fusion method is provided. This specification also involves a multi-source high-energy particle detection data fusion device, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0057] See also Figure 1 , Figure 1 A flow chart of a multi-source high-energy particle detection data fusion method provided according to an embodiment of this specification is shown, which specifically includes the following steps.

[0058] Step 101: Acquire initial detection data, perform geomagnetic coordinate conversion based on the initial detection data, and determine the coordinate conversion result.

[0059] In one possible implementation, geomagnetic coordinate conversion is performed based on initial detection data to determine the coordinate conversion result, including: determining the endogenous magnetic field and the exogenous magnetic field, determining a mapping relationship based on the endogenous magnetic field and the exogenous magnetic field; and determining the coordinate conversion result based on the mapping relationship.

[0060] In practical applications, since charged particles in Earth's orbital space basically follow three geomagnetic adiabatic invariants, it is necessary to convert the latitude, longitude, and altitude parameters into geomagnetic coordinates for a more accurate description. When performing geomagnetic coordinate conversion, it is necessary to combine the internal magnetic field model with the external magnetic field model. That is, to construct the mapping relationship {Lat(t), Long(t), Alt(t)}→{B→ inner (t)+ B→ outer (t)}→{|B|(t),B0(t),L(t)}. Where B→ is the magnetic field vector at the corresponding position, which is respectively composed of the internal magnetic field B→ inner With external magnetic field B→ outer The superposition of the two is calculated using an internal magnetic field model and an external magnetic field model. The internal magnetic field can be derived from geomagnetic field models such as IGRF, while the external magnetic field can be derived from magnetic field models such as T96 and TA16. |B| is the modulus of the magnetic field strength at that location, B0 is the magnetic field strength from the magnetic field lines at that location to the magnetic equator, and L is the distance from the magnetic field lines at that location to the center of the Earth at the magnetic equator. B0 and L are calculated using magnetic field line tracing.

[0061] Specifically, based on the detection data obtained by two on-orbit high-energy proton detectors, the high-energy particle detection dataset DF is formed by processing the data using the detector's geometric factors. i =Data(UT(time),>Energy, Counts i , {X Positions}). Where i={1,2}. The position coordinate dataset DP can be extracted i =Data( UT(time), {X Positions})= Data( UT(time), { Lat, Long, Alt}). Use a variety of spatial environment data to build a spatial environment dataset with time stamps DE=Data(UT(time), {X Environments}).

[0062] To convert geomagnetic coordinates, use X Positions Information, using the endogenous field model to obtain B→ inner For example, when using the IGRF13 geomagnetic field model, DM1 i = Data (UT(time), IGRF({Lat,Long,Alt}, Year(time))); use the external field model to obtain B→ outer For example, when using the T96 external source field model, {X Environments}Including the Dst index, solar wind speed, and solar wind dynamic pressure at each UT (time). For missing or low-resolution spatial environmental data, interpolation can be used to obtain the corresponding value at the time. That is, DM2 i = Data (UT(time), T96({Lat,Long,Alt}, Dst, SW speed , SW preasure )). Integration of DM1 i and DM2 i Data that forms the magnetic field B → value: DM i (B)=DM1 i (B)+DM2 i (B), and based on the constructed global geomagnetic field DM i The vector B→data is used to track the magnetic field lines to obtain the distance between the magnetic field lines at the magnetic equator and the center of the earth, which is recorded as L value. The ratio of |B| to the magnetic field |B0| at this position can be used to obtain B / B0, thus constructing DM i (B, L)={B, L, B / B0}.

[0063] Step 102: Determine a baseline model based on the coordinate transformation result, and determine a fluctuation model based on the baseline model.

[0064] In one possible implementation, determining a baseline model based on a coordinate transformation result includes: determining baseline data based on the coordinate transformation result; constructing a regularity model corresponding to each energy parameter based on the baseline data; and performing weighted superposition based on the regularity model to determine the baseline model.

[0065] In practical applications, by performing coordinate transformation, the data of high-energy charged particles becomes Flux base (E, t, B / B0, L), so according to the Kolmogorov-Arnold superposition theorem, we can construct the regular model of each energy E under B / B0 or ​​L coordinate transformation, and perform weighted superposition to form the final high-energy particle environment change model with geomagnetic coordinates Fit base (E,B / B0,L).

[0066] Specifically, join DM i Update the detection data set to DF1 i = Data(UT(time),>Energy,Counts, B / B0, L, Lat,Long,Alt). Construct a generalized linear correlation model so that Fit i : DF1 i (Counts i )~DF1 i(>Energy, B / B0, L, Lat, Long, Alt), for model Fit i Iterative optimization is performed based on AIC (Akaike Information Criteria) or other similar features to reduce the irrelevant position factors, and finally the high-energy particle environment baseline model Fit is obtained. base_i .

[0067] In one possible implementation, determining a fluctuation model based on a baseline model includes: determining fluctuation data based on the baseline model; and constructing a data model based on the fluctuation data and a machine learning model to determine the fluctuation model.

[0068] In practical applications, it can be considered that the high-energy particle environment is formed by the superposition of the baseline environment and the fluctuation environment, so the high-energy particle fluctuation part data Flux can be extracted env =Flux all -Flux base . And through machine learning models, such as generalized linear correlation, random forest and other methods, to build Fit:F env ~{e rel} model, where e rel It is an environmental parameter with a high correlation to the fluctuation data. Use the existing data to obtain the relevant weights of the model and establish Flux env (E,t)=Fit env (E, e rel (t)).

[0069] Specifically, through the high-energy particle environment baseline model Fit base_i Can calculate fluctuation data Flux env_i (>Energy,time)= DF i (>Energy,Counts i )- Fit base_i (>Energy, B / B0, L, Lat, Long, Alt). And use machine learning models to perform Flux env_i Training. In the embodiment, the random forest algorithm is used for training, the number of trees in the forest is 100, and the eigenvalue M is 3. Finally, the high-energy particle environment fluctuation model Fit is obtained. env_i .

[0070] Step 103: Establish a data calibration model based on the baseline model and the fluctuation model.

[0071] In a possible implementation, a data calibration model is established based on the baseline model and the fluctuation model, including: superimposing the baseline model and the fluctuation model to obtain the data calibration model.

[0072] In practical applications, combining the above two parts of data, we can get Fit all (E,B / B0,L,e rel (t))= Fit base (E,B / B0,L)+ Fit env (E, e rel (t)).

[0073] Specifically, build a particle data calibration model Fit i (E,B / B0,L,e rel (t))= Fit base_i (E,B / B0,L)+Fit env_i (E, e rel (t)).

[0074] Step 104: Determine calibration data based on the data calibration model, and determine calibration coefficients based on the calibration data.

[0075] In a possible implementation, determining the calibration coefficient based on the calibration data includes: determining a calibration object based on the calibration data; and determining the calibration coefficient based on the calibration object through linear regression.

[0076] In practical applications, for each detector data, a set of Fit i (E,B / B0,L,e rel (t)). i=[1,..,n], where n is the number of satellite payloads that need to be calibrated.

[0077] For the raw data DF of each load i =Data(UTtime,Pos,E,Count), where UTtime is the universal time stamp, Pos is the latitude and longitude position information, E is the energy channel information, and Count is the particle count information. i Transform the data: DF i '=Data(E,Count i ', B / B0(Grid),L(Grid),e rel (UTtime))= Data(E, Fit i (E, B / B0(Grid), L(Grid), e rel (UTtime))). Grid is a sequence formed by discretizing the maximum range of B / B0, L covered by multiple detector payload tracks.

[0078] Furthermore, for DF i 'Data Count i 'Perform calibration, at this time each DFi The technical correction value has three main dimensions B / B0, L, e rel . Wherein since B / B0 and L are both discretized by the same set of Grid, they have consistent calibration standards, e rel Then since there is a difference in time, it has multiple states, but when the detection time span is relatively long (generally not less than 1 year), the environmental data distribution also tends to be consistent. When calibrating, since B / B0, L and e rel Based on Fit i The transformation process is equivalent to dimension reduction, so the transformed Count i ' data can be used as the calibration object. Assuming that the data of detector numbered i=1 is taken as the reference, detectors i=[2,..,n] all need to be calibrated with it. Then linear regression is used to construct Count i '~w i ·Count1'+ε i , the calibration coefficient A i =[w i , ε i ] can be obtained, wherein A1=[1,0].

[0079] Specifically, generate particle calibration data for each detector:

[0080] DF1'=Data(E,Count1', B / B0(Grid),L(Grid),e rel (Uttime1))= Data(E, Fit1(E,B / B0(Grid), L(Grid), e rel (Uttime1))),DF2'=Data(E,Count2', B / B0(Grid), L(Grid),e rel (Uttime2)) = Data(E, Fit2(E, B / B0(Grid), L(Grid), e rel (Uttime2)))。

[0081] Further, data calibration is performed: taking Count1' as the reference, let A1=[1,0], then linear regression Count2'~w2·Count1' + ε2, obtain A2=[w2, ε2].

[0082] Step 105: based on the calibration coefficient and the data calibration model, data conversion is performed to determine the fusion data.

[0083] In one possible implementation, the initial detection data includes detection data corresponding to at least two proton detectors; accordingly, data conversion is performed based on the calibration coefficient and the data calibration model to determine the fusion data, including: determining a first calibration coefficient and a first data calibration model corresponding to a first detector in the proton detector, converting the detection data of the first detector based on the first calibration coefficient and the first data calibration model to determine the first detection data; determining a second calibration coefficient and a second data calibration model corresponding to a second detector in the proton detector, converting the detection data of the second detector based on the second calibration coefficient and the second data calibration model to determine the second detection data; and determining the fusion data based on the first detection data and the second detection data.

[0084] In practical applications, after obtaining the calibration coefficient A i After that, we can use the normalized model Fit obtained by each detector i Combined with A i To convert the data:

[0085] DF i =Data(UTtime,Pos,E,Count)→DF i '=Data(E,Count i ', B / B0(UTtime),L(UTtime),e rel (UTtime)) →DF i ' fix =Data(E,( Count i '-ε i ) / w i , B / B0(UTtime),L(UTtime),e rel (UTtime)). Finally, the fusion data DF is formed total =Data(DF i ' fix ), i=[1,..,n].

[0086] Specifically, perform data fusion: DF1' fix = DF1', DF2' fix =Data(E,(Count2'-ε2) / w i , B / B0(Uttime2), L(Uttime2), e rel (Uttime2)), DF total =Data(DF1' fix , DF2' fix ) .

[0087] The embodiments of this specification do not directly select benchmark-consistent data from existing data for calibration. Instead, they use the existing data to form a continuous response model, and use the model to generate more benchmark-consistent secondary data for calibration. Calibration does not require the removal of fluctuating or benchmark-inconsistent data, allowing more data to provide information for calibration. The fused data after calibration can include more data, eliminating the need to remove or discard fluctuating or benchmark-inconsistent data. This allows the original calibration base data to be retained while also expanding the calibration and fusion of data in cases of benchmark inconsistencies (such as mismatched geomagnetic B / L coordinate ranges or mismatched space environments), effectively utilizing in-orbit exploration data.

[0088] The embodiments of this specification provide a multi-source high-energy particle detection data fusion method and device, wherein the multi-source high-energy particle detection data fusion method includes: obtaining initial detection data, performing geomagnetic coordinate conversion based on the initial detection data, and determining the coordinate conversion result; determining a baseline model based on the coordinate conversion result, and determining a fluctuation model based on the baseline model; establishing a data calibration model based on the baseline model and the fluctuation model; determining calibration data based on the data calibration model, and determining a calibration coefficient based on the calibration data; performing data conversion based on the calibration coefficient and the data calibration model, and determining fused data. By adding calibration of fluctuation data at the data calibration level, the fluctuating detection data can be retained in data fusion, which has a wider range of application scenarios.

[0089] Corresponding to the above method embodiment, this specification also provides an embodiment of a multi-source high-energy particle detection data fusion device, Figure 2 FIG1 shows a schematic diagram of the structure of a multi-source high-energy particle detection data fusion device provided by an embodiment of this specification. Figure 2 As shown, the device includes:

[0090] The coordinate conversion module 201 is configured to obtain initial detection data, perform geomagnetic coordinate conversion based on the initial detection data, and determine a coordinate conversion result;

[0091] The fluctuation model module 202 is configured to determine a baseline model based on the coordinate transformation result, and determine a fluctuation model based on the baseline model;

[0092] The calibration model module 203 is configured to establish a data calibration model based on the baseline model and the fluctuation model;

[0093] A calibration coefficient module 204 is configured to determine calibration data based on a data calibration model and to determine calibration coefficients based on the calibration data;

[0094] The data fusion module 205 is configured to perform data conversion based on the calibration coefficient and the data calibration model to determine fused data.

[0095] In a possible implementation, the coordinate conversion module 201 is further configured to:

[0096] Determining an internal magnetic field and an external magnetic field, and determining a mapping relationship based on the internal magnetic field and the external magnetic field;

[0097] The coordinate transformation result is determined based on the mapping relationship.

[0098] In a possible implementation, the fluctuation model module 202 is further configured to:

[0099] Determine baseline data based on the coordinate transformation results;

[0100] Build regularity models corresponding to various energy parameters based on baseline data;

[0101] Perform weighted superposition based on regular models to determine the baseline model.

[0102] In a possible implementation, the fluctuation model module 202 is further configured to:

[0103] Determine fluctuation data based on the baseline model;

[0104] Build a data model based on fluctuation data and machine learning model to determine the fluctuation model.

[0105] In a possible implementation, the calibration model module 203 is further configured to:

[0106] The baseline model and the fluctuation model are superimposed to obtain the data calibration model.

[0107] In a possible implementation, the calibration coefficient module 204 is further configured to:

[0108] Determine the calibration object based on the calibration data;

[0109] The calibration coefficients are determined by linear regression based on the calibration object.

[0110] In a possible implementation, the data fusion module 205 is further configured to:

[0111] The initial detection data includes detection data corresponding to at least two proton detectors;

[0112] Accordingly, data conversion is performed based on the calibration coefficient and data calibration model to determine the fused data, including:

[0113] Determining a first calibration coefficient and a first data calibration model corresponding to a first detector in the proton detector, and converting detection data of the first detector based on the first calibration coefficient and the first data calibration model to determine first detection data;

[0114] Determining a second calibration coefficient and a second data calibration model corresponding to a second detector in the proton detector, converting detection data of the second detector based on the second calibration coefficient and the second data calibration model to determine second detection data;

[0115] Fused data is determined based on the first detection data and the second detection data.

[0116] The embodiments of this specification provide a multi-source high-energy particle detection data fusion method and device, wherein the multi-source high-energy particle detection data fusion device includes: obtaining initial detection data, performing geomagnetic coordinate conversion based on the initial detection data, and determining the coordinate conversion result; determining a baseline model based on the coordinate conversion result, and determining a fluctuation model based on the baseline model; establishing a data calibration model based on the baseline model and the fluctuation model; determining calibration data based on the data calibration model, and determining a calibration coefficient based on the calibration data; performing data conversion based on the calibration coefficient and the data calibration model, and determining fused data. By adding calibration of fluctuation data at the data calibration level, the fluctuating detection data can be retained in data fusion, which has a wider range of application scenarios.

[0117] The above is a schematic diagram of a multi-source high-energy particle detection data fusion device according to this embodiment. It should be noted that the technical solution of this multi-source high-energy particle detection data fusion device and the technical solution of the multi-source high-energy particle detection data fusion method described above share the same concept. For details not described in detail in the technical solution of the multi-source high-energy particle detection data fusion device, please refer to the description of the technical solution of the multi-source high-energy particle detection data fusion method described above.

[0118] Figure 3 The block diagram of a computing device 300 according to one embodiment of the present disclosure is shown. Components of the computing device 300 include, but are not limited to, a memory 310 and a processor 320. The processor 320 is connected to the memory 310 via a bus 330, and a database 350 is used to store data.

[0119] Computing device 300 also includes an access device 340 that enables computing device 300 to communicate via one or more networks 360. Examples of such networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. Access device 340 may include one or more of any type of network interface (e.g., a network interface card (NIC)) whether wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, or a near field communication (NFC) interface.

[0120] In one embodiment of the present specification, the above components of the computing device 300 and Figure 3 Other components not shown in the figure may also be connected to each other, for example, via a bus. Figure 3 The computing device structure block diagram shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art may add or replace other components as needed.

[0121] Computing device 300 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, personal digital assistant, laptop computer, notebook computer, netbook computer, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or personal computer (PC). Computing device 300 can also be a mobile or stationary server.

[0122] The processor 320 is configured to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the multi-source high-energy particle detection data fusion method. The above is a schematic diagram of a computing device according to this embodiment. It should be noted that the technical solution of the computing device and the technical solution of the multi-source high-energy particle detection data fusion method described above are based on the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the multi-source high-energy particle detection data fusion method described above.

[0123] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the multi-source high-energy particle detection data fusion method are implemented.

[0124] The above is a schematic diagram of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium is based on the same concept as the technical solution of the multi-source high-energy particle detection data fusion method described above. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the multi-source high-energy particle detection data fusion method described above.

[0125] An embodiment of the present specification further provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned multi-source high-energy particle detection data fusion method.

[0126] The above is an illustrative embodiment of a computer program. It should be noted that the technical solution of this computer program is based on the same concept as the technical solution of the multi-source high-energy particle detection data fusion method described above. For details not described in detail in the technical solution of the computer program, please refer to the description of the technical solution of the multi-source high-energy particle detection data fusion method described above.

[0127] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0128] The computer instructions include computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium may be appropriately increased or decreased based on the requirements of legislation and patent practice within a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0129] It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0130] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] The preferred embodiments disclosed above are intended only to help illustrate this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made based on the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-source high-energy particle detection data fusion method, characterized in that: include: Acquiring initial detection data, performing geomagnetic coordinate conversion based on the initial detection data, and determining a coordinate conversion result; Determine a baseline model based on the coordinate transformation result, and determine a fluctuation model based on the baseline model; Establishing a data calibration model based on the baseline model and the fluctuation model; Determine calibration data based on the data calibration model, and determine calibration coefficients based on the calibration data; Performing data conversion based on the calibration coefficient and the data calibration model to determine fused data; Determining a baseline model based on the coordinate transformation result includes: determining baseline data based on the coordinate transformation result; Constructing a regularity model corresponding to each energy parameter based on the baseline data; Perform weighted superposition based on the regular model to determine a baseline model; Determining a fluctuation model based on the baseline model includes: determining fluctuation data based on the baseline model; Building a data model based on the fluctuation data and the machine learning model to determine a fluctuation model; Establishing a data calibration model based on the baseline model and the fluctuation model includes: Superimposing the baseline model and the fluctuation model to obtain the data calibration model; Determining the calibration coefficient based on the calibration data includes: determining a calibration object based on the calibration data; The calibration coefficients are determined by linear regression based on the calibration object.

2. The method according to claim 1, characterized in that The performing geomagnetic coordinate conversion based on the initial detection data and determining the coordinate conversion result includes: Determining an internal magnetic field and an external magnetic field, and determining a mapping relationship based on the internal magnetic field and the external magnetic field; A coordinate conversion result is determined based on the mapping relationship.

3. The method according to claim 1, characterized in that The initial detection data includes detection data corresponding to at least two proton detectors; Accordingly, performing data conversion based on the calibration coefficient and the data calibration model to determine fused data includes: determining a first calibration coefficient and a first data calibration model corresponding to a first detector in the proton detector, and converting detection data of the first detector based on the first calibration coefficient and the first data calibration model to determine first detection data; determining a second calibration coefficient and a second data calibration model corresponding to a second detector in the proton detector, and converting detection data of the second detector based on the second calibration coefficient and the second data calibration model to determine second detection data; Fused data is determined based on the first detection data and the second detection data.

4. A multi-source high-energy particle detection data fusion device, characterized in that: The steps for implementing the multi-source high-energy particle detection data fusion method according to any one of claims 1 to 3 include: a coordinate conversion module configured to obtain initial detection data, perform geomagnetic coordinate conversion based on the initial detection data, and determine a coordinate conversion result; a fluctuation model module configured to determine a baseline model based on the coordinate transformation result, and determine a fluctuation model based on the baseline model; a calibration model module, configured to establish a data calibration model based on the baseline model and the fluctuation model; a calibration coefficient module, configured to determine calibration data based on the data calibration model, and to determine calibration coefficients based on the calibration data; The data fusion module is configured to perform data conversion based on the calibration coefficient and the data calibration model to determine fused data.

5. A computing device, characterized in that include: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-source high-energy particle detection data fusion method described in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the multi-source high-energy particle detection data fusion method according to any one of claims 1 to 3.

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