Data processing method, device and equipment applied to electromagnetic navigation system

Abstract

The invention relates to a data processing method, device and equipment applied to an electromagnetic navigation system, and the method comprises the steps: determining a sample actual measurement magnetic field data set in actual measurement magnetic field data, and the sample actual measurement magnetic field data set comprises a plurality of pieces of sample actual measurement magnetic field data; for each piece of sample actual measurement magnetic field data in the sample actual measurement magnetic field data group, obtaining sample interpolation state data through an interpolation function according to the temporary interpolation parameter and a plurality of pieces of actual measurement state data in a sample time window corresponding to the sample actual measurement magnetic field data; determining sample theoretical magnetic field data corresponding to the sample actual measurement magnetic field data according to the sample interpolation state data; and optimizing the temporary interpolation parameters to obtain candidate interpolation parameters corresponding to a sample actual measurement magnetic field data group by taking minimization of differences between multiple groups of corresponding sample theoretical magnetic field data and sample actual measurement magnetic field data as an optimization target. Through the candidate interpolation parameters, the problem that data phase difference exists in an existing electromagnetic navigation system can be solved.

Description

Technical Field

[0001] This application relates to the field of electromagnetic navigation, and in particular, to a data processing method, apparatus, and device applied to an electromagnetic navigation system. Background Art

[0002] An electromagnetic navigation system (electro-magnetic transient simulator, EMTS) is one of the mainstream technical solutions for surgical navigation systems. The most typical basic principle of EMTS is to generate a time-varying magnetic field through a field emitter, and after detecting the time-varying magnetic field through a magnetic sensor, perform pose calculation of the magnetic sensor. The phase method is a commonly used positioning calculation method. This method first needs to calculate the theoretical magnetic field data corresponding to different moments according to the state data of the field emitter at different moments, and then calculate the target pose of the magnetic sensor at the corresponding moment according to the theoretical magnetic field data and the measured magnetic field data at the same moment.

[0003] However, it is very difficult to achieve phase consistency between the data of the state sensor of the field emitter and the data of the magnetic sensor on the tracking target, that is, there is a phase difference between the measured state data output by the state sensor and the measured magnetic field data output by the magnetic sensor. When there is a phase difference between the above two types of data, it will cause misalignment between the theoretical magnetic field data and the measured magnetic field data, resulting in positioning errors in the electromagnetic navigation system. Further, some state sensors measure the state changes of the field emitter. At this time, the theoretical magnetic field data is calculated according to the measured state data and the initial state data of the field emitter. Therefore, when there is a calibration error in the initial state data of the field emitter, it will also cause misalignment between the theoretical magnetic field data and the measured magnetic field data, thereby further increasing the data phase difference of the electromagnetic navigation system and ultimately increasing the positioning error of the electromagnetic navigation system.

[0004] Regarding the problem of data phase difference in the current electromagnetic navigation system, which ultimately leads to positioning errors in the electromagnetic navigation system, no effective solution has been proposed yet. Summary of the Invention

[0005] In the present invention, a data processing method, apparatus, and device applied to an electromagnetic navigation system are provided to solve the problem of data phase difference in the current electromagnetic navigation system, which ultimately leads to positioning errors in the electromagnetic navigation system.

[0006] In a first aspect, the present invention provides a data processing method applied to an electromagnetic navigation system. The electromagnetic navigation system includes a field emitter and a tracked magnetic sensor. The data processing method includes:

[0007] Real-time obtain the measured state data of the field emitter and the measured magnetic field data of the magnetic sensor;

[0008] Determine a group of measured sample magnetic field data from the measured magnetic field data, where the group of measured sample magnetic field data includes multiple measured sample magnetic field data;

[0009] For each of the measured sample magnetic field data in the group of measured sample magnetic field data, according to the temporary interpolation parameters and multiple measured state data within the sample time window corresponding to the measured sample magnetic field data, obtain sample interpolated state data through an interpolation function, and determine sample theoretical magnetic field data corresponding to the measured sample magnetic field data according to the sample interpolated state data;

[0010] Taking minimizing the difference between multiple corresponding groups of the sample theoretical magnetic field data and the measured sample magnetic field data as the optimization objective, optimize the temporary interpolation parameters to obtain candidate interpolation parameters corresponding to the group of measured sample magnetic field data.

[0011] In some embodiments, there are multiple groups of the group of measured sample magnetic field data; the data processing method further includes:

[0012] Determine target interpolation parameters according to the candidate interpolation parameters corresponding to multiple groups of the group of measured sample magnetic field data.

[0013] In some embodiments, the step of determining a group of measured sample magnetic field data from the measured magnetic field data includes:

[0014] Determine multiple groups of the group of measured sample magnetic field data corresponding to different moments from the measured magnetic field data within a real-time time window;

[0015] The data processing method further includes:

[0016] Determine current target interpolation parameters in real time according to the candidate interpolation parameters corresponding to multiple groups of the group of measured sample magnetic field data within the real-time time window.

[0017] In some embodiments, the data processing method further includes:

[0018] Determine a group of target measured magnetic field data from the measured magnetic field data, where the group of target measured magnetic field data includes multiple target measured magnetic field data;

[0019] For each of the target measured magnetic field data in the group of target measured magnetic field data, according to the target interpolation parameters and several measured state data within the target time window corresponding to the target measured magnetic field data, obtain target interpolated state data corresponding to the target measured magnetic field data through the interpolation function;

[0020] Determine the target pose of the magnetic sensor according to multiple corresponding groups of the target measured magnetic field data and the target interpolated state data.

[0021] In some of these embodiments, the data processing method further includes:

[0022] Determine a target measured magnetic field data group corresponding to the current moment in the measured magnetic field data, where the target measured magnetic field data group includes multiple target measured magnetic field data;

[0023] For each target measured magnetic field data in the target measured magnetic field data group, according to the current target interpolation parameter and several measured state data within the target time window corresponding to the target measured magnetic field data, obtain target interpolation state data corresponding to the target measured magnetic field data through the interpolation function;

[0024] Determine the current target pose of the magnetic sensor according to multiple sets of corresponding target measured magnetic field data and the target interpolation state data.

[0025] In some of these embodiments, the data processing method further includes:

[0026] Judge whether there is magnetic field interference in the electromagnetic navigation system according to the difference between the candidate interpolation parameter corresponding to the sample measured magnetic field data group corresponding to the current moment and the previous target interpolation parameter.

[0027] In some of these embodiments, the field emitter includes an electromagnetic coil, and the measured state data includes the working current or working voltage of the electromagnetic coil;

[0028] Alternatively, the field emitter includes a magnet, and the measured state data includes the rotation angle or rotation distance of the magnet.

[0029] In a second aspect, the present invention provides a data processing device applied to an electromagnetic navigation system. The electromagnetic navigation system includes a field emitter and a tracked magnetic sensor. The data processing device is characterized in that the data processing device includes:

[0030] A data acquisition module for real-time acquiring the measured state data of the field emitter and the measured magnetic field data of the magnetic sensor;

[0031] A data preparation module for determining a sample measured magnetic field data group in the measured magnetic field data, where the sample measured magnetic field data group includes multiple sample measured magnetic field data;;

[0032] A data calculation module, configured to, for each of the measured magnetic field data samples in the measured magnetic field data group of samples, obtain interpolated state data of the sample according to the temporary interpolation parameters and a plurality of the measured state data within the sample time window corresponding to the measured magnetic field data of the sample through an interpolation function, and determine theoretical magnetic field data of the sample corresponding to the measured magnetic field data of the sample according to the interpolated state data of the sample;

[0033] A parameter optimization module, configured to optimize the temporary interpolation parameters with the objective of minimizing the difference between multiple groups of corresponding theoretical magnetic field data of the sample and the measured magnetic field data of the sample, so as to obtain candidate interpolation parameters corresponding to the measured magnetic field data group of samples.

[0034] In a third aspect, a data processing device applied to an electromagnetic navigation system is provided in the present invention, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the data processing method applied to the electromagnetic navigation system described in the first aspect above is implemented.

[0035] In a fourth aspect, a storage medium is provided in the present invention, on which a computer program is stored. When the program is executed by a processor, the data processing method applied to the electromagnetic navigation system described in the first aspect above is implemented.

[0036] Compared with the related art, in the data processing method, device, and equipment applied to the electromagnetic navigation system provided in the present invention, the interpolated state data corresponding to each measured magnetic field data can be interpolated through the target interpolation parameters or candidate interpolation parameters, and the phase difference between the two is much smaller than the phase difference between the measured magnetic field data and the measured state data corresponding thereto. Therefore, according to the measured magnetic field data and the interpolated state data corresponding thereto, compared with according to the measured magnetic field data and the measured state data corresponding thereto, the pose of the magnetic sensor can be calculated more accurately. Furthermore, through the data processing method applied to the electromagnetic navigation system provided in the present invention, target interpolation parameters or candidate interpolation parameters for characterizing the data phase difference can be obtained. Based on the target interpolation parameters or candidate interpolation parameters, the problem of data phase difference existing in the current electromagnetic navigation system can be solved, and finally the positioning error problem of the electromagnetic navigation system can be solved.

[0037] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the present application, and form a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application, and do not constitute an improper limitation to the present application. In the drawings:

[0039] Figure 1 It is a hardware structure block diagram of a terminal that executes the data processing method provided by the present invention and applied to an electromagnetic navigation system;

[0040] Figure 2 It is a flowchart of the data processing method provided by the present invention and applied to an electromagnetic navigation system;

[0041] Figure 3 It is a flowchart of the data processing method provided by the present invention in an embodiment and applied to an electromagnetic navigation system;

[0042] Figure 4 It is a flowchart of the data processing method provided by the present invention in an embodiment and applied to an electromagnetic navigation system;

[0043] Figure 5 It is a flowchart of the data processing method provided by the present invention in an embodiment and applied to an electromagnetic navigation system;

[0044] Figure 6 It is a schematic diagram of the acquisition trajectory of measured state data in a specific embodiment of the present invention;

[0045] Figure 7 It is a flowchart of the data processing method of the electromagnetic navigation system in a specific embodiment of the present invention;

[0046] Figure 8 It is a structure block diagram of the data processing device provided by the present invention and applied to an electromagnetic navigation system. Detailed implementation manners

[0047] To more clearly understand the purpose, technical solution, and advantages of the present application, the present application will be described and explained below with reference to the accompanying drawings and embodiments.

[0048] Unless otherwise defined, technical terms or scientific terms involved in this application shall have the general meanings understood by those with ordinary skills in the technical field to which this application belongs. In this application, words such as "a", "an", "one kind", "the", "these", etc. do not indicate a limitation in quantity, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent in these processes, methods, products or devices. The terms "connect", "be connected", "couple" and other similar words involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "plurality" involved in this application means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist simultaneously, and B exists alone. Usually, the character " / " indicates that the objects associated before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific sorting of the objects.

[0049] The method embodiment provided in this embodiment can be executed on a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 is a hardware structure block diagram of a terminal that executes the data processing method applied to an electromagnetic navigation system provided by the present invention. As Figure 1 shown, the terminal may include one or more ( Figure 1 only one is shown in the figure) processors 102 and a memory 104 for storing data. Among them, the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above terminal. For example, the terminal may further include more or fewer components than Figure 1 shown in the figure, or have a different configuration from Figure 1 shown in the figure.

[0050] The memory 104 can be used to store computer programs, for example, software programs of application software and modules, such as the computer program corresponding to the data processing method applied to the electromagnetic navigation system provided in the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above-mentioned method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.

[0051] The transmission device 106 is used to receive or send data via a network. The above-mentioned network includes a wireless network provided by a communication provider of the terminal. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0052] In the present invention, a data processing method applied to an electromagnetic navigation system is provided. The electromagnetic navigation system includes a field emitter and a magnetic sensor to be tracked. Figure 2 is a flowchart of the data processing method applied to the electromagnetic navigation system provided by the present invention, as Figure 2 shown, and this process includes the following steps:

[0053] Step S210, obtain the measured status data of the field emitter and the measured magnetic field data of the magnetic sensor in real time.

[0054] Step S220, determine a sample measured magnetic field data group in the measured magnetic field data. The sample measured magnetic field data group includes multiple sample measured magnetic field data.

[0055] Step S230, for each sample measured magnetic field data in the sample measured magnetic field data group, according to the temporary interpolation parameter and multiple measured status data within the sample time window corresponding to the sample measured magnetic field data, obtain the sample interpolated status data through an interpolation function, and determine the sample theoretical magnetic field data corresponding to the sample measured magnetic field data according to the sample interpolated status data.

[0056] Step S240: Taking the minimization of the difference between multiple groups of corresponding sample theoretical magnetic field data and sample measured magnetic field data as the optimization objective, optimize the temporary interpolation parameters to obtain candidate interpolation parameters corresponding to the sample measured magnetic field data group.

[0057] Specifically, during the electromagnetic navigation process, the field emitter continuously generates a time-varying magnetic field. The tracked magnetic sensor moves in the time-varying magnetic field and continuously detects the time-varying magnetic field, thereby continuously outputting corresponding measured magnetic field data. Different measured magnetic field data correspond to different moments. At the same time, the status sensor of the field emitter also continuously detects the working status of the field emitter, thereby continuously outputting corresponding measured status data. Different measured status data correspond to different moments.

[0058] Therefore, for the data processing method applied to the electromagnetic navigation system provided by the present invention, firstly, during the electromagnetic navigation process, continuously and real-time obtain the measured magnetic field data and the measured status data. At the same time, during the data acquisition process, determine at least part of the already acquired measured magnetic field data as the sample measured magnetic field data.

[0059] Furthermore, for the traditional electromagnetic navigation data processing method, it is necessary to match the measured status data and the measured magnetic field data, that is, to match the measured status data and the measured magnetic field data corresponding to the same moment. However, due to the different acquisition moments of the two types of data, there will be a data phase difference. Therefore, in the present invention, for each sample measured magnetic field data, determine the sample time window corresponding to the sample measured magnetic field data. The relative position of the sample time window and the sample measured magnetic field data is preset, that is, the relative position of the start and end moments of the sample time window and the acquisition moment of the sample measured magnetic field data is preset. Among them, the sample time window can be determined according to time. For example, first determine the acquisition moment of the sample measured magnetic field data, and then extend the acquisition moment to both sides or one side to determine the start and end moments of the sample time window. It can also be determined according to the data position. For example, first determine the status data corresponding to the sample measured magnetic field data, and then extend the status data to both sides or one side by several data positions to determine the start and end positions of the sample time window. Since the data is collected in sequence and has a sequential number, the data position can be determined according to the data serial number.

[0060] By presetting an appropriate relative position, the sample time window can cover the acquisition moment of the sample measured magnetic field data, that is, even if there is no measured status data in the sample time window that is at the same moment as the sample measured magnetic field data, but considering that the measured status data changes continuously, then perform interpolation processing on the measured status data within the sample time window, and the sample interpolated status data that is (or approximately) at the same moment as the sample measured magnetic field data can be obtained.

[0061] Therefore, the focus of the present invention is to interpolate the measured state data within the sample time window to obtain sample interpolated state data that has no phase difference (or a very small phase difference) from the sample measured magnetic field data. Among them, it is crucial to determine the interpolation parameters of the interpolation function. Specifically, a temporary interpolation parameter can be constructed first, and interpolation processing is performed through the temporary interpolation parameter to obtain sample interpolated state data, and then the sample theoretical magnetic field data can be determined. If the temporary interpolation parameter is accurate, that is, sample interpolated state data without a phase difference from the sample measured magnetic field data can be obtained through this parameter, then the sample theoretical magnetic field data is the same as the sample measured magnetic field data. Therefore, with the optimization goal of minimizing the difference between the sample theoretical magnetic field data and the sample measured magnetic field data, the temporary interpolation parameter is continuously iteratively optimized, and the final temporary interpolation parameter is determined as the candidate interpolation parameter.

[0062] To ensure the optimization accuracy of the candidate interpolation parameter, it is necessary to optimize the cumulative difference between the sample measured magnetic field data at multiple moments and their corresponding sample theoretical magnetic field data, and iteratively update the temporary interpolation parameter to obtain the candidate interpolation parameter. Therefore, it is necessary to determine multiple sample measured magnetic field data in the measured magnetic field data to form a sample measured magnetic field data group.

[0063] Exemplarily, the candidate interpolation parameter can be calculated using the pose solution model (full solution) in electromagnetic navigation:

[0064]

[0065] where T obj represents the pose of the magnetic sensor, λ represents the interpolation parameter, f represents the interpolation function, H represents the theoretical relationship among the pose of the magnetic sensor, the state data of the field emitter, and the magnetic field data of the magnetic sensor, and is used to calculate the corresponding theoretical magnetic field data according to the state data of the field emitter and the pose of the magnetic sensor, n represents the data serial number, and n 1 -n 2 represents the set of data serial numbers in a sample measured magnetic field data group, B(n) represents the measured magnetic field data of the magnetic sensor at time t n , X(n) represents the measured state data of the field emitter at time t n , and f(X(n - N 1 ),..., X(n),..., X(n + N 2 ), λ) represents the interpolation state data corresponding to B(n). Among them, t nThe moment usually represents the current moment. In the interpolation function, the measured state data at multiple different moments are weighted and summed to obtain the interpolated state data, and the weight of each data is determined by the interpolation parameter. Compared with the traditional positioning algorithm model, the main difference is that the interpolated state data is used instead of the measured state data for pose calculation, and the interpolation parameter to be solved is introduced in the calculation process of the interpolated state data. The principle of the above pose calculation model is as follows: If the interpolation parameter is accurate, the calculated interpolated state data and the measured magnetic field data correspond to the same physical moment, and when the pose of the magnetic sensor is accurate, the calculated theoretical magnetic field data is the same as the measured magnetic field data. Taking the minimization of the difference between the theoretical magnetic field data and the measured magnetic field data as the optimization goal, the accurate interpolation parameter and the pose of the magnetic sensor can be obtained. Furthermore, by substituting the sample measured magnetic field data and the measured state data within its corresponding sample time window into the algorithm model for calculation, the corresponding candidate interpolation parameter can be obtained.

[0066] It should be noted that according to the above algorithm model, in a specific determination method of the sample time window, the measured state data corresponding to the sample measured magnetic field data can be determined first, and then extended to both sides based on the measured state data to determine the width of the sample time window, that is, to determine the measured state data included in the sample time window.

[0067] In summary, through the data processing method applied to the electromagnetic navigation system provided by the present invention, candidate interpolation parameters for characterizing the data phase difference can be obtained. During the data matching process, the interpolated state data corresponding to each measured magnetic field data can be interpolated through the candidate interpolation parameters, and the phase difference between the measured magnetic field data and its corresponding interpolated state data is much smaller than the phase difference between the measured magnetic field data and the measured state data.

[0068] Since the data phase difference between the measured state data and the measured magnetic field data is usually fixed, the candidate interpolation parameters corresponding to different groups of sample measured magnetic field data should be close. At this time, the target interpolation parameter can be determined according to the candidate interpolation parameters corresponding to multiple groups of sample measured magnetic field data. For example, the candidate interpolation parameters with obvious differences from other candidate interpolation parameters can be removed, and the relatively close candidate interpolation parameters can be retained, and the candidate interpolation parameter with the most repeated occurrences can be determined as the target interpolation parameter. Of course, the target interpolation parameter can also be determined according to the mean value of multiple candidate interpolation parameters. In this way, a target interpolation parameter with higher accuracy can be obtained.

[0069] Correspondingly, in one embodiment, there are multiple groups of measured magnetic field data of the sample; the data processing method further includes: step S241, determining the target interpolation parameter according to the candidate interpolation parameters respectively corresponding to the multiple groups of measured magnetic field data of the sample. Specifically, the target interpolation parameter can be determined according to the mean value of the candidate interpolation parameters respectively corresponding to the multiple groups of measured magnetic field data of the sample. In this embodiment, the mean value of multiple candidate interpolation parameters can be determined as the target interpolation parameter, or the mean value of multiple candidate interpolation parameters can be appropriately transformed and used as the target interpolation parameter.

[0070] Figure 3 It is a flowchart of a data processing method applied to an electromagnetic navigation system in an embodiment of the present invention. In this embodiment, an application of the target interpolation parameter is provided. Refer to Figure 3 , the data processing method further includes:

[0071] Step S250, determining the target measured magnetic field data group in the measured magnetic field data, where the target measured magnetic field data group includes multiple target measured magnetic field data.

[0072] Step S251, for each target measured magnetic field data in the target measured magnetic field data group, according to the target interpolation parameter and several measured state data within the target time window corresponding to the target measured magnetic field data, obtaining the target interpolation state data corresponding to the target measured magnetic field data through an interpolation function.

[0073] Step S260, determining the target pose of the magnetic sensor according to multiple groups of corresponding target measured magnetic field data and target interpolation state data.

[0074] Specifically, in this embodiment, the target interpolation parameter is applied to the positioning of the magnetic sensor. Through the target interpolation parameter, the interpolation state data corresponding to each measured magnetic field data can be interpolated, and then according to the measured magnetic field data and its corresponding interpolation state data, the target pose of the magnetic sensor is calculated through a positioning algorithm model. Different from the solution of the candidate interpolation parameter, at this time, the interpolation parameter in the positioning algorithm model is the known target interpolation parameter, not the quantity to be solved.

[0075] To ensure the accuracy of the solution of the target pose, it is necessary to calculate the target pose of the magnetic sensor at a certain moment according to the target measured magnetic field data and its corresponding measured state data at multiple moments. For example, the target pose of the magnetic sensor at time n can be calculated according to the target measured magnetic field data and its corresponding measured state data at time n - 1, time n, and time n + 1.

[0076] Specifically, the pose calculation model (simplified solution) of the target pose of the magnetic sensor is as follows:

[0077]

[0078] Among them, is the target interpolation parameter, which represents the mean value of multiple candidate interpolation parameters in this example.

[0079] It should be noted that according to the determination principle of the target interpolation parameter, in order to accurately obtain the target interpolation state data corresponding to the target measured magnetic field data, the relative position of the target time window and the target measured magnetic field data is the same as the relative position of the sample time window and the sample measured magnetic field data. For example, both extend the same time width to both sides corresponding to the measured state data.

[0080] It should be noted that in other embodiments, a certain candidate interpolation parameter can also be directly applied to the positioning of the magnetic sensor. Therefore, in this embodiment, the data processing method further includes:

[0081] Determine a group of target measured magnetic field data in the measured magnetic field data, and the group of target measured magnetic field data includes multiple target measured magnetic field data; for each target measured magnetic field data in the group of target measured magnetic field data, according to the candidate interpolation parameter and several measured state data within the target time window corresponding to the target measured magnetic field data, obtain the target interpolation state data corresponding to the target measured magnetic field data through an interpolation function; determine the target pose of the magnetic sensor according to multiple groups of corresponding target measured magnetic field data and target interpolation state data. Its calculation principle is the same as that of the above embodiment and will not be elaborated here.

[0082] As can be seen from the above embodiments, the target interpolation parameter or the candidate interpolation parameter can be applied to the positioning of the magnetic sensor. Through the target interpolation parameter or the candidate interpolation parameter, the interpolation state data corresponding to each measured magnetic field data can be interpolated, and the phase difference between the two is much smaller than the phase difference between the measured magnetic field data and its corresponding measured state data. Therefore, according to the measured magnetic field data and its corresponding interpolation state data, compared with according to the measured magnetic field data and its corresponding measured state data, the pose of the magnetic sensor can be calculated more accurately. Furthermore, through the data processing method applied to the electromagnetic navigation system provided by the present invention, the target interpolation parameter or the candidate interpolation parameter for characterizing the data phase difference can be obtained. Based on the target interpolation parameter or the candidate interpolation parameter, the problem of data phase difference existing in the current electromagnetic navigation system can be solved, and finally the positioning error problem of the electromagnetic navigation system can be solved.

[0083] In the actual electromagnetic navigation process, the data processing device continuously and real-time acquires the measured state data and the measured magnetic field data, and thus can continuously update the target interpolation parameter according to the latest acquired data.

[0084] Therefore, in one embodiment, in step S220, to determine a set of sample measured magnetic field data from the measured magnetic field data, it includes: determining multiple sets of sample measured magnetic field data corresponding to different moments from the measured magnetic field data within the real-time time window. Correspondingly, the data processing method further includes: determining the current target interpolation parameter in real time according to the candidate interpolation parameters corresponding to multiple sets of sample measured magnetic field data within the real-time time window.

[0085] In this embodiment, both the candidate interpolation parameter and the target interpolation parameter are continuously calculated and updated. Specifically, the measured magnetic field data at multiple latest moments can be used as the sample measured magnetic field data to form the latest set of sample measured magnetic field data, and the candidate interpolation parameter corresponding to the current moment is calculated using this set of sample measured magnetic field data. Exemplarily, assuming the current moment is n, the set of measured magnetic field data corresponding to moments n - 2, n - 1, and n can be used as the set of sample measured magnetic field data for calculating the candidate interpolation parameter at the current moment n. Through the above method, the candidate interpolation parameter corresponding to each moment can be calculated in real time. Therefore, a real-time time window with a certain width can be preset, and the real-time time window is continuously updated and moved as the current moment changes. Furthermore, the measured magnetic field data within the real-time time window is also continuously updated and changed, that is, multiple sets of sample measured magnetic field data are continuously updated and changed. Since the target interpolation parameter is determined according to the candidate interpolation parameters corresponding to multiple sets of sample measured magnetic field data, the target interpolation parameter also changes continuously. Correspondingly, the current target interpolation parameter is determined according to the candidate interpolation parameters corresponding to multiple sets of sample measured magnetic field data within the real-time time window. Exemplarily, assuming the current moment is n and the real-time time window is (n - 5, n), the candidate interpolation parameter corresponding to moment n - 3 can be calculated according to the sample measured magnetic field data corresponding to moments n - 5, n - 4, and n - 3 (the set of sample measured magnetic field data corresponding to moment n - 3), and the candidate interpolation parameter corresponding to moment n - 2 can be calculated according to the sample measured magnetic field data corresponding to moments n - 4, n - 3, and n - 2, and so on. The candidate interpolation parameters corresponding to moments n - 1 and n can be obtained, and finally, the average value of these 4 candidate interpolation parameters can be used as the current target interpolation parameter.

[0086] Combined with the embodiment where the target interpolation parameter is applied to the positioning of the magnetic sensor, since the positioning of the magnetic sensor needs to meet the real-time requirement as much as possible, during the process of real-time obtaining the measured magnetic field data, the current measured magnetic field data will be continuously determined as the target measured magnetic field data, and at the same time, the target interpolation parameter is continuously updated, that is, the interpolation state data corresponding to the current measured magnetic field data is calculated according to the current target interpolation parameter, and the current pose of the magnetic sensor is further determined.

[0087] Figure 4It is a flowchart of a data processing method applied to an electromagnetic navigation system in an embodiment of the present invention. This embodiment is a combination of the above two embodiments. Refer to Figure 4 , the data processing method further includes:

[0088] Step 270, determine a target measured magnetic field data group corresponding to the current moment in the measured magnetic field data, and the target measured magnetic field data group includes a plurality of target measured magnetic field data.

[0089] Step S271, for each target measured magnetic field data in the target measured magnetic field data group, obtain target interpolation state data corresponding to the target measured magnetic field data through an interpolation function according to the current target interpolation parameter and several measured state data within the target time window corresponding to the target measured magnetic field data.

[0090] Step 280, determine the current target pose of the magnetic sensor according to multiple groups of corresponding target measured magnetic field data and target interpolation state data.

[0091] Specifically, this embodiment is closer to the actual data processing situation, and calculates the current target pose of the magnetic sensor in real time according to the latest multiple measured magnetic field data and the current target interpolation parameter. It should be noted that the calculation of the target interpolation parameter and the calculation of the target pose of the magnetic sensor do not interfere with each other, and the two can be carried out simultaneously. On the one hand, calculate the sample interpolation state data corresponding to the measured magnetic field data within the real-time time window, and continuously update the current target interpolation parameter; on the other hand, calculate the current target pose of the magnetic sensor according to the current target interpolation parameter and the latest multiple measured magnetic field data. It also shows that for the same measured magnetic field data, it can be either sample measured magnetic field data or target measured magnetic field data.

[0092] Correspondingly, the target interpolation parameter determined according to the measured magnetic field data within the real-time time window can be used not only for the positioning of the magnetic sensor, but also for the interference judgment in electromagnetic navigation. Since the data phase difference between the measured state data of the field emitter and the measured magnetic field data of the magnetic sensor is stable within a period of time. Therefore, the change of the interpolation parameter calculated based on the real-time measured magnetic field data and measured state data is stable. If the interpolation parameter changes greatly in a short time, it indicates that the electromagnetic navigation system is affected by magnetic field interference.

[0093] Figure 5 It is a flowchart of a data processing method applied to an electromagnetic navigation system in an embodiment of the present invention. In this embodiment, another application of the target interpolation parameter is provided. Refer to Figure 5 , the data processing method further includes:

[0094] Step 290: Determine whether there is magnetic field interference in the electromagnetic navigation system according to the difference between the candidate interpolation parameters corresponding to the measured magnetic field data set of the sample at the current moment and the previous target interpolation parameter.

[0095] Specifically, in this embodiment, the measured magnetic field data within the real-time time window is used to determine the current target interpolation parameter in real time. The specific determination process can refer to the description of the calculation of the current target interpolation parameter in the foregoing embodiment. Therefore, the target interpolation parameter is updated in real time, and the previous target interpolation parameter is calculated based on the measured magnetic field data of the sample within the real-time time window at the previous moment. Compare the candidate interpolation parameter corresponding to the measured magnetic field data set of the sample at the current moment with the previous target interpolation parameter. If the difference between the two is large and exceeds the preset threshold, it means that the candidate interpolation parameter corresponding to the current measured magnetic field data of the sample has fluctuated greatly compared with the historical candidate interpolation parameter, and it can be determined that the electromagnetic navigation system is affected by magnetic field interference at the current moment.

[0096] Finally, it should be noted that different types of field emitters correspond to different measured state data. When the field emitter includes an electromagnetic coil, the measured state data includes the working current or working voltage of the electromagnetic coil. At this time, according to the working current or working voltage of the electromagnetic coil, the magnetic field generated by the electromagnetic coil can be calculated. When the field emitter includes a magnet (such as a permanent magnet), the measured state data includes the rotation angle or rotation distance of the permanent magnet. At this time, according to the rotation angle or rotation distance of the permanent magnet, the magnetic moment of the permanent magnet, that is, the magnetic field generated by the permanent magnet, can be obtained.

[0097] The principle of the technical solution of this application is specifically described as follows.

[0098] 1. Description of technical problems.

[0099] Assume that the measured state data of the field emitter is represented by X, and the measured magnetic field data of the tracked magnetic sensor is represented by B. The data pair corresponding to the same moment is represented as D(n) = [B(n), X(n)]. Assume that the pose of the magnetic sensor at this time is T obj , then the relationship between the data is as follows (the influence of random noise is ignored in this embodiment):

[0100] B(n) = H(X(n), T obj )

[0101] where H represents the relationship between different data. Calculating the pose T of the magnetic sensor obj is to solve the following optimization problem:

[0102]

[0103] The above algorithm model is the pose calculation model in current electromagnetic navigation. Its principle is that, first, the theoretical magnetic field data is determined based on the measured state data and the pose of the magnetic sensor. With the minimization of the difference between the theoretical magnetic field data and the measured magnetic field data as the optimization objective, the pose of the magnetic sensor is continuously optimized to approach its true pose. In the algorithm model, error summation calculation is performed, indicating that the algorithm model may need to perform a pose calculation of the magnetic sensor on D(n) = [B(n), X(n)] through multiple groups of data at different times.

[0104] There are many types of field emitters. For example, it consists of multiple groups of static electromagnetic coils. At this time, the state sensor may measure the voltage U(n) applied across the electromagnetic coils. Therefore, the measured state data X(n) = U(n) / Z(n), and the current is obtained through the relationship between voltage and impedance. The field emitter can also consist of multiple moving permanent magnets. At this time, the state sensor measures the rotation angle or displacement θ(n) of the permanent magnets. Therefore, X(n) = [M(n), P(n)], where [M(n), P(n)] = F(θ(n), X 0 ) represents the position P(n) and magnetic moment M(n) of each permanent magnet at a certain moment, which are related to the actual motion mode of the permanent magnets and the initial pose X 0 of the permanent magnets.

[0105] There are the following two situations for the time misalignment between the measured magnetic field data and the measured state data.

[0106] (1) There is a time misalignment between the acquisition time of the measured magnetic field data and the acquisition time of the measured state data.

[0107] Assume that X(n) is the measured state data of the field emitter at time t n , and n is an integer representing the data serial number. Due to the time misalignment, B(n) = H(X(m), T obj ), where B(n) is the measured magnetic field data of the magnetic sensor at time t n . Its actual corresponding state data is X(m) of the field emitter at time t m , that is, the times of the magnetic sensor at time t n and the field emitter at time t m correspond to the same physical time. Among them: t n = t m + Δt s , and m is not necessarily an integer. Figure 6 is the schematic diagram of the acquisition trajectory of the measured state data in a specific embodiment of the present invention. Refer to Figure 6, the measured magnetic field data X(n) corresponds to the state data X(m) = [M(m), P(m)] on the acquisition trajectory of the measured state data. X(m) is located between two existing measured state data, but X(m) has not been actually acquired. At this time, if the pose calculation model continues to calculate through X(n) = [M(n), P(n)], positioning errors will be introduced.

[0108] (2) The initial pose calibration result X′ of the permanent magnet 0 and the actual value X 0 have a phase difference.

[0109] That is, X′ 0 = F(Δθ 0 , X 0 ). At this time, all subsequent measured state data calculated from [M(n), P(n)] = F(θ(n), X′ 0 ) have a misalignment with the actual value (which can be equivalent to a time misalignment Δt 0 ). From the results, it is similar to the situation described in Figure 6 .

[0110] In summary, the above two errors will both cause a time misalignment between the measured magnetic field data and the measured state data. This time misalignment can be expressed as: t n = t m + Δt eff ; where, Δt eff = Δt s + Δt 0 .

[0111] 2. Technical solution description.

[0112] Since the state data of the field emitter changes continuously, X(m) can be obtained by interpolating the measured state data, that is:

[0113] X(m) = f(X(n - N 1 ),..., X(n - 1), X(n), X(n + 1)..., X(n + N 2 ), λ)

[0114] where, f represents the interpolation function and λ is the interpolation parameter. The measured state data within the time window n - N 1 to n + N 2 is used in the above formula. When N 2 > 0, it means that the interpolation function needs to use subsequent states, so this interpolation has a delay. Similarly, taking the situation shown in Figure 6 as an example, and without loss of generality, the f function is linearly interpolated using the measured state data of ±1 nearest neighbors:

[0115] P(m) = λP(n - 1)+(1 - λ)P(n + 1)

[0116]

[0117] where represents the unit direction vector of the magnetic moment; M is the magnetic moment strength, and λ is a known parameter. It should be noted that in addition to the interpolation function shown in this embodiment, the function f can also adopt any existing interpolation function. Through the interpolation function, interpolation state data that is approximately or even equivalent to X(m) can be obtained.

[0118] When the state data of the field emitter changes continuously and differentiably and the state data sampling rate is relatively high (such as 100 - 10000 Hz), the above-mentioned nearest-neighbor linear interpolation has sufficient accuracy and small computational complexity, which is convenient for solving.

[0119] It should be noted that if the phase difference between the initial pose calibration result of the permanent magnet and the actual value is relatively large, the interpolation function f may need to use relatively later data, that is, N 2 > 0, which will cause a delay in the algorithm (because data at time n + N 2 needs to be collected to process the data at time n). Using the extrapolation method can solve this problem to a certain extent. There are many interpolation or extrapolation methods, which are not limited here.

[0120] Based on the above description, the calculation of the pose T obj can be obtained by solving the following updated optimization problem (solving the full amount):

[0121]

[0122] Compared with the existing pose calculation model, in this embodiment, f(X(n - N 1 ),..., X(n),..., X(n + N 2 ), λ) is used to replace X(n) in the existing pose calculation model, and the interpolation parameter λ to be solved is newly introduced. The number of λ is related to the interpolation method and the number of state sensors in the field emitter. For example, λ = [λ 1 , λ 2 ,..., λ M . Exemplarily, if there are 2 state sensors in the field emitter and their equivalent time misalignments with a certain magnetic sensor are different, then they need to be solved separately.

[0123] Within a certain time period, the time misalignment between the sensors of the system is relatively stable, that is where represent the time fluctuation and the average time difference respectively. And the initial state phase difference of the permanent magnet (equivalent time misalignment Δt 0) is also a fixed parameter (it may vary each time the system starts, but is constant during a single continuous use). Therefore, within a certain period of time, Δt eff = Δt s + Δt 0 is relatively fixed. Therefore, after the system calculates the pose solution model using the full - scale solution for a period of time, the mean value of λ within a certain real - time time window (with a length of W) can be taken as the target interpolation parameter, that is:

[0124]

[0125] Then, using to interpolate the subsequent measured state data, the aligned measured magnetic field data and interpolated state data are obtained, and finally pose calculation (simplified solution) is performed, that is:

[0126]

[0127] All optimization solutions have solution errors, and an increase in the number of unknowns will also introduce more uncertainties. Using the pose solution model with simplified solution for calculation reduces the number of unknowns, simplifies the solution difficulty, and further improves the system stability and accuracy. Therefore, during the actual calculation process, the system will continuously use the pose solution model with full - scale solution to solve for λ, and then set a sliding real - time time window [n - W + 1, n], and calculate the mean value of multiple λ for simplified solution and output as the result. According to the above description, if the sliding window width W is large enough, should be relatively stable or have a slow change.

[0128] 3. Extended application description.

[0129] As introduced above, within a certain time range, the data phase difference of the system is relatively fixed, and thus the interpolation parameter λ is relatively stable. Considering data noise and system fluctuations, the λ(n) solved by the pose solution model with full - scale solution each time conforms to where is the mean value of multiple λ(n) within the real - time time window W, and δλ(n) represents the uncertainty of λ(n) within this real - time time window, such as the standard deviation. This distribution is only related to the system itself and has nothing to do with the environment.

[0130] When there are interferences such as metals in the environment, the magnetic field of the field emitter will induce eddy currents in the metal, and the eddy currents will further form a secondary magnetic field, interfering with the original magnetic field and causing the measured magnetic field of the tracked object to be distorted. Usually, the secondary magnetic field signal has the same frequency as the original signal but has a phase difference, and the simple model is expressed as:

[0131]

[0132] Among them, B m (n) is the measured magnetic field data; B 0 (n) is the theoretical magnetic field data; represents the interference magnetic field, a is the intensity modulation parameter, Δω is the phase shift between the interference signal and the original signal, which is related to the material, shape, volume, pose, etc. of the interference source. Considering the randomness of the interference source, these parameters are difficult to estimate.

[0133] Based on the above description, it can be known that when there is magnetic field interference, the phase difference between the measured magnetic field data and the measured state data will change, which will cause a large change in the real-time λ(n) obtained by the pose calculation model of the full-scale solution. For example:

[0134]

[0135] The above is the interference judgment formula. Among them, v is a multiple rate, such as 1 to 3. Therefore, the above formula can be used as the basis for judging magnetic field interference. In an embodiment where λ(n) is a multi-dimensional vector, that is, it contains multiple parameters, such as λ = [λ 1 , λ 2 ,..., λ M , at this time, each parameter is independently compared through the interference judgment formula. When at least one parameter satisfies the above formula, it is considered that there is interference.

[0136] Based on the above principle description, a specific embodiment is provided as follows.

[0137] Figure 7 is the flowchart of the data processing method of the electromagnetic navigation system in a specific embodiment of the present invention. Referring to Figure 7 , in this specific embodiment, a data processing method of an electromagnetic navigation system is provided. The data processing method includes:

[0138] Step S710, data acquisition and state calculation.

[0139] Specifically, continuously acquire the measured magnetic field data output by the magnetic field sensor and the measured state data output by the state sensor. During the data acquisition process, calculate the state of the field emitter through the measured state data, such as the working current of the electromagnetic coil, or the position and magnetic moment vector of the permanent magnet, etc.

[0140] Step S720, full-scale solution.

[0141] Specifically, calculate the interpolation parameter according to the pose calculation model of the full-scale solution, and calculate the average value of the interpolation parameters within the real-time time window.

[0142] Step S730, simplified solution.

[0143] Specifically, based on the mean value of interpolation parameters, measured magnetic field data, and measured state data, the pose of the magnetic sensor is calculated through a simplified pose calculation model for solution.

[0144] Step S740, interference detection.

[0145] Specifically, using the interference judgment formula, it is judged whether there is magnetic field interference in the electromagnetic navigation system, such as metal interference, etc. When the interference judgment formula holds, it is determined that there is interference, and the system can prompt the user and pause the update of the result.

[0146] The present invention also provides a data processing device applied to an electromagnetic navigation system. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated here. The following terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0147] Figure 8 is the structural block diagram of the data processing device applied to the electromagnetic navigation system provided by the present invention, as Figure 8 shown, this device includes:

[0148] A data acquisition module 810, configured to acquire in real time the measured state data of the field emitter and the measured magnetic field data of the magnetic sensor;

[0149] A data preparation module 820, configured to determine a sample measured magnetic field data group in the measured magnetic field data, and the sample measured magnetic field data group includes multiple sample measured magnetic field data;

[0150] A data calculation module 830, for each sample measured magnetic field data in the sample measured magnetic field data group, according to the temporary interpolation parameter and multiple measured state data within the sample time window corresponding to the sample measured magnetic field data, obtaining sample interpolation state data through an interpolation function, and determining sample theoretical magnetic field data corresponding to the sample measured magnetic field data according to the sample interpolation state data;

[0151] A parameter optimization module 840, with the optimization goal of minimizing the difference between multiple groups of corresponding sample theoretical magnetic field data and sample measured magnetic field data, optimizing the temporary interpolation parameter to obtain candidate interpolation parameters corresponding to the sample measured magnetic field data group.

[0152] The target interpolation parameter can be applied to the positioning of the magnetic sensor. Through the target interpolation parameter, the interpolation state data corresponding to each measured magnetic field data can be interpolated, and the phase difference between the two is much smaller than the phase difference between the measured magnetic field data and its corresponding measured state data. Therefore, based on the measured magnetic field data and its corresponding interpolation state data, compared with based on the measured magnetic field data and its corresponding measured state data, the pose of the magnetic sensor can be calculated more accurately. Furthermore, through the data processing method applied to the electromagnetic navigation system provided by the present invention, the target interpolation parameter for characterizing the data phase difference can be obtained. Based on the target interpolation parameter, the data phase difference existing in the current electromagnetic navigation system can be solved, and finally the positioning error problem of the electromagnetic navigation system can be solved.

[0153] It should be noted that the above-mentioned various modules can be functional modules or program modules, which can be implemented by software or by hardware. For the modules implemented by hardware, the above-mentioned various modules can be located in the same processor; or the above-mentioned various modules can also be located in different processors in any combined form.

[0154] In the present invention, a data processing device applied to an electromagnetic navigation system is also provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0155] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be repeated in this embodiment.

[0156] In addition, in combination with the data processing method applied to the electromagnetic navigation system provided by the present invention, a storage medium can also be provided in the present invention to implement it. A computer program is stored on the storage medium; when the computer program is executed by the processor, it implements any one of the data processing methods applied to the electromagnetic navigation system in the above embodiments.

[0157] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties.

[0158] It should be understood that the specific embodiments described here are only used to explain this application, rather than to limit it. According to the embodiments provided by the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present application.

[0159] Obviously, the accompanying drawings are only some examples or embodiments of the present application. For those of ordinary skill in the art, the present application can also be applied to other similar situations based on these drawings without creative work. Additionally, it can be understood that although the work done during this development process may be complex and time-consuming, for those of ordinary skill in the art, certain design, manufacturing, or production changes based on the technical content disclosed in the present application are only routine technical means and should not be regarded as insufficient disclosure of the present application.

[0160] The term "embodiment" in this application means that the specific features, structures, or characteristics described in connection with the embodiments may be included in at least one embodiment of the present application. The phrase appears in various positions in the specification and does not necessarily mean the same embodiment, nor does it mean independence or alternative to other embodiments that are mutually exclusive. Those of ordinary skill in the art can clearly or implicitly understand that the embodiments described in this application can be combined with other embodiments without conflict.

[0161] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of patent protection. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A data processing method applied to an electromagnetic navigation system, the electromagnetic navigation system including a field emitter and a magnetic sensor, characterized in that, the data processing method includes: obtaining in real time the measured status data of the field emitter and the measured magnetic field data of the magnetic sensor; determining a sample measured magnetic field data group in the measured magnetic field data, the sample measured magnetic field data group including a plurality of sample measured magnetic field data; for each sample measured magnetic field data in the sample measured magnetic field data group, according to the temporary interpolation parameter and a plurality of the measured status data within the sample time window corresponding to the sample measured magnetic field data, obtaining sample interpolated status data through an interpolation function, and determining sample theoretical magnetic field data corresponding to the sample measured magnetic field data according to the sample interpolated status data; taking minimizing the difference between multiple groups of corresponding sample theoretical magnetic field data and sample measured magnetic field data as an optimization objective, optimizing the temporary interpolation parameter, and obtaining candidate interpolation parameters corresponding to the sample measured magnetic field data group.

2. The data processing method applied to an electromagnetic navigation system according to claim 1, characterized in that, there are multiple groups of the sample measured magnetic field data groups; the data processing method further includes: determining target interpolation parameters according to the candidate interpolation parameters respectively corresponding to multiple groups of the sample measured magnetic field data groups.

3. The data processing method applied to an electromagnetic navigation system according to claim 1, characterized in that, determining the sample measured magnetic field data group in the measured magnetic field data includes: determining multiple groups of the sample measured magnetic field data groups corresponding to different moments in the measured magnetic field data within a real-time time window; the data processing method further includes: determining current target interpolation parameters in real time according to the candidate interpolation parameters respectively corresponding to multiple groups of the sample measured magnetic field data groups within the real-time time window.

4. The data processing method applied to an electromagnetic navigation system according to claim 2, characterized in that, the data processing method further includes: determining a target measured magnetic field data group in the measured magnetic field data, the target measured magnetic field data group including a plurality of target measured magnetic field data; for each target measured magnetic field data in the target measured magnetic field data group, according to the target interpolation parameter and several of the measured status data within the target time window corresponding to the target measured magnetic field data, obtaining target interpolated status data corresponding to the target measured magnetic field data through the interpolation function; determining the target pose of the magnetic sensor according to multiple groups of corresponding target measured magnetic field data and target interpolated status data.

5. The data processing method applied to an electromagnetic navigation system according to claim 3, characterized in that, the data processing method further includes: determining a target measured magnetic field data group corresponding to the current moment in the measured magnetic field data, the target measured magnetic field data group including a plurality of target measured magnetic field data; For each of the target measured magnetic field data in the target measured magnetic field data set, according to the current target interpolation parameter and several of the measured state data within the target time window corresponding to the target measured magnetic field data, obtain the target interpolation state data corresponding to the target measured magnetic field data through the interpolation function; Determine the current target pose of the magnetic sensor according to multiple groups of corresponding target measured magnetic field data and the target interpolation state data.

6. The data processing method applied to an electromagnetic navigation system according to claim 3, wherein, the data processing method further includes: Judging whether there is magnetic field interference in the electromagnetic navigation system according to the difference between the candidate interpolation parameter corresponding to the sample measured magnetic field data set corresponding to the current moment and the previous target interpolation parameter.

7. The data processing method applied to an electromagnetic navigation system according to claim 1, wherein, the field emitter includes an electromagnetic coil, and the measured state data includes the working current or working voltage of the electromagnetic coil; Or, the field emitter includes a magnet, and the measured state data includes the rotation angle or rotation distance of the magnet.

8. A data processing device applied to an electromagnetic navigation system, the electromagnetic navigation system includes a field emitter and a magnetic sensor, wherein, the data processing device includes: A data acquisition module, configured to acquire the measured state data of the field emitter and the measured magnetic field data of the magnetic sensor in real time; A data preparation module, configured to determine a sample measured magnetic field data set in the measured magnetic field data, and the sample measured magnetic field data set includes multiple sample measured magnetic field data; A data calculation module, configured to, for each of the sample measured magnetic field data in the sample measured magnetic field data set, according to a temporary interpolation parameter and multiple of the measured state data within the sample time window corresponding to the sample measured magnetic field data, obtain sample interpolation state data through an interpolation function, and determine sample theoretical magnetic field data corresponding to the sample measured magnetic field data according to the sample interpolation state data; A parameter optimization module, configured to optimize the temporary interpolation parameter with the goal of minimizing the difference between multiple groups of corresponding sample theoretical magnetic field data and the sample measured magnetic field data, and obtain a candidate interpolation parameter corresponding to the sample measured magnetic field data set.

9. A data processing device applied to an electromagnetic navigation system, including a memory and a processor, wherein, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the data processing method applied to the electromagnetic navigation system according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the steps of the data processing method applied to the electromagnetic navigation system according to any one of claims 1 to 7 are implemented.

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