Anti-interference six-dimensional sensor acquisition system

Abstract

The invention relates to the technical field of data processing, in particular to an anti-interference six-dimensional sensor acquisition system, which comprises a multi-modal signal fusion module for receiving original acquisition data of a six-dimensional sensor and realizing fusion of the acquisition data of all sensors based on a self-adaptive dynamic weight distribution algorithm; the intelligent error compensation module is used for performing forward compensation on errors in the six-dimensional sensor data so as to improve the accuracy of the data; the correction feedback module is used for carrying out self-correction on the data acquired by the six-dimensional sensor at an application end and carrying out feedback adjustment on sampling parameters according to a correction result; according to the method, a self-adaptive covariance matched filtering algorithm is adopted, a noise covariance matrix is dynamically adjusted and measured in combination with Kalman filtering, the denoising effect is improved, and the optimal signal quality is ensured to be kept under different noise levels by setting a dynamic smoothing factor and adaptively adjusting a data smoothing process.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to an interference-resistant six-dimensional sensor acquisition system. Background Art

[0002] Six-dimensional sensors are mainly used for high-precision measurement, such as industrial robot control, drone attitude detection, and autonomous driving systems. However, in complex environments, data acquisition of six-dimensional sensors faces many challenges, mainly including signal interference, error accumulation, noise influence, etc., which leads to reduced data reliability and stability.

[0003] Therefore, anti-interference design needs to fully consider the stability of the signal and the reliability of the data. However, the current six-dimensional sensor still faces the following difficulties in anti-interference design during data acquisition: Sensors are susceptible to noise in environments with high electromagnetic interference and high mechanical vibration, resulting in data jitter or distortion; Traditional weighted fusion methods use fixed weights, which makes it difficult to adjust the weights in real time according to the sensor status, resulting in unstable data fusion effects; Most existing systems use fixed compensation models, which cannot accurately compensate for different types of errors (such as sensor zero bias drift, electromagnetic interference, temperature drift, etc.); Traditional filtering methods (such as mean filtering and low-pass filtering) work well in fixed noise environments, but the filtering effect decreases in complex environments (such as dynamic noise and sudden interference). Traditional systems lack a self-correction mechanism based on feedback control. When the environment changes, they cannot automatically adjust sampling parameters and filtering strategies, resulting in reduced data stability. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an anti-interference six-dimensional sensor acquisition system, comprising: The multimodal signal fusion module also includes: For the original data collected by the six-dimensional sensor, the adaptive dynamic weight allocation algorithm is used to adaptively allocate weights to different sensors, and the data collected by all sensors are fused according to the allocated weights. At the same time, the adaptive covariance allocation algorithm is used to denoise the fused data, and the filtering threshold is set to determine whether the data is completely filtered. Intelligent error compensation module, also includes, Based on the historical distribution of sensor data, a dynamic error model is constructed. The error type is automatically identified using the constructed dynamic error model. According to the identified error type, the compensation strategy is adaptively adjusted to compensate for the data error positively. And the correction feedback module also includes, By performing real-time self-correction on the collected data at the application end, and building a two-layer feedback control mechanism to dynamically adjust the sampling parameters, the filtering threshold is adaptively adjusted based on environmental changes.

[0006] As a preferred solution of the anti-interference six-dimensional sensor acquisition system described in the present invention, the adaptive dynamic weight allocation algorithm is specifically implemented as follows: Set the confidence evaluation index and calculate the confidence corresponding to each sensor based on the standard deviation of the data collected by the sensor ; According to the calculated sensor confidence, the adaptive weight allocation of the sensor is performed, then, in, Indicates The confidence level corresponding to each sensor is Indicates The weight coefficient corresponding to each sensor; According to the assigned weights, data fusion is performed, then we have: in, Indicates The weight coefficient corresponding to each sensor is: Represents the total number of sensors, Indicates The data collected by the sensors, Represents the data after all sensors are fused.

[0007] As a preferred solution of the anti-interference six-dimensional sensor acquisition system described in the present invention, an abnormality detection factor is introduced to realize secondary regulation of the sensor weight coefficient, specifically: Calculate the mutation rate of the data collected within the time window, then we have, in, Indicates The data collected by each sensor in the current time window is Indicates The data collected by the sensor in the previous time window, Indicates The anomaly detection factor of the data collected by the sensor is used to determine whether the data collected by the current sensor has an abnormal mutation. Specifically: Setting anomaly detection factor threshold , if the abnormal detection factor corresponding to the sensor is compared with the set abnormal detection factor threshold and satisfies the formula , indicating that the data collected by the current sensor has an abnormal mutation, reducing the corresponding weight of the current sensor.

[0008] As a preferred solution of the anti-interference six-dimensional sensor acquisition system described in the present invention, the adaptive covariance allocation algorithm is specifically implemented as follows: calculate The measurement noise variance at each moment and the state estimation error covariance ; Set the smoothing factor , to update the measurement noise covariance matrix and the process noise covariance matrix, we have, in, represents the smoothing factor, represents the measurement noise variance, represents the state estimation error covariance, , Respectively represent The measurement noise covariance matrix and process noise covariance matrix at each moment, , Respectively represent The measurement noise covariance matrix and process noise covariance matrix at each moment.

[0009] As a preferred solution of the anti-interference six-dimensional sensor acquisition system described in the present invention, wherein: for the denoised data , by setting the filtering threshold , judge the fused data Whether filtering is complete, specifically: If the denoised data meets the filter threshold, the formula , indicating that the current data is not completely filtered, and the abnormal data is not completely filtered. The data is filtered again by adjusting the smoothing factor until the formula is satisfied. , it means that the current data is completely filtered.

[0010] As a preferred solution of the anti-interference six-dimensional sensor acquisition system described in the present invention, the forward compensation of the data error of the adaptive adjustment compensation strategy is specifically implemented as follows: The types of prediction errors include errors caused by linear disturbances and errors caused by nonlinear disturbances; For the error caused by linear interference, the direct compensation strategy is used to compensate for the error, then, in, Indicates The error value of the prediction at the moment, Indicates Sensor measurement data at each moment, Represents the compensated sensor measurement data; For the error caused by nonlinear interference, the exponential smoothing compensation strategy is used to compensate for the error, then, in, Indicates The error value of the prediction at the moment, Indicates Sensor measurement data at each moment, Indicates Sensor measurement data at each moment, represents the compensated sensor measurement data, Represents the dynamic adjustment factor.

[0011] As a preferred solution of the anti-interference six-dimensional sensor acquisition system described in the present invention, the double-layer feedback control mechanism includes local feedback correction and global feedback correction, which is specifically implemented as follows: Local feedback correction, using the current sensor sampling value and recently The average of historical data , calculate the data deviation ; According to the calculated real-time deviation, determine whether sudden noise occurs, specifically: Setting the local deviation threshold , if the calculated data deviation satisfies the formula , indicating the occurrence of burst noise, self-correcting the data, we have, in, represents the dynamic adjustment factor, Indicates recent The mean of historical data, Represents the sensor sampling value after self-calibration and calculates the data deviation twice , and then compare it with the local deviation threshold twice. If the comparison result satisfies the formula , it means that the self-calibration is accurate, otherwise it will be re-calibrated until the formula is satisfied until; Global feedback correction uses a sliding window to calculate the long-term data error change trend, then we have: in, Indicates the current sensor sampling value, Indicates the sensor reference value at the current moment. Indicates the set sliding window size. Indicates the error within the current window size, represents the error within the previous window size, Indicates the data error change in the current window, which is used for global error compensation. Specifically: Setting the error change threshold , if the calculated error change satisfies the formula, , indicating that the data error in the current window exceeds the threshold, by adjusting the dynamic adjustment factor in the exponential smoothing compensation strategy until the error changes to satisfy the formula until.

[0012] As a preferred solution of the anti-interference six-dimensional sensor acquisition system described in the present invention, the adaptive adjustment of the filtering threshold based on environmental changes is specifically as follows: Ambient noise levels are based on recent The standard deviation of the data is shown as follows: in, Indicates the current sensor sampling value, Indicates recent The mean of historical data, Indicates the ambient noise level, used to dynamically adjust the filtering threshold The size of , then, in, Indicates the set filtering threshold size. represents the smoothing factor in the filtering process, Indicates the ambient noise level, Represents the adjusted filtering threshold used for data filtering under the current environmental noise level.

[0013] A computer device comprises a memory and a processor, wherein the memory stores a computer program and the processor implements an interference-resistant six-dimensional sensor acquisition system when executing the computer program.

[0014] A computer-readable storage medium stores a computer program, which realizes an interference-resistant six-dimensional sensor acquisition system when executed by a processor.

[0015] Beneficial effects of the present invention: The present invention adopts an adaptive dynamic weight allocation algorithm to dynamically adjust the weight according to the confidence of sensor data, so that the data fusion process has stronger anti-interference ability and improves data reliability. At the same time, an abnormal detection factor is introduced to identify and adjust the sudden noise, reducing the impact of abnormal data on the data fusion results. Adopt adaptive covariance matching filter algorithm, combined with Kalman filter to dynamically adjust the measurement noise covariance matrix to improve the denoising effect, and adaptively adjust the data smoothing process by setting dynamic smoothing factor to ensure the best signal quality under different noise levels; Adopting the LSTM-based time series error prediction model, dynamically learning the error change trend, improving the intelligence of error compensation, and adopting linear interference compensation strategy (zero bias drift, electromagnetic interference) and nonlinear interference compensation strategy (temperature drift, low-frequency interference) for different types of errors to significantly reduce system errors; Local feedback correction is used to quickly correct short-term burst noise to improve the stability of data in a short period of time. Global feedback correction is used to calculate the long-term error trend using a sliding window, and correction is performed for long-term error accumulation to improve the long-term reliability of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 This is a schematic diagram of the overall system step structure of an interference-resistant six-dimensional sensor acquisition system of the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0020] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0021] Meanwhile, in the description of the present invention, it should be noted that the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0022] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] Example 1: Reference Figure 1 , is an embodiment of the present invention, and provides an anti-interference six-dimensional sensor acquisition system, including a multi-modal signal fusion module, an intelligent error compensation module and a correction feedback module; Specifically, the multimodal signal fusion module is used to receive the original collected data of the six-dimensional sensor, and realize the fusion of all sensor collected data based on the adaptive dynamic weight allocation algorithm; the intelligent error compensation module is used to positively compensate for the errors in the six-dimensional sensor data to improve the accuracy of the data; the correction feedback module is used to self-correct the data collected by the six-dimensional sensor at the application end, and provide feedback to adjust the sampling parameters according to the correction results.

[0024] Furthermore, the multimodal signal fusion module is used to receive the original collected data of the six-dimensional sensor, and based on the adaptive dynamic weight allocation algorithm, realize the fusion of all sensor collected data. At the same time, based on the adaptive covariance allocation algorithm, the data is denoised to ensure the accuracy of the collected data.

[0025] Furthermore, the adaptive dynamic weight allocation algorithm is based on the real-time stability and reliability of each sensor, and adaptively adjusts its contribution weight in the data fusion process. The specific implementation is as follows: Before data fusion, set the confidence evaluation index , indicating the The reliability of the data collected by each sensor is as follows: in, Indicates The standard deviation of the data collected by each sensor is used to reflect the stability of the data collected by the sensor. The smaller the standard deviation, the more stable the data and the higher the confidence level. On the contrary, it means that the data is unstable and the confidence level is low. Indicates The confidence level corresponding to each sensor; Based on the calculated confidence, an adaptive weight allocation mechanism is constructed, specifically: in, Indicates The confidence level corresponding to each sensor is Indicates The weight coefficients corresponding to the sensors are used to realize the fusion of the data collected by the six-dimensional sensors, then, in, Indicates The weight coefficient corresponding to each sensor is: Represents the total number of sensors, Indicates The data collected by the sensors, Represents the data after all sensors are fused.

[0026] It should be noted that an abnormal detection factor is introduced at the same time to achieve secondary regulation of the sensor weight coefficient, specifically: The time when the sensor collects data is divided into multiple time windows of the same time interval length. According to the mutation rate of the collected data in the time window, the weight coefficient of the corresponding sensor during data fusion is adjusted twice. Then, in, Indicates The data collected by each sensor in the current time window is Indicates The data collected by the sensor in the previous time window, No. The anomaly detection factor of the data collected by each sensor is used to determine whether the data collected by the current sensor has an abnormal mutation. The details are as follows: Setting anomaly detection factor threshold , if the abnormal detection factor corresponding to the sensor is compared with the set abnormal detection factor threshold and satisfies the formula , indicating that the data collected by the current sensor has an abnormal mutation, reducing the corresponding weight of the current sensor.

[0027] Furthermore, the adaptive covariance allocation algorithm is aimed at the Kalman filter algorithm. According to the noise characteristics of the sensor, the process noise covariance matrix and the measurement noise covariance matrix in the denoising process of the Kalman filter algorithm are dynamically adjusted to improve the data denoising effect. The specific implementation is as follows: Setting the sliding window , respectively calculate The measurement noise variance and state estimation error covariance at each moment are: The measurement noise variance, in, Indicates The measurement data at each moment, Indicates the lower limit of the time in the current sliding window, represents the mean of the measured data in the sliding window, Represents the measurement noise variance, which is used to update the measurement noise covariance matrix; The state estimation error covariance, in, Indicates The estimated state value at the moment, The mean of the state estimates within the sliding window, Represents the state estimation error covariance, which is used to update the process noise covariance matrix; Update the measurement noise covariance matrix and process noise covariance matrix, specifically: Set the smoothing factor , to update the measurement noise covariance matrix and the process noise covariance matrix, we have, in, Represents the smoothing factor, which is set by the implementer according to the actual application scenario. represents the measurement noise variance, represents the state estimation error covariance, , Respectively represent The measurement noise covariance matrix and process noise covariance matrix at each moment, , Respectively represent The measurement noise covariance matrix and process noise covariance matrix at each moment are the adjusted measurement noise covariance matrix and process noise covariance matrix.

[0028] It should be noted that for the denoised data , by setting the filtering threshold , judge the fused data Whether filtering is complete, specifically: If the denoised data meets the filter threshold, the formula , indicating that the current data is not completely filtered, and the abnormal data is not completely filtered. The data is filtered again by adjusting the smoothing factor until the formula is satisfied. , it means that the current data is completely filtered.

[0029] It should be noted that through the adaptive dynamic weight allocation algorithm, when a sensor is interfered with, its contribution weight is automatically reduced to avoid erroneous data affecting the overall system. Combined with the adaptive covariance matching Kalman filter, the filtering parameters can be adaptively adjusted so that the system can obtain the optimal data fusion effect in different noise environments. Through adaptive covariance matching filtering, the system can dynamically adjust the filtering strategy according to different external interference conditions to improve adaptability.

[0030] Furthermore, the intelligent error compensation module is used to positively compensate for the errors in the six-dimensional sensor data to improve the accuracy of the data. It constructs a dynamic error model based on the historical distribution of the sensor data. It uses the constructed dynamic error model to automatically identify the error type and dynamically adjust the compensation strategy.

[0031] Furthermore, the dynamic error model is a time series prediction model based on LSTM, which learns the dynamic change trend of the error in real time and adjusts the adaptive compensation strategy. The specific implementation is as follows: According to the sensor measurement data And the real reference value , calculate the historical error, then we have, in, Represents the historical error value of the calculation, Represents sensor measurement data, Indicates the true reference value; Calculate the past The error value at each moment is calculated and the result is used as the input data of the time series prediction model. , and the input data satisfies the formula ; According to the input data, predict the future error value, then we have, in, Represents a set of predicted error values ​​that satisfies the formula ,and for As the error value prediction result of the input data, represents an LSTM network, including an input layer, an LSTM layer, a fully connected layer, and a loss function. The LSTM network is a well-known technology that can be easily associated with those skilled in the art, and will not be described in detail in this embodiment; Based on the set of error values ​​predicted , including errors caused by linear interference, including sensor zero bias drift and electromagnetic interference, and errors caused by nonlinear interference, including temperature offset and low-frequency interference. The compensation strategy is adaptively adjusted according to the error type, specifically: When the LSTM network determines that the error type is caused by linear interference, a direct compensation strategy is adopted to compensate the error, then, in, Indicates The error value of the prediction at the moment, Indicates Sensor measurement data at each moment, Represents the compensated sensor measurement data; When the LSTM network determines that the error type is caused by nonlinear interference, an exponential smoothing compensation strategy is adopted to compensate for the error, then, in, Indicates The error value of the prediction at the moment, Indicates Sensor measurement data at each moment, Indicates Sensor measurement data at each moment, represents the compensated sensor measurement data, Represents a dynamic adjustment factor, which is set by the implementer based on the actual application scenario.

[0032] It should be noted that by using LSTM prediction errors and combining a variety of compensation strategies, the anti-interference ability and accuracy of six-dimensional sensor data can be effectively improved, which is particularly suitable for precision measurement and control systems in complex environments.

[0033] Furthermore, the correction feedback module is used to perform self-correction on the data collected by the six-dimensional sensor at the application end, and to provide feedback to adjust the sampling parameters based on the correction results. It performs real-time self-correction on the collected data at the application end, builds a double-layer feedback control mechanism to dynamically adjust the sampling parameters, and adaptively adjusts the filtering threshold based on environmental changes, so as to improve the data stability and accuracy of the entire system in complex environments.

[0034] Furthermore, the double-layer feedback control mechanism introduces double-layer feedback control to dynamically adjust the sampling parameters of the sensor to improve the system's adaptability to different environments, including local feedback correction and global feedback correction, which is specifically implemented as follows: Local feedback correction is to detect and suppress the interference of sudden noise and is used for short-term correction. Specifically: Using the current sensor sampling value and recently The average of historical data , calculate the data deviation ; According to the calculated real-time deviation, determine whether sudden noise occurs, specifically: Setting the local deviation threshold , if the calculated data deviation satisfies the formula , indicating the occurrence of burst noise, self-correcting the data, we have, in, represents the dynamic adjustment factor, Indicates recent The mean of the data, Represents the sensor sampling value after self-calibration and calculates the data deviation twice , and then compare it with the local deviation threshold twice. If the comparison result satisfies the formula , it means that the self-calibration is accurate, otherwise it will be re-calibrated until the formula is satisfied until; Global feedback correction is to detect and suppress nonlinear interference for long-term correction, specifically: Using the sliding window to calculate the long-term data error change trend, we have: in, Indicates the current sensor sampling value, Indicates the sensor reference value at the current moment, which is set by the implementer according to the actual application scenario. Indicates the size of the sliding window, which is set by the implementer according to the actual application scenario. Indicates the error within the current window size, represents the error within the previous window size, Indicates the data error change in the current window, which is used for global error compensation. Specifically: Setting the error change threshold , if the calculated error change satisfies the formula , indicating that the data error in the current window exceeds the threshold, by adjusting the dynamic adjustment factor in the exponential smoothing compensation strategy until the error changes to satisfy the formula until.

[0035] Furthermore, the adaptive adjustment of the filtering threshold based on environmental changes is to dynamically adjust the filtering threshold by calculating the environmental noise level. The size of , in order to achieve signal quality changes in different environments, to avoid excessive filtering or false detection, the specific implementation is as follows: Ambient noise levels are based on recent The standard deviation of the data is shown as follows: in, Indicates the current sensor sampling value, Indicates recent The mean of historical data, Indicates the ambient noise level, used to dynamically adjust the filtering threshold The size of , then, in, Indicates the set filtering threshold size. represents the smoothing factor in the filtering process, Indicates the ambient noise level, Represents the adjusted filtering threshold used for data filtering under the current environmental noise level.

[0036] It should be noted that by introducing local feedback (short-time correction) + global feedback (long-term trend correction), the sampling parameters of the sensor are dynamically adjusted to improve the system's adaptability to different environments; based on the real-time calculated environmental noise level, the filtering threshold is dynamically adjusted so that the system can maintain the optimal sampling accuracy under different interference conditions.

[0037] Furthermore, if the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the system described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0038] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0039] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0040] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment may not be described (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those features that are not relevant to implementing the invention).

[0041] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0042] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An anti-interference six-dimensional sensor acquisition system, characterized in that: include, The multimodal signal fusion module also includes: For the original data collected by the six-dimensional sensor, the adaptive dynamic weight allocation algorithm is used to adaptively allocate weights to different sensors, and the data collected by all sensors are fused according to the allocated weights. At the same time, the adaptive covariance allocation algorithm is used to denoise the fused data, and the filtering threshold is set to determine whether the data is completely filtered. Intelligent error compensation module, also includes, Based on the historical distribution of sensor data, a dynamic error model is constructed. The error type is automatically identified using the constructed dynamic error model. According to the identified error type, the compensation strategy is adaptively adjusted to compensate for the data error positively. And the correction feedback module also includes, By performing real-time self-correction on the collected data at the application end, and building a two-layer feedback control mechanism to dynamically adjust the sampling parameters, the filtering threshold is adaptively adjusted based on environmental changes.

2. The anti-interference six-dimensional sensor acquisition system according to claim 1, characterized in that: The adaptive dynamic weight allocation algorithm is specifically implemented as follows: Set the confidence evaluation index and calculate the confidence corresponding to each sensor based on the standard deviation of the data collected by the sensor ; According to the calculated sensor confidence, the adaptive weight allocation of the sensor is performed, then, in, Indicates The confidence level corresponding to each sensor is Indicates The weight coefficient corresponding to each sensor; According to the assigned weights, data fusion is performed, then we have: in, Indicates The weight coefficient corresponding to each sensor is: Represents the total number of sensors, Indicates The data collected by the sensors, Represents the data after all sensors are fused.

3. The anti-interference six-dimensional sensor acquisition system according to claim 2, characterized in that: The anomaly detection factor is introduced to achieve secondary regulation of the sensor weight coefficient, specifically: Calculate the mutation rate of the data collected in the time window, then we have, in, Indicates The data collected by each sensor in the current time window is Indicates The data collected by the sensor in the previous time window, Indicates The anomaly detection factor of the data collected by the sensor is used to determine whether the data collected by the current sensor has an abnormal mutation. Specifically: Setting anomaly detection factor threshold , if the abnormal detection factor corresponding to the sensor is compared with the set abnormal detection factor threshold and satisfies the formula , indicating that the data collected by the current sensor has an abnormal mutation, reducing the corresponding weight of the current sensor.

4. The anti-interference six-dimensional sensor acquisition system according to claim 3, characterized in that: The adaptive covariance allocation algorithm is specifically implemented as follows: calculate The measurement noise variance at each moment and the state estimation error covariance ; Set the smoothing factor , to update the measurement noise covariance matrix and the process noise covariance matrix, we have, in, represents the smoothing factor, represents the measurement noise variance, represents the state estimation error covariance, , Respectively represent The measurement noise covariance matrix and process noise covariance matrix at each moment, , Respectively represent The measurement noise covariance matrix and process noise covariance matrix at each moment.

5. The anti-interference six-dimensional sensor acquisition system according to claim 4, characterized in that: For the denoised data , by setting the filtering threshold , judge the fused data Whether filtering is complete, specifically: If the denoised data meets the filter threshold, the formula , indicating that the current data is not completely filtered, and the abnormal data is not completely filtered. The data is filtered again by adjusting the smoothing factor until the formula is satisfied. , it means that the current data is completely filtered.

6. The anti-interference six-dimensional sensor acquisition system according to claim 5, characterized in that: The forward compensation of the adaptive adjustment compensation strategy data error is specifically implemented as follows: The types of prediction errors include errors caused by linear disturbances and errors caused by nonlinear disturbances; For the error caused by linear interference, the direct compensation strategy is used to compensate for the error, then, in, Indicates The error value of the prediction at the moment, Indicates The sensor measures data at each moment. Represents the compensated sensor measurement data; For the error caused by nonlinear interference, the exponential smoothing compensation strategy is used to compensate for the error, then, in, Indicates The error value of the prediction at the moment, Indicates The sensor measures data at each moment. Indicates The sensor measures data at each moment. represents the compensated sensor measurement data, Represents the dynamic adjustment factor.

7. The anti-interference six-dimensional sensor acquisition system according to claim 6, characterized in that: The dual-layer feedback control mechanism includes local feedback correction and global feedback correction, which is specifically implemented as follows: Local feedback correction, using the current sensor sampling value and recently The average of historical data , calculate the data deviation ; According to the calculated real-time deviation, determine whether sudden noise occurs, specifically: Setting the local deviation threshold , if the calculated data deviation satisfies the formula , indicating the occurrence of burst noise, self-correcting the data, we have, in, represents the dynamic adjustment factor, Indicates recent The mean of historical data, Represents the sensor sampling value after self-calibration and calculates the data deviation twice , and then compare it with the local deviation threshold twice. If the comparison result satisfies the formula , it means that the self-calibration is accurate, otherwise it will be re-calibrated until the formula is satisfied until; Global feedback correction uses a sliding window to calculate the long-term data error change trend, then we have: in, Indicates the current sensor sampling value, Indicates the sensor reference value at the current moment. Indicates the set sliding window size. Indicates the error within the current window size, represents the error within the previous window size, Indicates the data error change in the current window, which is used for global error compensation. Specifically: Setting the error change threshold , if the calculated error change satisfies the formula , indicating that the data error in the current window exceeds the threshold, by adjusting the dynamic adjustment factor in the exponential smoothing compensation strategy until the error changes to satisfy the formula until.

8. The anti-interference six-dimensional sensor acquisition system according to claim 7, characterized in that: The adaptive adjustment of the filtering threshold based on environmental changes is specifically as follows: Ambient noise levels are based on recent The standard deviation of the data is shown as follows: in, Indicates the current sensor sampling value, Indicates recent The mean of historical data, Indicates the ambient noise level, used to dynamically adjust the filtering threshold The size of , then, in, Indicates the set filtering threshold size. represents the smoothing factor in the filtering process, Indicates the ambient noise level, Represents the adjusted filtering threshold used for data filtering under the current environmental noise level.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the system according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the system according to any one of claims 1 to 8 is implemented.

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