Percutaneous puncture needle force feedback data real-time processing system

Abstract

The invention relates to the technical field of multi-dimensional analysis, in particular to a percutaneous puncture needle force feedback data real-time processing system. The system comprises a data acquisition module used for acquiring stress data of all dimensions of a percutaneous puncture needle; the dimension influence analysis module is used for determining an abnormal fluctuation coefficient of a single dimension according to an unsmooth rule condition of change of the stress data on the single dimension, and determining an influence coefficient of the stress data of each dimension in combination with the relation of multi-dimensional force feedback; and the feedback transmission adjustment module is used for carrying out weighted adjustment on all dimensions during LOF anomaly detection to obtain a more accurate adjusted LOF value and carrying out regulation and control judgment on whether transmission feedback can be carried out or not. According to the method, single-dimensional and multi-dimensional comparative analysis is carried out on the multi-dimensional stress data to measure the influence credible condition of the current participation of each dimension in the abnormity, LOF abnormity detection is adjusted, and real-time data are transmitted and fed back more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of multidimensional analysis, and in particular to a real-time processing system for percutaneous puncture needle force feedback data. Background Art

[0002] With the widespread adoption of percutaneous needle technology, accurate force feedback data is crucial to ensuring safe and effective procedures. During the puncture process, controlling the applied force ensures proper and precise insertion. Force feedback helps physicians perceive needle-tissue contact in real time. Furthermore, force data provides real-time operational information, helping to adjust the puncture angle and force, preventing misoperation and improving puncture accuracy to ensure a safe procedure.

[0003] Generally, real-time processing of percutaneous puncture needle force feedback data involves continuously collecting force feedback data from the puncture needle during operation and continuously inputting it into the system to form a real-time data stream. The LOF algorithm is then used to evaluate the abnormality of the data points based on the local density of the data points. For force feedback data marked as abnormal by the LOF algorithm, the system will directly filter out this data to prevent inaccurate feedback from affecting subsequent work.

[0004] However, when the puncture needle contacts different tissues or penetrates skin of different thicknesses, certain fluctuations will occur. In addition, due to the different elasticity, hardness and density of tissues, the puncture needle may be subjected to different force feedback fluctuations during operation. These force feedback changes represent normal physiological reactions, but they will also be misjudged as outliers by the LOF algorithm and identified as abnormal data, causing the system to mistakenly discard some normal data, thereby affecting the accuracy of the feedback. Summary of the Invention

[0005] In order to solve the technical problem in the prior art that force feedback changes of normal physiological reactions may be misjudged as outliers by the LOF algorithm, identified as abnormal data and discarded, the purpose of the present invention is to provide a real-time processing system for percutaneous puncture needle force feedback data. The technical solutions adopted are as follows: The present invention provides a real-time processing system for percutaneous puncture needle force feedback data, the system comprising: A data acquisition module is used to obtain the force data of the percutaneous puncture needle in different dimensions at each acquisition moment; The dimension impact analysis module is used to obtain the abnormal fluctuation coefficient of the force data of each dimension based on the regularity of the speed of change of the force data of each dimension in the time series before the current moment; and determine the influence coefficient of the force data of each dimension based on the correlation between the time series change of the force data of each dimension and other dimensions and the influence of the abnormal fluctuation coefficient; The feedback transmission adjustment module is used to perform weighted adjustment on the force data of each dimension based on the influence coefficient when performing LOF algorithm detection on the force data at the current moment to obtain the adjusted LOF value at the current moment; and perform transmission feedback adjustment based on the adjusted LOF value at the current moment.

[0006] Furthermore, the method for obtaining the abnormal fluctuation coefficient includes: For any dimension of force data, the initial dimension coefficient of the dimension force data at the current moment is obtained based on the deviation between the change degree of the dimension force data at the current moment and the time series change degree within the preset time series range before the current moment; According to the vibration frequency of the force data of the dimension before the current moment, combined with the initial dimension coefficient, the instability coefficient of the force data of the dimension at the current moment is obtained; According to the difference in the instability coefficient of the force data of this dimension between the current moment and the previous moment, the abnormal fluctuation coefficient of the force data of this dimension is obtained.

[0007] Furthermore, the method for obtaining the initial dimension coefficient includes: Within the preset time series range before the current moment, after calculating the numerical difference between each two adjacent sampling moments of the force data of the dimension, the mean of all numerical differences is used as the preceding mean square variable of the dimension; The difference between the force data of the dimension at the current moment and the previous sampling moment is used as the current change degree of the dimension; The ratio of the current change degree of the dimension to the previous mean variable is used as the initial dimension coefficient of the force data of the dimension at the current moment.

[0008] Furthermore, the method for obtaining the instability coefficient includes: Within the preset time series range before the current moment, the force data of the current dimension is Fourier transformed to obtain the frequency domain space of the dimension; the frequency at the highest peak in the frequency domain space is normalized to obtain the high-frequency anomaly of the dimension; The product of the high-frequency anomaly of the dimension and the initial dimension coefficient is used as the instability coefficient of the force data of the dimension at the current moment.

[0009] Furthermore, obtaining the abnormal fluctuation coefficient of the force data of the dimension according to the difference in the instability coefficient between the current moment and the previous moment includes: The product of the value after negative correlation mapping of the instability coefficient at the previous sampling moment of the current moment and the instability coefficient at the current moment is used as the abnormal fluctuation coefficient.

[0010] Furthermore, the method for obtaining the influence coefficient includes: For any dimension of force data, based on the temporal correlation between the dimension of force data and the other dimension of force data, the comparative availability of the dimension of force data and the other dimension of force data is obtained; The influence coefficient of the force data of this dimension is obtained by combining the comparative availability of the force data of this dimension with the force data of each other dimension and the abnormal fluctuation coefficient of the force data of this dimension and the force data of other dimensions.

[0011] Furthermore, the method for obtaining the comparative availability includes: In a preset local range before the current moment, calculate the Pearson correlation coefficient between the force data of this dimension and the force data of each other dimension in the time series, and use the absolute value of the Pearson correlation coefficient as the correlation degree of each other dimension; Calculate the correlation and value of other dimensions of this dimension as the correlation and value of this dimension; take the ratio of the correlation and value of each other dimension as the comparative availability of the stress data of this dimension and the stress data of each other dimension.

[0012] Furthermore, the influence coefficient of the force data of the dimension is obtained by combining the comparative availability of the force data of the dimension with the force data of each other dimension, and the abnormal fluctuation coefficient of the force data of the dimension and the force data of other dimensions, including: Each of the other dimensions of the stress data of the dimension is taken as the analysis dimension in turn, and the product of the abnormal fluctuation coefficient of the analysis dimension and the comparative availability is taken as the synchronization interference degree of the analysis dimension; the sum of the synchronization interference degrees of all other dimensions of the stress data of the dimension is taken as the relative influence degree of the dimension; The product of the abnormal fluctuation coefficient of the force data of this dimension and the relative influence degree is normalized and used as the influence coefficient of the force data of this dimension.

[0013] Furthermore, the method for obtaining the adjusted LOF value includes: Within the preset detection range before the current moment, obtain the difference distance between the force data in each dimension at every two moments; The product of the difference distance between the force data of each dimension at every two moments and the influence coefficient is taken as the dimensional distance of each dimension at every two moments; the dimensional distance of all dimensions between every two moments is combined to obtain the adjustment distance of every two moments; The adjusted LOF value at the current moment is obtained by using the LOF algorithm based on the adjusted distance between the times within the preset detection range before the current moment.

[0014] Furthermore, the transmission feedback adjustment based on the adjusted LOF value at the current moment includes: If the adjusted LOF value at the current moment is greater than the preset abnormal threshold, the force data at the current moment will not be transmitted for feedback.

[0015] The present invention has the following beneficial effects: For all dimensions of the current force data collected by the percutaneous puncture needle, the present invention first preliminarily determines the abnormal fluctuation coefficient of this dimension for abnormal detection of current real-time data based on the uneven regularity of the change of the force data in a single dimension, and then combines the connectivity of multi-dimensional force feedback, uses the correlation comparison of the force data in different dimensions in time series, and combines the abnormal fluctuation coefficient to determine the influence coefficient of each dimension of force data on the abnormal detection analysis of the current real-time data to improve the accuracy of abnormal point identification. Finally, all dimensions are weighted and adjusted during LOF abnormality detection to obtain a more accurate adjusted LOF value, and to make a regulatory judgment on whether transmission feedback can be performed. The present invention performs single-dimensional and multi-dimensional comparative analysis on multi-dimensional force data to measure the credibility of the influence of each dimension participating in the abnormality, adjust the LOF abnormality detection, and transmit feedback to real-time data more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. 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 paying any creative work.

[0017] Figure 1 This is a structural diagram of a real-time processing system for percutaneous puncture needle force feedback data provided by one embodiment of the present invention; Figure 2 A schematic diagram of single-dimensional force data provided by an embodiment of the present invention; Figure 3 A flow chart of a method for obtaining an abnormal fluctuation coefficient provided by one embodiment of the present invention; Figure 4 A flow chart of a method for obtaining an influence coefficient provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0018] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a real-time processing system for percutaneous puncture needle force feedback data proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of the real-time processing system for percutaneous puncture needle force feedback data provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a structural diagram of a real-time processing system for percutaneous puncture needle force feedback data provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a dimensional impact analysis module 102 and a feedback transmission adjustment module 103.

[0022] The data acquisition module 101 is used to obtain the force data of the percutaneous puncture needle in different dimensions at each acquisition moment. In the embodiment of the present invention, a 100Hz acquisition frequency is used to continuously and in real time acquire the force data of the puncture needle during the puncture process. Each force data contains multiple dimensions, including the magnitude of the force, the direction of the force, the speed of the force, etc., and the data of each dimension are dimensioned to avoid the influence of dimension. It should be noted that the acquisition process and dimensioning are technical means well known to those skilled in the art. The implementer of the acquisition frequency can adjust it by himself. Dimensioning can be achieved by Z-score standardization or normalization, etc., which will not be limited or elaborated here. Please refer to Figure 2 , which shows a schematic diagram of single-dimensional force data provided by an embodiment of the present invention, in which the time series changes of the data are represented when the dimension is the magnitude of the force.

[0023] The dimension impact analysis module 102 is used to obtain the abnormal fluctuation coefficient of the force data of each dimension based on the regularity of the speed of change of the force data of each dimension in the time series before the current moment; and determine the influence coefficient of the force data of each dimension based on the correlation between the time series changes of the force data of each dimension and other dimensions and the influence of the abnormal fluctuation coefficient.

[0024] Abnormal data caused by physiological fluctuations in percutaneous needle force feedback data is typically due to differences in hardness and elasticity between different tissue layers, such as skin, fat, muscle, and blood vessels. These fluctuations are a normal reaction to the needle's contact with tissue and do not represent any abnormality.

[0025] Human tissue is not completely broken; it has a certain degree of elasticity and transition between different layers. Therefore, when a puncture needle penetrates from one tissue layer to another, even if there is a fluctuation, the change in force data is not very abrupt. For example, when a puncture needle passes from the skin to the fat layer and then to the muscle layer, the force data in each dimension does not change drastically, but rather gradually transitions.

[0026] Therefore, combined with the speed of the previous time series changes, the analysis of the degree of abnormality that a single dimension can be used for real abnormality monitoring is conducted. Preferably, in the embodiment of the present invention, the method for obtaining the abnormal fluctuation coefficient can be found in Figure 3 , which shows a flow chart of a method for obtaining an abnormal fluctuation coefficient provided by one embodiment of the present invention, the method comprising the following steps: S201: For any dimension of force data, the initial dimension coefficient of the dimension force data at the current moment is obtained according to the deviation between the degree of change of the dimension force data at the current moment and the degree of temporal change within a preset time range before the current moment.

[0027] Therefore, if the stress data in a certain dimension of the current real-time data changes rapidly, the data is more likely to be abnormally fluctuating, and can be used for subsequent real abnormal data detection with higher participation.

[0028] In an embodiment of the present invention, within a preset time series range before the current moment, after calculating the numerical difference between each two adjacent sampling moments of the force data of the dimension, the mean of all numerical differences is used as the preceding mean square of the dimension, and the degree of change trend is reflected by the overall situation of the change difference between the preceding adjacent sampling moments. The preset time series range can be set to the range of 6 sampling moments before the current moment. The specific range setting can be limited by the implementer according to the specific implementation scenario and is not limited here.

[0029] The numerical difference between the force data of this dimension at the current moment and the previous sampling moment is further used as the current change degree of this dimension to reflect the instantaneous deviation at the current moment.

[0030] Finally, the ratio of the current change degree of the dimension to the previous mean variable is used as the initial dimension coefficient of the force data of the dimension at the current moment.

[0031] S202: Obtain the instability coefficient of the dimensional force data at the current moment based on the vibration frequency of the dimensional force data before the current moment and in combination with the initial dimensional coefficient.

[0032] Given the rapid changes in percutaneous needle force feedback data, when the needle contacts different tissue layers, the tissue undergoes elastic deformation, and deformation of elastic materials typically manifests as low-frequency vibrations. This low-frequency characteristic reflects the elastic deformation of the tissue. In the feedback of force data, abnormal data differs from the actual tissue response. Therefore, the vibration frequency can be combined to help distinguish physiological fluctuations from true abnormal changes, improving the reliability of the current dimensional analysis of true abnormalities.

[0033] In this embodiment of the present invention, a Fourier transform is performed on the force data of the current dimension within a preset time series range before the current moment to obtain the frequency domain space of that dimension. The frequency at the highest peak in the frequency domain space is then normalized to obtain the high-frequency anomaly degree of that dimension. Because physiological fluctuations in tissue elastic deformation are mostly low-frequency, while anomalies such as equipment vibration are mostly high-frequency, the Fourier transform is used to convert the time domain data to the frequency domain. The higher the peak value is in the high-frequency range, the more likely it is a true anomaly.

[0034] It should be noted that Fourier transform and normalization processing are technical means well known to those skilled in the art. The normalization option can be linear normalization or standard normalization, etc., which will not be elaborated or limited here.

[0035] Then, the product of the high-frequency anomaly of this dimension and the initial dimension coefficient is used as the instability coefficient of the force data of this dimension at the current moment. The combined fluctuation and frequency vibration conditions reflect the possible degree of abnormal instability of the force data of this dimension at the current moment.

[0036] S203: Obtaining an abnormal fluctuation coefficient of the force data of the dimension according to a difference in the instability coefficient between the current moment and the previous moment.

[0037] True abnormal interference data is primarily caused by factors such as external noise, equipment failure, operational errors, errors in signal processing, or the inertial effects of physical systems. These interference data are short-lived and discontinuous. Once the interference source disappears, the system quickly returns to normal. Therefore, unlike physiological signals and persistent abnormal data, these transient fluctuations typically disappear within a short period of time.

[0038] Therefore, the instantaneous difference in the instability coefficient is used to further identify abnormal errors. In this embodiment of the present invention, the product of the instability coefficient at the sampling moment before the current moment, after negative correlation mapping, and the instability coefficient at the current moment is used as the abnormal fluctuation coefficient. If the instability coefficient at the current sampling moment is significantly higher than the instability coefficient at the previous sampling moment, it indicates a higher degree of sudden instability and a higher possibility of true abnormal interference at the current moment. Therefore, the larger the abnormal fluctuation coefficient.

[0039] It should be noted that negative correlation mapping is a technical means well known to those skilled in the art, such as using a negative exponential power form or an inverse proportional form, and will not be elaborated or limited here.

[0040] At this point, the possible abnormal analysis of single-dimensional data is completed.

[0041] Due to the multidimensionality of force feedback data, correlation analysis is used to determine comparative usability based on the physical relationships between dimensions, such as the inherent connection between force magnitude and direction. This information is then integrated with the relative correlations across multiple dimensions to determine the ultimate degree of participation in anomaly detection. When comparing data from different dimensions of the puncture needle to determine the final dimensionality coefficient for each dimension, if the historical data trends of two dimensions are correlated, it indicates that they may reflect similar physical phenomena or interrelated operational conditions. For example, during the use of a puncture needle, there is an inherent connection between force magnitude and direction, especially during the movement or insertion of the needle tip, making the dimensions comparatively usable.

[0042] Therefore, the final influence coefficient of each dimension's force data is determined by combining the time series change correlation of the multi-dimensional data with the abnormal fluctuation coefficient. Preferably, in the embodiment of the present invention, the method for obtaining the influence coefficient can be found in Figure 4 , which shows a flow chart of a method for obtaining an influence coefficient provided by an embodiment of the present invention, the method comprising the following steps: S211: For any dimension of force data, according to the time series correlation between the dimension of force data and each other dimension of force data, obtain the comparative availability of the dimension of force data with each other dimension of force data.

[0043] The degree of synchronization availability of other dimensions is reflected by the correlation of changes in time series data. In an embodiment of the present invention, within a preset local range before the current moment, the Pearson correlation coefficient of the numerical values ​​between the force data of this dimension in the time series and the force data of each other dimension is calculated, and the absolute value of the Pearson correlation coefficient is used as the correlation of each other dimension. The Pearson correlation coefficient is used to reflect the degree of correlation of the data changes in the time series. The larger the absolute value of the Pearson correlation coefficient, the higher the correlation between the two sets of data. The preset local range can be set to the range of 10 sampling moments before the current moment, which can be adjusted by the implementer at will and is not limited here.

[0044] It should be noted that the Pearson correlation coefficient is a technical means well known to those skilled in the art and is not limited here.

[0045] Then calculate the correlation and value of other dimensions of this dimension as the correlation and value of this dimension, and take the ratio of the correlation and value of each other dimension as the comparative availability of the stress data of this dimension and the stress data of each other dimension, combined with the correlation proportion of all dimensions, to reflect the different degrees of synergistic influence of other dimensions.

[0046] S212: Obtain the influence coefficient of the force data of the dimension by combining the comparative availability of the force data of the dimension and the force data of each other dimension, and the abnormal fluctuation coefficient of the force data of the dimension and the force data of the other dimensions.

[0047] Physiological fluctuations typically arise from local physiological changes or the contact state between the needle and tissue, such as changes in tissue hardness or increased local resistance. These factors typically primarily affect force magnitude, with less influence on direction or velocity. For example, when a needle encounters relatively hard tissue, the magnitude of the force may increase suddenly, while the direction or velocity changes more gradually. At this point, other dimensions, such as direction and velocity, may not be affected to the same degree, resulting in no significant abrupt changes. Furthermore, physiological fluctuations are often localized and do not significantly affect all dimensions simultaneously, so only force magnitude will exhibit a sudden change.

[0048] During anomaly detection, if data in multiple dimensions fluctuates significantly under comparative availability, it typically indicates an anomaly that requires detection, such as external interference or equipment failure. Therefore, we analyze the final impact coefficient by combining comparative availability with the anomaly fluctuation coefficient.

[0049] In this embodiment of the present invention, each other dimension of the stress data for that dimension is sequentially used as an analysis dimension. The product of the abnormal fluctuation coefficient of the analysis dimension and the comparative availability is used as the synchronization interference degree of the analysis dimension, reflecting the changes in other dimensions under the comparative availability. The sum of the synchronization interference degrees of all other dimensions of the stress data for that dimension is used as the relative influence of that dimension. The higher the overall degree of change in the relevant influence of other dimensions, the more significant the abnormal problem.

[0050] Finally, the product of the abnormal fluctuation coefficient of the force data of this dimension and the relative influence is normalized as the influence coefficient of the force data of this dimension. By combining the associated fluctuation changes of all dimensions, it reflects the credibility of the force data of this dimension being abnormal at the current moment.

[0051] At this point, by combining single-dimensional analysis with multi-dimensional impact analysis, the influence coefficient of each dimension's force data on the current reliability of anomaly detection is obtained.

[0052] The feedback transmission adjustment module 103 is used to perform weighted adjustment on the force data of each dimension based on the influence coefficient when performing LOF algorithm detection on the force data at the current moment to obtain the adjusted LOF value at the current moment; and perform transmission feedback adjustment based on the adjusted LOF value at the current moment.

[0053] The LOF algorithm is used to determine the LOF value of the currently collected real-time force feedback data. Generally speaking, the coefficient of each dimension is fixed across all data, but not all dimensions can be effectively used for LOF detection. Because the puncture process is a continuous dynamic process, the interaction between the puncture needle and tissue, such as from skin to fat and then to muscle, has a temporal correlation. The force feedback data at the current moment is closely related to the data from the previous period. For example, if the puncture needle was in the fat layer 0.5 seconds ago, it may have just entered the muscle layer at this moment. Abnormal changes in its force feedback should be compared with the data within this 0.5 second period to eliminate abnormal effects.

[0054] In order to improve the accuracy of detection, the influence coefficient of each dimension in the current real-time data anomaly detection is used. When analyzing the LOF value, each dimension is weighted for the data at any two moments, and the local distribution distance between the data is adjusted to make the LOF value more accurate. In an embodiment of the present invention, the method for obtaining the adjusted LOF value includes: obtaining the difference distance between the force data of each dimension at every two moments within the preset detection range before the current moment. The difference distance reflects the numerical difference between the force data in a single dimension, and characterizes the degree of deviation between the two moments in this dimension. The preset detection range can be set to the range of 1 second before the current moment, and the specific numerical value can be adjusted by the implementer.

[0055] Then, the product of the difference distance between the force data in each dimension at every two moments and the influence coefficient is used as the dimensional distance in each dimension at every two moments. The influence coefficient is used to adjust the credibility of each dimension for local anomaly detection, making anomaly analysis more accurate.

[0056] Further combined with the dimensional distance of all dimensions between every two moments, the adjusted distance between every two moments is obtained. In an embodiment of the present invention, the sum of the dimensional distances of all dimensions between every two moments is used as the adjusted distance between every two moments to reflect the overall degree of deviation. Finally, based on the deviation distance between the adjusted moments and the adjusted distance between moments within the preset detection range before the current moment, the adjusted LOF value of the current moment is obtained through the LOF algorithm. The possibility of an abnormal point at the current moment is reflected by the LOF value. The larger the LOF value, the higher the possibility of being an abnormal point. It should be noted that the LOF algorithm is a well-known technical means well known to those skilled in the art and will not be elaborated here.

[0057] Therefore, transmission feedback adjustment is performed based on the adjusted LOF value at the current moment. In an embodiment of the present invention, if the adjusted LOF value at the current moment is greater than the preset abnormality threshold, the force data at the current moment will not be transmitted and fed back. The preset abnormality threshold is set to 2, and the implementer can adjust it by himself. Data exceeding the preset abnormality threshold is determined to be interference abnormal data, and its transmission cannot provide assistance for subsequent analysis of force feedback data. It is interference data, so there is no need for transmission feedback to improve the accuracy and reliability of force feedback data.

[0058] In summary, for all dimensions of the current force data collected by the percutaneous puncture needle, the present invention first preliminarily determines the abnormal fluctuation coefficient of this dimension for abnormal detection of current real-time data based on the uneven regularity of the change of the force data in a single dimension, and then combines the connectivity of multi-dimensional force feedback, uses the correlation comparison of the force data in different dimensions in time series, and combines the abnormal fluctuation coefficient to determine the influence coefficient of each dimension of force data on the abnormal detection analysis of the current real-time data to improve the accuracy of abnormal point identification. Finally, all dimensions are weighted and adjusted during LOF abnormality detection to obtain a more accurate adjusted LOF value, and to make a regulatory judgment on whether transmission feedback can be performed. The present invention performs single-dimensional and multi-dimensional comparative analysis on multi-dimensional force data to measure the credibility of the influence of each dimension on the abnormality, adjust the LOF abnormality detection, and transmit feedback to real-time data more accurately.

[0059] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A real-time processing system for percutaneous puncture needle force feedback data, characterized in that: The system comprises: A data acquisition module is used to obtain the force data of the percutaneous puncture needle in different dimensions at each acquisition moment; The dimension impact analysis module is used to obtain the abnormal fluctuation coefficient of the force data of each dimension based on the regularity of the speed of change of the force data of each dimension in the time series before the current moment; and determine the influence coefficient of the force data of each dimension based on the correlation between the time series change of the force data of each dimension and other dimensions and the influence of the abnormal fluctuation coefficient; The feedback transmission adjustment module is used to perform weighted adjustment on the force data of each dimension based on the influence coefficient when performing LOF algorithm detection on the force data at the current moment to obtain the adjusted LOF value at the current moment; and perform transmission feedback adjustment based on the adjusted LOF value at the current moment.

2. A real-time processing system for percutaneous puncture needle force feedback data according to claim 1, characterized in that: The method for obtaining the abnormal fluctuation coefficient includes: For any dimension of force data, the initial dimension coefficient of the dimension force data at the current moment is obtained based on the deviation between the change degree of the dimension force data at the current moment and the time series change degree within the preset time series range before the current moment; According to the vibration frequency of the force data of the dimension before the current moment, combined with the initial dimension coefficient, the instability coefficient of the force data of the dimension at the current moment is obtained; According to the difference in the instability coefficient of the force data of this dimension between the current moment and the previous moment, the abnormal fluctuation coefficient of the force data of this dimension is obtained.

3. A real-time processing system for percutaneous puncture needle force feedback data according to claim 2, characterized in that: The method for obtaining the initial dimension coefficient includes: Within the preset time series range before the current moment, after calculating the numerical difference between each two adjacent sampling moments of the force data of the dimension, the mean of all numerical differences is used as the preceding mean square variable of the dimension; The difference between the force data of the dimension at the current moment and the previous sampling moment is used as the current change degree of the dimension; The ratio of the current change degree of the dimension to the previous mean variable is used as the initial dimension coefficient of the force data of the dimension at the current moment.

4. A real-time processing system for percutaneous puncture needle force feedback data according to claim 2, characterized in that: The method for obtaining the instability coefficient includes: Within the preset time series range before the current moment, the force data of the current dimension is Fourier transformed to obtain the frequency domain space of the dimension; the frequency at the highest peak in the frequency domain space is normalized to obtain the high-frequency anomaly of the dimension; The product of the high-frequency anomaly of the dimension and the initial dimension coefficient is used as the instability coefficient of the force data of the dimension at the current moment.

5. A real-time processing system for percutaneous puncture needle force feedback data according to claim 2, characterized in that: The abnormal fluctuation coefficient of the force data of the dimension is obtained according to the difference in the instability coefficient between the current moment and the previous moment, including: The product of the value after negative correlation mapping of the instability coefficient at the previous sampling moment of the current moment and the instability coefficient at the current moment is used as the abnormal fluctuation coefficient.

6. A real-time processing system for percutaneous puncture needle force feedback data according to claim 1, characterized in that: The method for obtaining the influence coefficient includes: For any dimension of force data, based on the temporal correlation between the dimension of force data and the other dimension of force data, the comparative availability of the dimension of force data and the other dimension of force data is obtained; The influence coefficient of the force data of this dimension is obtained by combining the comparative availability of the force data of this dimension with the force data of each other dimension and the abnormal fluctuation coefficient of the force data of this dimension and the force data of other dimensions.

7. A real-time processing system for percutaneous puncture needle force feedback data according to claim 6, characterized in that: The method for obtaining the comparative availability includes: In a preset local range before the current moment, calculate the Pearson correlation coefficient between the force data of this dimension and the force data of each other dimension in the time series, and use the absolute value of the Pearson correlation coefficient as the correlation degree of each other dimension; Calculate the correlation and value of other dimensions of this dimension as the correlation and value of this dimension; take the ratio of the correlation and value of each other dimension as the comparative availability of the stress data of this dimension and the stress data of each other dimension.

8. A real-time processing system for percutaneous puncture needle force feedback data according to claim 6, characterized in that: The influence coefficient of the force data of the dimension is obtained by combining the comparative availability of the force data of the dimension with the force data of each other dimension, and the abnormal fluctuation coefficient of the force data of the dimension and the force data of other dimensions, including: Each of the other dimensions of the stress data of the dimension is taken as the analysis dimension in turn, and the product of the abnormal fluctuation coefficient of the analysis dimension and the comparative availability is taken as the synchronization interference degree of the analysis dimension; the sum of the synchronization interference degrees of all other dimensions of the stress data of the dimension is taken as the relative influence degree of the dimension; The product of the abnormal fluctuation coefficient of the force data of this dimension and the relative influence degree is normalized and used as the influence coefficient of the force data of this dimension.

9. A real-time processing system for percutaneous puncture needle force feedback data according to claim 1, characterized in that: The method for obtaining the adjusted LOF value includes: Within the preset detection range before the current moment, obtain the difference distance between the force data in each dimension at every two moments; The product of the difference distance between the force data of each dimension at every two moments and the influence coefficient is taken as the dimensional distance of each dimension at every two moments; the dimensional distance of all dimensions between every two moments is combined to obtain the adjustment distance of every two moments; The adjusted LOF value at the current moment is obtained by using the LOF algorithm based on the adjusted distance between the times within the preset detection range before the current moment.

10. A real-time processing system for percutaneous puncture needle force feedback data according to claim 1, characterized in that: The transmission feedback adjustment according to the adjusted LOF value at the current moment includes: If the adjusted LOF value at the current moment is greater than the preset abnormal threshold, the force data at the current moment will not be transmitted for feedback.

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