Equipment target control data acquisition system and acquisition method thereof

Abstract

The invention relates to the technical field of data acquisition, in particular to an equipment target control data acquisition system and method, and the system comprises a state potential energy module, a speed projection module, a trajectory fusion module, a collaborative preloading module and an acquisition trigger module. According to the method, a potential function sequence is constructed by obtaining an angular velocity change rate and angular momentum, a characteristic stable section is determined by comparing an identification change point with a fall-back point, attitude stability description is improved, characteristic capture under interference is carried out, an included angle rate is calculated by using inertial navigation velocity projection filtering, and a motion mode analysis section and a data quality focusing key interval are determined by comparing. A multi-node early warning section is determined by fusing radar coordinate trajectory recognition composite section and analysis section time overlapping, early warning accuracy is enhanced by combining internal and external information, a unified scheduling instruction is generated to cooperate with preloading to improve timeliness, a sensor is activated according to the instruction to record and store, accurate and loss-free cooperative acquisition in a key period is guaranteed, and data integrity is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular to an equipment target control data acquisition system and an acquisition method thereof. Background Art

[0002] The technical field of data acquisition includes technical links such as physical signal perception, analog signal conversion, digital signal transmission and storage processing. The core content involves optimizing the acquisition accuracy of sensor signals, synchronously integrating multi-source heterogeneous data, improving the stability of real-time transmission, and storing massive data. This field systematically integrates the design of sensor hardware, the development of signal conditioning circuits, the optimization of analog-to-digital conversion interfaces, the adaptation of communication protocols, and the construction of database architectures, and forms a complete technical chain from the signal source to the storage terminal through anti-interference design of hardware circuits, dynamic adjustment of sampling rates, embedding of data compression algorithms, and allocation of transmission bandwidths.

[0003] One kind of equipment target control data acquisition system refers to a dedicated acquisition device designed for the target state monitoring requirements in weapon test scenarios. Technical matters include synchronous acquisition of multiple parameters such as target displacement, shock wave intensity, and temperature gradient. It adopts a dynamic binding mechanism between control instructions and data acquisition channels, establishes a multi-channel independent trigger acquisition timing sequence through programmable logic devices, eliminates electromagnetic interference by combining differential signal transmission, uses a data packet verification and retransmission mechanism to ensure transmission integrity, automatically switches the range according to preset thresholds, and performs high-frequency data lossless caching based on a hardware circular buffer.

[0004] Existing data acquisition technologies have limitations in processing high-speed dynamic targets. The fineness of its state perception is insufficient, relying on direct physical quantities such as displacement and impact, lacking the monitoring and analysis of rotational dynamic characteristics, including being unable to distinguish between stable acceleration and the initial stage of instability oscillation, which limits risk prediction. Its data processing and analysis are lagging, mostly for post-event analysis or passive responses based on fixed thresholds, lacking the ability to predict future motion trends. For example, when the temperature exceeds the threshold and alarms, it is estimated that the best intervention time has been missed. Its application of multi-source information fusion is not deep enough, focusing on parallel recording of data, lacking in-depth spatio-temporal correlation and fusion analysis of information such as internal states and external trajectories, and it is difficult to quickly integrate and judge sensor failures or real complex motions. Its acquisition trigger mechanism is passive and isolated, and it is difficult to actively prepare in advance according to predicted complex events. For short-term unexpected events, passive triggering will miss the acquisition window due to delays. It lacks intelligent collaborative acquisition optimization. Multiple channels lack dynamic collaboration based on overall situation prediction and cannot adjust node parameters according to expected events, resulting in waste of resources or poor quality of key data. Summary of the Invention

[0005] In order to solve the disadvantages existing in the prior art, embodiments of the present invention provide an equipment target control data acquisition system and an acquisition method thereof. The technical solutions are as follows: On the one hand, an equipment target control data acquisition system is provided, and the system includes: A state potential energy module, which obtains the angular velocity change rate and angular momentum vector value of the projectile through a real-time sensor, identifies the change and fall point of the projectile motion state, performs difference comparison, determines the characteristic stable section, and transmits it to the velocity projection module; A velocity projection module, which obtains the real-time velocity vector of the flying projectile through an inertial navigation device, adjusts the Kalman filter gain coefficient based on the covariance matrix of the angular velocity change rate of the projectile, calculates the projection angle change rate, and determines the projectile motion mode analysis section by comparing the motion state change of the characteristic stable section, and transmits it to the trajectory fusion module; A trajectory fusion module, which obtains the projectile coordinates and turning trajectory point set through a radar device, identifies the moving path composite section of the projectile coordinates, and marks the intersection section overlapping with the execution time of the projectile motion mode analysis section as the multi-node warning section, and transmits it to the collaborative preloading module; A collaborative preloading module, which receives the multi-node warning section to generate multiple prompt instructions, and integrates the instructions into a unified scheduling instruction and outputs it to the acquisition trigger module; An acquisition trigger module, which receives the unified scheduling instruction, activates each acquisition node sensor, and performs buffer initialization and data recording and storage.

[0006] As a further solution of the present invention, the characteristic stable section includes a stable time window, a set of state parameters, and a stability identifier. The projectile motion mode analysis section includes a projection vector sequence, an angle rate data, and a mode matching result. The multi-node warning section includes a warning time interval, a set of key coordinate points, and a fused trajectory segment. The unified scheduling instruction includes a target node list, a trigger timing arrangement, and sensor configuration parameters. The buffer initialization includes an initialization state identifier, a recorded data block, and a storage position pointer.

[0007] As a further solution of the present invention, the state potential energy module includes: A motion data acquisition sub-module: obtaining the angular velocity change rate time series and angular momentum vector value time series provided by the real-time sensor, which are used to characterize the basic data set of the projectile rotation motion state within a continuous time period, and obtaining the instantaneous motion state quantity; A state function generation sub-module: calling the instantaneous motion state quantity, performing a difference operation on the angular velocity change rate time series to obtain the second-order time derivative, combining the angular velocity second-order time derivative, performing Z-score normalization processing on the angular velocity second-order time derivative and the angular momentum vector value, and calculating the state index value at each time point to establish a state function time series; Stable feature discrimination sub-module: Based on the time series of the state function, calculate the absolute difference between the state index values at adjacent time points in the series, compare the calculated difference with the preset state potential difference threshold point by point to determine the change in the motion state of the projectile and the fallback point, and determine the stable feature segment; The state potential difference threshold is calibrated and set by analyzing the wind tunnel test data of the projectile and based on the extreme values of fluctuations or the extreme values of change rates of key parameters such as angular velocity or attitude angle related to the sudden change of the flight state.

[0008] As a further solution of the present invention, the velocity projection module includes: Velocity vector acquisition sub-module: Use the inertial navigation device to record the velocity information of the projectile at each flight moment, extract the velocity components at each moment, and obtain the velocity vector at the specified moment based on the motion state and position data of the projectile; Velocity projection processing sub-module: Based on the velocity vector at the specified moment, select the projection direction matching the velocity vector, perform a vector projection operation, map the velocity vector onto the corresponding reference axis, calculate the vector value after projection at each moment, and obtain the velocity projection vector; Kalman filter sub-module: According to the obtained velocity projection vector, by introducing prior information, use the Kalman filter to estimate the true value of the projection vector at each moment, eliminate noise and errors, and adjust the filtering result according to the difference between the current measurement data and the predicted data to obtain a smooth projection vector; The statistical characteristics of the measurement noise in the prior information are obtained by fitting a Gaussian distribution model based on the sensor noise sampling data set collected when the projectile is in a stationary state; Motion mode comparison sub-module: Based on the smooth projection vector, calculate the change rate of the projection angle at each moment, combine the change in the motion state of the stable feature segment, and perform a comparative analysis of the change rate of the projection angle through angle calculation and time series to obtain the projectile motion mode analysis segment.

[0009] As a further solution of the present invention, the difference between the current measurement data and the predicted data is processed using the formula: ; where is the Kalman gain, H represents the measurement matrix, the transpose of the measurement matrix H, represents the predicted state covariance matrix, describing the error propagation result of the state estimation during the process of predicting from time step i - 1 to time step i, R represents the covariance matrix of the measurement noise, is the covariance of the predicted measurement value.

[0010] As a further solution of the present invention, the trajectory fusion module includes: Coordinate acquisition sub-module: Obtain the real-time projectile coordinate data provided by the radar device. By analyzing the signals returned by the radar, extract the coordinate points of the projectile and record the timestamps, and unify the coordinate data into the position data in the global coordinate system to obtain the projectile coordinate point set; Trajectory recognition sub-module: Based on the projectile coordinate point set, identify continuous trajectory points, analyze the spatial changes between trajectory points, combine the time interval and path changes, and identify and determine the composite segments of the projectile movement path through the least squares method; Overlap judgment sub-module: Based on the obtained composite segments of the projectile movement path and the projectile movement pattern analysis segment, calculate the time intervals of the composite segments of the projectile movement path and the projectile movement pattern analysis segment, compare and calculate their time ranges, and perform a time overlap threshold determination to extract the overlapping time periods to obtain the multi-node warning segments; The time overlap threshold is calculated based on the standard deviation of the duration distribution of the pattern analysis segment of the projectile movement data, and the window length is set through the standard deviation to determine whether the intersection length of the time periods is greater than or equal to half of the window length.

[0011] As a further solution of the present invention, process the difference between the actual observation data points and the predicted values of the fitting model, using the formula: ; where E is the sum of squared errors, which is an index to measure the overall difference between the fitting model and the actual data. j represents the index of the data points, traversing from 1 to N, and N is the total number of data points. represents the spatial coordinate parameter of the j-th data point. is the dependent variable value of the j-th data point, corresponding to the independent variable , is the constant term of the polynomial. is the first-order term in the polynomial. is the second-order term in the polynomial is the coefficient of is the predicted value of the quadratic polynomial model when the independent variable is .

[0012] As a further solution of the present invention, the collaborative preloading module includes: Node distribution generation sub-module: Receive the multi-node warning segments, divide the spatial distribution of the nodes by using the clustering analysis method according to the time marks and spatial coordinates of each node, calculate the position and time intervals between the multi-nodes, and generate a set of prompt instructions according to the distribution of the nodes; Instruction integration sub-module: Based on the obtained set of prompt instructions, merge the time and spatial ranges of multiple prompt instructions, eliminate redundant instructions, optimize and adjust the instructions for the same time period, generate a unified scheduling instruction, and output it to the acquisition trigger module.

[0013] As a further solution of the present invention, the acquisition trigger module includes: Node activation sub-module: Based on the unified scheduling instruction, according to the time and space information in the instruction, activate the sensors of each acquisition node, and control the corresponding sensors to turn on and start data acquisition according to the geographical location and scheduling requirements of each node. The node sensors respond in a timely manner and start working, obtaining a list of activated acquisition nodes; Buffer initialization sub-module: Based on the list of activated acquisition nodes, allocate an independent buffer for each activated node, perform memory initialization according to the size and storage requirements of the buffer, clear the data, so that the buffer of each node can store the real-time acquired data, obtaining the initialized buffer information; Data storage sub-module: Based on the initialized buffer information, store the acquired data in partitions according to the timestamp and node ID, perform the data writing operation and redundancy check, and save the data at the specified location, obtaining the stored acquisition data.

[0014] On the other hand, a method for collecting equipment target control data is provided. This method is applied to an equipment target control data acquisition system, and the method includes: S1: Obtain the angular velocity change rate and angular momentum vector value through a real-time sensor, construct a sequence of state potential energy functions based on the data, compare and analyze the state potential energy in adjacent time periods through difference comparison, determine whether the motion state changes, and use it as the basis for identifying the fallback point to determine the characteristic stable section; S2: Obtain the real-time velocity vector of the flying projectile through an inertial navigation device, perform a velocity vector projection operation, and smooth the projection vector through the Kalman filter algorithm, calculate the projection angle change rate representing the second derivative of the included angle between the velocity vector projection and the projectile axis, and perform a comparative analysis based on the motion state change of the characteristic stable section and the projection angle change rate to generate a projectile motion mode analysis section; S3: Obtain the projectile coordinates and turning trajectory point set through a radar device, identify the path composite section in combination with the trajectory point and coordinate information, and compare it with the time overlap range of the projectile motion mode analysis section to determine whether it meets the time overlap threshold. If it meets, it is determined as the multi-node warning section; S4: Receive the multi-node warning section, generate multiple prompt instructions according to the node distribution, and integrate the instructions into a unified scheduling instruction; S5: Activate the sensors of each acquisition node by receiving the unified scheduling instruction, perform buffer initialization, and record and store the real-time acquired data to obtain the stored acquisition data.

[0015] The beneficial effects brought by the technical solution provided by the embodiments of the present invention at least include: By obtaining the angular velocity change rate and angular momentum vector value of the projectile, constructing a state potential function sequence analysis, it can finely identify the change of the motion state and the falling point, perform difference comparison to determine the characteristic stable section, improve the ability to describe complex motion postures and stability, and enhance the capture of stable characteristics under interference; Use inertial navigation to obtain the velocity vector, perform velocity projection and Kalman filter smoothing processing, calculate the change rate of the projection angle, and compare it with the state change of the characteristic stable section to accurately lock the motion mode analysis section, improve the quality and anti-interference ability of the velocity data, and focus on the key interval; Integrate the radar coordinates and the turning trajectory point set, identify the composite section of the moving path, perform time overlap judgment with the motion mode analysis section to determine the multi-node warning section, combine the internal state and the external trajectory, and enhance the warning accuracy; Generate prompt instructions according to the multi-node warning section and the node distribution, and integrate them into a unified scheduling instruction for output to achieve collaborative preloading based on prediction, and improve the pertinence and timeliness of the acquisition task; In the acquisition trigger link, activate the sensor according to the unified scheduling instruction, perform buffer initialization and data recording and storage to ensure accurate and lossless collaborative acquisition during the key warning period, and improve the integrity and effectiveness of data acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 is the system flow chart of the present invention; Figure 2 is the system block diagram of the present invention; Figure 3 is the schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will describe the technical solutions in the present invention with reference to the drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "example" in the present invention should not be construed as being more preferred or more advantageous than other embodiments or design solutions. Exactly, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either of the two can be selected.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, their intended meanings are the same. "Of", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, their intended meanings are the same.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, their intended meanings are the same.

[0022] To make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0023] The embodiments of the present invention provide an equipment target control data acquisition system. Please refer to Figure 1 , the present invention provides a technical solution. An equipment target control data acquisition system includes: A state potential energy module, which obtains the angular velocity change rate and angular momentum vector value of the projectile through a real-time sensor, identifies the change and fall point of the projectile motion state, performs difference comparison, determines the characteristic stable section, and transmits it to the velocity projection module; A velocity projection module, which obtains the real-time velocity vector of the flying projectile through an inertial navigation device, adjusts the Kalman filter gain coefficient based on the covariance matrix of the angular velocity change rate of the projectile, calculates the projection angle change rate, and determines the projectile motion mode analysis section by comparing the motion state change of the characteristic stable section, and transmits it to the trajectory fusion module; A trajectory fusion module, which obtains the projectile coordinates and turning trajectory point set through a radar device, identifies the moving path composite section of the projectile coordinates, and marks the intersection section overlapping with the execution time of the projectile motion mode analysis section as a multi-node warning section, and transmits it to the collaborative preloading module; A collaborative preloading module, which receives the multi-node warning section to generate multiple prompt instructions, and integrates the instructions into a unified scheduling instruction and outputs it to the acquisition trigger module; An acquisition trigger module, which receives the unified scheduling instruction, activates each acquisition node sensor, and performs buffer initialization and data recording and storage.

[0024] The characteristic stable section includes a stable time window, a set of state parameters, and a stability identifier. The projectile motion mode analysis section includes a projection vector sequence, angle rate data, and a pattern matching result. The multi-node warning section includes a warning time interval, a set of key coordinate points, and a fused trajectory segment. The unified scheduling instruction includes a target node list, a trigger timing arrangement, and sensor configuration parameters. The buffer initialization includes an initialization state identifier, a recorded data block, and a storage location pointer.

[0025] Please refer to Figure 2 , the state potential energy module includes: Motion data acquisition sub-module: Obtain the time series of the angular velocity change rate of the projectile and the time series of the angular momentum vector value provided by the real-time sensor, which are used to characterize the basic data set of the projectile's rotational motion state in a continuous time period, and obtain the instantaneous motion state quantity; Obtain the time series of the angular velocity change rate of the projectile and the time series of the angular momentum vector value provided by the real-time sensor. The execution process is as follows: First, initialize the data acquisition system, set the sampling frequency to 100Hz, that is, collect data every 0.01 seconds. Then, start the gyroscope sensor group and the angular momentum measurement unit installed inside the projectile or at key positions. The sensor continuously monitors the angular velocity (unit: rad / s) of the projectile around three mutually orthogonal axes (defined as the roll axis, pitch axis, and yaw axis) and the angular momentum vector (unit: kg·m² / s). The system continuously reads the analog signal or digital signal output by the sensor, and performs a time difference processing on the read angular velocity signal , using first-order backward difference, and calculates the angular velocity change rate , at time of the angular velocity change rate, at time of the collected angular velocity, at time of the collected angular velocity, is the i-th sampling moment, is the sampling time interval, that is, 0.01 seconds, i represents the i-th sampling moment among them, and synchronously record the three component values of the angular momentum vector at the corresponding time . At t = 1.00 seconds, the collected angular velocity is rad / s. At t = 1.01 seconds, the collected angular velocity is rad / s. Then, the angular velocity change rate rad / s² at t = 1.01 seconds is calculated. At the same time, the angular momentum vector value at t = 1.01 seconds is recorded as kg·m² / s. For each sampling time point obtained, the angular velocity change rate vector and the angular momentum vector together with the timestamp Stored together to form a time-series data stream, a data structure or table can be established that includes time, the X component of the angular velocity change rate, the Y component of the angular velocity change rate, the Z component of the angular velocity change rate, the X component of the angular momentum, the Y component of the angular momentum, and the Z component of the angular momentum. During the continuous acquisition and calculation process, covering the entire flight time period of the projectile to be analyzed, including from the 5th second to the 25th second after launch, a data set consisting of (25 - 5) × 100 = 2000 data points is formed. The following table 1 shows a small segment of sample data. Table 1 Fragment Table of Projectile Motion Sensor Sampling Data

[0026] As shown in Table 1, for the angular velocity change rate and angular momentum data collected and calculated from t = 5.00 s to t = 5.04 s, the data set completely records the dynamic information of the projectile's rotational motion within the specified time period. The set of data points arranged in chronological order generates a basic data set representing the projectile's rotational motion state within a continuous time period, and the instantaneous motion state quantities are obtained.

[0027] State function generation sub-module: Call the instantaneous motion state quantities, perform a difference operation on the angular velocity change rate time series to obtain the second-order time derivative, combine the second-order time derivative of the angular velocity, perform Z-score normalization processing on the second-order time derivative of the angular velocity and the angular momentum vector value, and calculate the state index value at each time point to establish a time series of state functions. Calling the instantaneous motion state quantities means calling the angular velocity change rate time series recorded in the basic data set generated by the previous sub-module and the angular momentum vector time series , perform a difference operation on the angular velocity change rate time series to obtain the second-order time derivative , and the numerical difference method, second-order backward difference, is selected for the operation , represents the second-order time derivative of the angular velocity at time , represents the angular velocity collected at time , represents the angular velocity collected at time , represents the angular velocity collected at time , represents the sampling time interval, , represents the discrete sampling moments. Taking the second-order backward difference as an example, take seconds, and use the data in Table 1 to calculate the second-order time derivative of the angular velocity at t = 5.02 s, which requires the angular velocity at t = 5.02 s, t = 5.01 s, and t = 5.00 s Value: , , , rad / s³, and then calculate the square value of the angular velocity second-order time derivative vector, i.e., the square of the modulus , for the example of t = 5.02 s, (rad / s³)². Subsequently, obtain the angular momentum vector at the current time point , and calculate the square value of the magnitude , represents the square value of the magnitude of the angular momentum vector at the discrete time point . represents the instantaneous angular momentum vector at the discrete time point . represents the square of the component of the angular momentum vector on the x-axis at the discrete time point . represents the square of the component of the angular momentum vector on the y-axis at the discrete time point . represents the square of the component of the angular momentum vector on the z-axis at the discrete time point . represents the i-th sampling moment in a discrete time point or time series. For the example of t = 5.02 s, take kg·m² / s from Table 1, then (kg·m² / s)². Sum the square value of the angular velocity second-order time derivative and the square value of the magnitude of the current angular momentum vector to calculate the state index value at each time point. For the example of t = 5.02 s, . Repeat the above difference calculation, square calculation, and summation calculation process for all time points in the basic dataset (differential calculation requires the data points before and after, and the starting and ending few points cannot be calculated). Arrange the obtained state index values in chronological order to establish a state function time series. Stable feature discrimination sub-module: Based on the state function time series, calculate the absolute difference between the state index values at adjacent time points in the series, and compare the calculated difference with the preset state potential difference threshold point by point to determine the change in the motion state of the projectile and the falling point, and determine the characteristic stable segment; The state potential difference threshold is calibrated and set by analyzing the wind tunnel test data of the projectile according to the extreme values of fluctuations or the extreme values of change rates of key parameters of angular velocity or attitude angle related to the sudden change of flight state; Based on the state function time series , that is, the sequence of state index values covering the observation time period calculated by the previous sub-module, including the sequence , calculate the absolute difference between the state index values at adjacent time points in the sequence , the calculation is performed for all adjacent point pairs where i ranges from 1 to N - 1 in the sequence. For example, if S(5.02s) = 27000533.34 has been calculated and S(5.03s) = 28500700.00 is then calculated, the absolute difference is , the calculated difference is compared point by point with the preset state function difference threshold . The setting of the state function difference threshold needs to be based on the projectile flight test data or simulation data. Analyze the degree of change of the state function S(t) of the projectile during the known stable flight stage and the stage of maneuvering orbit change or being disturbed. Statistically analyze the maximum value and fluctuation range of the stable stage , as well as the typical values of the non-stable stage . Select a value that can distinguish these two states as the threshold. For example, through data analysis, during stable flight does not exceed , while during attitude adjustment or being disturbed it will jump to or above. To be able to identify significant changes and filter out normal fluctuations during stable flight, the threshold can be set between the two. It can be taken as 1.5 times the maximum fluctuation value of the stable stage. If the maximum observed during the stable stage is , then set . Now compare the calculated difference with the threshold. For the example , since , it is determined that a change in the motion state has occurred between t = 5.02s and t = 5.03s. The difference of the point , then mark as a point near the time of the change in the motion state. Continue to check the sequence backward. If at (m > k), the difference first drops back to , then can be regarded as a fallback point after the state change. By this point-by-point comparison, determine the change and fallback points of the projectile's motion state. Identify the time intervals that continuously satisfy , including all the differences corresponding to the time series are all less than or equal to , then the time period is determined as a characteristic stable segment, and determine the characteristic stable segment.

[0028] Please refer to Figure 2 , the velocity projection module includes: Velocity vector acquisition sub-module: Using an inertial navigation device to record the velocity information of the projectile at each flight moment, extracting the velocity components at each moment, and obtaining the velocity vector at a specified moment based on the motion state and position data of the projectile; Using the velocity information of the projectile at each flight moment recorded by an inertial navigation device (INS), perform actions. The system accesses the inertial navigation unit installed on the projectile. The unit is built-in with a gyroscope and an accelerometer, and outputs the velocity information of the projectile in a predetermined coordinate system (including the earth-fixed coordinate system at the launch point or the projectile coordinate system) in real time through integral operations. At each preset sampling time point (the sampling frequency is consistent with the aforementioned data acquisition, including 100Hz, seconds), read the velocity measurement value from the INS data stream. The measurement value is given in the form of three orthogonal components, including the velocity at moment is , where , , are the velocity components along the X, Y, and Z axes of the coordinate system respectively, with the unit of meters per second (m / s). Including at the time point t = 15.20 seconds, the velocity components output by the inertial navigation device are m / s, m / s, m / s, then extract these three component values, and based on the current motion state of the projectile (including whether it is in the afterburner or glide phase, which affects the selection or conversion of the coordinate system) and position data (also provided by the INS, used for coordinate conversion or reference), combine these three velocity components to construct the velocity vector at the moment, that is m / s, the velocity vector is for the reference coordinate system used in the INS internal solution. If the velocity in the target reference system is required, coordinate transformation operations need to be combined with the attitude data (including Euler angles or quaternions) provided by the INS. If it is set that the currently obtained is the velocity in the required reference system, then repeat this extraction and combination process to cover the entire time period required for analysis and obtain the velocity vector at the specified moment.

[0029] Velocity projection processing sub-module: Based on the velocity vector at a specified moment, select the projection direction that matches the velocity vector, perform a vector projection operation, map the velocity vector onto the corresponding reference axis, calculate the vector value after projection at each moment, and obtain the velocity projection vector; Based on the velocity vector at a specified moment , first, one or more projection directions with physical meanings need to be selected as reference axes. The selection is based on the analysis requirements, including the option to select the longitudinal axis direction of the projectile (reflecting the velocity component along the warhead direction), the ballistic tangent direction, or the normal direction perpendicular to the ballistic trajectory, etc. Set to select the longitudinal axis of the projectile itself as the projection direction, and the direction information of the axis (unit vector) is provided by the attitude measurement system (including a part of the INS or an independent attitude sensor) at the same moment Perform the vector projection operation, which is to project the velocity vector onto the selected reference axis direction The projection operation is completed through the vector dot product (inner product): First, calculate the scalar projection of the velocity vector in the reference axis direction , represents the instantaneous velocity vector at the discrete time point , represents the unit vector in the reference axis direction at the discrete time point , represents the component of the velocity vector on the x-axis at the discrete time point , represents the component of the reference axis unit vector on the x-axis at the discrete time point , represents the component of the velocity vector on the y-axis at the discrete time point , represents the component of the reference axis unit vector on the y-axis at the discrete time point , represents the component of the velocity vector on the z-axis at the discrete time point , represents the component of the reference axis unit vector on the z-axis at the discrete time point , represents the i-th sampling moment in a discrete time point or time series. Then, multiply the scalar projection value by the unit vector in the reference axis direction to obtain the projected vector value, i.e., the velocity projection vector . Taking t = 15.20 seconds as an example, the obtained velocity vector is m / s. Set the unit vector in the longitudinal axis direction of the projectile obtained simultaneously as (this is a unit vector, ), then calculate the scalar projection , Then calculate the projection vector: m / s, for each specified moment in the time series The operations of selecting the projection direction, calculating the dot product, and multiplying by the unit vector are repeatedly performed to obtain the velocity projection vector.

[0030] Kalman filter sub-module: According to the obtained velocity projection vector, by introducing prior information, the Kalman filter is used to estimate the true value of the projection vector at each moment, eliminate noise and errors, and adjust the filtering result according to the difference between the current measurement data and the predicted data to obtain the smoothed projection vector; The statistical characteristics of the measurement noise in the prior information are obtained by fitting a Gaussian distribution model based on the sensor noise sampling data set collected under the static state of the projectile; According to the obtained time series of velocity projection vectors , which is the directly calculated measurement value. i is the projection error introduced by the velocity measurement error and attitude measurement error of the INS at the i-th sampling moment in the sequence. It needs to be processed by introducing prior information and using the Kalman filter. First, define the state vector of the filter , including the projection vector itself and the estimated velocity change rate, including the assumption , the state transition model describes the evolution of the state from moment to moment. F is the state transition matrix, is the process noise, and the measurement model describes the relationship between the measurement value (i.e., the directly calculated ) and the true state, , H is the measurement matrix (if the state is directly the projection vector, then H is the identity matrix I), is the measurement noise. The prior information is: the statistical characteristics of the process noise, that is, the covariance matrix Q, which characterizes the uncertainty of the state transition model, including the setting of the estimated maneuvering acceleration range of the projectile. Set the standard deviation of the acceleration of the projectile in the projection direction to =5 m² / s, then the diagonal elements of Q can be set to m² / s², the statistical characteristics of the measurement noise, that is, the covariance matrix, which characterizes the uncertainty of the directly calculated projection vector , and the value is evaluated according to the projection error caused by the INS velocity error (including =0.1 m / s) and the attitude error (including =0.05°≈0.00087 rad). The diagonal elements of R are set to . The dynamic constraint conditions are reflected in the construction of the state transition matrix F, including setting the projection velocity to change approximately uniformly in a short time, then F = I. The time synchronization parameter is used to calculate The INS data and the attitude data used for calculation are strictly aligned in time stamp, and the parameters need to be obtained through actual measurement calibration, including collecting sensor outputs under known inputs through flight tests or simulations, analyzing the error statistical characteristics to set Q and R. The execution process of the Kalman filter is at each time step to perform two steps: prediction and update. The prediction step is based on the optimal estimate at the moment and the state transition model to predict the state at the moment and its covariance . The update step uses the measurement value at the moment to correct the prediction result and calculate the Kalman gain , where is the Kalman gain, H represents the measurement matrix, the transpose of the measurement matrix H, represents the predicted state covariance matrix, which describes the error propagation result of the state estimate during the process of predicting from time step i - 1 to time step i. R represents the covariance matrix of the measurement noise, is the covariance of the predicted measurement value, and the predicted state covariance matrix After the prediction step, we obtain the covariance of the prediction error as , and the units here, the diagonal elements are (m / s)² and (m / s²)² respectively, representing the variance of the predicted velocity and the variance of the predicted acceleration respectively. Calculate (the transpose of H): , calculate : , calculate , which represents the predicted uncertainty mapped to the measurement space: , , , and finally calculate the Kalman gain . The measurement value is the directly measured projected velocity , that is . Therefore, the measurement matrix H, which maps the state vector to the measurement space, is , and then calculate the optimal estimate at the current moment and the updated covariance process according to the difference between the current measurement data and the predicted data , and dynamically adjust the filtering result through the Kalman gain to perform filtering operations on all in the time series to obtain a smoothed projection vector.

[0031] Motion mode comparison sub-module: Based on the smooth projection vector, calculate the change rate of the projection angle at each moment. Combining the motion state changes in the feature stable section, through angle calculation and time series, perform a comparative analysis of the change rate of the projection angle to obtain the projectile motion mode analysis section; Based on the smooth projection vector time series , which is the result output by the Kalman filter sub-module. First, calculate the change rate of the projection angle at each moment. It is necessary to first define the projection angle , which is the smooth projection vector and a fixed reference direction The included angle between them. The reference direction can be the direction of the projection vector at the initial moment , or the vertically upward direction of the geodetic coordinate system . Select , then the included angle is calculated by the dot product: , then , in radians or degrees. For example, when t = 20.00 seconds, the filtered projection vector is m / s, then its dot product with is -80.5, and its modulus is ≈ ≈ ≈769.3m / s, so , calculated to get . Next, calculate the change rate of the projection angle , using the first-order backward difference , represents the previous discrete time point of the current time point . For example, if the calculated is obtained, then . Then, combining the information of the feature stable section determined in the previous stable feature discrimination sub-module, including determining that the time period is the stable section, and the identified motion state change points (including at the moment ), perform a comparative analysis of the change rate of the projection angle . The operation is to calculate the statistical characteristics of in the feature stable section time period (including ) and the non-stable section (including the time window near the state change point ), including calculating the average absolute value and the maximum absolute value of in the stable section, and comparing them with the corresponding statistical values in the non-stable section. For example, if the stable section The average absolute angular rate calculated internally is 8° / s, and the maximum absolute angular rate is 15° / s. In the state change region identified near t = 15.2 s, the calculated average absolute angular rate is 45° / s, and the maximum absolute angular rate reaches 70° / s. This comparison is obtained through angle calculation (acquiring ), and time series analysis (calculating and statistically analyzing in segments), to determine the degree of difference in the projection angle change rate under multiple motion states, including setting a change rate discrimination threshold , including = 25° / s. If the average of a single segment is considered a stable mode. If the average or the peak significantly exceeds this value, it is considered a maneuver or disturbance mode. The comparison analysis results are associated with the corresponding time period to obtain the analysis segment of the projectile motion mode.

[0032] Please refer to Figure 2 , the trajectory fusion module includes: Coordinate acquisition sub-module: Obtain the real-time projectile coordinate data provided by the radar device. By analyzing the radar return signal, extract the coordinate points of the projectile and record the time stamp, and unify the coordinate data into the position data in the global coordinate system to obtain the projectile coordinate point set; Obtain the real-time projectile coordinate data provided by the radar device. The operations are as follows: Start the ground or airborne radar system to scan the target airspace. When the radar beam detects the projectile target, receive the reflected echo signal. The signal processing unit analyzes the echo signal, extracts the time delay information, calculates the slant range R between the radar station and the projectile, and measures the antenna beam pointing when the signal returns to obtain the azimuth angle and the pitch angle , and record them together with the measurement time stamp . For example, at Coordinated Universal Time (UTC) 14:35:22.100 (corresponding to the flight time t = 30.10 seconds), the data measured by a single ground radar station is: slant range R = 65200 meters, azimuth angle , pitch angle . The system reads the original measurement data and the time stamp , and converts from the spherical coordinate data of the radar station itself to the specified global coordinate system, including the World Geodetic System 1984 Earth-centered, Earth-fixed (ECEF) coordinate system or the East-North-Up (ENU) coordinate system with the launch point as the origin. The conversion requires the known global coordinates of the radar station and the local coordinate axis directions, and calculates the coordinate rotation and translation operations. For example, when converting to the ENU coordinate system and setting the radar station at the origin of the ENU coordinate system, then , , , substitute the example data: x(30.10 s) = 65200×cos(25.8°)×sin(125.2°) ≈ 65200×0.899×0.817 ≈ 47950 m, y(30.10 s) = 65200×cos(25.8°)×cos(125.2°) ≈ 65200×0.899×(-0.576) ≈ -33830 m, z(30.10 s) = 65200×sin(25.8°) ≈ 65200×0.435 ≈ 28360 m, The calculated global coordinate points together with the time stamp are stored together, continuously performing radar tracking and data processing, and obtaining and recording coordinate points at fixed time intervals (including the estimated radar data update rate is 1 Hz, set to 10 Hz, that is seconds), as shown in Table 2, Table 2 Radar measurement of missile body coordinate data fragment table

[0033] As shown in Table 2, a part of the converted missile body global coordinate data from t = 30.00 s to t = 30.40 s is listed, and a set of coordinate points arranged in chronological order is obtained to get the missile body coordinate point set.

[0034] Trajectory recognition sub-module: Based on the missile body coordinate point set, identify continuous trajectory points, analyze the spatial changes between trajectory points, and combine the time interval and path changes to identify and determine the composite segments of the missile body movement path by the least squares method; Based on the missile body coordinate point set, which is the time series data output by the coordinate acquisition sub-module, first identify continuous trajectory points, that is, select a sequence of data points within a time window from the point set, including all points from to Analyze the spatial changes between trajectory points, calculate the displacement vectors and velocity estimates between adjacent points, monitor the magnitude and direction change trends of the velocity vectors, and combine the time interval And path changes (including judging the curvature change of the trajectory by calculating the change in the angle between consecutive displacement vectors), determine whether the projectile motion path is an approximate straight line segment, a curved line segment, or a composite segment composed of multiple characteristic paths. Specifically, if the direction change of the displacement vectors in consecutive multiple time steps is very small (including the angle between adjacent displacement vectors being less than 1°), it is a straight-line motion. If the direction change is continuous and significant (including the angle being continuously greater than 5°), it is a curved-line motion. Based on this analysis, divide the entire trajectory into several segments, and perform path determination operations on each segment (or the points within the entire time window). Use the least squares method to fit the trajectory model, and set a quadratic polynomial model to fit the change of the X coordinate with time , select the time window and take N=(k - j)+1 coordinate points within it. The goal is to find the coefficients such that the sum of squared errors is minimized. Here, E is the sum of squared errors, which is an index measuring the overall difference between the fitting model and the actual data. j represents the index of the data point, traversing from 1 to N, and N is the total number of data points. represents the spatial coordinate parameter of the j-th data point. is the dependent variable value of the j-th data point, corresponding to the independent variable . is the constant term of the polynomial. is the first-order term in the polynomial. is the second-order term in the polynomial coefficient. is the predicted value of the quadratic polynomial model when the independent variable is . By taking the partial derivatives of E with respect to respectively and setting them equal to zero, a system of linear equations is obtained, and solving the system of equations can obtain the optimal coefficients . Perform similar operations on the Y and Z coordinates to obtain the fitted path function , where each component is a polynomial or a selected function form with respect to time t. Specifically, perform a quadratic polynomial fitting on the 5 data points from t = 30.00s to t = 30.40s in Table 2. The fitted function segment represents a determined section of the projectile motion path within the corresponding time interval. If the entire trajectory is divided into multiple parts for fitting, what is obtained is a composite segment of the projectile motion path composed of multiple fitted function segments.

[0035] Overlap judgment sub-module: Based on the composite section of the projectile motion path and the projectile motion mode analysis section obtained, calculate the time intervals of the composite section of the projectile motion path and the projectile motion mode analysis section, compare and calculate their time ranges, and perform a time overlap threshold determination to extract the overlapping time periods to obtain a multi-node early warning section; the time overlap threshold is calculated based on the standard deviation of the duration distribution of the mode analysis section of the projectile motion data, and the window length is set through the standard deviation to determine whether the intersection length of the time periods is greater than or equal to half of the window length; Based on the composite section of the projectile motion path, that is, the time interval output by the trajectory recognition sub-module and the corresponding fitted path function , call the motion mode analysis section obtained previously in the motion mode comparison sub-module, and the analysis section also corresponds to the time interval , and each interval is associated with a motion mode (including stable flight mode, maneuver mode, perturbed mode, etc.). By calculating the time intervals of the path composite section and the motion mode analysis section, the operation is to traverse all path segments m and all mode segments n, and extract the time intervals for each pair (m, n) and , Calculate the intersection of these two time intervals: , Compare and calculate their time ranges, that is, calculate the duration of the intersection interval: , and then perform a time overlap threshold determination. A time overlap threshold needs to be set in advance , and the setting of the threshold refers to the minimum time required for the projectile to perform typical tactical maneuvers or experience perturbations, as well as the duration characteristics of the stable flight mode. For example, if the typical duration of a projectile's one-time maneuver is about 1 second, and the minimum analysis duration for radar trajectory segment fitting and mode recognition is 0.5 second, then the threshold can be set to = 0.8 second. The value is both greater than the pseudo-overlap caused by noise or short-term fluctuations and can capture the real associations with a certain persistence. Compare the calculated overlap duration with the threshold , including , then it is determined that there is a significant time overlap between the path segment m and the mode segment n, and the overlapping time period is extracted , including, if the time interval of identifying a path composite segment is , and at the same time the motion mode analysis segment identifies that the maneuver mode occurs at , then the intersection is , and the overlap duration = 31.8 - 30.5 = 1.3 seconds. Since , the time period Extract and collect all overlapping time periods that meet the overlapping threshold conditions , to obtain a multi-node warning segment.

[0036] Please refer to Figure 2 , the collaborative preloading module includes:[[]]END]] Node distribution generation sub-module: Receive the multi-node warning segment. According to the time stamps and spatial coordinates of each node, use the clustering analysis method to divide the spatial distribution of the nodes, calculate the position and time intervals between the multi-nodes, and generate a prompt instruction set based on the distribution of the nodes; Receive the multi-node warning segment. The warning segment is a set of time intervals where the projectile motion path and the target motion pattern significantly overlap in time determined in the previous step. It includes receiving two warning segments. Segment 1 is , associated with path segment m = 1 and maneuver mode n = 1. Segment 2 is , associated with path segment m = 2 and perturbed mode n = 2. First, extract the corresponding spatial coordinate nodes according to the time stamps of each warning segment. From the fitted path function output by the trajectory recognition sub-module, sample according to the time range of the warning segment and the preset time resolution (including = 0.1s) to obtain spatial coordinate points with time stamps. For segment 1 , the sampled node sequence is , for segment , the sampled result is , set the calculated node coordinates, including , … etc. Next, use the clustering analysis method to divide the spatial distribution of all the extracted nodes . Select the density-based spatial clustering algorithm (DBSCAN) and set two key parameters: neighborhood radius and minimum number of neighborhood points MinPts. The setting of refers to the radar positioning accuracy and the typical spatial scale during the maneuver of the projectile. It includes setting = 1000 meters, indicating that nodes with a spatial distance less than 1000 meters are considered adjacent. The setting of MinPts is based on the minimum number of nodes required to form a meaningful cluster, including setting MinPts = 3, indicating that at least 3 nodes (including itself) in the neighborhood of a node are considered core points. The algorithm execution process is to traverse all nodes and calculate the Euclidean distance between any two nodes . If , they are neighborhood points to each other. Find all core points. Starting from any core point, expand through the neighborhood relationship and divide all density-reachable nodes (core points or boundary points) into the same cluster. Points that cannot be assigned to any cluster are marked as noise points. Through clustering analysis, all nodes in segment 1 form cluster C1, and all nodes in segment 2 form cluster C2. Then calculate the position and time interval between multiple nodes. For nodes within a cluster, calculate the centroid coordinates of the cluster and the time span of the cluster , for multiple clusters C1, C2, calculate the distance between the centroids and the time interval (including the difference between the end time and the start time ). It includes that the centroid of cluster C1 is approximately , the time span , the centroid of cluster C2 is approximately , the time span , the centroid distance is about 45 kilometers, and the time interval is 45.2 - 31.8 = 13.4 seconds. Generate a prompt instruction set based on the distribution of nodes (the position, size, density, time span of the cluster, and the relationship between clusters). Rule example: If the number of nodes within a cluster C exceeds = 10 (high-density threshold, set with reference to the sampling rate and time span, (31.8 - 30.5) / 0.1 + 1 = 14, so C1 is high-density), then generate the instruction "Focus on key points", if the time span of the cluster is greater than = 1.0 second (duration threshold, set with reference to the typical maneuver duration), then generate the instruction "Continuously track", if the time interval between two clusters C1, C2 is less than = 5.0 seconds (associated time threshold, set with reference to the tactical response time), then generate the instruction "Sequential warning". According to the above rules and calculation results, cluster C1 (number of nodes 14 > 10, time span 1.3s > 1.0s) generates the instruction: "Instruction 1: Focus on key points, continuously track, area [approximate centroid coordinates of cluster C1], time [30.5s, 31.8s]", cluster C2 (set number of nodes 14 > 10, time span 1.3s > 1.0s) generates the instruction: "Instruction 2: Focus on key points, continuously track, area [approximate centroid coordinates of cluster C2], time [45.2s, 46.5s]". Since the time interval between clusters is 13.4s > 5.0s, no sequential warning instruction is generated, and a prompt instruction set is formed.

[0037] Instruction integration sub-module: Based on the obtained prompt instruction set, merge the time and space ranges of multiple prompt instructions, eliminate redundant instructions, optimize and adjust the instructions for the same time period, generate a unified scheduling instruction, and output it to the acquisition trigger module; The hint instruction set obtained by generating sub-modules based on node distribution includes Instruction 1: "Pay key attention, continuously track, Region A, time [30.5s, 31.8s]" and Instruction 2: "Pay key attention, continuously track, Region B, time [45.2s, 46.5s]", as well as other existing instructions, including warning segments with partial overlaps generated due to multiple analysis paths, generating Instruction 3: "Generally pay attention, Region A, time [31.5s, 32.5s]". Region A partially overlaps with Region A in space (including the centroid distance being less than the preset spatial merging threshold = 2 km). First, perform a merging check on the time and spatial ranges of multiple hint instructions, comparing any two instructions and 's time intervals and as well as spatial regions and , calculate the time overlap and spatial overlap (including judging the centroid distance of the regions ). If the time overlap > 0 and the spatial overlap meets the conditions (including ), then merge. Comparing Instruction 1 and Instruction 3, the time overlap interval is , with a duration of 0.3 seconds greater than 0. Set the centroid distance between Region A and A to 1.5 km, which is less than 2 km, so the merging condition is met. The merged time range is = [min(30.5, 31.5), max(31.8, 32.5)] = [30.5s, 32.5s]. The merged spatial region is (represented by a larger area that covers both or an updated centroid, including area A"), the priority of the merged instruction takes the higher of the two (key attention > general attention), and the merged instruction is obtained: "Instruction 1: Key attention, continuous tracking, area A, time [30.5s, 32.5s]". Next, redundant instructions are removed. During the merging process, if a single instruction is completely included within the time and space range of another instruction and its priority is not higher than the instruction that includes it, it is removed. For example, if there is Instruction 4: "Key attention, area A, time [31.0s, 31.5s]", it is completely covered by the merged Instruction 1 and its priority is not higher than Instruction 1, so Instruction 4 is removed. By optimizing and adjusting instructions for the same or adjacent time periods and spaces, including adjusting the start and end times of instructions to align with the standard time grid or fine-tuning the boundaries of the spatial area, the logical consistency between instructions is ensured. The adjusted instruction set is: "Instruction A: Key attention, continuous tracking, area A, time [30.5s, 32.5s]", "Instruction B: Key attention, continuous tracking, area B, time [45.2s, 46.5s]". Format the content of the integrated and optimized instructions (attention level, operation requirements, spatial range, time interval) to generate a unified scheduling instruction and output it to the acquisition trigger module.

[0038] Please refer to Figure 2 , the acquisition trigger module includes: Node activation sub-module: Based on the unified scheduling instruction, according to the time and space information in the instruction, activate the sensors of each acquisition node, and control the corresponding sensors to turn on and start data acquisition according to the geographical location and scheduling requirements of each node. The node sensors respond in a timely manner and start working, and a list of activated acquisition nodes is obtained; Based on the unified scheduling instruction output by the instruction integration sub-module, including Instruction A: "Key attention, continuous tracking, area A time [30.5s, 32.5s]" and Instruction B: "Key attention, continuous tracking, area B, time [45.2s, 46.5s]", the system first parses the time and space information in Instruction A. The time information is the start time and the end time , and the space information is that area A is defined by a coordinate range or volume, including a center point and a sphere defined by a radius r, or a cube defined by minimum / maximum coordinates. The system queries the pre-established acquisition node database, which stores the unique identifier (NodeID), geographical location coordinates of each available acquisition node (including seismic wave sensors, acoustic sensors, optical sensors), as well as the sensor type and capabilities, and performs a screening operation to obtain the geographical location of each node Compare with the spatial region A of instruction A to determine whether the node position falls within the specified spatial range of the instruction. Specifically, if region A is defined as a sphere with a center of meters and a radius r = 5000 meters, and the position of node NodeID101 is meters, calculate the distance from the node to the center of the region ; Since d = 2086m ≤ r = 5000m, node 101 is located within region A and is determined to be a node that needs to be activated. Repeat this spatial position comparison process for all nodes in the database to filter out the set of nodes located within region A , and according to the scheduling requirements of instruction A (paying key attention and continuously tracking) and the capabilities of the nodes (whether the sensor type matches the task requirements), send activation control instructions to each node in the filtered node set . The instructions include the node ID, the sensor identifier to be enabled, the start time of data collection and the end time . The instructions are sent through a preset communication network (including a wireless sensor network or a dedicated wired link). For example, at t = 30.4s (slightly earlier than the start time to reserve response time), send the activation instruction {NodeID:101,Command:Activate,Sensor:S1,StartTime:30.5,EndTime:32.5} to node 101. After receiving the instruction, the node controls the corresponding sensor S1 to turn on precisely at t = 30.5s and start data collection. The collected data stream includes sensor readings and timestamps. The node also sends back a status message verifying successful activation to the control center. When the control center receives the verification information from node 101 (including receiving {NodeID:101,Status:Activated,Sensor:S1} at t = 30.48s ), it is considered that the node sensor has responded in a timely manner and started working, and the node ID is added to the current list of activated collection nodes. The same spatial filtering, instruction sending, and status verification processes are also performed for instruction B. The IDs of all successfully activated nodes (regardless of whether they respond to instruction A or instruction B) are aggregated to obtain the list of activated collection nodes.

[0039] Buffer initialization sub-module: Based on the list of activated collection nodes, allocate an independent buffer for each activated node, initialize the memory according to the size and storage requirements of the buffer, and clear the data so that the buffer of each node can store the real-time collected data, obtaining the initialized buffer information; The list of activated acquisition nodes based on the output of the node activation sub-module, including the list [Node101, Node105, Node108, Node210, Node215]. The system needs to allocate an independent memory buffer for each activated node in the list to temporarily store the data collected by the node in real-time. Memory initialization is performed according to the size and storage requirements of the buffer. First, determine the buffer size required for each node. The size is generated based on the sensor data generation rate of the node (including the unit of bytes per second, Bytes / s) and the duration for which the node is activated (determined by the in the scheduling instruction) and multiply by a safety factor greater than 1 (including ), and the calculation formula is . The safety factor is set to account for the accumulation caused by short-term data transmission peaks or network delays. Setting it to 1.2 means reserving 20% of extra space. For example, node 101 responds to instruction A, and the activation time =32.5s - 30.5s = 2.0s. Suppose the data rate of its sensor S1 =10000Bytes / s (10KB / s), then the required buffer size =10000×2.0×1.2 = 24000Bytes (about 23.4KB). The system will allocate 24KB of memory space. Node 210 responds to instruction B, and the activation time =46.5s - 45.2s = 1.3s. Set the data rate =50000Bytes / s (50KB / s), then the required buffer size =50000×1.3×1.2 = 78000Bytes (about 76.2KB). The system will allocate 80KB (rounded up to the matching memory block size) of memory space for it. Repeat this calculation for all activated nodes in the list to determine the required buffer sizes, as shown in Table 3 below Table 3 Activation Node Buffer Allocation Table

[0040] As shown in Table 3, the buffer size calculation and allocation results for each activated node are listed. According to the calculated allocation size, the system applies for a continuous memory block of the specified size (including 24KB) for each node (including Node101) in the memory, and associates the starting address of the memory block with the node ID. After completing the memory allocation, the system performs a memory initialization operation, which is to clear all bytes of each allocated memory block (including calling the memset function to fill the memory area with 0). The buffer does not include any old or invalid data before starting to receive data, so that the buffer of each node is in a ready state and can store the real-time collected data. Information such as the ID of each node, the starting address of the allocated buffer, and the buffer size is recorded to obtain the initialized buffer information.

[0041] Data storage sub-module: Based on the initialized buffer information, the collected data is partitioned and stored according to the timestamp and node ID, the data write operation is executed and redundancy check is performed, and the data is saved at the specified location to obtain the stored collected data; Based on the initialized buffer information provided by the buffer initialization sub-module, including the buffer address Addr101 corresponding to the known node 101 with a size of 24KB, the buffer address Addr210 corresponding to the node 210 with a size of 80KB, etc. When the activated acquisition nodes (including Node101) start working and send the collected data packets to the data processing center through the network, the data receiving module receives the data packets. Each data packet includes the ID of the source node (including 101), the timestamp of data acquisition (including t = 30.505s), and the sensor readings (including numerical values or binary data blocks). The system looks up the initialized buffer information according to the node ID (101) in the data packet, the corresponding buffer address (Addr101) and the current written data position (initially 0), writes (copies) the sensor readings in the data packet to the current position of the node-specific buffer in timestamp order, updates the written data position pointer, stores the collected data in the memory buffer partitioned by timestamp and node ID, and performs redundancy check while executing the data writing operation. The check can be to verify whether the checksum of the data packet (including CRC checksum) matches the data content. If not, mark the data packet as an error or request retransmission. It can also be to check whether the timestamp of the data packet roughly conforms to the expected order. For example, if the timestamps of the data packets received continuously from Node101 are 30.505s, 30.515s, 30.535s, and the timestamp of 30.525S is less than the previous 30.535s, record a disorder event, still store the data in the buffer, and then perform sorting or correction. It can also check whether the buffer is about to be full. For example, when the used space of Node101's buffer reaches 95% of the allocated size (24KB) (this is a writing threshold, 24×0.95 = 22.8KB), trigger a buffer overflow warning and perform a data transfer operation. Move the data in the buffer (including 22.8KB of data starting from Addr101) to the permanent storage medium (including hard disk or database), and reset or adjust the written position pointer. The data writing operation continues until the acquisition end time of this node arrives (including t = 32.5s), and save the data at the specified location. The location is a structured file system path or a database table. The path structure can be designed based on the task ID, node ID, and time period, including / data / mission-XYZ / node-101 / 30.5-32.5.dat, or stored in a database table, including fields: node ID, timestamp, data value, etc. After completing the data writing and transfer of all activated nodes, the stored acquisition data is obtained.

[0042] Please refer to Figure 3 , the method includes: S1: Obtain the angular velocity change rate and angular momentum vector value through real-time sensors, construct a sequence of state potential energy functions based on the data, compare and analyze the state potential energy in adjacent time periods through difference comparison to determine whether there is a change in the motion state, and use it as the basis for identifying the fallback point to determine the characteristic stable section; S2: Obtain the real-time velocity vector of the flying projectile through the inertial navigation device, perform a velocity vector projection operation, and smooth the projection vector through the Kalman filter algorithm to calculate the projection angle change rate representing the second derivative of the angle between the velocity vector projection and the projectile axis. Compare and analyze based on the motion state change in the characteristic stable section and the projection angle change rate to generate the projectile motion mode analysis section; S3: Obtain the projectile coordinates and turning trajectory point set through the radar device, identify the path composite section in combination with the trajectory point and coordinate information, and compare it with the time overlap range of the projectile motion mode analysis section to determine whether it meets the time overlap threshold. If it meets, it is determined as the multi-node warning section; S4: Receive the multi-node warning section, generate multiple prompt instructions according to the node distribution, and integrate the instructions into a unified scheduling instruction; S5: Activate the sensors of each acquisition node by receiving the unified scheduling instruction, perform buffer initialization, and record and store the real-time acquired data to obtain the stored acquisition data.

[0043] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0044] In the present invention, "at least one" means one or more, and "multiple" means two or more. "At least one (item)" or similar expressions refer to any combination of these items, including any combination of single item (s) or plural item (s). For example, at least one (item) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0045] It should be understood that in various embodiments of the present invention, the magnitudes of the serial numbers of the above processes do not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0046] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0047] Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0048] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0049] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0050] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0051] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0052] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An equipment target control data acquisition system, characterized in that, The system includes: A state potential energy module that obtains the angular velocity change rate and angular momentum vector value of the projectile through real-time sensors, identifies the change in the motion state of the projectile and the fallback point, performs difference comparison, determines the characteristic stable section, and transmits it to the velocity projection module; A velocity projection module that obtains the real-time velocity vector of the flying projectile through an inertial navigation device, adjusts the Kalman filter gain coefficient based on the covariance matrix of the angular velocity change rate of the projectile, calculates the projection angle change rate, determines the projectile motion mode analysis section by comparing the motion state change of the characteristic stable section, and transmits it to the trajectory fusion module; A trajectory fusion module that obtains the projectile coordinates and turning trajectory point set through a radar device, identifies the moving path composite section of the projectile coordinates, marks the intersection section that overlaps with the execution time of the projectile motion mode analysis section as the multi-node warning section, and transmits it to the collaborative preloading module; A collaborative preloading module that receives the multi-node warning section, generates multiple prompt instructions, and integrates the instructions into a unified scheduling instruction and outputs it to the acquisition trigger module; An acquisition trigger module that receives the unified scheduling instruction, activates each acquisition node sensor, and performs buffer initialization and data recording and storage.

2. The equipment target control data acquisition system according to claim 1, wherein The characteristic stable section includes a stable time window, a set of state parameters, and a stability identifier. The projectile motion mode analysis section includes a projection vector sequence, an included angle rate data, and a pattern matching result. The multi-node warning section includes a warning time interval, a set of key coordinate points, and a fused trajectory segment. The unified scheduling instruction includes a target node list, a trigger timing arrangement, and sensor configuration parameters. The buffer initialization includes an initialization state identifier, a recorded data block, and a storage position pointer.

3. The equipment target control data acquisition system according to claim 1, characterized in that The state potential energy module includes: A motion data acquisition sub-module: obtains the angular velocity change rate time series and angular momentum vector value time series provided by the real-time sensor, which are used to characterize the basic data set of the projectile rotation motion state within a continuous time period, and obtains the instantaneous motion state quantity; A state function generation sub-module: calls the instantaneous motion state quantity, performs a difference operation on the angular velocity change rate time series to obtain the second-order time derivative, combines the angular velocity second-order time derivative, performs Z-score normalization processing on the angular velocity second-order time derivative and the angular momentum vector value, calculates the state index value at each time point, and establishes a state function time series; A stable feature discrimination sub-module: based on the state function time series, calculates the absolute difference between the state index values of adjacent time points in the series, compares the calculated difference with the preset state potential energy difference threshold point by point to determine the change in the motion state of the projectile and the fallback point, and determines the characteristic stable section; The state potential energy difference threshold is calibrated and set by analyzing the projectile wind tunnel test data according to the extreme values of fluctuations or change rates of key parameters of angular velocity or attitude angle related to sudden changes in flight state.

4. The equipment target control data acquisition system according to claim 1, characterized in that The velocity projection module includes: A velocity vector acquisition sub-module: uses an inertial navigation device to record the velocity information of the projectile at each flight moment, extracts the velocity components at each moment, and obtains the velocity vector at a specified moment according to the motion state and position data of the projectile; Velocity Projection Processing Sub-module: Based on the velocity vector at the specified moment, select the projection direction that matches the velocity vector, perform a vector projection operation, map the velocity vector onto the corresponding reference axis, calculate the vector value after projection at each moment, and obtain the velocity projection vector; Kalman Filtering Sub-module: According to the obtained velocity projection vector, by introducing prior information, use the Kalman filter to estimate the true value of the projection vector at each moment, eliminate noise and errors, and adjust the filtering result according to the difference between the current measurement data and the predicted data to obtain a smoothed projection vector; The statistical characteristics of the measurement noise in the prior information are obtained by fitting a Gaussian distribution model based on the sensor noise sampling data set collected when the projectile is in a stationary state; Motion Mode Comparison Sub-module: Based on the smoothed projection vector, calculate the change rate of the projection angle at each moment, combine the change of the motion state in the characteristic stable section, and perform a comparative analysis of the change rate of the projection angle through angle calculation and time series to obtain the projectile motion mode analysis section.

5. The equipment target control data acquisition system according to claim 4, wherein Process the difference between the current measurement data and the predicted data using the formula: ； where is the Kalman gain, H represents the measurement matrix, the transpose of the measurement matrix H, represents the predicted state covariance matrix, which describes the error propagation result of the state estimation during the process of predicting from time step i - 1 to time step i. R represents the covariance matrix of the measurement noise, is the covariance of the predicted measurement value.

6. The equipment target control data acquisition system according to claim 1, characterized in that The trajectory fusion module includes: Coordinate Acquisition Sub-module: Acquire the real-time projectile coordinate data provided by the radar device, extract the coordinate points of the projectile and record the time stamps by analyzing the radar return signal, and unify the coordinate data into the position data in the global coordinate system to obtain the projectile coordinate point set; Trajectory Recognition Sub-module: Based on the projectile coordinate point set, identify continuous trajectory points, analyze the spatial changes between trajectory points, and combine the time interval and path changes to identify and determine the composite section of the projectile motion path by the least squares method; Overlap Judgment Sub-module: Based on the obtained composite section of the projectile motion path and the projectile motion mode analysis section, calculate the time intervals of the composite section of the projectile motion path and the projectile motion mode analysis section, compare and calculate their time ranges, and perform a time overlap threshold determination to extract the overlapping time periods to obtain the multi-node warning section; the time overlap threshold is calculated based on the standard deviation of the duration distribution of the mode analysis section of the projectile motion data, and the window length is set by the standard deviation to determine whether the intersection length of the time periods is greater than or equal to half of the window length.

7. The equipment target control data acquisition system according to claim 6, characterized in that, Process the difference between the actual observed data points and the predicted values of the fitting model using the formula: ; Among them, E is the sum of squared errors, which is an index to measure the overall difference between the fitting model and the actual data. j represents the index of the data point, traversing from 1 to N, where N is the total number of data points. represents the spatial coordinate parameters of the j-th data point. is the dependent variable value of the j-th data point, corresponding to the independent variable , is the constant term of the polynomial. is the first-order term in the polynomial. is the second-order term in the polynomial. is the coefficient of is the predicted value of the quadratic polynomial model when the independent variable is .

8. The equipment target control data acquisition system according to claim 1, characterized in that, The collaborative preloading module includes: Node Distribution Generation Sub-module: Receive the multi-node warning section, divide the spatial distribution of the nodes using the clustering analysis method according to the time marks and spatial coordinates of each node, calculate the position and time intervals between multiple nodes, and generate a set of prompt instructions according to the node distribution; Instruction Integration Sub-module: Based on the obtained set of prompt instructions, merge the time and spatial ranges of multiple prompt instructions, eliminate redundant instructions, optimize and adjust the instructions in the same time period, generate a unified scheduling instruction, and output it to the acquisition trigger module.

9. The equipment target control data acquisition system according to claim 1, characterized in that The acquisition trigger module includes: Node activation sub-module: Based on the unified scheduling instruction, according to the time and space information in the instruction, activate the sensors of each acquisition node, and control the corresponding sensors to turn on and start data acquisition according to the geographical location and scheduling requirements of each node. The node sensors respond in a timely manner and start working to obtain a list of activated acquisition nodes; Buffer initialization sub-module: Based on the list of activated acquisition nodes, allocate independent buffers for each activated node, perform memory initialization according to the size and storage requirements of the buffers, and clear the data so that the buffers of each node can store the real-time acquired data to obtain the initialized buffer information; Data storage sub-module: Based on the initialized buffer information, partition and store the acquired data according to the time stamp and node ID, perform the data writing operation and redundancy check, and save the data at the specified location to obtain the stored acquired data.

10. A method for collecting equipment target control data, characterized in that, The method is used to implement the equipment target control data acquisition system according to any one of claims 1-9, and the method includes: S1: Obtain the angular velocity change rate and angular momentum vector value through real-time sensors, construct a sequence of state potential energy functions based on the data, compare and analyze the state potential energy in adjacent time periods through difference comparison to determine whether the motion state changes, and use it as the basis for identifying the fallback point to determine the characteristic stable section; S2: Obtain the real-time velocity vector of the flying projectile through the inertial navigation device, perform the velocity vector projection operation, and smooth the projection vector through the Kalman filter algorithm to calculate the projection angle change rate representing the second derivative of the included angle between the velocity vector projection and the projectile axis. Compare and analyze the motion state change of the characteristic stable section with the projection angle change rate to generate the projectile motion mode analysis section; S3: Obtain the projectile coordinates and turning trajectory point set through the radar device, identify the path composite section in combination with the trajectory point and coordinate information, and compare it with the time overlap range of the projectile motion mode analysis section to determine whether it meets the time overlap threshold. If it meets, it is determined as the multi-node warning section; S4: Receive the multi-node warning section, generate multiple prompt instructions according to the node distribution, and integrate the instructions into a unified scheduling instruction; S5: Activate each acquisition node sensor by receiving the unified scheduling instruction, perform buffer initialization, and record and store the real-time acquired data to obtain the stored acquired data.

Images