A cross-platform data acquisition method and monitoring control system based on big data

Abstract

The application relates to the technical field of big data, and discloses a cross-platform data acquisition method based on big data and a monitoring control system, which comprises the following steps: collecting vibration data, motor current data and rotating speed data of rotating equipment, performing angular domain conversion according to the rotating speed to obtain vibration angular domain data and motor current angular domain data; performing order analysis on the angular domain data to obtain vibration order spectrum data and motor current order spectrum data; further calculating non-normalized order cross bispectrum and a real part phase of the non-normalized order cross bispectrum, obtaining order cross bispectrum three-dimensional shear coefficients under order and constraint conditions, and calculating shear energy in a preset order range; finally, taking a preset shear tolerance as a constraint, dynamically updating two platform compression parameters by using shear energy statistics in a sliding window, so that the compression rate is improved and the shear energy does not exceed the tolerance, thereby obtaining a cross-platform data acquisition result with high compression rate and structural fidelity.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and more specifically, to a cross-platform data acquisition method and monitoring and control system based on big data. Background Technology

[0002] With the development of the Industrial Internet of Things (IIoT) and smart manufacturing, factory monitoring systems are generally deployed across multiple platforms in parallel. Different production lines or equipment use control systems, acquisition protocols, and data storage platforms from different manufacturers. Common platforms (such as AVEVA, Honeywell, Siemens, and Emerson) all employ their own compression mechanisms to improve data throughput and storage efficiency. For example, the swing gate algorithm dynamically selects points based on the signal slope, the deadband strategy performs sparsification based on time or amplitude thresholds, and error-bounded compression achieves high-ratio downsampling through numerical prediction. These methods can significantly reduce the amount of data, but they all share the characteristic of morphological processing of time-series signals: signal edges are smoothed, local oscillations are weakened, and the sampling times of different variables are no longer strictly aligned. In multi-platform integrated monitoring scenarios, this difference is not only manifested as a loss of accuracy but also alters the temporal structure and spectral distribution of the data.

[0003] In real-world operating environments, data acquisition links across different platforms often incorporate different transmission mechanisms and time bases. Some devices employ OPCUAPubSuboverTSN, which has a defined upper limit on latency; others use MQTT or lightweight edge gateways, resulting in out-of-order or duplicate data. Furthermore, inconsistent compression and segmentation strategies across platforms cause subtle shifts in the time markers and forms of the same event across different platforms. This shift may not be noticeable under steady-state conditions, but when devices enter acceleration, deceleration, or frequency adjustment phases, the relative phase and energy distribution of the signal undergo complex changes. Different platform compression algorithms sample this dynamic response differently, causing the same change to be encoded in different shapes across multiple platforms. This leads to structural shifts in cross-platform monitoring results: the system surface data appears continuous and numerically reasonable, but in higher-level state calculations, anomaly detection, and trend modeling, it may exhibit spurious changes, lags, or distortions. Summary of the Invention

[0004] This invention provides a cross-platform data acquisition method and monitoring and control system based on big data, which solves the technical problem that in a multi-platform acquisition system with high compression ratio, traditional data consistency and time synchronization methods cannot characterize the morphological deviation caused by the compression strategy, thus failing to effectively guarantee the comparability and reliability of cross-platform monitoring data.

[0005] In a first aspect, the present invention provides a cross-platform data acquisition method based on big data, comprising:

[0006] Collect vibration data of rotating equipment, current data of the motor driving the rotating equipment, and rotational speed data of the rotating equipment. Based on the rotational speed data, the vibration data and motor current data are transformed into angular domain data to obtain vibration angular domain data and motor current angular domain data. Order analysis was performed on the vibration angular domain data and the motor current angular domain data to obtain vibration order spectrum data and motor current order spectrum data; Based on vibration order spectrum data and motor current order spectrum data, the unnormalized order cross-bispectrum and its real part phase are calculated. Under the conditions of order and constraint, the order-intersecting bispectral ternary shear coefficient is calculated based on the real part phase, and the shear energy is obtained within the preset order range; Using a preset shear tolerance as a constraint, the compression parameters of the first and second platforms are updated by utilizing the shear energy statistics within the sliding window, so as to improve the compression ratio and ensure that the shear energy does not exceed the preset shear tolerance, thereby obtaining cross-platform data acquisition results.

[0007] Secondly, a cross-platform data monitoring and control system based on big data compares the shear energy with a preset judgment threshold and outputs the judgment result of the cross-platform data acquisition results, including: Set preset judgment threshold Preset judgment threshold The pre-defined critical value for shear energy used to distinguish between normal and abnormal cross-platform data acquisition results; when Greater than the preset judgment threshold When the time comes, the result is determined to be abnormal; when Less than or equal to the preset judgment threshold At that time, the judgment result was normal.

[0008] The beneficial effects of this invention include: by introducing the order-order bispectral ternary shear coefficient and its shear energy within a preset order range as a quantitative indicator of cross-platform data consistency, synchronous control of compression ratio improvement and cross-platform structural fidelity is achieved during high-compression-ratio data acquisition across multiple industrial platforms. This invention does not rely on traditional joint synchronization mechanisms; it dynamically adjusts the compression parameters of different platforms through shear energy statistics within a sliding window, ensuring the stability of the correlation structure of the compression strategy across multiple platforms. This not only effectively reduces the redundancy and storage bandwidth consumption of cross-platform data but also significantly improves the comparability, real-time performance, and analytical reliability of multi-platform monitoring data, ensuring accurate reflection of the operating status and dynamic changes of industrial equipment even under high compression ratio conditions. Attached Figure Description

[0009] Figure 1This is a flowchart of a cross-platform data acquisition method based on big data according to the present invention. Detailed Implementation

[0010] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0011] like Figure 1 As shown, a cross-platform data acquisition method based on big data includes: Collect vibration data of rotating equipment, current data of the motor driving the rotating equipment, and rotational speed data of the rotating equipment. Based on the rotational speed data, the vibration data and motor current data are transformed into angular domain data to obtain vibration angular domain data and motor current angular domain data. Order analysis was performed on the vibration angular domain data and the motor current angular domain data to obtain vibration order spectrum data and motor current order spectrum data; Based on vibration order spectrum data and motor current order spectrum data, the unnormalized order cross-bispectrum and its real part phase are calculated. Under the conditions of order and constraint, the order-intersecting bispectral ternary shear coefficient is calculated based on the real part phase, and the shear energy is obtained within the preset order range; Using a preset shear tolerance as a constraint, the compression parameters of the first and second platforms are updated by utilizing the shear energy statistics within the sliding window, so as to improve the compression ratio and ensure that the shear energy does not exceed the preset shear tolerance, thereby obtaining cross-platform data acquisition results.

[0012] The data acquisition phase targets rotating equipment (such as fans, gearboxes, pumps, and other rotating machinery) and simultaneously acquires three types of operational data, as detailed below: 1. Vibration data is collected from the mechanically sensitive components of rotating equipment (such as spindles, bearings, and meshing gear sets). Vibration sensors (such as piezoelectric and magnetoelectric sensors) are used to capture mechanical vibration signals during equipment operation, which are used to reflect abnormal conditions such as wear, imbalance, misalignment, and impact of the mechanical structure.

[0013] 2. Motor current data: The data is collected from the motor that directly drives the rotating equipment (such as asynchronous motor, servo motor, etc.). The stator or rotor current signal of the motor during operation is collected by a current sensor and used to correlate with mechanical load changes, motor winding faults, and electromagnetic coupling abnormalities.

[0014] 3. Rotational speed data: The data is collected from the core rotating components of the rotating equipment (such as the spindle and rotor). Real-time rotational speed values ​​(including dynamic speed changes under constant speed and variable speed conditions) are obtained through speed sensors (such as photoelectric encoders and Hall sensors). This data is used to convert vibration and current data to the angular domain (to eliminate variable speed spectrum broadening) and to provide a reference time base for order analysis.

[0015] In one embodiment of the present invention, vibration data and motor current data are transformed into angular domain data based on rotational speed data to obtain vibration angular domain data and motor current angular domain data, including: Set a time window, with the start time of the time window being... Vibration data are acquired within the time interval corresponding to the time window. Motor current data and speed data ;in, For any point in time within the time interval, For speed data at time points Angular velocity value at; Calculate cumulative angle ;in, The independent variable for integration is used to iterate through the time window from the start time. to any point in time The interval; Preset angle step Number of data points in the corner domain angular step size The difference between two adjacent target angles in the angular domain, and the number of angular domain data points. The total number of data points to be acquired after angle domain transformation; calculate the initial angle. initial angle Cumulative angle At the start time of the time window The value at that location.

[0016] A time window is a defined continuous time segment used to limit the scope of a single data acquisition and processing, preventing unexpected speed changes under variable speed conditions due to excessive data volume or too long time span.

[0017] The start time is the beginning of the time window. It can be set according to the equipment startup time, the restart time after maintenance, or the data acquisition trigger signal (such as when the equipment enters a stable operation phase). It needs to be synchronized with the acquisition trigger time of vibration, current, and speed sensors.

[0018] The time interval is the continuous period of time covered by the time window (i.e., the interval from the start time to the start time plus the window length). It needs to be set according to the equipment rotation cycle (e.g., the time for the spindle to rotate 10-50 times) to ensure that it includes enough rotation cycles to reflect the equipment operating characteristics. Specifically, the end time of the time interval is equal to the start time plus the time window length. The time window length is equal to (target number of rotations multiplied by 60) divided by the average speed of the equipment (unit: minutes, speed unit: revolutions per minute).

[0019] Vibration data within a time interval are continuous vibration signals collected by vibration sensors (piezoelectric or magnetoelectric) installed on the mechanically sensitive parts of the equipment (such as spindle bearings or gearboxes) within a time interval. The units are usually meters per square second or gravitational acceleration, reflecting the operating status of the equipment's mechanical structure.

[0020] Motor current data within a time interval refers to the stator or rotor current signal of the motor driving rotating equipment, which is collected by a current sensor within a time interval. The unit is amperes, and it reflects the changes in motor load and electromagnetic coupling status.

[0021] Rotational speed data within a time interval is the real-time rotational speed signal of the core components (spindle, rotor) of rotating equipment, collected by a speed sensor (photoelectric encoder, Hall sensor) within the time interval. The unit is usually revolutions per minute or radians per second, and it is the reference data for angular domain conversion.

[0022] Any point in time within the time interval is a specific instant within the time interval, used to describe the instantaneous value of the rotational speed data. It must correspond to the sampling time of the sensor to ensure that the angular velocity value can accurately match the equipment rotational speed at that moment.

[0023] The angular velocity value at a given time point is the rotational angular velocity of the device at any given time point. It is an instantaneous quantized value of the rotational speed data, usually expressed in radians per second, and is used to calculate the cumulative angle. Specifically, the angular velocity value is equal to (the rotational speed value collected by the speed sensor at that time point multiplied by 2 multiplied by pi) divided by 60.

[0024] The cumulative angle is the total angle traversed by the rotating parts of a device from the start time of the time window to any other time point. The unit is usually radians. It is the core intermediate quantity for angular domain conversion and is used to unify the time base of vibration and current data. Specifically, the cumulative angle is equal to the integral of the angular velocity value at each instant from the start time to any other time point. Specifically, the time interval from the start time to any other time point is divided into several small time intervals, and the angular velocity in each small interval is taken as the instantaneous value at the midpoint of that interval. The cumulative angle is equal to the sum of all (small time intervals multiplied by the corresponding interval angular velocity value).

[0025] The integral independent variable is used to iterate through the time interval from the start time to any point in time. Each value corresponds to an instant within the time interval. The purpose is to cover all angular velocity values ​​within that time interval and ensure that the cumulative angle calculation is complete.

[0026] The angular step size is the interval between two adjacent target angles in the angular domain, usually in radians. It determines the resolution of the angular domain data. The smaller the step size, the higher the resolution, but the larger the data volume. Specifically, the angular step size is equal to (2 times pi multiplied by the target order resolution). The target order resolution is set according to the frequency of equipment fault characteristics (such as the bearing fault characteristic order), usually taking the angular interval corresponding to 0.1-1 order.

[0027] The number of angular domain data points is the total number of vibration and current data points after angular domain conversion, which determines the total amount of angular domain data and needs to be matched with the angular step size and the total rotation angle of the device within the time interval. Specifically, the number of angular domain data points is equal to (the total rotation angle of the device within the time interval) divided by the angular step size, and the total rotation angle of the device within the time interval is equal to the value of the cumulative angle at the end of the time interval.

[0028] The initial angle is the total angle that the rotating parts of the device have rotated through at the beginning of the time window. The unit is usually radians. It is used to construct the first target angle in the angle domain. Specifically, if the starting time is the moment the device starts, the initial angle is 0 radians. If the starting time is a certain moment during the operation of the device, the initial angle is equal to the cumulative rotation angle of the speed sensor from the start of the device to the starting time (calculated by querying or backtracking the sensor's historical data).

[0029] In one embodiment of the present invention, the vibration data and motor current data are subjected to angular domain transformation processing based on rotational speed data to obtain vibration angular domain data and motor current angular domain data, and the method further includes: Construct targets at various angles within the angular domain. , ; Based on cumulative angle With time The correspondence is obtained by solving for the target at each angle. Corresponding target time Target time satisfy: ; Extracting vibration data The sampling time sequence is used to select the target time. Two adjacent sampling times and ;in, The smaller of two adjacent sampling times. It is the time with the larger time among two adjacent sampling times, and satisfies Calculate the target angle Corresponding vibration angular domain data : ;in, For vibration data at the sampling time The value at that location, For vibration data at the sampling time The value at; Simultaneously extract motor current data The sampling time sequence is used to select the target time. Two adjacent sampling times and ;in, The smaller of two adjacent sampling times. It is the time with the larger time among two adjacent sampling times, and satisfies Calculate the target angle Corresponding motor current angular domain data : ;in, For motor current data at the sampling time The value at that location, For motor current data at the sampling time The value at that location.

[0030] The angle target is the core reference point for angle domain conversion, used to map vibration and current data in the time domain to the angle domain, and to achieve angle alignment under variable speed conditions. The angle target index is used to distinguish different angle targets, and the number of angle domain data points is equal to the number of angle domain data points. Specifically, the k-th angle target is equal to the initial angle plus (angle target index multiplied by angle step size). The angle target index starts from 0 and the maximum value is the number of angle domain data points minus 1.

[0031] The correspondence between cumulative angle and time is a discrete data set of cumulative angle changes over time (i.e., each sampling time point corresponds to a cumulative angle value), which is used to look up the actual time corresponding to the angle target; extraction method: extract the data pairs of sampling time and cumulative angle in ascending order of time from the raw data stored by the speed sensor to form a correspondence table.

[0032] The target time is the instantaneous moment corresponding to the angle target in actual operation, serving as a bridge connecting the time domain data and the angle domain target. Specifically, the first step is to find the left time point and corresponding cumulative angle (the value of the cumulative angle at the left time point) where the cumulative angle is exactly less than or equal to the angle target, and the right time point and corresponding cumulative angle (the value of the cumulative angle at the right time point) where the cumulative angle is exactly greater than or equal to the angle target. The second step is to determine the target time by subtracting the cumulative angle at the left time point from the value at the right time point, if the result is not zero, the target time is equal to the left time point plus ((the angle target minus the value of the cumulative angle at the left time point) divided by (the value of the cumulative angle at the right time point minus the value of the cumulative angle at the left time point)) multiplied by (the right time point minus the left time point). If the result of subtracting the cumulative angle at the left time point from the right time point is zero (rotation speed is zero), the target time is the left time point.

[0033] The sampling time sequence of vibration data is all the time points recorded when the vibration sensor collects raw data, arranged in ascending order of time. The selection rule is: find the time with the largest time value that is less than or equal to the target time in the sequence as the left sampling time, and the time with the smallest time value that is greater than or equal to the target time as the right sampling time, ensuring that the left sampling time is less than or equal to the target time and the target time is less than or equal to the right sampling time.

[0034] The left sampling time is the leftmost time in the vibration sampling time sequence that wraps the target time, and is used to provide the left reference for vibration data interpolation.

[0035] The right-side sampling time is the right-side time of the target time in the vibration sampling time sequence, used to provide the right reference for vibration data interpolation.

[0036] The vibration data at the sampling time on the left side is the original vibration signal value collected by the vibration sensor at the sampling time on the left side, which is the left reference value for calculating the vibration angular domain data.

[0037] The vibration data at the sampling time on the right side is the original vibration signal value collected by the vibration sensor at the sampling time on the right side, which is the right reference value for calculating the vibration angular domain data.

[0038] The vibration angular domain data corresponding to the angular target is the vibration data value corresponding to the angular target in the angular domain, which is obtained by linear interpolation (since there is no direct vibration sampling value at the target time), ensuring angle alignment; specifically, the vibration angular domain data is equal to the value of the vibration data at the left sampling time plus ((the angular target minus the cumulative angle at the left sampling time) divided by (the cumulative angle at the right sampling time minus the cumulative angle at the left sampling time)) multiplied by (the vibration data at the right sampling time minus the vibration data at the left sampling time).

[0039] The sampling time sequence of motor current data is all the time points recorded when the current sensor collects raw data, arranged in ascending order of time (independent of the vibration sampling time sequence, as the sampling frequencies of the two may be different); the selection rule is: the time with the largest time value that is less than or equal to the target time is taken as the sampling time on the left side of the current, and the time with the smallest time value that is greater than or equal to the target time is taken as the sampling time on the right side of the current, ensuring that the sampling time on the left side of the current is less than or equal to the target time and the target time is less than or equal to the sampling time on the right side of the current.

[0040] The left sampling time of the current is the leftmost time in the current sampling time sequence that wraps the target time, and is used to provide the left reference for current data interpolation.

[0041] The right-side sampling time of the current is the right-side time of the target time in the current sampling time sequence, which is used to provide the right reference for current data interpolation.

[0042] The value of the motor current data at the sampling time on the left side of the current is the original current signal value collected by the current sensor at the sampling time on the left side of the current, and the unit is amperes. It is the left reference value for calculating the motor current angular domain data.

[0043] The value of the motor current data at the sampling time on the right side of the current is the original current signal value collected by the current sensor at the sampling time on the right side of the current, in amperes, and is the right reference value for calculating the motor current angular domain data.

[0044] The motor current angular domain data corresponding to the angular target is the current data value corresponding to the angular target in the angular domain, which is obtained through linear interpolation to ensure alignment with the vibration angular domain data at the same angular reference. Specifically, the motor current angular domain data is equal to the value of the motor current data at the sampling time on the left side of the current plus ((the angular target minus the cumulative angle at the sampling time on the left side of the current) divided by (the cumulative angle at the sampling time on the right side of the current minus the cumulative angle at the sampling time on the left side of the current)) multiplied by (the value of the motor current data at the sampling time on the right side of the current minus the value of the motor current data at the sampling time on the left side of the current).

[0045] In one embodiment of the present invention, order analysis is performed on the vibration angular domain data and the motor current angular domain data to obtain vibration order spectrum data and motor current order spectrum data, including: Define the order set Order set Each order in satisfy: ;in, The parameter is an integer, and its value ranges from 0 to... , For order set The first in Each order; Based on vibration angular domain data Calculate vibration order spectrum data : ;in, For vibration angular domain data in the first The value of the target at each angle, The parameter is an integer, and its value ranges from 0 to... , The imaginary unit, It is a natural exponential function. For vibrational order spectrum data at order Complex values ​​at; Based on motor current angular domain data Calculate the order spectrum data of motor current. : ;in, For motor current angular domain data in the first... The value of the target at each angle, The parameter is an integer, and its value ranges from 0 to... , For motor current order spectrum data at order Complex values ​​at the location.

[0046] The order set is the baseline order set for order analysis, containing consecutive integer orders starting from 0, used to correspond to different multiples of the rotational characteristics of rotating equipment (e.g., order 1 corresponds to the spindle rotation frequency, order 2 corresponds to twice the spindle frequency).

[0047] The m-th order in the order set is a single order value in the order set, which directly corresponds to the integer parameter m. It is used to characterize the m-fold characteristic of the rotation frequency of the rotating equipment. For example, m=1 corresponds to the first harmonic of the spindle, and m=2 corresponds to the second harmonic. Specifically, the m-th order in the order set is equal to the integer parameter m.

[0048] The integer parameter m is the index that distinguishes different orders in the order set. It is used to traverse all orders in the order set. The value starts from 0 and does not exceed the maximum value of the integer parameter m.

[0049] The maximum value of the integer parameter m is the upper limit of the integer parameter m, which determines the total number of orders included in the order set. It needs to match the number of angle targets to ensure the integrity of the order spectrum calculation. Specifically, the maximum value of the integer parameter m is equal to the maximum value of the integer parameter k, and the maximum value of the integer parameter k is equal to the total number of angle targets minus 1.

[0050] The integer parameter k is an index that distinguishes targets at different angles. It is used to traverse the vibration / current angular domain data corresponding to all targets at different angles. The value starts from 0 and does not exceed the maximum value of the integer parameter k.

[0051] The maximum value of the integer parameter k is the upper limit of the integer parameter k, which is equal to the total number of angle targets minus 1, ensuring that all angle target data is traversed; specifically, the maximum value of the integer parameter k is equal to the total number of angle targets minus 1.

[0052] The imaginary unit is a symbol in mathematics that represents an imaginary number. It is used to describe the phase information of an order spectrum, and its square is equal to -1. In complex numerical calculations, it is used to distinguish between the real and imaginary parts.

[0053] The natural exponential function is used to construct frequency components in order spectrum calculation. Here, the independent variable is the negative imaginary unit multiplied by pi, multiplied by the m-th order in the order set, multiplied by the integer parameter k, and then divided by the maximum value of the integer parameter k. It is used to convert angular domain data into complex values ​​in the frequency domain (order domain). Specifically, the result of the natural exponential function under this independent variable is equal to the cosine (pi multiplied by the m-th order in the order set, multiplied by the integer parameter k, and then divided by the maximum value of the integer parameter k) plus the imaginary unit multiplied by the sine (pi multiplied by the m-th order in the order set, multiplied by the integer parameter k, and then divided by the maximum value of the integer parameter k).

[0054] The complex value of the vibration order spectrum data at the m-th order is the result of order analysis of the vibration angular domain data. It contains the amplitude (modulus of the complex value) and phase (argument of the complex value) information at that order, which is used for subsequent cross-spectral calculations. Specifically, the first step is to calculate the value of the vibration angular domain data at the k-th angle target by multiplying it by the natural exponential function (the natural exponential function is calculated according to the above formula) for each integer parameter k (from 0 to the maximum value of the integer parameter k). The second step is to add up the calculation results corresponding to all k to obtain the complex value of the vibration order spectrum data at the m-th order.

[0055] The complex value of the motor current order spectrum data at the m-th order is the result of order analysis of the current angular domain data. It includes the current amplitude (modulus of the complex value) and phase (argument of the complex value) information at that order, which is used for subsequent cross-spectral calculation. Specifically, the first step is to calculate the value of the motor current angular domain data at the k-th angle target by multiplying it by the natural exponential function (the natural exponential function is calculated according to the above formula) for each integer parameter k (from 0 to the maximum value of the integer parameter k). The second step is to add up the calculation results corresponding to all k to obtain the complex value of the motor current order spectrum data at the m-th order.

[0056] In one embodiment of the present invention, the calculation of the unnormalized order bispectral and its real part phase based on vibration order spectrum data and motor current order spectrum data includes: Define order for sets order of set satisfy: ;in, For a set of order, For order set Any order in For order set Any order in For the set of order pairs Any pair of orders in the sequence; Based on vibration order spectrum data and motor current order spectrum data, calculate the unnormalized order cross-bispectrum. : ;in, For motor current order spectrum data at order Complex values ​​at that location, For vibrational order spectrum data at order Complex values ​​at that location, For vibrational order spectrum data at order Complex conjugate of complex values.

[0057] The order pair set is the set of order combinations used to calculate the cross-bispectrum. By constraining the order a plus order b in the order set, it is ensured that the quadratic coupling relationship represented by the cross-bispectrum (the coupling of order a and order b produces order a plus order b) has physical meaning. Specifically, the first step is to list all possible combinations of two orders in the order set (each combination is a potential order pair); the second step is to select combinations in the order set whose result of order a plus order b is also in the order set. These combinations together constitute the order pair set.

[0058] The order 'a' is a single order in the order set, serving as the corresponding order of the motor current order spectrum data in the cross-spectral calculation, and characterizing a certain rotational characteristic order of the current signal.

[0059] The order b is a single order in the order set, serving as the corresponding order of the vibration order spectrum data (first) in the cross-spectral calculation, and characterizing a certain rotational characteristic order of the vibration signal.

[0060] Any order pair in the order pair set is a single element in the order pair set, consisting of order a and order b, and is the basic unit for single-intersection bispectral calculation.

[0061] The complex conjugate of the complex values ​​of the vibration order spectrum data at order a plus order b is the conjugate form of the complex values ​​of the vibration order spectrum data at order a plus order b. It is used to multiply with the first two complex values ​​to construct a quadratic coupling relationship. Specifically, if the complex values ​​of the vibration order spectrum data at order a plus order b are the real part plus the imaginary unit multiplied by the imaginary part, then its complex conjugate is the real part minus the imaginary unit multiplied by the imaginary part.

[0062] It should be noted that the unnormalized order bispectral is a feature quantity for trans-domain (motor current-vibration) signals of rotating equipment, which quantifies the second-order nonlinear coupling relationship in the order domain. Specifically, it is constructed based on vibration order spectrum data and motor current order spectrum data through specific complex numerical operations, without any normalization processing to retain the original coupling strength information. Unnormalized order cross-spectral density relies on order domain data rather than time domain data. This is because the order domain has eliminated the spectral broadening problem under variable speed conditions through angular domain transformation, allowing signal characteristics (such as spindle frequency and gear meshing order) at different speed stages to be stably aligned, ensuring that the coupling relationship analysis is not affected by speed changes. Unnormalized order cross-spectral density requires first constructing a set of order pairs that satisfy the sum-frequency constraint (i.e., the sum of the two orders in the order pair is also within the order set, matching the pattern of secondary coupling of rotating equipment signals to generate the sum-frequency order). Then, for each order pair, perform a complex conjugate operation: the complex value of the motor current order spectrum at the first order × the complex value of the vibration order spectrum at the second order × the complex value of the vibration order spectrum at the sum-frequency of the two orders. The role of complex conjugation is to strengthen the phase correlation of secondary coupling through phase conjugate matching. From the perspective of unnormalized characteristics, it does not introduce magnitude normalization (such as dividing by the product of the magnitudes of each order spectrum). Therefore, the magnitude of unnormalized order cross-spectral density reflects the original strength of cross-domain secondary coupling. The stronger the coupling, the larger the absolute value of the unnormalized order bispectral spectrum, and vice versa. The unnormalized order bispectral spectrum can capture the secondary effects of the non-direct linear relationship between current and vibration signal (such as the nonlinear modulation generated by the vibration response and the transmission of electromagnetic excitation of motor through mechanical structure). Furthermore, based on bispectral theory, it has a natural suppression effect on zero-mean Gaussian noise and can highlight the weak coupling characteristics in noisy environments.

[0063] In one embodiment of the present invention, the calculation of the unnormalized order bispectral and its real part phase based on vibration order spectrum data and motor current order spectrum data further includes: Calculate the real part, modulus, and real phase of the unnormalized order cross-bispectrum, including:

[0064]

[0065]

[0066] in, For order pairs The real part of the non-normalized order bispectral intersection, The operator for retrieving the real part of a complex number; For order pairs The modulus of the unnormalized order bispectral intersection. The operator for taking the modulus of a complex number; For order pairs The real part of the phase of the unnormalized order bispectral cross. It is an inverse cosine function.

[0067] The real part of the unnormalized order bispectral at the order pair is the real component of the unnormalized order bispectral (complex value) corresponding to that order pair. It reflects the real contribution of the cross-domain (current-vibration) secondary coupling strength and eliminates the interference of imaginary noise on the real component of the coupling strength. Specifically, if the complex value of the unnormalized order bispectral at the order pair is expressed as the real value plus the imaginary unit multiplied by the imaginary value, then the real part is equal to the real value in the complex value.

[0068] The operator for extracting the real part of a complex number is used to extract the real part of a complex number. Its function is to separate the value that reflects only the size of the real part from a complex number in the form of real part + imaginary unit × imaginary part. Specifically, for any complex number, performing the operator for extracting the real part of the complex number will result in the part of the complex number that does not contain the imaginary unit.

[0069] The modulus of the unnormalized bispectral of an order pair is the absolute value of the unnormalized bispectral (complex value) of the corresponding order pair, reflecting the total strength of the cross-domain quadratic coupling (the superposition of the contributions of the real and imaginary parts). The larger the value, the stronger the coupling. Specifically, if the real part of the complex value of the unnormalized bispectral of an order pair is A and the imaginary part is B, then the modulus is equal to the square root of (the square of A plus the square of B).

[0070] The modulus operator is used to calculate the modulus of a complex number. Its function is to convert a complex number into a non-negative real number that reflects its total magnitude, eliminating the influence of the imaginary unit. Specifically, for any complex number (real part A, imaginary part B), performing the modulus operator will result in the square root of (the square of A plus the square of B).

[0071] It should be noted that the unnormalized order bispectral cross-spectrum at the order pair is the core complex numerical feature for quantifying the second-order nonlinear coupling relationship of the cross-domain (motor current-vibration) signal of rotating equipment under a specific order pair. It relies on the vibration / motor current order spectrum data and the sum-frequency constrained order pair (the sum of the two orders in the order pair is also within the order set), and does not perform any normalization processing to preserve the original coupling strength information. The unnormalized order cross-spectrum at order pairs is based on order pairs as units (each order pair consists of two orders in the order set that satisfy the sum-frequency constraint, such as order a and order b, and order a plus order b are within the order set), ensuring that the calculation results closely match the rules of the sum-frequency order generated by the secondary coupling of rotating equipment signals (e.g., the gear meshing order is generated by the coupling of the spindle rotation frequency order). The unnormalized order cross-spectrum at order pairs is constructed by the complex conjugate operation of the complex value of the motor current order spectrum at the first order of the order pair × the complex value of the vibration order spectrum at the second order of the order pair × the complex value of the vibration order spectrum at the sum-frequency of the two orders. The role of complex conjugation is to strengthen the phase correlation of the secondary coupling through phase conjugate matching and avoid non-coupled phase interference. The unnormalized order cross-spectrum at order pairs does not introduce normalization factors such as the product of the magnitudes of each order spectrum, so the absolute value of the complex value directly reflects the original strength of the cross-domain secondary coupling under that order pair. The stronger the coupling (such as the enhanced current-vibration correlation caused by mechanical failure), the larger the absolute value, and vice versa. The unnormalized bispectral order at the order pair can capture the secondary effect of the non-direct linear correlation between the current and vibration signal (such as the nonlinear modulation generated by the vibration response after the electromagnetic excitation of the motor is transmitted through the mechanical structure). Moreover, because it is based on bispectral theory, it has a natural suppression effect on zero-mean Gaussian noise, and can highlight the weak coupling characteristics in industrial noise environments.

[0072] In one embodiment of the present invention, under certain order and constraint conditions, the order-order cross-spectral ternary shear coefficient is calculated based on the real part of the phase, and the shear energy is obtained within a preset order range, including: Set the order sampling step size and preset order range Order sampling step size The difference between two adjacent sampling points in the order domain, with a preset order range. The predefined interval of the order to be analyzed; For the preset order range Each constraint order within Define the order of a set order of set satisfy:

[0073] in, For order set Any order in For order set Any order in For the set of order pairs Any pair of orders in the sequence; For the set of order pairs Each order pair in The local second-order mixture difference result is calculated according to the following formula. : ;in, For unnormalized order cross-spectrums in order pair The real phase at that point, For unnormalized order cross-spectrums in order pair The real phase at that point, For unnormalized order cross-spectrums in order pair The real phase at that point, For unnormalized order cross-spectrums in order pair The real phase at that point, For order pairs The local second-order mixture difference result at the location.

[0074] The order sampling step size is the interval between selecting adjacent order points in the order domain. It is used to control the accuracy of local second-order mixed differences. That is, the smaller the step size, the more refined the difference result, but the greater the computational load. Specifically, the order sampling step size is equal to the total length of the preset order range divided by (the total number of sampling points in the preset order range minus 1). The total number of sampling points in the preset order range is usually taken as 50 to 200 to ensure the stability of the difference.

[0075] The preset order range is the order interval that focuses on the key fault characteristics of rotating equipment (such as the first to fifth harmonics of the spindle, and the gear meshing order), to avoid meaningless orders that increase the amount of calculation. Specifically, the starting order of the preset order range is the lowest key characteristic order of the equipment (such as the first harmonic of the spindle), and the ending order is the starting order plus (the order corresponding to 5 to 10 times the spindle fundamental frequency). The specific adjustment is made according to the equipment type (such as fan, gearbox).

[0076] The constraint order is a single order within a preset order range. It serves as the frequency constraint benchmark for the set of order pairs (i.e., the sum of the two orders in the order pair equals the constraint order) and is the core of constructing the local difference analysis unit. Specifically, the constraint order is taken sequentially from each order within the preset order range, and the interval between adjacent constraint orders is equal to the order sampling step size to ensure coverage of the entire preset range.

[0077] The order pair set is a selection of order pairs for a single constraint order. Multiple constraints (where the sum frequency is the constraint order, and order a ± step size and order b + step size are within the order set) ensure that subsequent difference calculations are free of boundary missing values. Specifically, the first step is to select all order pairs in the order set where order a plus order b equals the constraint order. The second step is to further select order pairs from these order pairs where order a minus the order sampling step size, order a plus the order sampling step size, and order b plus the order sampling step size are all within the order set, thus forming the order pair set for that constraint order.

[0078] The order 'a' is a single order in the order set. As the first element of an order pair, it must satisfy that the order a ± the order sampling step size is within the order set to ensure that the difference calculation has enough adjacent order points.

[0079] The order b is a single order in the order set. As the second element of the order pair, it must satisfy that the order b plus the order sampling step size is within the order set to ensure that the difference calculation has a corresponding right-hand order point.

[0080] The local second-order mixed difference result at the order pair (order a, order b) reflects the degree of shear distortion of the real phase around the order pair (order a, order b). The larger the value, the more obvious the phase distortion. Specifically, the local second-order mixed difference result is equal to the real phase at (order a + order sampling step size, order b) minus the real phase at (order a, order b), then minus the real phase at (order a, order b + order sampling step size), and finally adding the real phase at (order a - order sampling step size, order b + order sampling step size).

[0081] In one embodiment of the present invention, under order and constraint conditions, the order-intersecting bispectral ternary shear coefficient is calculated based on the real part phase, and the shear energy is obtained within a preset order range, further comprising: Calculate the order of the set Number of elements Number of elements For the set of order pairs The order pairs included Total number of constraints; calculate constraint order The order of cross-bispectral ternary shear coefficients : ;in, To constrain the order The order of the bispectral ternary shear coefficient; Set weight function Weighting function For predefined, and constraint order The corresponding weight values; calculate the preset order range. internal shear energy : ;in, Preset order range Shear energy within.

[0082] The number of elements in the order pair set is the total number of order pairs after filtering for a single constraint order. It is used to average the local second-order mixed difference results and reduce the calculation bias caused by noise from a single order pair. Specifically, it iterates through the order pair set for the constraint order and counts the total number of order pairs contained in the set, which is the number of elements.

[0083] The order cross-bispectral ternary shear coefficient under the constraint order is the average shear distortion of the cross-domain (current-vibration) coupling phase under the constraint order. The greater the value deviates from zero, the more significant the distortion of the coupled structure caused by compression / speed variation under that order. Specifically, the first step is to sum the local second-order mixed difference results corresponding to all order pairs in the order pair set; the second step is to calculate the product of the number of elements and (the square of the order sampling step size); the third step is to divide the sum of the first step by the product of the second step to obtain the order cross-bispectral ternary shear coefficient under the constraint order.

[0084] The weighting function is a coefficient set according to the importance of the fault characteristics of the constraint order. It is used to give higher weight to critical orders (such as gear meshing order, spindle 2nd harmonic) in shear energy calculation, highlighting the shear and torsional effects of important orders. Specifically, if the constraint order is a critical fault characteristic order of the equipment (such as spindle 1st harmonic, bearing fault order), the weighting function value is set to 0.8 to 1.0; if it is a non-critical order, the weighting function value is set to 0.2 to 0.5.

[0085] Shear energy is a quantitative indicator of the total shear-torsion energy of a cross-domain coupled structure within a preset order range. The larger the value, the more severe the overall coupled structure is torsion under compression / variable speed. Specifically, the first step is to calculate the weight function value multiplied by the square of the bispectral ternary shear coefficient of the next lower order for each constraint order within the preset order range. The second step is to sum the above calculation results for all constraint orders to obtain the shear energy within the preset order range.

[0086] In one embodiment of the present invention, with a preset shear tolerance as a constraint, the compression parameters of the first and second platforms are updated using the shear energy statistics within a sliding window, so that the compression ratio is improved and the shear energy does not exceed the preset shear tolerance, and cross-platform data acquisition results are output, including: Set the length of the sliding window Sliding window length The number of consecutive windows used to calculate the statistic; for the current window index Calculate the shear energy statistics within the sliding window. : ;in, Window count is Shear energy at that time, Counts the number of windows and the range of values ​​is arrive , Index for the current window The corresponding shear energy statistics; Set preset shear tolerance Preset shear tolerance The maximum allowable shear energy is preset. Calculate tolerance margin factor : ;in, Index for the current window The corresponding tolerance margin coefficient; Set the current compression parameters for the first platform Current compression parameters of the second platform Current compression parameters of the first platform For the first platform in window index Compression configuration parameters at the time, current compression parameters on the second platform For the second platform in window index Compression configuration parameters at that time; Set the first update step size Second update step First update step size The adjustment range coefficient for the compression parameters of the first platform, and the second update step size. This is the adjustment range coefficient for the compression parameters of the second platform; Define the first unit vector Second unit vector First unit vector The first unit vector is the unit vector pointing in the direction of increasing compression ratio within the compression parameter space of the first platform, and the second unit vector is... This is a unit vector pointing in the direction of increasing the compression ratio within the compression parameter space of the second platform; The next window compression parameters for the first platform are calculated and updated. Second platform next window compression parameters :

[0087]

[0088] in, This refers to the adjustment amount of the compression parameters for the first platform. This refers to the adjustment amount of the compression parameters for the second platform; Calculate cross-platform compression ratio Cross-platform compression rate satisfy: ;in, For the first platform in window index The amount of data before compression For the second platform in window index The amount of data before compression For the first platform in window index The amount of data after compression For the second platform in window index The amount of data after compression; Output cross-platform data acquisition results, including the compression parameters of the next window on the first platform. Second platform next window compression parameters and cross-platform compression ratio .

[0089] The sliding window length is the number of consecutive data windows selected to smooth the shearing energy and avoid the instantaneous noise of a single window from causing incorrect adjustment of compression parameters. Specifically, the sliding window length is set according to the data stability, usually between 5 and 20 consecutive windows. A larger value (15 to 20) is used when the equipment operating conditions fluctuate greatly, and a smaller value (5 to 10) is used when the fluctuations are small.

[0090] The current window index is the number that identifies the currently processed data window. It starts from 1 and increments sequentially over time. It is used to determine the range of the sliding window (e.g., if the current window index is 10 and the sliding window length is 5, then it covers windows with indices 6 to 10).

[0091] The shear energy statistic within the sliding window is the average level of shear energy within the sliding window. It is used to reflect the overall trend of cross-domain coupling distortion over a period of time, avoiding the impact of fluctuations in a single window on parameter updates. Specifically, the first step is to determine the range of window counts: the starting window count equals the current window index minus the sliding window length plus 1, and the ending window count equals the current window index. The second step is to sum the shear energy corresponding to all window counts within this range. The third step is to divide the summation result by the sliding window length to obtain the shear energy statistic within the sliding window.

[0092] Window count is the number of each data window within the sliding window, used to iterate and obtain the shear energy of the corresponding window. Each window count corresponds to the shear energy of a time segment.

[0093] The preset shear tolerance is a threshold for controlling the distortion of the cross-domain coupling structure. Exceeding this value indicates that the distortion of the coupling structure exceeds the allowable range, and the compression ratio should be limited. Specifically, the preset shear tolerance is determined based on the shear energy under normal operating conditions, and is taken as 1.2 to 1.5 times the maximum shear energy under normal operating conditions to ensure that normal fluctuations do not trigger over-adjustment.

[0094] The tolerance margin coefficient is an indicator that reflects the difference between the current shear energy statistic and the preset shear tolerance. The value ranges from 0 to 1. The larger the value, the greater the space for improving the compression ratio. Specifically, the tolerance margin coefficient is equal to 1 minus (the shear energy statistic within the sliding window divided by the preset shear tolerance). If the calculation result is less than 0, the tolerance margin coefficient is 0.

[0095] The current compression parameters of the first platform are the compression settings currently used by the first platform (such as a vibration data acquisition platform), including the slope threshold and amplitude threshold of the swing gate algorithm or the time threshold and amplitude threshold of the deadband algorithm.

[0096] The current compression parameters of the second platform are the compression settings currently used by the second platform (such as the motor current data acquisition platform), and the parameter types are consistent with the current compression parameters of the first platform.

[0097] The first update step size is a coefficient that controls the adjustment range of the compression parameters of the first platform each time, so as to avoid the shear energy exceeding the tolerance due to excessive parameter adjustment. Specifically, the first update step size is set according to the sensitivity of the compression parameters, usually between 0.1 and 0.5. When the parameter has a large impact on the compression ratio, a smaller value (0.1 to 0.2) is used, and when the impact is small, a larger value (0.3 to 0.5) is used.

[0098] The second update step size is a coefficient that controls the adjustment range of the compression parameters of the second platform each time. Its function is the same as that of the first update step size, and its value can be the same as or different from that of the first update step size. Specifically, the setting method of the second update step size is the same as that of the first update step size, and it is taken as 0.1 to 0.5 according to the sensitivity of the compression parameters of the second platform.

[0099] The first unit vector is a directional identifier in the compression parameter space of the first platform that can improve the compression ratio. The component of each compression parameter dimension represents the direction of the parameter's influence on the compression ratio. Specifically, for each compression parameter of the first platform, if the compression ratio increases when the parameter increases (such as increasing the slope threshold of the swing gate algorithm, reducing the number of retained data points, and thus increasing the compression ratio), then the component of that parameter in the first unit vector is 1; if the compression ratio increases when the parameter decreases, then the component is -1. The vector formed by all components, after normalization, is the first unit vector.

[0100] The second unit vector is a directional indicator in the compression parameter space of the second platform that can improve the compression ratio. The setting logic is the same as that of the first unit vector. Specifically, the setting method of the second unit vector is the same as that of the first unit vector. The components are determined according to the influence direction of each compression parameter of the second platform on the compression ratio, and then normalized to obtain the result.

[0101] The adjustment amount of the compression parameters of the first platform is the change value of the current compression parameters of the first platform, ensuring that the adjustment direction is to increase the compression ratio, and the magnitude is controlled by the tolerance margin coefficient; specifically, the adjustment amount of the compression parameters of the first platform is equal to the first update step size multiplied by the tolerance margin coefficient, and then the result is multiplied by each component of the first unit vector to obtain the specific adjustment value of each compression parameter.

[0102] The adjustment amount of the compression parameters of the second platform is the change value of the current compression parameters of the second platform, and its function is the same as that of the adjustment amount of the first platform. Specifically, the adjustment amount of the compression parameters of the second platform is equal to the second update step size multiplied by the tolerance margin coefficient, and then the result is multiplied by each component of the second unit vector to obtain the specific adjustment value of each compression parameter.

[0103] The compression parameters of the next window of the first platform are the compression configurations that will be used in the next data window of the first platform, ensuring that the compression ratio is improved while the shear energy does not exceed the tolerance. Specifically, each component of the compression parameters of the next window of the first platform is equal to the corresponding component of the current compression parameters of the first platform plus the corresponding component of the adjustment amount of the compression parameters of the first platform.

[0104] The compression parameters for the next window of the second platform are the compression configurations that will be used in the next data window of the second platform. The setting logic is the same as that for the compression parameters for the next window of the first platform. Specifically, each component of the compression parameters for the next window of the second platform is equal to the corresponding component of the current compression parameters of the second platform plus the corresponding component of the adjustment amount of the compression parameters of the second platform.

[0105] Cross-platform compression ratio is an indicator that reflects the overall data compression effect of two platforms. The higher the value, the better the compression effect. Specifically, the first step is to calculate the total amount of data before compression on the first and second platforms, that is, the amount of data before compression on the first platform plus the amount of data before compression on the second platform. The second step is to calculate the total amount of data after compression on both platforms, that is, the amount of data after compression on the first platform plus the amount of data after compression on the second platform. The third step is to divide the sum of the first step by the sum of the second step to obtain the cross-platform compression ratio.

[0106] The amount of data before compression on the first platform is the total amount of raw data on the first platform within the time segment corresponding to the current window index, which has not undergone compression processing. The unit is usually bytes.

[0107] The compressed data volume of the first platform is the total amount of data processed by the first platform within the time segment corresponding to the current window index, and the unit is usually bytes.

[0108] The amount of data before compression on the second platform is the total amount of raw data on the second platform within the time segment corresponding to the current window index, which has not undergone compression processing. The unit is usually bytes.

[0109] The compressed data volume of the second platform is the total amount of original data that has been compressed within the time segment corresponding to the current window index of the second platform, and the unit is usually bytes.

[0110] Cross-platform data acquisition results are the core output of cross-platform data acquisition and can be directly used for compression configuration and compression effect evaluation in the next window.

[0111] Example 2 A cross-platform data monitoring and control system based on big data compares shear energy with a preset judgment threshold and outputs a judgment result of the cross-platform data acquisition results, including: Set preset judgment threshold Preset judgment threshold The pre-defined critical value for shear energy used to distinguish between normal and abnormal cross-platform data acquisition results; when Greater than the preset judgment threshold When the time comes, the result is determined to be abnormal; when Less than or equal to the preset judgment threshold At that time, the judgment result was normal.

[0112] The preset judgment threshold is the core critical value that distinguishes between normal fidelity and abnormal distortion in cross-platform data coupling structures. If the shear energy exceeds this value, it indicates that the coupling distortion caused by data compression / transmission has affected subsequent analysis (such as fault diagnosis), and an early warning needs to be triggered. Specifically, the first step is to collect the shear energy of at least 100 consecutive windows under normal operating conditions of the equipment; the second step is to calculate the 99th percentile of these shear energies (i.e., 99% of the normal shear energy is less than this value); the third step is to multiply the 99th percentile by a fault safety factor of 1.1 to 1.3 times, and the result is the preset judgment threshold.

[0113] The anomaly detection results indicate that the coupling structure distortion of cross-platform data exceeds the allowable range, which may lead to deviations in subsequent fault feature extraction (such as failure of current-vibration sideband correlation). It is necessary to output a warning signal and check the compression parameters or transmission link.

[0114] The normal judgment result indicates that the fidelity of the coupling structure of the cross-platform data meets the requirements, and there is no need to adjust the current acquisition configuration.

[0115] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A cross-platform data acquisition method based on big data, characterized in that, include: Collect vibration data of rotating equipment, current data of the motor driving the rotating equipment, and rotational speed data of the rotating equipment. Based on the rotational speed data, the vibration data and motor current data are transformed into angular domain data to obtain vibration angular domain data and motor current angular domain data. Order analysis was performed on the vibration angular domain data and the motor current angular domain data to obtain vibration order spectrum data and motor current order spectrum data; Based on vibration order spectrum data and motor current order spectrum data, the unnormalized order cross-bispectrum and its real part phase are calculated. Under the conditions of order and constraint, the order-intersecting bispectral ternary shear coefficient is calculated based on the real part phase, and the shear energy is obtained within the preset order range; Using a preset shear tolerance as a constraint, the compression parameters of the first and second platforms are updated by utilizing the shear energy statistics within the sliding window, so as to improve the compression ratio and ensure that the shear energy does not exceed the preset shear tolerance, thereby obtaining cross-platform data acquisition results.

2. The cross-platform data acquisition method based on big data according to claim 1, characterized in that, Based on the rotational speed data, the vibration data and motor current data are transformed into angular domain data to obtain vibration angular domain data and motor current angular domain data, including: Set a time window, with the start time of the time window being... Vibration data are acquired within the time interval corresponding to the time window. Motor current data and speed data ;in, For any point in time within the time interval, For speed data at time points Angular velocity value at; Calculate cumulative angle ;in, The independent variable for integration is used to iterate through the time window from the start time. to any point in time The interval; Preset angle step Number of data points in the corner domain angular step size The difference between two adjacent target angles in the angular domain, and the number of angular domain data points. The total number of data points to be acquired after angle domain transformation; calculate the initial angle. initial angle Cumulative angle At the start time of the time window The value at that location.

3. The cross-platform data acquisition method based on big data according to claim 2, characterized in that, Based on the rotational speed data, angular domain transformation is performed on the vibration data and motor current data to obtain vibration angular domain data and motor current angular domain data, which also includes: Construct targets at various angles within the angular domain. , ; Based on cumulative angle With time The correspondence is obtained by solving for the target at each angle. Corresponding target time Target time satisfy: ; Extracting vibration data The sampling time sequence is used to select the target time. Two adjacent sampling times and ;in, The smaller of two adjacent sampling times. It is the time with the larger time among two adjacent sampling times, and satisfies Calculate the target angle Corresponding vibration angular domain data : ;in, For vibration data at the sampling time The value at that location, For vibration data at the sampling time The value at; Simultaneously extract motor current data The sampling time sequence is used to select the target time. Two adjacent sampling times and ;in, The smaller of two adjacent sampling times. It is the time with the larger time among two adjacent sampling times, and satisfies Calculate the target angle Corresponding motor current angular domain data : ;in, For motor current data at the sampling time The value at that location, For motor current data at the sampling time The value at that location.

4. The cross-platform data acquisition method based on big data according to claim 3, characterized in that, Order analysis was performed on the vibration angular domain data and the motor current angular domain data to obtain the vibration order spectrum data and the motor current order spectrum data, including: Define the order set Order set Each order in satisfy: ;in, The parameter is an integer, and its value ranges from 0 to... , For order set The first in Each order; Based on vibration angular domain data Calculate vibration order spectrum data : ;in, For vibration angular domain data in the first The value of the target at each angle, The parameter is an integer, and its value ranges from 0 to... , The imaginary unit, It is a natural exponential function. For vibrational order spectrum data at order Complex values ​​at; Based on motor current angular domain data Calculate the order spectrum data of motor current. : ;in, For motor current angular domain data in the first... The value of the target at each angle, The parameter is an integer, and its value ranges from 0 to... , For motor current order spectrum data at order Complex values ​​at the location.

5. The cross-platform data acquisition method based on big data according to claim 4, characterized in that, Based on vibration order spectrum data and motor current order spectrum data, the unnormalized order cross-bispectrum and its real part phase are calculated, including: Define order for sets order of set satisfy: ;in, For a set of order, For order set Any order in For order set Any order in For the set of order pairs Any pair of orders in the sequence; Based on vibration order spectrum data and motor current order spectrum data, calculate the unnormalized order cross-bispectrum. : ;in, For motor current order spectrum data at order Complex values ​​at that location, For vibrational order spectrum data at order Complex values ​​at that location, For vibrational order spectrum data at order Complex conjugate of complex values.

6. The cross-platform data acquisition method based on big data according to claim 5, characterized in that, Based on vibration order spectrum data and motor current order spectrum data, the unnormalized order bispectral and its real part phase are calculated, which also includes: Calculate the real part, modulus, and real phase of the unnormalized order cross-bispectrum, including: in, For order pairs The real part of the non-normalized order bispectral intersection, The operator for retrieving the real part of a complex number; For order pairs The modulus of the unnormalized order bispectral intersection. The operator for taking the modulus of a complex number; For order pairs The real part of the phase of the unnormalized order bispectral cross. It is an inverse cosine function.

7. The cross-platform data acquisition method based on big data according to claim 6, characterized in that, Under certain order and constraints, the order-order cross-spectral ternary shear coefficient is calculated based on the real part of the phase, and the shear energy is obtained within a preset order range, including: Set the order sampling step size and preset order range Order sampling step size The difference between two adjacent sampling points in the order domain, with a preset order range. The predefined interval of the order to be analyzed; For the preset order range Each constraint order within Define the order of a set order of set satisfy: in, For order set Any order in For order set Any order in For the set of order pairs Any pair of orders in the sequence; For the set of order pairs Each order pair in The local second-order mixture difference result is calculated according to the following formula. : ;in, For unnormalized order cross-spectrums in order pair The real phase at that point, For unnormalized order cross-spectrums in order pair The real phase at that point, For unnormalized order cross-spectrums in order pair The real phase at that point, For unnormalized order cross-spectrums in order pair The real phase at that point, For order pairs The local second-order mixture difference result at the location.

8. The cross-platform data acquisition method based on big data according to claim 7, characterized in that, Under the conditions of order and constraints, the order-order cross-bispectral ternary shear coefficient is calculated based on the real part of the phase, and the shear energy is obtained within a preset order range. This also includes: Calculate the order of the set Number of elements Number of elements For the set of order pairs The order pairs included Total number of constraints; calculate constraint order The order of cross-bispectral ternary shear coefficients : ;in, To constrain the order The order of the bispectral ternary shear coefficient; Set weight function Weighting function For predefined, and constraint order The corresponding weight values; calculate the preset order range. internal shear energy : ;in, Preset order range Shear energy within.

9. The cross-platform data acquisition method based on big data according to claim 8, characterized in that, Using a preset shear tolerance as a constraint, the compression parameters of the first and second platforms are updated using the shear energy statistics within a sliding window. This improves the compression ratio while ensuring the shear energy does not exceed the preset shear tolerance. The cross-platform data acquisition results are then output, including: Set the length of the sliding window Sliding window length The number of consecutive windows used to calculate the statistic; for the current window index Calculate the shear energy statistics within the sliding window. : ;in, Window count is Shear energy at that time, Counts the number of windows and the range of values ​​is arrive , Index for the current window The corresponding shear energy statistics; Set preset shear tolerance Preset shear tolerance The maximum allowable shear energy is preset. Calculate tolerance margin factor : ;in, Index for the current window The corresponding tolerance margin coefficient; Set the current compression parameters for the first platform Current compression parameters of the second platform Current compression parameters of the first platform For the first platform in window index Compression configuration parameters at the time, current compression parameters on the second platform For the second platform in window index Compression configuration parameters at that time; Set the first update step size Second update step First update step size The adjustment range coefficient for the compression parameters of the first platform, and the second update step size. This is the adjustment range coefficient for the compression parameters of the second platform; Define the first unit vector Second unit vector First unit vector The first unit vector is the unit vector pointing in the direction of increasing compression ratio within the compression parameter space of the first platform, and the second unit vector is... This is a unit vector pointing in the direction of increasing the compression ratio within the compression parameter space of the second platform; The next window compression parameters for the first platform are calculated and updated. Second platform next window compression parameters : in, This refers to the adjustment amount of the compression parameters for the first platform. This refers to the adjustment amount of the compression parameters for the second platform; Calculate cross-platform compression ratio Cross-platform compression rate satisfy: ;in, For the first platform in window index The amount of data before compression For the second platform in window index The amount of data before compression For the first platform in window index The amount of data after compression For the second platform in window index The amount of data after compression; Output cross-platform data acquisition results, including the compression parameters of the next window on the first platform. Second platform next window compression parameters and cross-platform compression ratio .

10. A cross-platform data monitoring and control system based on big data, characterized in that, The shear energy is compared with a preset threshold, and the judgment result of the cross-platform data acquisition is output, including: Set preset judgment threshold Preset judgment threshold The pre-defined critical value for shear energy used to distinguish between normal and abnormal cross-platform data acquisition results; when Greater than the preset judgment threshold When the time comes, the result is determined to be abnormal; when Less than or equal to the preset judgment threshold At that time, the judgment result was normal.

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