Multifunctional metering detection system

Abstract

The invention relates to the technical field of multifunctional detection, in particular to a multifunctional metering detection system which comprises a time alignment module, an analog-to-digital conversion module, a data coupling module, a vector construction module and an event packaging module. According to the invention, the time alignment is carried out on the measurement data in multiple channels, the synchronization consistency of heterogeneous data is improved, the dynamic analysis of amplitude fluctuation characteristics is combined, the real-time adjustment of the analog-to-digital conversion resolution is realized, the response sensitivity of the system to unsteady signals is enhanced, and the reliability of the system is improved. A composite parameter sequence is constructed by using a cross-channel amplitude and trend relationship, the coupling expression capability among multiple parameters is optimized, the recognition precision of a multi-node linkage trend is improved through state vector modeling and direction difference judgment, an abnormal event path is reconstructed by adopting a time sequence, and a process record is packaged. And the detection data is converted into structured event chain data from original sampling, so that the organizability and traceability of the anomaly detection result are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-functional detection, and particularly to a multi-functional metrology detection system. Background Art

[0002] The technical field of multi-functional detection involves comprehensive testing and metrology of multiple physical quantities or performance parameters of the object to be measured. The core content of this technical field is to use a combined sensor, a signal acquisition module and a control system to synchronously measure and process various parameters such as temperature, pressure, flow rate, voltage, current, displacement, vibration, etc. The technical field mainly includes a multi-parameter perception mechanism, cooperative acquisition of detection signals, a unified analysis mechanism for electrical and non-electrical quantities, and an automatic control logic for the testing process. Through an integrated structure design, the coordinated work between multiple measurement modules is realized, which is applicable to various application scenarios such as industrial manufacturing, equipment commissioning, operation and maintenance, and experimental detection.

[0003] Among them, a multi-functional metrology detection system refers to a device system with multiple detection units that can perform comprehensive parameter acquisition and metrology on different types of objects to be measured. This patent theme aims at complex detection requirements involving multiple test items, covering measurement matters of physical quantities such as length detection, current detection, voltage detection, temperature detection, etc. Specifically, a combined module equipped with a displacement sensor, a voltage sensor, a current transformer, a thermocouple or a thermistor is used to independently collect different signals, and then the detection signals are converted into a processable data form through a channel switching mechanism and an analog-to-digital conversion module in the main control unit. With the help of an embedded sampling control circuit and a storage management circuit, multi-channel data is synchronously collected and itemized metered to complete the structured detection process of various parameters on the same platform, and a functional detection platform with an integrated feature is constructed.

[0004] In the process of comprehensive measurement of traditional multi-functional metrology detection technology, the synchronous acquisition of multi-channel physical quantities usually relies on a unified sampling period and a fixed resolution mode, and it is impossible to adjust in real time according to the dynamic characteristics of the data itself, resulting in poor sampling accuracy of non-steady-state data. Especially, the ability to capture short-time mutation and high-dynamic characteristic signals is insufficient, and the data is prone to be fuzzy or distorted. In the data processing process, there is a lack of in-depth analysis of the cross-channel data coupling relationship, resulting in insufficient sensitivity to the identification of potential abnormal states and events, limiting the early identification ability of complex faults, delaying the detection opportunity, lacking means for multi-dimensional vector processing of the change process of measurement data, and the data correlation analysis is limited to the change trend of a single parameter itself, and the dynamic linkage relationship between parameters is not effectively utilized for in-depth mining, limiting the analysis depth and comprehensiveness of abnormal events, and significantly affecting the detection efficiency and accuracy. Summary of the Invention

[0005] The object of the present invention is to solve the deficiencies existing in the prior art, and a multifunctional metering and detection system is proposed.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A multifunctional metering and detection system includes: The time alignment module obtains the continuous sampling value sequences of each measurement channel, calculates the change rate between adjacent sampling points, aligns the time of the data by extracting the time positions of the peak nodes and comparing the time offsets of the peak nodes in each channel, and generates a heterogeneous data alignment sequence; The analog-to-digital conversion module calls the heterogeneous data alignment sequence, extracts the change amplitude of the measurement data within a continuous period and calculates the rate fluctuation characteristics, and adjusts the resolution parameters of the analog-to-digital conversion in real time according to the rate change to generate a dynamic sampling configuration interval; The data coupling module calls the dynamic sampling configuration interval, obtains the sampling values of multiple measurement channels at the same time node, performs ratio combination using the amplitude difference and the trend change value, constructs a parameter sequence describing the fluctuation relationship between channels, and obtains a composite parameter coupling sequence; The vector construction module calls the composite parameter coupling sequence, analyzes the state value changes of each parameter node within a continuous time period, extracts the change direction, change amplitude and continuous change frequency, constructs a node state change vector, and obtains a multi-node state linkage trend coefficient by combining the angle operation and the comparison of the vector projection lengths.

[0007] As a further solution of the present invention, the heterogeneous data alignment sequence includes a time alignment channel number, a key node timestamp, and an interpolated sampling value; the dynamic sampling configuration interval includes a rate threshold section, a resolution parameter level, and a configuration index number; the composite parameter coupling sequence is specifically an amplitude difference sequence, a trend change value sequence, and a trend amplitude ratio; the multi-node state linkage trend coefficient includes a state change direction vector, a state change amplitude vector, and a change frequency eigenvalue.

[0008] As a further solution of the present invention, the time alignment module includes: The rate identification sub-module obtains the continuous sampling value sequences of each measurement channel, extracts the numerical differences between adjacent sampling points, performs difference operations on adjacent sampling values in the continuous sampling segments within each channel, calculates the change rate, locates the peak nodes of the change rate in each channel, and generates a set of key nodes of the rate change; The offset comparison sub-module calls the set of key nodes of the rate change, extracts the timestamp data of the peak nodes in the channel, arranges the times of the key nodes in each channel in a sequence according to the time dimension, identifies the time offset situation between the key nodes within the same time period, and generates an offset time interval; The node interpolation sub-module calls the offset time interval and uses the formula: ; Calculate the reconstructed sampled values, perform time alignment on the measurement data of multiple channels through data interpolation, and generate a heterogeneous data alignment sequence; Among them, is the reconstructed sampled value of the interpolation point, is the known sampled value before the interpolation point, is the known sampled value after the interpolation point, is the sampled value corresponding time stamp, is the sampled value corresponding time stamp, is the target time position of the interpolation point on the time axis, is the index number of the previous sampling point in the time interval where the interpolation point is located, is the index number of the next sampling point in the time interval where the interpolation point is located, is the index number of the current interpolation target point on the time axis.

[0009] As a further solution of the present invention, the analog-to-digital conversion module includes: The amplitude extraction sub-module calls the heterogeneous data alignment sequence to calculate the amplitude change range of the sampled values of each channel within a preset time window in real time, and obtains the continuous amplitude change interval value; The rate fluctuation sub-module calls the continuous amplitude change interval value, calculates the sampling change rate of each channel within the time window, extracts the difference change between the rates in adjacent time periods, compares the rate fluctuation frequency and the difference interval in multiple time periods, establishes a rate change ratio group, and generates a sampling rate fluctuation characteristic factor; The resolution configuration sub-module calls the sampling rate fluctuation characteristic factor, and according to the rate change amplitude, the fluctuation frequency per unit time, and the difference in the sampling value fluctuation direction, uses the formula: ; Calculate the resolution parameter adjustment value of the analog-to-digital conversion, and generate a dynamic sampling configuration interval; Among them, is the resolution parameter adjustment value of the analog-to-digital conversion, is the rate value of the th sampling point within the jump section, is the average value of all rate values within the jump section, is the fluctuation frequency per unit time within the jump section, is the difference in the rate direction change within the th time period, is the maximum amplitude of the sampling rate change within the jump section, is the average value of the measured amplitudes before and after this section, is the peak value of the measured amplitude for this section, is the number of sampling points participating in the rate fluctuation statistics within the jump section, is the number of time periods participating in the direction difference calculation within the jump section, is the index number of the rate sampling point, is the index number of the rate direction difference participation section.

[0010] As a further solution of the present invention, the data coupling module includes: The channel data acquisition sub-module calls the dynamic sampling configuration interval, extracts the synchronous sampling values of multiple measurement channels at the same time node, and generates a channel synchronous sampling matrix; The trend difference extraction sub-module calls the channel synchronous sampling matrix, calculates the sampling amplitude difference of multiple channels at the same time node, combines the change direction of the sampling values within the continuous time period of each channel, extracts the trend change direction difference between channels, and generates a trend amplitude difference structure group; The coupling feature construction sub-module calls the trend amplitude difference structure group, performs time dimension ratio combination processing on the amplitude difference sequence and trend difference sequence between channels, constructs a structure feature sequence for describing the change relationship between channels, and generates a composite parameter coupling sequence.

[0011] As a further solution of the present invention, the vector construction module includes: The node extraction sub-module calls the composite parameter coupling sequence, extracts the state value changes of each parameter node within the continuous time period, and extracts the change direction, change amplitude, and change frequency according to the direction, amplitude increment, and change frequency within the adjacent time period of the state change, and obtains the node state feature information; The vector generation sub-module calls the node state feature information, maps each node state feature to the three-dimensional vector axis value, constructs a node state change vector, and generates a node state vector structure set; The linkage judgment sub-module calls the node state vector structure set, performs angle calculation and vector projection difference comparison on multiple node state vector pairs, and uses the formula: ; Calculates the state linkage trend coefficients of multiple node pairs and obtains the multi-node state linkage trend coefficients; Among them, represents the state linkage trend coefficient between node and node , represents the projection value of node on the -dimensional vector coordinate, represents the projection value of node on the The projection value on the coordinate of the n-dimensional vector, indicating the node at the projection value on the coordinate of the n-dimensional vector, indicating the node at the projection value on the coordinate of the m-dimensional vector, indicating the dimension number of the state vector, indicating the node state change vector of, indicating the node state change vector of, indicating the serial number of the first node in the state node pair, indicating the comparison node number in the state node pair, indicating the vector dimension index for difference calculation, indicating the vector dimension index for total amplitude statistics.

[0012] As a further solution of the present invention, the system further includes: The event encapsulation module calls the multi-node state linkage trend coefficient, detects abnormal measurement events by identifying the direction differences of state vectors among multiple parameter nodes, extracts the corresponding parameter types and timestamps, reconstructs the evolution path of abnormal events according to the timestamp sequence, and encapsulates the measurement data according to the path sequence to generate an event process encapsulation record; The event process encapsulation record specifically refers to the abnormal parameter type, abnormal timestamp, and event path number.

[0013] As a further solution of the present invention, the event encapsulation module includes: The vector difference identification sub-module calls the multi-node state linkage trend coefficient, obtains the state change vectors of multiple parameter nodes, detects abnormal measurement events by analyzing the direction differences of state vectors among nodes, and generates an abnormal event identification result; The abnormal event path reconstruction sub-module extracts the parameter types and timestamp data corresponding to the target abnormal event based on the abnormal event identification result, and reconstructs the evolution path of the abnormal event according to the timestamp sequence to generate event evolution path data; The event encapsulation record sub-module calls the event evolution path data, encapsulates the measurement data according to the path sequence, and generates an event process encapsulation record.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by performing time alignment on the measurement data in multiple channels, the synchronization consistency of heterogeneous data is improved. Combining with the dynamic analysis of amplitude fluctuation characteristics, real-time adjustment of the analog-to-digital conversion resolution is achieved, enhancing the response sensitivity of the system to non-steady signals. By constructing a composite parameter sequence using the relationship between cross-channel amplitude and trend, the coupling expression ability between multiple parameters is optimized. Through state vector modeling and direction difference judgment, the recognition accuracy of the multi-node linkage trend is improved. By reconstructing the abnormal event path using time series and encapsulating the process record, the detection data is transformed from the original sampling into structured event chain data, enhancing the organization and traceability of the abnormal detection results. Description of the Drawings

[0015] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the time alignment module of the present invention; Figure 3 is the flow chart of the analog-to-digital conversion module of the present invention; Figure 4 is the flow chart of the data coupling module of the present invention; Figure 5 is the flow chart of the vector construction module of the present invention; Figure 6 is the flow chart of the event encapsulation module of the present invention. Detailed Embodiments

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Embodiment 1: Please refer to Figure 1 , a multi-functional metering and detection system includes: The time alignment module obtains the continuous sampling value sequence of each measurement channel, calculates the change rate between adjacent sampling points, extracts the time positions of the peak nodes by comparing the time offsets of the peak nodes in each channel, aligns the data in time, and generates a heterogeneous data alignment sequence; The analog-to-digital conversion module calls the heterogeneous data alignment sequence, extracts the change amplitude of the measurement data within a continuous period and calculates the rate fluctuation characteristics, and adjusts the resolution parameters of the analog-to-digital conversion in real time according to the rate change to generate a dynamic sampling configuration interval; The data coupling module calls the dynamic sampling configuration interval, obtains the sampling values of multiple measurement channels at the same time node, performs ratio combination using the amplitude difference and the trend change value, constructs a parameter sequence describing the fluctuation relationship between channels, and obtains a composite parameter coupling sequence; The vector construction module calls the composite parameter coupling sequence, analyzes the state value changes of each parameter node within a continuous period, extracts the change direction, change amplitude, and continuous change frequency, constructs a node state change vector, and obtains a multi-node state linkage trend coefficient by combining angle operation and vector projection length comparison; The event encapsulation module calls the multi-node state linkage trend coefficient, detects abnormal measurement events by identifying the direction differences of the state vectors between multiple parameter nodes, extracts the corresponding parameter types and timestamps, reconstructs the evolution path of the abnormal events according to the timestamp order, and encapsulates the measurement data using the path order to generate an event process encapsulation record.

[0019] The heterogeneous data alignment sequence includes the time alignment channel number, the key node timestamp, and the interpolated sampling value. The dynamic sampling configuration interval includes the rate threshold section, the resolution parameter level, and the configuration index number. The composite parameter coupling sequence is specifically the amplitude difference sequence, the trend change value sequence, and the trend amplitude ratio. The multi-node state linkage trend coefficient includes the state change direction vector, the state change amplitude vector, and the change frequency eigenvalue. The event process encapsulation record specifically refers to the abnormal parameter type, the abnormal timestamp, and the event path number.

[0020] Please refer to Figure 2 , the time alignment module includes: The rate identification sub-module obtains the continuous sampling value sequence of each measurement channel, extracts the numerical difference between adjacent sampling points, performs difference operations on adjacent sampling values in each continuous sampling segment within each channel, calculates the change rate, locates the peak nodes of the change rate in each channel, and generates a set of key nodes of rate change; Obtain a sequence of continuous sampled values for each measurement channel. Extract the numerical difference and time difference between any pair of adjacent sampling points in each channel in the order of sampling time. Divide the difference by the time interval to obtain the rate of change per unit time. For channel A, if the amplitudes of a pair of adjacent sampling points are 2.5 and 3.0 respectively, and the timestamps are 100 ms and 110 ms respectively, then the rate of change is (3.0 - 2.5) / (110 - 100) = 0.05 / ms. If the sampling points in channel B are 2.7 and 3.1 and the corresponding timestamps are the same, then the rate of change is 0.04 / ms. The system repeats the above operations for the complete sequence of each channel to form a rate sequence within the channel. Subsequently, adopt a sliding window mechanism, set the window width to 3 sampling points, perform a local extreme value comparison on the rate values within any continuous window in the rate sequence, identify the time index number corresponding to the maximum rate within the window, map this index number to the original data sequence, and obtain its corresponding timestamp as the rate peak node of this window. Assume that the rates at the 4th, 7th, and 11th sampling point positions in channel A are 0.02, 0.06, and 0.05 / ms respectively. When the window slides to the 7th point, it is identified as the local maximum, and the time point is 170 ms. Finally, repeat the positioning for each channel. The time of the rate peak nodes of channel A is {170, 190, 250} ms, and that of channel B is {180, 200, 260} ms, obtaining the key node set of rate change.

[0021] The offset comparison sub-module calls the key node set of rate change, extracts the timestamp data of the peak nodes in the channel, arranges the key node times in each channel in a sequence according to the time dimension, identifies the time offset situation between the key nodes within the same time period, and generates an offset time interval; Call the key node set of the call rate change, extract the timestamp data of the identified key nodes in each channel, construct a time series index in ascending order along the time axis, set a target time range such as [160ms, 200ms], filter the key node times of each channel within this time period. If there are two nodes at 170ms and 190ms in channel A, and two nodes at 180ms and 200ms in channel B, then calculate the time difference between the corresponding positions for each pair of key time nodes. For example, the difference between 170ms and 180ms is 10ms, and the difference between 190ms and 200ms is also 10ms. Merge them into an offset length vector [10, 10]ms. The system sets the offset time judgment threshold to 5ms as the acceptable time error benchmark value for cross-channel nodes. Since the differences of the above two node pairs are both greater than this threshold, the system judges that there is a synchronous offset between the key nodes of channel A and channel B during this time period. This judgment operation specifically compares the offset value with the benchmark value. If the offset value is greater than or equal to this benchmark value, it is considered that the offset exists; otherwise, synchronization exists. Finally, combine and encapsulate the offsets between multiple channels and their time period identifiers to generate an offset time interval.

[0022] The node interpolation sub-module calls the offset time interval and uses the formula: ; Calculate the reconstructed sampling values, and through data interpolation, perform time alignment on the measurement data of multiple channels to generate a heterogeneous data alignment sequence; Among them, is the reconstructed sampling value of the interpolation point, is the known sampling value before the interpolation point, is the known sampling value after the interpolation point, is the sampling value corresponding to the timestamp, is the sampling value corresponding to the timestamp, is the target time position of the interpolation point on the time axis, is the index number of the previous sampling point in the time interval where the interpolation point is located, is the index number of the next sampling point in the time interval where the interpolation point is located, is the index number of the current interpolation target point on the time axis; When calling the offset time interval to perform time alignment processing on the measurement data of multiple channels, first, based on the key node time information of each channel in the offset time interval, extract the known sampling values on both sides of the offset section. The sampling value at the previous moment of the sampling point is set as , and the sampling value at the next moment is set as , and extract the corresponding timestamps and , and then determine the time interval offset of the interpolation point through the time difference between the target time and , calculate by combining the amplitude difference between known values to obtain the interpolation amplitude , and specifically use the formula: ; In a specific example, the two sampling values of channel A are 3.0 ( ) and 3.5 ( ), and their corresponding timestamps are 110 ms ( ) and 120 ms ( ). If the target interpolation point time is 115 ms ( ), then the interpolation value at this point is: ; Thus, the interpolation value of channel A at 115 ms is 3.25. Process channel B in the same way. Given , , , , then its interpolation value at 115 ms is: ; Taking all the interpolation points between channels as reference nodes, reconstruct the interpolation values of each channel in chronological order, and combine them to form a complete time-aligned data stream sequence, ensuring that each channel has a corresponding amplitude sampling point at any moment. Correct the unaligned data through interpolation to generate a heterogeneous data alignment sequence. Among them, represents the reconstructed sampling value of the point to be interpolated, and respectively represent the amplitudes of the previous sampling point and the subsequent sampling point within the interpolation interval, , are the timestamps of the two sampling points, represents the time position of the target interpolation point on the time axis, is the index number of the previous sampling point within the current interpolation interval, is the index number of the current interpolation target point in the time series. By using the actual time displacement amount of the interpolation point position as a multiplication factor and combining the adjacent amplitude differences to achieve dynamic amplitude generation, the reconstructed sampling value represents that in the case of time offset between different measurement channels, the sampling value at a certain target time point is estimated through interpolation calculation, which is used to eliminate the time asynchronization problem between channels and achieve time alignment, effectively establishing a continuous mapping relationship between time nodes and signal amplitudes.

[0023] Please refer to Figure 3 , the analog-to-digital conversion module includes: The amplitude extraction sub-module calls the heterogeneous data alignment sequence to calculate in real time the amplitude change range of the sampled values of each channel within a preset time window, and obtains the continuous amplitude change interval value; Call the heterogeneous data alignment sequence. First, obtain the continuous sampled value sequence after time interpolation processing in each measurement channel, and segment it according to the set time window. Taking a 5ms sliding window as an example, the system extracts the difference between the maximum amplitude and the minimum amplitude in each group of windows as the amplitude change interval value of this time window. If the sampled values of channel A in a certain time window are {2.5, 2.8, 3.0, 3.1, 3.3}, then the maximum value in this window is 3.3, the minimum value is 2.5, and the change amplitude is 3.3 - 2.5 = 0.8. If the sampled values of channel B in the same time period are {2.6, 2.9, 3.0, 3.2, 3.4}, then its amplitude change interval value is 3.4 - 2.6 = 0.8. The system slides the window in turn to extract the amplitude intervals of each time period, forming the amplitude change flow sequence under the time series of this channel. Set the amplitude change interval judgment threshold to 0.5 unit amplitude. If the amplitude interval values of two consecutive windows both exceed this threshold, it is determined that this section is in the jump behavior stage, record the start and end times of the window and the corresponding channel number, form a continuous amplitude change record sequence, and generate the continuous amplitude change interval value based on this.

[0024] The rate fluctuation sub-module calls the continuous amplitude change interval value, calculates the sampling change rate of each channel within the time window, extracts the difference change between the rates of adjacent time periods, compares the rate fluctuation frequency and the difference interval in multiple time periods, establishes a rate change ratio group, and generates the sampling rate fluctuation characteristic factor; Call the continuously varying amplitude range value, set the change rate within a time period for each channel. Based on the sampling points, the system performs a difference operation on the amplitude changes between two adjacent sampling times and divides it by the corresponding time interval to form a change rate sequence per unit time. If channel A has an amplitude sequence {2.5, 2.8, 3.1, 3.0, 3.3} within a certain period, and the corresponding timestamps are 100ms, 105ms, 110ms, 115ms, 120ms respectively, then the change rates are: (2.8 - 2.5) / 5 = 0.06, (3.1 - 2.8) / 5 = 0.06, (3.0 - 3.1) / 5 = -0.02, (3.3 - 3.0) / 5 = 0.06, forming a rate sequence {0.06, 0.06, -0.02, 0.06}. Subsequently, the system calculates the fluctuation change amount of the rates in different time periods, that is, subtracts the rate of the current time period from the rate of the previous time period and takes the absolute value. For example, |0.06 - 0.06| = 0.00, |0.06 - (-0.02)| = 0.08, |-0.02 - 0.06| = 0.08, generating a difference fluctuation sequence. Set the fluctuation difference judgment threshold to 0.05. If the difference exceeds this value, it is counted as a fluctuation event once. The fluctuation frequency within the unit time window is counted as 4, and the maximum rate change amplitude under the current jump behavior is recorded as 0.11 - 0.07 = 0.04. Finally, based on this fluctuation behavior, the frequency value, direction change difference, and total rate change of each channel's jump segment are constructed, and combined to form a sampling rate fluctuation characteristic factor.

[0025] The resolution configuration sub-module calls the sampling rate fluctuation characteristic factor, and according to the rate change amplitude, the fluctuation frequency per unit time, and the sampling value fluctuation direction difference, uses the formula: ; Calculate the resolution parameter adjustment value for analog-to-digital conversion and generate a dynamic sampling configuration range; Among them, is the resolution parameter adjustment value for analog-to-digital conversion, is the rate value of the th sampling point within the jump section, is the average value of all rate values within the jump section, is the fluctuation frequency per unit time within the jump section, is the rate direction change difference in the th time period, is the maximum amplitude of the sampling rate change within the jump section, is the average value of the measurement amplitudes before and after this section, is the peak value of the measurement amplitude in this section, is the number of sampling points participating in the rate fluctuation statistics within the jump section, is the number of time periods participating in the direction difference calculation within the jump section, is the index number of the rate sampling point, is the index number of the segment where the rate direction difference participates; Call the sampling rate fluctuation characteristic factor. The rate sampling values of the selected channel within the jump segment are {0.08, 0.10, 0.07, 0.11, 0.09}, and its average value is: ; Further calculate the total absolute difference between this rate value and the average rate value: ; Fluctuation frequency Is set to 4, and its square root is: ; The direction change difference is set to {0.02, 0.03, 0.01}, and its absolute value sum is: ; Set the maximum rate change amplitude , average amplitude , amplitude peak , then their difference is: ; Substitute all the above values into the formula: ; Finally, calculate the resolution adjustment parameter value for analog-to-digital conversion , and generate a dynamic sampling configuration interval accordingly.

[0026] Among them, represents the value of the th rate sampling point, is the average rate within the jump segment, is the fluctuation frequency within the jump section, is the rate direction change difference in the th time period, is the maximum rate amplitude difference within the jump segment, is the average amplitude, is the amplitude peak of the current segment, is the dynamic quantity finally used to adjust the analog-to-digital conversion accuracy.

[0027] Table 1 Resolution Parameter Calculation Input Table ; As shown in Table 1, the jump segment rate values and direction change differences involved in the calculation are listed. The rate values are based on the amplitude in adjacent time periods divided by the time interval, and the direction change differences are calculated through the scalar differences of the change trend directions. Both are used for the actual values of each parameter in the formula. From the data in the table and the calculation, the resolution adjustment value is 0.000727. This result indicates that the analog-to-digital conversion module in this stage needs to adopt a more precise conversion step to adapt to the sampling rate fluctuation characteristics.

[0028] Please refer to Figure 4 , the data coupling module includes: The channel data acquisition sub-module calls the dynamic sampling configuration interval, extracts the synchronous sampling values of multiple measurement channels at the same time node, and generates a channel synchronous sampling matrix; After calling the dynamic sampling configuration interval, the system enters the multi-channel sampling data synchronization stage, and it is necessary to ensure that multiple channels have complete measurement value records at the same time node. Here, taking Channel A and Channel B as examples, the sampling values of Channel A at time points 100 ms, 105 ms, 110 ms, 115 ms, and 120 ms are 2.5, 2.8, 3.0, 3.1, and 3.3 respectively, and the sampling values of Channel B at the same time points are 2.6, 2.9, 3.0, 3.2, and 3.4. The system sequentially traverses each channel, extracts the amplitude values at the corresponding moments from the sampled data that has been interpolated and time-aligned, and vertically concatenates the measurement values of all channels in the time dimension to generate a synchronous sampling matrix. Each matrix unit corresponds to the measurement value of a certain channel at a specific time point, each row represents a unified time node, and the columns correspond to different channels. During the matrix generation process, the system performs a one-by-one comparison on the time stamp fields of all channels, eliminates the time deviation caused by sampling delay or signal loss, and retains the time nodes where all channels have complete sampling values. The construction of the synchronous sampling matrix depends on the dynamic sampling configuration interval generated by the previous module, which contains information such as the current sampling status, jump window, and effective time period of each channel. The system uses this as the sampling window benchmark to screen synchronization points. In the above example, the system constructs a set of channel synchronous records for the data of the five time nodes in the range of 100 ms to 120 ms for subsequent amplitude and trend difference analysis.

[0029] The trend difference extraction sub-module calls the channel synchronous sampling matrix, calculates the sampling amplitude differences of multiple channels at the same time node, combines the change directions of the sampled values within the continuous time period of each channel, extracts the trend change direction differences between channels, and generates a trend amplitude difference structure group; Calling the channel synchronous sampling matrix constructed in the previous step, the system starts the trend difference extraction operation. First, calculate the amplitude difference of the sampling values of different channels at each time node. Taking channels A and B as an example, at 100 milliseconds, the sampling values of the two channels are 2.5 and 2.6 respectively. The system subtracts the smaller value from the larger value and takes the absolute value, and the amplitude difference at this time point is 0.1. Process 105 milliseconds, 110 milliseconds, 115 milliseconds, and 120 milliseconds in turn to obtain a sequence of amplitude difference values. Subsequently, the system enters the trend recognition stage, and respectively judges the difference between the sampling values of two consecutive sampling points in the time dimension for each channel. If the latter sampling value is greater than the former, it is recorded as an upward trend, represented by 1; if the latter is less than the former, it is recorded as a downward trend, represented by -1; if they are equal, it is recorded as stable, represented by 0. After processing the sampling values {2.5, 2.8, 3.0, 3.1, 3.3} of channel A, the trend sequence is {1, 1, 1, 1}; the sampling values {2.6, 2.9, 3.0, 3.2, 3.4} of channel B also obtain the trend sequence {1, 1, 1, 1}. The system compares the trend encodings of the two channels in the same time period one by one. A difference of 0 represents the same trend, a difference of 2 represents a reverse trend of one rising and one falling, and a difference of 1 represents that one is stable and the other is changing. The system extracts the trend difference of each time period in turn, and binds its structure with the aforementioned amplitude difference to form a corresponding structure of the trend difference and the amplitude difference, and finally forms a group of trend amplitude difference structures.

[0030] The coupling feature construction sub-module calls the group of trend amplitude difference structures, performs time dimension ratio combination processing on the amplitude difference sequence and the trend difference sequence between channels, constructs a structural feature sequence for describing the change relationship between channels, and generates a composite parameter coupling sequence; After obtaining the trend amplitude difference structure group, the system performs ratio combination processing on the structural units at each time node. Specifically, the system extracts the trend difference and the amplitude difference, and sequentially performs the ratio combination operation. To avoid the situation of a zero denominator, the system adds a constant term 1 when processing the trend difference, and defines the ratio as the amplitude difference divided by the trend difference plus 1. For example, if the amplitude difference at a certain time point is 0.1 and the trend difference is 0, the calculated value is 0.1 divided by 1, that is, the result is 0.1; if the amplitude difference is 0.2 and the trend difference is 1, the result is 0.2 divided by 2, getting 0.1; if the trend difference is 2 and the amplitude difference is 0.1, the result is 0.1 divided by 3, getting approximately 0.033. The system records the result of this ratio operation as a new structural feature point, and repeats the above calculation steps for each time node to generate a sequence of structural feature values. The sequence of structural feature values represents the degree of coordination of the sampled value changes between different channels at each sampling moment. A high value represents a large amplitude difference but a consistent trend, and a low value represents a small amplitude difference in the presence of a direction conflict. The time series of the structural feature values will be used as the input for the subsequent model or an auxiliary indicator for state decision-making. Finally, the system stores the combined values at all time nodes as structured data entries, organizes them in chronological order into a structure sequence, and generates a composite parameter coupling sequence to describe the relative coupling of the change amounts during the multi-channel signal linkage process.

[0031] Please refer to Figure 5 , the vector construction module includes: The node extraction sub-module calls the composite parameter coupling sequence, extracts the state value changes of each parameter node within a continuous time period, and extracts the change direction, change amplitude, and change frequency according to the direction of the state change, the amplitude increment, and the change frequency within adjacent time periods, so as to obtain the node state feature information; During the execution of the node extraction sub-module, it is first necessary to call the composite parameter coupling sequence to extract the state value changes of each measurement node within a continuous time period. At this time, the state value change of each node is regarded as a time series. For example, assume that the sampled data of node A within a continuous time period is: . This data sequence represents how the measured value of this node changes at each time point. Next, for this sequence, an analysis of the state value changes is performed. First, the direction of the state change, the amplitude increment, and the change frequency within each time period are determined. Specifically, the direction of the state change is judged by the positive or negative of the difference between the current time point and the previous time point. If the difference is positive, the direction is considered to be "+1"; if the difference is negative, the direction is considered to be "-1". For example, in the data sequence of node A, the measured value from the 1st to the 2nd time point increases from 12 to 14, and the difference is +2, so the direction is +1; the measured value from the 2nd to the 3rd time point increases from 14 to 17, and the difference is +3, so the direction is still +1. And so on, until all time points have been calculated, and finally the direction sequence is In addition, the change in amplitude is defined as the absolute difference between the current sampled value and the previous sampled value. For example, the amplitude change from the 1st to the 2nd time point is , and the amplitude change from the 2nd to the 3rd time point is , and so on, resulting in an amplitude change sequence of . Next, the change frequency is calculated. The change frequency refers to the number of times the state direction changes within a given time window. Taking the direction change sequence of node A as an example, the state direction changes from +1 to -1 and from -1 to +1 both occur 2 times, and the frequency value is 2. By extracting the state direction, amplitude, and change frequency of node A during this time period, the state feature information of the node is obtained. This feature information includes three dimensions: direction, amplitude, and frequency, and is structured and recorded in the form of a triple, forming a set of node state feature information with a consistent time interval. For example, the direction, amplitude, and frequency data of node A are respectively , and the frequency 2. This node state feature information is used as input for further processing and analysis by subsequent modules. Through this structured extraction, the system maintains time synchronization among multiple nodes, ensuring the temporal consistency of node data, thereby providing the necessary data basis for the subsequent vector generation sub-module.

[0032] The vector generation sub-module calls the node state feature information, maps each node state feature to the axis values of a three-dimensional vector, constructs a node state change vector, and generates a set of node state vector structures; Evaluate the state linkage trend among each node by analyzing the set of node state vector structures. First, the sub-module processes multiple pairs of node state vectors. Each pair of node state vectors comes from different measurement points or sensors in the system, and their changes reflect the correlation between various parameters in the system. On this basis, it is first necessary to calculate the included angle between the state vectors of each pair of nodes. The size of the included angle reflects whether the change directions of the two nodes are the same. The smaller the included angle, the more similar the change trends of the two nodes, and vice versa. Next, perform a comparison of the vector projection differences. In each pair of node state vectors, the system calculates the projection values of the node state vectors in each dimension, and then obtains the projection differences of the two nodes in the same dimension. This difference reveals the degree of consistency of the node state changes in this dimension. Specifically, the smaller the projection difference, the more similar the change characteristics of the nodes in this dimension, and vice versa. Then calculate the state linkage trend coefficients for multiple node pairs. Calculate the included angle value and the projection difference, and then combine the two to obtain the final state linkage trend coefficient. The coefficient is used to represent the linkage degree between different nodes. The higher the coefficient value, the stronger the linkage between the nodes, and vice versa. These coefficients provide multi-dimensional data support to help the system judge whether different nodes have the same or similar change trends during the measurement process. Through the operation of the linkage judgment sub-module, the system can effectively identify the correlation between multiple nodes and quantify their linkage strength through the trend coefficient. This process can not only reveal the correlation between different parameters or measurement channels, but also provide a scientific basis for system fault diagnosis, trend prediction, etc. For example, if under a certain working condition, the state linkage trend coefficient between node A and node B increases significantly, this may indicate that these two nodes have a strong linkage response under the current working condition, and the system should pay attention, and there may be a strongly correlated physical phenomenon or potential fault. Finally, the calculation results of the state linkage trend coefficients of multiple nodes will be used as the basis for system judgment and decision-making. These results not only provide the necessary data support for subsequent anomaly detection and prediction, but also help the system evaluate whether the current state is within the normal range by comparing with historical data, and provide a reference for adjusting the system operation.

[0033] The linkage judgment sub-module calls the set of node state vector structures, performs included angle calculation and vector projection difference comparison on multiple pairs of node state vectors, and uses the formula: ; Calculate the state linkage trend coefficients of multiple node pairs to obtain the multi-node state linkage trend coefficients; Among them, represents the state linkage trend coefficient between node and node , represents node in the The projection value on the coordinate of the indicating the node on the coordinate of the indicating the node on the coordinate of the indicating the node on the coordinate of the indicating the dimension number of the state vector, indicating the node state change vector of indicating the node state change vector of indicating the serial number of the first node in the state node pair, indicating the comparison node number in the state node pair, indicating the vector dimension index for difference calculation, indicating the vector dimension index for total amplitude statistics; Call the node state vector structure set to identify the state change relationship between multiple node pairs. It is necessary to pair the state change three-dimensional vectors expressed by multiple nodes in each time period pairwise, and perform the included angle operation and projection difference comparison on each group of node vectors. Among them, the included angle reflects the consistency of the vector direction, and the projection difference reflects the closeness of the amplitude configuration. Taking the three node vectors in a certain time period as an example, nodes A, B, and C have state change components (change direction, amplitude, frequency) in three dimensions respectively, as shown in the table, where the values have been unified in dimension.

[0034] Table 2 Vector Dimension Component Representation Table ; As shown in Table 2, the numerical distributions of nodes A, B, and C in the three state feature dimensions (direction, amplitude, frequency) provide basic data for subsequent vector included angle calculation and trend coefficient determination.

[0035] According to the values in Table 2, the state change vectors of nodes A and B can be calculated as follows: ; Calculate their inner product to get: ; The modulus lengths are respectively: ; Then the cosine of the included angle is: ; Perform vector projection difference calculation simultaneously. Let the projection differences of each dimension between A and B be as follows: ; Then the total projection difference is: ; The total amplitude of each dimension is: ; Then the final formula operation is as follows: ; This result indicates that the directions of nodes A and B on the state change vector are similar, the amplitudes are slightly different, and the linkage trend is highly close. The obtained state linkage trend coefficient is 0.818. This value is used to construct the trend correlation matrix between nodes, and all node pairs can be executed according to this method. Indicates the state linkage trend coefficient between node and node , reflecting the consistency in direction and amplitude of their state behaviors. Indicates the projection value of node on the -dimensional coordinate (direction, amplitude, frequency). Indicates the projection value of node on the same dimension. , Similarly, it is used for the total amplitude normalization calculation. is the number of dimensions of the state vector, usually three dimensions. , are the vector forms of the node state changes. , are the number indexes of the state nodes. , are the dimension indexes respectively participating in the projection difference calculation and amplitude summation. By introducing the cosine of the included angle and the vector projection difference normalization coefficient, the consistency and intensity of the state behavior are coupled and evaluated as a single trend coefficient, improving the comprehensive judgment ability of the node state covariation. The multi-node state linkage trend coefficient is used to measure the synergy between different measurement nodes in state changes, indicating the synergy trend strength between multiple node state changes. The higher the value, the more consistent and stronger the linkage of these node changes. Referring to the node vector values shown in Table 2, the trend matrix between all nodes can be quickly deduced for use by subsequent modules.

[0036] Please refer to Figure 6 , the event encapsulation module includes: The vector difference identification sub-module calls the multi-node state linkage trend coefficient to obtain the state change vectors of multiple parameter nodes. By analyzing the direction differences of the state vectors between nodes, it detects abnormal measurement events and generates abnormal event identification results. First, it is necessary to obtain the state change vectors of multiple measurement channels and use these vectors to reflect the changes in the state of each channel over time. The state change vector is an array containing the state values at multiple time points. By analyzing these state values, the dynamic change trend of each channel can be identified. During the execution of this module, by comparing the differences in the state values within each channel, especially by detecting the directional differences in the state vectors of each measurement point, the nodes with significant changes are identified. For example, assume that within a certain time period, the state vector of channel 1 is (0.2, 0.5, 0.8, 1.0, 1.2), while the state vector of channel 2 is (0.1, 0.4, 0.7, 1.1, 1.5). The state value differences between the two are large, and the change directions are significantly different. Then, this difference may represent the occurrence of an abnormal event. At this time, by setting a certain threshold to calibrate these nodes with significant differences, these nodes will be considered as the trigger points of abnormal events and marked as candidate data for abnormal events. Further, the directional differences of these state vectors are quantified by calculating the vector angle or the magnitude of the direction change. When the vector difference exceeds the set threshold, the system determines that this node is the identification point of an abnormal measurement event. For example, if the set threshold is 0.3, when the state vector difference is greater than this value, it can be determined that an abnormality occurred at that moment. Therefore, by analyzing the directional differences of the state change vectors of multiple channels, it is possible to locate which measurement data have significant abnormal fluctuations within a specific time period, providing key data support for subsequent abnormal event identification. These identified abnormal nodes provide an important basis for the subsequent steps, laying the foundation for the further tracking and path reconstruction of abnormal events, and finally generating the abnormal event identification result.

[0037] Based on the abnormal event identification result, the abnormal event path reconstruction sub-module extracts the parameter type and timestamp data corresponding to the target abnormal event, and reconstructs the evolution path of the abnormal event according to the timestamp order to generate event evolution path data; Extract relevant parameter types and timestamp data from the anomaly event identifier. By analyzing the timestamps of each anomaly event and its corresponding status change data, identify and reconstruct a complete anomaly event path. For example, assume that during the measurement process, the statuses of Channel 1, Channel 2, and Channel 3 change significantly at certain time points, and the timestamps of these changes are 5 seconds, 10 seconds, 15 seconds, and 20 seconds respectively. Then these timestamps form the evolution path of the anomaly event. During the path reconstruction process, the system first arranges the timestamps of these events in chronological order to establish a continuous and gradually evolving event sequence. During this process, the system reorganizes the status values corresponding to each timestamp, combines the status changes of multiple channels, and ensures that the status data at each time point is accurately mapped to the event path. For example, assume the event path data is [5 seconds, 10 seconds, 15 seconds, 20 seconds], then the data at each time point (such as the status value of Channel 1 at 5 seconds is 0.2, at 10 seconds is 0.5, etc.) will be extracted one by one, combined in chronological order, and finally a complete anomaly event evolution path is reconstructed. This reconstruction process can provide a clear time frame and detailed data for each measurement point, enabling the entire process of the anomaly event from occurrence to evolution to be clearly presented, and generating event evolution path data. These data provide strong support for subsequent event encapsulation and analysis, ensuring the accuracy and reliability of the entire process.

[0038] The event encapsulation record sub-module calls the event evolution path data, encapsulates the measurement data according to the path order, and generates an event process encapsulation record; By analyzing the order of timestamps extracted from the abnormal event path, the measurement data associated with these timestamps are obtained one by one and encapsulated in chronological order. For example, assume the event path is [5 seconds, 10 seconds, 15 seconds, 20 seconds], and the measurement data corresponding to these moments are: at 5 seconds, the status value of channel 1 is 0.2, the status value of channel 2 is 0.1, and the status value of channel 3 is 0.3; at 10 seconds, the status value of channel 1 is 0.5, the status value of channel 2 is 0.4, and the status value of channel 3 is 0.6; at 15 seconds, the status value of channel 1 is 0.8, the status value of channel 2 is 0.7, and the status value of channel 3 is 1.0; at 20 seconds, the status value of channel 1 is 1.0, the status value of channel 2 is 1.1, and the status value of channel 3 is 1.3; these data will be extracted one by one according to the order of timestamps and used to construct a complete record of the event process. Finally, the encapsulated data will form a unified record format for subsequent use and analysis. For example, the event process record is: [5 seconds: [0.2, 0.1, 0.3], 10 seconds: [0.5, 0.4, 0.6], 15 seconds: [0.8, 0.7, 1.0], 20 seconds: [1.0, 1.1, 1.3]]. This encapsulated record not only includes the time information of the event occurrence but also contains the status values of each related channel. This encapsulation method makes the event data more structured, facilitating storage, transmission, and subsequent analysis. Especially in complex monitoring and maintenance processes, these encapsulated records can effectively support the rapid query and backtracking of data, providing a convenient tool for subsequent fault diagnosis and event tracking. The core of this process is to ensure the timeliness and accuracy of the data, and finally generate an encapsulated record of the event process.

[0039] The above is only the preferred embodiment of the present invention, and it is not intended to limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as the content of the technical solution of the present invention is not departed from, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A multifunctional metrology and detection system, characterized in that The system includes: The time alignment module obtains the continuous sampling value sequence of each measurement channel, calculates the change rate between adjacent sampling points, aligns the data in time by extracting the time positions of peak nodes and comparing the time offsets of peak nodes in each channel, and generates a heterogeneous data alignment sequence; The analog-to-digital conversion module calls the heterogeneous data alignment sequence, extracts the change amplitude of the measurement data within a continuous time period and calculates the rate fluctuation characteristics, and adjusts the resolution parameters of the analog-to-digital conversion in real time according to the rate change to generate a dynamic sampling configuration interval; The data coupling module calls the dynamic sampling configuration interval, obtains the sampling values of multiple measurement channels at the same time node, performs ratio combination using the amplitude difference and the trend change value, constructs a parameter sequence describing the fluctuation relationship between channels, and obtains a composite parameter coupling sequence; The vector construction module calls the composite parameter coupling sequence, extracts the change direction, change amplitude and continuous change frequency by analyzing the state value changes of each parameter node within a continuous time period, constructs a node state change vector, and obtains a multi-node state linkage trend coefficient by combining angle operation and vector projection length comparison.

2. The multifunctional metering and detection system according to claim 1, wherein The heterogeneous data alignment sequence includes the time alignment channel number, the key node timestamp, and the interpolated sampling value. The dynamic sampling configuration interval includes the rate threshold section, the resolution parameter level, and the configuration index number. The composite parameter coupling sequence is specifically the amplitude difference sequence, the trend change value sequence, and the trend amplitude ratio. The multi-node state linkage trend coefficient includes the state change direction vector, the state change amplitude vector, and the change frequency characteristic value.

3. The multi-functional metering and detection system according to claim 1, wherein The time alignment module includes: The rate identification sub-module obtains the continuous sampling value sequence of each measurement channel, extracts the numerical difference between adjacent sampling points, performs difference operation on adjacent sampling values in each continuous sampling segment within each channel, calculates the change rate, locates the peak nodes of the change rate in each channel, and generates a set of key nodes of the rate change; The offset comparison sub-module calls the set of key nodes of the rate change, extracts the timestamp data of the peak nodes in the channel, arranges the key node times in each channel in a sequence according to the time dimension, identifies the time offset situation between key nodes within the same time period, and generates an offset time interval; The node interpolation sub-module calls the offset time interval and uses the formula: ； Calculate the reconstructed sampling value, and perform time alignment on the measurement data of multiple channels through data interpolation to generate a heterogeneous data alignment sequence; Wherein, is the reconstructed sampling value of the interpolation point, is the known sampling value before the interpolation point, is the known sampling value after the interpolation point, is the sampling value corresponding time stamp, is the sampling value corresponding time stamp, is the target time position of the interpolation point on the time axis, is the index number of the previous sampling point in the time interval where the interpolation point is located, is the index number of the next sampling point in the time interval where the interpolation point is located, is the index number of the current interpolation target point on the time axis.

4. The multifunctional metering and detection system according to claim 3, wherein, The analog-to-digital conversion module includes: The amplitude extraction sub-module calls the heterogeneous data alignment sequence and calculates the amplitude change range of the sampling value of each channel within a preset time window in real time to obtain the continuous amplitude change interval value; The rate fluctuation sub-module calls the continuous amplitude change interval value, calculates the sampling change rate of each channel within the time window, extracts the difference change between the rates of adjacent time periods, compares the rate fluctuation frequency and the difference interval within multiple time periods, establishes a rate change ratio group, and generates a sampling rate fluctuation characteristic factor; The discrimination configuration sub-module calls the sampling rate fluctuation characteristic factor, and according to the rate change amplitude, the fluctuation frequency within a unit time, and the sampling value fluctuation direction difference, uses the formula: ； Calculate the resolution parameter adjustment value of the analog-to-digital conversion, and generate a dynamic sampling configuration interval; Among them, is the resolution parameter adjustment value for analog-to-digital conversion, is the rate value of the th sampling point within the jump section, is the average value of all rate values within the jump section, is the fluctuation frequency per unit time within the jump section, is the rate direction change difference within the th time period, is the maximum amplitude of the sampling rate change within the jump section, is the average value of the measurement amplitudes before and after this section, is the peak value of the measurement amplitude of this section, is the number of sampling points participating in the rate fluctuation statistics within the jump section, is the number of time periods participating in the direction difference calculation within the jump section, is the index number of the rate sampling point, is the index number of the section where the rate direction difference participates.

5. The multi-functional metrology and detection system according to claim 4, characterized in that, The data coupling module includes: The channel data acquisition sub-module calls the dynamic sampling configuration interval, extracts the synchronous sampling values of multiple measurement channels at the same time node, and generates a channel synchronous sampling matrix; The trend difference extraction sub-module calls the channel synchronous sampling matrix, calculates the sampling amplitude difference of multiple channels at the same time node, combines the sampling value change directions within the continuous time period of each channel, extracts the trend change direction difference between channels, and generates a trend amplitude difference structure group; The coupling feature construction sub-module calls the trend amplitude difference structure group, performs time dimension ratio combination processing on the amplitude difference sequence and trend difference sequence between channels, constructs a structure feature sequence for describing the change relationship between channels, and generates a composite parameter coupling sequence.

6. The multifunctional metrology and detection system according to claim 5, wherein The vector construction module includes: The node extraction sub-module calls the composite parameter coupling sequence, extracts the state value changes of each parameter node within a continuous time period, and according to the direction, amplitude increment, and change frequency within adjacent time periods of the state change, extracts the change direction, change amplitude, and change frequency to obtain node state feature information; The vector generation sub-module calls the node state feature information, maps each node state feature to the three-dimensional vector axis value, constructs a node state change vector, and generates a node state vector structure set; The linkage judgment sub-module calls the node state vector structure set, performs angle calculation and vector projection difference comparison on multiple node state vector pairs, and uses the formula: ； Calculate the state linkage trend coefficient of multiple node pairs, and obtain the multi-node state linkage trend coefficient; Among them, represents the node and the state linkage trend coefficient of node , represents the projection value of node on the -dimensional vector coordinate, represents the projection value of node on the -dimensional vector coordinate, represents the projection value of node on the -dimensional vector coordinate, represents the projection value of node on the -dimensional vector coordinate, represents the dimension number of the state vector, represents the state change vector of node , represents the state change vector of node , represents the serial number of the first node in the state node pair, represents the serial number of the comparison node in the state node pair, represents the vector dimension index for difference calculation, represents the vector dimension index for total amplitude statistics.

7. The multifunctional metrology and detection system according to claim 1, wherein, The system further includes: The event encapsulation module calls the multi-node state linkage trend coefficient, detects abnormal measurement events by identifying the direction differences of the state vectors between multiple parameter nodes, extracts the corresponding parameter types and timestamps, and reconstructs the evolution path of the abnormal event according to the timestamp order, and encapsulates the measurement data according to the path order to generate an event process encapsulation record; The event process encapsulation record specifically refers to the abnormal parameter type, abnormal timestamp, and event path number.

8. The multi-functional metering and detection system according to claim 7, characterized in that The event encapsulation module includes: The vector difference identification sub-module calls the multi-node state linkage trend coefficient, obtains the state change vectors of multiple parameter nodes, and detects abnormal measurement events by analyzing the direction differences of the state vectors between nodes, and generates an abnormal event identification result; The abnormal event path reconstruction sub-module, based on the abnormal event identification result, extracts the parameter type and timestamp data corresponding to the target abnormal event, and reconstructs the evolution path of the abnormal event according to the timestamp order to generate event evolution path data; The event encapsulation record sub-module calls the event evolution path data, encapsulates the measurement data according to the path order, and generates an event process encapsulation record.

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