A data acquisition system for differential pressure transmitter

Abstract

The present invention provides a data acquisition system for a differential pressure transmitter, relating to the field of data processing technology. The system is configured to: collect differential pressure signal values ​​through a differential pressure sensor, generate a standardized signal sequence, set a three-dimensional space vector of a sampling point, and generate a signal mapping tensor from the three-dimensional space vector of the sampling point within a preset time window. The similarity between each time point is used to construct a structural relationship matrix. The structural relationship matrices of each differential pressure transmitter are fused according to the layout position of each differential pressure transmitter to obtain a fused structural relationship matrix. The element values ​​on each matrix diagonal are extracted to calculate asynchrony. When the asynchrony exceeds a preset asynchrony threshold, sampling asynchrony is determined, the time points are readjusted, a synchronized fused structural relationship matrix is ​​obtained, and the synchronized fused structural relationship matrix is ​​converted into a transmittable data packet format for transmission to a remote server. The present invention can strengthen the digital signal of the differential pressure transmitter and ensure a stable transmission process.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, in particular to a data acquisition system for a differential pressure transmitter. Background Art

[0002] Differential pressure transmitters are widely used in fields such as liquid and gas flow measurement and liquid level monitoring. They mainly realize the conversion of physical quantities by sensing the differential pressure signal in the fluid pipeline. Traditional differential pressure transmitters use analog signal processing technology to directly convert the sensor signal into the corresponding voltage or current signal output. This analog signal processing method may have great instability in the data transmission and acquisition process, and it is difficult to achieve multi-point remote monitoring. Especially when the data transmission distance is long or multiple transmitters are used in conjunction, it is often affected by signal attenuation or interference, resulting in reduced signal accuracy and reliability.

[0003] Existing differential pressure transmitter systems generally have the defect of being easily interfered with by the external environment during data transmission. For example, in pipeline flow monitoring in the petrochemical industry, differential pressure transmitters are often used to remotely monitor the flow of high-temperature, high-pressure or corrosive gases. However, since traditional analog signals cannot remain stable during long-distance transmission, signal attenuation may occur, affecting data accuracy. Summary of the Invention

[0004] The object of the present invention is to provide a data acquisition system for a differential pressure transmitter, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A data acquisition system for a differential pressure transmitter, the system comprising:

[0007] A standardization module is used to collect differential pressure signal values ​​through a differential pressure sensor to obtain a differential pressure signal sequence, and generate a standardized signal sequence based on the number of local extreme value points within a preset sample window;

[0008] The enhancement module is used to set the three-dimensional space vector of the sampling point according to the standardized signal sequence, and generate a signal mapping tensor from the three-dimensional space vector of the sampling point within a preset time window to obtain tensor data;

[0009] The structural matrix module is used to segment the tensor data, obtain tensor data segments, calculate the similarity between each time point in the tensor data segment, and construct the structural relationship matrix;

[0010] The fusion matrix module is used to determine the weight value of each differential pressure transmitter according to its layout position, and fuse the structural relationship matrix of each differential pressure transmitter based on the weight value to obtain a fusion structural relationship matrix;

[0011] The asynchronous module is used to extract the element values ​​on each matrix diagonal according to the fusion structure relationship matrix, obtain the change rate sequence, and calculate the asynchrony based on it. When the asynchrony exceeds the preset asynchrony threshold, it is determined that sampling asynchrony exists, and the time point is readjusted to obtain the synchronous fusion structure relationship matrix;

[0012] The transmission module is used to convert the synchronous fusion structure relationship matrix into a transmittable data packet format and send it to the remote server.

[0013] Furthermore, the standardization module includes:

[0014] The acquisition unit is used to obtain the analog voltage value of the differential pressure signal through the differential pressure sensor and sort it according to the sampling time to obtain a differential pressure signal sequence;

[0015] A sliding window segmentation unit is used to perform window sliding on the differential pressure signal sequence according to a preset sample window to obtain a differential pressure signal sequence window;

[0016] An extreme value statistics unit is used to count the total number of local maximum value points and local minimum value points according to the differential pressure signal sequence window to obtain the number of local extreme value points;

[0017] The normalization unit is used to normalize the extreme value points in the differential pressure signal sequence according to the number of local extreme values, map the value of each extreme value point to the range of 0-1, and obtain a standardized signal sequence.

[0018] Furthermore, the strengthening module includes:

[0019] an extraction unit, configured to extract a standard value of each sampling point according to the standardized signal sequence to obtain a first space vector component;

[0020] a difference calculation unit, configured to compare a standard value between a current sampling point and a previous sampling point, calculate the difference therebetween, and obtain a second space vector component;

[0021] a sliding mean deviation unit, configured to calculate a sliding mean of the sampling points according to a preset sample window, and calculate the square of the difference between the standard value of the sampling points and the sliding mean to obtain a third space vector component;

[0022] The three-dimensional space vector unit is used to generate a three-dimensional space vector of the sampling point according to the first space vector component, the second space vector component and the third space vector component.

[0023] Furthermore, the strengthening module also includes:

[0024] The window division unit is used to perform sliding slicing on the three-dimensional space vectors of the sampling points at different time points according to the preset time window, and divide them into vector sets of multiple continuous time periods to obtain a vector time series;

[0025] A vector splicing unit is used to splice the three-dimensional space vectors of multiple consecutive sampling points according to the time dimension according to the vector time series to obtain a vector matrix block;

[0026] The tensor construction unit is used to superimpose vector matrix blocks according to the time dimension to obtain tensor data.

[0027] Furthermore, the structure matrix module includes:

[0028] A tensor segmentation unit is used to obtain multiple tensor data segments by dividing the tensor data into equal intervals, and extract the three-dimensional space vectors of the sampling points in each tensor data segment at each time point to obtain a tensor segment vector set;

[0029] A similarity calculation unit is used to extract the three-dimensional space vector of the sampling point at any time point based on the tensor segmentation vector set, calculate the dimensional difference mean and the three-dimensional space vector mean of the sampling point at any time point; and calculate the similarity between the sampling points at any two time points based on the dimensional difference mean and the three-dimensional space vector mean of the sampling point at the two time points;

[0030] The structural relationship matrix unit is used to arrange the similarities into a two-dimensional matrix according to the time point index to obtain the structural relationship matrix.

[0031] Furthermore, the fusion matrix module includes:

[0032] The layout index unit is used to number the different differential pressure transmitters from upstream to downstream according to the physical installation order of the differential pressure transmitters on the target pipeline to obtain a layout index sequence;

[0033] A weight generation unit is used to construct a linear proportional sequence according to the layout index sequence, calculate the weight values ​​of different differential pressure transmitters, and obtain a weight value set;

[0034] The weighted fusion unit is used to perform linear weighting processing on the structural relationship matrices of different differential pressure transmitters according to the corresponding weight values ​​according to the weight value set to obtain a fused structural relationship matrix.

[0035] Furthermore, the weight generating unit includes:

[0036] The terminal distance unit is used to measure the distance from each differential pressure transmitter to the downstream terminal position of the target pipeline to obtain the terminal distance data;

[0037] The spacing unit is used to measure the installation spacing between every two adjacent differential pressure transmitters to obtain adjacent spacing data;

[0038] The distance factor unit is used to calculate the distance factor value of each differential pressure transmitter according to the terminal distance data and the adjacent spacing data, and to correct the distance factor value according to the numbering order in the layout index sequence to obtain a set of corrected weight original values;

[0039] The normalized weight unit is used to normalize all the original weight values, and scale the original values ​​of the corrected weights of all transmitters according to their sum to obtain a weight value set.

[0040] Furthermore, the asynchronous module includes:

[0041] An extreme value element unit is used to extract all element values ​​from the fusion structure relationship matrix and determine the maximum element value and the minimum element value based on the values;

[0042] A difference mapping unit is used to calculate the difference between each element value and the minimum value in the fusion structure relationship matrix to obtain a mapping value, and obtain a normalized value based on the mapping value and the difference between the maximum element value and the minimum element value, and replace all elements with the corresponding normalized values ​​to obtain a normalized structure relationship matrix;

[0043] A diagonal extraction unit is used to extract all normalized values ​​on the main diagonal in time sequence according to the normalized structural relationship matrix to obtain a diagonal value sequence;

[0044] The change calculation unit is used to calculate the change rate between each time point and the previous time point according to the diagonal value sequence to obtain a change rate sequence.

[0045] Furthermore, the asynchronous module further includes:

[0046] an asynchrony calculation unit, configured to calculate the difference between the vector change rates based on the change rate sequence to obtain a jump amount; calculate the mean of the change rates to obtain a normalization factor; and calculate the asynchrony of the differential pressure signal based on the jump amount and the normalization factor to obtain an asynchrony judgment indicator sequence;

[0047] A threshold comparison unit is used to compare the maximum value in the asynchrony judgment indicator sequence with a preset asynchrony threshold value, and when the maximum value exceeds the asynchrony threshold value, it is determined that sampling asynchrony exists;

[0048] A time offset calculation unit is used to calculate the time offset of each differential pressure transmitter according to the time point when the asynchronous phenomenon occurs, and form an offset mapping table;

[0049] The time correction unit is used to rearrange and adjust the sampling time points of each differential pressure transmitter according to the offset mapping table to obtain a synchronous differential pressure signal sequence.

[0050] Furthermore, the time correction unit includes:

[0051] A sampling sequence positioning unit is used to locate the start time point and the end time point that need to be adjusted in the differential pressure signal sequence of the differential pressure transmitter according to the offset mapping table, and obtain a sampling time index set;

[0052] A time reconstruction unit is used to perform time reconstruction on the sampling time index set, adjust the sampling point of each transmitter by the corresponding time offset, and obtain an intermediate adjustment sequence;

[0053] A time alignment unit is used to fill in the gaps in the intermediate adjustment sequence caused by the offset according to adjacent sampling points to obtain a time-aligned sequence;

[0054] The synchronous output unit is used to uniformly sort all the completed time-aligned sequences according to the global time order to obtain a synchronous differential pressure signal sequence.

[0055] The above solution of the present invention includes at least the following beneficial effects:

[0056] The present invention introduces a sliding window mechanism to perform segmented processing on the signal, counts the number of local maximum and minimum points in each window, and then normalizes these extreme points, thereby improving the anti-interference ability of signal processing and solving problems such as signal drift and amplitude distortion caused by factors such as ambient temperature changes, electromagnetic interference or sensor aging during the acquisition process of traditional analog signals. By extracting the fluctuation characteristics of the signal through the statistical method of local extreme points, the dynamic trend of the differential pressure change can be more accurately characterized, avoiding the information loss caused by the extraction based solely on the average value or maximum and minimum values, fundamentally enhancing the system's adaptability to changes in complex working conditions, and serving as the basic guarantee for the entire data acquisition system.

[0057] The present invention effectively retains the instantaneous amplitude, local change trend and degree of deviation from the overall trend of the signal by combining and constructing three-dimensional space vectors. This spatial modeling method of triple feature fusion significantly improves the distinguishability of sampling points in high-dimensional space, and avoids the problem of insufficient expression ability caused by a single feature dimension. The time series composed of three-dimensional vectors provides rich structural information before constructing tensor data, ensuring that subsequent tensor mapping has more semantic depth and time consistency. Through the component construction and combination mechanism, the original one-dimensional signal is promoted to a higher-dimensional space, effectively solving the problems of slow response to transient changes and weak outlier detection ability in the existing technology, and providing a traceable structural basis for asynchronous detection and time alignment, thereby improving the system's analytical ability and robustness for non-stationary signals.

[0058] The present invention significantly improves the accuracy and depth of signal timing structure analysis by quantifying the changing relationship of sampling points in different time periods. The similarity calculation not only integrates the amplitude deviation of the vector, but also considers the overall mean difference of multi-dimensional features, thereby accurately capturing key behaviors such as periodic characteristics, local mutations, and trend inflection points. Compared with the statistical averaging-based method in the existing technology, the structural relationship matrix provides a means of dynamic structural modeling of time series, which helps to accurately identify the inherent logical connection between multi-point signals. It not only enriches the representation of differential pressure signals, but also provides a clear and standard two-dimensional matrix basis for subsequent modules to perform spatial fusion and time asynchronous judgment. It is the core bridge in the entire system analysis architecture.

[0059] The present invention effectively solves the problem of uneven influence caused by differences in installation positions during the fusion of multi-point signals by fusing a global fusion structure relationship matrix. By setting indexes and correcting distance factors, the contribution of each transmitter is made more reasonable. Linear weighted processing not only improves computational efficiency, but also enhances information integration capabilities while retaining the structural characteristics of the original matrix. The fusion structure relationship matrix provides a two-dimensional expression that fully reflects the temporal correlation and spatial differences of all sampling points, facilitates subsequent unified judgment and processing, enhances the objectivity and repeatability of parameter generation, and effectively improves the adaptability and accuracy of the system in complex fluid pipeline environments.

[0060] By modeling the rate of change of diagonal elements, the present invention can accurately locate the mutation points and inconsistent sampling periods in the time series structure, effectively identify the asynchronous behavior of the system, and rearrange and synchronize time points. This not only ensures the consistency of data in the time dimension, but also provides a high-precision time-base unified sample for subsequent analysis. Compared with the existing technology that relies on clock synchronization protocols or physical calibration methods, the system uses mathematical modeling to achieve dynamic perception and repair of asynchrony, greatly reducing dependence on hardware systems and improving system deployment flexibility and maintenance convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of a data acquisition system for a differential pressure transmitter provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0063] like Figure 1 As shown, an embodiment of the present invention provides a data acquisition system for a differential pressure transmitter, the system comprising:

[0064] A standardization module is used to collect differential pressure signal values ​​through a differential pressure sensor to obtain a differential pressure signal sequence, and generate a standardized signal sequence based on the number of local extreme value points within a preset sample window;

[0065] The enhancement module is used to set the three-dimensional space vector of the sampling point according to the standardized signal sequence, and generate a signal mapping tensor from the three-dimensional space vector of the sampling point within a preset time window to obtain tensor data;

[0066] The structural matrix module is used to segment the tensor data, obtain tensor data segments, calculate the similarity between each time point in the tensor data segment, and construct the structural relationship matrix;

[0067] The fusion matrix module is used to determine the weight value of each differential pressure transmitter according to its layout position, and fuse the structural relationship matrix of each differential pressure transmitter based on the weight value to obtain a fusion structural relationship matrix;

[0068] The asynchronous module is used to extract the element values ​​on each matrix diagonal according to the fusion structure relationship matrix, obtain the change rate sequence, and calculate the asynchrony based on it. When the asynchrony exceeds the preset asynchrony threshold, it is determined that sampling asynchrony exists, and the time point is readjusted to obtain the synchronous fusion structure relationship matrix;

[0069] The transmission module is used to convert the synchronous fusion structure relationship matrix into a transmittable data packet format and send it to the remote server.

[0070] In an embodiment of the present invention, a standardization module is used to collect differential pressure signal values ​​through a differential pressure sensor to obtain a differential pressure signal sequence, and generate a standardized signal sequence based on the number of local extreme points in a preset sample window. The signal features are extracted by statistical methods of local extreme points to more accurately capture weak fluctuations in a short period of time, thereby enhancing the ability to express the microscopic dynamic characteristics of the signal; an enhancement module is used to set the three-dimensional space vector of the sampling point according to the standardized signal sequence, and generate a signal mapping tensor for the three-dimensional space vector of the sampling point in a preset time window to obtain tensor data, which can fully capture the three aspects of information of the current signal amplitude, change trend and deviation degree, and can better reflect the dynamic change form of the differential pressure signal than traditional single-dimensional features; a structural matrix module is used to segment the tensor data to obtain tensor data segments, and calculate the similarity between each time point in the tensor data segment to construct a structural relationship matrix. By quantifying the similarity of sampling behavior between time points, a structural relationship matrix containing time topology information is constructed, so that the system has the ability to identify changes in time series patterns.

[0071] The fusion matrix module is used to determine the weight value of each differential pressure transmitter according to its layout position, and fuse the structural relationship matrix of each differential pressure transmitter based on the weight value to obtain a fusion structural relationship matrix, realizing the fusion of multi-point information and enhancing the scientific nature and field adaptability of weight distribution through the physical layout weight mechanism;

[0072] The asynchronous module is used to extract the element values ​​on each matrix diagonal based on the fusion structure relationship matrix to obtain the change rate sequence and calculate the asynchrony based on it. When the asynchrony exceeds the preset asynchrony threshold, it is determined that sampling asynchrony exists and the time points are readjusted to obtain the synchronous fusion structure relationship matrix. The synchronization between the sampling points is quantified mathematically and the presence of asynchrony is determined by indexing. The active correction of time offset ensures that the data from different sensors have a high degree of time base alignment, thereby significantly improving the accuracy of analysis and control.

[0073] The transmission module is used to convert the synchronous fusion structure relationship matrix into a transmittable data packet format and send it to the remote server. Combined with the matrix structure after asynchronous correction, it ensures that the transmitted data has high synchronization and semantic consistency, thereby enhancing the deployment reliability of the system.

[0074] The transmission module is used to convert the synchronous fusion structure relationship matrix into a transmittable data packet format and send it to the remote server, specifically including:

[0075] First, it is necessary to perform standardized data encapsulation processing on the synchronous fusion structure relationship matrix, and encode the two-dimensional floating-point numerical data in the synchronous fusion structure relationship matrix into a linear array in row-major order. The array will be embedded as a data body into a predefined data structure template, which contains a field header, data body and check digit; the field header includes the data packet identifier, data source number, timestamp information, matrix dimension information and compression encoding method identifier to ensure the identifiability and parsability of the data packet; the data body carries the encoded matrix numerical content. To ensure cross-platform compatibility, the data body content is uniformly represented in the 32-bit floating-point format of the IEEE 754 standard during the conversion process and encoded using Base64 to adapt to the formatting requirements of JSON encapsulation or binary protocol encapsulation; the check digit uses the CRC32 algorithm or SHA-1 digest mechanism to generate an integrity check code for the entire data packet content to verify the integrity of the data packet during data transmission.

[0076] Then, select the specific communication protocol and interface according to the network deployment environment. For example, the Ethernet interface is based on the TCP / IP protocol. The interface control unit is responsible for packaging the data packets layer by layer according to the protocol stack, and calling the driver module to send the data. If the target system is an edge computing node or a local gateway device, the transmission path is set to a local area network or a dedicated line channel; if it is a cloud platform, the public network or VPN channel is enabled, and identity authentication information such as tokens or certificates are attached to ensure communication security. The system supports real-time monitoring of the network status. If abnormal conditions such as data transmission failure or excessive delay occur, the retransmission mechanism will be triggered or the backup channel will be switched to redundant transmission.

[0077] The receiving end is a remote server. After receiving the data packet, it reads the basic metadata according to the field header, confirms the source, type and timestamp of the data packet, and uses the preset key or verification algorithm to verify the check bit. After the verification is passed, the data body content is decoded into the original matrix format and stored in the database system for further analysis and processing. During the entire transmission process, the system can be configured with a logging mechanism to record the sending time, receiving feedback and abnormal status of each data packet, which is convenient for later troubleshooting or data consistency backtracking; through the above process, the transmission module realizes the reliable and efficient sending of structured and synchronized high-dimensional data collected locally to the remote system, forming a closed-loop data link from edge collection to remote processing.

[0078] In a preferred embodiment of the present invention, the standardization module includes:

[0079] The acquisition unit is used to obtain the analog voltage value of the differential pressure signal through the differential pressure sensor and sort it according to the sampling time to obtain a differential pressure signal sequence;

[0080] A sliding window segmentation unit is used to perform window sliding on the differential pressure signal sequence according to a preset sample window to obtain a differential pressure signal sequence window;

[0081] An extreme value statistics unit is used to count the total number of local maximum value points and local minimum value points according to the differential pressure signal sequence window to obtain the number of local extreme value points;

[0082] The normalization unit is used to normalize the extreme value points in the differential pressure signal sequence according to the number of local extreme values, map the value of each extreme value point to the range of 0-1, and obtain a standardized signal sequence.

[0083] In an embodiment of the present invention, the acquisition unit is used to obtain the analog voltage value of the differential pressure signal through the differential pressure sensor, and sort it according to the sampling time to obtain a differential pressure signal sequence. The time order sorting lays the foundation for subsequent window division and extreme value analysis, thereby improving the time consistency and processing efficiency of the data; the sliding window segmentation unit is used to slide the window of the differential pressure signal sequence according to the preset sample window to obtain the differential pressure signal sequence window, thereby realizing the local time domain analysis of the differential pressure signal, so that the system can extract a stable and representative time segment from the continuously changing signal; the extreme value statistics unit is used to extract a stable and representative time segment from the continuously changing signal according to the differential pressure signal sequence. The window counts the total number of local maximum points and local minimum points to obtain the number of local extreme points. The extreme value statistics can reflect the fluctuation density of the signal, and the number of extreme values ​​indirectly measures the frequency of change and dynamic volatility of the fluid pressure. The normalization unit is used to normalize the extreme points in the differential pressure signal sequence according to the number of local extreme values, and map the value of each extreme point to the interval of 0-1 to obtain a standardized signal sequence, eliminating the amplitude difference between each sliding window, so that the system can compare and analyze signals in different time periods on a unified scale, thereby improving the consistency and reliability of subsequent vector modeling and matrix construction.

[0084] In a preferred embodiment of the present invention, the strengthening module includes:

[0085] an extraction unit, configured to extract a standard value of each sampling point according to the standardized signal sequence to obtain a first space vector component;

[0086] a difference calculation unit, configured to compare a standard value between a current sampling point and a previous sampling point, calculate the difference therebetween, and obtain a second space vector component;

[0087] a sliding mean deviation unit, configured to calculate a sliding mean of the sampling points according to a preset sample window, and calculate the square of the difference between the standard value of the sampling points and the sliding mean to obtain a third space vector component;

[0088] The three-dimensional space vector unit is used to generate a three-dimensional space vector of the sampling point according to the first space vector component, the second space vector component and the third space vector component.

[0089] In an embodiment of the present invention, an extraction unit is used to extract the standard value of each sampling point according to the standardized signal sequence, obtain the first space vector component, map the one-dimensional standardized signal to the first dimension of the vector space, and form an initial spatial structure expression of the differential pressure signal change process; a difference calculation unit is used to compare the standard value between the current sampling point and the previous sampling point, calculate the difference, and obtain the second space vector component. By introducing the difference between adjacent points, the system can capture the local change trend of the differential pressure signal and enhance the sensitivity to dynamic characteristics such as abnormal fluctuations and mutation points; a sliding deviation unit is used to calculate the difference between the current sampling point and the previous sampling point according to the preset sample window. , calculate the sliding mean of the sampling point, and calculate the square of the difference between the standard value of the sampling point and the sliding mean to obtain the third space vector component. The square of the deviation can characterize the degree of outlier of the sampling point in the local window, reflect the stability of the signal, and enhance the sensitivity to local disturbances; the three-dimensional space vector unit is used to generate the three-dimensional space vector of the sampling point according to the first space vector component, the second space vector component and the third space vector component. The three-dimensional vector has stronger expression ability and can comprehensively characterize the spatiotemporal behavior of each sampling point, providing a rich and reliable data basis for subsequent tensor construction and structural matrix analysis.

[0090] The three-dimensional space vector unit is used to generate a three-dimensional space vector of the sampling point based on the first space vector component, the second space vector component and the third space vector component, specifically including:

[0091] First, a vector construction index is established for each sampling time point, and all sampling points in the standardized signal sequence are traversed. Three preset eigenvalues ​​are extracted for each sampling point, namely the standard value of the point itself, the difference between the point and the previous sampling point, and the square of the deviation between the point and the local sliding average.

[0092] If the first The three components of the sampling points are 、 、 , then the three-dimensional space vector unit is assembled according to the following structure , each three-dimensional vector is stored as an element in the vector sequence in chronological order, forming a time-driven vector flow. In order to ensure the consistency and comparability of the numerical scale of each dimension, a normalization mechanism can be introduced to perform unified scale standardization on the three components of the three-dimensional vector. For example, all difference components and deviation components can be remapped to the [0,1] interval to ensure that different features will not be biased in subsequent calculations due to differences in numerical levels.

[0093] In a preferred embodiment of the present invention, the strengthening module further includes:

[0094] The window division unit is used to perform sliding slicing on the three-dimensional space vectors of the sampling points at different time points according to the preset time window, and divide them into vector sets of multiple continuous time periods to obtain a vector time series;

[0095] A vector splicing unit is used to splice the three-dimensional space vectors of multiple consecutive sampling points according to the time dimension according to the vector time series to obtain a vector matrix block;

[0096] The tensor construction unit is used to superimpose vector matrix blocks according to the time dimension to obtain tensor data.

[0097] In an embodiment of the present invention, a window division unit is used to perform sliding slicing on the three-dimensional space vectors of the sampling points at different time points according to a preset time window, and divide them into vector sets of multiple continuous time periods to obtain a vector time series. By introducing a time window division mechanism, the unstructured sampling point time series is constructed into local segments with short-term temporal logic, which is convenient for subsequent construction of a tensor form; a vector splicing unit is used to splice the three-dimensional space vectors of multiple continuous sampling points according to the time dimension according to the vector time series to obtain a vector matrix block, and solidify the spatial vector data in the time window in a matrix form, which not only retains the continuity of the time sequence, but also enhances the correlation between vectors; a tensor construction unit is used to superimpose the vector matrix blocks according to the time dimension to obtain tensor data. The tensor can simultaneously retain the timing characteristics, spatial change information and segment continuity of the signal in multiple dimensions.

[0098] The vector splicing unit is used to splice the three-dimensional space vectors of multiple consecutive sampling points according to the time dimension according to the vector time series to obtain a vector matrix block, which specifically includes:

[0099] First, receive the vector time series output by the window division unit. Each vector time series is composed of multiple sampling points arranged in time. Each sampling point corresponds to a three-dimensional space vector. The vector dimensions are: the current standard value, the difference from the standard value at the previous time point, and the square of the deviation between the standard value and the sliding mean. The three are recorded as , , In the actual processing process, the vector splicing unit uses a fixed-length time window as the processing unit and sequentially reads and arranges the three-dimensional vectors of all sampling points in each time window. Specifically, assuming the time window length is , then within a time window we will get three-dimensional vectors, represented as The core of the splicing operation is to Three-dimensional vectors are stacked vertically to form a two-dimensional matrix block according to the time dimension. , where the matrix The row corresponds to the time window This splicing operation not only preserves the time series information between sampling points, but also structurally represents the spatial variation characteristics within the local sequence, so that the dynamic evolution pattern within each time window can be expressed in a matrix form, which improves the convenience and stability of data visualization and subsequent operations.

[0100] The tensor construction unit is used to superimpose vector matrix blocks according to the time dimension to obtain tensor data, specifically including:

[0101] After completing the construction of vector matrix blocks in multiple time windows, the tensor construction unit will enter the working state. Its main function is to perform high-order stacking operations on these vector matrix blocks in the time axis dimension, thereby generating tensor data that can be used for multi-dimensional structure analysis. In the specific implementation process, the tensor construction unit will receive all vector matrix blocks one by one in the order of time window sliding. Set the total number of sliding windows to , each window corresponds to a completed matrix ,in Value 1~ , the tensor construction unit indexes these matrix blocks according to the time window The order of the first axis of the newly introduced third dimension is stacked to generate a third-order tensor. The first A tensor slice is the first dimension of the tensor Layer, corresponding to Through this multi-window, multi-time period continuous stacking operation, the system successfully expands the original one-dimensional time series into a high-order tensor representation with three dimensions: local segment structure, spatial variation characteristics, and time series evolution, thus providing a semantically complete mathematical object for the subsequent structural relationship matrix construction and similarity measurement.

[0102] In a preferred embodiment of the present invention, the structure matrix module includes:

[0103] A tensor segmentation unit is used to obtain multiple tensor data segments by dividing the tensor data into equal intervals, and extract the three-dimensional space vectors of the sampling points in each tensor data segment at each time point to obtain a tensor segment vector set;

[0104] A similarity calculation unit is used to extract the three-dimensional space vector of the sampling point at any time point based on the tensor segmentation vector set, calculate the dimensional difference mean and the three-dimensional space vector mean of the sampling point at any time point; and calculate the similarity between the sampling points at any two time points based on the dimensional difference mean and the three-dimensional space vector mean of the sampling point at the two time points;

[0105] The structural relationship matrix unit is used to arrange the similarities into a two-dimensional matrix according to the time point index to obtain the structural relationship matrix.

[0106] In an embodiment of the present invention, a tensor segmentation unit is used to divide the tensor data into equal intervals to obtain multiple tensor data segments, and extract the three-dimensional space vectors of the sampling points in each tensor data segment at each time point to obtain a tensor segmentation vector set, and divide the continuous tensor into time periods so that the time series change information in the original tensor data can be captured locally, and each tensor segment concentrates on expressing the dynamic characteristics of pressure in a short period of time; a similarity calculation unit is used to extract the three-dimensional space vectors of the sampling points at any time point according to the tensor segmentation vector set, and calculate the dimensional difference of the sampling points at any time point. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical. The method is simple and practical.

[0107] The calculation formula of the similarity is:

[0108] ;

[0109] in, The sampling point is time point and The similarity of the time points, and is the index of the time point, The sampling point is The mean of the dimension difference at each time point, , The sampling point is The first space vector component of the time point, The sampling point is The second space vector component at a time point, The sampling point is The third space vector component at each time point, The sampling point is The mean of the dimension difference at each time point, and Similarly, The sampling point is The first time point Space vector components, is the index of the space vector component, The sampling point is The first time point Space vector components, and Similarly, The sampling point is The mean of the three-dimensional space vector at each time point, , The sampling point is The mean of the three-dimensional space vector at each time point, and Similarly, is the minimum constant.

[0110] In a preferred embodiment of the present invention, the fusion matrix module includes:

[0111] The layout index unit is used to number the different differential pressure transmitters from upstream to downstream according to the physical installation order of the differential pressure transmitters on the target pipeline to obtain a layout index sequence;

[0112] A weight generation unit is used to construct a linear proportional sequence according to the layout index sequence, calculate the weight values ​​of different differential pressure transmitters, and obtain a weight value set;

[0113] The weighted fusion unit is used to perform linear weighting processing on the structural relationship matrices of different differential pressure transmitters according to the corresponding weight values ​​according to the weight value set to obtain a fused structural relationship matrix.

[0114] In an embodiment of the present invention, a layout index unit is used to number different differential pressure transmitters in sequence from upstream to downstream according to the physical installation order of different differential pressure transmitters on the target pipeline to obtain a layout index sequence, which solves the problem of data fusion confusion caused by chaotic sensor installation and inconsistent numbering in the existing system; a weight generation unit is used to construct a linear proportional sequence according to the layout index sequence, calculate the weight values ​​of different differential pressure transmitters, and obtain a weight value set to ensure that a mathematically operable structure is formed between the weights of each sensor, provide a standardized weight coefficient for subsequent matrix fusion, and ensure the linear consistency and operational stability of the fusion calculation; a weighted fusion unit is used to linearly weight the structural relationship matrices of different differential pressure transmitters according to the corresponding weight values ​​according to the weight value set to obtain a fused structural relationship matrix, and scientifically merge the structural matrices of multiple differential pressure transmitters through a refined weight distribution mechanism, breaking through the adaptability problem of traditional averaging processing methods in scenarios with uneven multi-point distribution.

[0115] Among them, the weighted fusion unit is used to perform linear weighted processing on the structural relationship matrices of different differential pressure transmitters according to the corresponding weight values ​​according to the weight value set to obtain a fusion structural relationship matrix, which specifically includes:

[0116] After receiving the structural relationship matrices corresponding to multiple differential pressure transmitters and their corresponding weight value sets, a one-to-one correspondence is first established between the structural matrices. The system will call the structural relationship matrix of each transmitter one by one according to the physical layout order of the transmitters. These matrices usually come from the results of enhanced processing and structural modeling of their collected data. Each structural relationship matrix is ​​a square two-dimensional data set, which is used to describe the degree of numerical correlation between the transmitter at multiple sampling time points. At the same time as these structural relationship matrices, the weight value set calculated by the weight generation unit is obtained. Each weight value represents the influence weight that the corresponding transmitter should have in the overall system structure.

[0117] In the weighted calculation stage, the system will perform linear weighted operations on each structural relationship matrix in turn, that is, multiply all elements in the matrix with their corresponding weight values ​​one by one. This process is not a simple global scaling of the matrix, but element-by-element multiplication, so that the degree of structural correlation between each time point is scaled according to the weight of the transmitter, which can reflect the importance of different transmitters in the global structural relationship and avoid interference with the overall structure by low-impact sensors. After all structural relationship matrices are weighted, the system will add the values ​​of these matrices at the corresponding positions to form a new unified structural relationship matrix. This matrix integrates the similarity evaluation results of multiple transmitters at different sampling times to form a fusion matrix covering the global time series.

[0118] After generating the preliminary fusion matrix, the system will also perform overall normalization on the fusion matrix to prevent numerical bias caused by different scales of the original data or uneven numerical distribution. This normalization process is based on the gap between the minimum and maximum element values ​​in the fusion matrix, and all values ​​are proportionally mapped to ensure that the fusion results have good numerical stability and comparability. The normalized fusion structure relationship matrix will become an important input basis for the subsequent asynchrony detection and time correction modules. Through the above operations, the weighted fusion unit realizes the unified integration of multi-point data structure information, so that the data signals of the differential pressure transmitter under complex physical space layout conditions can be unified and fused for analysis, solving the problem that multi-source structure data cannot be effectively integrated in the existing technology, and providing a solid data foundation for subsequent time consistency correction and high-precision trend analysis.

[0119] In a preferred embodiment of the present invention, the weight generating unit includes:

[0120] The terminal distance unit is used to measure the distance from each differential pressure transmitter to the downstream terminal position of the target pipeline to obtain the terminal distance data;

[0121] The spacing unit is used to measure the installation spacing between every two adjacent differential pressure transmitters to obtain adjacent spacing data;

[0122] The distance factor unit is used to calculate the distance factor value of each differential pressure transmitter according to the terminal distance data and the adjacent spacing data, and to correct the distance factor value according to the numbering order in the layout index sequence to obtain a set of corrected weight original values;

[0123] The normalized weight unit is used to normalize all the original weight values, and scale the original values ​​of the corrected weights of all transmitters according to their sum to obtain a weight value set.

[0124] In the embodiment of the present invention, the terminal distance unit is used to measure the distance from each differential pressure transmitter to the downstream terminal position of the target pipeline to obtain the terminal distance data. By accurately obtaining the spatial position relationship between each transmitter and the terminal, it is ensured that the data of each transmitter in the subsequent fusion structure can be weighted according to its distance from the terminal; the spacing unit is used to measure the installation distance between each two adjacent differential pressure transmitters to obtain adjacent spacing data. Compared with the traditional system that only relies on the layout number to judge the position difference, the spacing measurement provides a more continuous and detailed spatial resolution; the distance factor unit is used to calculate the distance between the terminal distance data. and adjacent spacing data, calculate the distance factor value of each differential pressure transmitter, and correct the distance factor value according to the numbering order in the layout index sequence to obtain a set of corrected weight original values, which is suitable for actual working conditions such as irregular layout of transmitters and limited installation points, and can ensure that each collection point obtains a quantitative weight that conforms to the degree of its physical influence; the normalized weight unit is used to normalize all the original weight values, scale the corrected weight original values ​​of all transmitters according to their sum, and obtain a weight value set to ensure that the fusion structure relationship matrix will not accumulate calculation errors due to imbalance of numerical scales during the calculation process.

[0125] The distance factor unit is used to calculate the distance factor value of each differential pressure transmitter based on the terminal distance data and the adjacent spacing data, and to correct the distance factor value according to the numbering order in the layout index sequence to obtain a set of corrected weight original values, specifically including:

[0126] First, the terminal distance data provided by the terminal distance unit is received. This data represents the distance from each differential pressure transmitter to the downstream pipeline terminal, such as the drain port, detection valve, main outlet, etc., and has real physical space significance. At the same time, the installation spacing data between adjacent differential pressure transmitters provided by the spacing unit is received. This data reflects the distribution density and relative position of the differential pressure transmitter in the direction along the target pipeline. The data of the above two dimensions constitute the basic input for the unit to construct the distance factor. In the specific calculation process, the distance factor unit first aligns the original data according to the layout number sequence of the differential pressure transmitter to ensure the one-to-one correspondence of the data mapping, and then introduces the composite distance factor calculation model to weightedly fuse the terminal distance and the cumulative spacing value to construct the influence value of the differential pressure transmitter in the spatial structure. Specifically, for the first A differential pressure transmitter, its distance factor value Determined by this formula, , Indicates the distance from the differential pressure transmitter to the terminal. Indicates the cumulative installation distance from the upstream starting point to the transmitter location. and This is a system-preset proportional factor used to determine the weighted relationship between terminal distance and installation spacing based on the application scenario, preventing a single distance dimension from dominating the entire factor evaluation. The above calculation logic ensures that the distance factor reflects not only the position of a measuring point itself, but also its relative structural density with other measuring points.

[0127] set up Take the value 0.6, The value of 0.4 is based on actual engineering experience and multiple system test evaluation results, which show that this proportional relationship can achieve a good compromise between structural fusion stability and data consistency in most complex distribution scenarios. If the system is deployed in a short-range, high-density environment, it can also be adjusted. Take the value 0.5, The value is set to 0.5 to highlight the influence of the deployment density; in long-distance and low-density deployment environments, it can be set to The value is 0.7, The value is set to 0.3 to enhance the dominant weight of terminal distance.

[0128] After completing the preliminary distance factor calculation, the system will perform consistency corrections on all layout index sequences. When there are skipped layout numbers, reverse installations, or changes in the construction site, the distance factor unit will call the mapping table to automatically rearrange the calculation order to ensure that the logical order of the calculation results completely matches the physical order. For local spacing anomalies such as extreme weight value offsets caused by densely laid transmitter sections, the system introduces a median adjustment mechanism to appropriately limit the factor deviation within the set range, so that the overall distance factor distribution presents a smooth change trend that is approximately linear or exponentially increasing, and the final output is a set of original weight values ​​that have been corrected in sequence.

[0129] In a preferred embodiment of the present invention, the asynchronous module includes:

[0130] An extreme value element unit is used to extract all element values ​​from the fusion structure relationship matrix and determine the maximum element value and the minimum element value based on the values;

[0131] A difference mapping unit is used to calculate the difference between each element value and the minimum value in the fusion structure relationship matrix to obtain a mapping value, and obtain a normalized value based on the mapping value and the difference between the maximum element value and the minimum element value, and replace all elements with the corresponding normalized values ​​to obtain a normalized structure relationship matrix;

[0132] A diagonal extraction unit is used to extract all normalized values ​​on the main diagonal in time sequence according to the normalized structural relationship matrix to obtain a diagonal value sequence;

[0133] The change calculation unit is used to calculate the change rate between each time point and the previous time point according to the diagonal value sequence to obtain a change rate sequence.

[0134] In the embodiment of the present invention, the extreme value element unit is used to extract all element values ​​from the fusion structure relationship matrix, and determine the maximum element value and the minimum element value based on them, providing the necessary interval basis for the subsequent normalization operation, so that all values ​​can be mapped to a unified standard scale; the difference mapping unit is used to calculate the difference between each element value and the minimum value in the fusion structure relationship matrix to obtain a mapping value, and obtain a normalized value based on the mapping value and the difference between the maximum element value and the minimum element value, replace all elements with the corresponding normalized values, and obtain a normalized structure relationship matrix, eliminating the possible numerical offset in the original structure relationship matrix, so that the values ​​from different layout positions and different weight sources are normalized. The signal maintains consistency in statistical processing; the diagonal extraction unit is used to extract all normalized values ​​on the main diagonal in chronological order according to the normalized structural relationship matrix to obtain a diagonal value sequence. The main diagonal reflects the similarity trend of the sampling points of the same differential pressure transmitter itself in the time dimension, and its sequence can keenly capture the fluctuation amplitude and mutation behavior between time points; the change calculation unit is used to calculate the change rate of each time point and its previous time point according to the diagonal value sequence to obtain a change rate sequence, which provides an important quantitative indicator for the continuity and stability of the sampling process, especially with extremely high sensitivity in identifying asynchronous phenomena caused by sensor state drift, network jitter or sampling delay.

[0135] In a preferred embodiment of the present invention, the asynchronous module further includes:

[0136] an asynchrony calculation unit, configured to calculate the difference between the vector change rates based on the change rate sequence to obtain a jump amount; calculate the mean of the change rates to obtain a normalization factor; and calculate the asynchrony of the differential pressure signal based on the jump amount and the normalization factor to obtain an asynchrony judgment indicator sequence;

[0137] A threshold comparison unit is used to compare the maximum value in the asynchrony judgment indicator sequence with a preset asynchrony threshold value, and when the maximum value exceeds the asynchrony threshold value, it is determined that sampling asynchrony exists;

[0138] A time offset calculation unit is used to calculate the time offset of each differential pressure transmitter according to the time point when the asynchronous phenomenon occurs, and form an offset mapping table;

[0139] The time correction unit is used to rearrange and adjust the sampling time points of each differential pressure transmitter according to the offset mapping table to obtain a synchronous differential pressure signal sequence.

[0140] In an embodiment of the present invention, an asynchrony calculation unit is used to calculate the difference between the vector change rates according to the change rate sequence to obtain the jump variable; calculate the mean of the change rate to obtain the normalization factor; calculate the asynchrony of the differential pressure signal according to the jump variable and the normalization factor to obtain an asynchrony judgment index sequence, effectively transforming the asynchrony problem from qualitative perception to quantitative evaluation, providing a clear basis for adjusting the threshold for the system, and improving the automation level of fault diagnosis; a threshold comparison unit is used to compare the maximum value in the asynchrony judgment index sequence with the preset asynchrony threshold. When the maximum value exceeds the asynchrony threshold, it is determined that there is a sampling asynchronous phenomenon, and the maximum value judgment method is used to determine the presence of the sampling asynchronous phenomenon. The method simplifies the asynchronous judgment process, avoids false triggering, and improves the robustness of the judgment result; the time offset calculation unit is used to calculate the time offset of each differential pressure transmitter according to the time point when the asynchronous phenomenon occurs, and form an offset mapping table. By locating the asynchronous abnormal point and calculating the time offset in combination with the spatial position index, it can more accurately estimate the degree of misalignment of each sampling signal on the time axis, and avoid distortion of other normal signals due to unified timing adjustment; the time correction unit is used to rearrange and adjust the sampling time points of each differential pressure transmitter according to the offset mapping table to obtain a synchronous differential pressure signal sequence, thereby ensuring the uniformity and continuity of multi-point differential pressure data under complex field conditions.

[0141] The calculation formula of the asynchrony is:

[0142] ;

[0143] in, To integrate the asynchrony of the structural relationship matrix, is the length of the diagonal value sequence, and is the index of the elements on the main diagonal of the fusion structure relationship matrix, For the The normalized values ​​of the elements on the main diagonal, For the The normalized values ​​of the elements on the main diagonal, For the The normalized values ​​of the elements on the two main diagonals, For the The normalized values ​​of the elements on the main diagonal, For the The normalized values ​​of the elements on the main diagonal, is the mean of the change rate series, .

[0144] The time offset calculation unit is used to calculate the time offset of each differential pressure transmitter according to the time point when the asynchronous phenomenon occurs, and form an offset mapping table, which specifically includes:

[0145] The system first determines the time point corresponding to the maximum value in the indicator sequence through asynchrony, marks it as the key time point when the asynchronous phenomenon occurs, and records it as the reference time point. At this time, the sampling data corresponding to this time point reflects that there is a significant inconsistency in the time dimension between multiple groups of differential pressure transmitters, which provides a unified starting point benchmark for the subsequent calculation of the offset of each sensor.

[0146] Then, the system extracts the differential pressure transmitter from its original sampling data. The three-dimensional space vectors of multiple consecutive time periods before and after are calculated, and the three-dimensional space vectors of each sampling point in the time period are compared with the The Euclidean distance between the three-dimensional spatial vectors at the corresponding moments. Through this method based on spatial vector similarity, the system can detect the actual sampling time of each transmitter that is closest to the reference time point in terms of spatial structure change, thereby determining the degree to which the transmitter data deviates from the reference time point in terms of timing.

[0147] After completing the similarity analysis, the system calculates the sampling time of each transmitter and the reference time point. The time difference between them is used to calculate the time offset of each differential pressure transmitter. ,in For the The offset reflects the sampling time skew trend of each transmitter in the current asynchronous state. It may be positive, negative or zero, corresponding to the sampling too fast, sampling lagging or synchronization state respectively.

[0148] Furthermore, the system integrates the offset information of all transmitters in a structured manner to form an offset mapping table containing the transmitter number, the original sampling time index and the corresponding time offset. The mapping table can be stored in a key-value pair structure, where the key is the unique identifier of the transmitter and the value is the time adjustment amount. To ensure the accuracy and consistency of the correction operation, the system will also limit the resolution of the offset to ensure that the offset unit matches the sampling period, thereby avoiding redundant processing caused by small disturbances.

[0149] In a preferred embodiment of the present invention, the time correction unit includes:

[0150] A sampling sequence positioning unit is used to locate the start time point and the end time point that need to be adjusted in the differential pressure signal sequence of the differential pressure transmitter according to the offset mapping table, and obtain a sampling time index set;

[0151] A time reconstruction unit is used to perform time reconstruction on the sampling time index set, adjust the sampling point of each transmitter by the corresponding time offset, and obtain an intermediate adjustment sequence;

[0152] A time alignment unit is used to fill in the gaps in the intermediate adjustment sequence caused by the offset according to adjacent sampling points to obtain a time-aligned sequence;

[0153] The synchronous output unit is used to uniformly sort all the completed time-aligned sequences according to the global time order to obtain a synchronous differential pressure signal sequence.

[0154] In an embodiment of the present invention, a sampling sequence positioning unit is used to locate the start time point and the end time point that need to be adjusted in the differential pressure signal sequence of the differential pressure transmitter according to the offset mapping table, and obtain a sampling time index set, thereby achieving accurate calibration of the time interval with asynchrony in the sampling sequence, and laying a clear foundation for subsequent time correction and synchronization; a time reconstruction unit is used to perform time reconstruction on the sampling time index set, adjust the sampling point of each transmitter by the corresponding time offset, and obtain an intermediate adjustment sequence. Through precise time shift operations, the time misalignment problem caused by clock inconsistency is eliminated; a time alignment unit is used to fill the vacant positions in the intermediate adjustment sequence caused by the offset according to adjacent sampling points, and obtain a time-aligned sequence, thereby preventing data loss due to time discontinuities after reconstruction; a synchronous output unit is used to uniformly sort all the filled time-aligned sequences according to the global time order, and obtain a synchronous differential pressure signal sequence, which achieves complete alignment in the time dimension and can significantly improve the correctness of the structural matrix and the sensitivity to asynchronous signal detection.

[0155] The time reconstruction unit is used to reconstruct the sampling time index set, adjust the sampling point of each transmitter by the corresponding time offset, and obtain the intermediate adjustment sequence, which specifically includes:

[0156] First, the system extracts the time offset value corresponding to each differential pressure transmitter in the current monitoring cycle according to the offset mapping table output by the asynchronous module. Then the system reads the original sampling timestamp sequence of the transmitter in the period from the sampling time index set , apply weighted offset processing to each of the timestamps to obtain a new adjusted timestamp sequence This processing method ensures that the sampling points are shifted on the time axis as a whole without changing the original differential pressure signal data itself, so that they are aligned with the theoretical synchronization timeline. In order to prevent timestamps from crossing the boundary or overlapping, the system will also perform boundary verification on the adjustment results and trigger data fault tolerance strategies when necessary, such as setting the maximum allowable offset range. The intermediate adjustment sequence after completing the time correction will serve as the basic input for subsequent completion and sorting processing, providing a structured and processable time reference sequence for achieving true sampling synchronization.

[0157] The time alignment unit is used to fill in the gaps in the intermediate adjustment sequence due to the offset based on adjacent sampling points to obtain a time-aligned sequence, specifically including:

[0158] After the intermediate adjustment sequence is reconstructed, differences in initial time offsets between different transmitters may result in blank areas on the unified time axis where certain time points are not covered, resulting in discontinuous time gaps in the sampling sequence. The main function of the time alignment unit is to identify these time breakpoints caused by offsets and perform data completion to restore time continuity. The system first performs a unified analysis of the intermediate adjustment sequences of all transmitters, constructs a global time baseline, and divides the time axis into equal steps based on a set time interval to generate an ideal sampling time set. The system then checks the intermediate adjustment sequence of each transmitter in turn to determine whether it covers all time points in the sampling time set. For missing time points, the system uses the numerical information of the bilateral sampling points to interpolate and complete them. The interpolation strategy can use linear interpolation to construct an estimated value based on the change trend between adjacent known points to fill in the numerical value and timestamp corresponding to the missing position. After the interpolation is completed, the system re-checks the sequence of the completed sequence to ensure that its timestamp is monotonically increasing and the sampling interval is consistent with the synchronous sampling period set by the system. Finally, the sampling sequence after time completion constitutes a time-aligned sequence, which has a strictly unified time scale and ensures that all transmitters have corresponding data at each global sampling point, thereby providing a highly consistent and high-density input basis for the subsequent synchronous construction of the structural relationship matrix.

[0159] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A data acquisition system for a differential pressure transmitter, characterized in that: The system comprises: A standardization module is used to collect differential pressure signal values ​​through a differential pressure sensor to obtain a differential pressure signal sequence, and generate a standardized signal sequence based on the number of local extreme value points within a preset sample window; The enhancement module is used to set the three-dimensional space vector of the sampling point according to the standardized signal sequence, and generate a signal mapping tensor from the three-dimensional space vector of the sampling point within a preset time window to obtain tensor data; The structural matrix module is used to segment the tensor data, obtain tensor data segments, calculate the similarity between each time point in the tensor data segment, and construct the structural relationship matrix; The fusion matrix module is used to determine the weight value of each differential pressure transmitter according to its layout position, and fuse the structural relationship matrix of each differential pressure transmitter based on the weight value to obtain a fusion structural relationship matrix; The asynchronous module is used to extract the element values ​​on each matrix diagonal according to the fusion structure relationship matrix, obtain the change rate sequence, and calculate the asynchrony based on it. When the asynchrony exceeds the preset asynchrony threshold, it is determined that sampling asynchrony exists, and the time point is readjusted to obtain the synchronous fusion structure relationship matrix; The transmission module is used to convert the synchronous fusion structure relationship matrix into a transmittable data packet format and send it to the remote server.

2. The data acquisition system for a differential pressure transmitter according to claim 1, characterized in that: The standardization module includes: The acquisition unit is used to obtain the analog voltage value of the differential pressure signal through the differential pressure sensor and sort it according to the sampling time to obtain a differential pressure signal sequence; A sliding window segmentation unit is used to perform window sliding on the differential pressure signal sequence according to a preset sample window to obtain a differential pressure signal sequence window; An extreme value statistics unit is used to count the total number of local maximum value points and local minimum value points according to the differential pressure signal sequence window to obtain the number of local extreme value points; The normalization unit is used to normalize the extreme value points in the differential pressure signal sequence according to the number of local extreme values, map the value of each extreme value point to the range of 0-1, and obtain a standardized signal sequence.

3. The data acquisition system for a differential pressure transmitter according to claim 2, characterized in that: The strengthening module includes: an extraction unit, configured to extract a standard value of each sampling point according to the standardized signal sequence to obtain a first space vector component; a difference calculation unit, configured to compare a standard value between a current sampling point and a previous sampling point, calculate the difference therebetween, and obtain a second space vector component; a sliding mean deviation unit, configured to calculate a sliding mean of the sampling points according to a preset sample window, and calculate the square of the difference between the standard value of the sampling points and the sliding mean to obtain a third space vector component; The three-dimensional space vector unit is used to generate a three-dimensional space vector of the sampling point according to the first space vector component, the second space vector component and the third space vector component.

4. The data acquisition system for a differential pressure transmitter according to claim 3, characterized in that: The strengthening module also includes: The window division unit is used to perform sliding slicing on the three-dimensional space vectors of the sampling points at different time points according to the preset time window, and divide them into vector sets of multiple continuous time periods to obtain a vector time series; A vector splicing unit is used to splice the three-dimensional space vectors of multiple consecutive sampling points according to the time dimension according to the vector time series to obtain a vector matrix block; The tensor construction unit is used to superimpose vector matrix blocks according to the time dimension to obtain tensor data.

5. The data acquisition system for a differential pressure transmitter according to claim 4, characterized in that: The structure matrix module includes: A tensor segmentation unit is used to obtain multiple tensor data segments by dividing the tensor data into equal intervals, and extract the three-dimensional space vectors of the sampling points in each tensor data segment at each time point to obtain a tensor segment vector set; A similarity calculation unit is used to extract the three-dimensional space vector of the sampling point at any time point based on the tensor segmentation vector set, calculate the dimensional difference mean and the three-dimensional space vector mean of the sampling point at any time point; and calculate the similarity between the sampling points at any two time points based on the dimensional difference mean and the three-dimensional space vector mean of the sampling point at the two time points; The structural relationship matrix unit is used to arrange the similarities into a two-dimensional matrix according to the time point index to obtain the structural relationship matrix.

6. The data acquisition system for a differential pressure transmitter according to claim 5, characterized in that: The fusion matrix module includes: The layout index unit is used to number the different differential pressure transmitters from upstream to downstream according to the physical installation order of the differential pressure transmitters on the target pipeline to obtain a layout index sequence; A weight generation unit is used to construct a linear proportional sequence according to the layout index sequence, calculate the weight values ​​of different differential pressure transmitters, and obtain a weight value set; The weighted fusion unit is used to perform linear weighting processing on the structural relationship matrices of different differential pressure transmitters according to the corresponding weight values ​​according to the weight value set to obtain a fused structural relationship matrix.

7. The data acquisition system for a differential pressure transmitter according to claim 6, characterized in that: The weight generating unit includes: The terminal distance unit is used to measure the distance from each differential pressure transmitter to the downstream terminal position of the target pipeline to obtain the terminal distance data; The spacing unit is used to measure the installation spacing between every two adjacent differential pressure transmitters to obtain adjacent spacing data; The distance factor unit is used to calculate the distance factor value of each differential pressure transmitter according to the terminal distance data and the adjacent spacing data, and to correct the distance factor value according to the numbering order in the layout index sequence to obtain a set of corrected weight original values; The normalized weight unit is used to normalize all the original weight values, and scale the original values ​​of the corrected weights of all transmitters according to their sum to obtain a weight value set.

8. The data acquisition system for a differential pressure transmitter according to claim 7, characterized in that: The asynchronous module includes: An extreme value element unit is used to extract all element values ​​from the fusion structure relationship matrix and determine the maximum element value and the minimum element value based on the values; A difference mapping unit is used to calculate the difference between each element value and the minimum value in the fusion structure relationship matrix to obtain a mapping value, and obtain a normalized value based on the mapping value and the difference between the maximum element value and the minimum element value, and replace all elements with the corresponding normalized values ​​to obtain a normalized structure relationship matrix; A diagonal extraction unit is used to extract all normalized values ​​on the main diagonal in time sequence according to the normalized structural relationship matrix to obtain a diagonal value sequence; The change calculation unit is used to calculate the change rate between each time point and the previous time point according to the diagonal value sequence to obtain a change rate sequence.

9. The data acquisition system for a differential pressure transmitter according to claim 8, characterized in that: The asynchronous module also includes: an asynchrony calculation unit, configured to calculate the difference between the vector change rates based on the change rate sequence to obtain a jump amount; calculate the mean of the change rates to obtain a normalization factor; and calculate the asynchrony of the differential pressure signal based on the jump amount and the normalization factor to obtain an asynchrony judgment indicator sequence; A threshold comparison unit is used to compare the maximum value in the asynchrony judgment indicator sequence with a preset asynchrony threshold value, and when the maximum value exceeds the asynchrony threshold value, it is determined that sampling asynchrony exists; A time offset calculation unit is used to calculate the time offset of each differential pressure transmitter according to the time point when the asynchronous phenomenon occurs, and form an offset mapping table; The time correction unit is used to rearrange and adjust the sampling time points of each differential pressure transmitter according to the offset mapping table to obtain a synchronous differential pressure signal sequence.

10. The data acquisition system for a differential pressure transmitter according to claim 9, characterized in that: The time correction unit includes: A sampling sequence positioning unit is used to locate the start time point and the end time point that need to be adjusted in the differential pressure signal sequence of the differential pressure transmitter according to the offset mapping table, and obtain a sampling time index set; A time reconstruction unit is used to perform time reconstruction on the sampling time index set, adjust the sampling point of each transmitter by the corresponding time offset, and obtain an intermediate adjustment sequence; A time alignment unit is used to fill in the gaps in the intermediate adjustment sequence caused by the offset according to adjacent sampling points to obtain a time-aligned sequence; The synchronous output unit is used to uniformly sort all the completed time-aligned sequences according to the global time order to obtain a synchronous differential pressure signal sequence.

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