A slurry pipeline state identification method and device

Abstract

The present application relates to a kind of slurry supply pipeline state identification method and device, the method comprises: obtaining the n-dimensional detection data of slurry supply pipeline;The n-dimensional detection data includes the specified parameter value of the slurry supply pipeline at n different time points;Make the n-dimensional detection data multiply by preset conversion matrix, obtain target vector;The preset conversion matrix is calculated according to first matrix and second matrix;The first matrix includes the n-dimensional detection data sample of the slurry supply pipeline in N pipeline state;The second matrix includes the coordinates of the N pipeline state in M-dimensional space;Wherein, M≥N-1, N≥2;According to the target vector and the coordinates of the N pipeline state in M-dimensional space, determine the state of the slurry supply pipeline.Based on this, the present application is high in recognition efficiency, and it can be applicable to PLC and DCS etc. Industrial control machine, application scene is wider.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of slurry pipeline state identification, and particularly relates to a slurry pipeline state identification method and device. BACKGROUND

[0002] In a limestone-gypsum wet desulfurization process, a limestone slurry supply system is an important factor for maintaining the quality of slurry in an absorption tower and the desulfurization efficiency. When the slurry supply pipeline of the limestone slurry supply system is blocked or leaks, etc., the normal operation of the limestone slurry supply system is affected. Therefore, it is necessary to identify the state of the slurry supply pipeline.

[0003] At present, the internal relationship between historical data parameters and the state of the slurry supply pipeline is found by learning the rules from historical reference samples of the slurry supply pipeline, and subsequently, the internal relationship and the real-time collected data parameters of the slurry supply pipeline are used to identify the blocking condition of the slurry supply pipeline.

[0004] However, the above technical solution has a large amount of calculation, which makes the identification efficiency of the solution low, and the solution is difficult to be directly applied to industrial control machines that cannot realize a large amount of calculation, such as PLC (Programmable Logic Controller) and DCS (Distributed Control System), etc., so that the application scenarios are limited. SUMMARY

[0005] In order to solve the above technical problems, the present application provides a slurry pipeline state identification method and device.

[0006] The technical solution of the present application is as follows:

[0007] The present application provides a slurry pipeline state identification method, comprising:

[0008] n-dimensional detection data of the slurry pipeline are acquired; the n-dimensional detection data contain specified parameter values of the slurry pipeline at n different time points;

[0009] The n-dimensional detection data are multiplied by a preset conversion matrix to obtain a target vector; the preset conversion matrix is calculated according to a first matrix and a second matrix; the first matrix contains n-dimensional detection data samples of the slurry pipeline in N pipeline states; the second matrix contains coordinates of the N pipeline states in M-dimensional space; wherein M≥N-1, N≥2;

[0010] The state of the slurry pipeline is determined according to the target vector and the coordinates of the N pipeline states in M-dimensional space.

[0011] Optionally, the M-dimensional space is divided into N partitions which are adjacent to each other and do not overlap with each other, each partition representing a pipe state; the coordinates of any one pipe state are located on the center line of the partition of the any one pipe state; the vector module length corresponding to each coordinate is equal; the center line of the partition refers to a line formed by points with equal perpendicular distance to each partition boundary of the partition;

[0012] The method further comprises:

[0013] If the vector direction of the target vector coincides with the vector direction corresponding to the coordinates of the target pipe state, the state of the slurry supply pipe is determined as the target pipe state; the target pipe state is one of the N pipe states;

[0014] If the vector direction of the target vector does not coincide with the vector direction corresponding to the coordinates of the N pipe states, the state of the slurry supply pipe is determined as the pipe state corresponding to the coordinates closest to the target vector, and there is a tendency to transfer to the pipe state corresponding to the coordinates second closest to the target vector.

[0015] The method further comprises:

[0016] The target vector is divided by its own module length to obtain the unit vector coordinates of the target vector;

[0017] The distance values between the unit vector coordinates and the coordinates of the N pipe states are calculated respectively; the vector module length corresponding to the coordinates is 1;

[0018] If the minimum distance value in each distance value is 0, the state of the slurry supply pipe is determined as the pipe state corresponding to the minimum distance value;

[0019] If the minimum distance value in each distance value is not 0, the state of the slurry supply pipe is determined as the pipe state corresponding to the minimum distance value, and there is a tendency to transfer to the pipe state corresponding to the second minimum distance value in each distance value.

[0020] Optionally, the specified parameter value is the current of the slurry supply pipe pump, the pipe pressure of the slurry supply pipe, or the pipe flow of the slurry supply pipe.

[0021] Optionally, before the n-dimensional detection data of the slurry supply pipe is obtained, the method further comprises:

[0022] acquire a first matrix sent by a user; the first matrix contains n-dimensional detection data samples of a preset pipeline state simulation device in N pipeline states; the n-dimensional detection data samples contain specified parameter values of the preset pipeline state simulation device at different time points;

[0023] acquire a second matrix sent by a user;

[0024] calculate the preset conversion matrix according to the first matrix and the second matrix.

[0025] Optionally, one pipeline state corresponds to multiple n-dimensional detection data samples, and the number of n-dimensional detection data samples corresponding to each pipeline state is the same or different.

[0026] Optionally, the calculation of the preset conversion matrix according to the first matrix and the second matrix specifically includes:

[0027] the preset conversion matrix is calculated according to the following calculation formula:

[0028] B=(AT*A)-1*AT*C

[0029] wherein B is the preset conversion matrix; A is the first matrix; and C is the second matrix.

[0030] Optionally, the n-dimensional detection data samples are data acquired in a slurry supply pipeline pump starting process of a preset pipeline state simulation device.

[0031] Optionally, the N pipeline states include at least two of normal, blockage and leakage.

[0032] The application further provides a slurry supply pipeline state recognition device, which comprises:

[0033] an acquisition module for acquiring n-dimensional detection data of a slurry supply pipeline; the n-dimensional detection data contains specified parameter values of the slurry supply pipeline at different time points;

[0034] a target vector calculation module for multiplying the n-dimensional detection data by a preset conversion matrix to obtain a target vector; the preset conversion matrix is calculated according to a first matrix and a second matrix; the first matrix contains n-dimensional detection data samples of the slurry supply pipeline in N pipeline states; and the second matrix contains coordinates of the N pipeline states in M-dimensional space; wherein M≥N-1 and N≥2.

[0035] a pipeline state recognition module for determining a state of the slurry supply pipeline according to the target vector and the coordinates of the N pipeline states in M-dimensional space.

[0036] The present application has the following beneficial effects by adopting the above technical scheme.

[0037] A slurry supply pipeline state recognition method, comprising: acquiring n-dimensional detection data of a slurry supply pipeline; the n-dimensional detection data contains specified parameter values of the slurry supply pipeline at n different time points; multiplying the n-dimensional detection data by a preset conversion matrix to obtain a target vector; the preset conversion matrix is calculated according to a first matrix and a second matrix; the first matrix contains n-dimensional detection data samples of the slurry supply pipeline in N pipeline states; the second matrix contains coordinates of the N pipeline states in M-dimensional space; wherein M≥N-1, N≥2; determining the state of the slurry supply pipeline according to the target vector and the coordinates of the N pipeline states in M-dimensional space. Based on this, since the amount of data required for calculating the conversion matrix is small (only a few groups of data are required for each pipeline state), the recognition efficiency of the present application is high, and it is suitable for industrial control machines such as PLC and DCS, and has a wide application scenario. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without creative labor.

[0039] Figure 1 is a flowchart of a preset conversion matrix acquisition method provided by an embodiment of the present application;

[0040] Figure 2 is a 2-dimensional space division result schematic diagram provided by an embodiment of the present application;

[0041] Figure 3 is a flowchart of a slurry supply pipeline state recognition method provided by an embodiment of the present application;

[0042] Figure 4 is a structural schematic diagram of a slurry supply pipeline state recognition device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0044] In the limestone-gypsum wet desulfurization process, the limestone slurry supply system is an important factor for maintaining the slurry quality and desulfurization efficiency of the absorption tower. In order to ensure the slurry quality balance of the absorption tower, it is necessary to continuously adjust the limestone slurry flow size in the slurry supply pipeline of the limestone slurry supply system, or intermittently start and stop the limestone slurry supply system. Because the limestone slurry concentration is large, and the pipe diameter of the slurry supply pipeline is often small, when the slurry supply flow of the slurry supply pipeline is small, or the slurry supply pipeline is stopped for a short time, the slurry supply pipeline is prone to blockage, affecting the normal operation of the limestone slurry supply system. In addition, the slurry supply pipeline may also leak, which will also affect the normal operation of the limestone slurry supply system. Therefore, it is necessary to identify the state of the slurry supply pipeline.

[0045] At present, the law is learned from the historical reference sample of the slurry supply pipeline, and the internal relationship between the historical data parameters and the slurry supply pipeline condition is found by using the law. Subsequently, the internal relationship and the real-time collected slurry supply pipeline data parameters are used to identify the blockage of the slurry supply pipeline. However, the above technical solution has a large amount of calculation, so the identification efficiency of the solution is low. In addition, the characteristics of PLC and CS and other industrial control machines make them unable to realize large amount of calculation (such as iteration) and store historical data, and the above technical solution has a large amount of calculation, so the solution cannot be directly applied to PLC and CS and other industrial control machines, which limits its application scenarios.

[0046] In order to solve the above technical problems, the present application provides a slurry supply pipeline state identification method and device. The technical solution is described in detail below in conjunction with the drawings.

[0047] Figure 1 is a flowchart of a preset conversion matrix acquisition method provided by an embodiment of the present application. As shown in Figure 1 , the preset conversion matrix acquisition method comprises:

[0048] Step 101: obtaining a first matrix sent by a user; the first matrix contains n-dimensional detection data samples of a preset pipeline state simulation device in N pipeline states; the n-dimensional detection data samples contain specified parameter values of the preset pipeline state simulation device at different time points.

[0049] In the embodiments of the present application, those skilled in the art can divide the pipeline state of the slurry supply pipeline according to actual needs, for example, the pipeline state of the slurry supply pipeline can be divided into three kinds of normal, blockage and leakage, and further, the pipeline state of the slurry supply pipeline can be further divided into normal, slight blockage, serious blockage, slight leakage and serious leakage. The N pipeline states can include at least two of the aforementioned pipeline states.

[0050] In the embodiments of the present specification, the preset pipeline state simulation device can be an existing limestone slurry supply system, that is, the present application can use the existing limestone slurry supply system to simulate the N pipeline states. Specifically, the user can simulate the N pipeline states by controlling the valve opening degree of the slurry supply pipeline in the limestone slurry supply system, for example, control the opening of the drain valve of the slurry supply pipeline to 75% to simulate the slight blockage state, control the opening of the drain valve of the slurry supply pipeline to 50% to simulate the serious leakage state, and so on.

[0051] In the embodiments of the present specification, for each pipeline state in the N pipeline states, K n-dimensional detection data samples of the preset pipeline state simulation device in the pipeline state can be collected, and the number of n-dimensional detection data samples corresponding to each pipeline state in the N pipeline states is the same. Wherein, K is greater than and equal to 1. And the n-dimensional detection data sample can include the specified parameter value obtained at different time points in the pump starting process of the preset pipeline state simulation device, for example, the specified parameter value obtained every second in the pump starting process of the preset pipeline state simulation device. The specified parameter value can be the current of the pump, the pipeline pressure of the slurry supply pipeline, or the pipeline flow of the slurry supply pipeline.

[0052] In order to more clearly illustrate the present scheme, the following will be described taking K = 3 as an example. It is assumed that N = 3, and the N pipeline states are normal, slight blockage and serious blockage respectively, and the specified parameter value is the current of the pump. In the process of obtaining the n-dimensional detection data sample, for each pipeline state, the preset pipeline state simulation device is controlled to simulate the pipeline state for 3 times, 3 n-dimensional current values (i.e. n-dimensional detection data samples) of the pipeline state are obtained, and are recorded as:

[0053] Normal: A 1，k,n

[0054] Slight blockage: A2 k,n

[0055] Serious leakage: A3 k,n

[0056] Wherein, A1 k,n = [A1 1,1 A1 1,2 A1 1,3 ...A1 1,n ; A1 2,1 A1 2,2 A1 2,3 ...A1 2,n ; A1 3,1 A1 3,2 , A1 3,3 ...A1 3,n , A2 k,n and A3 k,n are the same.

[0057] wherein, A1 1,1 represents the first collected current value when the preset pipeline state simulation device simulates the normal state for the first time; A1 1,2 represents the second collected current value when the preset pipeline state simulation device simulates the normal state for the first time; A1 1,3 represents the third collected current value when the preset pipeline state simulation device simulates the normal state for the first time; A1 1,n represents the n-th collected current value when the preset pipeline state simulation device simulates the normal state for the first time; A1 2,1 represents the first collected current value when the preset pipeline state simulation device simulates the normal state for the second time; A1 2,n represents the n-th collected current value when the preset pipeline state simulation device simulates the normal state for the second time.

[0058] Optionally, after each n-dimensional detection data sample is collected, the n-dimensional detection data sample can be divided by its own module length to obtain a unit vector of the n-dimensional detection data sample. After obtaining the unit vectors of the n-dimensional detection data samples, the unit vectors of the n-dimensional detection data samples are used to construct a first matrix A as follows:

[0059]

[0060] wherein, the first module length is a module length of a vector (A1 1,1 A1 1,2 A1 1,3 ...A1 1,n ) composed of the first collected current values when the preset pipeline state simulation device simulates the normal state for the first time; the second module length is a module length of a vector (A1 2,1 A1 2,2 A1 2,3 ...A1 2,n ) composed of the second collected current values when the preset pipeline state simulation device simulates the normal state for the first time; and the third module length is a module length of a vector (A1 3,1 A1 3,2 A1 3,3 ...A1 3,n ) composed of the third collected current values when the preset pipeline state simulation device simulates the normal state for the first time. The meanings of other module lengths can be similarly deduced and will not be described herein.

[0061] Step 102: obtaining the second matrix sent by the user; the second matrix contains coordinates of the N pipeline states in an M-dimensional space; wherein, M≥N-1, N≥2.

[0062] In the embodiments of the present specification, the M-dimensional space is divided into N partitions that are adjacent to each other and do not overlap with each other, each partition represents a pipeline state; the coordinates of any one pipeline state are located on the center line of the partition of the any one pipeline state; the vector lengths corresponding to each coordinate are equal; the center line of the partition refers to a line formed by points with equal perpendicular distances to each partition boundary of the partition. It can be understood by those skilled in the art that when M = 1, the partition boundary is a point; when M = 2, the partition boundary is a line; when M = 3, the partition boundary is a surface; and when M ≥ 4, the partition boundary is a hypersurface. Hereinafter, M = 2 and N = 3 are taken as examples for description.

[0063] Figure 2 is a schematic diagram of a 2-dimensional space division result provided by an embodiment of the present application. Referring to Figure 2 The method for determining the partitions and coordinates corresponding to N pipeline states in the M-dimensional space can specifically include the following steps.

[0064] Step 1: Randomly selecting N points around the 0 point in the M-dimensional space.

[0065] In the embodiments of the present specification, the three solid points in Figure 2 , and the distances between the N points and the 0 point are all equal, in the present embodiment, the distances between the N points and the 0 point are all 1, in addition, those skilled in the art can select other values as the distance value according to actual needs.

[0066] Step 2: Connecting the N points through the 0 point, and dividing the M-dimensional space into N partitions that are adjacent to each other and do not overlap with each other, each partition representing a pipeline state.

[0067] In the embodiments of the present specification, the pipeline state represented by each partition can be arbitrarily set by a user, as long as the pipeline states represented by the partitions are different. For example, Figure 2 , the first partition (i.e., the partition where point A is located, i.e., the first quadrant) represents slight blockage; the second partition (i.e., the partition where point B is located, i.e., the second quadrant and the third quadrant) represents normal; and the third partition (i.e., the partition where point C is located, i.e., the fourth quadrant) represents severe blockage.

[0068] Step 3: Determining the coordinates corresponding to the N pipeline states in the M-dimensional space.

[0069] In the embodiments of the present specification, the coordinates corresponding to the pipeline state can be the center line of the partition corresponding to the pipeline state and Figure 2The coordinates of the intersection of the dotted circles. Wherein, the dotted circle radius is equal to the distance between N points and 0 point respectively, according to the foregoing, in the embodiment, the distance between N points and 0 point is 1, therefore, the dotted circle is a unit circle. Therefore, the coordinates of the slightly blocked point A in the M-dimensional space, the coordinates of the normally blocked point B in the M-dimensional space, and the coordinates of the seriously blocked point C in the M-dimensional space.

[0070] Next, using the coordinates corresponding to the N pipe states, a second matrix is constructed.

[0071] In the embodiment of the present specification, in the first matrix and the second matrix, the pipe states corresponding to the two rows with the same position are the same, for example, the first row of the first matrix is the n-dimensional detection data sample collected when the control preset pipe state simulation device simulates the normal state, and the first row of the second matrix is the coordinates corresponding to the normal state in the M-dimensional space. Based on this, according to the above example, the second matrix C is:

[0072]

[0073] Step 103: calculating the preset conversion matrix according to the first matrix and the second matrix.

[0074] In the embodiment of the present specification, the preset conversion matrix B can be calculated according to the following calculation formula:

[0075] B=(A T *A) -1 *A T *C

[0076] Wherein, A is the first matrix; C is the second matrix.

[0077] Subsequently, the pipe state of the pulp supply pipe can be identified by using the preset conversion matrix obtained above. The method of identifying the pipe state of the pulp supply pipe using the preset conversion matrix will be described in detail below with reference to the accompanying drawings.

[0078] Figure 3 is a flowchart of a pulp supply pipe state identification method provided by an embodiment of the present application. As shown in the figure, the flowchart includes: Figure 3

[0079] Step 301: acquiring n-dimensional detection data of a pulp supply pipe; the n-dimensional detection data contains specified parameter values of the pulp supply pipe at n different time instants.

[0080] In the embodiment of the present specification, the specified parameters in the n-dimensional detection data are the same as the specified parameters in the aforementioned n-dimensional detection data sample, for example, the specified parameters in the aforementioned n-dimensional detection data sample are the current of the pump, and the specified parameters in the n-dimensional detection data are also the current of the pump.​

[0081] Step 302: multiply the n-dimensional detection data by a preset conversion matrix to obtain a target vector.

[0082] Step 303: determine the state of the slurry supply pipeline according to the target vector and the coordinates of the N pipeline states in the M-dimensional space.

[0083] In the embodiments of the present specification, the M-dimensional space can be divided into N partitions that are adjacent to each other and do not overlap with each other, and each partition represents a pipeline state; the coordinates of any one pipeline state are located on the center line of the partition of the any one pipeline state; the center line is a line passing through the 0 point and bisecting the corresponding partition.

[0084] Step 303: determining the state of the slurry supply pipeline according to the target vector and the coordinates of the N pipeline states in the M-dimensional space, which can specifically include:

[0085] If the vector direction of the target vector coincides with the vector direction corresponding to the coordinates of the target pipeline state, it is determined that the state of the slurry supply pipeline is the target pipeline state; the target pipeline state is one of the N pipeline states.

[0086] And, if the vector direction of the target vector does not coincide with the vector direction corresponding to the coordinates of the N pipeline states, it is determined that the state of the slurry supply pipeline is the pipeline state corresponding to the coordinates closest to the target vector, and there is a tendency to transfer to the pipeline state corresponding to the coordinates second closest to the target vector.

[0087] More specifically, step 303: determining the state of the slurry supply pipeline according to the target vector and the coordinates of the N pipeline states in the M-dimensional space, which can specifically include:

[0088] First, divide the target vector by its own module length to obtain the unit vector coordinates of the target vector. Then, the distance values between the unit vector coordinates and the coordinates of the N pipeline states are calculated respectively; the vector module length corresponding to the coordinates is 1.

[0089] Finally, it is determined whether the minimum distance value in each distance value is 0, if the minimum distance value in each distance value is 0, it is determined that the state of the slurry supply pipeline is the pipeline state corresponding to the minimum distance value; if the minimum distance value in each distance value is not 0, it is determined that the state of the slurry supply pipeline is the pipeline state corresponding to the minimum distance value, and there is a tendency to transfer to the pipeline state corresponding to the second smallest distance value in each distance value.

[0090] In one specific example, refer to Figure 3, assuming that the coordinates of the target vector are (0, 1), the pipe state coordinates corresponding to the minimum distance value are the coordinates of point A, and the pipe state coordinates corresponding to the second minimum distance value are the coordinates of point B, therefore, it is determined that the state of the pulp supply pipe is blocked and has a tendency to transfer to the normal state.

[0091] In the embodiments of the present specification, the M-dimensional space is divided into partitions of the same number of pipe states through mechanism analysis, each partition represents a pipe state, and each partition is adjacent to each other and does not overlap with each other, so that the present scheme fully considers the correlation between different pipe states (different pipe states are directly related and can be converted to each other, for example, in a certain period of time, the pipe state of the pulp supply pipe can be converted from normal to blocked, or converted to leakage due to damage of the pulp supply pipe), and further, the present application can determine the current pipe state of the pulp supply pipe and the transfer tendency of the pipe state according to the coordinates of each pipe state in the M-dimensional space and the target vector, so that the recognition result of the present application is more accurate.

[0092] In addition, in the embodiments of the present specification, the amount of data required for calculating the conversion matrix is small (only a few groups of data are required for each pipe state), so that the recognition efficiency of the present application is high, and the present application is suitable for PLC and DCS and other industrial control machines, and has a wide application scenario.

[0093] Based on the same inventive concept, the embodiments of the present application also provide a pulp supply pipe state recognition device. Figure 4 is a structural schematic diagram of a pulp supply pipe state recognition device provided by the embodiments of the present application. As shown in Figure 4 , the device comprises:

[0094] The acquisition detection data module 41 is configured to acquire n-dimensional detection data of the pulp supply pipe; the n-dimensional detection data comprises specified parameter values of the pulp supply pipe at n different time points;

[0095] The target vector calculation module 42 is configured to multiply the n-dimensional detection data by a preset conversion matrix to obtain a target vector; the preset conversion matrix is calculated according to a first matrix and a second matrix; the first matrix comprises n-dimensional detection data samples of the pulp supply pipe in N pipe states; the second matrix comprises coordinates of the N pipe states in M-dimensional space; wherein M≥N-1, N≥2;

[0096] The pipe state recognition module 43 is configured to determine the state of the pulp supply pipe according to the target vector and the coordinates of the N pipe states in the M-dimensional space.

[0097] Optionally, the M-dimensional space is divided into N adjacent and non-overlapping partitions, each partition representing a pipeline state; the coordinates of any pipeline state are located on the center line of the partition of that pipeline state; the vector magnitudes corresponding to each coordinate are equal; the center line of the partition refers to the line formed by points that are equidistant from the boundaries of each partition.

[0098] The pipeline status identification module 43 may specifically include:

[0099] If the direction of the target vector coincides with the direction of the vector corresponding to the coordinates of the target pipeline state, then the state of the slurry supply pipeline is determined to be the target pipeline state; the target pipeline state is one of the N pipeline states.

[0100] If the direction of the target vector does not coincide with the direction of the coordinates of the N pipe states, then the state of the slurry supply pipe is determined to be the pipe state corresponding to the coordinate closest to the target vector, and there is a tendency to shift to the pipe state corresponding to the second closest coordinate to the target vector.

[0101] Optionally, the pipeline status identification module 43 may specifically include:

[0102] Divide the target vector by its own magnitude to obtain the unit vector coordinates of the target vector;

[0103] Calculate the distance between the unit vector coordinates and the coordinates of the N pipe states; the vector magnitude corresponding to the coordinates is 1.

[0104] If the minimum distance value among all the distance values ​​is 0, then the state of the slurry supply pipeline is defined as the pipeline state corresponding to the minimum distance value;

[0105] If the minimum distance value among all the distance values ​​is not 0, then the state of the slurry supply pipeline is defined as the pipeline state corresponding to the minimum distance value, and there is a tendency to transfer to the pipeline state corresponding to the second smallest distance value among all the distance values.

[0106] Optionally, the specified parameter value is the current of the slurry supply pipeline pump, the pipeline pressure of the slurry supply pipeline, or the pipeline flow rate of the slurry supply pipeline.

[0107] Optionally, the apparatus in the embodiments of this specification may further include:

[0108] The module for obtaining the first matrix is ​​used to obtain a first matrix sent by the user; the first matrix contains n-dimensional detection data samples of a preset pipeline state simulation device in N pipeline states; the n-dimensional detection data samples contain specified parameter values ​​of the preset pipeline state simulation device at n different times.

[0109] The second matrix acquisition module is configured to acquire the second matrix sent by the user.

[0110] The conversion matrix calculation module is configured to calculate the preset conversion matrix according to the first matrix and the second matrix.

[0111] Optionally, one pipeline state corresponds to a plurality of n-dimensional detection data samples, and the number of n-dimensional detection data samples corresponding to each pipeline state is the same.

[0112] Optionally, the conversion matrix calculation module can be specifically configured to:

[0113] The preset conversion matrix is calculated according to the following calculation formula:

[0114] B = (A T *A) -1 *A T *C

[0115] Wherein, B is the preset conversion matrix; A is the first matrix; C is the second matrix.

[0116] Optionally, the n-dimensional detection data sample is data acquired in a pump starting process of a slurry supply pipeline of a preset pipeline state simulation device.

[0117] Optionally, the N pipeline states include at least two of normal, blockage and leakage.

[0118] For each method embodiment described above, in order to simply describe, the method is described as a series of action combinations, but those skilled in the art should know that the present application is not limited to the action sequence described, because according to the present application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0119] It should be noted that each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same and similar parts of each embodiment can be referred to. For device embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0120] The steps in the method of each embodiment of the present application can be adjusted, combined and reduced in sequence according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0121] The modules and sub-modules in the device and terminal of the embodiments of the present application can be combined, divided, and deleted according to actual needs.

[0122] It should be understood that the disclosed terminal, device, and method can be implemented in other ways in several embodiments of the present application. For example, the terminal embodiments described above are only illustrative, and the division of the modules or sub-modules is only a logical function division. In actual implementation, another division manner can be used, for example, a plurality of sub-modules or modules can be combined or integrated into another module, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules or sub-modules can be indirect coupling or communication connection through some interfaces, devices, or modules, and can be electrical, mechanical, or other forms.

[0123] The modules or sub-modules described as separate components can or can not be physically separated, and the components of the modules or sub-modules can or can not be physical modules or sub-modules, that is, can be located in one place or can be distributed on a plurality of network modules or sub-modules. Part or all of the modules or sub-modules can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0124] In addition, each functional module or sub-module in each embodiment of the present application can be integrated in one processing module, or each module or sub-module can exist physically, or two or more modules or sub-modules can be integrated in one module. The integrated module or sub-module can be realized in the form of hardware or software functional module or sub-module.

[0125] The skilled person can further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, computer software, or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0126] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be directly implemented by hardware, a software unit executed by a processor, or a combination of both. The software unit can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0127] Finally, it should be noted that the terms "first" and "second", and the like, merely serve to distinguish one entity or action from another without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0128] The above description of disclosed embodiments provides enabling concepts for practicing or using the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for slurry pipeline condition identification, characterized by, The method comprises the following steps: acquiring n-dimensional detection data of a slurry supply pipeline; the n-dimensional detection data comprises specified parameter values of the slurry supply pipeline at different time points, and the specified parameter values are current of a slurry supply pipeline pump, pipeline pressure of the slurry supply pipeline, or pipeline flow of the slurry supply pipeline; multiplying the n-dimensional detection data by a preset conversion matrix to obtain a target vector; the preset conversion matrix is calculated according to a first matrix and a second matrix; the first matrix comprises n-dimensional detection data samples of the slurry supply pipeline in N pipeline states; the second matrix comprises coordinates of the N pipeline states in M-dimensional space; M≥N-1, and N≥2; the preset conversion matrix is calculated according to the following calculation formula: B = (A T *A) -1 *A T *C wherein B is the preset conversion matrix, A is the first matrix, and C is the second matrix; determining a state of the slurry supply pipeline according to the target vector and the coordinates of the N pipeline states in the M-dimensional space.

2. The method of claim 1, wherein, The M-dimensional space is divided into N partitions that are adjacent to each other and do not overlap with each other, and each partition represents a pipeline state; the coordinates of any pipeline state are located on a center line of the partition of the any pipeline state; the vector lengths corresponding to the coordinates are equal; the center line of the partition refers to a line formed by points that are equal in perpendicular distance to each partition boundary of the partition; The determination of the state of the slurry supply pipeline according to the target vector and the coordinates of the N pipeline states in the M-dimensional space specifically comprises the following steps: if the vector direction of the target vector coincides with the vector direction corresponding to the coordinates of a target pipeline state, the state of the slurry supply pipeline is determined as the target pipeline state; the target pipeline state is one of the N pipeline states; if the vector direction of the target vector does not coincide with the vector direction corresponding to the coordinates of any of the N pipeline states, the state of the slurry supply pipeline is determined as the pipeline state corresponding to the coordinates closest to the target vector, and there is a tendency to transfer to the pipeline state corresponding to the coordinates next closest to the target vector.

3. The method of claim 2, wherein, The determination of the state of the slurry supply pipeline according to the target vector and the coordinates of the N pipeline states in the M-dimensional space specifically comprises the following steps: dividing the target vector by its own length to obtain a unit vector coordinate of the target vector; calculating distance values between the unit vector coordinate and the coordinates of the N pipeline states respectively; the vector length corresponding to the coordinates is 1; if the minimum distance value in the distance values is 0, the state of the slurry supply pipeline is determined as the pipeline state corresponding to the minimum distance value; if the minimum distance value in the distance values is not 0, the state of the slurry supply pipeline is determined as the pipeline state corresponding to the minimum distance value, and there is a tendency to transfer to the pipeline state corresponding to the second minimum distance value in the distance values.

4. The method of claim 1, wherein, Before the n-dimensional detection data of the slurry supply pipeline is acquired, the following steps are further included: obtaining a first matrix sent by a user; the first matrix containing n-dimensional detection data samples of a preset pipeline state simulation device in N pipeline states; the n-dimensional detection data samples containing specified parameter values of the preset pipeline state simulation device at different time points; obtaining a second matrix sent by a user; calculating the preset conversion matrix according to the first matrix and the second matrix.

5. The method of claim 4, wherein, One pipeline state corresponds to multiple n-dimensional detection data samples, and the number of n-dimensional detection data samples corresponding to each pipeline state is the same or different.

6. The method of claim 4, wherein, The n-dimensional detection data samples are data obtained in a slurry supply pipeline pump starting process of a preset pipeline state simulation device.

7. The method of claim 1, wherein, The N pipeline states include at least two of normal, blockage and leakage.

8. A slurry pipeline condition identification apparatus, comprising: Comprise: an acquisition detection data module for acquiring n-dimensional detection data of a slurry supply pipeline; the n-dimensional detection data containing specified parameter values of the slurry supply pipeline at different time points, and the specified parameter values being current of a slurry supply pipeline pump, pipeline pressure of the slurry supply pipeline, or pipeline flow of the slurry supply pipeline; a target vector calculation module for multiplying the n-dimensional detection data by a preset conversion matrix to obtain a target vector; the preset conversion matrix being calculated according to a first matrix and a second matrix; the first matrix containing n-dimensional detection data samples of the slurry supply pipeline in N pipeline states; the second matrix containing coordinates of the N pipeline states in M-dimensional space; wherein M≥N-1, N≥2; a pipeline state identification module for determining a state of the slurry supply pipeline according to the target vector and the coordinates of the N pipeline states in M-dimensional space; a conversion matrix calculation module for calculating the preset conversion matrix according to the following calculation formula: B = (A T *A) -1 *A T *C wherein B is the preset conversion matrix; A is the first matrix; and C is the second matrix.

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