Mine filling pipeline blockage monitoring method and device

Abstract

The present invention relates to a method and device for monitoring blockage of a mine filling pipeline, and belongs to the field of mine filling detection technology, wherein the method includes obtaining first temperature vibration data of the mine filling pipeline at the current moment and second temperature vibration data at the previous moment; processing the first temperature vibration data at the current moment to obtain multiple initial prediction results; and obtaining a target prediction result based on the multiple initial prediction results; monitoring the blockage of the mine filling pipeline based on the target prediction result and the second temperature vibration data at the previous moment, so that the blockage of the mine filling pipeline can be predicted in real time based on the first temperature vibration data at the current moment and the second temperature vibration data at the previous moment, and anomalies can be detected before the filling pipeline is blocked, so that staff can handle it according to the monitoring situation, thereby ensuring the normal production and safety of the mine.

Description

Technical Field

[0001] The present invention relates to the technical field of mine filling detection, and in particular to a method and device for monitoring blockage of a mine filling pipeline. Background Art

[0002] The backfill mining method is favored by mines for reducing safety hazards associated with mining activities and improving the reliability of the human living environment and property safety. The transportation of backfill is a crucial step in backfill mining. Blockage of mine backfill pipelines is a common problem during mining operations, seriously impacting normal mine production and safety. As a critical channel for conveying backfill materials, blockage of backfill pipelines can not only interrupt backfill operations but also potentially cause equipment damage, environmental pollution, and safety accidents. As the backfill slurry flows through the pipeline, heat generation and heat transfer occur constantly, resulting in different rates of temperature change of the slurry at different flow locations, and the backfill pipeline constantly vibrates. This can affect the transportation of the backfill slurry, causing it to stagnate and ultimately block the pipeline.

[0003] In the existing technology, pressure detection method, pulse echo method, resistivity analysis method, and acoustic technology are generally used to detect filling pipes. However, these methods cannot predict the phenomenon of filling pipes in a timely manner. They can only detect blockages when they occur, thus affecting the normal production and safety of mines.

[0004] Therefore, it is urgent to propose a method and device for monitoring blockage of mine filling pipes to solve the technical problem that the existing methods in the prior art cannot predict the phenomenon of filling pipes in a timely manner and can only detect it when blockage occurs, thereby affecting the normal production and safety of mines. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and device for monitoring blockage of mine filling pipelines to solve the technical problem that the existing methods in the prior art cannot predict the phenomenon of filling pipelines in a timely manner and can only detect it when blockage occurs, thereby affecting the normal production and safety of mines.

[0006] In order to solve the above problems, the present invention provides a method for monitoring blockage of a mine filling pipeline, comprising:

[0007] Obtaining first temperature and vibration data of the mine filling pipeline at the current moment and second temperature and vibration data at the previous moment;

[0008] processing the first temperature-vibration data to obtain a plurality of initial prediction results;

[0009] Obtaining a target prediction result based on the multiple initial prediction results;

[0010] The blockage condition of the mine filling pipeline is monitored according to the target prediction result and the second temperature and vibration data.

[0011] In a possible implementation, the processing of the first temperature-vibration data to obtain a plurality of initial prediction results includes:

[0012] Decomposing the first temperature-vibration data to obtain multiple sequences;

[0013] Each sequence is predicted separately to obtain multiple initial prediction results.

[0014] In a possible implementation, the multiple sequences include a residual sequence and k modal component IMF sequences, and the decomposition of the first temperature-vibration data to obtain multiple sequences includes:

[0015] Based on the preset optimization algorithm, the core parameters of the VMD decomposition algorithm, the modal number k value and the penalty factor, are optimized to obtain the target VMD decomposition algorithm;

[0016] The first temperature-vibration data is decomposed based on the target VMD decomposition algorithm to obtain k modal component IMF sequences and a residual sequence.

[0017] In a possible implementation, the multiple initial prediction results include multiple subsequence prediction results and residual prediction results, and the prediction processing is performed on each sequence separately to obtain multiple initial prediction results, including:

[0018] Processing each of the k modal component IMF sequences respectively to obtain multiple subsequence prediction results;

[0019] The residual sequence is decomposed twice to obtain a residual prediction result.

[0020] In a possible implementation, performing secondary decomposition on the residual sequence to obtain a residual prediction result includes:

[0021] Performing secondary decomposition on the residual sequence based on a preset number of neural networks to obtain a residual subsequence corresponding to each neural network;

[0022] Combining a preset number of residual subsequences to obtain a combined prediction model;

[0023] The combined prediction model is calculated according to the preset number of residual subsequences to obtain a residual prediction result.

[0024] In a possible implementation, obtaining a target prediction result based on the multiple initial prediction results includes:

[0025] The multiple subsequence prediction results and the residual prediction result are superimposed and reconstructed to obtain a target prediction result.

[0026] In a possible implementation, monitoring the blockage of the mine filling pipeline according to the target prediction result and the second temperature and vibration data includes:

[0027] Obtaining a pipe diameter change trend value according to the target prediction result and the second temperature-vibration data;

[0028] The blockage condition of the mine filling pipeline is monitored according to the pipe diameter change trend value.

[0029] In a possible implementation, monitoring the blockage of the mine filling pipeline according to the pipeline diameter change trend value includes:

[0030] Setting a classification level for the pipeline transportation status of the mine filling pipeline;

[0031] Determining a target level according to the pipeline transportation status classification level and the pipeline diameter change trend value;

[0032] The blockage of the mine filling pipeline is monitored according to the target level.

[0033] In a possible implementation, combining a preset number of residual subsequences to obtain a combined prediction model includes:

[0034] Combining a preset number of residual subsequences to obtain an initial combined prediction model;

[0035] The initial combined prediction model is optimized based on the crow search algorithm to obtain a combined prediction model.

[0036] On the other hand, the present invention also provides a mine filling pipeline blockage monitoring device, comprising:

[0037] A data acquisition module, used to acquire first temperature and vibration data of the mine filling pipeline at the current moment and second temperature and vibration data at the previous moment;

[0038] a data prediction module, configured to process the first temperature-vibration data to obtain a plurality of initial prediction results;

[0039] A result prediction module, configured to obtain a target prediction result based on the multiple initial prediction results;

[0040] A pipeline monitoring module is used to monitor the blockage of the mine filling pipeline according to the target prediction result and the second temperature and vibration data.

[0041] The beneficial effect of the present invention is to obtain the first temperature vibration data of the mine filling pipeline at the current moment and the second temperature vibration data at the previous moment; then the first temperature vibration data at the current moment can be processed to obtain multiple initial prediction results; and based on the multiple initial prediction results, the target prediction result is obtained; then the blockage of the mine filling pipeline can be monitored according to the target prediction result and the second temperature vibration data at the previous moment, so that the blockage of the mine filling pipeline can be predicted in real time based on the first temperature vibration data at the current moment and the second temperature vibration data at the previous moment, so that the abnormality can be detected before the filling pipeline is blocked, so that the staff can handle it according to the monitoring situation, thereby ensuring the normal production and safety of the mine. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of an embodiment of a method for monitoring blockage in a mine filling pipeline provided by the present invention;

[0043] Figure 2 A schematic diagram of an embodiment of the residual prediction result provided by the present invention;

[0044] Figure 3 A schematic diagram of an embodiment of the process of monitoring a mine filling pipeline provided by the present invention;

[0045] Figure 4 A schematic structural diagram of an embodiment of a mine filling pipeline blockage monitoring device provided by the present invention;

[0046] Figure 5 This is a schematic structural diagram of an embodiment of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0047] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0048] like Figure 1 As shown, a specific embodiment of the present invention discloses a method for monitoring blockage of a mine filling pipeline, comprising:

[0049] S101, obtaining first temperature and vibration data of a mine filling pipeline at a current moment and second temperature and vibration data at a previous moment;

[0050] S102, processing the first temperature-vibration data to obtain multiple initial prediction results;

[0051] S103, obtaining a target prediction result based on multiple initial prediction results;

[0052] S104. Monitor the blockage of the mine filling pipeline according to the target prediction result and the second temperature vibration data.

[0053] It should be understood that: a temperature vibration sensor can be installed on the mine filling pipe, and the temperature vibration sensor can be used to collect temperature vibration data with time series information, such as the first temperature vibration data at the current moment and the second temperature vibration data at the previous moment. The specific acquisition process can be set according to actual conditions, and the embodiment of the present invention is not limited here.

[0054] In a specific embodiment of the present invention, the first temperature vibration data of the mine filling pipeline at the current moment and the second temperature vibration data at the previous moment can be obtained, and then the first temperature vibration data can be processed through VMD decomposition to obtain multiple initial prediction results of multiple subsequences, and then the target prediction result can be obtained based on the multiple initial prediction results. Then, calculation and judgment can be performed based on the target prediction result and the second temperature vibration data, so that detection can be performed based on the judgment result. For example, when the judgment result is that there is an abnormality, it means that it may be blocked or has been blocked. At this time, the staff can process the mine filling pipeline according to the detection results to ensure the normal operation and safety of the mine filling pipeline.

[0055] Compared with the prior art, the present embodiment provides a method for obtaining the first temperature-vibration data of the mine filling pipeline at the current moment and the second temperature-vibration data at the previous moment; and then the first temperature-vibration data at the current moment can be processed to obtain a plurality of initial prediction results; and based on the plurality of initial prediction results, a target prediction result can be obtained; and then the blockage of the mine filling pipeline can be monitored according to the target prediction result and the second temperature-vibration data at the previous moment, so that the mine filling pipeline can be monitored in real time according to the first temperature-vibration data at the current moment and the second temperature-vibration data at the previous moment, so that anomalies can be detected before the filling pipeline is blocked, so that the staff can handle the situation according to the monitoring situation, thereby ensuring the normal production and safety of the mine.

[0056] In some embodiments of the present invention, step S102 includes:

[0057] Decomposing the first temperature-vibration data to obtain multiple sequences;

[0058] Each sequence is predicted separately to obtain multiple initial prediction results.

[0059] In a specific embodiment of the present invention, the temperature-vibration data can be decomposed using a preset algorithm to obtain multiple sequences. The specific preset algorithm can be set according to actual conditions and is not limited in this embodiment of the present invention. A prediction process can then be performed on each sequence to obtain an initial prediction result corresponding to each sequence. Multiple sequences can then be used to obtain multiple initial prediction results.

[0060] In some embodiments of the present invention, the multiple sequences include a residual sequence and k modal component IMF sequences. The first temperature-vibration data is decomposed to obtain multiple sequences, including:

[0061] Based on the preset optimization algorithm, the core parameters of the VMD decomposition algorithm, the modal number k value and the penalty factor, are optimized to obtain the target VMD decomposition algorithm;

[0062] The first temperature vibration data is decomposed based on the target VMD decomposition algorithm to obtain k modal component IMF sequences and a residual sequence.

[0063] In a specific embodiment of the present invention, the core parameters of the VMD decomposition algorithm, namely, the modal number k value and the penalty factor, are optimized based on a preset optimization algorithm to obtain the optimal parameter combination of the modal component k value and the penalty factor α, that is, to obtain the target VMD decomposition algorithm, wherein the preset optimization algorithm may be a GOOSE optimization algorithm. The specific optimization process may be:

[0064] Step 1: Initialize the parameters. The position of the goose is x, and the specific variable is represented by [ k , α ]. Initialize the goose flock Xi ( i= 1,2，...，n ). When rnd≥0.5, the exploration phase is activated; in the exploration phase, if pro>0.2, and S_W ≥12, perform location exploration, as shown in formula (1):

[0065] (1)

[0066] Where: S_W it Indicates the number of iterations, the value range is [5~25]; The_O_O it represents the time it takes for the stone to reach the ground when it falls, and it is random, ranging from 1 to the number of dimensions in each iteration of the loop; T_o_A_S it When the object hits the ground, the sound is made and transmitted to each goose in the flock; T_o_A it Indicates the time during the iteration process; T_ T represents the total time required for the sound to propagate throughout the entire iteration and reach a single goose in the flock; T_A Indicates the average time required; F_F_S represents the free fall velocity; S_S Indicates the speed of sound; D_S_T it Sound propagation distance; D_Git Keep distance between a guard goose and another goose that is resting or feeding; X represents the optimal position solution obtained.

[0067] When rnd≥0.5, the exploration phase is activated; in the exploration phase, if pro≤0.2, and S_W When <12, the following formula is used for iteration, as shown in formula (2):

[0068]

[0069]

[0070] (2)

[0071] All the formulas in formula (2) are iterated to find the optimal position solution for the goose.

[0072] When rnd < 0.5, the exploration phase is started directly and iterated as shown in formula (3):

[0073]

[0074] (3)

[0075] Where: Best_pos is the best location found in the search area.

[0076] Step 2: If you exceed the limit, you will adapt X , evaluate the objective function and return the optimal solution to obtain the optimal k and α Combination, we can get the optimized target VMD decomposition algorithm. X , it will return to step 1 and cycle again.

[0077] Then the first temperature-vibration data can be decomposed based on the target VMD decomposition algorithm to obtain k The solution is shown in formula (4):

[0078] Data initialization, IMF sequence collection , residual sequence set , Lagrange multipliers 、 n All are set to 0, where u k 、 oh k Represents each submodal component and center frequency ( k =1,2,⋯, K ); l Represents the Lagrange multiplier and selects the optimal decomposition order k As the default value; updated by introducing the augmented Lagrangian equation and the alternating direction multiplier method u k and oh k ; then update ,Right now ,in, oh Represents the angular velocity; when the following convergence conditions are met, the iteration stops. The convergence conditions are ; Otherwise, return to the step data initialization. When the iteration stops, get The k modal component IMF sequences in A residual sequence in .

[0079] In some embodiments of the present invention, the multiple initial prediction results include multiple subsequence prediction results and residual prediction results. Prediction processing is performed on each sequence to obtain multiple initial prediction results, including:

[0080] respectively k Each modal component IMF sequence in the modal component IMF sequence is processed to obtain multiple subsequence prediction results;

[0081] The residual sequence is decomposed twice to obtain the residual prediction result.

[0082] In a specific embodiment of the present invention, the decomposed k The IMF sequences of the modal components are fed into LSTM for prediction. The basic formula of LSTM is shown in formula (4):

[0083] (4)

[0084] Where: f t 、 i t 、 c t 、 o t express t The forget gate, input gate, cell state and output gate at each moment; Indicates the creation of an alternative c t ; h t Indicates the output result; s Represents the sigmoid function; W f 、 W i 、 Wc 、 W o Represent the weight matrices of the forget gate, input gate, cell state, and output gate respectively; h t express t The hidden layer output at time t; x t express t Input at the moment; b f 、 b i 、 b c 、 b o They represent the bias terms of the forget gate, input gate, cell state, and output gate respectively; tanh represents the hyperbolic tangent function.

[0085] Therefore, each subsequence can be predicted through the LSTM prediction neural network, and the subsequence prediction result corresponding to each subsequence can be output.

[0086] In some embodiments of the present invention, Figure 2 As shown, the residual sequence is decomposed twice to obtain the residual prediction results, including:

[0087] S201, performing secondary decomposition on the residual sequence based on a preset number of neural networks to obtain a residual subsequence corresponding to each neural network;

[0088] S202, combining a preset number of residual subsequences to obtain a combined prediction model;

[0089] S203: Calculate the combined prediction model according to a preset number of residual subsequences to obtain a residual prediction result.

[0090] In a specific embodiment of the present invention, the residual sequence can be decomposed twice using the SSA algorithm to obtain a residual subsequence corresponding to each neural network, that is, a preset number of residual subsequences. The preset number of residual subsequences are used to establish a combined prediction model including a preset number of neural networks, wherein the preset number of neural networks may include KAN, SVM and MLP. Then, the combined prediction model can be calculated based on the preset number of residual subsequences to obtain a residual prediction result.

[0091] In some embodiments of the present invention, step S202 includes:

[0092] Combining a preset number of residual subsequences to obtain an initial combined prediction model;

[0093] The initial combined prediction model is optimized based on the crow search algorithm to obtain the combined prediction model.

[0094] In a specific embodiment of the present invention, the three neural network models KAN, SVM and MLP are combined through formula (5) to obtain an initial combined prediction model:

[0095] (5)

[0096] Where: P i express i Combined forecast value at time; , , Indicates that the three sub-models are i The predicted value at the moment; oh 1. oh 2 and oh 3 represents the combination coefficient of 3 sub-models.

[0097] Furthermore, the initial combined prediction model can be optimized based on the crow search algorithm. Specifically: 1. Formulate the optimization problem and its decision variables and constraints, and set the value of the adjustable parameter, i.e., the number of crows ( or c ), maximum number of iterations (Niter), flight length ( or l ) and perceived probability ( c AP ).

[0098] 2. Design a or c Line and or d A matrix consisting of columns, where or c 、 or d are the number of crows and decision variables, respectively. Each crow represents a feasible solution, and its memory is set. Furthermore, at the beginning, all crows have no experience, so it is assumed that each crow hides food in a random location.

[0099] 3. Calculate the value of the fitness function.

[0100] 4. Calculate the new position of the crow. First, generate a random number and compare it with c AP Compare, if the random number value is less than c AP , then the crow moves randomly in the search space. Otherwise, the crow (x i ) Randomly select any crows ( m j ), followed by mj Determine where it has hidden its food. i ) is calculated as shown in formula (6):

[0101] (6)

[0102] In the formula: r represents a random number; t represents the number of iterations. This process is performed for all η c Repeat for each crow.

[0103] 5. Calculate the fitness of the new position.

[0104] 6. Determine whether the crow's memory is updated based on the fitness values ​​of the new position and the remembered position. That is, if the fitness value of the new position is better than that of the remembered position, the crow's memory will be updated. Otherwise, the crow stays at the current position.

[0105] 7. Repeat steps 4 to 6 until the maximum number of iterations or the termination criterion is met. The best position remembered by the crow will represent the final solution to the optimization problem.

[0106] 8. Solve the combination coefficient in formula (5) according to steps 1-7, and select the root mean square error as the fitness function value of the crow search algorithm. The optimal solution of the crow search algorithm is recorded as: , , , then the optimized combined prediction model is obtained, as shown in formula (7):

[0107] (7)

[0108] In some embodiments of the present invention, step S103 includes:

[0109] Multiple subsequence prediction results and residual prediction results are superimposed and reconstructed to obtain the target prediction result.

[0110] In a specific embodiment of the present invention, multiple subsequence prediction results are reconstructed according to formula (8) to obtain the final target prediction result, as shown in formula (8):

[0111] (8)

[0112] Where, Represents the subsequence prediction result, 、 and They represent the prediction results of the 1st, 2nd and k+1th subsequences respectively.

[0113] In some embodiments of the present invention, step S104 includes:

[0114] According to the target prediction results and the second temperature-vibration data, the pipe diameter change trend value is obtained;

[0115] The blockage of mine filling pipelines is monitored based on the trend value of pipe diameter changes.

[0116] In a specific embodiment of the present invention, the change in the diameter of the filling pipe in the future can be predicted, including: recording the obtained target prediction result as S 0, the temperature vibration data obtained by setting the interval time t is S t , that is, the second temperature vibration data at the previous moment is S t .pass D p =S 0 / S t To indicate the change trend of pipe diameter, the blockage of mine filling pipeline can be monitored according to the change trend.

[0117] In some embodiments of the present invention, Figure 3 As shown, the blockage of mine filling pipelines is monitored based on the trend value of pipe diameter changes, including:

[0118] S301. Setting a classification level for the pipeline transportation status of the mine filling pipeline;

[0119] S302, determining a target level based on the pipeline transportation status classification level and the pipeline diameter change trend value;

[0120] S303. Monitor the blockage of the mine filling pipeline according to the target level.

[0121] In a specific embodiment of the present invention, the pipeline transportation status classification level of the mine filling pipeline can be set, for example, level one, level two and level three, where level one represents a blocked state, level two represents a blocked state, and level three represents a normal state, wherein a value greater than 1.2 is level three, between 1.2 and 1.5 is level two, and greater than 1.5 is level one. The specific pipeline transportation status classification level and the corresponding value of each level can be set according to the actual situation, and the embodiment of the present invention is not limited here. Then, the calculated pipe diameter change trend value can be used to calculate the pipeline transportation status classification level. D p , determine the target level in the pipeline transportation status classification level, when D p When the value is less than 1.2, the pipe diameter is normal; when D p When the value is between 1.2 and 1.5, it is classified as Level 2, warning state; when D pWhen the value is greater than 1.5, it is classified as level 1, indicating a blocked state. Based on the pipeline temperature and vibration prediction model, the pipe diameter change level is predicted and the predicted status level is sent to the terminal, completing the filling pipeline temperature and vibration monitoring and pipe blockage warning.

[0122] The embodiment of the present invention obtains the time series of temperature vibration data by installing a temperature vibration sensor on the filling pipe, decomposes the temperature vibration data using the VMD data decomposition method, and solves the optimal K and α This approach expands the scope of the prediction algorithm. The resulting data sequences are fed into the LSTM prediction model, which performs a secondary decomposition of the residual sequence. A combined prediction model optimized by the Crow algorithm is then built for the decomposed subsequences, significantly improving the accuracy of the prediction results. The prediction results enable quantitative characterization of pipeline diameter changes, creating emergency processing time for blockages and providing a new approach to mine pipeline monitoring.

[0123] In order to better implement the mine filling pipeline blockage monitoring method in the embodiment of the present invention, based on the mine filling pipeline blockage monitoring method, the embodiment of the present invention also provides a mine filling pipeline blockage monitoring device, such as Figure 4 As shown, the mine filling pipeline blockage monitoring device 400 includes:

[0124] The data acquisition module 401 is used to acquire the first temperature and vibration data of the mine filling pipeline at the current moment and the second temperature and vibration data at the previous moment;

[0125] A data prediction module 402 is configured to process the first temperature-vibration data to obtain a plurality of initial prediction results;

[0126] The result prediction module 403 is used to obtain a target prediction result based on multiple initial prediction results;

[0127] The pipeline monitoring module 404 is used to monitor the blockage of the mine filling pipeline according to the target prediction result and the second temperature and vibration data.

[0128] The mine filling pipeline blockage monitoring device 400 provided in the above embodiment can implement the technical solution described in the above mine filling pipeline blockage monitoring method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above mine filling pipeline blockage monitoring method embodiment, which will not be repeated here.

[0129] like Figure 5 As shown, the present invention also provides an electronic device 500. The electronic device 500 includes a processor 501, a memory 502 and a display 503. Figure 5Only some of the components of the electronic device 500 are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0130] In some embodiments, the memory 502 may be an internal storage unit of the electronic device 500, such as a hard disk or memory of the electronic device 500. In other embodiments, the memory 502 may also be an external storage device of the electronic device 500, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 500.

[0131] Furthermore, the memory 502 may include both an internal storage unit of the electronic device 500 and an external storage device. The memory 502 is used to store application software installed in the electronic device 500 and various data.

[0132] In some embodiments, the processor 501 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 502, such as the mine filling pipeline blockage monitoring method of the present invention.

[0133] In some embodiments, display 503 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 503 is used to display information about electronic device 500 and to display a visual user interface. Components 501-503 of electronic device 500 communicate with each other via a system bus.

[0134] In some embodiments of the present invention, when the processor 501 executes the mine filling pipeline blockage monitoring program in the memory 502, the following steps may be implemented:

[0135] Obtaining first temperature and vibration data of the mine filling pipeline at the current moment and second temperature and vibration data at the previous moment;

[0136] Processing the first temperature-vibration data to obtain a plurality of initial prediction results;

[0137] According to multiple initial prediction results, a target prediction result is obtained;

[0138] The blockage of the mine filling pipeline is monitored based on the target prediction results and the second temperature vibration data.

[0139] It should be understood that, when the processor 501 executes the mine filling pipeline blockage monitoring program in the memory 502 , in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.

[0140] Furthermore, the embodiments of the present invention do not specifically limit the type of electronic device 500 mentioned. The electronic device 500 may be a portable electronic device such as a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, or laptop computer. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The portable electronic devices mentioned above may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 500 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0141] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by the processor, the steps or functions of the mine filling pipeline blockage monitoring method provided by the above-mentioned method embodiments can be implemented.

[0142] Those skilled in the art will appreciate that all or part of the process flow of the above-described method embodiment can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0143] The above is a detailed introduction to the mine filling pipeline blockage monitoring method and device provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for monitoring blockage of a mine filling pipeline, characterized in that: include: Obtaining first temperature and vibration data of the mine filling pipeline at the current moment and second temperature and vibration data at the previous moment; processing the first temperature-vibration data to obtain a plurality of initial prediction results; Obtaining a target prediction result based on the multiple initial prediction results; The blockage condition of the mine filling pipeline is monitored according to the target prediction result and the second temperature and vibration data.

2. The mine filling pipeline blockage monitoring method according to claim 1 is characterized in that: The processing of the first temperature-vibration data to obtain a plurality of initial prediction results includes: Decomposing the first temperature-vibration data to obtain multiple sequences; Each sequence is predicted separately to obtain multiple initial prediction results.

3. The mine filling pipeline blockage monitoring method according to claim 2, characterized in that: The multiple sequences include a residual sequence and k modal component IMF sequences. The decomposition of the first temperature-vibration data to obtain multiple sequences includes: Based on the preset optimization algorithm, the core parameters of the VMD decomposition algorithm, the modal number k value and the penalty factor, are optimized to obtain the target VMD decomposition algorithm; The first temperature-vibration data is decomposed based on the target VMD decomposition algorithm to obtain k modal component IMF sequences and a residual sequence.

4. The mine filling pipeline blockage monitoring method according to claim 3 is characterized in that: The multiple initial prediction results include multiple subsequence prediction results and residual prediction results. The prediction process is performed on each sequence respectively to obtain multiple initial prediction results, including: Processing each of the k modal component IMF sequences respectively to obtain multiple subsequence prediction results; The residual sequence is decomposed twice to obtain a residual prediction result.

5. The mine filling pipeline blockage monitoring method according to claim 4 is characterized in that: The performing secondary decomposition on the residual sequence to obtain a residual prediction result includes: Performing secondary decomposition on the residual sequence based on a preset number of neural networks to obtain a residual subsequence corresponding to each neural network; Combining a preset number of residual subsequences to obtain a combined prediction model; The combined prediction model is calculated according to the preset number of residual subsequences to obtain a residual prediction result.

6. The method for monitoring blockage of a mine filling pipeline according to claim 4, characterized in that: Obtaining a target prediction result based on the multiple initial prediction results includes: The multiple subsequence prediction results and the residual prediction result are superimposed and reconstructed to obtain a target prediction result.

7. The method for monitoring blockage of a mine filling pipeline according to claim 1, characterized in that: The monitoring of the blockage condition of the mine filling pipeline according to the target prediction result and the second temperature and vibration data includes: Obtaining a pipe diameter change trend value according to the target prediction result and the second temperature-vibration data; The blockage condition of the mine filling pipeline is monitored according to the pipe diameter change trend value.

8. The method for monitoring blockage of a mine filling pipeline according to claim 7, characterized in that: The monitoring of the blockage condition of the mine filling pipeline according to the pipe diameter change trend value includes: Setting a classification level for the pipeline transportation status of the mine filling pipeline; Determining a target level according to the pipeline transportation status classification level and the pipeline diameter change trend value; The blockage of the mine filling pipeline is monitored according to the target level.

9. The method for monitoring blockage of a mine filling pipeline according to claim 5, characterized in that: The combining of the preset number of residual subsequences to obtain a combined prediction model includes: Combining a preset number of residual subsequences to obtain an initial combined prediction model; The initial combined prediction model is optimized based on the crow search algorithm to obtain a combined prediction model.

10. A mine filling pipeline blockage monitoring device, characterized in that: include: A data acquisition module, used to acquire first temperature and vibration data of the mine filling pipeline at the current moment and second temperature and vibration data at the previous moment; a data prediction module, configured to process the first temperature-vibration data to obtain a plurality of initial prediction results; A result prediction module, configured to obtain a target prediction result based on the multiple initial prediction results; A pipeline monitoring module is used to monitor the blockage of the mine filling pipeline according to the target prediction result and the second temperature and vibration data.

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