Remote control method and system for underground filling equipment

Abstract

The invention discloses a remote control method and system for underground filling equipment, and relates to the technical field of equipment remote control, and the method comprises the steps: monitoring and collecting pipeline pressure data in real time through a pressure sensor, and monitoring and collecting a filling material flow value in real time; a data processing and analysis algorithm based on artificial intelligence and deep learning is introduced into a remote control server to carry out time sequence analysis and information propagation aggregation representation on the pipeline pressure time sequence data and the filling material flow time sequence data; then, core component fusion semantics between pipeline pressure time sequence propagation aggregation characteristics and filling material flow time sequence propagation aggregation characteristics are used for conducting filling rate recommendation at the next time point, and a variable frequency driver and a servo motor are used for achieving accurate control over a filling and pumping system. And more intelligent remote control of underground filling equipment can be realized, so that the material filling rate is adaptively controlled based on the time sequence collaborative change condition between the pipeline pressure and the filling material flow.

Description

Technical Field

[0001] The present application relates to the technical field of remote control of equipment, and in particular, to a remote control method and system for underground filling equipment. Background Art

[0002] With the continuous pursuit of safety and efficiency in the mining industry, underground filling operations have gradually become an important part of modern mining. Filling operations can not only effectively improve the resource utilization rate of mines, but also significantly reduce the risk of geological disasters caused by mine voids. In underground mine filling operations, tailings or other filling materials need to be pumped into the goaf to maintain surface stability, control surrounding rock deformation, and recover valuable resources. This process requires precise control of the delivery rate of filling materials to ensure the quality and stability of the filling body.

[0003] However, traditional filling operations usually rely on the experience and manual adjustments of on-site operators. The experience and judgment of operators directly affect the quality of the filling process. Different operators may adopt different operating strategies under the same conditions, resulting in inconsistent and uneven filling results. In addition, the manual adjustment of the filling process cannot respond to changes in on-site conditions in a timely manner. For example, excessive pressure caused by too fast a filling rate may cause a pipeline rupture, and when abnormal pressure or unstable flow occurs, the operator may not be able to respond quickly, increasing safety risks.

[0004] Therefore, an optimized underground filling control solution is needed to solve the above technical problems. Summary of the invention

[0005] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] In a first aspect, the present application provides a remote control method for underground filling equipment, the method comprising: Obtaining a time queue of pipeline pressure and a time queue of filling material flow values ​​collected by a pressure sensor; Transmitting the time queue of the pipeline pressure and the time queue of the filling material flow value to the remote control server through the wireless communication module; On the remote control server, locally time-series encoding is performed on the time queue of the pipeline pressure and the time queue of the filling material flow value to obtain a sequence of local time-series associated feature vectors of the pipeline pressure and a sequence of local time-series associated feature vectors of the filling material flow; Inputting the sequence of the pipeline pressure local time series associated feature vectors and the sequence of the filling material flow local time series associated feature vectors into a feature forward propagation network based on significant time series attenuation of node energy to obtain a pipeline pressure time series propagation representation vector and a filling material flow time series propagation representation vector; Inputting the pipeline pressure time series propagation representation vector and the filling material flow rate time series propagation representation vector into a query matching interaction network based on characteristic principal components to obtain a pipeline pressure-filling material flow rate core component significant fusion representation vector; The pipeline pressure-filling material flow rate core component significant fusion representation vector is input into a decoder-based remote control instruction generator to obtain a remote operation instruction, and the remote operation instruction includes a recommended decoded value of the filling rate at the next time point.

[0007] Optionally, in the remote control server, the time queue of the pipeline pressure and the time queue of the filling material flow value are locally time-series encoded respectively to obtain a sequence of local time-series associated feature vectors of pipeline pressure and a sequence of local time-series associated feature vectors of filling material flow, including: in the remote control server, the time queue of the pipeline pressure and the time queue of the filling material flow value are input into a sequence encoder based on the 1D-CNN model to obtain a sequence of local time-series associated feature vectors of pipeline pressure and a sequence of local time-series associated feature vectors of filling material flow.

[0008] Optionally, the sequence of the pipeline pressure local time series associated feature vectors and the sequence of the filling material flow local time series associated feature vectors are input into a feature forward propagation network based on node energy significant time series attenuation to obtain the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector, including: based on the mean and standard deviation of each pipeline pressure local time series associated feature vector in the sequence of the pipeline pressure local time series associated feature vectors, calculating the energy significant descriptor of each pipeline pressure local time series associated feature vector to obtain a sequence of pipeline pressure local time series energy significant descriptors; extracting the timestamp of each pipeline pressure local time series associated feature vector in the sequence of the pipeline pressure local time series associated feature vector to obtain a sequence of pipeline pressure timestamps; using the pipeline pressure local time series associated feature vector corresponding to the current time point in the sequence of the pipeline pressure local time series associated feature vector as the current pipeline pressure local time series associated feature vector and using the pipeline pressure local time series associated feature vectors corresponding to other time points as the historical pipeline pressure local time series associated feature vectors to obtain the current time series associated feature vector. a sequence of previous pipeline pressure local time series associated feature vectors and historical pipeline pressure local time series associated feature vectors; calculating the energy time decay factor of each historical pipeline pressure local time series associated feature vector in the sequence of historical pipeline pressure local time series associated feature vectors relative to the current pipeline pressure local time series associated feature vector to obtain a sequence of pipeline pressure local time series energy time decay factors; calculating the positional division of the sequence of pipeline pressure local time series energy significant descriptors and the sequence of pipeline pressure local time series energy time decay factors, using the obtained pipeline pressure local time series energy weight as a weighting coefficient, calculating the positional weighted sum of the sequence of historical pipeline pressure local time series associated feature vectors to obtain a pipeline pressure local time series historical node propagation aggregation representation feature vector; based on the energy significant descriptor corresponding to the current pipeline pressure local time series associated feature vector, calculating the positional weighted sum of the pipeline pressure local time series historical node propagation aggregation representation feature vector and the current pipeline pressure local time series associated feature vector to obtain the pipeline pressure time series propagation aggregation representation vector.

[0009] Optionally, based on the mean and standard deviation of each pipeline pressure local time series associated feature vector in the sequence of pipeline pressure local time series associated feature vectors, the energy significant descriptors of each pipeline pressure local time series associated feature vector are calculated to obtain a sequence of pipeline pressure local time series energy significant descriptors, including: calculating the mean and standard deviation of each pipeline pressure local time series associated feature vector in the sequence of pipeline pressure local time series associated feature vectors to obtain a sequence of pipeline pressure local means and a sequence of pipeline pressure local standard deviations; performing positional differentiation on each group of corresponding pipeline pressure local time series associated feature vectors and pipeline pressure local means in the sequence of pipeline pressure local time series associated feature vectors and the sequence of pipeline pressure local means, and then performing positional differentiation on the obtained differential features. Each position characteristic value in the sequence of vectors is modulated to the fourth power to obtain a sequence of pipeline pressure local timing modulation characteristic vectors; the expected value of each pipeline pressure local timing modulation characteristic vector in the sequence of pipeline pressure local timing modulation characteristic vectors is calculated to obtain a sequence of pipeline pressure local timing positive expected factors; the fourth power modulation value of each pipeline pressure local standard deviation in the sequence of pipeline pressure local standard deviations is calculated to obtain a sequence of pipeline pressure local timing inverse expected factors; each group of corresponding pipeline pressure local timing positive expected factors and pipeline pressure local timing inverse expected factors in the sequence of pipeline pressure local timing positive expected factors and the sequence of pipeline pressure local timing inverse expected factors are divided to obtain a sequence of pipeline pressure local timing energy significant descriptors.

[0010] Optionally, the energy time decay factor of each historical pipeline pressure local time series associated feature vector in the sequence of historical pipeline pressure local time series associated feature vectors relative to the current pipeline pressure local time series associated feature vector is calculated to obtain a sequence of pipeline pressure local time series energy time decay factors, including: respectively calculating the pipeline pressure timestamp corresponding to the current pipeline pressure local time series associated feature vector and the pipeline pressure timestamp corresponding to each historical pipeline pressure local time series associated feature vector, subtracting them from each other and rounding them down to obtain a sequence of time span values; after calculating the square of each time span value in the sequence of time span values, dividing the obtained sequence of span square modulation values ​​by the square of the time attenuation inverse scaling parameter by position and multiplying them by the attenuation rate positive scaling parameter by position to obtain a sequence of time series span energy decay coefficients; using each time series span energy decay coefficient in the sequence of time series span energy decay coefficients as an exponential power, calculating a natural exponential function value with the natural constant e as the base to obtain the sequence of pipeline pressure local time series energy time decay factors.

[0011] Optionally, the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector are input into a query matching interaction network based on characteristic principal components to obtain a pipeline pressure-filling material flow core component significant fusion representation vector, including: standardizing the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector to obtain a standardized pipeline pressure time series propagation representation vector and a standardized filling material flow time series propagation representation vector; calculating the sample covariance matrix of the standardized pipeline pressure time series propagation representation vector and the standardized filling material flow time series propagation representation vector to obtain a pipeline pressure time series sample covariance matrix and a filling material flow time series sample covariance matrix. The invention discloses a sample covariance matrix; extracting eigenvectors based on matrix decomposition for the pipeline pressure time series sample covariance matrix and the filling material flow time series sample covariance matrix to obtain a set of pipeline pressure time series principal component eigenvectors and a set of filling material flow time series principal component eigenvectors; inputting the set of pipeline pressure time series principal component eigenvectors and the set of filling material flow time series principal component eigenvectors into a maximum approximate query matching network to obtain a set of best matching pairs of pipeline pressure time series principal component eigenvectors and filling material flow time series principal component eigenvectors; and extracting eigenvectors based on matrix decomposition for the pipeline pressure time series sample covariance matrix and the filling material flow time series sample covariance matrix to obtain a set of best matching pairs of pipeline pressure time series principal component eigenvectors and filling material flow time series principal component eigenvectors. The best matching pairs of each pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector are input into the semantic fine-grained gated joint module to obtain a set of pipeline pressure-filling material flow time series principal component fusion feature vectors; each pipeline pressure-filling material flow time series principal component fusion feature vector in the set of pipeline pressure-filling material flow time series principal component fusion feature vectors is cascaded to obtain the pipeline pressure-filling material flow core component significant fusion representation vector; wherein, the sample covariance matrix of the standardized pipeline pressure time series propagation representation vector and the standardized filling material flow time series propagation representation vector is calculated to obtain the pipeline pressure time series sample covariance matrix and The filling material flow time series sample covariance matrix includes: calculating the vector multiplication between the transposed vector of the standardized pipeline pressure time series propagation representation vector and the standardized pipeline pressure time series propagation representation vector, and then dividing it by the number of eigenvalues ​​of the standardized pipeline pressure time series propagation representation vector minus one by position point to obtain the pipeline pressure time series sample covariance matrix; calculating the vector multiplication between the transposed vector of the standardized filling material flow time series propagation representation vector and the standardized filling material flow time series propagation representation vector, and then dividing it by the number of eigenvalues ​​of the standardized filling material flow time series propagation representation vector minus one by position point to obtain the filling material flow time series sample covariance matrix.

[0012] Optionally, the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector are standardized to obtain standardized pipeline pressure time series propagation representation vector and standardized filling material flow time series propagation representation vector, including: calculating the mean and standard deviation of the pipeline pressure time series propagation representation vector to obtain the pipeline pressure time series mean and the pipeline pressure time series standard deviation; calculating the position difference between the pipeline pressure time series propagation representation vector and the pipeline pressure time series mean, and then dividing it by the pipeline pressure time series standard deviation to obtain the standardized pipeline pressure time series propagation representation vector; calculating the mean and standard deviation of the filling material flow time series propagation representation vector to obtain the filling material flow time series mean and the filling material flow time series standard deviation; calculating the position difference between the filling material flow time series propagation representation vector and the filling material flow time series mean, and then dividing it by the filling material flow time series standard deviation to obtain the standardized filling material flow time series propagation representation vector.

[0013] Optionally, the set of the pipeline pressure time series principal component feature vectors and the set of the filling material flow time series principal component feature vectors are input into a maximum approximate query matching network to obtain a set of best matching pairs of the pipeline pressure time series principal component feature vectors and the filling material flow time series principal component feature vectors, including: taking the pipeline pressure time series principal component feature vector at a predetermined position in the set of the pipeline pressure time series principal component feature vectors to obtain a predetermined pipeline pressure time series principal component feature vector; calculating the inner product between the predetermined pipeline pressure time series principal component feature vector and each filling material flow time series principal component feature vector in the set of the filling material flow time series principal component feature vector to obtain a sequence of pipeline pressure-filling material flow inner product values; calculating a norm of the predetermined pipeline pressure time series principal component feature vector to obtain A norm of a principal component characteristic of a predetermined pipeline pressure time series is obtained; a norm of each filling material flow time series principal component characteristic vector in the set of the filling material flow time series principal component characteristic vectors is calculated to obtain a sequence of the principal component characteristic norm of the filling material flow time series; the predetermined pipeline pressure time series principal component characteristic norm and the sequence of the filling material flow time series principal component characteristic norm are multiplied by the position point to obtain a sequence of pipeline pressure-filling material flow semantic interaction values; after calculating the position point division between the sequence of pipeline pressure-filling material flow inner product values ​​and the sequence of pipeline pressure-filling material flow semantic interaction values, the filling material flow time series principal component characteristic vector corresponding to the maximum eigenvalue in the obtained sequence of eigenvalues ​​and the predetermined pipeline pressure time series principal component characteristic vector form a set of best matching pairs.

[0014] Optionally, the best matching pairs of each pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector in the set of the best matching pairs of the pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector are input into the semantic fine-grained gated joint module to obtain a set of pipeline pressure-filling material flow time series principal component fusion feature vectors, including: performing position difference, position dot multiplication and position summation processing on the best matching pairs of each pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector to obtain a set of pipeline pressure-filling material flow time series principal component difference feature vectors, a set of pipeline pressure-filling material flow time series principal component dot product feature vectors and a set of pipeline pressure-filling material flow time series principal component fusion feature vectors. The invention relates to a method for obtaining a set of principal component sum feature vectors of the pipeline pressure-filling material flow time series; a set of principal component difference feature vectors of the pipeline pressure-filling material flow time series, a set of principal component dot product feature vectors of the pipeline pressure-filling material flow time series and a set of principal component sum feature vectors of the pipeline pressure-filling material flow time series are cascaded by position to obtain a set of multi-dimensional fusion expression vectors of the principal component of the pipeline pressure-filling material flow time series; each multi-dimensional fusion expression vector of the principal component of the pipeline pressure-filling material flow time series in the set of multi-dimensional fusion expression vectors of the principal component of the pipeline pressure-filling material flow time series is processed by a one-dimensional convolution layer and then by a maximum pooling layer to obtain a set of principal component fusion feature vectors of the pipeline pressure-filling material flow time series.

[0015] In a second aspect, the present application provides a remote control system for underground filling equipment, the system comprising: A data acquisition module, used to acquire a time queue of pipeline pressure and a time queue of filling material flow value collected by a pressure sensor; A data transmission module, used for transmitting the time queue of the pipeline pressure and the time queue of the filling material flow value to the remote control server through a wireless communication module; A local time series encoding module is used to perform local time series encoding on the time series of the pipeline pressure and the time series of the filling material flow value on the remote control server to obtain a sequence of local time series associated feature vectors of the pipeline pressure and a sequence of local time series associated feature vectors of the filling material flow; A feature forward propagation module, used for inputting the sequence of the pipeline pressure local time series associated feature vectors and the sequence of the filling material flow local time series associated feature vectors into a feature forward propagation network based on significant time series attenuation of node energy to obtain a pipeline pressure time series propagation representation vector and a filling material flow time series propagation representation vector; A significant fusion module, used for inputting the pipeline pressure time series propagation representation vector and the filling material flow rate time series propagation representation vector into a query matching interaction network based on characteristic principal components to obtain a pipeline pressure-filling material flow rate core component significant fusion representation vector; A remote operation instruction determination module is used to input the pipeline pressure-filling material flow core component significant fusion representation vector into a decoder-based remote control instruction generator to obtain a remote operation instruction, wherein the remote operation instruction includes a recommended decoded value of the filling rate at the next time point.

[0016] This application has at least the following technical effects: Compared with the prior art, the present application uses a pressure sensor to monitor and collect pipeline pressure data in real time, and monitor and collect filling material flow values ​​in real time, and introduces a data processing and analysis algorithm based on artificial intelligence and deep learning in the remote control server to perform time series analysis and information propagation aggregation representation on the pipeline pressure time series data and the filling material flow time series data, and then uses the core component fusion semantics between the pipeline pressure time series propagation aggregation characteristics and the filling material flow time series propagation aggregation characteristics to recommend the filling rate at the next time point, and adopts variable frequency drives and servo motors to achieve precise control of the filling pumping system, so as to adaptively control the material filling rate based on the time series coordinated changes between the pipeline pressure and the filling material flow, and realize more intelligent remote control of underground filling equipment, thereby avoiding the problems of excessive pressure and uneven filling caused by unstable filling rate caused by traditional manual operation, and improving the safety and efficiency of the overall underground filling operation.

[0017] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale. In the drawings: Figure 1 The present invention is a flow chart of a remote control method of underground filling equipment according to an exemplary embodiment.

[0019] Figure 2 It is a block diagram of a remote control system for underground filling equipment according to an exemplary embodiment. DETAILED DESCRIPTION

[0020] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0021] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0022] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0023] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0024] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0025] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0026] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0027] It should be noted that the acquisition and processing of all information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0028] It should be understood that filling rate control is an important part of underground mine filling operations. A reasonable filling rate can not only ensure the quality of the filling body, but also effectively improve the efficiency of the filling operation, while reducing resource waste and potential safety risks. Specifically, an appropriate filling rate helps to ensure that the filling material is evenly distributed, avoiding voids or overcrowded areas, thereby improving the overall stability and bearing capacity of the filling body. In addition, reasonable control of the filling rate helps prevent accidents, such as pipe rupture caused by excessive pressure due to excessive filling rate.

[0029] Based on this, in the technical solution of the present application, a remote control method for underground filling equipment is proposed, which can monitor and collect pipeline pressure data in real time through a pressure sensor, and monitor and collect filling material flow value in real time, and then transmit the collected pipeline pressure time series data and filling material flow time series data to a remote control server through a wireless communication module, so as to introduce a data processing and analysis algorithm based on artificial intelligence and deep learning in the remote control server to perform time series analysis and information propagation aggregation representation on the pipeline pressure time series data and the filling material flow time series data, and then use the core component fusion semantics between the pipeline pressure time series propagation aggregation feature and the filling material flow time series propagation aggregation feature to recommend the filling rate at the next time point, and use a variable frequency drive and a servo motor to achieve precise control of the filling pumping system to achieve the required recommended filling rate, and can use the wireless communication module and the remote control server to realize more intelligent remote control of underground filling equipment, so as to adaptively control the material filling rate based on the time series coordinated changes between the pipeline pressure and the filling material flow, thereby avoiding the problems of excessive pressure and uneven filling caused by the unstable filling rate caused by the traditional manual operation method, and improving the safety and efficiency of the overall underground filling operation.

[0030] Figure 1 is a flow chart of a remote control method for underground filling equipment according to an exemplary embodiment. Figure 1 As shown, the method includes: Step S101, obtaining a time queue of pipeline pressure and a time queue of filling material flow value collected by a pressure sensor; Step S102, transmitting the time queue of the pipeline pressure and the time queue of the filling material flow value to the remote control server through the wireless communication module; Step S103: On the remote control server, locally time-series encoding is performed on the time queue of the pipeline pressure and the time queue of the filling material flow value to obtain a sequence of local time-series associated feature vectors of the pipeline pressure and a sequence of local time-series associated feature vectors of the filling material flow; Step S104, inputting the sequence of the pipeline pressure local time series associated feature vectors and the sequence of the filling material flow local time series associated feature vectors into a feature forward propagation network based on significant time series attenuation of node energy to obtain a pipeline pressure time series propagation representation vector and a filling material flow time series propagation representation vector; Step S105, inputting the pipeline pressure time series propagation representation vector and the filling material flow rate time series propagation representation vector into a query matching interaction network based on characteristic principal components to obtain a pipeline pressure-filling material flow rate core component significant fusion representation vector; Step S106: input the pipeline pressure-filling material flow rate core component significant fusion representation vector into a decoder-based remote control instruction generator to obtain a remote operation instruction, wherein the remote operation instruction includes a recommended decoded value of the filling rate at the next time point.

[0031] Specifically, in the technical solution of the present application, first, the time queue of the pipeline pressure and the time queue of the filling material flow value collected by the pressure sensor are obtained, and the time queue of the pipeline pressure and the time queue of the filling material flow value are transmitted to the remote control server through the wireless communication module.

[0032] In the remote control server, the time queue of the pipeline pressure and the time queue of the filling material flow value are input into a sequence encoder based on the 1D-CNN model for local temporal encoding, so as to respectively extract the local temporal correlation feature information of the pipeline pressure and the filling material flow in the time dimension, thereby obtaining a sequence of local temporal correlation feature vectors of the pipeline pressure and a sequence of local temporal correlation feature vectors of the filling material flow.

[0033] In one embodiment of the present application, in the remote control server, local time series encoding is performed on the time queue of the pipeline pressure and the time queue of the filling material flow value, respectively, to obtain a sequence of local time series correlation feature vectors of the pipeline pressure and a sequence of local time series correlation feature vectors of the filling material flow, including: in the remote control server, the time queue of the pipeline pressure and the time queue of the filling material flow value are input into a sequence encoder based on a 1D-CNN model to obtain a sequence of local time series correlation feature vectors of the pipeline pressure and a sequence of local time series correlation feature vectors of the filling material flow.

[0034] Then, in the sequence of the pipeline pressure local time series associated feature vectors, considering that each pipeline pressure local time series associated feature vector contains the local time series feature information of the pipeline pressure in the local time period, these pipeline pressure local time series semantics have a correlation relationship and interaction based on the global time series, and this influence will decay over time. Therefore, in order to more comprehensively and accurately characterize the current pressure state of the pipeline, so as to provide a basis for the subsequent inference of the filling rate, it is necessary to utilize the association characteristics and attenuation importance influence between the local time series semantics of each pipeline pressure. Based on this, in the technical solution of the present application, the sequence of the pipeline pressure local time series associated feature vectors is further input into the feature forward propagation network based on the significant time series attenuation of node energy to obtain the pipeline pressure time series propagation representation vector. In particular, the feature forward propagation network based on the significant time series attenuation of node energy can focus on aggregating the local time series features of each pipeline pressure through the significance and time series attenuation characteristics of node energy to generate a propagation aggregation representation vector. Specifically, in the feature forward propagation network based on the significant time series attenuation of node energy, the evaluation of node energy significance can help the network determine which local time period pipeline pressure features are more important. For the filling process, the pipeline pressure will continue to change with the passage of time, and the local time series semantics of the pipeline pressure closer to the current time period will be more important for the aggregation representation of the pipeline pressure time series information and the subsequent pressure speculation. Therefore, through the evaluation of energy significance and the consideration of time series attenuation, the network can finely adjust the feature weight of each pipeline pressure local time series associated feature vector, strengthen the influence of the pipeline pressure local time series characteristics of the key nodes, and properly consider the time factor. The network can learn the law of pipeline pressure changes in the filling process more quickly, which helps to strengthen the semantic expression of key nodes in the process of local time series semantic aggregation of pipeline pressure, and has a significant effect on improving the accuracy of the model for time series prediction, that is, it can predict the next pipeline pressure change, which is helpful for the subsequent filling rate speculation and control at the next time point. Correspondingly, the process of processing the sequence of the filling material flow local time series associated feature vector to obtain the filling material flow time series propagation representation vector is the same as the above method.

[0035] In one embodiment of the present application, the sequence of the pipeline pressure local time series association feature vectors and the sequence of the filling material flow local time series association feature vectors are input into a feature forward propagation network based on node energy significant time series attenuation to obtain a pipeline pressure time series propagation representation vector and a filling material flow time series propagation representation vector, including: based on the mean and standard deviation of each pipeline pressure local time series association feature vector in the sequence of the pipeline pressure local time series association feature vectors, calculating the energy significant descriptor of each pipeline pressure local time series association feature vector to obtain a sequence of pipeline pressure local time series energy significant descriptors; extracting the timestamp of each pipeline pressure local time series association feature vector in the sequence of the pipeline pressure local time series association feature vector to obtain a sequence of pipeline pressure timestamps; using the pipeline pressure local time series association feature vector corresponding to the current time point in the sequence of the pipeline pressure local time series association feature vector as the current pipeline pressure local time series association feature vector and using the pipeline pressure local time series association feature vectors corresponding to other time points as the historical pipeline pressure local time series association feature vectors To obtain a sequence of current pipeline pressure local time series correlation feature vectors and historical pipeline pressure local time series correlation feature vectors; calculate the energy time decay factor of each historical pipeline pressure local time series correlation feature vector in the sequence of historical pipeline pressure local time series correlation feature vectors relative to the current pipeline pressure local time series correlation feature vector to obtain a sequence of pipeline pressure local time series energy time decay factors; calculate the positional division of the sequence of pipeline pressure local time series energy significant descriptors and the sequence of pipeline pressure local time series energy time decay factors, use the obtained pipeline pressure local time series energy weight as a weighting coefficient, calculate the positional weighted sum of the sequence of historical pipeline pressure local time series correlation feature vectors to obtain the pipeline pressure local time series historical node propagation aggregation representation feature vector; based on the energy significant descriptor corresponding to the current pipeline pressure local time series correlation feature vector, calculate the positional weighted sum of the pipeline pressure local time series historical node propagation aggregation representation feature vector and the current pipeline pressure local time series correlation feature vector to obtain the pipeline pressure time series propagation aggregation representation vector.

[0036] Further, in one embodiment of the present application, based on the mean and standard deviation of each pipeline pressure local time series associated feature vector in the sequence of the pipeline pressure local time series associated feature vector, the energy significant descriptor of each pipeline pressure local time series associated feature vector is calculated to obtain a sequence of pipeline pressure local time series energy significant descriptors, including: calculating the mean and standard deviation of each pipeline pressure local time series associated feature vector in the sequence of the pipeline pressure local time series associated feature vector to obtain a sequence of pipeline pressure local means and a sequence of pipeline pressure local standard deviations; performing positional differences on each group of corresponding pipeline pressure local time series associated feature vectors and pipeline pressure local means in the sequence of the pipeline pressure local time series associated feature vector and the sequence of the pipeline pressure local means, and then performing positional differences on each group of corresponding pipeline pressure local time series associated feature vectors and ... Each position characteristic value in the obtained sequence of differential characteristic vectors is modulated to the fourth power to obtain a sequence of pipeline pressure local timing modulation characteristic vectors; the expected value of each pipeline pressure local timing modulation characteristic vector in the sequence of pipeline pressure local timing modulation characteristic vectors is calculated to obtain a sequence of pipeline pressure local timing positive expected factors; the fourth power modulation value of each pipeline pressure local standard deviation in the sequence of pipeline pressure local standard deviations is calculated to obtain a sequence of pipeline pressure local timing inverse expected factors; each group of corresponding pipeline pressure local timing positive expected factors and pipeline pressure local timing inverse expected factors in the sequence of pipeline pressure local timing positive expected factors and the sequence of pipeline pressure local timing inverse expected factors are divided to obtain a sequence of pipeline pressure local timing energy significant descriptors.

[0037] Furthermore, in one embodiment of the present application, the energy time decay factor of each historical pipeline pressure local time series associated feature vector in the sequence of the historical pipeline pressure local time series associated feature vector is calculated relative to the current pipeline pressure local time series associated feature vector to obtain a sequence of pipeline pressure local time series energy time decay factors, including: respectively calculating the pipeline pressure timestamp corresponding to the current pipeline pressure local time series associated feature vector and the pipeline pressure timestamp corresponding to each historical pipeline pressure local time series associated feature vector, subtracting them from each other and rounding them down to obtain a sequence of time span values; after calculating the square of each time span value in the sequence of time span values, dividing the obtained sequence of span square modulation values ​​by the square of the time attenuation inverse scaling parameter by position and multiplying them by the attenuation rate positive scaling parameter by position to obtain a sequence of time series span energy decay coefficients; using each time series span energy decay coefficient in the sequence of time series span energy decay coefficients as an exponential power, calculating a natural exponential function value with the natural constant e as the base to obtain the sequence of pipeline pressure local time series energy time decay factors.

[0038] Specifically, the sequence of the pipeline pressure local time series associated feature vectors is input into the feature forward propagation network based on the significant time series attenuation of node energy, and processed by the following node feature propagation formula to obtain the pipeline pressure time series propagation representation vector; Among them, the node feature propagation formula is: in, is the sequence of local time series correlation feature vectors of the pipeline pressure, and are respectively the first, second, ... ( ) and The local time series correlation feature vector of pipeline pressure, is the first in the sequence of the pipeline pressure local time series correlation feature vector The local time series correlation feature vector of pipeline pressure, For the The local time series correlation feature vector of pipeline pressure Position feature values, For the The length of the local time series correlation feature vector of the pipeline pressure, For the The mean of the local time series correlation eigenvectors of pipeline pressure, For the The standard deviation of the local time series correlation feature vector of pipeline pressure, represents the calculation of expected value, It is The energy significant descriptor of the local temporal correlation feature vector of pipeline pressure, represents the exponential function value with the natural constant e as the base, is a positive scaling parameter that controls the decay rate, is the descaling parameter that controls the time decay period, and Respectively represent and The timestamp of the local time series correlation feature vector of the pipeline pressure, Indicates a round-down operation. For the The energy significant descriptor of the local temporal correlation feature vector of pipeline pressure, and is the trainable weight hyperparameter, is a vector representing the pipeline pressure time series propagation.

[0039] It should be understood that the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector respectively contain the full time domain propagation aggregation representation information of the local time series characteristics of the pipeline pressure and the local time series characteristics of the filling material flow, and this information also contains the time series attenuation information and reasoning information of the local time series characteristics of the pipeline pressure and the filling material flow in the time series, which is conducive to the inference of the filling rate at the next time point. In addition, since there is an implicit correlation and mutual influence between the pipeline pressure and the filling material flow, this interactive information is of great significance for the control of the filling rate, such as the problem of pipeline rupture caused by excessive pipeline pressure caused by too fast filling rate. Based on this, in the technical solution of the present application, the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector are further input into the query matching interaction network based on the feature principal component to obtain the pipeline pressure-filling material flow core component significant fusion representation vector. The query matching interaction network based on the feature principal component is a feature extraction and fusion technology based on feature sparse processing, principal component analysis and significant fusion, which is used to generate sparse and significant feature association representations between feature vectors.

[0040] In one embodiment of the present application, the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector are input into a query matching interaction network based on characteristic principal components to obtain a pipeline pressure-filling material flow core component significant fusion representation vector, including: standardizing the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector to obtain a standardized pipeline pressure time series propagation representation vector and a standardized filling material flow time series propagation representation vector; calculating the sample covariance matrix of the standardized pipeline pressure time series propagation representation vector and the standardized filling material flow time series propagation representation vector to obtain a pipeline pressure time series sample covariance matrix and a filling material flow time series sample covariance matrix. The material flow time series sample covariance matrix; the pipeline pressure time series sample covariance matrix and the filling material flow time series sample covariance matrix are subjected to eigenvector extraction based on matrix decomposition to obtain a set of pipeline pressure time series principal component eigenvectors and a set of filling material flow time series principal component eigenvectors; the set of pipeline pressure time series principal component eigenvectors and the set of filling material flow time series principal component eigenvectors are input into a maximum approximate query matching network to obtain a set of best matching pairs of pipeline pressure time series principal component eigenvectors and filling material flow time series principal component eigenvectors; the best matching pairs of pipeline pressure time series principal component eigenvectors and filling material flow time series principal component eigenvectors are input into a maximum approximate query matching network to obtain a set of best matching pairs of pipeline pressure time series principal component eigenvectors and filling material flow time series principal component eigenvectors. The best matching pairs of each pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector in the set are input into the semantic fine-grained gated joint module to obtain a set of pipeline pressure-filling material flow time series principal component fusion feature vectors; each pipeline pressure-filling material flow time series principal component fusion feature vector in the set of pipeline pressure-filling material flow time series principal component fusion feature vectors is cascaded to obtain the pipeline pressure-filling material flow core component significant fusion representation vector; wherein the sample covariance matrix of the standardized pipeline pressure time series propagation representation vector and the standardized filling material flow time series propagation representation vector is calculated to obtain the pipeline pressure time series sample covariance matrix The invention relates to a matrix and a filling material flow time series sample covariance matrix, comprising: calculating the vector multiplication between the transposed vector of the standardized pipeline pressure time series propagation representation vector and the standardized pipeline pressure time series propagation representation vector, and then dividing the vector multiplication by the number of eigenvalues ​​of the standardized pipeline pressure time series propagation representation vector minus one by position points to obtain the pipeline pressure time series sample covariance matrix; calculating the vector multiplication between the transposed vector of the standardized filling material flow time series propagation representation vector and the standardized filling material flow time series propagation representation vector, and then dividing the vector multiplication by the number of eigenvalues ​​of the standardized filling material flow time series propagation representation vector minus one by position points to obtain the filling material flow time series sample covariance matrix.

[0041] Specifically, first, the pipeline pressure and the filling material flow time series propagation representation vectors are standardized to ensure that the pipeline pressure and the filling material flow time series propagation characteristics are distributed on a zero mean scale and have unit variance. This can eliminate the influence of different dimensions and magnitudes between the two, so that the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector can be analyzed and compared.

[0042] In one embodiment of the present application, the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector are standardized to obtain standardized pipeline pressure time series propagation representation vector and standardized filling material flow time series propagation representation vector, including: calculating the mean and standard deviation of the pipeline pressure time series propagation representation vector to obtain the pipeline pressure time series mean and the pipeline pressure time series standard deviation; calculating the position difference between the pipeline pressure time series propagation representation vector and the pipeline pressure time series mean, and then dividing it by the pipeline pressure time series standard deviation to obtain the standardized pipeline pressure time series propagation representation vector; calculating the mean and standard deviation of the filling material flow time series propagation representation vector to obtain the filling material flow time series mean and the filling material flow time series standard deviation; calculating the position difference between the filling material flow time series propagation representation vector and the filling material flow time series mean, and then dividing it by the filling material flow time series standard deviation to obtain the standardized filling material flow time series propagation representation vector.

[0043] Next, the covariance matrix of the normalized pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector is calculated. The covariance matrix reflects the linear correlation between the pipeline pressure time series propagation features and the filling material flow time series propagation features. This step helps to understand how the features in the pipeline pressure and filling material flow time series propagation representation vectors are interdependent.

[0044] Then, the principal component analysis algorithm is used to perform feature sparse processing on the standardized pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector. Principal component analysis can identify the most important change direction in the pipeline pressure time series characteristics and the filling material flow time series characteristics, and represent it as the principal component. Through principal component analysis, the dimension of the data can be reduced while retaining the key information in the data feature set, which helps to extract the core time series characteristics of the pipeline pressure and the filling material flow from the standardized pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector.

[0045] After extracting the principal component feature vectors, it is necessary to determine which feature vectors are most similar to each other, that is, have similar principal component features. Then, based on the maximum approximate query matching between the set of principal component feature vectors of the pipeline pressure time series and the set of principal component feature vectors of the filling material flow time series, the best matching pair is found by calculating and comparing the similarity between the principal component feature vector sets of pipeline pressure and filling material flow, which involves a distance metric or similarity scoring mechanism. The maximum approximate query matching network helps to understand the intrinsic connection between pipeline pressure and filling material flow by identifying the most similar principal component feature vector pairs, and provides an accurate correspondence for the fusion of the two features.

[0046] In one embodiment of the present application, the set of the pipeline pressure time series principal component feature vectors and the set of the filling material flow time series principal component feature vectors are input into a maximum approximate query matching network to obtain a set of the best matching pairs of the pipeline pressure time series principal component feature vectors and the filling material flow time series principal component feature vectors, including: taking the pipeline pressure time series principal component feature vector at a predetermined position in the set of the pipeline pressure time series principal component feature vectors to obtain a predetermined pipeline pressure time series principal component feature vector; calculating the inner product between the predetermined pipeline pressure time series principal component feature vector and each filling material flow time series principal component feature vector in the set of the filling material flow time series principal component feature vector to obtain a sequence of pipeline pressure-filling material flow inner product values; calculating a range of the predetermined pipeline pressure time series principal component feature vector. to obtain a predetermined pipeline pressure time series principal component characteristic one-norm; calculate a norm of each filling material flow time series principal component characteristic vector in the set of the filling material flow time series principal component characteristic vectors to obtain a sequence of filling material flow time series principal component characteristic one-norm; multiply the predetermined pipeline pressure time series principal component characteristic one-norm with the sequence of the filling material flow time series principal component characteristic one-norm by position point to obtain a sequence of pipeline pressure-filling material flow semantic interaction values; calculate the position point division between the sequence of pipeline pressure-filling material flow inner product values ​​and the sequence of pipeline pressure-filling material flow semantic interaction values, and the filling material flow time series principal component characteristic vector corresponding to the maximum eigenvalue in the obtained eigenvalue sequence and the predetermined pipeline pressure time series principal component characteristic vector form a set of best matching pairs.

[0047] Then, the gating mechanism is used to perform a fine-grained semantic union on the best matching pairs of the pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector. The gating mechanism can dynamically adjust the feature weights according to the importance and contextual information of the pipeline pressure time series principal component feature and the filling material flow time series principal component feature. Through the gating union, not only the information with high significance in the best matching pairs of each group of pipeline pressure time series principal component feature vector and filling material flow time series principal component feature vector is enhanced, but also the model's ability to identify the importance of features is improved through fine-grained control, providing rich semantic information for generating the final pipeline pressure-filling material flow time series principal component fusion feature representation.

[0048] Finally, the set of pipeline pressure-filling material flow time series principal component fusion feature vectors obtained through gated joint processing is cascaded and fused to generate the pipeline pressure-filling material flow core component significant fusion representation vector. This vector contains the key information of pipeline pressure and filling material flow and their mutual correlation. This representation helps to improve the model's prediction accuracy for filling rate, especially when dealing with complex underground mine filling operations.

[0049] In one embodiment of the present application, the best matching pairs of each pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector in the set of the best matching pairs of the pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector are input into the semantic fine-grained gating joint module to obtain a set of pipeline pressure-filling material flow time series principal component fusion feature vectors, including: performing position difference, position dot multiplication and position summation processing on the best matching pairs of each pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector to obtain a set of pipeline pressure-filling material flow time series principal component difference feature vectors, a set of pipeline pressure-filling material flow time series principal component dot product feature vectors and a pipeline pressure-filling material flow time series principal component fusion feature vector. A set of principal component sum feature vectors of the pressure-filling material flow time series; cascading the set of principal component difference feature vectors of the pipeline pressure-filling material flow time series, the set of principal component dot product feature vectors of the pipeline pressure-filling material flow time series and the set of principal component sum feature vectors of the pipeline pressure-filling material flow time series by position to obtain a set of multi-dimensional fusion expression vectors of the principal component of the pipeline pressure-filling material flow time series; processing each multi-dimensional fusion expression vector of the principal component of the pipeline pressure-filling material flow time series in the set of multi-dimensional fusion expression vectors of the principal component of the pipeline pressure-filling material flow time series respectively through a one-dimensional convolution layer and then through a maximum pooling layer to obtain a set of principal component fusion feature vectors of the pipeline pressure-filling material flow time series.

[0050] Specifically, the pipeline pressure time series propagation representation vector and the filling material flow rate time series propagation representation vector are input into the query matching interactive network based on the characteristic principal component and processed by the following characteristic principal component query matching formula to obtain the pipeline pressure-filling material flow rate core component significant fusion representation vector; The characteristic principal component query matching formula is: in, is the pipeline pressure time series propagation representation vector, is the filling material flow timing propagation representation vector, is the mean value of the pipeline pressure time series propagation representation vector, is the standard deviation of the pipeline pressure time series propagation representation vector, is the mean value of the filling material flow time series propagation representation vector, is the standard deviation of the filling material flow time series propagation representation vector, represents the difference by position, is the normalized pipeline pressure time series propagation representation vector, is the normalized filling material flow time series propagation representation vector, is the number of eigenvalues ​​in the normalized pipeline pressure time series propagation representation vector, is the number of eigenvalues ​​in the normalized filling material flow time series propagation representation vector, is the transpose operation, is the pipeline pressure time series sample covariance matrix, is the filling material flow time series sample covariance matrix, is the principal component eigenvector of each pipeline pressure time series in the set of principal component eigenvectors of pipeline pressure time series, is the orthogonal matrix of the principal components of the pipeline pressure time series, The diagonal elements of the matrix are The diagonal matrix of Respectively represent the weight parameters of the principal component eigenvectors of each pipeline pressure time series, is the pipeline pressure time series diagonal matrix, is each filling material flow time series principal component eigenvector in the set of filling material flow time series principal component eigenvectors, is the orthogonal matrix of the principal components of the filling material flow time series, The diagonal elements of the matrix are The diagonal matrix of is the diagonal matrix of filling material flow time series, They respectively represent the weight parameters of the principal component eigenvectors of the time series of each filling material flow, is the first in the set of principal component eigenvectors of the pipeline pressure time series The principal component eigenvectors of pipeline pressure time series, and are the first and The principal component eigenvectors of the filling material flow time series, represents the inner product of vectors, represents the one-norm of a vector, Indicates that the maximum value point is returned. value, is the maximum approximate query matching value, and Respectively represent positional dot multiplication and positional addition, represents a one-dimensional convolutional layer, represents maximum pooling, is the first in the set of principal component fusion feature vectors of pipeline pressure-filling material flow time series The principal component fusion feature vector of pipeline pressure-filling material flow time series, is the number of eigenvectors in the set of principal component fusion eigenvectors of the pipeline pressure-filling material flow time series, is the significant fusion representation vector of the core component of pipeline pressure-filling material flow.

[0051] Then, the pipeline pressure-filling material flow core component significant fusion representation vector is input into the decoder-based remote control instruction generator to obtain a remote operation instruction, which includes a recommended decoded value of the filling rate at the next time point. In other words, the core component significant fusion representation information between the pipeline pressure time series propagation aggregation feature and the filling material flow time series propagation aggregation feature is used for decoding regression, so as to recommend the filling rate at the next time point, and the variable frequency drive and servo motor are used to achieve precise control of the filling pumping system to achieve the required recommended filling rate. In this way, wireless communication modules and remote control servers can be used to achieve more intelligent remote control of underground filling equipment, so as to adaptively control the material filling rate based on the time series coordinated changes between pipeline pressure and filling material flow, thereby avoiding the problems of excessive pressure and uneven filling caused by the unstable filling rate caused by traditional manual operation, and improving the safety and efficiency of the overall underground filling operation.

[0052] In a preferred example, considering that the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector respectively represent the one-dimensional local time series correlation characteristics of the pipeline pressure and the filling material flow value, the significant attenuation propagation characteristics based on the time series node energy, therefore, when performing significant fusion based on feature principal component query matching, due to the difference in the distribution of time series correlation characteristics caused by the difference in source time series distribution, superimposed on the significant propagation difference across time series nodes, the pipeline pressure-filling material flow core component significant fusion representation vector will also have time series feature diversified spatial structure differences, affecting the convergence consistency of the decoder, therefore, it is expected that the pipeline pressure-filling material flow core component significant fusion representation vector input based on the decoder remote control instruction generator for decoding regression regression regression consistency, thereby improving the accuracy of the remote operation instruction obtained. Therefore, in one example, the pipeline pressure-filling material flow core component significant fusion representation vector is optimized.

[0053] Based on this, in the preferred example, the pipeline pressure-filling material flow rate core component significant fusion representation vector is input into the decoder-based optimal drilling pressure recommender to obtain the optimal drilling pressure recommended decoded value, which specifically includes: The absolute value sum and the square root of the square sum of all eigenvalues ​​of the pipeline pressure-filling material flow core component significant fusion representation vector are calculated to obtain the first pipeline pressure-filling material flow core component significant fusion spatial structure value and the second pipeline pressure-filling material flow core component significant fusion spatial structure value, that is: in, The pipeline pressure-filling material flow core component significant fusion representation vector eigenvalues, Indicates the significant fusion spatial structure value of the core component of the first pipeline pressure-filling material flow, Indicates that the core component of the second pipeline pressure-filling material flow rate significantly merges the spatial structure value; Determine the number of eigenvalues ​​of all eigenvalues ​​of the pipeline pressure-filling material flow core component that significantly integrates the representation vector , that is, the length of the vector representing the significant fusion of the pipeline pressure-filling material flow core component; For each eigenvalue of the pipeline pressure-filling material flow core component significant fusion representation vector, the first pipeline pressure-filling material flow core component significant fusion spatial structure value minus the product of the eigenvalue and the number of eigenvalues ​​is calculated to obtain the first pipeline pressure-filling material flow core component significant fusion long-range dependency value ,in, Indicates the significant fusion spatial structure value of the core component of the first pipeline pressure-filling material flow, The pipeline pressure-filling material flow core component significant fusion representation vector eigenvalues, The number of eigenvalues ​​representing all eigenvalues ​​of the pipeline pressure-filling material flow core component that significantly merges the representation vector, It indicates that the core component of the first pipeline pressure-filling material flow rate significantly integrates the long-range dependence value; Calculate the square root of the number of eigenvalues ​​multiplied by the product of the eigenvalues ​​minus the significant fusion spatial structure value of the second pipeline pressure-filling material flow core component to obtain the significant fusion long-range dependence value of the second pipeline pressure-filling material flow core component ,in, Indicates that the core component of the second pipeline pressure-filling material flow rate significantly integrates the spatial structure value, The pipeline pressure-filling material flow core component significant fusion representation vector eigenvalues, The number of eigenvalues ​​representing all eigenvalues ​​of the pipeline pressure-filling material flow core component that significantly merges the representation vector, It indicates that the core component of the second pipeline pressure-filling material flow rate significantly integrates the long-range dependence value; The index value calculated by taking the significant fusion long-range dependence value of the first pipeline pressure-filling material flow core component as the exponent of the natural constant and the inverse of the significant fusion long-range dependence value of the second pipeline pressure-filling material flow core component are weighted summed to obtain the optimized eigenvalue corresponding to each eigenvalue ,in, It indicates that the core component of the first pipeline pressure-filling material flow rate significantly integrates the long-range dependence value, It indicates that the core component of the second pipeline pressure-filling material flow rate significantly integrates the long-range dependence value, represents a natural constant, and represents the weighted hyperparameter, Indicates the optimized eigenvalue corresponding to each eigenvalue; The optimized characteristic values ​​are combined into an optimized pipeline pressure-filling material flow core component significant fusion representation vector, and the optimized pipeline pressure-filling material flow core component significant fusion representation vector is input into a decoder-based remote control instruction generator to obtain a remote operation instruction.

[0054] That is, in view of the possible spatial structure loss in the high-dimensional space of the feature set of the pipeline pressure-filling material flow core component significant fusion representation vector, which causes the decoder weight to implicitly infer spatial structure information based on the features, resulting in inconsistent convergence. A long-distance feature dependency relationship is established based on the spatial structure representation of the pipeline pressure-filling material flow core component significant fusion representation vector relative to the overall feature scale of the pipeline pressure-filling material flow core component significant fusion representation vector, so as to establish the feature local connectivity of the pipeline pressure-filling material flow core component significant fusion representation vector, and capture the spatial ambiguous information of the object feature value through the unstructured feature value point prediction of the pipeline pressure-filling material flow core component significant fusion representation vector, thereby improving the spatial inductive bias perception ability of the feature set of the pipeline pressure-filling material flow core component significant fusion representation vector, improving the convergence consistency of the decoder, and improving the accuracy of the remote operation instructions obtained by the remote control instruction generator based on the decoder input of the pipeline pressure-filling material flow core component significant fusion representation vector. In this way, the material filling rate can be more accurately and adaptively controlled based on the time-series coordinated changes between pipeline pressure and filling material flow, thereby avoiding the problems of excessive pressure and uneven filling caused by unstable filling rate due to traditional manual operation, and improving the safety and efficiency of the overall underground filling operation.

[0055] In summary, the above scheme is adopted to monitor and collect pipeline pressure data in real time through pressure sensors, and to monitor and collect filling material flow values ​​in real time, and then transmit the collected pipeline pressure time series data and filling material flow time series data to the remote control server through the wireless communication module, so as to introduce data processing and analysis algorithms based on artificial intelligence and deep learning in the remote control server to perform time series analysis and information propagation aggregation representation on the pipeline pressure time series data and the filling material flow time series data, and then use the core component fusion semantics between the pipeline pressure time series propagation aggregation features and the filling material flow time series propagation aggregation features to recommend the filling rate at the next time point, and use variable frequency drives and servo motors to achieve precise control of the filling pumping system to achieve the required recommended filling rate. In this way, wireless communication modules and remote control servers can be used to realize more intelligent remote control of underground filling equipment, so as to adaptively control the material filling rate based on the time series coordinated changes between pipeline pressure and filling material flow, thereby avoiding the problems of excessive pressure and uneven filling caused by the unstable filling rate caused by traditional manual operation, and improving the safety and efficiency of the overall underground filling operation.

[0056] Figure 2 FIG. 1 is a block diagram of a remote control system for underground filling equipment according to an exemplary embodiment. Figure 2 As shown, the system 200 includes: The data acquisition module 201 is used to acquire the time queue of the pipeline pressure and the time queue of the filling material flow value collected by the pressure sensor; A data transmission module 202, for transmitting the time queue of the pipeline pressure and the time queue of the filling material flow value to a remote control server via a wireless communication module; A local time series encoding module 203 is used to perform local time series encoding on the time series of the pipeline pressure and the time series of the filling material flow value on the remote control server to obtain a sequence of local time series associated feature vectors of the pipeline pressure and a sequence of local time series associated feature vectors of the filling material flow; A feature forward propagation module 204 is used to input the sequence of the pipeline pressure local time series associated feature vectors and the sequence of the filling material flow local time series associated feature vectors into a feature forward propagation network based on significant time series attenuation of node energy to obtain a pipeline pressure time series propagation representation vector and a filling material flow time series propagation representation vector; A significant fusion module 205 is used to input the pipeline pressure time series propagation representation vector and the filling material flow rate time series propagation representation vector into a query matching interaction network based on characteristic principal components to obtain a pipeline pressure-filling material flow rate core component significant fusion representation vector; The remote operation instruction determination module 206 is used to input the pipeline pressure-filling material flow core component significant fusion representation vector into a decoder-based remote control instruction generator to obtain a remote operation instruction, wherein the remote operation instruction includes a recommended decoded value of the filling rate at the next time point.

[0057] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0058] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

[0059] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or to be performed in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0060] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely exemplary forms of implementation. With respect to the apparatus in the above-mentioned embodiments, the specific manner in which each module performs the operation has been described in detail in the embodiments related to the method, and will not be described in detail here.

Claims

1. A remote control method for underground filling equipment, characterized in that: include: Obtaining a time queue of pipeline pressure and a time queue of filling material flow values ​​collected by a pressure sensor; Transmitting the time queue of the pipeline pressure and the time queue of the filling material flow value to the remote control server through the wireless communication module; On the remote control server, locally time-series encoding is performed on the time queue of the pipeline pressure and the time queue of the filling material flow value to obtain a sequence of local time-series associated feature vectors of the pipeline pressure and a sequence of local time-series associated feature vectors of the filling material flow; Inputting the sequence of the pipeline pressure local time series associated feature vectors and the sequence of the filling material flow local time series associated feature vectors into a feature forward propagation network based on significant time series attenuation of node energy to obtain a pipeline pressure time series propagation representation vector and a filling material flow time series propagation representation vector; Inputting the pipeline pressure time series propagation representation vector and the filling material flow rate time series propagation representation vector into a query matching interaction network based on characteristic principal components to obtain a pipeline pressure-filling material flow rate core component significant fusion representation vector; The pipeline pressure-filling material flow rate core component significant fusion representation vector is input into a decoder-based remote control instruction generator to obtain a remote operation instruction, and the remote operation instruction includes a recommended decoded value of the filling rate at the next time point.

2. The remote control method of underground filling equipment according to claim 1, characterized in that: On the remote control server, local time series encoding is performed on the time queue of the pipeline pressure and the time queue of the filling material flow value respectively to obtain a sequence of local time series correlation feature vectors of the pipeline pressure and a sequence of local time series correlation feature vectors of the filling material flow, including: on the remote control server, the time queue of the pipeline pressure and the time queue of the filling material flow value are input into a sequence encoder based on a 1D-CNN model to obtain a sequence of local time series correlation feature vectors of the pipeline pressure and a sequence of local time series correlation feature vectors of the filling material flow.

3. The remote control method of underground filling equipment according to claim 2, characterized in that: Inputting the sequence of the pipeline pressure local time series associated feature vectors and the sequence of the filling material flow local time series associated feature vectors into a feature forward propagation network based on significant time series attenuation of node energy to obtain a pipeline pressure time series propagation representation vector and a filling material flow time series propagation representation vector, including: Based on the mean and standard deviation of each pipeline pressure local time series associated feature vector in the sequence of pipeline pressure local time series associated feature vectors, calculating the energy significant descriptor of each pipeline pressure local time series associated feature vector to obtain a sequence of pipeline pressure local time series energy significant descriptors; Extracting the timestamp of each pipeline pressure local time series correlation feature vector in the sequence of pipeline pressure local time series correlation feature vectors to obtain a sequence of pipeline pressure timestamps; The pipeline pressure local time series correlation feature vector corresponding to the current time point in the sequence of the pipeline pressure local time series correlation feature vectors is used as the current pipeline pressure local time series correlation feature vector and the pipeline pressure local time series correlation feature vectors corresponding to other time points are used as the historical pipeline pressure local time series correlation feature vectors to obtain a sequence of the current pipeline pressure local time series correlation feature vector and the historical pipeline pressure local time series correlation feature vector; Calculating the energy time decay factor of each historical pipeline pressure local time series associated characteristic vector in the sequence of historical pipeline pressure local time series associated characteristic vectors relative to the current pipeline pressure local time series associated characteristic vector to obtain a sequence of pipeline pressure local time series energy time decay factors; After calculating the positional division of the sequence of the pipeline pressure local time series energy significant descriptors and the sequence of the pipeline pressure local time series energy time decay factors, the obtained pipeline pressure local time series energy weight is used as a weighting coefficient, and the positional weighted sum of the sequence of the historical pipeline pressure local time series associated feature vectors is calculated to obtain the pipeline pressure local time series historical node propagation aggregation representation feature vector; Based on the energy significant descriptor corresponding to the current pipeline pressure local time series associated feature vector, the position-weighted sum of the pipeline pressure local time series historical node propagation aggregation representation feature vector and the current pipeline pressure local time series associated feature vector is calculated to obtain the pipeline pressure time series propagation aggregation representation vector.

4. The remote control method of underground filling equipment according to claim 3, characterized in that: Based on the mean and standard deviation of each pipeline pressure local time series associated feature vector in the sequence of pipeline pressure local time series associated feature vectors, calculating the energy significant descriptors of each pipeline pressure local time series associated feature vector to obtain a sequence of pipeline pressure local time series energy significant descriptors, including: Calculating the mean and standard deviation of each pipeline pressure local time series associated characteristic vector in the sequence of pipeline pressure local time series associated characteristic vectors to obtain a sequence of pipeline pressure local means and a sequence of pipeline pressure local standard deviations; After performing positional differentiation on each group of corresponding local time series correlation feature vectors of pipeline pressure and local mean values ​​in the sequence of local time series correlation feature vectors of pipeline pressure and the sequence of local mean values ​​of pipeline pressure, each positional feature value in the obtained sequence of differential feature vectors is modulated to the fourth power to obtain a sequence of local time series modulation feature vectors of pipeline pressure; Calculating the expected value of each pipeline pressure local time series modulation characteristic vector in the sequence of pipeline pressure local time series modulation characteristic vectors to obtain a sequence of pipeline pressure local time series positive expected factors; Calculating the fourth power modulation value of each local standard deviation of pipeline pressure in the sequence of local standard deviations of pipeline pressure to obtain a sequence of local time series inverse expectation factors of pipeline pressure; Each group of corresponding pipeline pressure local timing positive expected factors and pipeline pressure local timing negative expected factors in the sequence of pipeline pressure local timing positive expected factors and the sequence of pipeline pressure local timing negative expected factors are divided to obtain the sequence of pipeline pressure local timing energy significant descriptors.

5. The remote control method of underground filling equipment according to claim 4, characterized in that: Calculating the energy time decay factor of each historical pipeline pressure local time series associated feature vector in the sequence of the historical pipeline pressure local time series associated feature vectors relative to the current pipeline pressure local time series associated feature vector to obtain a sequence of pipeline pressure local time series energy time decay factors, including: Respectively calculate the pipeline pressure timestamp corresponding to the current pipeline pressure local time series correlation feature vector and the pipeline pressure timestamp corresponding to each historical pipeline pressure local time series correlation feature vector, subtract them and round them down to obtain a sequence of time span values; After calculating the square of each time span value in the sequence of time span values, the obtained sequence of span square modulation values ​​is divided by the square of the time decay inverse scaling parameter by position and multiplied by the decay rate positive scaling parameter by position to obtain a sequence of time series span energy decay coefficients; Taking each time series span energy attenuation coefficient in the sequence of the time series span energy attenuation coefficient as an exponential power, a natural exponential function value with a natural constant e as a base is calculated to obtain a sequence of the local time series energy time attenuation factors of the pipeline pressure.

6. The remote control method of underground filling equipment according to claim 5, characterized in that: Inputting the pipeline pressure time series propagation representation vector and the filling material flow time series propagation representation vector into a query matching interaction network based on characteristic principal components to obtain a pipeline pressure-filling material flow core component significant fusion representation vector, including: Standardizing the pipeline pressure time series propagation representation vector and the filling material flow rate time series propagation representation vector to obtain a standardized pipeline pressure time series propagation representation vector and a standardized filling material flow rate time series propagation representation vector; Calculating the sample covariance matrix of the standardized pipeline pressure time series propagation representation vector and the standardized filling material flow time series propagation representation vector to obtain a pipeline pressure time series sample covariance matrix and a filling material flow time series sample covariance matrix; Performing eigenvector extraction based on matrix decomposition on the pipeline pressure time series sample covariance matrix and the filling material flow time series sample covariance matrix to obtain a set of pipeline pressure time series principal component eigenvectors and a set of filling material flow time series principal component eigenvectors; Inputting the set of pipeline pressure time series principal component feature vectors and the set of filling material flow time series principal component feature vectors into a maximum approximate query matching network to obtain a set of best matching pairs of pipeline pressure time series principal component feature vectors and filling material flow time series principal component feature vectors; Inputting each best matching pair of the pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector in the set of best matching pairs of the pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector into the semantic fine-grained gating joint module to obtain a set of pipeline pressure-filling material flow time series principal component fusion feature vectors; Cascading each pipeline pressure-filling material flow time series principal component fusion feature vector in the set of pipeline pressure-filling material flow time series principal component fusion feature vectors to obtain the pipeline pressure-filling material flow core component significant fusion representation vector; The step of calculating the sample covariance matrix of the standardized pipeline pressure time series propagation representation vector and the standardized filling material flow time series propagation representation vector to obtain the pipeline pressure time series sample covariance matrix and the filling material flow time series sample covariance matrix includes: Calculate the vector multiplication between the transposed vector of the standardized pipeline pressure time series propagation representation vector and the standardized pipeline pressure time series propagation representation vector, and then divide it by the number of eigenvalues ​​of the standardized pipeline pressure time series propagation representation vector minus one to obtain the pipeline pressure time series sample covariance matrix; The filling material flow time series sample covariance matrix is ​​obtained by calculating the vector multiplication between the transposed vector of the standardized filling material flow time series propagation representation vector and the standardized filling material flow time series propagation representation vector and then dividing it by the number of eigenvalues ​​of the standardized filling material flow time series propagation representation vector minus one by position point.

7. The remote control method of underground filling equipment according to claim 6, characterized in that: The pipeline pressure time series propagation representation vector and the filling material flow rate time series propagation representation vector are normalized to obtain a normalized pipeline pressure time series propagation representation vector and a normalized filling material flow rate time series propagation representation vector, including: Calculating the mean and standard deviation of the pipeline pressure time series propagation representation vector to obtain the pipeline pressure time series mean and pipeline pressure time series standard deviation; Calculating the position difference between the pipeline pressure time series propagation representation vector and the pipeline pressure time series mean value, and then dividing the position difference by the pipeline pressure time series standard deviation to obtain the standardized pipeline pressure time series propagation representation vector; Calculating the mean and standard deviation of the filling material flow time series propagation representation vector to obtain the filling material flow time series mean and the filling material flow time series standard deviation; The positional difference between the filling material flow timing propagation representation vector and the filling material flow timing mean is calculated and then divided by the filling material flow timing standard deviation to obtain the standardized filling material flow timing propagation representation vector.

8. The remote control method of underground filling equipment according to claim 7, characterized in that: Inputting the set of pipeline pressure time series principal component feature vectors and the set of filling material flow time series principal component feature vectors into a maximum approximate query matching network to obtain a set of best matching pairs of pipeline pressure time series principal component feature vectors and filling material flow time series principal component feature vectors, including: Taking the pipeline pressure time series principal component feature vector at a predetermined position in the set of pipeline pressure time series principal component feature vectors to obtain a predetermined pipeline pressure time series principal component feature vector; Calculating the inner products of the predetermined pipeline pressure time series principal component eigenvector and each filling material flow time series principal component eigenvector in the set of filling material flow time series principal component eigenvectors to obtain a sequence of pipeline pressure-filling material flow inner product values; Calculating a norm of a principal component characteristic vector of the predetermined pipeline pressure time series to obtain a norm of a principal component characteristic of the predetermined pipeline pressure time series; Calculating a norm of each filling material flow time series principal component characteristic vector in the set of filling material flow time series principal component characteristic vectors to obtain a sequence of a norm of the filling material flow time series principal component characteristic; Multiplying the sequence of the predetermined pipeline pressure time series principal component feature one norm and the filling material flow time series principal component feature one norm by position points to obtain a sequence of pipeline pressure-filling material flow semantic interaction values; After calculating the point-by-point division between the sequence of pipeline pressure-filling material flow inner product values ​​and the sequence of pipeline pressure-filling material flow semantic interaction values, the filling material flow time series principal component eigenvector corresponding to the maximum eigenvalue in the obtained eigenvalue sequence and the predetermined pipeline pressure time series principal component eigenvector form a set of optimal matching pairs.

9. The remote control method of underground filling equipment according to claim 8, characterized in that: Inputting each best matching pair of the pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector in the set of best matching pairs of the pipeline pressure time series principal component feature vector and the filling material flow time series principal component feature vector into the semantic fine-grained gating joint module to obtain a set of pipeline pressure-filling material flow time series principal component fusion feature vectors, including: The best matching pairs of the pipeline pressure time series principal component eigenvectors and the filling material flow time series principal component eigenvectors are respectively subjected to position difference, position dot multiplication and position summation processing to obtain a set of pipeline pressure-filling material flow time series principal component difference eigenvectors, a set of pipeline pressure-filling material flow time series principal component dot product eigenvectors and a set of pipeline pressure-filling material flow time series principal component summation eigenvectors; The set of pipeline pressure-filling material flow time series principal component difference eigenvectors, the set of pipeline pressure-filling material flow time series principal component dot product eigenvectors and the set of pipeline pressure-filling material flow time series principal component sum eigenvectors are cascaded by position to obtain a set of pipeline pressure-filling material flow time series principal component multi-dimensional fusion expression vectors; Each pipeline pressure-filling material flow time series principal component multidimensional fusion expression vector in the set of the pipeline pressure-filling material flow time series principal component multidimensional fusion expression vector is processed by a one-dimensional convolution layer and then by a maximum pooling layer to obtain a set of pipeline pressure-filling material flow time series principal component fusion feature vectors.

10. A remote control system for underground filling equipment, characterized in that: include: A data acquisition module, used to acquire a time queue of pipeline pressure and a time queue of filling material flow value collected by a pressure sensor; A data transmission module, used for transmitting the time queue of the pipeline pressure and the time queue of the filling material flow value to the remote control server through a wireless communication module; A local time series encoding module is used to perform local time series encoding on the time series of the pipeline pressure and the time series of the filling material flow value on the remote control server to obtain a sequence of local time series associated feature vectors of the pipeline pressure and a sequence of local time series associated feature vectors of the filling material flow; A feature forward propagation module, used for inputting the sequence of the pipeline pressure local time series associated feature vectors and the sequence of the filling material flow local time series associated feature vectors into a feature forward propagation network based on significant time series attenuation of node energy to obtain a pipeline pressure time series propagation representation vector and a filling material flow time series propagation representation vector; A significant fusion module, used for inputting the pipeline pressure time series propagation representation vector and the filling material flow rate time series propagation representation vector into a query matching interaction network based on characteristic principal components to obtain a pipeline pressure-filling material flow rate core component significant fusion representation vector; A remote operation instruction determination module is used to input the pipeline pressure-filling material flow core component significant fusion representation vector into a decoder-based remote control instruction generator to obtain a remote operation instruction, wherein the remote operation instruction includes a recommended decoded value of the filling rate at the next time point.

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