A Smart Monitoring Method and System for High-Pressure Gate Valves

Abstract

This invention provides an intelligent monitoring method and system for high-pressure gate valves, comprising: acquiring the location data of the high-pressure gate valve and converting the location data into node network data; calculating energy consumption surplus / deficit data through the energy consumption model; obtaining the electrical energy distribution status data of the energy storage device based on the energy consumption surplus / deficit data; inputting the electrical energy distribution status data into a time-regularized matrix decomposition model to obtain predicted active nodes and dormant nodes; monitoring the high-pressure gate valve based on the predicted active nodes and dormant nodes to obtain output monitoring data; controlling the operation of the high-pressure gate valve through the monitoring data; and further obtaining active sensor nodes by supplementing the electrical energy distribution status data through an artificial intelligence model, and monitoring the high-pressure gate valve corresponding to the active sensor node, thereby improving the intelligence of high-pressure gate valve monitoring and ensuring the safety of high-pressure gate valve operation.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an intelligent monitoring method and system for high-pressure gate valves, a computer device, and a storage medium. Background Technology

[0002] High-pressure gate valves are forced-sealing valves, so when the valve is closed, pressure must be applied to the gate to force a leak-proof seal. When the medium enters the valve from below the gate, the resistance that the operating force needs to overcome is the frictional force of the valve stem and packing, plus the thrust generated by the pressure of the medium. The force required to close the valve is greater than the force required to open it.

[0003] Existing high-pressure gate valves can be equipped with certain monitoring devices to achieve functions such as monitoring current, but their functionality is very limited. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide an intelligent monitoring method for a high-pressure gate valve, an intelligent monitoring system for a high-pressure gate valve, a computer device, and a storage medium to overcome or at least partially solve the above problems.

[0005] To address the aforementioned problems, this invention discloses an intelligent monitoring method for high-pressure gate valves. Multiple high-pressure gate valves are installed on a pipeline network. Each high-pressure gate valve is connected to various sensors and an energy storage device. The energy storage device is connected to a piezoelectric transducer. The sensors include temperature sensors and pressure sensors.

[0006] The location data of the high-pressure gate valve is acquired and converted into node network data;

[0007] Under the node network, the first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and the second energy consumption data of multiple sensors are obtained;

[0008] An energy consumption model is constructed based on the first energy consumption data and the second energy consumption data;

[0009] Energy consumption profit and loss data are calculated using the energy consumption model.

[0010] The energy distribution status data of the energy storage device is obtained based on the energy consumption surplus and deficit data.

[0011] The power distribution state data is input into a time-regularized matrix decomposition model to obtain the predicted active nodes and dormant nodes.

[0012] The high-pressure gate valve is monitored based on the predicted active and dormant nodes to obtain output monitoring data, and the operation of the high-pressure gate valve is controlled by the monitoring data.

[0013] Preferably, the acquisition of first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and second energy consumption data of multiple sensors under the node network includes:

[0014] The conversion efficiency and functional capacity of the piezoelectric transducer of the high-pressure gate valve were obtained;

[0015] The startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors are obtained.

[0016] Preferably, the step of constructing an energy consumption model based on the first energy consumption data and the second energy consumption data includes:

[0017] The startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors are combined into a sensor energy consumption layer;

[0018] The conversion efficiency and functional capacity of the piezoelectric transducer are set as the energy consumption layer;

[0019] The initial energy, energy collection power, and energy consumption power of the energy storage device are obtained. Based on the initial energy, energy collection power, and energy consumption power of the energy storage device, energy consumption constraints are constructed. The energy consumption model is composed of the energy consumption constraints, the sensor energy consumption layer, and the energy consumption layer.

[0020] Preferably, the step of calculating energy consumption profit and loss data through the energy consumption model includes:

[0021] The energy data output by the energy consumption layer under constraints is used to form an energy sequence;

[0022] The energy consumption data output by the sensor energy consumption layer under constrained conditions is used to form a sensor energy consumption sequence.

[0023] Energy consumption surplus / deficit data are calculated using the energy sequence and sensor energy consumption sequence.

[0024] Preferably, obtaining the electrical energy distribution status data of the energy storage device based on the energy consumption surplus / deficit data includes:

[0025] Identify the number of each sensor in the energy consumption surplus / deficit data;

[0026] The energy consumption surplus and deficit data are allocated to each sensor according to the numbering example to obtain the power distribution status data of the energy storage device.

[0027] Preferably, the step of inputting the power distribution state data into a time-regularized matrix factorization model to obtain the predicted active and dormant nodes includes:

[0028] The power distribution state data is converted into an observation data matrix, which is then decomposed into a multidimensional low-rank matrix. The regularization parameters of the high-pressure gate valve are then set.

[0029] The objective function of the gate valve is established based on the multidimensional low-rank matrix and the regularization parameters of the high-pressure gate valve.

[0030] The gate valve objective function is optimized by the ADNM optimizer to obtain the output predicted sensor data. The predicted sensor data and the original sensor data are combined to form complete sensor data. Active nodes and dormant nodes are identified based on the complete sensor data.

[0031] Preferably, the step of monitoring the high-pressure gate valve based on the predicted active and dormant nodes to obtain output monitoring data, and controlling the operation of the high-pressure gate valve through the monitoring data, includes:

[0032] Extract the sensor locations corresponding to the active nodes, and identify the number of a specific high-pressure gate valve through the sensor locations;

[0033] The sensor data related to the number of the specific high-pressure gate valve is calculated and a threshold identification operation is performed to obtain the output sensor monitoring data. The operation of the high-pressure gate valve is controlled by the monitoring data.

[0034] This invention discloses an intelligent monitoring system for high-pressure gate valves. Multiple high-pressure gate valves are installed on a pipeline network. Each high-pressure gate valve is connected to various sensors and an energy storage device. The energy storage device is connected to a piezoelectric transducer. The sensors include temperature sensors and pressure sensors.

[0035] The first acquisition module is used to acquire the position data of the high-pressure gate valve and convert the position data into node network data;

[0036] The second acquisition module is used to acquire the first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and the second energy consumption data of multiple sensors in the node network.

[0037] The construction module is used to construct an energy consumption model based on the first energy consumption data and the second energy consumption data;

[0038] The calculation module is used to calculate energy consumption profit and loss data through the energy consumption model;

[0039] The power distribution module is used to obtain the power distribution status data of the energy storage device based on the energy consumption surplus and deficit data.

[0040] The prediction module is used to input the power distribution state data into the time-regularized matrix decomposition model to obtain the predicted active nodes and dormant nodes.

[0041] The monitoring module is used to monitor the high-pressure gate valve based on the predicted active and dormant nodes, obtain output monitoring data, and control the operation of the high-pressure gate valve through the monitoring data.

[0042] This invention also discloses a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described steps for intelligent monitoring of the high-pressure gate valve.

[0043] This invention also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described steps for intelligent monitoring of the high-pressure gate valve.

[0044] The embodiments of the present invention have the following advantages:

[0045] In this embodiment of the invention, multiple high-pressure gate valves are arranged on a pipeline network. Each high-pressure gate valve is connected to various sensors and energy storage devices. These sensors include piezoelectric transducers, temperature sensors, and pressure sensors. The intelligent monitoring method for the high-pressure gate valves includes: acquiring the position data of the high-pressure gate valves and converting the position data into node network data; acquiring first energy consumption data of the piezoelectric transducer of the high-pressure gate valves and second energy consumption data from multiple sensors within the node network; constructing an energy consumption model based on the first and second energy consumption data; calculating energy consumption profit and loss data using the energy consumption model; and calculating the energy consumption profit and loss data based on the energy consumption profit and loss data. The data obtains the power distribution status data of the energy storage device; the power distribution status data is input into a time-regularized matrix decomposition model to obtain predicted active nodes and dormant nodes; based on the predicted active nodes and dormant nodes, the high-pressure gate valve is monitored to obtain output monitoring data, and the operation of the high-pressure gate valve is controlled by the monitoring data; by using an artificial intelligence model to complete the power distribution status data, active sensor nodes can be further obtained, and the high-pressure gate valve corresponding to the active sensor node is monitored, which improves the intelligence of the high-pressure gate valve monitoring program and ensures the safety of the high-pressure gate valve operation. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the steps of an embodiment of an intelligent monitoring method for a high-pressure gate valve according to an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of node network data according to an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of an energy consumption model according to an embodiment of the present invention;

[0050] Figure 4 This is a structural block diagram of an embodiment of an intelligent monitoring system for a high-pressure gate valve according to an embodiment of the present invention;

[0051] Figure 5 This is an internal structural diagram of a computer device according to one embodiment. Detailed Implementation

[0052] To make the technical problems, technical solutions, and beneficial effects solved by the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0053] In this embodiment of the invention, a piezoelectric transducer is installed in the pipeline and paired with an energy storage device. The energy from the piezoelectric transducer is supplied to multiple monitoring sensors, thus diversifying the energy supply for the pipeline sensors and ensuring the operational performance of the sensor equipment. The energy consumption model constructed by the piezoelectric transducer and sensors yields the electrical energy distribution data of the energy storage device. This data is then supplemented by an artificial intelligence model to identify active sensor nodes. Monitoring is then performed on the high-pressure gate valves corresponding to these active sensor nodes, improving the intelligence of the high-pressure gate valve monitoring process and ensuring the safety of the high-pressure gate valve operation.

[0054] Reference Figure 1 This diagram illustrates a flowchart of an embodiment of an intelligent monitoring method for high-pressure gate valves according to an invention. Multiple high-pressure gate valves are arranged on a pipeline network. The high-pressure gate valves are connected to various sensors and energy storage devices. The energy storage devices are connected to piezoelectric transducers. The sensors include temperature sensors and pressure sensors. Specifically, the method may include the following steps:

[0055] Step 101: Obtain the position data of the high-pressure gate valve and convert the position data into node network data;

[0056] In this embodiment of the invention, the method is applied to a pipeline network system, which may include various pipelines, high-pressure gate valves, flow meters, and various sensors. High-pressure gate valves are installed on various pipelines. The pipeline network system is also connected via a terminal. The terminal can obtain information about various high-pressure gate valves and sensors and control the operation of high-pressure gate valves or sensors through the pipeline network's control system. This embodiment of the invention does not impose excessive restrictions on the structure and components of the pipeline network. Furthermore, the terminal may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. This embodiment of the invention does not limit the specific type of terminal. The operating system of the terminal may include Android, Harmony OS, iOS, Windows Phone, Windows, etc. This invention does not impose excessive restrictions in this regard.

[0057] In this embodiment of the invention, the pipeline network system is further equipped with an energy storage device and a piezoelectric transducer. Specifically, the energy storage device may include various energy storage batteries, such as lithium-ion batteries, lead-acid batteries, flow batteries, sodium-ion batteries, solid-state batteries, and nickel-metal hydride batteries. This embodiment of the invention does not impose excessive restrictions on these. In addition, the piezoelectric transducer can be installed inside the pipeline to convert mechanical energy into electrical energy through the pressure of the liquid inside the pipeline, thereby powering the sensors. Specifically, the piezoelectric transducer can transmit the converted electrical energy to the energy storage device, i.e., the energy storage battery, which provides electrical energy to various sensors. Furthermore, the electrical energy converted by the piezoelectric transducer can also be directly provided to various sensors. It should be noted that the sampling frequency of the sensors can be 50Hz, and the sampling accuracy can be 10 bits or higher. This embodiment of the invention does not impose excessive restrictions on these.

[0058] In this embodiment of the invention, the piezoelectric transducer may include a piezoelectric material. When mechanical stress (pressure, tension, shear force) is applied to the piezoelectric material, polarization will be generated inside the material, thereby generating charge or voltage on its surface. The number of piezoelectric transducers may be multiple. This embodiment of the invention does not impose too many restrictions on the number of piezoelectric transducers.

[0059] In one specific configuration, the piezoelectric transducer can be placed inside a pipe, converting kinetic energy into electrical energy through the liquid flowing inside the pipe.

[0060] In a specific application of this invention, the location information may refer to the planar coordinates of the high-pressure gate valve in the pipeline network plan design drawing;

[0061] In this embodiment of the invention, the position data of the high-pressure gate valve is obtained and converted into node network data; the position data of the high-pressure gate valve is determined and converted into the node positions of the plane, and the edge between two nodes represents the pipeline. By traversing the position data of all high-pressure gate valves and the connection relationship of the pipelines, a node network containing nodes and edges can be generated. Each node can also be connected to smaller nodes representing sensors, such as... Figure 2 As shown, the embodiments of the present invention do not impose excessive limitations on this;

[0062] Step 102: Under the node network, obtain the first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and the second energy consumption data of multiple sensors;

[0063] In this embodiment of the invention, after converting the high-pressure gate valve and its corresponding sensors and pipelines into a node network, the corresponding energy consumption data is obtained and an energy consumption model is established.

[0064] In a preferred embodiment of the present invention, the step of acquiring first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and second energy consumption data of multiple sensors in the node network includes:

[0065] The conversion efficiency and functional capacity of the piezoelectric transducer of the high-pressure gate valve can be obtained; furthermore, the start-up energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors can also be obtained.

[0066] The conversion efficiency of a piezoelectric transducer refers to the ratio of output energy to input energy when electrical energy is converted into mechanical energy. For example, the conversion efficiency can be 60%. The functional capacity refers to the continuous or peak electrical power that a piezoelectric transducer can withstand, and the unit is W (watt).

[0067] The startup energy consumption of a sensor refers to the average startup energy consumption of the sensor's receiver and transmitter; the data reception and transmission energy consumption refers to the energy consumption of the sensor's receiver or transmitter during a single data transmission process, which can include data transmission energy consumption and data reception energy consumption; and the data encoding and decoding energy consumption refers to the energy consumption of the receiver in decoding the received data or the transmitter in encoding the transmitted data, which can include data decoding energy consumption and data encoding energy consumption.

[0068] Step 103: Construct an energy consumption model based on the first energy consumption data and the second energy consumption data;

[0069] Further applied to embodiments of the present invention, an energy consumption model can be constructed based on the first energy consumption data and the second energy consumption data; the construction of the energy consumption model based on the first energy consumption data and the second energy consumption data includes: combining the start-up energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the plurality of sensors into a sensor energy consumption layer; setting the conversion efficiency and functional capacity of the piezoelectric transducer as an energy consumption layer; obtaining the initial energy, energy collection power, and energy consumption power of the energy storage device, constructing energy consumption constraints based on the initial energy, energy collection power, and energy consumption power of the energy storage device, and forming the energy consumption model based on the energy consumption constraints, the sensor energy consumption layer, and the energy consumption layer, as shown below. Figure 3 As shown.

[0070] Specifically, in this embodiment of the invention, the expression for the sensor energy consumption layer Et is as follows:

[0071]

[0072] Where Ec represents the startup energy consumption of the sensor's receiver or transmitter, E1 represents the energy consumed to transmit each bit of data, E2 represents the energy consumed to receive each bit of data, β1 represents the parameters of the free space amplifier circuit, β2 represents the parameters of the multipath space amplifier circuit, and d represents the information transmission distance.

[0073]

[0074] Where Es represents the energy consumption for data reception and transmission, t represents the running time, L represents the length of the data transmitted, B represents the bit rate, and Eex represents the additional power of the sensor.

[0075]

[0076] Where Er represents the energy consumption for data encoding and decoding, t represents the running time, L represents the length of the data transmitted, Bc represents the encoding rate of the data transmitted, and Eex represents the additional power of the sensor.

[0077] Et = Es + Er;

[0078] Here, Et represents the sensor energy consumption layer, Es represents the data reception and transmission energy consumption, and Er represents the data encoding and decoding energy consumption. The two together constitute the sensor energy consumption layer.

[0079] Energy consumption layer E b It can be represented as follows:

[0080] Eb=CA·E f ;

[0081] Where Eb represents the energy consumption layer, E f CA represents conversion efficiency, and CA represents functional capacity.

[0082] In this embodiment of the invention, the initial energy, energy collection power, and energy consumption power of the energy storage device are obtained, and energy consumption constraints are generated based on the initial energy, energy collection power, and energy consumption power of the energy storage device.

[0083] The expression for the energy consumption constraint is as follows:

[0084] Ph(t)≥Pc(t)+κ, t∈[0,∞);

[0085] Where Ph(t) represents the energy harvesting power of the energy storage device, Pc(t) represents the energy consumption power of the energy storage device, κ represents the initial energy of the energy storage device, and t represents the operating time.

[0086] Step 104: Calculate the energy consumption profit and loss data using the energy consumption model.

[0087] Further applied to embodiments of the present invention, the step of calculating energy consumption profit and loss data through the energy consumption model includes:

[0088] The energy data output by the energy consumption layer under constraints is used to form an energy sequence;

[0089] The energy consumption data output by the sensor energy consumption layer under constrained conditions is used to form a sensor energy consumption sequence.

[0090] Energy consumption surplus / deficit data are calculated using the energy sequence and sensor energy consumption sequence.

[0091] In this embodiment of the invention, the energy sequence output by the energy consumption layer can be represented as follows:

[0092] a1, a2, a3, a4···an-1, an;

[0093] The sensor energy consumption sequence output by the sensor energy consumption layer can be represented as follows:

[0094] b1, b2, b3, b4···bn-1, bn;

[0095] The energy consumption profit and loss data can then be represented as follows:

[0096] a1-b1,a2-b2,a3-b3,a4-b4···a-1-bn-1,an-bn

[0097] Where n represents the number of sequence data, that is, the difference operation between the elements of the energy sequence and the sensor energy consumption sequence can be performed to obtain each element of the energy consumption surplus data, which can represent the energy that the energy storage device needs to replenish or the excess energy.

[0098] Step 105: Obtain the power distribution status data of the energy storage device based on the energy consumption surplus and deficit data;

[0099] Further applied to embodiments of the present invention, obtaining the electrical energy distribution status data of the energy storage device based on the energy consumption surplus / deficit data includes:

[0100] Identify the number of each sensor in the energy consumption surplus / deficit data;

[0101] The energy consumption surplus and deficit data are allocated to each sensor according to the numbering example to obtain the power distribution status data of the energy storage device.

[0102] In this embodiment of the invention, it is also possible to identify, such as Figure 3 The sensor number shown is used to establish a correlation between the sequence data in the energy consumption surplus / deficit data obtained by the difference operation and the number of each sensor. By matching the two one by one, the power distribution status data can be obtained. The specific example of the power distribution status data is as follows: (number 1, a1, b1, a1-b1), (number 2, a2, b2, a2-b2), (number 2, a3, b3, a3-b3)·······, (number n, an, bn, an-bn).

[0103] Step 106: Input the power distribution state data into the time regularized matrix decomposition model to obtain the predicted active nodes and dormant nodes;

[0104] Further applied to embodiments of the present invention, energy consumption surplus / deficit data in the power distribution status data can be categorized by adding labels. Specifically, when the energy consumption surplus / deficit data in the power distribution status data is negative, the corresponding data is labeled as a dormant node; and when the energy consumption surplus / deficit data in the power distribution status data is positive, the corresponding data is labeled as an active node, thereby obtaining training data. Specifically, in embodiments of the present invention, the step of inputting the power distribution status data into a time-regularized matrix factorization model to obtain predicted active and dormant nodes includes:

[0105] The power distribution state data is converted into an observation data matrix, which is then decomposed into a multidimensional low-rank matrix. The regularization parameters of the high-pressure gate valve are then set.

[0106] The high-pressure gate valve regularization parameter can refer to a regularization term parameter related to the high-pressure gate valve's operating time, which can improve the model's generalization ability and limit its complexity. The gate valve objective function is established based on the multidimensional low-rank matrix and the high-pressure gate valve regularization parameter.

[0107] The objective function for this gate valve can be expressed as follows:

[0108]

[0109] Where Y represents the observation data matrix, U represents the first low-rank matrix of the decomposition, X represents the first low-rank matrix of the decomposition, W indicates that any column of matrix X is a combination of the first two columns, Zt represents the regularization parameter related to the opening duration of the high-pressure gate valve, and Z A The regularization parameter represents the duration of closure of the high-pressure gate valve. F This represents the Frobenius norm operation.

[0110] The gate valve objective function is optimized by the ADNM optimizer to obtain the output predicted sensor data. This predicted sensor data, together with the original sensor data, constitutes complete sensor data. Based on this complete sensor data, active and dormant nodes are identified. Specifically, if the amount of data in the complete sensor data is less than a preset amount or is zero, then that node is determined to be a dormant node; the remaining nodes are identified as active nodes. In this embodiment of the invention, the ADNM optimizer solves the problem of high-dimensional data containing many missing values.

[0111] In this embodiment of the invention, the first energy consumption data of multiple new piezoelectric transducers and the second energy consumption data of multiple sensors can also be input into the trained time-regularized matrix factorization model to obtain the output predicted active nodes and dormant nodes.

[0112] Step 107: Monitor the high-pressure gate valve based on the predicted active and dormant nodes to obtain output monitoring data, and control the operation of the high-pressure gate valve through the monitoring data.

[0113] In this embodiment of the invention, the step of monitoring the high-pressure gate valve based on the predicted active and dormant nodes to obtain output monitoring data, and controlling the operation of the high-pressure gate valve through the monitoring data, includes:

[0114] Extract the sensor locations corresponding to the active nodes, and identify the number of a specific high-pressure gate valve through the sensor locations;

[0115] The sensor data related to the number of the specific high-pressure gate valve is calculated and threshold identification is performed to obtain the output sensor monitoring data. The operation of the high-pressure gate valve is controlled by the monitoring data, and data acquisition and operation control are performed for the specific high-pressure gate valve of the active node.

[0116] In this embodiment of the invention, multiple high-pressure gate valves are arranged on a pipeline network. Each high-pressure gate valve is connected to various sensors and energy storage devices. These sensors include piezoelectric transducers, temperature sensors, and pressure sensors. The intelligent monitoring method for the high-pressure gate valves includes: acquiring the position data of the high-pressure gate valves and converting the position data into node network data; acquiring first energy consumption data of the piezoelectric transducer of the high-pressure gate valves and second energy consumption data from multiple sensors within the node network; constructing an energy consumption model based on the first and second energy consumption data; calculating energy consumption profit and loss data using the energy consumption model; and calculating the energy consumption profit and loss data based on the energy consumption profit and loss data. The data obtains the power distribution status data of the energy storage device; the power distribution status data is input into a time-regularized matrix decomposition model to obtain predicted active nodes and dormant nodes; based on the predicted active nodes and dormant nodes, the high-pressure gate valve is monitored to obtain output monitoring data, and the operation of the high-pressure gate valve is controlled by the monitoring data; by using an artificial intelligence model to complete the power distribution status data, active sensor nodes can be further obtained, and the high-pressure gate valve corresponding to the active sensor node is monitored, which improves the intelligence of the high-pressure gate valve monitoring program and ensures the safety of the high-pressure gate valve operation.

[0117] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0118] Reference Figure 4 This diagram illustrates a structural block diagram of an embodiment of an intelligent monitoring system for high-pressure gate valves according to an invention. Multiple high-pressure gate valves are arranged on a pipeline network. Each high-pressure gate valve is connected to various sensors and an energy storage device. The energy storage device is connected to a piezoelectric transducer. The sensors include temperature sensors and pressure sensors, and may specifically include the following modules:

[0119] The first acquisition module 301 is used to acquire the position data of the high-pressure gate valve and convert the position data into node network data;

[0120] The second acquisition module 302 is used to acquire, in the node network, the first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and the second energy consumption data of multiple sensors;

[0121] Construction module 303 is used to construct an energy consumption model based on the first energy consumption data and the second energy consumption data;

[0122] Calculation module 304 is used to calculate energy consumption profit and loss data through the energy consumption model;

[0123] The power distribution module 305 is used to obtain the power distribution status data of the energy storage device based on the energy consumption surplus and deficit data.

[0124] Prediction module 306 is used to input the power distribution state data into the time regularized matrix decomposition model to obtain the predicted active nodes and dormant nodes.

[0125] The monitoring module 307 is used to monitor the high-pressure gate valve based on the predicted active and dormant nodes, obtain output monitoring data, and control the operation of the high-pressure gate valve through the monitoring data.

[0126] Preferably, the second acquisition module includes:

[0127] The first acquisition sub-model is used to obtain the conversion efficiency and functional capacity of the piezoelectric transducer of the high-pressure gate valve;

[0128] The second acquisition sub-model is used to acquire the startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors.

[0129] Preferably, the building module includes:

[0130] The combination submodule is used to combine the startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors into a sensor energy consumption layer;

[0131] A submodule is set up to set the conversion efficiency and functional capacity of the piezoelectric transducer as the energy consumption layer.

[0132] A submodule is constructed to obtain the initial energy, energy collection power, and energy consumption power of the energy storage device. Based on the initial energy, energy collection power, and energy consumption power of the energy storage device, an energy consumption constraint is constructed. The energy consumption model is composed of the energy consumption constraint, the sensor energy consumption layer, and the energy consumption layer.

[0133] Preferably, the computing module includes:

[0134] The first output submodule is used to assemble the energy data output by the energy consumption layer under constraints into an energy sequence;

[0135] The second output submodule is used to assemble the energy consumption data output by the sensor energy consumption layer under constrained conditions into a sensor energy consumption sequence.

[0136] The calculation submodule is used to calculate energy consumption surplus and deficit data through the energy sequence and sensor energy consumption sequence.

[0137] Preferably, the power distribution module includes:

[0138] The identification submodule is used to identify the number of each sensor in the energy consumption surplus and deficit data;

[0139] The allocation submodule is used to allocate the energy consumption surplus and deficit data to each sensor according to the number example, so as to obtain the power distribution status data of the energy storage device.

[0140] Preferably, the prediction module includes:

[0141] The conversion submodule is used to convert the power distribution state data into an observation data matrix, decompose the observation data matrix into a multi-dimensional low-rank matrix, and set the regularization parameters of the high-voltage gate valve.

[0142] A submodule is established to establish the gate valve objective function based on the multidimensional low-rank matrix and the high-pressure gate valve regularization parameters;

[0143] The identification submodule is used to optimize the objective function of the gate valve through the ADNM optimizer to obtain the output predicted sensor data. The predicted sensor data and the original sensor data are combined to form complete sensor data. Based on the complete sensor data, active nodes and dormant nodes are identified.

[0144] Preferably, the monitoring module includes:

[0145] The extraction module is used to extract the sensor location corresponding to the active node and identify the number of a specific high-pressure gate valve through the sensor location;

[0146] The calculation module is used to calculate the sensor data related to the number of the specific high-pressure gate valve, perform threshold identification operation, obtain the output sensor monitoring data, and control the operation of the high-pressure gate valve through the monitoring data.

[0147] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0148] Specific limitations regarding the intelligent monitoring system for high-pressure gate valves can be found in the above description of the intelligent monitoring method for high-pressure gate valves, and will not be repeated here. Each module in the aforementioned intelligent monitoring system for high-pressure gate valves can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0149] The intelligent monitoring system for high-pressure gate valves provided above can be used to execute the intelligent monitoring method for high-pressure gate valves provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0150] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent monitoring method for high-pressure gate valves. The display screen can be an LCD screen or an e-ink display screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0151] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0152] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0153] The location data of the high-pressure gate valve is acquired and converted into node network data;

[0154] Under the node network, the first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and the second energy consumption data of multiple sensors are obtained;

[0155] An energy consumption model is constructed based on the first energy consumption data and the second energy consumption data;

[0156] Energy consumption profit and loss data are calculated using the energy consumption model.

[0157] The energy distribution status data of the energy storage device is obtained based on the energy consumption surplus and deficit data.

[0158] The power distribution state data is input into a time-regularized matrix decomposition model to obtain the predicted active nodes and dormant nodes.

[0159] The high-pressure gate valve is monitored based on the predicted active and dormant nodes to obtain output monitoring data, and the operation of the high-pressure gate valve is controlled by the monitoring data.

[0160] Preferably, the acquisition of first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and second energy consumption data of multiple sensors under the node network includes:

[0161] The conversion efficiency and functional capacity of the piezoelectric transducer of the high-pressure gate valve were obtained;

[0162] The startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors are obtained.

[0163] Preferably, the step of constructing an energy consumption model based on the first energy consumption data and the second energy consumption data includes:

[0164] The startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors are combined into a sensor energy consumption layer;

[0165] The conversion efficiency and functional capacity of the piezoelectric transducer are set as the energy consumption layer;

[0166] The initial energy, energy collection power, and energy consumption power of the energy storage device are obtained. Based on the initial energy, energy collection power, and energy consumption power of the energy storage device, energy consumption constraints are constructed. The energy consumption model is composed of the energy consumption constraints, the sensor energy consumption layer, and the energy consumption layer.

[0167] Preferably, the step of calculating energy consumption profit and loss data through the energy consumption model includes:

[0168] The energy data output by the energy consumption layer under constraints is used to form an energy sequence;

[0169] The energy consumption data output by the sensor energy consumption layer under constrained conditions is used to form a sensor energy consumption sequence.

[0170] Energy consumption surplus / deficit data are calculated using the energy sequence and sensor energy consumption sequence.

[0171] Preferably, obtaining the electrical energy distribution status data of the energy storage device based on the energy consumption surplus / deficit data includes:

[0172] Identify the number of each sensor in the energy consumption surplus / deficit data;

[0173] The energy consumption surplus and deficit data are allocated to each sensor according to the numbering example to obtain the power distribution status data of the energy storage device.

[0174] Preferably, the step of inputting the power distribution state data into a time-regularized matrix factorization model to obtain the predicted active and dormant nodes includes:

[0175] The power distribution state data is converted into an observation data matrix, which is then decomposed into a multidimensional low-rank matrix. The regularization parameters of the high-pressure gate valve are then set.

[0176] The objective function of the gate valve is established based on the multidimensional low-rank matrix and the regularization parameters of the high-pressure gate valve.

[0177] The gate valve objective function is optimized by the ADNM optimizer to obtain the output predicted sensor data. The predicted sensor data and the original sensor data are combined to form complete sensor data. Active nodes and dormant nodes are identified based on the complete sensor data.

[0178] Preferably, the step of monitoring the high-pressure gate valve based on the predicted active and dormant nodes to obtain output monitoring data, and controlling the operation of the high-pressure gate valve through the monitoring data, includes:

[0179] Extract the sensor locations corresponding to the active nodes, and identify the number of a specific high-pressure gate valve through the sensor locations;

[0180] The sensor data related to the number of the specific high-pressure gate valve is calculated and a threshold identification operation is performed to obtain the output sensor monitoring data. The operation of the high-pressure gate valve is controlled by the monitoring data.

[0181] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0182] The location data of the high-pressure gate valve is acquired and converted into node network data;

[0183] Under the node network, the first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and the second energy consumption data of multiple sensors are obtained;

[0184] An energy consumption model is constructed based on the first energy consumption data and the second energy consumption data;

[0185] Energy consumption profit and loss data are calculated using the energy consumption model.

[0186] The energy distribution status data of the energy storage device is obtained based on the energy consumption surplus and deficit data.

[0187] The power distribution state data is input into a time-regularized matrix decomposition model to obtain the predicted active nodes and dormant nodes.

[0188] The high-pressure gate valve is monitored based on the predicted active and dormant nodes to obtain output monitoring data, and the operation of the high-pressure gate valve is controlled by the monitoring data.

[0189] Preferably, the acquisition of first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and second energy consumption data of multiple sensors under the node network includes:

[0190] The conversion efficiency and functional capacity of the piezoelectric transducer of the high-pressure gate valve were obtained;

[0191] The startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors are obtained.

[0192] Preferably, the step of constructing an energy consumption model based on the first energy consumption data and the second energy consumption data includes:

[0193] The startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors are combined into a sensor energy consumption layer;

[0194] The conversion efficiency and functional capacity of the piezoelectric transducer are set as the energy consumption layer;

[0195] The initial energy, energy collection power, and energy consumption power of the energy storage device are obtained. Based on the initial energy, energy collection power, and energy consumption power of the energy storage device, energy consumption constraints are constructed. The energy consumption model is composed of the energy consumption constraints, the sensor energy consumption layer, and the energy consumption layer.

[0196] Preferably, the step of calculating energy consumption profit and loss data through the energy consumption model includes:

[0197] The energy data output by the energy consumption layer under constraints is used to form an energy sequence;

[0198] The energy consumption data output by the sensor energy consumption layer under constrained conditions is used to form a sensor energy consumption sequence.

[0199] Energy consumption surplus / deficit data are calculated using the energy sequence and sensor energy consumption sequence.

[0200] Preferably, obtaining the electrical energy distribution status data of the energy storage device based on the energy consumption surplus / deficit data includes:

[0201] Identify the number of each sensor in the energy consumption surplus / deficit data;

[0202] The energy consumption surplus and deficit data are allocated to each sensor according to the numbering example to obtain the power distribution status data of the energy storage device.

[0203] Preferably, the step of inputting the power distribution state data into a time-regularized matrix factorization model to obtain the predicted active and dormant nodes includes:

[0204] The power distribution state data is converted into an observation data matrix, which is then decomposed into a multidimensional low-rank matrix. The regularization parameters of the high-pressure gate valve are then set.

[0205] The objective function of the gate valve is established based on the multidimensional low-rank matrix and the regularization parameters of the high-pressure gate valve.

[0206] The gate valve objective function is optimized by the ADNM optimizer to obtain the output predicted sensor data. The predicted sensor data and the original sensor data are combined to form complete sensor data. Active nodes and dormant nodes are identified based on the complete sensor data.

[0207] Preferably, the step of monitoring the high-pressure gate valve based on the predicted active and dormant nodes to obtain output monitoring data, and controlling the operation of the high-pressure gate valve through the monitoring data, includes:

[0208] Extract the sensor locations corresponding to the active nodes, and identify the number of a specific high-pressure gate valve through the sensor locations;

[0209] The sensor data related to the number of the specific high-pressure gate valve is calculated and a threshold identification operation is performed to obtain the output sensor monitoring data. The operation of the high-pressure gate valve is controlled by the monitoring data.

[0210] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0211] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0212] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0213] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of apparatus, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0214] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0215] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0216] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0217] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or terminal device that includes said element.

[0218] The present invention provides a detailed description of an intelligent monitoring method for a high-pressure gate valve, an intelligent monitoring system for a high-pressure gate valve, a computer device, and a storage medium. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for intelligent monitoring of high-pressure gate valves, characterized in that, Multiple high-pressure gate valves are installed on a pipeline network. These high-pressure gate valves are connected to various sensors and energy storage devices. The energy storage devices are connected to piezoelectric transducers. The sensors include temperature sensors and pressure sensors. The location data of the high-pressure gate valve is acquired and converted into node network data; Under the node network, the first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and the second energy consumption data of multiple sensors are obtained; An energy consumption model is constructed based on the first energy consumption data and the second energy consumption data; Energy consumption profit and loss data are calculated using the energy consumption model. The energy distribution status data of the energy storage device is obtained based on the energy consumption surplus and deficit data. The power distribution state data is input into a time-regularized matrix decomposition model to obtain the predicted active nodes and dormant nodes. The high-pressure gate valve is monitored based on the predicted active and dormant nodes to obtain output monitoring data, and the operation of the high-pressure gate valve is controlled by the monitoring data. The step of inputting the power distribution state data into a time-regularized matrix factorization model to obtain predicted active and dormant nodes includes: The power distribution state data is converted into an observation data matrix, which is then decomposed into a multidimensional low-rank matrix. The regularization parameters of the high-pressure gate valve are then set. The objective function of the gate valve is established based on the multidimensional low-rank matrix and the regularization parameters of the high-pressure gate valve. The gate valve objective function is optimized by the ADNM optimizer to obtain the output predicted sensor data. The predicted sensor data and the original sensor data are combined to form complete sensor data. Active nodes and dormant nodes are identified based on the complete sensor data. The objective function for this gate valve can be expressed as follows: ； in, Represents the observation data matrix, Let the first low-rank matrix be the decomposed matrix. Let this be the first low-rank matrix of the decomposition. This indicates that any column of matrix X is a combination of the first two columns. This indicates a regularization parameter related to the opening duration of the high-pressure gate valve. This indicates a regularization parameter related to the closing duration of the high-pressure gate valve. This represents the Frobenius norm operation.

2. The method according to claim 1, characterized in that, The first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and the second energy consumption data of multiple sensors are obtained in the node network, including: The conversion efficiency and functional capacity of the piezoelectric transducer of the high-pressure gate valve were obtained; The startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors are obtained.

3. The method according to claim 2, characterized in that, The step of constructing an energy consumption model based on the first energy consumption data and the second energy consumption data includes: The startup energy consumption, data reception and transmission energy consumption, and data encoding and decoding energy consumption of the multiple sensors are combined into a sensor energy consumption layer; The conversion efficiency and functional capacity of the piezoelectric transducer are set as the energy consumption layer; The initial energy, energy collection power, and energy consumption power of the energy storage device are obtained. Based on the initial energy, energy collection power, and energy consumption power of the energy storage device, energy consumption constraints are constructed. The energy consumption model is composed of the energy consumption constraints, the sensor energy consumption layer, and the energy consumption layer.

4. The method according to claim 3, characterized in that, The energy consumption profit and loss data calculated through the energy consumption model includes: The energy data output by the energy consumption layer under constraints is used to form an energy sequence; The energy consumption data output by the sensor energy consumption layer under constrained conditions is used to form a sensor energy consumption sequence. Energy consumption surplus / deficit data are calculated using the energy sequence and sensor energy consumption sequence.

5. The method according to claim 2, characterized in that, The step of obtaining the electrical energy distribution status data of the energy storage device based on the energy consumption surplus and deficit data includes: Identify the number of each sensor in the energy consumption surplus / deficit data; The energy consumption surplus and deficit data are allocated to each sensor according to the numbering to obtain the power distribution status data of the energy storage device.

6. The method according to claim 2, characterized in that, The step of monitoring the high-pressure gate valve based on the predicted active and dormant nodes, obtaining output monitoring data, and controlling the operation of the high-pressure gate valve using the monitoring data includes: Extract the sensor locations corresponding to the active nodes, and identify the number of a specific high-pressure gate valve through the sensor locations; The sensor data related to the number of the specific high-pressure gate valve is calculated and a threshold identification operation is performed to obtain the output sensor monitoring data. The operation of the high-pressure gate valve is controlled by the monitoring data.

7. An intelligent monitoring system for a high-pressure gate valve, characterized in that, Multiple high-pressure gate valves are installed on a pipeline network. These high-pressure gate valves are connected to various sensors and energy storage devices. The energy storage devices are connected to piezoelectric transducers. The sensors include temperature sensors and pressure sensors. The first acquisition module is used to acquire the position data of the high-pressure gate valve and convert the position data into node network data; The second acquisition module is used to acquire the first energy consumption data of the piezoelectric transducer of the high-pressure gate valve and the second energy consumption data of multiple sensors in the node network. The construction module is used to construct an energy consumption model based on the first energy consumption data and the second energy consumption data; The calculation module is used to calculate energy consumption profit and loss data through the energy consumption model; The power distribution module is used to obtain the power distribution status data of the energy storage device based on the energy consumption surplus and deficit data. The prediction module is used to input the power distribution state data into the time-regularized matrix decomposition model to obtain the predicted active nodes and dormant nodes. The monitoring module is used to monitor the high-pressure gate valve based on the predicted active and dormant nodes, obtain output monitoring data, and control the operation of the high-pressure gate valve through the monitoring data. The step of inputting the power distribution state data into a time-regularized matrix factorization model to obtain predicted active and dormant nodes includes: The power distribution state data is converted into an observation data matrix, which is then decomposed into a multidimensional low-rank matrix. The regularization parameters of the high-pressure gate valve are then set. The objective function of the gate valve is established based on the multidimensional low-rank matrix and the regularization parameters of the high-pressure gate valve. The gate valve objective function is optimized by the ADNM optimizer to obtain the output predicted sensor data. The predicted sensor data and the original sensor data are combined to form complete sensor data. Active nodes and dormant nodes are identified based on the complete sensor data. The objective function for this gate valve can be expressed as follows: ； in, Represents the observation data matrix, Let this be the first low-rank matrix of the decomposition. Let this be the first low-rank matrix of the decomposition. This indicates that any column of matrix X is a combination of the first two columns. This indicates a regularization parameter related to the opening duration of the high-pressure gate valve. This indicates a regularization parameter related to the closing duration of the high-pressure gate valve. This represents the Frobenius norm operation.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent monitoring method for the high-pressure gate valve as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent monitoring method for the high-pressure gate valve as described in any one of claims 1 to 6.

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