System and method for monitoring opening and closing times of valve

Abstract

The invention discloses a system and method for monitoring the opening and closing times of a valve, and belongs to the technical field of valves. The system for monitoring the opening and closing times of the valve comprises a data detection unit, a data acquisition unit, a data processing unit and a man-machine interaction unit. The problem that the opening and closing times of the valve cannot be effectively monitored in the prior art is solved, the model is established by using a machine learning method and is trained by collecting the historical action data of the valve operating mechanism, the state of the valve can be recognized, and meanwhile, the valve opening and closing times can be effectively monitored. The opening and closing times of the valve are recorded and compared with a preset valve opening and closing safety threshold value, and when the opening and closing times exceed the valve opening and closing safety threshold value, an alarm is given immediately, so that the valve can be overhauled or replaced in time, and the information of the environment where the sensor is located can be known in real time by deploying various detection devices. The detection precision of the sensor is prevented from being influenced by the ambient temperature and humidity and magnetic field.

Description

Technical Field

[0001] The present invention relates to the field of valve technology, and in particular to a system and method for monitoring the number of times a valve is opened and closed. Background Art

[0002] In the industrial production process, various types of flow control equipment, such as valves and pumps, often need to be maintained and managed to ensure their normal operation and extend their service life. Among them, valve maintenance is particularly important because the number of times a valve is opened and closed is directly related to its service life and reliability. However, many flow control devices on the market are not equipped with devices that can accurately monitor the number of times a valve is opened and closed, and often rely on manual counting; therefore, they do not meet existing needs. In this regard, we propose a system and method for monitoring the number of times a valve is opened and closed. Summary of the invention

[0003] The object of the present invention is to provide a system and method for monitoring the number of times a valve is opened and closed. The opening and closing actions of the valve are detected by a sensor and converted into an electrical signal for output. The electrical signal output by the sensor is then amplified and filtered and converted into a digital signal to obtain valve detection data. A model is established by using a machine learning method, and the action history data of the valve operating mechanism is collected to train the model. The analysis of the valve detection data by the model can identify the state of the valve and display it. At the same time, the number of times the valve is opened and closed is recorded and compared with a pre-set valve opening and closing safety threshold. When the number of opening and closing exceeds the valve opening and closing safety threshold, an alarm is immediately issued, thereby solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a system for monitoring the number of times a valve is opened and closed, comprising:

[0005] Data detection unit, used for:

[0006] Install a sensor on the operating mechanism of the valve, detect the opening and closing actions of the valve through the sensor and convert it into an electrical signal for output;

[0007] Data acquisition unit for:

[0008] Responsible for receiving the output of the sensor's electrical signal, amplifying and filtering the electrical signal. After processing, the electrical signal is converted into a digital signal, and then the valve detection data is obtained and transmitted to the data processing unit;

[0009] Data processing unit for:

[0010] Use machine learning methods to build a model, collect historical data on the valve operating mechanism to train the model, and identify the valve status through the model's analysis of valve detection data;

[0011] Human-computer interaction unit, used for:

[0012] Provide staff with a human-computer interaction interface, display the opening and closing status of the valve based on the analysis results of the data processing unit, and record the number of times the valve is opened and closed. The recorded number of times the valve is opened and closed is compared with the pre-set valve opening and closing safety threshold. When the number of times the valve is opened and closed exceeds the valve opening and closing safety threshold, an alarm is immediately issued.

[0013] Furthermore, the data detection unit, the data acquisition module and the data processing module communicate with each other by wire or wireless means, specifically:

[0014] Wired methods, including using RS-485 or Modbus RTU serial communication protocols;

[0015] Wireless methods include using Wi-Fi, Bluetooth or LoRa wireless communication protocols.

[0016] Furthermore, the data detection unit further includes:

[0017] Environmental monitoring module for:

[0018] Deploy various testing equipment to test the environment around the valve, including testing temperature, humidity and magnetic field, obtain environmental testing data, and analyze the environmental testing data to determine whether the environmental testing data exceeds the standard range;

[0019] Among them, the testing equipment includes:

[0020] Temperature and humidity sensor, used to detect the temperature and humidity of the environment around the valve;

[0021] Magnetic field measuring instrument, used to detect the magnetic field strength around the valve;

[0022] Temperature standard range: 0℃~40℃;

[0023] Humidity standard range: 0-20% for dry environment; 20-60% for normal environment; 60-90% for humid environment;

[0024] The standard range of magnetic field strength is 20mT-150mT.

[0025] Furthermore, the data acquisition unit comprises:

[0026] Data acquisition module for:

[0027] Receive and collect the electrical signals output by the sensor, and amplify and filter the electrical signals;

[0028] Amplification: Amplify the electrical signal through an amplifier;

[0029] Filtering: Remove noise from electrical signals through filters;

[0030] Data conversion module for:

[0031] The electrical signal is converted into a data signal through a converter, and finally the valve detection data is obtained;

[0032] Data transmission module for:

[0033] Encrypting and transmitting the valve detection data to a data processing unit, and decrypting the valve detection data using a key on the data processing unit;

[0034] Use asymmetric encryption algorithm to encrypt valve detection data;

[0035] The encrypted valve detection data is transmitted to the data processing unit through the network;

[0036] The data processing unit decrypts the received valve detection data using the corresponding public key.

[0037] Furthermore, the data processing unit comprises:

[0038] Data collection module for:

[0039] Collect the historical action data of the valve operating mechanism, mark the status of each historical action data, and after marking, integrate all historical action data into a data set;

[0040] Data analysis module for:

[0041] Use machine learning methods to build a model, use data sets to train the model, use the trained model to analyze valve detection data, and identify the open and closed status of the valve based on the analysis results;

[0042] State generation module, used to:

[0043] Generate a status code output of the valve opening and closing status according to the analysis results of the data analysis module, and display the status code on the human-computer interaction unit;

[0044] Data storage module for:

[0045] The analysis results of the data analysis module are stored in the database. The analysis results are stored in units of days, and all the opening and closing states of the valves on that day are stored in a unified file in the database.

[0046] Furthermore, the data analysis module specifically performs the following process:

[0047] Use machine learning methods to build models;

[0048] Then divide the action history data into training set and test set;

[0049] Train the model through the training set so that the model can learn the characteristics of the valve status;

[0050] Test the model through the test set to verify the performance of the model;

[0051] After the model is trained and tested, it is deployed to actual applications;

[0052] The valve detection data is analyzed through the deployed model to identify the open and closed status of the valve.

[0053] Furthermore, the human-computer interaction unit comprises:

[0054] Valve management module for:

[0055] Based on the analysis results of the data processing unit, the number of times the valve is opened and closed is recorded and compared with the pre-set valve opening and closing safety threshold, and the comparison is used to determine whether the number of times the valve is opened and closed exceeds the safety range;

[0056] If it does not exceed the safety range, continue to record the number of times the valve is opened and closed;

[0057] If it exceeds the safety range, an early warning signal will be generated immediately;

[0058] Among them, the valve opening and closing safety threshold is 0-5000 times;

[0059] Human-computer interaction module for:

[0060] The opening and closing status of the valve is displayed based on the analysis results of the data processing unit, and according to the generated early warning signal, the early warning information is displayed and an alarm combined with sound and light is issued.

[0061] A method for implementing a system for monitoring the number of times a valve is opened and closed comprises the following steps:

[0062] The opening and closing actions of the valve are detected by the sensor and converted into electrical signals for output;

[0063] Amplify and filter the electrical signal output by the sensor and convert it into a digital signal;

[0064] Use machine learning methods to build a model, collect historical data on the valve operating mechanism to train the model, and identify the valve status through the model's analysis of valve detection data;

[0065] Based on the analysis results of the data processing unit, the open and closed status of the valve is displayed;

[0066] The number of times the valve is opened and closed is recorded and compared with the pre-set valve opening and closing safety threshold. When the number of opening and closing times exceeds the valve opening and closing safety threshold, an alarm is immediately issued.

[0067] Furthermore, the method for monitoring the number of times a valve is opened and closed also includes:

[0068] When the difference between the pre-set valve opening and closing safety threshold and the valve guidance safety threshold is within the preset difference range; obtaining the fluid parameters of the fluid in the pipeline where the valve is installed and the environmental parameters of the surrounding environment of the valve;

[0069] Quantify the fluid parameters and environmental parameters and arrange the quantified parameters to form an event set;

[0070] According to the event set, the corresponding update value is retrieved from the pre-set update library and the valve opening and closing safety threshold is updated;

[0071] Among them, the guiding safety threshold is the average value of the number of opening and closing times obtained by conducting multiple opening and closing experiments on the valve in advance; the update library is configured in advance by professionals, and the event sets in the library are associated with the update values ​​one by one; fluid parameters include: fluid type, temperature, flow rate; environmental parameters include: temperature, humidity, light, air composition, and magnetic field.

[0072] Furthermore, the method for monitoring the number of times a valve is opened and closed also includes:

[0073] Obtaining fluid parameters of the fluid in the pipeline where the valve is installed and environmental parameters of the surrounding environment of the valve;

[0074] Obtain a structural topology diagram of the system in which the valve is installed and intercept structural codes of a preset number of levels upstream and downstream of the valve;

[0075] A label set is constructed according to the intercepted structural code and the quantitative parameters for quantifying the fluid parameters and the environmental parameters;

[0076] Obtain fault data from the fault big data platform based on the label set;

[0077] Analyze fault data and build risk warning sets and correction values;

[0078] Based on the correction value, the valve opening and closing safety threshold is corrected;

[0079] Issue risk warnings based on the risk warning set;

[0080] Among them, based on the label set, the fault data obtained from the fault big data platform includes:

[0081] The similarity calculation is performed between the identification set and the standard identification set of the valve corresponding to the fault data; the similarity calculation formula is as follows:

[0082]

[0083] In the formula, δ represents the similarity; A ij Represents the data in the i-th row and j-th column of the local valve identification set; B ij Represents the data of the i-th row and j-th column in the valve identification set on the big data platform; M represents the total number of columns; A is the average value of the data in the local valve identification set; B is the average value of the data in the valve identification set on the big data platform; 2N+2 is the total number of rows of data in the standard identification set or identification set;

[0084] Fault data with a similarity greater than or equal to a preset similarity threshold are extracted.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

[0086] 1. The present invention detects the opening and closing actions of the valve through a sensor and converts them into electrical signals for output, then amplifies and filters the electrical signals output by the sensor and converts them into digital signals to obtain valve detection data. A model is established by using a machine learning method, and the action history data of the valve operating mechanism is collected to train the model. The analysis of the valve detection data by the model can identify the state of the valve and display it. At the same time, the number of valve openings and closings is recorded and compared with a pre-set valve opening and closing safety threshold. When the number of openings and closings exceeds the valve opening and closing safety threshold, an alarm is immediately issued, so that the valve can be inspected or replaced in time, ensuring the reliability and safety of the valve.

[0087] 2. The present invention can detect the temperature, humidity and magnetic field of the environment around the valve by deploying various detection equipment, so as to obtain environmental detection data. By analyzing the environmental detection data, it can be determined whether the environmental detection data exceeds the standard range. By deploying various detection equipment, the environmental information of the sensor can be understood in real time to prevent the temperature, humidity and magnetic field of the surrounding environment from affecting the detection accuracy of the sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 It is a schematic diagram of the structure of the system for monitoring the opening and closing times of a valve according to the present invention. DETAILED DESCRIPTION

[0089] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] In order to solve the technical problem that many existing flow control devices are not equipped with devices that can accurately monitor the number of valve opening and closing times, and often rely on manual counting, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0091] A system for monitoring the number of times a valve is opened and closed, comprising:

[0092] Data detection unit, used for:

[0093] Install a sensor on the operating mechanism of the valve, detect the opening and closing actions of the valve through the sensor and convert it into an electrical signal for output;

[0094] Data acquisition unit for:

[0095] Responsible for receiving the output of the sensor's electrical signal, amplifying and filtering the electrical signal. After processing, the electrical signal is converted into a digital signal, and then the valve detection data is obtained and transmitted to the data processing unit;

[0096] Data processing unit for:

[0097] Use machine learning methods to build a model, collect historical data on the valve operating mechanism to train the model, and identify the valve status through the model's analysis of valve detection data;

[0098] Human-computer interaction unit, used for:

[0099] Provide staff with a human-computer interaction interface, display the opening and closing status of the valve based on the analysis results of the data processing unit, and record the number of times the valve is opened and closed. The recorded number of times the valve is opened and closed is compared with the pre-set valve opening and closing safety threshold. When the number of times the valve is opened and closed exceeds the valve opening and closing safety threshold, an alarm is immediately issued.

[0100] The technical effect of the above content is: the data detection unit can detect the opening and closing actions of the valve through the installed sensor, and convert it into an electrical signal for output. Then, the data acquisition unit can receive the electrical signal output by the sensor for amplification and filtering. The quality of the electrical signal can be improved through amplification and filtering. The processed electrical signal is then converted into a digital signal to obtain valve detection data. The data processing unit establishes a model by using a machine learning method. At the same time, the action history data of the valve operating mechanism is collected to train the model, so that the model can be used to judge the state of the valve. The opening and closing state of the valve will be displayed through the human-computer interaction unit so that the staff can understand the valve state in time. At the same time, the human-computer interaction unit will also record the number of times the valve is opened and closed, and will issue an alarm when the number of opening and closing exceeds the safe level, thereby improving the reliability and safety of the valve.

[0101] The data detection unit, the data acquisition module and the data processing module communicate with each other by wired or wireless means, specifically:

[0102] Wired methods, including using RS-485 or Modbus RTU serial communication protocols;

[0103] Wireless methods include using Wi-Fi, Bluetooth or LoRa wireless communication protocols.

[0104] The technical effect of the above content is: when selecting a communication method, the transmission network is selected according to the communication rate, communication distance, communication protocol, installation flexibility, work efficiency, economy and environmental requirements, making data transmission more flexible.

[0105] The data detection unit further includes:

[0106] Environmental monitoring module for:

[0107] Deploy various testing equipment to test the environment around the valve, including testing temperature, humidity and magnetic field, obtain environmental testing data, and analyze the environmental testing data to determine whether the environmental testing data exceeds the standard range;

[0108] Among them, the testing equipment includes:

[0109] Temperature and humidity sensor, used to detect the temperature and humidity of the environment around the valve;

[0110] Magnetic field measuring instrument, used to detect the magnetic field strength around the valve;

[0111] Temperature standard range: 0℃~40℃;

[0112] Humidity standard range: 0-20% for dry environment; 20-60% for normal environment; 60-90% for humid environment;

[0113] The standard range of magnetic field strength is 20mT-150mT.

[0114] The technical effect of the above content is: the data detection unit can detect the environment around the valve by setting up an environmental detection module, so as to avoid the environment around the valve affecting the detection accuracy of the sensor, and the environmental detection module deploys various detection equipment to detect temperature, humidity and magnetic field. By deploying temperature and humidity sensors, the temperature and humidity of the environment around the valve can be detected, and the detected temperature and humidity data can be analyzed to analyze whether the detected temperature exceeds the preset standard range of 0℃~40℃, and whether the detected humidity exceeds the preset standard range. Because the environment is different, the standard range of humidity is different. If it is in a dry environment, the standard range is 0-20%, and if it is in a general environment, the standard range is 20-6 0%, if it is in a humid environment, the standard range is 60-90%. By analyzing the temperature and humidity, it is determined whether the temperature and humidity around the valve meet the appropriate range of the sensor. The magnetic field measuring instrument can detect the magnetic field strength of the valve environment and analyze the detected magnetic field data to analyze whether the detected magnetic field strength exceeds the preset standard range of 20mT-150mT to ensure that the surrounding magnetic field does not interfere with the sensor's detection work. By detecting the temperature, humidity and magnetic field of the surrounding environment, the temperature and humidity changes and the magnetic field strength can be understood in real time. When the temperature, humidity and magnetic field exceed the standard range, protective measures can be taken in time to reduce the impact of these factors on the sensor, thereby ensuring the sensor's detection accuracy and stability.

[0115] Data acquisition unit, including:

[0116] Data acquisition module for:

[0117] Receive and collect the electrical signals output by the sensor, and amplify and filter the electrical signals;

[0118] Amplification: Amplify the electrical signal through an amplifier;

[0119] Filtering: Remove noise from electrical signals through filters;

[0120] Data conversion module for:

[0121] The electrical signal is converted into a data signal through a converter, and finally the valve detection data is obtained;

[0122] Data transmission module for:

[0123] Encrypting and transmitting the valve detection data to a data processing unit, and decrypting the valve detection data using a key on the data processing unit;

[0124] Data encryption: Use an asymmetric encryption algorithm to encrypt valve detection data. In the asymmetric encryption algorithm, the public key is used to encrypt the data;

[0125] Data transmission: The encrypted data is transmitted to the data processing unit through the network. During the transmission process, even if the data is intercepted, it is impossible to decrypt and read the original data without the corresponding key;

[0126] Data decryption: The data processing unit uses the corresponding public key (asymmetric encryption) to decrypt the received ciphertext and restore it to the original data.

[0127] The technical effect of the above content is: the data acquisition module can receive and collect the electrical signal output by the sensor, and amplify and filter the electrical signal. The amplifier amplifies the electrical signal, and the amplifier can amplify the weak signal to a sufficient amplitude so that it can be smoothly transmitted and processed in the electronic system, thereby achieving the purpose of enhancing the signal strength. The noise in the electrical signal is removed by a filter (such as a low-pass filter, a high-pass filter, a band-pass filter, and a band-stop filter, etc.), which can reduce noise interference and improve the stability and reliability of the electrical signal. Then, the data conversion module converts the electrical signal into a data signal through a converter to obtain valve detection data, and the valve detection data is used for subsequent data analysis. Finally, the valve detection data is encrypted and transmitted through the data transmission module, and the data processing unit uses a key to decrypt the valve detection data to obtain the valve detection data. Through encrypted transmission, it can effectively resist potential risks such as information theft and tampering, ensuring that the data can be fully protected while flowing.

[0128] Data processing unit, including:

[0129] Data collection module for:

[0130] Collect the historical action data of the valve operating mechanism, mark the status of each historical action data, and after marking, integrate all historical action data into a data set;

[0131] Data analysis module for:

[0132] Use machine learning methods to build a model, use data sets to train the model, use the trained model to analyze valve detection data, and identify the open and closed status of the valve based on the analysis results;

[0133] State generation module, used to:

[0134] Generate a status code output of the valve opening and closing status according to the analysis results of the data analysis module, and display the status code on the human-computer interaction unit;

[0135] Data storage module for:

[0136] The analysis results of the data analysis module are stored in the database. The analysis results are stored in units of days, and all the opening and closing states of the valves on that day are stored in a unified file in the database.

[0137] Among them, the data analysis module specifically performs the following processes:

[0138] Use machine learning methods to build models;

[0139] Then divide the action history data into training set and test set;

[0140] Train the model through the training set so that the model can learn the characteristics of the valve status;

[0141] Test the model through the test set to verify the performance of the model;

[0142] After the model is trained and tested, it is deployed to actual applications;

[0143] The valve detection data is analyzed through the deployed model to identify the open and closed status of the valve.

[0144] The technical effects of the above content are as follows: first, through the data collection module, the action history data of the valve operating mechanism can be collected, and the state of each action history data can be marked. The valve opening and closing state in each action history data can be clearly identified through the state marking. All the collected action history data are integrated into a data set, and the data set is divided into a training set and a test set for training and testing the model. Then, the data analysis module establishes a model through machine learning methods (such as support vector machines, neural networks, and decision trees, etc.), and then, the model is trained and tested using the training set and the test set respectively. In the training set, the model is trained to learn the characteristics of the valve state, and in the test set, the performance of the model is verified. Through training and testing, if the model achieves the expected accuracy, the model is deployed in actual applications, and the valve detection data is analyzed through the model, so that the opening and closing state of the valve can be determined. The state generation module generates a state code of the valve opening and closing state according to the opening and closing state of the valve and outputs it to the human-computer interaction unit so that the human-computer interaction unit can display the valve opening and closing state. Finally, the analysis results will also be stored in the database through the data storage module for future query and management.

[0145] Human-computer interaction unit, including:

[0146] Valve management module for:

[0147] Based on the analysis results of the data processing unit, the number of times the valve is opened and closed is recorded and compared with the pre-set valve opening and closing safety threshold, and the comparison is used to determine whether the number of times the valve is opened and closed exceeds the safety range;

[0148] If it does not exceed the safety range, continue to record the number of times the valve is opened and closed;

[0149] If it exceeds the safety range, an early warning signal will be generated immediately;

[0150] Among them, the valve opening and closing safety threshold is 0-5000 times;

[0151] Human-computer interaction module for:

[0152] The opening and closing status of the valve is displayed based on the analysis results of the data processing unit, and according to the generated early warning signal, the early warning information is displayed and an alarm combined with sound and light is issued.

[0153] The technical effect of the above content is: through the valve management module, the number of times the valve is opened and closed can be recorded based on the analysis results of the data processing unit, and then the number of times the valve is opened and closed can be compared with the pre-set valve opening and closing safety threshold. Through the comparison, it is determined whether the number of times the valve is opened and closed exceeds the safety range. If it exceeds the safety range, an early warning signal is immediately generated. The human-computer interaction module can display the early warning information and issue an alarm combined with sound and light according to the generated early warning signal, so as to remind the staff to pay attention to the fact that the number of times the valve is opened and closed has exceeded the safety range, so as to timely inspect or replace the valve. At the same time, the human-computer interaction module can also display the opening and closing status of the valve based on the analysis results of the data processing unit, so that the staff can understand the opening and closing status of the valve in real time. Through the above operation, the human-computer interaction unit can provide the staff with a human-computer interaction interface, so that the staff can easily view the opening and closing status and number of the valve through it, and understand whether the number of times the valve is opened and closed exceeds the limit standard, so as to facilitate the staff to grasp the valve operation status in real time, so that the staff can take protective measures for the valve in a timely and rapid manner.

[0154] Specifically, this embodiment also proposes a method for implementing a system for monitoring the number of times a valve is opened and closed, comprising the following steps:

[0155] The opening and closing actions of the valve are detected by the sensor and converted into electrical signals for output;

[0156] Amplify and filter the electrical signal output by the sensor and convert it into a digital signal;

[0157] Use machine learning methods to build a model, collect historical data on the valve operating mechanism to train the model, and identify the valve status through the model's analysis of valve detection data;

[0158] Based on the analysis results of the data processing unit, the open and closed status of the valve is displayed;

[0159] Record the number of times the valve is opened and closed and compare it with the pre-set valve opening and closing safety threshold. When the number of times exceeds the valve opening and closing safety threshold, an alarm is immediately issued

[0160] Working principle: The data detection unit detects the opening and closing actions of the valve through sensors and converts them into electrical signals for output. In addition, the data detection unit deploys various detection equipment through the environmental detection module, which can understand the environmental information of the sensor in real time to avoid the temperature, humidity and magnetic field of the surrounding environment affecting the detection accuracy of the sensor. Then, the electrical signal output by the sensor is amplified and filtered by the data acquisition unit and converted into a digital signal to obtain the valve detection data. The quality and stability of the electrical signal can be improved by amplification and filtering. The data processing unit establishes a model by using machine learning methods. At the same time, the historical action data of the valve operating mechanism is collected to train the model, so that the model can be used to judge the state of the valve. The opening and closing state of the valve will be displayed through the human-computer interaction unit so that the staff can understand the valve state in time. At the same time, the human-computer interaction unit will also record the number of times the valve is opened and closed, and will issue an alarm when the number of opening and closing exceeds the safety limit, so as to improve the reliability and safety of the valve.

[0161] The method for monitoring the number of times a valve is opened and closed also includes:

[0162] When the difference between the pre-set valve opening and closing safety threshold and the valve guidance safety threshold is within the preset difference range; obtaining the fluid parameters of the fluid in the pipeline where the valve is installed and the environmental parameters of the surrounding environment of the valve;

[0163] Quantify the fluid parameters and environmental parameters and arrange the quantified parameters to form an event set;

[0164] According to the event set, the corresponding update value is retrieved from the pre-set update library and the valve opening and closing safety threshold is updated;

[0165] Among them, the guiding safety threshold is the average value of the number of opening and closing times obtained by conducting multiple opening and closing experiments on the valve in advance; the update library is configured in advance by professionals, and the event sets in the library are associated with the update values ​​one by one; fluid parameters include: fluid type, temperature, flow rate; environmental parameters include: temperature, humidity, light, air composition, and magnetic field.

[0166] The working principle and beneficial effects are:

[0167] Comprehensively analyze the impact of the fluid in the pipeline and the surrounding environment on the valve, and then update the valve opening and closing safety threshold to ensure that the valve opening and closing safety threshold is more accurate and the valve control is safer; this update occurs when the difference between the pre-set valve opening and closing safety threshold and the valve's guidance safety threshold is within the preset difference range; wherein, the guidance safety threshold is the average value of the number of opening and closing times obtained by conducting multiple opening and closing experiments on the valve in advance. The update library is also configured in advance by professionals, and the event set in the library is associated with the update value one-to-one; fluid parameters include: fluid type, temperature, flow rate, etc.; environmental parameters include: temperature, humidity, light, air composition, magnetic field, etc., and the event types in the update library have a certain impact on valve safety;

[0168] The method for monitoring the number of times a valve is opened and closed also includes:

[0169] Obtaining fluid parameters of the fluid in the pipeline where the valve is installed and environmental parameters of the surrounding environment of the valve;

[0170] Obtain a structural topology diagram of the system in which the valve is installed and intercept structural codes of a preset number of levels upstream and downstream of the valve;

[0171] A label set is constructed according to the intercepted structural code and the quantitative parameters for quantifying the fluid parameters and the environmental parameters;

[0172] Obtain fault data from the fault big data platform based on the label set;

[0173] Analyze fault data and build risk warning sets and correction values;

[0174] Based on the correction value, the valve opening and closing safety threshold is corrected;

[0175] Issue risk warnings based on the risk warning set.

[0176] The working principle and beneficial effects are:

[0177] A comprehensive analysis is performed on the position of the system in which the valve is located, the fluid in the pipeline, and the surrounding environment to form a label set; the label set is then used to accurately locate the fault data from the fault big data platform to ensure the accuracy of the risk warning set and the correction value analyzed, thereby improving the accuracy and effectiveness of valve safety monitoring; the label set has a total of 2N+2 rows of data, including: the first 2N+1 rows are the structure codes of the preset number (N) levels of the upstream and downstream of the valve (the system is the code configured for each structure, such as 0001 for the valve, 0010 for the pump, etc.), and the last row is the quantitative parameters for quantifying the fluid parameters and environmental parameters arranged in sequence; when obtaining the fault data from the fault big data platform based on the label set, the label set is calculated for similarity with the standard label set of the valve corresponding to the fault data; the similarity calculation formula is as follows:

[0178]

[0179] In the formula, δ represents the similarity; A ij Represents the data in the i-th row and j-th column of the local valve identification set; B ij Represents the data of the i-th row and j-th column in the valve identification set on the big data platform; M represents the total number of columns; A is the average value of the data in the local valve identification set; B is the average value of the data in the valve identification set on the big data platform; 2N+2 is the total number of rows of data in the standard identification set or identification set;

[0180] Fault data with a similarity greater than or equal to a preset similarity threshold (0.90) are extracted; then analysis is performed, and the specific steps of the analysis are as follows: construct a frequency axis (each coordinate point on the frequency axis represents the number of uses, increasing from left to right), correspond the fault data one by one to the frequency axis, extract the coordinate points where the ratio of the fault data at each coordinate point on the frequency axis to the total number of valves corresponding to the extracted fault data is greater than or equal to the preset ratio (0.85), and construct a risk warning set with the extracted data; then segment the frequency axis, and count the number of times at each coordinate point into an analysis set; for example, the frequency axis has a total of 5,000 points, which are segmented into 100 to obtain 50 data, and the 50 data are arranged into a 5*10 array to obtain an analysis set; match the analysis set with the standard analysis set corresponding to each correction value in the pre-configured correction library, and retrieve the corresponding correction value; the correction library is pre-configured, and the standard analysis set in the library is associated with the analysis set one by one; the standard analysis set is also a 5*10 array data.

[0181] In addition, when both the update value and the correction value are used for adjustment at the same time, it is necessary to calculate the weighted sum of the two, that is, the update value and the correction value are respectively multiplied by the pre-configured first coefficient and the second coefficient, and the results are summed to adjust the valve opening and closing safety threshold with the summed result; avoid interference between the two correction updates; and make the final valve opening and closing safety threshold more accurate and reasonable.

[0182] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0183] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A system for monitoring the number of times a valve is opened and closed, characterized in that: include: Data detection unit, used for: Install a sensor on the operating mechanism of the valve, detect the opening and closing actions of the valve through the sensor and convert it into an electrical signal for output; Data acquisition unit for: Responsible for receiving the output of the sensor's electrical signal, amplifying and filtering the electrical signal. After processing, the electrical signal is converted into a digital signal, and then the valve detection data is obtained and transmitted to the data processing unit; Data processing unit for: Use machine learning methods to build a model, collect historical data on the valve operating mechanism to train the model, and identify the valve status through the model's analysis of valve detection data; Human-computer interaction unit, used for: Provide staff with a human-computer interaction interface, display the opening and closing status of the valve based on the analysis results of the data processing unit, and record the number of times the valve is opened and closed. The recorded number of times the valve is opened and closed is compared with the pre-set valve opening and closing safety threshold. When the number of times the valve is opened and closed exceeds the valve opening and closing safety threshold, an alarm is immediately issued.

2. A system for monitoring valve opening and closing times according to claim 1, characterized in that: The data detection unit, the data acquisition module and the data processing module communicate with each other by wired or wireless means, specifically: Wired methods, including using RS-485 or Modbus RTU serial communication protocols; Wireless methods include using Wi-Fi, Bluetooth or LoRa wireless communication protocols.

3. A system for monitoring valve opening and closing times according to claim 1, characterized in that: The data detection unit further includes: Environmental monitoring module for: Deploy various testing equipment to test the environment around the valve, including testing temperature, humidity and magnetic field, obtain environmental testing data, and analyze the environmental testing data to determine whether the environmental testing data exceeds the standard range; Among them, the testing equipment includes: Temperature and humidity sensor, used to detect the temperature and humidity of the environment around the valve; Magnetic field measuring instrument, used to detect the magnetic field strength around the valve; Temperature standard range: 0℃~40℃; Humidity standard range: 0-20% for dry environment; 20-60% for normal environment; 60-90% for humid environment; The standard range of magnetic field strength is 20mT-150mT.

4. A system for monitoring valve opening and closing times according to claim 1, characterized in that: The data acquisition unit comprises: Data acquisition module for: Receive and collect the electrical signals output by the sensor, and amplify and filter the electrical signals; Amplification: Amplify the electrical signal through an amplifier; Filtering: Remove noise from electrical signals through filters; Data conversion module for: The electrical signal is converted into a data signal through a converter, and finally the valve detection data is obtained; Data transmission module for: Encrypting and transmitting the valve detection data to a data processing unit, and decrypting the valve detection data using a key on the data processing unit; Use asymmetric encryption algorithm to encrypt valve detection data; The encrypted valve detection data is transmitted to the data processing unit through the network; The data processing unit decrypts the received valve detection data using the corresponding public key.

5. A system for monitoring valve opening and closing times according to claim 1, characterized in that: The data processing unit comprises: Data collection module for: Collect the historical action data of the valve operating mechanism, mark the status of each historical action data, and after marking, integrate all historical action data into a data set; Data analysis module for: Use machine learning methods to build a model, use data sets to train the model, use the trained model to analyze valve detection data, and identify the open and closed status of the valve based on the analysis results; State generation module, used to: Generate a status code output of the valve opening and closing status according to the analysis results of the data analysis module, and display the status code on the human-computer interaction unit; Data storage module for: The analysis results of the data analysis module are stored in the database. The analysis results are stored in units of days, and all the opening and closing states of the valves on that day are stored in a unified file in the database.

6. A system for monitoring the number of valve openings and closings according to claim 5, characterized in that: The data analysis module specifically performs the following process: Use machine learning methods to build models; Then divide the action history data into training set and test set; Train the model through the training set so that the model can learn the characteristics of the valve status; Test the model through the test set to verify the performance of the model; After the model is trained and tested, it is deployed to actual applications; The valve detection data is analyzed through the deployed model to identify the open and closed status of the valve.

7. A system for monitoring valve opening and closing times according to claim 1, characterized in that: The human-computer interaction unit comprises: Valve management module for: Based on the analysis results of the data processing unit, the number of times the valve is opened and closed is recorded and compared with the pre-set valve opening and closing safety threshold, and the comparison is used to determine whether the number of times the valve is opened and closed exceeds the safety range; If it does not exceed the safety range, continue to record the number of times the valve is opened and closed; If it exceeds the safety range, an early warning signal will be generated immediately; Among them, the valve opening and closing safety threshold is 0-5000 times; Human-computer interaction module for: The opening and closing status of the valve is displayed based on the analysis results of the data processing unit, and according to the generated early warning signal, the early warning information is displayed and an alarm combined with sound and light is issued.

8. A method for monitoring the number of times a valve is opened and closed, based on the system for monitoring the number of times a valve is opened and closed as described in any one of claims 1 to 7, characterized in that: The following steps are involved: The opening and closing actions of the valve are detected by the sensor and converted into electrical signals for output; Amplify and filter the electrical signal output by the sensor and convert it into a digital signal; Use machine learning methods to build a model, collect historical data on the valve operating mechanism to train the model, and identify the valve status through the model's analysis of valve detection data; Based on the analysis results of the data processing unit, the open and closed status of the valve is displayed; The number of times the valve is opened and closed is recorded and compared with the pre-set valve opening and closing safety threshold. When the number of opening and closing times exceeds the valve opening and closing safety threshold, an alarm is immediately issued.

9. The method for monitoring the number of valve opening and closing times according to claim 8, characterized in that: Also includes: When the difference between the pre-set valve opening and closing safety threshold and the valve guidance safety threshold is within the preset difference range; obtaining the fluid parameters of the fluid in the pipeline where the valve is installed and the environmental parameters of the surrounding environment of the valve; Quantify the fluid parameters and environmental parameters and arrange the quantified parameters to form an event set; According to the event set, the corresponding update value is retrieved from the pre-set update library and the valve opening and closing safety threshold is updated; Among them, the guiding safety threshold is the average value of the number of opening and closing times obtained by conducting multiple opening and closing experiments on the valve in advance; the update library is configured in advance by professionals, and the event sets in the library are associated with the update values ​​one by one; fluid parameters include: fluid type, temperature, flow rate; environmental parameters include: temperature, humidity, light, air composition, and magnetic field.

10. The method for monitoring the number of valve opening and closing times according to claim 8, characterized in that: Also includes: Obtaining fluid parameters of the fluid in the pipeline where the valve is installed and environmental parameters of the surrounding environment of the valve; Obtain a structural topology diagram of the system in which the valve is installed and intercept structural codes of a preset number of levels upstream and downstream of the valve; A label set is constructed according to the intercepted structural code and the quantitative parameters for quantifying the fluid parameters and the environmental parameters; Obtain fault data from the fault big data platform based on the label set; Analyze fault data and build risk warning sets and correction values; Based on the correction value, the valve opening and closing safety threshold is corrected; Issue risk warnings based on the risk warning set; Among them, based on the label set, the fault data obtained from the fault big data platform includes: The similarity calculation is performed between the identification set and the standard identification set of the valve corresponding to the fault data; the similarity calculation formula is as follows: In the formula, δ represents the similarity; A ij Represents the data in the i-th row and j-th column of the local valve identification set; B ij Represents the data of the i-th row and j-th column in the valve identification set on the big data platform; M represents the total number of columns; A is the average value of the data in the local valve identification set; B is the average value of the data in the valve identification set on the big data platform; 2N+2 is the total number of rows of data in the standard identification set or identification set; Fault data with a similarity greater than or equal to a preset similarity threshold are extracted.

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