Bridge shooting combined action underground detonation monitoring device

Abstract

The invention discloses a bridge shooting combined action underground detonation monitoring device, and relates to the field of underground detonation monitoring, the bridge shooting combined action underground detonation monitoring device comprises a device body, spring needle shells are mounted on two sides of an inner cavity of the device body, a high-temperature battery is arranged on the right side of the left spring needle shell, and a microprocessor is arranged on the right side of the high-temperature battery; a vibration sensor and a temperature sensor are mounted on the outer wall of the microprocessor, and the microprocessor comprises a vibration waveform mode recognition module, a driving circuit, a power management circuit, a communication circuit and a data acquisition module. According to the bridge-shooting combined action underground detonation monitoring device, the vibration impact condition in the underground operation process can be monitored in real time, the safety and effectiveness of operation can be effectively improved, data collected by the temperature sensor and the vibration sensor can be transmitted in real time, potential safety hazards can be found in time, and the underground detonation monitoring device is safe and reliable. And through the arranged waveform recognition self-learning module, the recognition algorithm can be continuously optimized according to historical data, and the recognition accuracy and reliability are improved.

Description

Technical Field

[0001] The invention relates to the field of underground detonation monitoring, and in particular to an underground detonation monitoring device with bridge-shooting coupling effect. Background Art

[0002] At present, many downhole operations rely on traditional vibration monitoring equipment, which usually uses acceleration sensors to collect vibration data. However, the performance of traditional equipment is unstable in high temperature and high pressure environments, and it is difficult to achieve real-time data transmission, which limits its application in complex downhole environments. Secondly, the existing multi-channel logging system can provide monitoring of multiple parameters, but usually requires complex wiring and equipment configuration, which increases the complexity and cost of the operation. In addition, these systems often do not support long-distance communication, which limits their application in deep wells or complex formations. At the same time, some advanced vibration monitoring systems have begun to apply vibration waveform recognition technology, which can identify different operating states. However, most of these technologies rely on manually set thresholds and modes, lack self-learning capabilities, and are difficult to adapt to changes in different operating environments and conditions.

[0003] Therefore, it is necessary to propose a bridge-shot underground detonation monitoring device to solve the above problems. Summary of the invention

[0004] The main purpose of the present invention is to provide a bridge-shot underground detonation monitoring device, which can effectively solve the problems in the background technology.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A bridge-shot underground detonation monitoring device comprises a device body, spring needle shells are installed on both sides of the inner cavity of the device body, a spring and a needle shaft are installed in the inner cavity of the spring needle shell, a high-temperature battery is arranged on the right side of the left spring needle shell, a microprocessor is arranged on the right side of the high-temperature battery, and a vibration sensor and a temperature sensor are installed on the outer wall of the microprocessor;

[0007] The microprocessor includes a vibration waveform pattern recognition module, a driving circuit, a power management, a communication circuit and a data acquisition module, and the vibration waveform pattern recognition module is electrically connected to a waveform recognition self-learning module;

[0008] The vibration waveform pattern recognition module is used to identify vibration characteristics under different operating conditions, including perforating ignition cap detonation, perforating gun explosion, gunpowder setting tool setting, and electric setting tool setting. The waveform recognition self-learning module is used to continuously optimize the recognition algorithm based on historical data. The power management is used to manage high-temperature batteries. The drive circuit is used to continuously power the device body during operation to support long-term operation requirements. The communication circuit is used to transmit the information received and processed by the microprocessor to the ground monitoring system. The communication circuit adopts long-distance single bus communication technology to be compatible with single-core and multi-core logging cables for data transmission.

[0009] Preferably, the spring is wound around the outer wall of the needle shaft, a first copper block is installed on one side of the needle shaft, a second copper block is installed on the other side of the needle shaft, nuts are symmetrically installed on the opposite sides of the two needle shafts, and the microprocessor is electrically connected to the high-temperature battery.

[0010] Preferably, the outer walls of the two spring needle housings are each installed with a retaining ring, the retaining ring is installed in the inner cavity of the device body, a circuit board bracket is installed in the inner cavity of the device body, core seats are symmetrically installed on both sides of the circuit board bracket, and the microprocessor, high-temperature battery, vibration sensor and temperature sensor are all installed in the inner cavity of the circuit board bracket.

[0011] Preferably, the data acquisition module is electrically connected to the microprocessor, the data acquisition module is electrically connected to a vibration sensor data receiving module and a temperature sensor data receiving module, the vibration sensor data receiving module is electrically connected to the vibration sensor, and the temperature sensor data receiving module is electrically connected to the temperature sensor;

[0012] The vibration sensor adopts an acceleration sensor with a range of ±400 times the acceleration of gravity. The vibration sensor data receiving module is used to receive vibration information collected by the vibration sensor, and the temperature sensor data receiving module is used to collect temperature information collected by the temperature sensor.

[0013] Preferably, the microprocessor is electrically connected to a drive circuit, a power management circuit and a communication circuit.

[0014] Preferably, the vibration waveform pattern recognition module and the waveform recognition self-learning module are both electrically connected to the microprocessor, and the waveform recognition self-learning module is electrically connected to the vibration waveform pattern recognition module;

[0015] The vibration waveform pattern recognition module includes a signal acquisition unit, a feature extraction unit, a pattern recognition unit, a range setting unit, a scene confirmation and judgment unit, and a result output unit, wherein the signal acquisition unit is used to collect vibration and temperature data signals from the vibration sensor data receiving module and the temperature sensor data receiving module, and is also used to amplify, filter and perform noise reduction processing on the collected vibration and temperature data signals;

[0016] The range setting unit is used to set the range of vibration and temperature data, including the range of vibration data when idle and the range of temperature data when idle, and classify them into a normal range when idle and an abnormal range when idle;

[0017] It also includes the range of vibration data during operation and the range of temperature data during operation, which are divided into a normal range during operation and an abnormal range during operation. The range of vibration data during operation also includes the vibration range of perforating ignition detonator detonation, perforating gun explosion, and gunpowder setting tool setting;

[0018] The feature extraction unit is used to calculate the time domain feature parameters of the mean, variance, peak value, peak-to-peak value, kurtosis, and skewness of the vibration and temperature signals, convert the time domain feature parameters into frequency domain signals through the fast Fourier transform method based on the calculated time domain feature parameters, extract the frequency components, spectrum peaks, and bandwidth features, analyze the signals in the time domain and frequency domain at the same time, and extract the time-frequency domain features;

[0019] The pattern recognition unit classifies and recognizes the extracted time-frequency domain features based on a neural network, determines the pattern of the vibration waveform, and performs model training based on the obtained vibration waveform data;

[0020] The result output unit displays the result of the pattern recognition unit in the form of a waveform graph;

[0021] The scene confirmation and judgment unit confirms and judges the current working scene of the device body based on the range setting unit and the result output unit. The working scene of the device body is first judged based on the vibration and temperature data, which is divided into idle scenes and working scenes. Normal and abnormal judgments are made based on the range of idle scene and working scene data. After the judgment, the confirmed abnormal range is marked based on the scene it is in. When it is marked as the abnormal range in the working scene, it is judged that an explosion has occurred. When it is marked as the abnormal range in the idle scene, it is judged that the environment in which the device body is located and the device body itself have faults, and an alarm signal will be issued for the marked abnormal range.

[0022] Preferably, the waveform recognition self-learning module performs feature extraction based on the data of the vibration waveform pattern recognition module, the data acquisition module, and the vibration waveform pattern recognition module, so as to extract key features of the data waveform. The waveform recognition self-learning module includes a machine learning algorithm unit and an evaluation and verification unit, wherein the machine learning algorithm unit performs training and updating of the existing model based on key features of the supervised learning algorithm, and divides the data into standard data when idle, abnormal data when idle, standard data when working, and abnormal data when working. The data recognized by the vibration waveform pattern recognition module is re-learned through the machine learning algorithm unit, and rewards are given when the learning result is the same as the actual result, otherwise penalties are given, and the current data is re-learned after the penalty;

[0023] The evaluation and verification unit includes an evaluation index definition module, a cross-validation module, and a visual analysis module, wherein the evaluation index definition module defines the index as the accuracy rate to measure the model performance;

[0024] The cross-validation module uses a cross-validation method to validate the model;

[0025] The visualization analysis module displays the learning process and results of the model through visualization tools to help and assist users in understanding and analyzing. If users are not satisfied with the learning process and results, the machine learning algorithm unit is retrained and updated.

[0026] Compared with the prior art, the present invention provides a bridge-shot underground detonation monitoring device, which has the following beneficial effects:

[0027] The bridge-shot downhole detonation monitoring device can monitor the vibration and impact conditions during downhole operations in real time, effectively improve the safety and effectiveness of operations, transmit data collected by temperature sensors and vibration sensors, and promptly discover potential safety hazards. It adopts long-distance single-bus communication technology and is compatible with single-core and multi-core logging cables to ensure stable and reliable data transmission in complex downhole environments. Through the vibration waveform pattern recognition module, it can identify the vibration characteristics under different operating conditions, including perforating ignition detonator detonation, perforating gun explosion, gunpowder setting tool sealing, and electric setting tool sealing. Through the waveform recognition self-learning module, it can continuously optimize the recognition algorithm according to historical data to improve the accuracy and reliability of recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a cross-sectional view of the present invention;

[0029] Figure 2 The present invention Figure 1 The enlarged view of point A in the middle;

[0030] Figure 3 It is a system block diagram of the microprocessor of the present invention

[0031] In the figure: 1. device body; 2. circuit board bracket; 3. core seat; 4. spring needle housing; 5. nut; 6. first copper block; 7. second copper block; 8. spring; 9. retaining ring; 10. microprocessor; 11. high temperature battery; 12. vibration sensor; 13. temperature sensor; 14. needle shaft. DETAILED DESCRIPTION

[0032] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0033] Embodiment 1:

[0034] like Figure 1 , Figure 2 As shown, a bridge-shot downhole detonation monitoring device comprises a device body 1, spring needle shells 4 are installed on both sides of the inner cavity of the device body 1, a spring 8 is wound around the outer wall of a needle shaft 14, a first copper block 6 is installed on one side of the needle shaft 14, a second copper block 7 is installed on the other side of the needle shaft 14, nuts 5 are symmetrically installed on the opposite sides of the two needle shafts 14, a microprocessor 10 is electrically connected to a high-temperature battery 11, retaining rings 9 are installed on the outer walls of the two spring needle shells 4, the retaining rings 9 are installed in the inner cavity of the device body 1, a circuit board bracket 2 is installed in the inner cavity of the device body 1, core seats 3 are symmetrically installed on both sides of the circuit board bracket 2, the microprocessor 10, the high-temperature battery 11, the vibration sensor 12 and the temperature sensor 13 are all installed in the inner cavity of the circuit board bracket 2, a spring 8 and a needle shaft 14 are installed in the inner cavity of the spring needle shell 4, a high-temperature battery 11 is arranged on the right side of the left spring needle shell 4, a microprocessor 10 is arranged on the right side of the high-temperature battery 11, and a vibration sensor 12 and a temperature sensor 13 are installed on the outer wall of the microprocessor 10.

[0035] Embodiment 2:

[0036] like Figure 1 , Figure 3As shown, a bridge-shooting downhole detonation monitoring device, the microprocessor 10 includes a vibration waveform pattern recognition module, a drive circuit, a power management, a communication circuit and a data acquisition module. The vibration waveform pattern recognition module is electrically connected to a waveform recognition self-learning module. The vibration waveform pattern recognition module is used to identify vibration characteristics under different operating conditions. The operating conditions include perforating ignition detonator detonation, perforating gun explosion, gunpowder setting tool setting, and electric setting tool setting. The waveform recognition self-learning module is used to continuously optimize the recognition algorithm according to historical data, the power management is used to manage the high-temperature battery 11, the drive circuit is used to continuously power the device body 1 during the operation process, and support long-term operation requirements. The communication circuit is used to transmit the information received and processed by the microprocessor 10 to the ground monitoring system. The communication circuit adopts long-distance single bus communication technology and is used to be compatible with single-core and multi-core logging cables for data transmission.

[0037] The data acquisition module is electrically connected to the microprocessor 10, the data acquisition module is electrically connected to a vibration sensor data receiving module and a temperature sensor data receiving module, the vibration sensor data receiving module is electrically connected to the vibration sensor 12, and the temperature sensor data receiving module is electrically connected to the temperature sensor 13;

[0038] The vibration sensor 12 uses an acceleration sensor with a range of ±400 times the acceleration of gravity. The vibration sensor data receiving module is used to receive the vibration information collected by the vibration sensor 12. The temperature sensor data receiving module is used to collect the temperature information collected by the temperature sensor 13. The microprocessor 10 is electrically connected to the drive circuit, power management and communication circuit.

[0039] The vibration waveform pattern recognition module and the waveform recognition self-learning module are both electrically connected to the microprocessor 10, and the waveform recognition self-learning module is electrically connected to the vibration waveform pattern recognition module;

[0040] The vibration waveform pattern recognition module includes a signal acquisition unit, a feature extraction unit, a pattern recognition unit, a range setting unit, a scene confirmation and judgment unit, and a result output unit, wherein the signal acquisition unit is used to collect vibration and temperature data signals from the vibration sensor data receiving module and the temperature sensor data receiving module, and is also used to amplify, filter and perform noise reduction processing on the collected vibration and temperature data signals;

[0041] The range setting unit is used to set the range of vibration and temperature data, including the range of vibration data when idle and the range of temperature data when idle, and classify them into a normal range when idle and an abnormal range when idle;

[0042] It also includes the range of vibration data during operation and the range of temperature data during operation, which are divided into a normal range during operation and an abnormal range during operation. The range of vibration data during operation also includes the vibration range of perforating ignition detonator detonation, perforating gun explosion, and gunpowder setting tool setting;

[0043] The feature extraction unit is used to calculate the time domain feature parameters of the mean, variance, peak value, peak-to-peak value, kurtosis, and skewness of the vibration and temperature signals, and convert the time domain feature parameters into frequency domain signals through the fast Fourier transform method based on the calculated time domain feature parameters, extract the frequency components, spectrum peaks, and bandwidth features, and analyze the signals in the time domain and frequency domain at the same time to extract the time and frequency domain features;

[0044] The pattern recognition unit classifies and recognizes the extracted time-frequency domain features based on the neural network, determines the pattern of the vibration waveform, and performs model training based on the obtained vibration waveform data;

[0045] The result output unit displays the result of the pattern recognition unit in the form of a waveform graph;

[0046] The scene confirmation and judgment unit confirms and judges the current working scene of the device body 1 based on the range setting unit and the result output unit. The working scene of the device body 1 is first judged based on the vibration and temperature data, which is divided into idle scenes and working scenes. Normal and abnormal judgments are made based on the range of idle scene and working scene data. After the judgment, the confirmed abnormal range is marked based on the scene it is in. When it is marked as the abnormal range in the working scene, it is judged that an explosion has occurred. When it is marked as the abnormal range in the idle scene, it is judged that the environment in which the device body 1 is located and the device body itself has faults, and an alarm signal will be issued for the marked abnormal range.

[0047] The waveform recognition self-learning module performs feature extraction based on the data of the vibration waveform pattern recognition module, the data acquisition module, and the vibration waveform pattern recognition module, and is used to extract key features of the data waveform. The waveform recognition self-learning module includes a machine learning algorithm unit and an evaluation and verification unit, wherein the machine learning algorithm unit trains and updates the existing model based on key features of the supervised learning algorithm, and divides the data into standard data when idle, abnormal data when idle, standard data when working, and abnormal data when working. The data recognized by the vibration waveform pattern recognition module is re-learned through the machine learning algorithm unit. When the learning result is the same as the actual result, a reward is given, otherwise a penalty is given, and the current data is re-learned after the penalty;

[0048] The evaluation and verification unit includes an evaluation indicator definition module, a cross-validation module, and a visual analysis module. The evaluation indicator definition module defines the indicator as the accuracy rate to measure the model performance.

[0049] The cross-validation module uses the cross-validation method to validate the model;

[0050] The visual analysis module uses visual tools to display the learning process and results of the model to help and assist users in understanding and analyzing. If users are not satisfied with the learning process and results, the machine learning algorithm unit will be retrained and updated.

[0051] It can monitor the vibration and impact conditions during downhole operations in real time, effectively improve the safety and effectiveness of operations, transmit the data collected by the temperature sensor 13 and the vibration sensor 12, and promptly discover potential safety hazards. It adopts long-distance single-bus communication technology and is compatible with single-core and multi-core logging cables to ensure stable and reliable data transmission in complex downhole environments. Through the vibration waveform pattern recognition module, it can identify the vibration characteristics under different operating conditions, including perforating ignition detonator detonation, perforating gun explosion, gunpowder sealing tool sealing, and electric sealing tool sealing. Through the waveform recognition self-learning module that is set, it can continuously optimize the recognition algorithm according to historical data to improve the accuracy and reliability of recognition.

[0052] The present invention relates to a bridge-shot underground detonation monitoring device. When in use, a vibration sensor (12) and a temperature sensor (13) monitor the vibration and temperature conditions of the environment in which the device body (1) is located. A microprocessor (10) judges and confirms the current environment in which the device body (1) is located based on the data monitored by the vibration sensor (12) and the temperature sensor (13). When it is judged to be within a normal range, the device body (1) is in a normal working environment and an idle environment. When it is judged to be within an abnormal range, the device body (1) is judged to be in an abnormal working environment and an idle environment. When it is in an abnormal working environment, it is judged that detonation occurs at the location of the device body (1). If it is in an abnormal idle environment, it is judged that the environment in which the device body (1) is located and the environment in which the device body (1) is located and the device body (1) itself have a fault, and a current signal is sent out through a communication circuit.

[0053] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A bridge-shot underground detonation monitoring device, comprising a device body (1), characterized in that: A spring needle housing (4) is installed on both sides of the inner cavity of the device body (1), a spring (8) and a needle shaft (14) are installed in the inner cavity of the spring needle housing (4), a high-temperature battery (11) is arranged on the right side of the left spring needle housing (4), a microprocessor (10) is arranged on the right side of the high-temperature battery (11), and a vibration sensor (12) and a temperature sensor (13) are installed on the outer wall of the microprocessor (10); The microprocessor (10) comprises a vibration waveform pattern recognition module, a drive circuit, a power management, a communication circuit and a data acquisition module, wherein the vibration waveform pattern recognition module is electrically connected to a waveform recognition self-learning module; The vibration waveform pattern recognition module is used to recognize vibration characteristics under different operating conditions, including perforating ignition cap detonation, perforating gun explosion, gunpowder setting tool setting, and electric setting tool setting. The waveform recognition self-learning module is used to continuously optimize the recognition algorithm based on historical data. The power management is used to manage the high-temperature battery (11). The drive circuit is used to continuously power the device body (1) during the operation process to support long-term operation requirements. The communication circuit is used to transmit the information received and processed by the microprocessor (10) to the ground monitoring system. The communication circuit adopts long-distance single bus communication technology and is used to be compatible with single-core and multi-core logging cables for data transmission.

2. The bridge-shot underground detonation monitoring device according to claim 1 is characterized in that: The spring (8) is wound around the outer wall of the needle shaft (14); a first copper block (6) is installed on one side of the needle shaft (14); a second copper block (7) is installed on the other side of the needle shaft (14); nuts (5) are symmetrically installed on opposite sides of the two needle shafts (14); and the microprocessor (10) is electrically connected to the high-temperature battery (11).

3. The bridge-shot underground detonation monitoring device according to claim 1 is characterized in that: The outer walls of the two spring needle housings (4) are both installed with retaining rings (9), the retaining rings (9) are installed in the inner cavity of the device body (1), a circuit board bracket (2) is installed in the inner cavity of the device body (1), core seats (3) are symmetrically installed on both sides of the circuit board bracket (2), and the microprocessor (10), high-temperature battery (11), vibration sensor (12) and temperature sensor (13) are all installed in the inner cavity of the circuit board bracket (2).

4. The bridge-shot underground detonation monitoring device according to claim 1 is characterized in that: The data acquisition module is electrically connected to the microprocessor (10), the data acquisition module is electrically connected to a vibration sensor data receiving module and a temperature sensor data receiving module, the vibration sensor data receiving module is electrically connected to the vibration sensor (12), and the temperature sensor data receiving module is electrically connected to the temperature sensor (13); The vibration sensor (12) adopts an acceleration sensor with a range of ±400 times the acceleration of gravity, the vibration sensor data receiving module is used to receive vibration information collected by the vibration sensor (12), and the temperature sensor data receiving module is used to collect temperature information collected by the temperature sensor (13).

5. The bridge-shot underground detonation monitoring device according to claim 1 is characterized in that: The microprocessor (10) is electrically connected to a driving circuit, a power management circuit and a communication circuit.

6. The bridge-shot underground detonation monitoring device according to claim 1 is characterized in that: The vibration waveform pattern recognition module and the waveform recognition self-learning module are both electrically connected to the microprocessor (10), and the waveform recognition self-learning module is electrically connected to the vibration waveform pattern recognition module; The vibration waveform pattern recognition module includes a signal acquisition unit, a feature extraction unit, a pattern recognition unit, a range setting unit, a scene confirmation and judgment unit, and a result output unit, wherein the signal acquisition unit is used to collect vibration and temperature data signals from the vibration sensor data receiving module and the temperature sensor data receiving module, and is also used to amplify, filter and perform noise reduction processing on the collected vibration and temperature data signals; The range setting unit is used to set the range of vibration and temperature data, including the range of vibration data when idle and the range of temperature data when idle, and classify them into a normal range when idle and an abnormal range when idle; It also includes the range of vibration data during operation and the range of temperature data during operation, which are divided into a normal range during operation and an abnormal range during operation. The range of vibration data during operation also includes the vibration range of perforating ignition detonator detonation, perforating gun explosion, and gunpowder setting tool setting; The feature extraction unit is used to calculate the time domain feature parameters of the mean, variance, peak value, peak-to-peak value, kurtosis, and skewness of the vibration and temperature signals, convert the time domain feature parameters into frequency domain signals through the fast Fourier transform method based on the calculated time domain feature parameters, extract the frequency components, spectrum peaks, and bandwidth features, analyze the signals in the time domain and frequency domain at the same time, and extract the time-frequency domain features; The pattern recognition unit classifies and recognizes the extracted time-frequency domain features based on a neural network, determines the pattern of the vibration waveform, and performs model training based on the obtained vibration waveform data; The result output unit displays the result of the pattern recognition unit in the form of a waveform graph; The scene confirmation and judgment unit confirms and judges the current working scene of the device body (1) based on the range setting unit and the result output unit. The working scene of the device body (1) is first judged based on the vibration and temperature data, which is divided into an idle scene and a working scene. Normal and abnormal judgments are made based on the range of the idle scene and the working scene data. After the judgment, the confirmed abnormal range is marked based on the scene it is in. When it is marked as an abnormal range in the working scene, it is judged that an explosion has occurred. When it is marked as an abnormal range in the idle scene, it is judged that the environment in which the device body (1) is located and the device body itself has a fault, and an alarm signal is issued for the marked abnormal range.

7. The bridge-shot underground detonation monitoring device according to claim 6 is characterized by: The waveform recognition self-learning module performs feature extraction based on the data of the vibration waveform pattern recognition module, the data acquisition module, and the vibration waveform pattern recognition module, and is used to extract key features of the data waveform. The waveform recognition self-learning module includes a machine learning algorithm unit and an evaluation and verification unit, wherein the machine learning algorithm unit performs key feature training and updates on the existing model based on the supervised learning algorithm, and divides the data into standard data when idle, abnormal data when idle, standard data when working, and abnormal data when working. The data recognized by the vibration waveform pattern recognition module is re-learned through the machine learning algorithm unit, and rewards are given when the learning result is the same as the actual result, otherwise penalties are given, and the current data is re-learned after the penalty; The evaluation and verification unit includes an evaluation index definition module, a cross-validation module, and a visual analysis module, wherein the evaluation index definition module defines the index as the accuracy rate to measure the model performance; The cross-validation module uses a cross-validation method to validate the model; The visualization analysis module displays the learning process and results of the model through visualization tools to help and assist users in understanding and analyzing. If users are not satisfied with the learning process and results, the machine learning algorithm unit is retrained and updated.

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