Intelligent remote detection method for working condition of magnetic resonance cold head

Abstract

The invention discloses an intelligent remote detection method for the working condition of a magnetic resonance cold head. Comprising a remote monitoring data box and a detection module. The remote monitoring data box is connected with the magnetic resonance equipment host through a network cable. The detection module comprises a liquid level sensor, a pressure sensor, a temperature sensor and a voltage sensor, the liquid level sensor and the pressure sensor are arranged in a liquid helium bin of the magnetic resonance equipment, the voltage sensor is arranged on a power supply circuit of a helium compressor, and the temperature sensor is arranged on a water inlet and outlet pipe of the helium compressor; the liquid level sensor and the pressure sensor are in communication connection with a ddas control panel of the magnetic resonance equipment, and the remote monitoring data box is connected with the ddas control panel through an optical fiber data line; the remote monitoring data box is in communication connection with the temperature sensor and the voltage sensor. Spare parts are replaced in advance, quenching caused by equipment faults is prevented, and maintenance expenses of hospitals are saved.

Description

Technical field:

[0001] The invention belongs to the technical field of nuclear magnetic resonance equipment, and in particular relates to an intelligent remote detection method for the working condition of a magnetic resonance cold head. Background technology:

[0002] The magnetic field of the MRI device is formed by the superconducting coil immersed in ultra-low temperature liquid helium to form a zero resistance state, and the magnetic field formed by the superconducting current is stimulated. In order to maintain the ultra-low temperature of the liquid helium, the cold head needs to be continuously pressurized and condensed, and the cold head needs the helium compressor that powers it to work continuously to dissipate heat through high-purity helium, and the helium compressor needs to dissipate heat through a water cooling system.

[0003] Since the cold head cannot use motor technology to work continuously, it cannot be made of non-magnetic materials. In order to prevent the cold head from cutting the magnetic induction lines in the magnetic field to form interference signals when working, the magnetic resonance in some nuclear magnetic resonance equipment will automatically suspend the cold head and helium compressor during high-order uniform field sequence scanning, such as Siemens' Multiva nuclear magnetic resonance equipment. If the helium compressor that powers the cold head cannot determine the actual working condition of the cold head, once the cold head stops working for too long, the nuclear magnetic resonance equipment will easily lose supersonic power, which will cause great losses to the user. Therefore, detecting the working condition of the cold head is a problem that needs to be solved.

[0004] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention:

[0005] The object of the present invention is to provide a method for intelligent remote detection of the working condition of a magnetic resonance cold head, thereby overcoming the above-mentioned defects in the prior art.

[0006] To achieve the above-mentioned purpose, the present invention provides an intelligent remote detection method for the working condition of a magnetic resonance cold head, comprising a remote monitoring data box and a detection module; the remote monitoring data box is connected to a host of a magnetic resonance device through a network cable; the detection module comprises a liquid level sensor, a pressure sensor, a temperature sensor, and a voltage sensor, the liquid level sensor and the pressure sensor are arranged in a liquid helium tank of the magnetic resonance device, the voltage sensor is arranged on a power supply circuit of a helium compressor, and the temperature sensor is arranged on an inlet and outlet water pipe of the helium compressor; the liquid level sensor and the pressure sensor are connected in communication with a DDAS control board of the magnetic resonance device, and the remote monitoring data box is connected to the DDAS control board through an optical fiber data cable; the remote monitoring data box is connected in communication with the temperature sensor and the voltage sensor respectively;

[0007] The steps are:

[0008] (1) When the magnetic resonance device is working, the remote monitoring data box reads the scanning parameters of the high-order shim sequence from the host. The scanning parameters include the scanning duration and the scanning start time;

[0009] (2) The liquid level sensor and the pressure sensor respectively collect the liquid helium face value and the liquid helium pressure value in the liquid helium tank. The liquid helium face value is the percentage of the liquid helium stock in the liquid helium tank. The remote monitoring data box reads the liquid helium face value and the liquid helium pressure value from the DDAS control board.

[0010] (3) The voltage sensor detects the voltage value of the power supply circuit output from the helium compressor to the cold head, and the remote monitoring data box reads the voltage value from the voltage sensor;

[0011] (4) The temperature sensor detects the inlet water temperature and outlet water temperature of the helium compressor, and the remote monitoring data box reads the inlet water temperature and outlet water temperature from the temperature sensor;

[0012] (5) The remote monitoring data box uses the liquid helium face value, liquid helium pressure value, voltage value, water inlet temperature value and water outlet temperature value as refrigeration parameters, compares and analyzes the refrigeration parameters with the scanning parameters, determines whether the magnetic resonance cold head working condition is stable, and establishes a cold head life model.

[0013] Preferably, in the technical solution, the voltage sensor is connected to the remote monitoring data box using LoRa communication technology.

[0014] Preferably, in the technical solution, the temperature sensor is connected to the remote monitoring data box by using the modbus communication protocol.

[0015] Preferably, in the technical solution, the cold head life model is:

[0016] h = ∫ t m×∫ t p×∫ t w×∫ t e, (1)

[0017] Wherein, h is the life of the cold head, t is the time when the equipment becomes abnormal, m is the ratio of the daily pressure fluctuation time and the liquid helium surface fluctuation time divided by 24 hours, p is the daily scanning time of the high-order uniform field parameters divided by 24 hours, w is the daily time when the water inlet and outlet temperatures exceed the normal range divided by 24 hours, and e is the daily time when the power value is 0 divided by 24 hours.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The working status of the MRI cold head is monitored through a remote monitoring data box, and the refrigeration status of the MRI equipment and the life of the cold head are effectively judged through the cold head life model, so that spare parts can be replaced in advance to prevent equipment failure and malfunction, thus saving the hospital's maintenance expenses. Description of the drawings:

[0020] Figure 1 This is a flow chart of the intelligent remote detection method for the working condition of a magnetic resonance cold head of the present invention. Specific implementation method:

[0021] The specific embodiments of the present invention are described in detail below, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0022] Unless explicitly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising”, etc., will be understood to include the stated elements or components but not to exclude other elements or components.

[0023] like Figure 1 As shown, an intelligent remote detection method for the working condition of a magnetic resonance cold head includes a remote monitoring data box and a detection module; the remote monitoring data box is connected to a host of a magnetic resonance device through a network cable; the detection module includes a liquid level sensor, a pressure sensor, a temperature sensor, and a voltage sensor, the liquid level sensor and the pressure sensor are arranged in a liquid helium tank of the magnetic resonance device, the voltage sensor is arranged on a power supply circuit of a helium compressor, and the temperature sensor is arranged on an inlet and outlet water pipe of the helium compressor; the liquid level sensor and the pressure sensor are connected to a DDAS control board of the magnetic resonance device for communication, and the remote monitoring data box is connected to the DDAS control board through an optical fiber data cable; the voltage sensor is connected to the remote monitoring data box for communication using the LoRa communication technology; the temperature sensor is connected to the remote monitoring data box for communication using the Modbus communication protocol;

[0024] The steps are:

[0025] (1) When the magnetic resonance device is working, the remote monitoring data box reads the scanning parameters of the high-order shim sequence from the host. The scanning parameters include the scanning duration and the scanning start time;

[0026] (2) The liquid level sensor and the pressure sensor respectively collect the liquid helium face value and the liquid helium pressure value in the liquid helium tank. The liquid helium face value is the percentage of the liquid helium stock in the liquid helium tank. The remote monitoring data box reads the liquid helium face value and the liquid helium pressure value from the DDAS control board.

[0027] (3) The voltage sensor detects the voltage value of the power supply circuit output from the helium compressor to the cold head, and the remote monitoring data box reads the voltage value from the voltage sensor;

[0028] (4) The temperature sensor detects the inlet water temperature and outlet water temperature of the helium compressor, and the remote monitoring data box reads the inlet water temperature and outlet water temperature from the temperature sensor;

[0029] (5) The remote monitoring data box uses the liquid helium face value, liquid helium pressure value, voltage value, water inlet temperature value and water outlet temperature value as refrigeration parameters, compares and analyzes the refrigeration parameters with the scanning parameters, determines whether the magnetic resonance cold head working condition is stable, and establishes a cold head life model:

[0030] h = ∫ t m×∫ t p×∫ t w×∫ t e, (1)

[0031] Wherein, h is the life of the cold head, t is the time when the equipment becomes abnormal, m is the ratio of the daily pressure fluctuation time and the liquid helium surface fluctuation time divided by 24 hours, p is the daily scanning time of the high-order uniform field parameters divided by 24 hours, w is the daily time when the water inlet and outlet temperatures exceed the normal range divided by 24 hours, and e is the daily time when the power value is 0 divided by 24 hours.

[0032] The working status of the MRI cold head is monitored through a remote monitoring data box, and the refrigeration status of the MRI equipment and the life of the cold head are effectively judged through the cold head life model, so that spare parts can be replaced in advance to prevent equipment failure and malfunction, thus saving the hospital's maintenance expenses.

[0033] The foregoing description of specific exemplary embodiments of the present invention is for the purpose of illustration and demonstration. These descriptions are not intended to limit the present invention to the precise form disclosed, and it is clear that many changes and variations can be made based on the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical application, so that those skilled in the art can realize and utilize various different exemplary embodiments of the present invention and various different selections and changes. The scope of the present invention is intended to be limited by the claims and their equivalents.

Claims

1. An intelligent remote detection method for magnetic resonance cold head working conditions, characterized in that: It includes a remote monitoring data box and a detection module; the remote monitoring data box is connected to the main unit of the magnetic resonance device through a network cable; the detection module includes a liquid level sensor, a pressure sensor, a temperature sensor, and a voltage sensor; the liquid level sensor and the pressure sensor are arranged in the liquid helium tank of the magnetic resonance device, the voltage sensor is arranged on the power supply circuit of the helium compressor, and the temperature sensor is arranged on the inlet and outlet water pipes of the helium compressor; the liquid level sensor and the pressure sensor are connected to the DDAS control board of the magnetic resonance device through communication, and the remote monitoring data box is connected to the DDAS control board through an optical fiber data line; the remote monitoring data box is connected to the temperature sensor and the voltage sensor through communication respectively; The steps are: (1) When the magnetic resonance device is working, the remote monitoring data box reads the scanning parameters of the high-order shim sequence from the host. The scanning parameters include the scanning duration and the scanning start time; (2) The liquid level sensor and the pressure sensor respectively collect the liquid helium face value and the liquid helium pressure value in the liquid helium tank. The liquid helium face value is the percentage of the liquid helium stock in the liquid helium tank. The remote monitoring data box reads the liquid helium face value and the liquid helium pressure value from the DDAS control board. (3) The voltage sensor detects the voltage value of the power supply circuit output from the helium compressor to the cold head, and the remote monitoring data box reads the voltage value from the voltage sensor; (4) The temperature sensor detects the inlet water temperature and outlet water temperature of the helium compressor, and the remote monitoring data box reads the inlet water temperature and outlet water temperature from the temperature sensor; (5) The remote monitoring data box uses the liquid helium face value, liquid helium pressure value, voltage value, water inlet temperature value and water outlet temperature value as refrigeration parameters, compares and analyzes the refrigeration parameters with the scanning parameters, determines whether the magnetic resonance cold head working condition is stable, and establishes a cold head life model.

2. The method for intelligent remote detection of magnetic resonance cold head working conditions according to claim 1, characterized in that: The voltage sensor uses LoRa communication technology to communicate with the remote monitoring data box.

3. The method for intelligent remote detection of magnetic resonance cold head working conditions according to claim 1, characterized in that: The temperature sensor uses the modbus communication protocol to communicate with the remote monitoring data box.

4. The method for intelligent remote detection of magnetic resonance cold head working conditions according to claim 1, characterized in that: The cold head life model is: h=∫ t m×∫ t p×∫ t w×∫ t e,(1) Wherein, h is the life of the cold head, t is the time when the equipment becomes abnormal, m is the ratio of the daily pressure fluctuation time and the liquid helium surface fluctuation time divided by 24 hours, p is the daily scanning time of the high-order uniform field parameters divided by 24 hours, w is the daily time when the water inlet and outlet temperatures exceed the normal range divided by 24 hours, and e is the daily time when the power value is 0 divided by 24 hours.

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