A CT tube intelligent replacement prediction system

Abstract

The present invention discloses a CT tube intelligent replacement prediction system, including: step S1, establishing an independent database and inputting initial data; step S2, establishing prediction data; step S3, detecting the CT tube and recording the detected data, and uploading it to the independent database; step S4, comparing the independent database and the prediction data; step S5, confirming the status of the CT tube; step S6, correcting the prediction data; step S7, recording the data, uploading to the independent database, and updating the database center. The present invention provides a CT tube intelligent replacement prediction system, which can monitor the use of CT tubes in real time, predict in advance when the CT tubes need to be replaced, so as to reserve CT tubes in advance; and it can automatically correct the prediction system, and the accuracy of the prediction becomes more and more reliable as the number of uses increases.

Description

Technical Field

[0001] The present invention belongs to the technical field of CT tube management, and in particular relates to a CT tube intelligent replacement prediction system. Background Art

[0002] CT (Computed Tomography) is a computer tomography scan that uses a precisely collimated X-ray beam and a highly sensitive detector to scan sections of a certain part of the human body one by one. It has the characteristics of fast scanning time and clear images. As the X-ray signal source of CT equipment, the CT tube is one of the most core components of CT equipment. Its performance directly affects the image quality and service life of CT. With the development of multi-slice spiral CT technology, continuous scanning is required, and the X-ray dose requirements are getting higher and higher, so the CT tube is required to have a higher heat capacity. The improvement of heat capacity requires continuous improvement and improvement of the tube material and the structure of the rotating anode. Small (micro) focus, large capacity, high frequency and high voltage and its stability, high-speed rotating anode (low noise), and filament emission consistency have become the development direction of the tube.

[0003] CT tubes are consumables. When they do not meet the use requirements, they need to be replaced. CT tubes are expensive, and the cost of long-term storage of CT tubes is also high. However, the service life of CT tubes cannot be predicted, so it is impossible to reserve CT tubes at the right time. Summary of the invention

[0004] In order to solve the deficiencies of the prior art, the present invention aims to provide a CT tube intelligent replacement prediction system.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A CT tube intelligent replacement prediction system comprises the following steps:

[0007] Step S1: installing a CT tube on a CT device, establishing an independent database, and inputting initial data of the CT tube into the independent database, and uploading the independent database to a database center in real time;

[0008] Step S2: Compare the independent database with the database center to determine and establish the prediction data closest to the independent database;

[0009] Step S3: when the CT tube is in use, detecting the use status of the CT tube, recording the detected data, and uploading the detected data to the independent database;

[0010] Step S4: compare the independent database with the predicted data, and when the independent database does not reach the predicted data, return to step S3; when the independent database reaches the predicted data, execute step S5;

[0011] Step S5: Send an alarm signal, wait for professional inspection and confirmation, when the professional confirms that the CT tube does not need to be replaced, execute step S6; when the professional confirms that the CT tube needs to be replaced, execute step S7;

[0012] Step S6: setting correction parameters, modifying the prediction data, and returning to step S3;

[0013] Step S7: Record the corresponding data, upload it to the independent database, upload the independent database to the database center, and update the database center.

[0014] Preferably, the step S1 comprises the following steps:

[0015] Step S1.1: Establishing the independent database and generating the corresponding database code;

[0016] Step S1.2: automatically generating the installation time of the CT tube and uploading it to the independent database;

[0017] Step S1.3: manually inputting the manufacturer and product model of the CT tube and uploading it to the independent database;

[0018] Step S1.4: The independent database is synchronized to the database center, and the independent database can be retrieved from the database center through the database code.

[0019] Preferably, step S2 comprises the following steps:

[0020] Step S2.1: comparing the product model of the CT tube, when the database center has a database with the same product model as the independent database, calling the database and establishing the prediction data; when the database center does not have the same product model as the independent database, executing step S2.2;

[0021] Step S2.2: comparing the manufacturer of the CT tube, if the database center has a database of the same manufacturer as the independent database, calling the database and matching the correction factor to establish the prediction data; if the database center does not have the same manufacturer as the independent database, executing step S2.3;

[0022] Step S2.3: retrieve a database of commonly used product models and match the correction factors to establish prediction data.

[0023] Preferably, step S3 comprises the following steps:

[0024] Step S3.1: When the CT tube starts to be used, the use time of the CT tube is automatically recorded and uploaded to the independent database;

[0025] Step S3.2: During the use of the CT tube, the temperature data of the CT tube is recorded and uploaded to the independent database;

[0026] Step S3.3: When the use of the CT tube ends, the end time of the CT tube is automatically recorded, the use time is automatically calculated, and uploaded to the independent database;

[0027] Step S3.4: Automatically calculate the cumulative operating time and average operating time of the CT tube, and automatically calculate the usage frequency of the CT tube, and upload them to the independent database.

[0028] Preferably, step S4 comprises the following steps:

[0029] Step S4.1: Compare the cumulative running time of the independent database and the predicted data. When the cumulative running time of the independent database exceeds the cumulative running time of the predicted data, execute step S5; when the cumulative running time of the independent database does not exceed the cumulative running time of the predicted data, execute step S3.

[0030] Preferably, step S6 comprises the following steps:

[0031] Step S6.1: Compare the temperature data of the independent database and the temperature data of the database center, and correct the predicted data by matching the temperature correction factor according to the temperature correction factor function of the database center;

[0032] Step S6.2: Compare the usage frequencies of the independent database and the database center, and correct the prediction data according to the frequency correction factor matching frequency correction factor function of the database center;

[0033] Step S6.3: Compare the average operation time of the independent database and the database center, and correct the prediction data according to the operation correction factor function of the database center matching the operation correction factor;

[0034] Step S6.4: Synchronize the prediction data and return to step S3.

[0035] Preferably, step S7 comprises the following steps:

[0036] Step S7.1: uploading the independent database to the database center, and updating the database center;

[0037] Step S7.2: the database center adds the temperature correction factor to the database center, and updates and establishes the temperature correction factor function;

[0038] Step S7.3: the database center adds the frequency correction factor to the database center, and updates and establishes the frequency correction factor function;

[0039] Step S7.4: the database center adds the operation correction factor to the database center, and updates and establishes the frequency correction factor function;

[0040] Step S7.5: Update the database center and return to step S1.

[0041] Preferably, step S5 comprises the following steps:

[0042] Step S5.1: Send an alarm signal, open the monitoring screen for preliminary observation, and if the professional preliminarily determines that the CT tube does not need to be replaced, execute step S6; if the professional preliminarily determines that the CT tube may need to be replaced, execute step S5.2;

[0043] Step S5.2: Observe the CT tube on site. If the professional determines that the CT tube does not need to be replaced, execute step S6; if the professional determines that the CT tube needs to be replaced, execute step S7.

[0044] Preferably, step S3 comprises the following steps:

[0045] Step S3.5: During the use of the CT tube, a heat sink is used to dissipate heat from the CT tube. When the temperature of the CT tube drops to room temperature, the heat sink is stopped.

[0046] The present invention discloses a CT tube intelligent replacement prediction system, which has the beneficial effect of being able to monitor the use of CT tubes in real time and predict in advance when the CT tubes need to be replaced so as to reserve CT tubes in advance; and, it can automatically correct the prediction system, and the accuracy of the prediction becomes more and more reliable as the number of uses increases. DETAILED DESCRIPTION

[0047] The present invention discloses a CT tube intelligent replacement prediction system. The specific implementation of the present invention is further described below in conjunction with a preferred embodiment.

[0048] Preferred embodiments.

[0049] The present invention provides a CT tube intelligent replacement prediction system, comprising the following steps:

[0050] Step S1: installing a CT tube on a CT device, establishing an independent database, and inputting initial data of the CT tube into the independent database, and uploading the independent database to a database center in real time;

[0051] Step S2: Compare the independent database with the database center to determine and establish the prediction data closest to the independent database;

[0052] Step S3: when the CT tube is in use, detecting the use status of the CT tube, recording the detected data, and uploading the detected data to the independent database;

[0053] Step S4: compare the independent database with the predicted data, and when the independent database does not reach the predicted data, return to step S3; when the independent database reaches the predicted data, execute step S5;

[0054] Step S5: Send an alarm signal, wait for professional inspection and confirmation, when the professional confirms that the CT tube does not need to be replaced, execute step S6; when the professional confirms that the CT tube needs to be replaced, execute step S7;

[0055] Step S6: setting correction parameters, modifying the prediction data, and returning to step S3;

[0056] Step S7: Record the corresponding data, upload it to the independent database, upload the independent database to the database center, and update the database center.

[0057] Preferably, the step S1 comprises the following steps:

[0058] Step S1.1: Establishing the independent database and generating the corresponding database code;

[0059] Step S1.2: automatically generating the installation time of the CT tube and uploading it to the independent database;

[0060] Step S1.3: manually inputting the manufacturer and product model of the CT tube and uploading it to the independent database;

[0061] Step S1.4: The independent database is synchronized to the database center, and the independent database can be retrieved from the database center through the database code.

[0062] Preferably, step S2 comprises the following steps:

[0063] Step S2.1: comparing the product model of the CT tube, when the database center has a database with the same product model as the independent database, calling the database and establishing the prediction data; when the database center does not have the same product model as the independent database, executing step S2.2;

[0064] Step S2.2: comparing the manufacturer of the CT tube, if the database center has a database of the same manufacturer as the independent database, calling the database and matching the correction factor to establish the prediction data; if the database center does not have the same manufacturer as the independent database, executing step S2.3;

[0065] Step S2.3: retrieve a database of commonly used product models and match the correction factors to establish prediction data.

[0066] Preferably, step S3 comprises the following steps:

[0067] Step S3.1: When the CT tube starts to be used, the use time of the CT tube is automatically recorded and uploaded to the independent database;

[0068] Step S3.2: During the use of the CT tube, the temperature data of the CT tube is recorded and uploaded to the independent database;

[0069] Step S3.3: When the use of the CT tube ends, the end time of the CT tube is automatically recorded, the use time is automatically calculated, and uploaded to the independent database;

[0070] Step S3.4: Automatically calculate the cumulative operating time and average operating time of the CT tube, and automatically calculate the usage frequency of the CT tube, and upload them to the independent database.

[0071] Preferably, step S4 comprises the following steps:

[0072] Step S4.1: Compare the cumulative running time of the independent database and the predicted data. When the cumulative running time of the independent database exceeds the cumulative running time of the predicted data, execute step S5; when the cumulative running time of the independent database does not exceed the cumulative running time of the predicted data, execute step S3.

[0073] Preferably, step S6 comprises the following steps:

[0074] Step S6.1: Compare the temperature data of the independent database and the temperature data of the database center, and correct the predicted data by matching the temperature correction factor according to the temperature correction factor function of the database center;

[0075] Step S6.2: Compare the usage frequencies of the independent database and the database center, and correct the prediction data according to the frequency correction factor matching frequency correction factor function of the database center;

[0076] Step S6.3: Compare the average operation time of the independent database and the database center, and correct the prediction data according to the operation correction factor function of the database center matching the operation correction factor;

[0077] Step S6.4: Synchronize the prediction data and return to step S3.

[0078] Preferably, the step S7 comprises the following steps:

[0079] Step S7.1: uploading the independent database to the database center, and updating the database center;

[0080] Step S7.2: the database center adds the temperature correction factor to the database center, and updates and establishes the temperature correction factor function;

[0081] Step S7.3: the database center adds the frequency correction factor to the database center, and updates and establishes the frequency correction factor function;

[0082] Step S7.4: the database center adds the operation correction factor to the database center, and updates and establishes the frequency correction factor function;

[0083] Step S7.5: Update the database center and return to step S1.

[0084] Preferably, step S5 comprises the following steps:

[0085] Step S5.1: Send an alarm signal, open the monitoring screen for preliminary observation, and if the professional preliminarily determines that the CT tube does not need to be replaced, execute step S6; if the professional preliminarily determines that the CT tube may need to be replaced, execute step S5.2;

[0086] Step S5.2: Observe the CT tube on site. If the professional determines that the CT tube does not need to be replaced, execute step S6; if the professional determines that the CT tube needs to be replaced, execute step S7.

[0087] Preferably, step S3 comprises the following steps:

[0088] Step S3.5: During the use of the CT tube, a heat sink is used to dissipate heat from the CT tube. When the temperature of the CT tube drops to room temperature, the heat sink is stopped.

[0089] Among them, the database center is formed by inputting existing monitoring data, and based on the continuously accumulated supplement of the independent database, a prediction system with increasingly accurate prediction degree is formed, and the temperature correction factor function, frequency correction factor function and operation correction factor function are continuously improved.

[0090] It is worth mentioning that the technical features such as the database center and heat dissipation device involved in the patent application of this invention should be regarded as the prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected by conventional methods in the field, and should not be regarded as the inventive point of the patent of this invention. The patent of this invention will not be further elaborated.

[0091] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A CT tube intelligent replacement prediction system, characterized in that: The following steps are involved: Step S1: installing a CT tube on a CT device, establishing an independent database, and inputting initial data of the CT tube into the independent database, and uploading the independent database to a database center in real time; Step S2: Compare the independent database with the database center to determine and establish the prediction data closest to the independent database; Step S3: when the CT tube is in use, detecting the use status of the CT tube, recording the detected data, and uploading the detected data to the independent database; Step S4: compare the independent database with the predicted data. When the independent database does not reach the predicted data, return to step S3; when the independent database reaches the predicted data, execute step S5. Step S4 includes the following steps: Step S4.1: Compare the cumulative running time of the independent database and the predicted data. When the cumulative running time of the independent database exceeds the cumulative running time of the predicted data, execute step S5; when the cumulative running time of the independent database does not exceed the cumulative running time of the predicted data, execute step S3; Step S5: Send an alarm signal, wait for professional inspection and confirmation, when the professional confirms that the CT tube does not need to be replaced, execute step S6; when the professional confirms that the CT tube needs to be replaced, execute step S7; Step S6: setting correction parameters, modifying the prediction data, and returning to step S3; wherein step S6 includes the following steps: Step S6.1: Compare the temperature data of the independent database and the temperature data of the database center, and correct the predicted data by matching the temperature correction factor according to the temperature correction factor function of the database center; Step S6.2: Compare the usage frequencies of the independent database and the database center, and correct the prediction data according to the frequency correction factor matching frequency correction factor function of the database center; Step S6.3: Compare the average operation time of the independent database and the database center, and correct the prediction data according to the operation correction factor function of the database center matching the operation correction factor; Step S6.4: Synchronize the prediction data and return to step S3; Step S7: Record the corresponding data, upload it to the independent database, upload the independent database to the database center, and update the database center.

2. The CT tube intelligent replacement prediction system according to claim 1 is characterized in that: The step S1 comprises the following steps: Step S1.1: Establishing the independent database and generating the corresponding database code; Step S1.2: automatically generating the installation time of the CT tube and uploading it to the independent database; Step S1.3: manually inputting the manufacturer and product model of the CT tube and uploading it to the independent database; Step S1.4: The independent database is synchronized to the database center, and the independent database can be retrieved from the database center through the database code.

3. The CT tube intelligent replacement prediction system according to claim 2 is characterized in that: The step S2 comprises the following steps: Step S2.1: comparing the product model of the CT tube, when the database center has a database with the same product model as the independent database, calling the database and establishing the prediction data; when the database center does not have the same product model as the independent database, executing step S2.2; Step S2.2: comparing the manufacturer of the CT tube, if the database center has a database of the same manufacturer as the independent database, calling the database and matching the correction factor to establish the prediction data; if the database center does not have the same manufacturer as the independent database, executing step S2.3; Step S2.3: retrieve a database of commonly used product models and match the correction factors to establish prediction data.

4. The CT tube intelligent replacement prediction system according to claim 3 is characterized in that: The step S3 comprises the following steps: Step S3.1: When the CT tube starts to be used, the use time of the CT tube is automatically recorded and uploaded to the independent database; Step S3.2: During the use of the CT tube, the temperature data of the CT tube is recorded and uploaded to the independent database; Step S3.3: When the use of the CT tube ends, the end time of the CT tube is automatically recorded, the use time is automatically calculated, and uploaded to the independent database; Step S3.4: Automatically calculate the cumulative operating time and average operating time of the CT tube, and automatically calculate the usage frequency of the CT tube, and upload them to the independent database.

5. The CT tube intelligent replacement prediction system according to claim 4 is characterized in that: The step S7 comprises the following steps: Step S7.1: uploading the independent database to the database center, and updating the database center; Step S7.2: the database center adds the temperature correction factor to the database center, and updates and establishes the temperature correction factor function; Step S7.3: the database center adds the frequency correction factor to the database center, and updates and establishes the frequency correction factor function; Step S7.4: the database center adds the operation correction factor to the database center, and updates and establishes the frequency correction factor function; Step S7.5: Update the database center and return to step S1.

6. The CT tube intelligent replacement prediction system according to claim 5 is characterized in that: The step S5 comprises the following steps: Step S5.1: Send an alarm signal, open the monitoring screen for preliminary observation, and if the professional preliminarily determines that the CT tube does not need to be replaced, execute step S6; if the professional preliminarily determines that the CT tube may need to be replaced, execute step S5.2; Step S5.2: Observe the CT tube on site. If the professional determines that the CT tube does not need to be replaced, execute step S6; if the professional determines that the CT tube needs to be replaced, execute step S7.

7. The CT tube intelligent replacement prediction system according to claim 6 is characterized in that: The step S3 comprises the following steps: Step S3.5: During the use of the CT tube, a heat sink is used to dissipate heat from the CT tube. When the temperature of the CT tube drops to room temperature, the heat sink is stopped.