Clean air pipe air tightness detection method and device

Abstract

The invention provides a clean air pipe air tightness detection method and device, the clean air pipe air tightness detection method is applied to a pipeline robot, and the method comprises the steps: collecting clean air parameters in a clean air pipe based on a preset detection interval, and obtaining a group of clean air parameters corresponding to a plurality of detection points; comparing two adjacent groups of clean air parameters in the plurality of groups of clean air parameters to determine the air tightness of the detection point corresponding to each group of clean air parameters to obtain a first detection result; and according to the first detection result and the positioning data corresponding to each group of clean air parameters, a second detection result is obtained, and the second detection result represents an air tightness detection result of the clean air pipe. According to the detection method provided by the invention, the position of the abnormal air tightness of the air pipe can be accurately positioned, so that the position of the abnormal air tightness can be accurately overhauled, the efficiency of handling the abnormality of the air pipe is improved, and the labor cost is reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical clean air ducts, and in particular to a clean air duct air tightness detection method and device. Background Art

[0002] Hospital clean air ducts are important components for transmitting medical clean air. They need to maintain high requirements for cleanliness and air tightness to ensure the normal transportation of medical clean air. In actual use, clean air ducts may have air tightness abnormalities after being used for a certain period of time.

[0003] At present, when an abnormality occurs in the clean air duct, the conventional treatment method is to increase the fan frequency to compensate for the air volume. When the fan frequency cannot be increased or the fan frequency cannot be adjusted to balance, it is necessary to check for possible leakage points. The specific leakage location investigation work is relatively cumbersome, resulting in great manpower costs, and the accuracy is difficult to guarantee. Summary of the invention

[0004] Based on the above description, the present disclosure provides a clean air duct air tightness detection method and device, so as to quickly and accurately find the leakage point of the air duct while reducing labor costs.

[0005] The technical solution of the present disclosure to solve the above technical problems is as follows:

[0006] In a first aspect, the present application provides a clean air duct air tightness detection method, which is applied to a pipeline robot, and the method includes: collecting clean air parameters in the clean air duct based on a preset detection interval to obtain a group of clean air parameters corresponding to multiple detection points; comparing two adjacent groups of clean air parameters in the multiple groups of clean air parameters to determine the air tightness of the detection points corresponding to each group of clean air parameters, and obtain a first detection result; and, according to the first detection result and the positioning data corresponding to each group of clean air parameters, obtain a second detection result, and the second detection result represents the air tightness detection result of the clean air duct.

[0007] Preferably, each group of clean air parameters includes wind speed parameters, temperature parameters, pressure parameters and positioning parameters; wherein, the clean air parameters in the clean air duct are collected based on a preset detection interval to obtain a group of clean air parameters corresponding to each of the multiple detection points, including: when clean air is transmitted in the clean air duct, based on the preset detection interval, the wind speed parameters, temperature parameters, pressure parameters and positioning parameters at each detection point are collected in turn to obtain a group of clean air parameters corresponding to each of the multiple detection points.

[0008] Preferably, two adjacent groups of clean wind parameters among the multiple groups of clean wind parameters are compared to determine the air tightness of the detection points corresponding to each group of clean wind parameters to obtain a first detection result, including: comparing the wind speed data of the current detection point among the multiple groups of clean wind parameters with the wind speed data of the previous detection point, the temperature data of the current detection point with the temperature data of the previous detection point, and the pressure data of the current detection point with the pressure data of the previous detection point, respectively, to obtain a comparison result corresponding to the current detection point; and, based on the comparison result, obtaining a first detection result; wherein the first detection result includes the air tightness detection result of the current detection point.

[0009] Preferably, a first detection result is obtained based on the comparison result, including: determining whether the wind speed difference between the wind speed parameter of the current detection point in the comparison result and the wind speed parameter of the previous detection point satisfies the first preset gradient, whether the temperature difference between the temperature parameter of the current detection point and the temperature parameter of the previous detection point satisfies the second preset gradient, and whether the pressure difference between the pressure parameter of the current detection point and the pressure parameter of the previous detection point satisfies the third preset gradient; when the wind speed difference satisfies the first preset gradient, the temperature difference satisfies the second preset gradient, and the pressure difference satisfies the third preset gradient, the first detection result indicates that the airtightness detection result of the current detection point is normal.

[0010] Preferably, a first detection result is obtained based on the comparison result, including: determining whether the wind speed difference between the wind speed parameter of the current detection point in the comparison result and the wind speed parameter of the previous detection point satisfies the first preset gradient, whether the temperature difference between the temperature parameter of the current detection point and the temperature parameter of the previous detection point satisfies the second preset gradient, and whether the pressure difference between the pressure parameter of the current detection point and the pressure parameter of the previous detection point satisfies the third preset gradient; when one of the following conditions is met: the wind speed difference does not satisfy the first preset gradient; the temperature difference does not satisfy the second preset gradient; the pressure difference does not satisfy the third preset gradient; the first detection result indicates that the airtightness detection result of the current detection point is abnormal.

[0011] Preferably, two adjacent groups of clean wind parameters among multiple groups of clean wind parameters are compared to determine the air tightness of the detection points corresponding to each group of clean wind parameters to obtain a first detection result, including: when the current detection point is the initial detection point, the wind speed data of the initial detection point is compared with the wind speed reference data, the temperature data of the initial detection point is compared with the temperature reference data, and the pressure data of the initial detection point is compared with the pressure reference data to obtain a comparison result corresponding to the initial detection point; and, based on the comparison result, a first detection result is obtained; wherein the first detection result includes the air tightness detection result of the initial detection point.

[0012] Preferably, the method further includes: sending the second detection result to the terminal device.

[0013] In a second aspect, the present application provides a clean air duct air tightness detection device, which is applied to a pipeline robot, comprising: a data acquisition module, configured to collect clean air parameters in the clean air duct based on a preset detection interval, and obtain a group of clean air parameters corresponding to multiple detection points; a data comparison module, configured to compare two adjacent groups of clean air parameters in the multiple groups of clean air parameters to determine the air tightness of the detection points corresponding to each group of clean air parameters, and obtain a first detection result; and a data analysis module, configured to obtain a second detection result based on the first detection result and the positioning data corresponding to each group of clean air parameters, and the second detection result represents the air tightness detection result of the clean air duct.

[0014] Preferably, the data acquisition module includes: a wind speed sensing unit configured to obtain the wind speed parameters of each detection point; a temperature sensing unit configured to obtain the temperature parameters of each detection point; and a pressure sensing unit configured to obtain the pressure parameters of each detection point.

[0015] Preferably, the data comparison module is also configured to: compare the wind speed data of the current detection point in the multiple sets of clean wind parameters with the wind speed data of the previous detection point, the temperature data of the current detection point with the temperature data of the previous detection point, and the pressure data of the current detection point with the pressure data of the previous detection point, respectively, to obtain a comparison result corresponding to the current detection point; and, based on the comparison result, obtain a first detection result; wherein the first detection result includes the air tightness detection result of the current detection point.

[0016] Compared with the prior art, the technical solution of the present application has at least the following beneficial technical effects:

[0017] (1) In the detection method of the present application, the clean air parameters in the clean air duct are collected based on the preset detection interval to obtain a group of clean air parameters corresponding to each of the multiple detection points. By comparing two adjacent groups of clean air parameters, the air tightness results of the detection points corresponding to each group of clean air parameters can be determined, that is, whether the air tightness of the detection points corresponding to each group of clean air parameters is abnormal, and the positioning data corresponding to each detection point can reflect the specific position of the detection point in the air duct. After completing the collection of clean air parameters for all detection points in the entire air duct, the detection points with abnormal air tightness and the corresponding position information on the entire air duct can be obtained, thereby accurately locating the position of the abnormal air tightness of the air duct. After completing the precise positioning of the position of the abnormal air tightness of the air duct, the position of the abnormal air tightness can be accurately inspected and repaired, thereby improving the efficiency of the clean air duct abnormality handling work and reducing labor costs.

[0018] (2) In the detection method of the present application, based on the premise that when clean air is transported in an air duct with normal air tightness, the parameter changes along the transport direction should be kept within a reasonable range, for the clean air parameters obtained at the current detection point, the clean air parameters obtained at the previous detection point are used as a benchmark, and reasonable variation ranges are set according to specific wind speed parameters, temperature parameters and pressure parameters. It can be judged whether the clean air parameters obtained at the current detection point are abnormal, that is, whether the air tightness of the air duct at the current detection point is abnormal, thereby obtaining the air tightness detection result of the current detection point. The judgment method is simple and has high accuracy.

[0019] (3) In the detection method of the present application, the wind speed parameter, temperature parameter, and pressure parameter in the clean air parameters of the current detection point are compared with the wind speed parameter, temperature parameter, and pressure parameter in the clean air parameters of the previous detection point, respectively, to determine whether each parameter is abnormal, and set it to be normal only when all three parameters are normal, otherwise it is determined that the air tightness of the air duct at the current detection point is abnormal. The locations of all possible air tightness anomalies on the air duct can be determined to eliminate all leakage points during subsequent maintenance to ensure that the air tightness of the clean air duct returns to normal.

[0020] (4) In the detection method of the present application, the clean air parameters at the initial detection point are compared with the parameters when the clean air is input into the air duct to determine whether the air tightness of the air duct at the initial detection point is abnormal, thereby ensuring that the air tightness detection of the entire length of the clean air duct is completed.

[0021] (5) In the detection device of the present application, the clean air parameters are collected by the data acquisition module, the two adjacent groups of clean air parameters collected are compared by the data comparison module, and the air tightness of the detection point corresponding to each group of clean air parameters is determined. The air tightness result and the corresponding positioning data of each detection point are analyzed by the data analysis module, and the air tightness results and corresponding position information of all detection points are generated, so that the detection points with abnormal air tightness on the entire air duct and the corresponding position information can be determined, and the position of the abnormal air tightness of the air duct can be accurately located. The detection device has a simple structure and high detection accuracy. It is integrated on the pipeline robot and can be applied to the air tightness detection of various air ducts. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other purposes, features and advantages of the embodiments of the present disclosure will be made clearer by describing the embodiments of the present disclosure in conjunction with the accompanying drawings. It should be noted that throughout the accompanying drawings, the same elements are represented by the same or similar reference numerals. In the drawings:

[0023] Figure 1 The application scenario of the clean air duct air tightness detection method according to an embodiment of the present disclosure is shown;

[0024] Figure 2A flow chart of a clean air duct air tightness detection method according to an embodiment of the present disclosure is shown; and

[0025] Figure 3 A structural block diagram of a clean air duct air tightness detection device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0026] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0027] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0028] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0029] In the case of using expressions such as "at least one of A, B, and C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). In the case of using expressions such as "at least one of A, B, or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0030] Some block diagrams and / or flow charts are shown in the accompanying drawings. It should be understood that some boxes or combinations thereof in the block diagrams and / or flow charts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that these instructions can create a device for implementing the functions / operations described in these block diagrams and / or flow charts when executed by the processor. The technology of the present disclosure can be implemented in the form of hardware and / or software (including firmware, microcode, etc.). In addition, the technology of the present disclosure can take the form of a computer program product on a computer-readable storage medium storing instructions, which can be used by an instruction execution system or used in conjunction with an instruction execution system.

[0031] Figure 1 The exemplary system architecture 100 that can be applied to the clean air duct air tightness detection method according to the embodiment of the present disclosure is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0032] like Figure 1 As shown, the system architecture 100 according to this embodiment may include user terminals 101, 102, 103, a network 104, and a pipeline robot 105. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the pipeline robot 105. The network 104 may include various connection types, such as a wireless communication link, etc.

[0033] The user can use the terminal devices 101, 102, 103 to interact with the pipeline robot 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 101, 102, 103. For example, web browser applications, search applications, instant messaging tools, robot controllers, etc. (only examples).

[0034] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0035] The pipeline robot 105 may be a robot that can flexibly walk in a clean pipeline to perform airtightness detection, such as a micro pipeline robot. The micro pipeline robot may receive user requests from the terminal devices 101, 102, 103 to perform airtightness detection, perform data analysis, and feed back the processing results to the terminal devices 101, 102, 103.

[0036] For example, the pipeline robot 105 may include a communication module, a data acquisition module, and a data analysis and processing module. The communication module is used to realize wireless communication between the pipeline robot 105 and the terminal devices 101, 102, and 103. The data acquisition module is used to collect clean air parameters in the clean air duct. The data analysis and processing module is used to analyze and process the collected clean air parameters to obtain air tightness detection results.

[0037] For example, the clean air parameters may be wind speed parameters, temperature parameters, pressure parameters and positioning parameters. To collect the clean air parameters, the data collection module may include a wind speed sensing unit, a temperature sensing unit, a pressure sensing unit and a positioning unit.

[0038] For example, the wind speed sensing unit is used to obtain the wind speed parameters of each detection point. The temperature sensing unit is used to obtain the temperature parameters of each detection point. The pressure sensing unit is used to obtain the pressure parameters of each detection point. The positioning unit is used to obtain the positioning parameters to achieve accurate positioning of the detection point where the abnormality occurs.

[0039] It can be understood that the system architecture is to realize the air tightness detection of the clean air ducts in the hospital. The detection process needs to be carried out when clean air is transmitted in the clean air ducts to determine whether there are points with abnormal air tightness.

[0040] It should be noted that the clean air duct air tightness detection method provided in the embodiment of the present disclosure can be performed by the pipeline robot 105. Accordingly, the clean air duct air tightness detection device provided in the embodiment of the present disclosure can generally be arranged in the pipeline robot 105. The clean air duct air tightness detection method provided in the embodiment of the present disclosure can also be performed by a robot different from the pipeline robot 105 and capable of communicating with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the clean air duct air tightness detection device provided in the embodiment of the present disclosure can also be arranged in a robot different from the pipeline robot 105 and capable of communicating with the terminal devices 101, 102, 103 and / or the pipeline robot 105 for execution.

[0041] Figure 2 FIG. 1 is a flow chart of a clean air duct air tightness detection method according to an embodiment of the present disclosure. Figure 2 As shown, the clean air duct air tightness detection method can be applied to Figure 1 The pipeline robot shown includes operations S210 to S230.

[0042] In operation S210, clean air parameters in the clean air duct are collected based on a preset detection interval to obtain a group of clean air parameters corresponding to each of the plurality of detection points.

[0043] In operation S220, two adjacent groups of clean air parameters among the multiple groups of clean air parameters are compared to determine the air tightness of the detection points corresponding to each group of clean air parameters, thereby obtaining a first detection result.

[0044] In operation S230, a second detection result is obtained according to the first detection result and the positioning data corresponding to each group of clean air parameters. The second detection result represents the air tightness detection result of the clean air duct.

[0045] According to an embodiment of the present disclosure, the pipeline robot can collect clean air parameters in the clean air duct based on a preset detection interval. For example, the preset detection interval can collect a set of clean air parameters for every 10 cm interval, and locate the detection point. It should be noted that the detection interval can be set according to actual conditions, and the embodiment of the present disclosure does not limit this.

[0046] In the disclosed embodiment, each group of clean air parameters includes wind speed parameters, temperature parameters, pressure parameters and positioning parameters. Wind speed parameters, temperature parameters and pressure parameters are used to determine whether the air tightness of each detection point is normal to obtain the detection result. The positioning parameter is used to accurately locate the detection point with abnormality in combination with the detection result.

[0047] According to the embodiments of the present disclosure, since the air supply flow requirements in the clean air ducts used in different departments of the hospital or operating rooms with different purposes are different, the air tightness detection of the clean air ducts in different application environments cannot be judged based on a specific reference parameter. Based on this, the embodiments of the present disclosure compare the clean air parameters of two adjacent groups in pairs to obtain the air tightness detection results of each detection point, thereby ensuring the accuracy of the air tightness detection in the clean air duct.

[0048] In the disclosed embodiment, a pipeline robot is placed at the clean air duct opening at the interface with the blower. The pipeline robot collects clean air parameters in the clean air duct based on a preset detection interval. This process can avoid the process of manually arranging multiple detection probes in the clean air duct in advance, effectively improving the detection effect and reducing labor costs. This detection method is applicable to the case where air tightness abnormalities occur in the clean air duct after each department or operating room has been put into use for a period of time, avoiding the disadvantage of removing the indoor ceiling to perform pipeline air tightness detection, which not only reduces after-sales costs, but also improves the effectiveness of detection.

[0049] According to an embodiment of the present disclosure, in operation S210, clean air parameters in the clean air duct are collected based on a preset detection interval to obtain a group of clean air parameters corresponding to each of the multiple detection points, including: when clean air is transmitted in the clean air duct, based on the preset detection interval, the wind speed parameters, temperature parameters, pressure parameters and positioning parameters at each detection point are collected in turn to obtain a group of clean air parameters corresponding to each of the multiple detection points.

[0050] For example, if the length of a straight section of the clean air duct is 4m, a set of clean air parameters is collected every 10cm. Therefore, the clean air parameters of 40 detection points of the straight section of the 4m-long air duct need to be collected, and 40 sets of clean air parameters are generated. The 40 sets of clean air parameters are respectively the first set of clean air parameters, the second set of clean air parameters, the third set of clean air parameters, ..., the 39th set of clean air parameters, and the 40th set of clean air parameters.

[0051] For example, the first group of clean wind parameters, the second group of clean wind parameters, the third group of clean wind parameters, ..., the 39th group of clean wind parameters, and the 40th group of clean wind parameters all include wind speed parameters, temperature parameters, pressure parameters, and positioning parameters.

[0052] According to an embodiment of the present disclosure, in operation S220, two adjacent groups of clean air parameters among multiple groups of clean air parameters are compared to determine the air tightness of the detection points corresponding to each group of clean air parameters to obtain a first detection result, including: operations S221 to S222.

[0053] In operation S221, the wind speed data of the current detection point in the multiple sets of clean wind parameters are compared with the wind speed data of the previous detection point, the temperature data of the current detection point is compared with the temperature data of the previous detection point, and the pressure data of the current detection point is compared with the pressure data of the previous detection point to obtain a comparison result corresponding to the current detection point.

[0054] In operation S222, a first detection result is obtained according to the comparison result, wherein the first detection result includes an airtightness detection result of the current detection point.

[0055] In the embodiment of the present disclosure, the above embodiment is used. In operation S221, the second group of clean wind parameters is compared with the first group of clean wind parameters, the third group of clean wind parameters is compared with the second group of clean wind parameters, ..., and so on, the 40th group of clean wind parameters is compared with the 39th group of clean wind parameters to obtain a comparison result. The comparison result includes the comparison result of the detection point corresponding to the second group of clean wind parameters, the comparison result of the detection point corresponding to the third group of clean wind parameters, ..., and the comparison result of the detection point corresponding to the 40th group of clean wind parameters. Based on this, according to the comparison result, the first detection result obtained may include the airtightness detection result of the detection point corresponding to the second group of clean wind parameters, the airtightness detection result of the detection point corresponding to the third group of clean wind parameters, ..., and the airtightness detection result of the detection point corresponding to the 40th group of clean wind parameters.

[0056] Specifically, the comparison process is to progressively compare the wind speed data in the second group of clean wind parameters with the wind speed data in the first group of clean wind parameters, the temperature data in the second group of clean wind parameters with the temperature data in the first group of clean wind parameters, and the pressure data in the second group of clean wind parameters with the pressure data in the first group of clean wind parameters, to obtain the comparison results of the detection points corresponding to the second group of clean wind parameters.

[0057] Specifically, the wind speed data in the third group of clean wind parameters are compared with the wind speed data in the second group of clean wind parameters, the temperature data in the third group of clean wind parameters are compared with the temperature data in the second group of clean wind parameters, and the pressure data in the third group of clean wind parameters are compared with the pressure data in the second group of clean wind parameters to obtain the comparison result of the detection point corresponding to the third group of clean wind parameters. And so on.

[0058] It should be noted that the clean air parameter comparison process of other adjacent groups is consistent with the above content, and the embodiment of the present disclosure will not repeat the clean air parameter comparison process of other adjacent groups.

[0059] According to an embodiment of the present disclosure, in operation S222, a first detection result is obtained based on the comparison result, including: determining whether the wind speed difference between the wind speed parameter of the current detection point and the wind speed parameter of the previous detection point in the comparison result satisfies the first preset gradient, whether the temperature difference between the temperature parameter of the current detection point and the temperature parameter of the previous detection point satisfies the second preset gradient, and whether the pressure difference between the pressure parameter of the current detection point and the pressure parameter of the previous detection point satisfies the third preset gradient. Wherein, when the wind speed difference satisfies the first preset gradient, the temperature difference satisfies the second preset gradient, and the pressure difference satisfies the third preset gradient, the first detection result indicates that the airtightness detection result of the current detection point is normal. When one of the following conditions is met: the wind speed difference does not satisfy the first preset gradient, the temperature difference does not satisfy the second preset gradient, and the pressure difference does not satisfy the third preset gradient, the first detection result indicates that the airtightness detection result of the current detection point is abnormal.

[0060] In the embodiment of the present disclosure, by comparing whether the change gradient of each parameter in the adjacent groups of clean air parameters is within the preset gradient, it is confirmed whether the parameters of each detection point are abnormal, and then it is determined whether the air tightness of the detection point is abnormal.

[0061] For example, following the above embodiment, it is confirmed whether the wind speed difference between the wind speed parameter of the second group of clean wind parameters and the wind speed parameter of the first group of clean wind parameters satisfies the first preset gradient. If so, it indicates that the wind speed parameter of the second group of clean wind parameters is normal. Otherwise, it indicates that the wind speed parameter of the second group of clean wind parameters is abnormal.

[0062] For example, it is confirmed whether the temperature difference between the temperature parameter of the second group of clean air parameters and the temperature parameter of the first group of clean air parameters satisfies the second preset gradient. If so, it means that the temperature parameter of the second group of clean air parameters is normal. Otherwise, it means that the temperature parameter of the second group of clean air parameters is abnormal.

[0063] For example, it is confirmed whether the pressure difference between the pressure parameter of the second group of clean air parameters and the pressure parameter of the first group of clean air parameters satisfies the third preset gradient. If so, it means that the pressure parameter of the second group of clean air parameters is normal. Otherwise, it means that the pressure parameter of the second group of clean air parameters is abnormal.

[0064] In the embodiment of the present disclosure, the first preset gradient, the second preset gradient and the third preset gradient can be set according to actual application needs, and the embodiment of the present disclosure does not limit this. For example, the first preset gradient can be ±0.1m / s, the second preset gradient can be ±0.05°C, and the third preset gradient can be ±0.1Pa.

[0065] In the embodiment of the present disclosure, when the difference between the wind speed parameters of two adjacent groups of clean air satisfies the first preset gradient, the difference between the temperature parameters satisfies the second preset gradient, and the difference between the pressure parameters satisfies the third preset gradient, it indicates that the air tightness test result at the rear detection point is normal. Otherwise, it indicates that the air tightness test result at the rear detection point is abnormal.

[0066] According to an embodiment of the present disclosure, when the current detection point is the initial detection point (i.e., when the current detection point is the first detection point), the airtightness detection process of the detection point can be determined by comparing the wind speed data of the initial detection point with the wind speed reference data, the temperature data of the initial detection point with the temperature reference data, and the pressure data of the initial detection point with the pressure reference data, respectively, to obtain a comparison result corresponding to the initial detection point.

[0067] In the embodiment of the present disclosure, the wind speed reference data, the temperature reference data and the pressure reference data can be set according to the set air outlet air volume, and their values ​​are not fixed, and the embodiment of the present disclosure does not limit this.

[0068] According to an embodiment of the present disclosure, when the current detection point is the initial detection point, the air tightness detection process of determining the detection point can also be to compare two adjacent groups of clean air parameters to obtain a first detection result. When the first detection result indicates that the air tightness detection results of all detection points except the initial detection point are normal, the wind speed parameter, temperature parameter and pressure parameter of the air outlet in the clean room are detected. When the wind speed parameter of the air outlet in the clean room reaches the target wind speed parameter, it indicates that the air tightness of the initial detection point is normal. Otherwise, it indicates that the air tightness of the initial detection point is abnormal.

[0069] According to an embodiment of the present disclosure, the method further includes: sending the second detection result to the terminal device.

[0070] In the disclosed embodiment, the second detection result is sent to the terminal device through the communication module of the pipeline robot, so that the user can obtain and view the second detection result through the terminal device and take further maintenance measures according to the second detection result. This process can accurately locate the position of abnormal air tightness of the air duct. The detection process is simple and low-cost, and it can be applied to air tightness detection of various air ducts, with a wide range of applications.

[0071] In the disclosed embodiment, the data analysis and processing process may also be executed by the terminal device. In this case, the pipeline robot only needs to collect various data parameters in the clean air duct and then send the various data parameters to the terminal device.

[0072] Figure 3 A structural block diagram of a clean air duct air tightness detection device according to an embodiment of the present disclosure is shown.

[0073] like Figure 3 As shown, the clean air duct air tightness detection device 300 includes a data acquisition module 310, a data comparison module 320 and a data analysis module 330. The device 300 can be applied to a pipeline robot.

[0074] The data acquisition module 310 is configured to collect clean air parameters in the clean air duct based on a preset detection interval to obtain a group of clean air parameters corresponding to each of the multiple detection points.

[0075] According to an embodiment of the present disclosure, the data acquisition module 310 can be used to execute the above reference Figure 2 The described step S210 will not be repeated here.

[0076] The data comparison module 320 is configured to compare two adjacent groups of clean air parameters in the clean air parameters to determine the air tightness of the detection point corresponding to each group of clean air parameters to obtain a first detection result.

[0077] According to an embodiment of the present disclosure, the data comparison module 320 can be used to perform the above reference Figure 2 The described step S220 will not be repeated here.

[0078] The data analysis module 330 is configured to obtain a second detection result according to the first detection result and the positioning data corresponding to each group of clean air parameters. The second detection result represents the air tightness detection result of the clean air duct.

[0079] According to an embodiment of the present disclosure, the data analysis module 330 can be used to perform the above reference Figure 2 The described step S230 will not be repeated here.

[0080] According to an embodiment of the present disclosure, the data acquisition module 310 may include: a wind speed sensing unit configured to obtain the wind speed parameters of each detection point; a temperature sensing unit configured to obtain the temperature parameters of each detection point; and a pressure sensing unit configured to obtain the pressure parameters of each detection point.

[0081] According to an embodiment of the present disclosure, the data acquisition module 310 may further include: a positioning unit configured to obtain position parameters of each detection point. For example, the positioning unit may use a GPS locator. When the pipeline robot walks in the clean air duct, the position parameters of the pipeline robot are obtained through the positioning unit.

[0082] It should be noted that the data acquisition module 310 may also include other data acquisition units, and the structure of the other data acquisition units is set according to actual needs. The embodiment of the present disclosure does not limit the structure of the data acquisition module 310.

[0083] According to an embodiment of the present disclosure, the data comparison module 320 is further configured to: compare the wind speed data of the current detection point in the multiple groups of clean air parameters with the wind speed data of the previous detection point, the temperature data of the current detection point with the temperature data of the previous detection point, and the pressure data of the current detection point with the pressure data of the previous detection point, respectively, to obtain a comparison result corresponding to the current detection point; and, according to the comparison result, obtain a first detection result. The first detection result includes the airtightness detection result of the current detection point.

[0084] According to an embodiment of the present disclosure, the data comparison module 320 is also configured to: determine whether the wind speed difference between the wind speed parameter of the current detection point and the wind speed parameter of the previous detection point in the comparison result satisfies the first preset gradient, whether the temperature difference between the temperature parameter of the current detection point and the temperature parameter of the previous detection point satisfies the second preset gradient, and whether the pressure difference between the pressure parameter of the current detection point and the pressure parameter of the previous detection point satisfies the third preset gradient. Wherein, when the wind speed difference satisfies the first preset gradient, the temperature difference satisfies the second preset gradient, and the pressure difference satisfies the third preset gradient, the first detection result indicates that the airtightness detection result of the current detection point is normal. When one of the following conditions is met: the wind speed difference does not satisfy the first preset gradient, the temperature difference does not satisfy the second preset gradient; the pressure difference does not satisfy the third preset gradient, the first detection result indicates that the airtightness detection result of the current detection point is abnormal.

[0085] According to an embodiment of the present disclosure, the apparatus 300 further includes a communication module configured to send the collected multiple groups of clean air parameters and the second detection result to a terminal device.

[0086] Specifically, the pipeline robot walks in the clean air duct along the clean air conveying direction, collects a set of clean air parameters and obtains the position parameters corresponding to the detection point according to the preset interval, and sends them to the terminal device through the communication module as a set of detection data. The terminal device receives the detection data and displays the detection data of abnormal clean air parameters.

[0087] Specifically, the communication module can be a wireless communication module, including a wifi module and / or a 5G communication module, etc., which can be equipped at the same time or one of them can be equipped selectively to achieve communication connection between the pipeline robot and the terminal device.

[0088] For example, the terminal device may be a mobile device (such as a mobile phone, iPad, etc.), a desktop computer, a laptop computer, etc., which receives and processes the clean air parameters collected by the data collection module and the location information obtained by the positioning unit.

[0089] In the embodiment of the present disclosure, the process of processing and analyzing the collected clean air parameters can be performed by the pipeline robot or by the terminal device. The process performed by the pipeline robot is as shown in the above embodiment and will not be repeated here.

[0090] For example, the process of processing and analyzing the collected clean air parameters by the terminal device can be that the terminal device receives and processes the detection data. For the first set of detection data obtained, that is, the first set of clean air parameters and the corresponding robot position information, the terminal device uses a built-in algorithm to compare the set of clean air parameters with the parameters stored when the clean air is input into the air duct, and judge whether the set of clean air parameters is abnormal, so as to judge whether the air tightness of the air duct at the position where the parameters are obtained and before is abnormal. If abnormal, the set of detection data is displayed on the display port of the terminal device, and the acquisition position information of the set of clean air parameters is displayed. The operator determines the position and area of ​​the air tightness abnormality on the air duct based on the position information.

[0091] When the terminal device receives all clean air parameters except the first group of clean air parameters, for each group of test data, the built-in algorithm is used to compare the clean air parameters obtained in the previous group with the clean air parameters in the currently received test data to determine whether the clean air parameters in the currently received test data are abnormal, and thus determine whether the air tightness of the air duct at the current parameter acquisition position and the area between this position and the previous parameter acquisition position is abnormal. If the air tightness is abnormal, the currently received test data will be displayed on the display port of the terminal device, displaying the acquisition position information of the group of clean air parameters, and the operator will determine the position and area of ​​the air tightness abnormality on the air duct based on the position information.

[0092] When the pipeline robot moves to the duct outlet, it completes the collection of the last set of test data. After the terminal device receives and processes the last set of test data, the air tightness test of the entire duct is completed. The location of the air tightness anomaly on the duct is displayed on the terminal device, making it convenient for the inspector to accurately locate the location of the air tightness anomaly on the duct.

[0093] According to an embodiment of the present disclosure, the present disclosure also provides a pipeline robot, including: Figure 2 The clean air duct air tightness detection 300 is shown. The structure of the clean air duct air tightness detection 300 is as shown in the above embodiment. For the sake of simplicity, the clean air duct air tightness detection 300 is not described again here.

[0094] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0095] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A clean air duct air tightness detection method, characterized in that: Applied to a pipeline robot, the method comprises: The clean air parameters in the clean air duct are collected based on the preset detection interval to obtain a set of clean air parameters corresponding to each of the multiple detection points; comparing two adjacent groups of the clean air parameters among the multiple groups of the clean air parameters to determine the air tightness of the detection points corresponding to each group of the clean air parameters, and obtaining a first detection result; and A second detection result is obtained according to the first detection result and the positioning data corresponding to each group of clean air parameters, and the second detection result represents the air tightness detection result of the clean air duct.

2. The clean air duct air tightness detection method according to claim 1 is characterized in that: Each group of clean air parameters includes wind speed parameters, temperature parameters, pressure parameters and positioning parameters; wherein, the clean air parameters in the clean air duct are collected based on the preset detection interval to obtain a group of clean air parameters corresponding to each of the multiple detection points, including: When clean air is transmitted in the clean air duct, based on the preset detection interval, the wind speed parameters, temperature parameters, pressure parameters and positioning parameters at each detection point are collected in sequence to obtain a set of clean air parameters corresponding to each of the multiple detection points.

3. The clean air duct air tightness detection method according to claim 2 is characterized in that: The comparing two adjacent groups of clean air parameters among the multiple groups of clean air parameters to determine the air tightness of the detection points corresponding to each group of clean air parameters to obtain a first detection result includes: Compare the wind speed data of the current detection point with the wind speed data of the previous detection point, the temperature data of the current detection point with the temperature data of the previous detection point, and the pressure data of the current detection point with the pressure data of the previous detection point in the multiple groups of the clean air parameters to obtain a comparison result corresponding to the current detection point; and A first detection result is obtained according to the comparison result; wherein the first detection result includes an airtightness detection result of a current detection point.

4. The clean air duct air tightness detection method according to claim 3 is characterized in that: Obtaining a first detection result according to the comparison result includes: Determine whether a wind speed difference between the wind speed parameter of the current detection point and the wind speed parameter of the previous detection point in the comparison result satisfies a first preset gradient, whether a temperature difference between the temperature parameter of the current detection point and the temperature parameter of the previous detection point satisfies a second preset gradient, and whether a pressure difference between the pressure parameter of the current detection point and the pressure parameter of the previous detection point satisfies a third preset gradient; When the wind speed difference satisfies a first preset gradient, the temperature difference satisfies a second preset gradient, and the pressure difference satisfies a third preset gradient, the first detection result indicates that the airtightness detection result of the current detection point is normal.

5. The clean air duct air tightness detection method according to claim 3 is characterized in that: Obtaining a first detection result according to the comparison result includes: Determine whether a wind speed difference between the wind speed parameter of the current detection point and the wind speed parameter of the previous detection point in the comparison result satisfies a first preset gradient, whether a temperature difference between the temperature parameter of the current detection point and the temperature parameter of the previous detection point satisfies a second preset gradient, and whether a pressure difference between the pressure parameter of the current detection point and the pressure parameter of the previous detection point satisfies a third preset gradient; When one of the following conditions is met: The wind speed difference does not satisfy the first preset gradient; The temperature difference does not satisfy the second preset gradient; The pressure difference does not satisfy the third preset gradient; The first detection result indicates that the airtightness detection result of the current detection point is abnormal.

6. The clean air duct air tightness detection method according to claim 3 is characterized in that: The comparing two adjacent groups of clean air parameters among the multiple groups of clean air parameters to determine the air tightness of the detection points corresponding to each group of clean air parameters to obtain a first detection result includes: In the case where the current detection point is an initial detection point, Compare the wind speed data of the initial detection point with the wind speed reference data, the temperature data of the initial detection point with the temperature reference data, and the pressure data of the initial detection point with the pressure reference data, respectively, to obtain a comparison result corresponding to the initial detection point; and According to the comparison result, a first detection result is obtained; wherein the first detection result includes the airtightness detection result of the initial detection point.

7. The clean air duct air tightness detection method according to claim 1, characterized in that: The method further includes: The second detection result is sent to the terminal device.

8. A clean air duct air tightness detection device, characterized in that: Applied to pipeline robots, including: A data acquisition module is configured to collect clean air parameters in the clean air duct based on a preset detection interval to obtain a set of clean air parameters corresponding to each of the multiple detection points; a data comparison module, configured to compare two adjacent groups of the clean air parameters among the multiple groups of the clean air parameters to determine the air tightness of the detection points corresponding to each group of the clean air parameters, and obtain a first detection result; and The data analysis module is configured to obtain a second detection result based on the first detection result and the positioning data corresponding to each group of clean air parameters, wherein the second detection result represents the air tightness detection result of the clean air duct.

9. The clean air duct air tightness detection device according to claim 8, characterized in that: The data acquisition module comprises: A wind speed sensing unit configured to obtain wind speed parameters at each detection point; A temperature sensing unit configured to obtain temperature parameters of each detection point; and The pressure sensing unit is configured to obtain pressure parameters of each detection point.

10. The clean air duct air tightness detection device according to claim 8, characterized in that: The data comparison module is also configured as: Compare the wind speed data of the current detection point with the wind speed data of the previous detection point, the temperature data of the current detection point with the temperature data of the previous detection point, and the pressure data of the current detection point with the pressure data of the previous detection point in the multiple groups of the clean air parameters, respectively, to obtain a comparison result corresponding to the current detection point; as well as A first detection result is obtained according to the comparison result; wherein the first detection result includes an airtightness detection result of a current detection point.

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