Automatic sensor identification method for intelligent building robot

Abstract

The invention discloses a method for automatically identifying sensors of a building intelligent robot. The method comprises the following steps: S1, acquiring data information in a plurality of sensors on the robot; s2, based on the acquired data information, detecting whether there is an abnormality between the data information of the sensor and the data information when the sensor works normally; s3, for the data information which is determined to be abnormal, determining that the sensor from which the abnormal data information comes is an abnormal sensor; s4, the abnormal sensor is diagnosed, and the reason of data information abnormity is determined; and S5, detecting the working state of the sensor in each detection layer in the plurality of set detection layers for the data information without the abnormal sensor. By detecting the data information of the sensor and the working state of the sensor, the abnormal sensor can be rapidly and accurately detected, and the efficiency and accuracy of abnormal sensor identification can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction robots, and in particular to a sensor automatic recognition method of a building intelligent robot. Background Art

[0002] There are many types of construction robots. According to the construction process, robots can be divided into demolition, construction surveying, excavation and walling combined with intelligent construction robots on the construction site, prefabricated panels, construction, steel beam welding, steel wire and carbon fiber weaving, concrete spraying, decoration, floor laying, cleaning, construction service robots, etc. If classified by the post-construction use, robots can be divided into security, property management services, cleaning services, butler services, intelligent building management service robots, etc. These robots mainly serve the maintenance, repair, transportation, cleaning, security, rescue, and monitoring of buildings.

[0003] As is known to all, detection sensors (e.g., inertial measurement units) for detecting posture information of each front component are respectively installed on the multiple front components constituting the working machine (e.g., the boom, dipper arm, bucket, etc. constituting the front working machine such as a hydraulic excavator). Each posture information detection sensor inputs a detection signal to the control device via the vehicle network, and the posture of each front component is calculated in the control device. The calculated posture information of the front component is displayed on a display device such as a monitor, so as to achieve limitation of the movable range of the working machine.

[0004] However, due to the complex environment where the robot is used and the sensors working outdoors for a long time, it is inevitable that the sensors will be damaged, resulting in abnormal and inaccurate monitoring data, which in turn may lead to accidents. Summary of the invention

[0005] The present invention provides a sensor automatic identification method for a building intelligent robot. By detecting the data information of the sensor and the working status of the sensor itself, the sensor can be quickly and accurately detected, which helps to improve the efficiency and accuracy of abnormal sensor identification.

[0006] In order to achieve the above object, the present invention provides a sensor automatic identification method for a building intelligent robot, which comprises the following steps:

[0007] S1, obtain data information from multiple sensors on the robot;

[0008] S2. Based on the acquired data information, detecting whether there is an abnormality between the data information of the sensor and the data information when the sensor works normally;

[0009] S3. For the data information that is determined to be abnormal, determine that the sensor from which the abnormal data information originates is an abnormal sensor;

[0010] S4, diagnose the abnormal sensor and determine the cause of the abnormal data information;

[0011] S5. For data information in which there is no abnormality in the sensor, detecting the working state of the sensor at each of the set multiple detection levels;

[0012] S6. A sensor with an abnormal working state is detected at any detection level and is determined as an abnormal sensor.

[0013] As a further description of the above technical solution:

[0014] Steps to determine abnormal sensor data information:

[0015] Set the standard value of the sensor's normal working data information;

[0016] Record the working value of the sensor's normal working data information;

[0017] Calculate the fluctuation threshold of the sensor's normal working data information, where the fluctuation threshold is the absolute value of the working value minus the standard value;

[0018] The actual working value of the actual working data information of the sensor is collected, and the absolute value of the difference between the actual working value and the standard value is compared with the fluctuation threshold; if the absolute value of the difference between the actual working value and the standard value is not within the fluctuation threshold, the data information of the sensor is abnormal; otherwise, the data information of the sensor is normal.

[0019] As a further description of the above technical solution:

[0020] Determining the cause of abnormal data information includes: based on the data information collected by the sensor, detecting the corresponding data information at each detection level, and when abnormal data is detected at any detection level, determining the cause of the abnormal data information accordingly.

[0021] As a further description of the above technical solution:

[0022] The detection level includes a numerical detection level, a position detection level or an occlusion detection level.

[0023] As a further description of the above technical solution:

[0024] The sensor access success verification step is also included, and the verification step includes:

[0025] When the sensor is connected, connection information is sent, and the connection information includes sensor tag information;

[0026] The robot processor receives the connection information, identifies and verifies the connection information, and obtains a verification result;

[0027] Determine whether the sensor connection is successful based on the verification result.

[0028] As a further description of the above technical solution:

[0029] The verification result needs to last for a period of time, and needs to be re-verified at regular intervals.

[0030] As a further description of the above technical solution:

[0031] The verification steps of the verification result are:

[0032] Determine whether the sensor tag information included in the connection information is valid, and obtain a determination result;

[0033] If the judgment result is that the sensor identification information is valid, the sensor access is determined to be valid access; otherwise, the sensor access is determined to be invalid access.

[0034] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0035] The present invention provides a sensor automatic identification method for a building intelligent robot, which obtains data information of multiple sensors on the robot; based on the obtained data information, detects whether there is an abnormality between the data information of the sensor and the data information when the sensor is working normally; for the data information determined to be abnormal, determines that the sensor from which the abnormal data information originates is an abnormal sensor; diagnoses the abnormal sensor to determine the cause of the data information abnormality; for the data information of the sensor without abnormality, detects the working state of the sensor at each of the multiple detection levels set; and determines that the sensor with abnormal working state at any detection level is an abnormal sensor. By detecting the data information of the sensor and the working state of the sensor itself, the sensor with abnormality can be detected quickly and accurately, which helps to improve the efficiency and accuracy of abnormal sensor identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 The present invention is a flow chart of a sensor automatic identification method for a building intelligent robot. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0039] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0041] In the description of the embodiments of the present invention, it should be noted that the terms "upper", "inner", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the inventive product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0042] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, an indirect connection through an intermediate medium, or the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] See also Figure 1 The present invention provides a sensor automatic identification method for a building intelligent robot, comprising the following steps:

[0044] S1, obtain data information from multiple sensors on the robot;

[0045] S2. Based on the acquired data information, detecting whether there is an abnormality between the data information of the sensor and the data information when the sensor works normally;

[0046] S3. For the data information that is determined to be abnormal, determine that the sensor from which the abnormal data information originates is an abnormal sensor;

[0047] S4, diagnose the abnormal sensor and determine the cause of the abnormal data information;

[0048] S5. For data information in which there is no abnormality in the sensor, detecting the working state of the sensor at each of the set multiple detection levels;

[0049] S6. A sensor with an abnormal working state is detected at any detection level and is determined as an abnormal sensor.

[0050] Steps to determine abnormal sensor data information:

[0051] Set the standard value of the sensor's normal working data information;

[0052] Record the working value of the sensor's normal working data information;

[0053] Calculate the fluctuation threshold of the sensor's normal working data information, where the fluctuation threshold is the absolute value of the working value minus the standard value;

[0054] The actual working value of the actual working data information of the sensor is collected, and the absolute value of the difference between the actual working value and the standard value is compared with the fluctuation threshold; if the absolute value of the difference between the actual working value and the standard value is not within the fluctuation threshold, the sensor data information is abnormal; otherwise, the sensor data information is normal. This can prevent misjudgment and improve the accuracy of data information detection.

[0055] Determining the reason for abnormal data information includes: based on the data information collected by the sensor, detecting the corresponding data information at each detection level, and when abnormal data is detected at any detection level, determining the corresponding data information is abnormal. Performing hierarchical detection can further improve the accuracy of detection. Specifically, the detection level includes a numerical detection level, a position detection level, or an occlusion detection level.

[0056] In another embodiment, the method further includes a sensor access success verification step, wherein the verification step includes:

[0057] When the sensor is connected, connection information is sent, and the connection information includes sensor tag information;

[0058] The robot processor receives the connection information, identifies and verifies the connection information, and obtains a verification result;

[0059] Determine whether the sensor access is successful based on the verification result. This step is located in step S1, and can determine whether the sensor is normally connected to avoid misjudgment caused by inaccurate sensor access.

[0060] The verification results need to be continued for a period of time, and re-verification is required at regular intervals to prevent misjudgment caused by poor sensor contact.

[0061] The verification steps of the verification result are:

[0062] Determine whether the sensor tag information included in the connection information is valid, and obtain a determination result;

[0063] If the judgment result is that the sensor identification information is valid, the sensor access is determined to be valid access, otherwise the sensor access is determined to be invalid access. The tag information can be verified, thereby improving the accuracy of the judgment result.

[0064] In another implementation, the circuit can be used to verify whether the sensor is successfully connected. Specifically, the sampling circuit board is installed on the sensor, and then the sensor is installed on the main circuit board of the robot; the voltage acquisition circuit collects the voltage value across the two contacts, and outputs the voltage value to the microprocessor circuit of the robot; the robot microprocessor circuit determines whether the voltage value is within a preset voltage threshold range based on the collected voltage value. If so, the sensor is successfully connected to the robot main circuit board, otherwise, the sensor is not successfully connected to the robot main circuit board.

[0065] Working principle: by acquiring data information from multiple sensors on the robot; based on the acquired data information, detecting whether there is an abnormality between the data information of the sensor and the data information when the sensor is working normally; for data information that is determined to be abnormal, determining the sensor from which the abnormal data information originates as an abnormal sensor; diagnosing the abnormal sensor to determine the cause of the data information abnormality; for data information where the sensor does not have abnormalities, detecting the working status of the sensor at each of the multiple detection levels set; determining the sensor with an abnormal working status at any detection level as an abnormal sensor. By detecting the data information of the sensor and the working status of the sensor itself, the abnormal sensor can be detected quickly and accurately, which helps to improve the efficiency and accuracy of abnormal sensor identification.

[0066] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A sensor automatic identification method for a building intelligent robot, characterized in that: The following steps are involved: S1, obtain data information from multiple sensors on the robot; S2. Based on the acquired data information, detecting whether there is an abnormality between the data information of the sensor and the data information when the sensor works normally; S3. For the data information that is determined to be abnormal, determine that the sensor from which the abnormal data information originates is an abnormal sensor; S4, diagnose the abnormal sensor and determine the cause of the abnormal data information; S5. For data information in which there is no abnormality in the sensor, detecting the working state of the sensor at each of the set multiple detection levels; S6. A sensor with an abnormal working state is detected at any detection level and is determined as an abnormal sensor.

2. The sensor automatic identification method of a building intelligent robot according to claim 1 is characterized in that: Steps to determine abnormal sensor data information: Set the standard value of the sensor's normal working data information; Record the working value of the sensor's normal working data information; Calculate the fluctuation threshold of the sensor's normal working data information, where the fluctuation threshold is the absolute value of the working value minus the standard value; Collect the actual working value of the actual working data information of the sensor, and compare the absolute value of the difference between the actual working value and the standard value with the fluctuation threshold; If the absolute value of the difference between the actual working value and the standard value is not within the fluctuation threshold, the data information of the sensor is abnormal; otherwise, the data information of the sensor is normal.

3. The sensor automatic identification method of a building intelligent robot according to claim 1 is characterized in that: Determining the cause of abnormal data information includes: based on the data information collected by the sensor, detecting the corresponding data information at each detection level, and when abnormal data is detected at any detection level, determining the cause of the abnormal data information accordingly.

4. The sensor automatic identification method of a building intelligent robot according to claim 1 is characterized in that: The detection level includes a numerical detection level, a position detection level or an occlusion detection level.

5. The sensor automatic identification method of a building intelligent robot according to claim 1 is characterized in that: The sensor access success verification step is also included, and the verification step includes: When the sensor is connected, connection information is sent, and the connection information includes sensor tag information; The robot processor receives the connection information, and identifies and verifies the connection information to obtain a verification result; Determine whether the sensor connection is successful based on the verification result.

6. The sensor automatic identification method of a building intelligent robot according to claim 5 is characterized in that: The verification result needs to last for a period of time, and needs to be re-verified at regular intervals.

7. The sensor automatic identification method of a building intelligent robot according to claim 5 is characterized in that: The verification steps of the verification result are: Determine whether the sensor tag information included in the connection information is valid, and obtain a determination result; If the judgment result is that the sensor identification information is valid, the sensor access is determined to be valid access; otherwise, the sensor access is determined to be invalid access.

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