A digital acceptance robot and method for communication equipment rooms

Abstract

This invention relates to a digital acceptance robot for communication equipment rooms, comprising: a lidar, an inertial measurement unit (IMU), a drive motor, a motor drive unit, an encoder, an acceptance data acquisition unit, and a processing unit. The processing unit constructs a map using a lidar SLAM algorithm based on radar data acquired by the lidar and odometry data acquired by the encoder. Simultaneously, it obtains the robot's first pose data based on IMU measurement data acquired by the IMU, and combines this first pose data with the constructed map to generate a motion decision model. The acceptance data acquisition unit collects acceptance data from the communication equipment room and generates acceptance results based on this data. This approach avoids the difficulties caused by excessive manpower, improves acceptance efficiency, helps save on acceptance costs, and enables objective review using artificial intelligence, reducing the impact of subjective review opinions on the acceptance of communication equipment rooms.

Description

Technical Field

[0001] This application relates to the field of communication equipment room technology, specifically to a digital acceptance robot and method for communication equipment rooms. Background Technology

[0002] In traditional acceptance procedures, the acceptance of a communication equipment room requires joint inspection of on-site technical details by the construction unit, owner, maintenance unit, construction unit, design unit, and supervision unit, referring to communication equipment room construction specifications. Acceptance is granted only after all units reach a consensus. In actual acceptance, personnel from the construction unit participating in the acceptance process include those from the engineering management department, safety management department, investment management department, and maintenance management department. Existing technology has the following characteristics:

[0003] (1) There are many communication equipment rooms. It is difficult to coordinate the time of each acceptance through manual on-site acceptance mode to ensure that the relevant units can participate on time during the acceptance.

[0004] (2) Communication equipment rooms are geographically distributed, and the transportation time during the acceptance process is long;

[0005] (3) There are many participating units and departments. Even if each unit conducts acceptance in accordance with industry standards and construction standards, there are differences in the understanding and emphasis of each unit and department on the standards, which may easily lead to inconsistent acceptance conclusions and the possibility of repeated on-site acceptance. Summary of the Invention

[0006] In view of the above problems, this application provides a digital acceptance robot and method for communication equipment rooms, which solves the problems of the existing manual on-site acceptance mode, such as the need for personnel coordination and the impact of subjective review opinions on the acceptance of communication equipment rooms.

[0007] To achieve the above objectives, the inventors provide a digital acceptance robot for communication equipment rooms, comprising:

[0008] A lidar, which is used to collect radar data of the surrounding environment;

[0009] An inertial measurement unit (IMU) is used to collect IMU measurement data from the robot.

[0010] A drive motor, used to drive the robot to move;

[0011] A motor drive unit is connected to the drive motor and is used to control the drive motor;

[0012] An encoder is mounted on the robot's drive motor and is used to collect the robot's odometer data.

[0013] An acceptance data acquisition unit is used to collect acceptance data for the communication equipment room.

[0014] The processing unit is used to construct a map using a laser SLAM algorithm based on radar data collected by the lidar and robot odometry data collected by the encoder. Simultaneously, it obtains the robot's first pose data based on IMU measurement data collected by the inertial measurement unit, and combines the first pose data with the constructed map to generate a motion decision model. Based on the generated motion decision model, it controls the drive motor to drive the robot to move through the motor drive unit, and collects acceptance data from the communication equipment room through the acceptance data acquisition unit, and generates acceptance results based on the collected acceptance data.

[0015] In some embodiments, a vision camera is also included, which is used to acquire visual data of the surrounding environment;

[0016] The processing unit is used to capture the relative positions of each set key measurement point through LiDAR, form a two-dimensional grid map through LiDAR SLAM algorithm, capture the relative positions of each set key measurement point through a vision camera, form a two-dimensional image through a vision SLAM algorithm, and perform planar fitting between the obtained two-dimensional grid map and the two-dimensional image to construct a map.

[0017] In some embodiments, there are multiple inertial measurement units;

[0018] The processing unit is also used to perform time synchronization and weighted averaging of IMU measurement data collected by multiple inertial measurement units using a visual SLAM algorithm to obtain average IMU measurement data, and to integrate the average IMU measurement data to obtain the first pose data.

[0019] In some embodiments, the vision camera is also used to acquire the robot's second pose data;

[0020] The processing unit is also used to obtain the third pose data by passing the obtained first pose data and second pose data through the Kalman filter algorithm, and to combine the obtained third pose data with the constructed map to obtain a motion decision model.

[0021] In some embodiments, the acceptance data acquisition unit includes:

[0022] An infrared sensor, used to collect data on the civil engineering structure of the communication equipment room;

[0023] A strength sensor is used to collect the material mass of the communication equipment room;

[0024] A resistance measuring instrument, used to measure the resistance of external power input points and equipment access points in a communication equipment room;

[0025] A temperature and humidity sensor, used to collect temperature and humidity data of the environment inside the communication equipment room;

[0026] A high-precision camera is used to collect data on the operation of equipment in a communication equipment room.

[0027] Another technical solution is also provided: a method for digital acceptance testing of communication equipment rooms, including the following steps:

[0028] Radar data of the environment surrounding the communication equipment room is collected using lidar;

[0029] The robot's odometer data is collected via an encoder;

[0030] A map is constructed using laser SLAM algorithms based on radar and odometer data;

[0031] The robot's first pose data is obtained by collecting IMU measurement data from the inertial measurement unit.

[0032] The first pose data is then combined with the constructed map to generate a motion decision model;

[0033] Based on the generated motion decision model, the drive motor is controlled by the motor drive unit to drive the robot to move.

[0034] The acceptance data acquisition unit collects acceptance data from the communication equipment room and generates acceptance results based on the collected acceptance data.

[0035] In some embodiments, the "construction of a map based on radar data and odometry data using a laser SLAM algorithm" specifically includes the following steps:

[0036] The relative positions of each key measurement point are captured by LiDAR, and a two-dimensional grid pattern is formed by LiDAR SLAM algorithm;

[0037] The relative positions of each key measurement point are captured by a visual camera, and a two-dimensional image is formed by a visual SLAM algorithm.

[0038] The obtained two-dimensional grid pattern is fitted to the two-dimensional image to construct a map.

[0039] In some embodiments, there are multiple inertial measurement units;

[0040] The step of "obtaining the robot's first pose data based on the IMU measurement data collected by the inertial measurement unit" specifically includes the following steps:

[0041] The average IMU measurement data is obtained by time synchronization and weighted averaging of IMU measurement data collected by multiple inertial measurement units using a visual SLAM algorithm. The first pose data is then obtained by integrating the average IMU measurement data.

[0042] In some embodiments, the step of "combining the first pose data with the constructed map to generate a motion decision model" specifically includes the following steps:

[0043] The robot's second pose data is acquired using a vision camera;

[0044] The obtained first pose data and second pose data are used to obtain the third pose data through the Kalman filter algorithm;

[0045] The obtained third pose data is then combined with the constructed map to obtain a motion decision model.

[0046] In some embodiments, the "collection of acceptance data for the communication equipment room through the acceptance data acquisition unit" specifically includes the following steps:

[0047] The civil structure of the communication equipment room is collected using infrared sensors;

[0048] The material quality of the communication equipment room is collected using strength sensors;

[0049] The resistance of the external power supply points and equipment access points in the communication equipment room is measured using a resistance measuring instrument.

[0050] Temperature and humidity data of the communication equipment room are collected using temperature and humidity sensors.

[0051] The operation status of equipment in the communication equipment room is collected using high-precision cameras.

[0052] Unlike existing technologies, the above-mentioned technical solution, when requiring acceptance testing of a communication equipment room, uses a robot equipped with a LiDAR to scan the environment within the equipment room to obtain radar data. Simultaneously, an encoder collects odometer data from the robot. The collected radar and odometer data are used to construct a map using a LiDAR SLAM algorithm. An inertial measurement unit (IMU) collects IMU measurement data from the robot, and the robot's first pose data is obtained from the IMU data. This first pose data is combined with the constructed map to generate a motion decision model. The motion decision model then outputs drive signals to the motor drive unit, which controls the robot's drive motors to move the robot according to the motion decision model. Simultaneously, an acceptance data acquisition unit collects acceptance data from the communication equipment room, and the acceptance result is obtained based on this data. Using a robot to inspect the communication equipment room changes the "offline" on-site acceptance mode to an "online" model acceptance mode, avoiding various difficulties caused by excessive personnel, improving acceptance efficiency, saving acceptance costs, and enabling objective review by artificial intelligence, reducing the impact of subjective review opinions on the acceptance of the communication equipment room.

[0053] The above description of the invention is merely an overview of the technical solution of this application. In order to enable those skilled in the art to better understand the technical solution of this application and to implement it based on the description and drawings, and to make the above-mentioned objectives and other objectives, features and advantages of this application easier to understand, the following description is provided in conjunction with the specific embodiments and drawings of this application. Attached Figure Description

[0054] The accompanying drawings are only used to illustrate the principles, implementation methods, applications, features, and effects of specific embodiments of this application and other related content, and should not be considered as limitations on this application.

[0055] In the accompanying drawings of the instruction manual:

[0056] Figure 1 A schematic diagram of a structure of the digital acceptance robot for communication equipment rooms described in a specific implementation;

[0057] Figure 2 This is a schematic diagram of another structure of the digital acceptance robot for the communication equipment room described in a specific implementation;

[0058] Figure 3 This is a schematic diagram illustrating the construction of the motion decision-making model described in a specific implementation method;

[0059] Figure 4 This is a schematic diagram of the structure of the acceptance data acquisition unit described in a specific implementation method;

[0060] Figure 5 A schematic diagram of a technical framework for the digital acceptance robot for communication equipment rooms described in a specific implementation;

[0061] Figure 6 A schematic diagram of the online evaluation system described in the specific implementation method;

[0062] Figure 7 This is a flowchart illustrating one embodiment of the digital acceptance method for communication equipment rooms.

[0063] The reference numerals used in the above figures are explained as follows:

[0064] 110. LiDAR (Light Detection and Ranging)

[0065] 120. Inertial Measurement Unit

[0066] 130. Drive motor,

[0067] 140. Motor drive unit,

[0068] 150. Encoder

[0069] 160. Acceptance of the data acquisition unit.

[0070] 170. Processing Unit. Detailed Implementation

[0071] In order to explain in detail the possible application scenarios, technical principles, specific solutions that can be implemented, and the purpose and effects of this application, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of this application and are therefore only examples and are not intended to limit the scope of protection of this application.

[0072] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0073] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit this application.

[0074] In the description of this application, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" logical relationship.

[0075] In this application, terms such as “first” and “second” are used only to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy or order relationship between these entities or operations.

[0076] Unless otherwise specified, the use of terms such as “comprising,” “including,” “having,” or other similar expressions in this application is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0077] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceeding" are understood to exclude the stated number; expressions such as "above," "below," and "within" are understood to include the stated number. Furthermore, in the description of the embodiments in this application, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise explicitly specified.

[0078] In the description of the embodiments of this application, the space-related expressions used, such as "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "vertical," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiments or drawings. They are only for the purpose of describing the specific embodiments of this application or for the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0079] Unless otherwise expressly specified or limited, the terms "installation," "connection," "linking," "fixing," and "setting," as used in the description of the embodiments of this application, should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two components or the interaction between two components. For those skilled in the art to which this application pertains, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0080] Please see Figure 1 This embodiment provides a digital acceptance robot for communication equipment rooms, including:

[0081] A lidar 110 is used to collect radar data of the surrounding environment;

[0082] An inertial measurement unit 120 is used to collect IMU measurement data from the robot.

[0083] A drive motor 130 is used to drive the robot to move.

[0084] A motor drive unit 140 is connected to the drive motor 130 and is used to control the drive motor 130.

[0085] An encoder 150 is mounted on the robot's drive motor 130 and is used to collect the robot's odometer data.

[0086] Acceptance data acquisition unit 160, the acceptance data acquisition unit 160 is used to collect acceptance data of the communication equipment room;

[0087] The processing unit 170 is used to construct a map using a laser SLAM algorithm based on radar data collected by the lidar 110 and robot odometry data collected by the encoder 150. Simultaneously, it obtains the robot's first pose data based on IMU measurement data collected by the inertial measurement unit 120, and combines the first pose data with the constructed map to generate a motion decision model. Based on the generated motion decision model, it controls the drive motor 130 to drive the robot through the motor drive unit 140, and collects acceptance data from the communication equipment room through the acceptance data acquisition unit 160, and generates acceptance results based on the collected acceptance data.

[0088] When a communication equipment room needs to be inspected, the robot's LiDAR 110 scans the environment to obtain radar data, while the encoder 150 collects odometer data. The collected radar and odometer data are used to construct a map using a LiDAR SLAM algorithm. The inertial measurement unit 120 collects IMU measurement data, and the robot's first pose data is obtained from the IMU data. This first pose data is combined with the constructed map to generate a motion decision model. The motion decision model then outputs drive signals to the motor drive unit 140, which controls the robot's drive motors 130 to move the robot according to the motion decision model. Simultaneously, the acceptance data acquisition unit 160 collects acceptance data for the communication equipment room and obtains the acceptance result. Using a robot to inspect the communication equipment room changes the "offline" on-site inspection mode to an "online" model inspection mode, avoiding various difficulties caused by excessive personnel, improving inspection efficiency, saving inspection costs, and enabling objective artificial intelligence review, reducing the impact of subjective review opinions on the acceptance of the communication equipment room.

[0089] Please see Figure 3 In some embodiments, a vision camera is also included for acquiring visual data of the surrounding environment;

[0090] The processing unit 170 is used to capture the relative positions of each set key measurement point through the lidar 110, form a two-dimensional grid map through the lidar SLAM algorithm, capture the relative positions of each set key measurement point through the vision camera, form a two-dimensional image through the vision SLAM algorithm, and perform plane fitting between the obtained two-dimensional grid map and the two-dimensional image to construct a map.

[0091] Visual data of the environment surrounding the communication equipment room is collected using a visual camera. A projected 2D image is then generated using a visual SLAM algorithm. A 2D grid map is generated by combining laser data from the LiDAR 110 and odometer data from the encoder 150 using a laser SLAM algorithm. The projected 2D image and the 2D grid map are then fused using a grid occupancy rule to improve map accuracy. The relative positions of key measurement points are set using laser capture, and a 2D grid map is formed using the SLAM algorithm. The relative positions of key measurement points are also set using a visual camera, and a 2D image is formed using the SLAM algorithm. The 2D grid map and the 2D image are then fitted to a plane to confirm the positions of the key measurement points. If the key measurement point positions are consistent, the fitting is successful; if they are inconsistent, the corresponding key measurement points are captured again for secondary and tertiary fitting. During each fitting process, the success rate of key measurement point fitting gradually increases until a perfect fit is achieved. The reason for incomplete fitting of key measurement points is mainly due to measurement point capture errors; smooth fitting is performed within the allowable error range of the model.

[0092] Please see Figure 3 In some embodiments, there are multiple inertial measurement units 120;

[0093] The processing unit 170 is also used to perform time synchronization and weighted averaging of the IMU measurement data collected by multiple inertial measurement units 120 using a visual SLAM algorithm to obtain average IMU measurement data, and to integrate the average IMU measurement data to obtain the first pose data.

[0094] Multiple inertial measurement units 120 are used to collect robot IMU measurement data, thereby obtaining multiple IMU measurement data. The obtained IMU measurement data are time-synchronized and weighted averaged using a visual SLAM algorithm to obtain average IMU measurement data. The average IMU measurement data is then integrated to obtain the first pose data, making the acquired robot's first pose data more accurate.

[0095] Please see Figure 3 In some embodiments, the vision camera is also used to acquire the robot's second pose data;

[0096] The processing unit 170 is further configured to use the obtained first pose data and second pose data to obtain third pose data through a Kalman filter algorithm, and combine the obtained third pose data with the constructed map to obtain a motion decision model. Alternatively, the robot's second pose data is acquired through a vision camera, and the obtained first pose data and second pose data are used as the output of the Kalman filter algorithm to calculate the third pose data, which is then applied to motion decision-making.

[0097] Please see Figure 4 In some embodiments, the acceptance data acquisition unit 160 includes:

[0098] An infrared sensor, used to collect data on the civil engineering structure of the communication equipment room;

[0099] A strength sensor is used to collect the material mass of the communication equipment room;

[0100] A resistance measuring instrument, used to measure the resistance of external power input points and equipment access points in a communication equipment room;

[0101] A temperature and humidity sensor, used to collect temperature and humidity data of the environment inside the communication equipment room;

[0102] A high-precision camera is used to collect data on the operation of equipment in a communication equipment room.

[0103] The modeling structure was developed based on the actual acceptance standards for communication equipment rooms. The modeling principle adopts a multi-level scanning modeling structure, where each sensor or instrument independently models its respective acceptance component, merging all modeling elements into a unified model. Communication equipment room acceptance involves multiple levels and parameters, but smaller parameters are subordinate to larger parameters; that is, if the larger parameters pass acceptance, the smaller parameters are also likely to be acceptable. Taking communication equipment room transmission as an example, transmission includes cable trays, pigtails, ODF racks, equipment, and other aspects, exhibiting upstream and downstream technical relationships. However, once the equipment completes its on-loop connection to the network, it indicates that the corresponding transmission components are interconnected and there are no technical issues; only the compliance of other transmission components needs to be verified.

[0104] After processing, the model was used to perform layered scanning of the civil structure quality, power supply, temperature and humidity, and equipment operation of the communication equipment room. Based on the layered scanning structure, the first layer used infrared sensors to measure the relative distances between the civil structures of the communication equipment room and complete the modeling of the main structure. The second layer used strength sensors to subject the materials of the communication equipment room to slight impacts and overturning, measuring the material curvature to assess material quality. The third layer used an onboard resistance measuring instrument to measure the resistance of the external power supply and equipment connections to ensure electrical safety. The fourth layer used temperature and humidity sensors to measure temperature and humidity at various points while the infrared sensors were scanning. The fifth layer used a camera to capture the lighting status of various devices in the communication equipment room, comparing it with normal lighting conditions to output the equipment operation status. The model achieved layered scanning and layered modeling, and finally, the model was integrated and archived.

[0105] In some embodiments, the system further includes a positioning module, which acquires the robot's relative pose; converts the relative pose into a target pose that matches the constructed SLAM map coordinate system using a preset transformation matrix; determines the current positioning information in the SLAM map based on the target pose; and establishes a direct correspondence between the relative pose of the positioning module and the SLAM map, thereby improving the accuracy of the positioning function.

[0106] In some embodiments, the visual camera is a binocular camera. It selects pixels in the overlapping area of ​​the acquired images, converts the selected pixels into pixels corresponding to the binocular SLAM algorithm, calculates the three-dimensional coordinates of the converted pixels in the overlapping area based on the binocular SLAM algorithm, converts the three-dimensional coordinates of the pixels into camera coordinate information, and initializes the visual SLAM algorithm based on the camera coordinate information, thereby improving the accuracy and stability of the visual SLAM algorithm.

[0107] In some embodiments, a storage device is also included, which is connected to the processing unit 170. The robot stores the constructed motion decision model and the acceptance results in the storage device to achieve traceability of the acceptance process.

[0108] Please see Figure 2 In some embodiments, based on SLAM technology, this technical solution designs a robot with high modeling accuracy to achieve digital acceptance testing of communication equipment rooms. The intelligent modeling robot is equipped with a drive motor 130 and includes a main control MCU, a motor drive unit 140, a micro PC, a data acquisition unit, and a navigation unit. The micro PC is connected to a lidar 110 and a GPS. The navigation unit includes an encoder 150 mounted on the drive motor 130 and a gyroscope mounted on the machine as an inertial measurement unit 120. The encoder 150 and the gyroscope are connected to the main control MCU. The main control MCU and the micro PC constitute a processing unit 170.

[0109] Please see Figure 5 The technical framework comprises three aspects: on-site modeling, system comparison, and online review. First, by setting system parameters for the robot, SLAM and sensor technologies are comprehensively applied to capture key points and model entities in the communication equipment room, establishing and refining the associated model, and archiving and maintaining the model. Second, by setting the acceptance criteria for the communication equipment room, acceptance standards based on artificial intelligence are developed and compared with the actual conditions of the equipment room. Preliminary feedback on rectification items identified during the on-site acceptance is provided, and evaluation suggestions are output based on the machine. Finally, the acceptance unit and department call the model online through the system, compare the preliminary evaluation suggestions generated by the machine, form professional opinions from each unit and department, and output the final result after synthesizing various opinions.

[0110] Please see Figure 6In some embodiments, the processing unit 170 evaluates the acceptance results online through an online evaluation system, which includes an online assessment end and an individual review end. The online assessment end uses pre-learning to import the evaluation model system and input artificial intelligence training features and extract neural networks. When actual data is input, the evaluation results are compared, and an evaluation conclusion is output. The individual review end refers to the process where, after receiving the acceptance conclusion from the artificial intelligence, the acceptance personnel can access client data from the modeling site stored by the transportation bureau and generate an augmented reality model. By comparing the artificial intelligence judgment with the actual situation, a final individual acceptance review opinion is given.

[0111] The online evaluation employs a pre-built deep learning model using artificial intelligence. This model combines the evaluation standards for each layer of the communication equipment room's on-site acceptance process with key point capture and key value comparison. The results are compared to standard values ​​to form preliminary evaluation conclusions and provide feedback on areas requiring rectification. The online evaluation conclusions mainly include "veto items" and "suggested rectification items." For critical acceptance matters involving security, a "veto system" is applied; any deviation from the pre-built deep learning model results in failure to pass the evaluation. "Suggested rectification items" provide prompts and suggestions for modification to aspects that do not affect the overall condition of the communication equipment room but do not fully comply with the acceptance standards.

[0112] The following are the rectification measures for online evaluations:

[0113]

[0114] Online evaluations will generate AI-powered assessment and acceptance results (preliminary pass or preliminary fail) based on the "veto item" status. If there are no "veto items" resulting in a failure (preliminary pass), AI will be used to evaluate the "suggested rectification items" and provide suggested rectification opinions. After the online evaluation is completed, each unit and department can access the model through the "individual review terminal" to propose supplementary rectification items for their respective specialties. In principle, the AI-powered assessment and acceptance results cannot be overturned. The construction unit will conduct on-site rectification based on the acceptance results and feedback on the rectification items, and apply for re-acceptance after completing the rectification (if there are no rectification items, re-application for acceptance is not required).

[0115] By providing an online, data-traceable, and AI-evaluated digital acceptance model for communication equipment rooms, and using SLAM as the foundation for digital modeling technology, a large-scale improvement in the efficiency of communication equipment room acceptance can be achieved.

[0116] The beneficial effects include the following:

[0117] 1. Change the "offline" on-site acceptance mode of communication equipment rooms to an "online" model acceptance mode to avoid various difficulties caused by too many acceptance personnel and help save acceptance costs;

[0118] 2. By changing the subjective acceptance mode of communication equipment rooms to a combination of machine evaluation and manual evaluation, objective review by artificial intelligence can be achieved, reducing the impact of subjective review opinions on the acceptance of communication equipment rooms.

[0119] 3. Enable modeling and archiving of communication equipment rooms, achieve traceability of acceptance data and improve maintenance efficiency, and set difference coefficients according to the differences of communication equipment rooms to facilitate cross-regional and cross-industry promotion.

[0120] 4. Develop an "online" communication equipment room acceptance mode to avoid the manpower and material resources wasted by acceptance personnel going to the site repeatedly, and to achieve the traceability of acceptance data;

[0121] 5. Based on SLAM technology, robots are used to model the communication equipment room site to realize a digital communication equipment room acceptance mode;

[0122] 6. Based on artificial intelligence technology, using deep learning and key point capture technology strategies, and combined with construction standards, conduct on-site acceptance and evaluation of communication equipment rooms, and provide rectification feedback for any parts that do not meet the construction standards.

[0123] Please see Figure 7 In another embodiment, a digital acceptance method for a communication equipment room is implemented using the robot described in the above embodiment. The acceptance method includes the following steps:

[0124] Step S710: Collect radar data of the environment surrounding the communication equipment room using lidar;

[0125] Step S720: Collect the robot's odometry data using the encoder;

[0126] Step S730: Construct a map using a laser SLAM algorithm based on radar data and odometer data;

[0127] Step S740: Obtain the robot's first pose data by collecting IMU measurement data from the inertial measurement unit;

[0128] Step S750: Combine the first pose data with the constructed map to generate a motion decision model;

[0129] Step S760: Based on the generated motion decision model, control the drive motor through the motor drive unit to drive the robot to move;

[0130] Step S770: Collect acceptance data of the communication equipment room through the acceptance data acquisition unit, and generate acceptance results based on the collected acceptance data.

[0131] When accepting a communication equipment room, a robot uses a LiDAR scanner to scan the environment and obtain radar data. Simultaneously, an encoder collects odometry data. The collected radar and odometry data are used to construct a map using a LiDAR SLAM algorithm. An Inertial Measurement Unit (IMU) collects IMU data to determine the robot's first pose. This pose data is then combined with the constructed map to generate a motion decision model. The motion decision model outputs drive signals to the motor drive unit, which controls the robot's motors to move according to the motion decision model. Simultaneously, an acceptance data acquisition unit collects acceptance data for the communication equipment room, and the acceptance result is derived from this data. Using a robot for communication equipment room acceptance transforms the traditional "offline" on-site acceptance model into an "online" model acceptance model. This avoids the difficulties caused by excessive manpower, improves acceptance efficiency, helps save costs, and enables objective AI-based review, reducing the impact of subjective review opinions on the acceptance process.

[0132] In some embodiments, the "construction of a map based on radar data and odometry data using a laser SLAM algorithm" specifically includes the following steps:

[0133] The relative positions of each key measurement point are captured by LiDAR, and a two-dimensional grid pattern is formed by LiDAR SLAM algorithm;

[0134] The relative positions of each key measurement point are captured by a visual camera, and a two-dimensional image is formed by a visual SLAM algorithm.

[0135] The obtained two-dimensional grid pattern is fitted to the two-dimensional image to construct a map.

[0136] Visual data of the environment surrounding the communication equipment room is collected using a visual camera. A projected 2D image is then generated using a visual SLAM algorithm. A 2D grid map is generated by combining laser data from a lidar sensor and odometer data from an encoder using a laser SLAM algorithm. The projected 2D image and the 2D grid map are then fused using a grid occupancy rule to improve map accuracy. The relative positions of key measurement points are set using laser capture, and a 2D grid map is formed using the SLAM algorithm. The relative positions of key measurement points are also set using a visual camera, and a 2D image is formed using the SLAM algorithm. The 2D grid map and the 2D image are then fitted to a plane to confirm the positions of the key measurement points. If the key measurement point positions are consistent, the fit is successful; if they are inconsistent, the corresponding key measurement points are captured again for secondary and tertiary fitting. During each fitting process, the success rate of key measurement point fitting gradually increases until a perfect fit is achieved. The reason for incomplete fitting of key measurement points is mainly due to measurement point capture errors; smooth fitting is performed within the allowable error range of the model.

[0137] In some embodiments, there are multiple inertial measurement units;

[0138] The step of "obtaining the robot's first pose data based on the IMU measurement data collected by the inertial measurement unit" specifically includes the following steps:

[0139] The average IMU measurement data is obtained by time synchronization and weighted averaging of IMU measurement data collected by multiple inertial measurement units using the visual SLAM algorithm. The first pose data is obtained by integrating the average IMU measurement data.

[0140] Multiple inertial measurement units (IMUs) are used to collect robot IMU measurement data, resulting in multiple IMU measurement data. The obtained IMU measurement data is then time-synchronized and weighted by a visual SLAM algorithm to obtain average IMU measurement data. The average IMU measurement data is then integrated to obtain the first pose data, making the acquired robot's first pose data more accurate.

[0141] In some embodiments, the step of "combining the first pose data with the constructed map to generate a motion decision model" specifically includes the following steps:

[0142] The robot's second pose data is acquired using a vision camera;

[0143] The obtained first pose data and second pose data are used to obtain the third pose data through the Kalman filter algorithm;

[0144] The obtained third pose data is then combined with the constructed map to obtain a motion decision model.

[0145] The robot's second pose data is acquired by a vision camera. The first and second pose data are then used as the output of the Kalman filter algorithm to calculate the third pose data, which is then applied to motion decision-making.

[0146] In some embodiments, the "collection of acceptance data for the communication equipment room through the acceptance data acquisition unit" specifically includes the following steps:

[0147] The civil structure of the communication equipment room is collected using infrared sensors;

[0148] The material quality of the communication equipment room is collected using strength sensors;

[0149] The resistance of the external power supply points and equipment access points in the communication equipment room is measured using a resistance measuring instrument.

[0150] Temperature and humidity data of the communication equipment room are collected using temperature and humidity sensors.

[0151] The operation status of equipment in the communication equipment room is collected using high-precision cameras.

[0152] The modeling structure was developed based on the actual acceptance standards for communication equipment rooms. The modeling principle adopts a multi-level scanning modeling structure, where each sensor or instrument independently models its respective acceptance component, merging all modeling elements into a unified model. Communication equipment room acceptance involves multiple levels and parameters, but smaller parameters are subordinate to larger parameters; that is, if the larger parameters pass acceptance, the smaller parameters are also likely to be acceptable. Taking communication equipment room transmission as an example, transmission includes cable trays, pigtails, ODF racks, equipment, and other aspects, exhibiting upstream and downstream technical relationships. However, once the equipment completes its on-loop connection to the network, it indicates that the corresponding transmission components are interconnected and there are no technical issues; only the compliance of other transmission components needs to be verified.

[0153] After processing, the model was used to perform layered scanning of the civil structure quality, power supply, temperature and humidity, and equipment operation of the communication equipment room. Based on the layered scanning structure, the first layer used infrared sensors to measure the relative distances between the civil structures of the communication equipment room and complete the modeling of the main structure. The second layer used strength sensors to subject the materials of the communication equipment room to slight impacts and overturning, measuring the material curvature to assess material quality. The third layer used an onboard resistance measuring instrument to measure the resistance of the external power supply and equipment connections to ensure electrical safety. The fourth layer used temperature and humidity sensors to measure temperature and humidity at various points while the infrared sensors were scanning. The fifth layer used a camera to capture the lighting status of various devices in the communication equipment room, comparing it with normal lighting conditions to output the equipment operation status. The model achieved layered scanning and layered modeling, and finally, the model was integrated and archived.

[0154] In some embodiments, a method for digital acceptance testing of a communication equipment room includes the following steps:

[0155] (1) The robot uses SLAM technology to model the communication equipment room on-site;

[0156] (2) Based on artificial intelligence technology, key points of the communication equipment room model are captured, and the machine generates acceptance conclusions and rectification feedback;

[0157] (3) The participating units and departments shall conduct an online evaluation of the communication equipment room model within a specified period and issue supplementary opinions based on the acceptance conclusion.

[0158] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. A digital acceptance robot for communication equipment rooms, characterized in that, include: A lidar, which is used to collect radar data of the surrounding environment; An inertial measurement unit (IMU) is used to collect IMU measurement data from the robot. A drive motor, used to drive the robot to move; A motor drive unit is connected to the drive motor and is used to control the drive motor; An encoder is mounted on the robot's drive motor and is used to collect the robot's odometer data. An acceptance data acquisition unit is used to collect acceptance data for the communication equipment room. The processing unit is used to construct a map using a laser SLAM algorithm based on radar data collected by the lidar and robot odometry data collected by the encoder. At the same time, it obtains the robot's first pose data based on IMU measurement data collected by the inertial measurement unit, and combines the first pose data with the constructed map to generate a motion decision model. Based on the generated motion decision model, it controls the drive motor to drive the robot to move through the motor drive unit, and collects the acceptance data of the communication equipment room through the acceptance data acquisition unit, and generates the acceptance result based on the collected acceptance data. The acceptance data acquisition unit includes: An infrared sensor, used to collect data on the civil engineering structure of the communication equipment room; A strength sensor is used to collect the material mass of the communication equipment room; A resistance measuring instrument, used to measure the resistance of external power input points and equipment access points in a communication equipment room; A temperature and humidity sensor, used to collect temperature and humidity data of the environment inside the communication equipment room; A high-precision camera, used to collect data on the operation of equipment in a communication equipment room; The processing unit is further configured to layer the scanning structure as follows: the first layer uses infrared sensors to measure the relative distance between the civil engineering structures of the communication equipment room and complete the modeling of the main structure of the communication equipment room; the second layer uses strength sensors to subject the materials of the communication equipment room to slight impacts and flips, and measures the curvature of the materials to determine the material quality; the third layer uses an onboard resistance measuring instrument to measure the resistance of the external power supply and equipment access of the communication equipment room to ensure compliance with power safety; the fourth layer uses temperature and humidity sensors to measure the temperature and humidity at various points while the infrared sensors are scanning; and the fifth layer uses a camera to capture the lighting status of each device in the communication equipment room and compares it with the normal lighting status to output the equipment operation status.

2. The communication equipment room digital acceptance robot according to claim 1, characterized in that, It also includes a vision camera, which is used to collect visual data of the surrounding environment; The processing unit is used to capture the relative positions of each set key measurement point through LiDAR, form a two-dimensional grid map through LiDAR SLAM algorithm, capture the relative positions of each set key measurement point through a vision camera, form a two-dimensional image through a vision SLAM algorithm, and perform planar fitting between the obtained two-dimensional grid map and the two-dimensional image to construct a map.

3. The communication equipment room digital acceptance robot according to claim 2, characterized in that, The inertial measurement unit is multiple; The processing unit is also used to perform time synchronization and weighted averaging of IMU measurement data collected by multiple inertial measurement units using a visual SLAM algorithm to obtain average IMU measurement data, and to integrate the average IMU measurement data to obtain the first pose data.

4. The communication equipment room digital acceptance robot according to claim 3, characterized in that, The vision camera is also used to acquire the robot's second pose data; The processing unit is also used to calculate the third pose data by using the obtained first pose data and second pose data through the Kalman filter algorithm, and to combine the obtained third pose data with the constructed map to obtain a motion decision model.

5. A method for digital acceptance testing of a communication equipment room, characterized in that, Includes the following steps: Radar data of the environment surrounding the communication equipment room is collected using lidar; The robot's odometer data is collected via an encoder; A map is constructed using laser SLAM algorithms based on radar and odometer data; The robot's first pose data is obtained by collecting IMU measurement data from the inertial measurement unit. The first pose data is then combined with the constructed map to generate a motion decision model; Based on the generated motion decision model, the drive motor is controlled by the motor drive unit to drive the robot to move. The acceptance data acquisition unit collects acceptance data from the communication equipment room and generates acceptance results based on the collected acceptance data. The process of collecting acceptance data for the communication equipment room through the acceptance data acquisition unit specifically includes the following steps: The civil structure of the communication equipment room is collected using infrared sensors; The material quality of the communication equipment room is collected using strength sensors; The resistance of the external power supply points and equipment access points in the communication equipment room is measured using a resistance measuring instrument. Temperature and humidity data of the communication equipment room are collected using temperature and humidity sensors. The operation status of equipment in the communication equipment room is collected using high-precision cameras; Based on the layered scanning structure, the first layer uses infrared sensors to measure the relative distance between the civil engineering structures of the communication equipment room and completes the modeling of the main structure of the communication equipment room. The second layer uses strength sensors to slightly impact and flip the materials in the communication equipment room, measuring the material's curvature to determine its quality. The third layer uses onboard resistance measuring instruments to measure the resistance of external power supplies and equipment connections to the communication equipment room, ensuring compliance with power safety standards. The fourth layer uses temperature and humidity sensors to measure the temperature and humidity at various points while the infrared sensor is scanning. The fifth floor uses cameras to capture the lighting status of various devices in the communication equipment room, and outputs the equipment operation status by comparing it with the normal lighting status.

6. The digital acceptance method for communication equipment rooms according to claim 5, characterized in that, The process of constructing a map using a laser SLAM algorithm based on radar and odometry data specifically includes the following steps: The relative positions of each key measurement point are captured by LiDAR, and a two-dimensional grid pattern is formed by LiDAR SLAM algorithm; The relative positions of each key measurement point are captured by a visual camera, and a two-dimensional image is formed by a visual SLAM algorithm. The obtained two-dimensional grid pattern is fitted to the two-dimensional image to construct a map.

7. The digital acceptance method for communication equipment rooms according to claim 6, characterized in that, The inertial measurement unit is multiple; The process of obtaining the robot's first pose data based on the IMU measurement data collected by the inertial measurement unit specifically includes the following steps: The average IMU measurement data is obtained by time synchronization and weighted averaging of IMU measurement data collected by multiple inertial measurement units using a visual SLAM algorithm. The first pose data is then obtained by integrating the average IMU measurement data.

8. The digital acceptance method for communication equipment rooms according to claim 7, characterized in that, The process of combining the first pose data with the constructed map to generate a motion decision model specifically includes the following steps: The robot's second pose data is acquired using a vision camera; The obtained first pose data and second pose data are used to calculate the third pose data using the Kalman filter algorithm; The obtained third pose data is then combined with the constructed map to obtain a motion decision model.

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