CN211375602U - Mobile intelligent noise live-action cloud picture modeling system for transformer substation - Google Patents

Abstract

The utility model provides a substation mobile intelligent noise real scene cloud image modeling system. A positioning device is installed on the top of a support rod of an intelligent mobile robot and is connected with a control main board; the acoustic measurement device is installed on the support rod of the intelligent mobile robot and is connected with the support rod. The control board is connected; the lidar device is installed on the support rod of the intelligent mobile robot and connected with the control board; the control board is installed inside the intelligent mobile robot to receive the position coordinates collected by the positioning device and the acoustic measurement device. The noise sound pressure data, the point cloud data collected by the lidar device, and the noise real cloud map are generated. The utility model performs automatic online detection of the whole substation through intelligent mobile equipment, perceives the distribution and size of each building and equipment through sensors, and establishes a unified global three-dimensional stereoscopic scene model of the entire substation to further improve the prediction accuracy of the radiation noise of the substation.

Description

A mobile intelligent noise cloud map modeling system for substations

technical field

The utility model relates to the field of noise control implementation engineering, in particular to a mobile intelligent noise real scene cloud map modeling system of a substation.

Background technique

As the main source of noise pollution in the city, the substation has an impact on the physical and mental health of the surrounding residents. Therefore, the noise control of the substation has become an urgent problem to be solved. Although the State Grid attaches great importance to noise control of substations, and has invested a lot of human and financial resources to support the noise control project of substations, the actual effect is still not ideal from the analysis of the current situation after noise control. One of the important reasons is that it is impossible to truly understand the changes of the substation in the whole area and with the change of operating conditions, and the noise control work of the existing substation cannot respond in time or predict the trend of the sound field changing with the external conditions. It makes the noise control project of the substation meet the national regulations in a certain period (working condition), but does not meet the requirements of the national regulations in another period (working condition).

There are at least the following problems in the existing noise sound field simulation technology: (1) In the acoustic simulation of the substation, the most important data are the accurate three-dimensional model and the sound source intensity model of the substation field. When the actual substation performs acoustic simulation, for some substations with a long service life, the original construction data is missing, which makes it difficult to obtain the layout and size data of the substation. Hazardous environments cannot enter into measurement, etc., so it is difficult to establish an acoustic simulation model of accurate size; (2) The determination of the intensity of the sound source model used in the simulation is also based on the actual measurement on site, and the sound field of the substation varies with operating conditions and environmental factors. Due to the unstable characteristics of the sound source model, the strength of the sound source model only reflects the test results, and the simulation conclusion is only suitable for the test state at that time. When the operating conditions and environmental factors change, the acoustic simulation results and test results often appear different. big gap. (3) The model in the acoustic simulation is simplified to a certain extent, and some physical parameters need to be adjusted according to the operating conditions and environmental factors, such as the sound absorption coefficient and reflection coefficient of various physical interfaces, which are further corrected through continuous noise measurement. The results of the acoustic simulation reflect the consistency between the substation simulation and the real state, and improve the adaptability and pertinence of the substation simulation.

Utility model content

In order to overcome the above-mentioned defects, the present utility model provides a mobile intelligent noise cloud map modeling system for substations, which improves the high degree of matching between the noise source model and the influencing factors of operating conditions, and improves the adaptability of substation acoustic prediction and simulation to operating conditions. improve the accuracy of noise prediction in substations.

The technical scheme of the present utility model:

A mobile intelligent noise real-world cloud image modeling system in a substation, comprising an intelligent mobile robot, a power supply device, a robot drive device, a positioning device, an acoustic measurement device, a laser radar device, a driving wheel and a control board,

The power supply device is installed in the intelligent mobile robot to supply power to the entire modeling system;

The robot driving device is installed in the intelligent mobile robot and connected with the power supply device, the driving wheel and the control main board, so as to drive the intelligent mobile robot to move;

The positioning device is installed on the top of the support rod of the intelligent mobile robot and connected with the control main board, so as to perform real-time positioning of the intelligent mobile robot;

The acoustic measurement device is installed on the support rod of the intelligent mobile robot and connected with the control main board, so as to complete the acquisition of noise sound pressure data of the substation;

The lidar device is installed on the support rod of the intelligent mobile robot and connected with the control main board, so as to realize the acquisition of point cloud data of equipment and buildings in the substation field;

The control board is installed inside the intelligent mobile robot, and is used to receive the position coordinates collected by the positioning device, the noise sound pressure data collected by the acoustic measurement device, and the point cloud data collected by the laser radar device, and generate a real noise cloud map.

A processing chip is installed on the control main board, and the control chip includes a data analysis management module, a noise simulation module and a real cloud image generation module. The data analysis and management module is connected with a positioning device, an acoustic measurement device, and a laser radar device for receiving Position coordinate data, noise sound pressure data, and point cloud data, and store and manage the data. The noise simulation module is connected to the data analysis and management module, and simulates according to the noise sound pressure data to form a noise cloud map, and the real cloud map generation module is connected with the noise. The simulation module is connected to generate a spatial-temporal distribution map of sound pressure according to the position coordinate data, noise sound pressure data, and point cloud data, and superimpose it with the noise cloud map to generate a real noise cloud map.

The power supply device adopts a rechargeable lithium battery.

The positioning device is a Beidou positioning device or a GPS positioning device.

The acoustic measurement device uses a MEMS microphone.

The lidar device adopts the MEMS lidar of the Velarray series.

A Core I7 processing chip is installed on the control motherboard, and ANSYS Fluent software is solidified and installed in the processing chip for noise simulation and sound pressure time-space distribution diagram simulation.

Compared with the prior art, the beneficial effect of the utility model is: through intelligent mobile equipment, automatic online detection of the whole substation is carried out, the distribution and size of each building and equipment are sensed through sensors, and a unified global three-dimensional stereo of the entire substation is established. Scene model, and real-time acquisition and storage of real sound pressure everywhere. The use of intelligent mobile equipment can perform autonomous inspection and obstacle avoidance according to the established tasks, complete the monitoring tasks uninterruptedly, realize the automation and intelligence of noise monitoring, and overcome the problems of manual operation limited by working time and labor costs, and work fatigue. Improve the reliability of monitoring. Using the global three-dimensional scene model established by intelligent mobile devices, and the sound pressure data collected from various places, the spatial and temporal distribution map of noise can be drawn through technologies such as coordinate conversion and data processing, and it is possible to intuitively "see" the sound pressure along the path. The change law truly reflects the distribution state of the sound field of the substation. Because it is the real state without any artificial parameter settings, the spatiotemporal distribution law of sound pressure can reflect the current situation of the noise in the substation, and can scientifically guide the design of the substation noise control plan. Through continuous testing all year round and drawing noise spatiotemporal distribution map, we can clearly understand the change of substation sound field with operating conditions and environmental factors. This information is very important for substation noise management. Based on a large amount of data accumulated in continuous noise testing, and according to seasons, operating loads, weather conditions and other information, the relationship between external influencing factors and noise prediction model parameters will be studied, which will further improve the accuracy of radiated noise prediction in substations.

Description of drawings

Fig. 1 is the principle block diagram of the utility model system;

FIG. 2 is a schematic structural diagram of an intelligent mobile robot of the present invention.

Detailed ways

The technical solutions in the embodiments of the present utility model will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present utility model. Obviously, the described embodiments are only a part of the embodiments of the present utility model, rather than all the implementations. example. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

Please refer to Fig. 1 and Fig. 2, the utility model provides a kind of technical scheme:

A substation mobile intelligent noise real scene cloud image modeling system, comprising an intelligent mobile robot 1, a power supply device 2, a robot driving device 3, a positioning device 4, an acoustic measurement device 5, a laser radar device 6, a driving wheel 10 and a control motherboard 11,

The power supply device 2 is installed in the intelligent mobile robot 1 to supply power to the entire modeling system;

The robot driving device 3 is installed in the intelligent mobile robot 1 and connected with the power supply device 2, the driving wheel 10 and the control main board 11 to drive the intelligent mobile robot 1 to move;

The positioning device 4 is installed on the top of the support rod of the intelligent mobile robot 1 and is connected with the control main board 11 to perform real-time positioning of the intelligent mobile robot 1, so as to control the intelligent mobile robot to walk in the substation field according to a predetermined route and to be able to Stay at the planned measuring point to complete the collection of cloud data of the substation, and can record the coordinate value of the measuring point;

The acoustic measurement device 5 is installed on the support rod of the intelligent mobile robot 1 and connected with the control main board 11 to complete the acquisition of noise and sound pressure data of the substation;

The lidar device 6 is installed on the support rod of the intelligent mobile robot 1 and connected to the control main board 11 to realize the acquisition of point cloud data of equipment and buildings in the substation field; the lidar obtains the buildings and equipment around the current position of the robot distribution and size information, and extract effective feature points from it. When moving to the next position, the feature points are matched to calculate the change matrix of the robot pose, and the two positions are monitored according to the calculated change matrix. Maps are stitched into one.

The control board 11 is installed inside the intelligent mobile robot 1 to receive the position coordinates collected by the positioning device 4, the noise sound pressure data collected by the acoustic measurement device 5, and the point cloud data collected by the lidar device 6, and generate a real noise cloud map .

A processing chip is installed on the control motherboard 11, and the control chip includes a data analysis and management module 7, a noise simulation module 8 and a real cloud image generation module 9. The data analysis and management module 7 is connected with the positioning device 4, the acoustic measurement device 5, and the laser radar. The device 6 is connected to receive position coordinate data, noise sound pressure data and point cloud data and store and manage the data. The noise simulation module 8 is connected to the data analysis and management module 7, and the noise simulation module 8 is used to complete the data analysis. Prediction and calculation of the overall sound field distribution of the substation; compare the measured data of the substation, and correct the acoustic parameter settings: material reflection coefficient, material sound absorption coefficient, diffraction boundary, acoustic field point settings, etc., so that the noise simulation and field measurement remain acceptable. error. And perform simulation according to noise sound pressure data to form a noise cloud map. The real cloud map generation module 9 is connected with the noise simulation module 8 to generate a sound pressure spatiotemporal distribution map according to the position coordinate data, noise sound pressure data, and point cloud data and superimpose it with the noise cloud map. Generate a real noise cloud map, display the distribution of noise in the substation field from a global perspective, and make the noise "visible".

The power supply device 2 uses a rechargeable lithium battery.

The positioning device 4 is a Beidou positioning device or a GPS positioning device.

The acoustic measurement device 5 uses a MEMS microphone.

The lidar device 6 adopts the MEMS lidar of the Velarray series.

A Core I7 processing chip is installed on the control motherboard 11, and ANSYS Fluent software is solidified and installed in the processing chip for noise simulation and sound pressure time-space distribution diagram simulation.

Although the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes and modifications can be made to these embodiments without departing from the principles and spirit of the present invention , alternatives and modifications, the scope of the present invention is defined by the appended claims and their equivalents.

Claims (5)

1. A transformer substation mobile intelligent noise live-action cloud picture modeling system is characterized by comprising an intelligent mobile robot (1), a power supply device (2), a robot driving device (3), a positioning device (4), an acoustic measuring device (5), a laser radar device (6), a driving wheel (10) and a control main board (11),

the power supply device (2) is arranged in the intelligent mobile robot (1) and used for supplying power to the whole modeling system;

the robot driving device (3) is installed in the intelligent mobile robot (1), connected with the power supply device (2), the driving wheel (10) and the control main board (11) and used for driving the intelligent mobile robot (1) to move;

the positioning device (4) is installed at the top of a support rod of the intelligent mobile robot (1) and connected with the control main board (11) and used for positioning the intelligent mobile robot (1) in real time;

the acoustic measurement device (5) is arranged on a support rod of the intelligent mobile robot (1) and connected with the control main board (11) to complete collection of noise sound pressure data of the transformer substation;

the laser radar device (6) is installed on a support rod of the intelligent mobile robot (1) and connected with the control main board (11) to achieve acquisition of point cloud data of equipment and buildings in the substation field;

the control main board (11) is installed in the intelligent mobile robot (1) and used for receiving position coordinates collected by the positioning device (4), noise sound pressure data collected by the acoustic measurement device (5) and point cloud data collected by the laser radar device (6) and generating a noise live-action cloud picture.

2. The substation mobile intelligent noise live-action cloud picture modeling system according to claim 1, characterized in that the power supply device (2) employs a rechargeable lithium battery.

3. The substation mobile intelligent noise live-action cloud picture modeling system according to claim 1, characterized in that the positioning device (4) is a Beidou positioning device or a GPS positioning device.

4. A substation mobile intelligent noise live-action cloud picture modeling system according to claim 1, characterized in that said acoustic measurement device (5) employs a MEMS microphone.

5. A substation mobile intelligent noise live-action cloud picture modeling system according to claim 1, characterized in that said lidar device (6) employs a MEMS lidar of the veilarray series.

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