Method and system for identifying and locating fire sources using optical quantum radar for fire-fighting robots

Abstract

The present invention relates to the field of radar fire source positioning technology, and specifically to a method and system for identifying and positioning a fire source using a photon radar for a fire-fighting robot. The radar point cloud data, temperature time series data, and smoke concentration time series data of the fire scene are obtained; the radar point cloud data are compared with the preset radar point cloud data to identify the smoke data points, and their motion characteristics are analyzed to determine the smoke diffusion path. At the same time, the changes in the temperature and smoke concentration time series data are analyzed to determine the fire spread path; the two are compared and combined with the temperature and smoke concentration change characteristics to identify the true fire evolution trend and screen the suspected fire source location; at each suspected fire source location, the initial power and resolution of the photon radar are adaptively and intelligently adjusted according to indicators such as the area to be measured, the complexity of the environment, and the smoke concentration, so as to identify and locate the fire source with the best performance and improve accuracy and stability. The present invention dynamically narrows the range and adaptively adjusts the radar parameters, which helps to accurately locate the fire source.

Description

Technical Field

[0001] The present invention relates to the technical field of radar fire source positioning, and in particular to a method and system for identifying and positioning fire sources using a light quantum radar for a fire extinguishing robot. Background Art

[0002] Fire monitoring and firefighting operations often involve complex environments and smoke obscuration, making simple image acquisition ineffective in identifying the fire source. Photonic quantum radar is an advanced, high-precision detection device that emits specific photonic quantum signals. When these signals interact with the target and return, the quantum properties of the returned signals are analyzed to obtain a variety of target information. Combining photonic quantum radar with firefighting robots (such as drones and fire trucks) can intelligently identify and locate the fire source at the scene, enabling firefighting operations to be carried out effectively.

[0003] Existing technologies using optical quantum radar to identify and locate fire sources typically use fixed power and resolution to collect and analyze data from the entire fire scene, thereby identifying and locating the fire source. However, when a fire occurs, the environment is often complex, with smoke spreading and temperature and other data changing in real time. Therefore, if the optical quantum radar continues to use fixed parameters to perform a random, comprehensive scan of the fire scene, it will not only increase the difficulty of locating the fire source, but also affect the accuracy of the optical quantum radar's fire source identification and location. Summary of the Invention

[0004] In order to solve the technical problem that when a fire occurs, the environment at the scene is usually complex, smoke will diffuse, and data such as temperature will change in real time, if fixed parameters are used to perform a comprehensive and aimless scan of the fire scene, it will not only increase the difficulty of positioning, but also affect the accuracy of positioning. The purpose of the present invention is to provide a method and system for identifying and locating fire sources using a light quantum radar for fire-fighting robots. The technical solutions adopted are as follows:

[0005] A method for identifying and locating fire sources using a light quantum radar for a fire-fighting robot, comprising:

[0006] Obtain radar point cloud data of the fire scene detected by the optical quantum radar, as well as temperature time series data and smoke concentration time series data at each monitoring location;

[0007] Compare the radar point cloud data at each moment with the preset radar point cloud data to determine the smoke data points; determine the smoke diffusion path at the fire scene based on the movement of the smoke data points in the radar point cloud data at different moments; analyze the changes in the temperature time series data and the changes in the smoke concentration time series data to determine the fire spread path;

[0008] Compare the smoke diffusion path and the fire spread path, and screen the suspected fire source location among all monitoring locations based on the changes in the temperature and smoke concentration values ​​at the current moment;

[0009] The monitoring range of each suspected fire source location is used as the range to be measured. Within each range to be measured, indicators are selected from the area value of the range to be measured, the complexity of the environment, and the smoke concentration value at the current moment to adjust the initial power and initial resolution of the photonic quantum radar, so as to identify and locate the fire source in the range to be measured.

[0010] Furthermore, the method for obtaining the smoke diffusion path includes:

[0011] In the radar point cloud data at each moment, the position of each smoke data point is used as the starting point, and the position of the same smoke data point in the radar point cloud data at the next adjacent moment is used as the end point. The motion vector between the radar point cloud data at adjacent moments of each smoke data point is obtained.

[0012] In the radar point cloud data at each moment, for any xoz plane, the motion vectors of the radar point cloud data in the xoz plane are synthesized to determine the motion direction of the xoz plane;

[0013] The movement directions of the same xoz plane in the radar point cloud data at all times are connected to obtain all the smoke diffusion paths.

[0014] Furthermore, the method for obtaining the fire spread path includes:

[0015] Obtain a temperature curve of the temperature time series data at each monitoring location, obtain a slope value at each data point on the temperature curve, and use the sum of the slope values ​​at all data points as a temperature change trend value at each monitoring location;

[0016] Obtaining a concentration curve of the smoke concentration time series data at each monitoring location, obtaining a slope value at each data point on the concentration curve, and using the sum of the slope values ​​at all data points as a smoke concentration change trend value at each monitoring location;

[0017] A preset neighborhood is determined with each monitoring location as the center. Within the preset neighborhood corresponding to each monitoring location, the difference between the temperature change trend values ​​of the central monitoring location and the neighboring monitoring locations, as well as the difference between the smoke concentration change trend values, are analyzed to obtain a direction determination factor.

[0018] When the direction determination factor between the central monitoring position and each neighboring monitoring position is greater than a preset direction determination threshold, the fire spread direction between the central monitoring position and each neighboring monitoring position is from the neighboring monitoring position to the central monitoring position; otherwise, the fire spread direction between the central monitoring position and each neighboring monitoring position is from the neighboring monitoring position to the central monitoring position;

[0019] The fire spread directions between all monitoring locations and neighboring monitoring locations are integrated to obtain all fire spread paths.

[0020] Furthermore, the method for obtaining the direction determination factor includes:

[0021] The normalized value of the temperature change trend difference between the central monitoring location and each neighboring monitoring location is used as the temperature change difference value, and the normalized value of the smoke concentration change trend difference between the central monitoring location and each neighboring monitoring location is used as the smoke concentration change difference value;

[0022] The value obtained by normalizing the product of the temperature change difference and the smoke concentration change difference is used as the direction determination factor.

[0023] Furthermore, the method for obtaining the suspected fire source location includes:

[0024] Compare the fire spread path and smoke diffusion path to select the locations to be analyzed from all monitoring locations;

[0025] For any position to be analyzed, the sum of the temperature change trend value and the smoke concentration change trend value at the position to be analyzed is taken as the first fire source factor;

[0026] The sum of the temperature value and the smoke density value of the position to be analyzed at the current moment is used as the second fire source factor;

[0027] The product of the first fire source factor and the second fire source factor at the position to be analyzed is normalized to obtain a value as the suspected fire source index at the position to be analyzed;

[0028] The location to be analyzed whose suspected fire source index is greater than the preset fire source threshold is regarded as the suspected fire source location.

[0029] Furthermore, the method for obtaining the position to be analyzed includes:

[0030] Each fire spread path is mapped to the three-dimensional radar point cloud data and compared with the smoke diffusion path. If the overlap length of a fire spread path with a smoke diffusion path is greater than or equal to half of the smoke diffusion path, the fire spread path is taken as the path to be analyzed, and the monitoring position on the target path is taken as the position to be analyzed.

[0031] Furthermore, within each range to be measured, the initial power and initial resolution of the optical quantum radar are adjusted by selecting indicators based on the area value of the range to be measured, the complexity of the environment, and the smoke concentration value at the current moment, so as to identify and locate the fire source in the range to be measured, including:

[0032] In each test range, the normalized value of the smoke concentration at the suspected fire source location corresponding to the test range at the current moment and the sum of the normalized value and the preset constant are used as the power adjustment coefficient;

[0033] The product of the power adjustment coefficient and the initial power is used as the adjustment power of the optical quantum radar;

[0034] Determine the environmental complexity of each range to be measured based on the quantity characteristics and grayscale characteristics of the data points in the radar point cloud data within each range to be measured;

[0035] The sum of the normalized value of the product of the environmental complexity and the area value of each measured range and a preset constant is used as the resolution adjustment coefficient;

[0036] The product of the resolution adjustment coefficient and the initial range resolution is used as the adjusted range resolution of the photon radar, and the product of the resolution adjustment coefficient and the initial angular resolution is used as the adjusted angular resolution of the photon radar;

[0037] Based on the adjusted power, adjusted distance resolution, and adjusted angular resolution corresponding to each range to be measured, the optical quantum radar is used to detect the range to be measured and identify the location of the fire source.

[0038] Furthermore, the method for obtaining the environmental complexity includes:

[0039] In each measured range, the variance of the grayscale values ​​of all data points is used as the environmental complexity factor;

[0040] The product of the environmental complexity factor and the number of smoke data points is normalized to obtain a value which is used as the environmental complexity of each range to be measured.

[0041] Furthermore, the method for obtaining the smoke data points includes:

[0042] The radar point cloud data at each moment is compared with the preset radar point cloud data, and the newly appeared data points are used as smoke data points; among them, the preset radar point cloud data is the radar point cloud data at the scene when no fire occurs.

[0043] A light quantum radar fire source identification and positioning system for a fire-fighting robot includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. When the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor, the steps of a light quantum radar fire source identification and positioning method for a fire-fighting robot are implemented.

[0044] The present invention has the following beneficial effects:

[0045] The firefighting robot acquires radar point cloud data from the fire scene. Because laser radar is easily affected by smoke particles, which can interfere with its signal, multiple monitoring locations are set up at the fire scene. Time-series data for temperature and smoke concentration are acquired at each monitoring location. This allows for a coordinated analysis of the spatial structure of the fire environment and the monitoring data, improving the reliability of subsequent fire source location. By comparing the radar point cloud data with pre-set radar point cloud data, smoke data points can be identified and extracted. Since smoke typically spreads as the fire spreads, analyzing the motion characteristics of the smoke data points to determine the smoke diffusion path helps narrow the scope and aids the firefighting robot in accurately locating and identifying the fire source. Furthermore, the spread of the fire directly reflects the fire's expansion trend and is also helpful for fire source location analysis. Therefore, the differences in temperature and smoke concentration time-series data between monitoring locations are analyzed to determine the fire spread path. Then, by comparing and analyzing the smoke diffusion path and the fire spread path, and combining the changing characteristics of the temperature value and the smoke concentration value, it is possible to penetrate the thick smoke interference and identify the real fire evolution trend, screen out the suspected fire source location in all monitoring positions, and narrow the monitoring range of the fire scene. Finally, within the test range of each suspected fire source location, the present invention adaptively adjusts the initial power and initial resolution of the photon radar according to indicators such as the test area, environmental complexity and smoke concentration. This intelligent parameter adjustment method enables the photon radar carried by the fire-fighting robot to perform at its best according to the actual needs in different scenarios, further improving the accuracy and stability of fire source identification and positioning. In summary, the present invention quickly locks the suspected fire source area and dynamically narrows the scanning range by integrating the point cloud spatial information with the changing laws of temperature and smoke concentration, and adaptively adjusts the parameters of the photon radar carried by the fire-fighting robot, which helps to accurately locate the fire source and improves the timeliness of the fire-fighting robot's emergency rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A flowchart of a method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot according to one embodiment of the present invention;

[0048] Figure 2 A flow chart of a method for obtaining a fire spread path provided by one embodiment of the present invention;

[0049] Figure 3 A flow chart of a method for obtaining a suspected fire source location provided by one embodiment of the present invention;

[0050] Figure 4 This is a system block diagram of a light quantum radar fire source identification and positioning system for a fire-fighting robot provided by one embodiment of the present invention;

[0051] Figure 5 A schematic diagram of the system structure of a light quantum radar fire source identification and positioning system for a fire-fighting robot provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0052] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a method and system for identifying and locating fire sources using a quantum radar for firefighting robots, according to the present invention. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0053] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0054] The following describes in detail a method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot provided by the present invention with reference to the accompanying drawings.

[0055] See also Figure 1 , which shows a method flow chart of a method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot provided by one embodiment of the present invention, the method comprising the following steps:

[0056] Step S1: Obtain radar point cloud data of the fire scene detected by the optical quantum radar, as well as temperature time series data and smoke concentration time series data at each monitoring location.

[0057] In chemical industrial parks, chemical fires are often accompanied by the combustion of chemicals, which produces a large amount of smoke containing special chemical components, such as black smoke formed by a mixture of gases such as chlorine and hydrogen sulfide and soot. When the fire is large, this smoke will spread upward along the ventilation ducts, corridors and other passages inside the building, forming a complex smoke field. In this environment, simple image acquisition often makes it difficult to accurately identify and locate the fire source.

[0058] Photonic quantum radar is an advanced, high-precision detection device that emits specific photonic quantum signals. When these signals interact with the target and return, the device can obtain a variety of target information by analyzing the quantum characteristics of the returned signals. In addition, it has a certain ability to penetrate the smoke at the fire scene, so the photonic quantum radar can be combined with a fire-fighting robot to identify and locate the fire source.

[0059] First, spatial data of the fire scene can be acquired based on the photonic radar carried by the fire-fighting robot. Specifically, the fire-fighting robot needs to go deep into the fire scene and use the photonic radar to scan the fire scene. By emitting photonic signals and receiving reflected signals, radar point cloud data is generated. Radar point cloud data helps to understand the spatial layout of the fire scene and facilitates the subsequent identification and location of the fire source.

[0060] At the same time, the temperature and smoke concentration at the fire scene are also important clues for determining the location of the fire source. Therefore, based on the multi-parameter sensors pre-installed at the fire scene, the temperature time series data and smoke concentration time series data at the monitoring location of each multi-parameter sensor are obtained, providing important information for the subsequent identification and location of the fire source. In the embodiment of the present invention, the installation location of the multi-parameter sensors can be set at key locations such as ceilings, walls, columns, electrical equipment, and stairways. The specific number, location, and density can be adjusted according to the implementation scenario and are not limited here. Each multi-parameter sensor has a certain monitoring range. The temperature time series data and smoke concentration time series data both represent the temperature and smoke concentration conditions within this local monitoring range.

[0061] It should be noted that the data acquisition frequency of the multi-parameter sensor should be consistent with the scanning frequency of the radar point cloud data. Therefore, at each moment, the radar point cloud data of the fire scene, the temperature values ​​at each monitoring location, and the smoke concentration values ​​are all available. The specific data acquisition frequency and scanning frequency can be set to once per second. The specific frequency value can be adjusted according to the implementation scenario and is not limited here. The monitoring range of the multi-parameter sensor at each monitoring location can be set to a circular area with the multi-parameter sensor as the center and a radius of 3 meters. The specific range can be adjusted according to the implementation scenario and is not limited here.

[0062] In an embodiment of the present invention, since there may be multiple fire-fighting robots working simultaneously in the space of the fire scene, when acquiring radar point cloud data, a random fire-fighting robot is used as the center to integrate the radar point cloud data of all fire-fighting robots (fusion is performed based on three-dimensional coordinates, and point cloud particles with consistent coordinates are merged) to obtain the radar point cloud data of the fire scene.

[0063] Step S2: Compare the radar point cloud data at each moment with the preset radar point cloud data to determine the smoke data points; determine the smoke diffusion path at the fire scene based on the movement of the smoke data points in the radar point cloud data at different moments; analyze the change differences between the temperature time series data and the change differences between the smoke concentration time series data to determine the fire spread path.

[0064] When a fire is large, although optical quantum radar has good penetration, it may not be able to quickly and accurately locate the fire source due to the dense smoke and high temperatures. Untargeted scanning only increases the time required to identify the fire source, leading to further damage. Smoke is a key characteristic of fires, and its diffusion path is often closely related to factors such as the fire source location, ventilation conditions, and building layout. By comparing radar point cloud data at different times, smoke data points can be identified. Further analysis of their movement can help determine the smoke diffusion path, indirectly inferring the possible location of the fire source and the direction of fire spread. Temperature and smoke concentration time series data provide real-time information on temperature and smoke concentration changes at the fire scene. By analyzing the differences in these data, the speed and direction of fire spread can also be understood. Therefore, analyzing the various data collected at the fire scene can help reduce blind searches and ineffective actions in subsequent operations and improve the accuracy of fire source identification and location.

[0065] First, the radar point cloud data at each moment can be compared with the preset radar point cloud data to obtain the smoke data points.

[0066] Preferably, in one embodiment of the present invention, the method for acquiring smoke data points includes:

[0067] Smoke is one of the important characteristics of a fire. By comparing the radar point cloud data at each moment with the preset radar point cloud data, we can identify which data points are new data points generated by smoke. Therefore, the newly appearing data points are regarded as smoke data points. Smoke data points are the basis for subsequent analysis of the smoke diffusion path. Among them, the preset radar point cloud data is the radar point cloud data at the scene when no fire occurs.

[0068] The smoke diffusion path is often related to the location and size of the fire source, and the smoke diffusion path usually points to the fire source or its surrounding area. Therefore, by observing the movement trajectory of smoke data points, we can help determine the approximate location of the fire source.

[0069] Preferably, in one embodiment of the present invention, the method for obtaining the smoke diffusion path includes:

[0070] The diffusion of smoke at a fire scene is a dynamic process, and the position of smoke data points will change over time. Therefore, in the radar point cloud data at each moment, the position of each smoke data point is used as the starting point, and the position of the same smoke data point in the radar point cloud data at the next adjacent moment is used as the end point. The motion vector between the radar point cloud data of each smoke data point at adjacent moments can be obtained.

[0071] Then, in the radar point cloud data at each moment, for any xoz plane, the motion vectors of the radar point cloud data in the xoz plane are synthesized to determine the movement direction of the xoz plane. At this time, the movement direction of each xoz plane represents the overall movement direction of the plane, which helps to more intuitively understand the diffusion of smoke on different planes.

[0072] At this time, in the radar point cloud data at each moment, each xoz plane has a movement direction. Finally, by connecting the movement directions of the same xoz plane in the radar point cloud data at all moments, all the smoke diffusion paths when the smoke diffuses at the fire scene can be obtained, thereby intuitively reflecting the diffusion of smoke over time up to the current moment.

[0073] When a fire occurs, the temperature around the fire source will rise significantly and spread to the surrounding area, and the smoke concentration will also change as the fire spreads. Therefore, by analyzing the changes in the temperature time series data and the smoke concentration time series data between different monitoring locations, the fire spread path can be determined based on the monitoring location at the fire scene, and it can also assist in determining the approximate location of the fire source in the subsequent process.

[0074] Preferably, in one embodiment of the present invention, the method for obtaining the fire spread path includes:

[0075] See also Figure 2 , which shows a flow chart of a method for obtaining a fire spread path in one embodiment of the present invention, the method comprising the following steps:

[0076] Step S201: analyzing the change characteristics of the temperature values ​​in the temperature time series data at each monitoring location, and determining the temperature change trend value at each monitoring location.

[0077] Obtain a temperature curve for the temperature time series data at each monitoring location, and obtain the slope value at each data point on the temperature curve. The slope value here can reflect the temperature change trend at each data point. The larger the positive value, the faster the temperature rises; conversely, the smaller the negative value, the faster the temperature falls. Finally, the slope values ​​at all data points are combined, and the sum of the slope values ​​at all data points is used as the temperature change trend value at each monitoring location. Based on the above analysis, it can be seen that when the temperature change trend value at a monitoring location is positive and larger, it means that the overall temperature at that monitoring location is in a rapidly rising trend.

[0078] Step S202: analyzing the variation characteristics of the smoke concentration values ​​in the smoke concentration time series data at each monitoring location, and determining the smoke concentration variation trend value at each monitoring location.

[0079] Obtain a concentration curve for the smoke concentration time series data at each monitoring location, and obtain the slope value at each data point on the concentration curve. The slope value here can reflect the trend of change in smoke concentration at each data point. A positive and larger value indicates that the smoke concentration has an increasing trend and a faster rate of increase; conversely, a negative and smaller value indicates that the smoke concentration has a decreasing trend and a faster rate of decrease. Finally, the slope values ​​at all data points are combined, and the sum of the slope values ​​at all data points is used as the smoke concentration change trend value at each monitoring location. Based on the above analysis, it can be seen that a positive and larger value of the smoke concentration change trend at a monitoring location indicates that the overall smoke concentration at that monitoring location is on a rapidly increasing trend.

[0080] Step S203: Determine a preset neighborhood with each monitoring position as the center. Within the preset neighborhood corresponding to each monitoring position, analyze the difference between the temperature change trend values ​​of the central monitoring position and the neighboring monitoring positions, and the difference between the smoke concentration change trend values ​​to obtain a direction determination factor.

[0081] Given that when a fire occurs, the smoke concentration and temperature values ​​at the monitoring location tend to increase rapidly as the fire source approaches, the direction determination factor can be obtained by comparing the temperature change trend values ​​and smoke concentration change trend values ​​between adjacent monitoring locations, thereby determining the direction of fire spread in the subsequent process.

[0082] In the preset neighborhood corresponding to each monitoring position, the difference between the temperature change trend value of the central monitoring position and each neighboring monitoring position is normalized and used as the temperature change difference. The larger the temperature change difference, the more obvious the temperature rise trend at the central monitoring position is compared with the neighboring monitoring position. Similarly, the difference between the smoke concentration change trend value of the central monitoring position and each neighboring monitoring position is normalized and used as the smoke concentration change difference. The larger the smoke concentration change difference, the more obvious the smoke concentration rise trend at the central monitoring position is compared with the neighboring monitoring position. Since the difference may be positive or negative, the normalization method here adopts function.

[0083] Finally, the product of the temperature change difference and the smoke concentration change difference is normalized and used as the direction determination factor. Based on the above analysis, it can be seen that the larger the direction determination factor, the more obvious the upward trend of the temperature and smoke concentration at the central monitoring location. Normalization is a technical method well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0084] It should be noted that in an embodiment of the present invention, the preset neighborhood corresponding to each monitoring location should include at least three monitoring locations, and the size of the preset neighborhood is larger than the monitoring range of the monitoring location. The size of the neighborhood can be adjusted according to the implementation scenario and is not limited here.

[0085] Step S204: Determine factors based on the directions between all monitoring locations and neighboring monitoring locations to obtain the fire spread path.

[0086] As the fire source approaches, for two adjacent monitoring positions a and b, if the rising trend of the smoke concentration and temperature values ​​at monitoring position a is greater than that at monitoring position b, then it can be considered that the fire spreads from b to a. That is, when a is the central monitoring position and b is the neighboring monitoring position of a, the larger the direction determination factor between a and b, the more likely the fire spreads from b to a.

[0087] Therefore, when the direction determination factor between the central monitoring position and each neighboring monitoring position is greater than the preset direction determination threshold, the direction of fire spread between the central monitoring position and each neighboring monitoring position is from the neighboring monitoring position to the central monitoring position; otherwise, the direction of fire spread between the central monitoring position and each neighboring monitoring position is from the neighboring monitoring position to the central monitoring position.

[0088] Finally, the fire spread directions between all monitoring locations and neighboring monitoring locations are integrated to obtain all fire spread paths.

[0089] It should be noted that the preset direction determination threshold can be set to 0.5, and the specific value can be adjusted according to the implementation scenario and is not limited here.

[0090] Step S3: Compare the smoke diffusion path and the fire spread path, and screen suspected fire source locations from all monitoring locations based on changes in temperature and smoke concentration at the monitoring location at the current moment.

[0091] Smoke and flames spread differently in fires, but they share the same source: the area where the flames are burning or have burned. Therefore, by comparing their paths, we can gain a more comprehensive understanding of the dynamic changes in a fire and help identify the suspected fire source. Furthermore, temperature and smoke concentration are two key indicators in fire monitoring. The vicinity of a fire source is often accompanied by a sharp rise in temperature and a significant increase in smoke concentration. Therefore, by monitoring changes in these parameters, potential fire sources can be discovered. While a single data source may not provide sufficient information to determine the fire source's location, fusing and analyzing multiple data sources, such as smoke diffusion paths, fire spread paths, temperature changes, and smoke concentration changes, can greatly improve the accuracy and reliability of determining the suspected fire source's location.

[0092] Preferably, in one embodiment of the present invention, the method for obtaining the suspected fire source location includes:

[0093] See also Figure 3 , which shows a flow chart of a method for obtaining a suspected fire source location in one embodiment of the present invention, the method comprising the following steps:

[0094] Step S301: Compare the fire spread path and the smoke diffusion path, thereby selecting the location to be analyzed from all monitoring locations.

[0095] Fire sources typically produce both flames and smoke. Flames spread along specific paths, while smoke also spreads with airflow. Therefore, areas where the fire and smoke spread paths overlap significantly are likely to be at or near the fire source.

[0096] Therefore, each fire spread path is mapped to the 3D radar point cloud data and compared with the smoke diffusion path. Given that smoke often spreads before flames, a screening condition is set where the overlap length is greater than or equal to half of the smoke diffusion path. This ensures sufficient correlation between the selected fire spread paths and the smoke diffusion path, thereby improving screening accuracy. If a fire spread path overlaps with a smoke diffusion path for a length greater than or equal to half of that smoke diffusion path, that fire spread path is selected as the path to be analyzed, and the monitoring location on the target path is selected as the location to be analyzed.

[0097] Step S302: The temperature change trend value and the smoke concentration change trend value at each location to be analyzed are integrated to obtain the first fire source factor at each location to be analyzed.

[0098] Near the fire source, the temperature and smoke concentration will tend to increase over time. Therefore, for any location to be analyzed, the sum of the temperature change trend value and the smoke concentration change trend value at the location to be analyzed is used as the first fire source factor. The larger the first fire source factor, the more obvious the temperature increase trend and smoke concentration increase trend at the location to be analyzed, and the more likely it is to be a suspected fire source location.

[0099] Step S303: The temperature value and smoke density value at each position to be analyzed at the current moment are integrated to obtain the second fire source factor at each position to be analyzed.

[0100] The closer to the fire source, the more intuitive the data is, such as higher temperature values ​​and higher smoke concentration values. Therefore, the sum of the temperature value and smoke concentration value of the position to be analyzed at the current moment is used as the second fire source factor. Similarly, the larger the second fire source factor, the larger the temperature value and smoke concentration value at the position to be analyzed, and the more likely it is to be a suspected fire source location.

[0101] Step S304: Based on the first fire source factor and the second fire source factor at each position to be analyzed, a suspected fire source position is screened out from all positions to be analyzed.

[0102] Based on the above analysis, it can be seen that both the first and second fire source factors are positively correlated with the likelihood that the location to be analyzed is a suspected fire source. Therefore, the product of the first and second fire source factors at the location to be analyzed is normalized to form the suspected fire source index for the location to be analyzed. A larger suspected fire source index indicates a greater likelihood that the location to be analyzed is a suspected fire source. Normalization is a technique well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, among others. The specific normalization method is not limited here.

[0103] Finally, the location to be analyzed whose suspected fire source index is greater than the preset fire source threshold is regarded as the suspected fire source location.

[0104] It should be noted that the preset fire source threshold can be set to 0.6, and the specific value can be adjusted according to the implementation scenario and is not limited here.

[0105] Step S4: The monitoring range of each suspected fire source location is used as the range to be measured; within each range to be measured, the initial power and initial resolution of the photon radar are adjusted based on the area value of the range to be measured, the complexity of the environment, and the smoke concentration value at the current moment, so as to identify and locate the fire source in the range to be measured.

[0106] Based on the above steps, the suspected fire source location can be screened out from the numerous monitoring locations at the fire scene. The screening of the suspected fire source location narrows the working range of the fire extinguishing robot, thereby helping to improve the accuracy of fire source identification and positioning. After obtaining all the suspected fire source locations, the optical quantum radar can be used to scan the monitoring range of the suspected fire source location to identify the fire source point. However, given that the environment at the fire scene is relatively complex, the environmental conditions at the scene will affect the penetration of the optical quantum radar. Therefore, in order to match the working parameters of the optical quantum radar with the environmental conditions within the monitoring range of the suspected fire source location, ensure that the optical quantum radar can maintain good performance in a complex environment, and improve the accuracy of fire source identification and positioning, in an embodiment of the present invention, the monitoring range of each suspected fire source location is used as the test range, and then within each test range, the area value of the test range, the environmental complexity, and the smoke concentration value at the current moment are selected to adaptively adjust the initial power and initial resolution of the optical quantum radar, thereby identifying and positioning the fire source within the test range.

[0107] Preferably, in one embodiment of the present invention, the process of adjusting the initial power and initial resolution of the optical quantum radar to identify and locate the fire source in the detection range includes:

[0108] The power of a photon radar determines the number and energy of the photons it emits. Smoke significantly attenuates the photon signal intensity, resulting in a decrease in the efficiency of quantum state propagation, which in turn affects the detection sensitivity. Therefore, by adjusting the power of the photon radar, the penetration probability of photons can be increased, ensuring that the photon radar can still maintain sufficient detection capabilities in environments with different smoke concentrations.

[0109] Therefore, within each test range, the normalized value of the smoke concentration at the suspected fire source location corresponding to the test range at the current moment is summed with the preset constant as the power adjustment coefficient. The larger the smoke concentration value, the greater the degree of smoke interference within the test range, and thus the photonic radar requires higher penetration ability, so the power adjustment coefficient is larger, that is, within the test range, the photonic radar requires higher working power when scanning.

[0110] The product of the power adjustment coefficient and the initial power is then used as the adjustment power of the optical quantum radar. At this time, the adjustment power will be more closely matched with the environmental conditions within the test range, thereby better identifying and locating the fire source within the test range.

[0111] The resolution determines the minimum target size and distance interval that the optical quantum radar can distinguish. Improving the resolution helps to accurately identify targets in complex environments. The area value of the test range determines the size of the test range. A larger test range requires a higher resolution to ensure that every corner can be effectively covered. In addition, multi-target reflections in a complex environment will produce quantum state aliasing. This situation also requires improving the resolution to achieve signal separation. The number characteristics and grayscale characteristics of the data points in the radar point cloud data can reflect the complexity of the environment. Therefore, in each test range, the variance of the grayscale values ​​of all data points is used as the environmental complexity factor. The larger the environmental complexity factor, the more chaotic the grayscale distribution of the data points in the test range, and the greater the change in grayscale value, which usually means a more complex environment.

[0112] The number of smoke data points can also be used as an indicator to measure the complexity of the environment. The more smoke data points there are, the denser the smoke distribution is within the test range, further increasing the complexity of the environment. Therefore, the product of the environmental complexity factor and the number of smoke data points is normalized and used as the environmental complexity of each test range. At this time, the greater the environmental complexity, the higher the environmental complexity within the test range, and the higher the resolution of the optical quantum radar is required.

[0113] Based on the above analysis, it can be seen that there is a positive correlation between the area value of the measured range and the resolution of the photon radar. Therefore, the normalized value of the product of the environmental complexity and the area value of each measured range and the sum of the values ​​with a preset constant are used as the resolution adjustment coefficient. The larger the resolution adjustment coefficient, the higher the resolution required for the photon radar to scan within the measured range. Normalization is a technical means well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0114] Then the product of the resolution adjustment coefficient and the initial distance resolution is used as the adjusted distance resolution of the photonic quantum radar, and the product of the resolution adjustment coefficient and the initial angular resolution is used as the adjusted angular resolution of the photonic quantum radar. At this time, the adjusted distance resolution and the adjusted angular resolution will be more closely matched with the environmental conditions within the range to be measured, so that the photonic quantum radar can more accurately measure the distance and direction of the target and other information, which is helpful for identifying and locating the fire source within the range to be measured.

[0115] Finally, based on the adjustment power and resolution corresponding to each range to be measured, the optical quantum radar is used to detect the range to be measured and identify the location of the fire source, so the location of the fire source can be accurately identified.

[0116] It should be noted that in this embodiment of the present invention, in order to avoid over-adjustment of power and resolution, the preset constant is set to 1. The specific value can also be adjusted according to the implementation scenario and is not limited here; the initial power of the optical quantum radar can be set to 1 watt, the initial distance resolution can be set to 3 meters, and the initial angular resolution can be set to 0.1°. The specific values ​​can be adjusted according to the implementation scenario and are not limited here.

[0117] After identifying the location of the fire source based on the above process, the fire-fighting robot can select different fire-extinguishing materials to extinguish the fire source according to the type of burning material at the fire scene. For example, for carbon-containing solids, water-type fire extinguishers, foam fire extinguishers, etc. can be used, and for liquid or meltable solid materials, dry powder fire extinguishers, carbon dioxide fire extinguishers, etc. can be used.

[0118] In summary, acquiring radar point cloud data from the fire scene, along with temperature and smoke concentration time series data at each monitoring location, allows for a coordinated analysis of the fire environment's spatial structure and monitoring data, improving the reliability of subsequent fire source location. Comparing the radar point cloud data with pre-set radar point cloud data allows for the identification and extraction of smoke data points. Given that smoke typically spreads as the fire spreads, analyzing the motion characteristics of smoke data points to determine the smoke diffusion path helps narrow the scope for accurate fire source location and identification. Furthermore, the spread of the fire directly reflects its expansion trend, further aiding in fire source location analysis. Therefore, the differences in temperature and smoke concentration time series data between monitoring locations are analyzed to determine the fire spread path. Comparing the smoke diffusion path with the fire spread path, combined with the changing characteristics of temperature and smoke concentration values, allows for the identification of the true fire evolution trend through smoke interference, screening suspected fire sources from all monitoring locations and narrowing the monitoring scope of the fire scene. Finally, within the detection range of each suspected fire source, the embodiment of the present invention adaptively adjusts the initial power and initial resolution of the photonic radar based on indicators such as the detection area, environmental complexity, and smoke concentration. This intelligent parameter adjustment method enables the photonic radar to achieve optimal performance based on the actual needs of different scenarios, further improving the accuracy and stability of fire source identification and location. By integrating point cloud spatial information with the changing patterns of temperature and smoke concentration, the embodiment of the present invention quickly locates the suspected fire source area and dynamically narrows the scanning range. Furthermore, the adaptive adjustment of the photonic radar parameters facilitates accurate location of the fire source and improves the timeliness of emergency rescue.

[0119] The present invention also provides a light quantum radar fire source identification and positioning system for fire extinguishing robots, see Figure 4 , which shows a system block diagram, including a data acquisition module 401, used to implement step S1 in the above method embodiment; a path analysis module 402, used to implement step S2 in the above method embodiment; a suspected fire source location screening module 403, used to implement step S3 in the above method embodiment; and a fire source positioning and identification module 404, used to implement step S4 in the above method embodiment.

[0120] It should be noted that the system provided in the above embodiment is merely an example of the division of the aforementioned functional modules. In actual applications, the aforementioned functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the above embodiment provides a light quantum radar fire source identification and positioning system for a fire-fighting robot and an embodiment of a light quantum radar fire source identification and positioning method for a fire-fighting robot, which are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0121] See also Figure 5 , which shows a system structure diagram of a light quantum radar fire source identification and positioning system for a fire-fighting robot provided by an embodiment of the present invention, including a processor 500, a memory 501, a bus 502 and a communication interface 503, wherein the processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; wherein the memory 501 may include a high-speed random access memory, the bus 502 may be an ISA bus, a PCI bus or an EISA bus, etc., and the processor 500 may be an integrated circuit chip with signal processing capabilities; the memory 501 stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps in a light quantum radar fire source identification and positioning method for a fire-fighting robot are implemented.

[0122] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for identifying and locating fire sources using a light quantum radar for a fire-fighting robot, characterized in that: The method comprises: Obtain radar point cloud data of the fire scene detected by the optical quantum radar, as well as temperature time series data and smoke concentration time series data at each monitoring location; Compare the radar point cloud data at each moment with the preset radar point cloud data to determine the smoke data points; determine the smoke diffusion path at the fire scene based on the movement of the smoke data points in the radar point cloud data at different moments; analyze the changes in the temperature time series data and the changes in the smoke concentration time series data to determine the fire spread path; Compare the smoke diffusion path and the fire spread path, and screen the suspected fire source location among all monitoring locations based on the changes in the temperature and smoke concentration values ​​at the current moment; The monitoring range of each suspected fire source location is used as the range to be measured. Within each range to be measured, indicators are selected from the area value of the range to be measured, the complexity of the environment, and the smoke concentration value at the current moment to adjust the initial power and initial resolution of the photonic quantum radar, so as to identify and locate the fire source in the range to be measured.

2. The method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot according to claim 1, characterized in that: The method for obtaining the smoke diffusion path includes: In the radar point cloud data at each moment, the position of each smoke data point is used as the starting point, and the position of the same smoke data point in the radar point cloud data at the next adjacent moment is used as the end point. The motion vector between the radar point cloud data at adjacent moments of each smoke data point is obtained. In the radar point cloud data at each moment, for any xoz plane, the motion vectors of the radar point cloud data in the xoz plane are synthesized to determine the motion direction of the xoz plane; The movement directions of the same xoz plane in the radar point cloud data at all times are connected to obtain all the smoke diffusion paths.

3. The method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot according to claim 1, characterized in that: The method for obtaining the fire spread path includes: Obtain a temperature curve of the temperature time series data at each monitoring location, obtain a slope value at each data point on the temperature curve, and use the sum of the slope values ​​at all data points as a temperature change trend value at each monitoring location; Obtaining a concentration curve of the smoke concentration time series data at each monitoring location, obtaining a slope value at each data point on the concentration curve, and using the sum of the slope values ​​at all data points as a smoke concentration change trend value at each monitoring location; A preset neighborhood is determined with each monitoring location as the center. Within the preset neighborhood corresponding to each monitoring location, the difference between the temperature change trend values ​​of the central monitoring location and the neighboring monitoring locations, as well as the difference between the smoke concentration change trend values, are analyzed to obtain a direction determination factor. When the direction determination factor between the central monitoring position and each neighboring monitoring position is greater than a preset direction determination threshold, the fire spread direction between the central monitoring position and each neighboring monitoring position is from the neighboring monitoring position to the central monitoring position; otherwise, the fire spread direction between the central monitoring position and each neighboring monitoring position is from the neighboring monitoring position to the central monitoring position; The fire spread directions between all monitoring locations and neighboring monitoring locations are integrated to obtain all fire spread paths.

4. The method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot according to claim 3, characterized in that: The method for obtaining the direction determination factor includes: The normalized value of the temperature change trend difference between the central monitoring location and each neighboring monitoring location is used as the temperature change difference value, and the normalized value of the smoke concentration change trend difference between the central monitoring location and each neighboring monitoring location is used as the smoke concentration change difference value; The value obtained by normalizing the product of the temperature change difference and the smoke concentration change difference is used as the direction determination factor.

5. The method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot according to claim 3, characterized in that: The method for obtaining the suspected fire source location includes: Compare the fire spread path and smoke diffusion path to select the locations to be analyzed from all monitoring locations; For any position to be analyzed, the sum of the temperature change trend value and the smoke concentration change trend value at the position to be analyzed is taken as the first fire source factor; The sum of the temperature value and the smoke density value of the position to be analyzed at the current moment is used as the second fire source factor; The product of the first fire source factor and the second fire source factor at the position to be analyzed is normalized to obtain a value as the suspected fire source index at the position to be analyzed; The location to be analyzed whose suspected fire source index is greater than the preset fire source threshold is regarded as the suspected fire source location.

6. The method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot according to claim 5, characterized in that: The method for obtaining the position to be analyzed includes: Each fire spread path is mapped to the three-dimensional radar point cloud data and compared with the smoke diffusion path. If the overlap length of a fire spread path with a smoke diffusion path is greater than or equal to half of the smoke diffusion path, the fire spread path is taken as the path to be analyzed, and the monitoring position on the target path is taken as the position to be analyzed.

7. The method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot according to claim 1, characterized in that: In each test range, the initial power and initial resolution of the optical quantum radar are adjusted by selecting indicators based on the area value of the test range, the complexity of the environment, and the smoke concentration value at the current moment, so as to identify and locate the fire source in the test range, including: In each test range, the normalized value of the smoke concentration at the suspected fire source location corresponding to the test range at the current moment and the sum of the normalized value and the preset constant are used as the power adjustment coefficient; The product of the power adjustment coefficient and the initial power is used as the adjustment power of the optical quantum radar; Determine the environmental complexity of each range to be measured based on the quantity characteristics and grayscale characteristics of the data points in the radar point cloud data within each range to be measured; The sum of the normalized value of the product of the environmental complexity and the area value of each measured range and a preset constant is used as the resolution adjustment coefficient; The product of the resolution adjustment coefficient and the initial range resolution is used as the adjusted range resolution of the photon radar, and the product of the resolution adjustment coefficient and the initial angular resolution is used as the adjusted angular resolution of the photon radar; Based on the adjusted power, adjusted distance resolution, and adjusted angular resolution corresponding to each range to be measured, the optical quantum radar is used to detect the range to be measured and identify the location of the fire source.

8. The method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot according to claim 7, characterized in that: The method for obtaining the environmental complexity includes: In each measured range, the variance of the grayscale values ​​of all data points is used as the environmental complexity factor; The product of the environmental complexity factor and the number of smoke data points is normalized to obtain a value which is used as the environmental complexity of each range to be measured.

9. The method for identifying and locating a fire source using a light quantum radar for a fire-fighting robot according to claim 1, characterized in that: The method for obtaining the smoke data points includes: The radar point cloud data at each moment is compared with the preset radar point cloud data, and the newly appeared data points are used as smoke data points; among them, the preset radar point cloud data is the radar point cloud data at the scene when no fire occurs.

10. A light quantum radar fire source identification and positioning system for fire-fighting robots, characterized in that: The invention comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, the steps of a method for identifying and locating a fire source by an optical quantum radar for a fire-fighting robot as described in any one of claims 1 to 9 are implemented.

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