Multi-source data fusion high-altitude falling object detection system and high-altitude falling object detection method

Abstract

The invention provides a multi-source data fusion high-altitude falling object detection system and a high-altitude falling object detection method. The system comprises an acceleration sensor, a sound sensor, a processor and an alarm module. The acceleration sensor monitors the acceleration change of the target falling object and preliminarily judges an object falling event; when the sound sensor preliminarily judges that an object falling event occurs, a sound signal generated by a target falling object is monitored, and sound source positioning is achieved; the processor receives data of the sensor and verifies an object falling event again through a data fusion algorithm, so that misjudgment is avoided. In addition, the alarm module further generates alarm information including the falling time, the object type and the falling position to give an alarm. The system solves the problem that in the prior art, the accuracy of object falling detection through a single-type sensor is low.

Description

Technical Field

[0001] The present invention relates to the technical field of high-altitude falling object detection, and in particular to a high-altitude falling object detection system and a high-altitude falling object detection method, device and computer-readable storage medium that integrates multi-source data. Background Art

[0002] With the rapid development of social economy, incidents of objects being thrown from high places occur frequently, seriously threatening public security and personal safety and bringing great hidden dangers to society.

[0003] At present, the detection of falling objects from high altitudes is usually carried out by video surveillance systems. Through the video surveillance system, the occurrence of falling objects from high altitudes can be monitored in real time, and the video of the falling objects from high altitudes can be obtained through the video surveillance system afterwards for evidence collection. However, the video surveillance system has the following defects: First, there are blind spots in the video surveillance system. For areas that are unsupervised or not covered by video surveillance equipment, it is impossible to detect falling objects from high altitudes; second, the video surveillance system can only collect evidence afterwards, and cannot detect the occurrence of falling objects from high altitudes in real time, and there is a serious lag; third, the detection accuracy of the video surveillance system depends on the quality of video surveillance. For areas with poor video surveillance quality, the detection accuracy of falling objects from high altitudes is low.

[0004] In order to solve the above defects, the existing technical document [Publication No. CN110911164A] proposes a high-altitude falling object detection method based on video monitoring. The method monitors the occurrence of high-altitude falling object events in real time through a video monitoring system, determines the occurrence of falling object events by analyzing the change in the number of objects in the video, and determines the falling time, falling position and falling time of the falling object event by analyzing the movement trajectory of the object. However, this method depends on the quality of video monitoring. For areas with poor video monitoring quality, the detection accuracy of high-altitude falling object events is low, and the detection accuracy of this method depends on the time resolution of the video monitoring system. For areas with low time resolution of the video monitoring system, the detection accuracy of high-altitude falling object events is also low. Summary of the invention

[0005] The main purpose of the present application is to provide a high-altitude falling object detection system and a high-altitude falling object detection method, device and computer-readable storage medium with multi-source data fusion, so as to at least solve the problem of low accuracy of object falling detection through a single type of sensor in the prior art.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a high-altitude falling object detection system with multi-source data fusion is provided, including: an acceleration sensor, used to monitor the acceleration change of the target falling object and preliminarily determine whether there is a falling object event; a sound sensor, which is communicated with the acceleration sensor, and is used to monitor the sound signal generated by the target falling object when a falling object event is detected, so as to realize the sound source positioning and the type of the target falling object; a processor, which is communicated with the acceleration sensor and the sound sensor, and is used to receive sensor data and re-verify the falling object event through a data fusion algorithm to avoid misjudgment.

[0007] Optionally, the high-altitude falling object detection system also includes: an alarm module, which is communicatively connected to the processor, and when the processor determines that a falling object event has occurred, an alarm message is issued to prompt the staff to handle it, and the alarm information at least includes the falling time, the type of target falling object and the falling location; a power supply module, which is electrically connected to the acceleration sensor, the sound sensor, the processor and the alarm module, and is used to power the acceleration sensor, the sound sensor, the processor and the alarm module.

[0008] According to another aspect of the present application, a high-altitude falling object detection method with multi-source data fusion is provided, and the high-altitude falling object detection method is used in a high-altitude falling object detection system, and the method includes: controlling an acceleration sensor to monitor the acceleration change of a target falling object to obtain a first target signal, and determining whether there is a falling object event based on the first target signal; when the acceleration sensor determines that there is a falling object event, controlling each sound sensor to monitor the sound signal of the target falling object to obtain multiple second target signals; controlling a processor to receive the first target signal and the second target signal, and performing data fusion based on the first target signal and the second target signal to obtain a third target signal, and verifying the first target signal based on the third target signal.

[0009] Optionally, determining whether a falling object event occurs based on the first target signal includes: calculating the product of gravity acceleration and a preset ratio to obtain a first threshold; and determining that a falling object event occurs when the acceleration of the target falling object is greater than or equal to the first threshold.

[0010] Optionally, data fusion is performed based on the first target signal and the second target signal to obtain a third target signal, including: determining the falling acceleration of the target falling object based on the first target signal; determining the target sound pressure level of the sound generated by the target falling object based on the second target signal; obtaining a first preset weight and a second preset weight, wherein the first preset weight and the second preset weight are weight parameters of the falling acceleration and the target sound pressure level, respectively; performing weighted calculation based on the falling acceleration, the target sound pressure level, the first preset weight and the second preset weight to obtain the third target signal; and determining that a falling object event exists when the third target signal is greater than or equal to the second threshold.

[0011] Optionally, after controlling each sound sensor to monitor the sound signal of the target falling object and obtaining multiple second target signals, the method also includes: determining the time when the sound sensor corresponding to each second target signal receives the second target signal to obtain multiple target moments; determining the time difference when each sound sensor receives the second target signal based on the target moment to obtain the target duration; determining the distance difference between each sound sensor and the target falling object based on each target duration to obtain the first target distance; obtaining the position deviation between each sound sensor to obtain the second target distance, and locating the target falling object based on the first target distance and the second target distance to obtain target position information; determining the frequency of the sound generated by the target falling object according to the second target signal to obtain the target frequency.

[0012] Optionally, after controlling the acceleration sensor to monitor the acceleration change of the target falling object and obtaining the first target signal, the method further includes: integrating based on the first target signal to determine the falling speed of the target falling object.

[0013] Optionally, after verifying the first target signal based on the third target signal, the method also includes: when the target frequency is greater than or equal to the third threshold and less than or equal to the fourth threshold, determining the type of the target falling object is a heavy object; when the target frequency is greater than the fourth threshold and less than or equal to the fifth threshold, determining the type of the target falling object is a light object; determining the falling height of the target falling object based on the target position information, determining the falling time of the target falling object based on the falling height and the falling speed, and determining the falling position of the target falling object based on the target position information, the falling speed and the falling time; and generating alarm information based on the type, falling time and falling position of the falling object.

[0014] According to another aspect of the present application, a high-altitude falling object detection device with multi-source data fusion is provided, and the high-altitude falling object detection device is used for the above-mentioned high-altitude falling object detection system, including: a first control unit, used to control the acceleration sensor to monitor the acceleration change of the target falling object, obtain a first target signal, and determine whether there is a falling object event based on the first target signal; a second control unit, used to control each sound sensor to monitor the sound signal of the target falling object when the acceleration sensor determines that there is a falling object event, and obtain multiple second target signals; a third control unit, used to control the processor to receive the first target signal and the second target signal, and perform data fusion based on the first target signal and the second target signal to obtain a third target signal, and verify the first target signal based on the third target signal.

[0015] According to another aspect of the present application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein when the program is executed, a device where the computer-readable storage medium is located is controlled to execute any one of the methods.

[0016] By applying the technical solution of the present application, the accuracy and reliability of falling object detection are improved by combining the data of the acceleration sensor and the sound sensor. The acceleration sensor can respond quickly to falling object events, while the sound sensor can locate the sound source. The combination of the two can not only detect falling objects in time, but also accurately determine the type and location of the falling objects, effectively avoiding the misjudgment that may be caused by a single sensor. In addition, verification through the data fusion algorithm further improves the accuracy of detection, providing strong technical support for the safe management of falling objects. In practical applications, the system can significantly improve public safety and reduce potential risks and losses caused by falling objects. The above embodiments solve the problem of low accuracy in detecting falling objects using a single type of sensor in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A structural block diagram of a high-altitude falling object detection system with multi-source data fusion provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic diagram of a process of a high-altitude falling object detection method using multi-source data fusion according to an embodiment of the present application is shown;

[0019] Figure 3 A structural block diagram of a high-altitude falling object detection device with multi-source data fusion provided according to an embodiment of the present application is shown.

[0020] The above drawings include the following reference numerals:

[0021] 11. Acceleration sensor; 12. Sound sensor; 13. Processor; 14. Alarm module; 15. Power module. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] As introduced in the background technology, the existing technology uses a video surveillance system to detect falling objects from a high altitude, which depends on the quality of video surveillance. For areas with poor video surveillance quality, the detection accuracy of falling objects from a high altitude is low. In order to solve the problem of low accuracy in detecting falling objects using a single type of sensor in the existing technology, the embodiments of the present application provide a multi-source data fusion falling object detection system and a falling object detection method, device and computer-readable storage medium.

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] In this embodiment, a high-altitude falling object detection system with multi-source data fusion is provided. Figure 1 As shown, the system includes: an acceleration sensor 11, a sound sensor 12, a processor 13, an alarm module 14 and a power module 15, wherein:

[0028] The acceleration sensor 11 is used to monitor the acceleration change of the target falling object and preliminarily determine whether there is a falling object event;

[0029] The sound sensor 12 is connected to the acceleration sensor for monitoring the sound signal generated by the target falling object when the falling object event is detected, so as to locate the sound source and the type of the target falling object;

[0030] The processor 13 is connected to the acceleration sensor and the sound sensor for receiving sensor data and rechecking the falling object event through a data fusion algorithm to avoid misjudgment;

[0031] The alarm module 14 is connected to the processor for communication. When the processor determines that a falling object event has occurred, the alarm module 14 issues an alarm message to prompt the staff to handle the event. The alarm message includes at least the falling time, the type of the target falling object, and the falling location.

[0032] The power module 15 is electrically connected to the acceleration sensor, the sound sensor, the processor and the alarm module, and is used to supply power to the acceleration sensor, the sound sensor, the processor and the alarm module.

[0033] Based on the above embodiment, in a specific implementation, the installation of the sensor and data collection include:

[0034] 1.1. Arrangement of acceleration sensors: Multiple acceleration sensors 11 are installed at key positions on the top, bottom and surrounding of the transmission tower to ensure that all facades and height ranges of the tower can be fully covered to capture the acceleration changes when the target object falls.

[0035] 1.2. Acceleration sensor data collection and preliminary time diagnosis: After installation, the acceleration sensor 11 continuously monitors the acceleration changes in the target area. Once the acceleration is detected to be abnormally increased and exceeds the preset value, it is preliminarily determined that there may be a high-altitude falling object. The acceleration sensor 11 synchronously transmits this judgment result to the processor and the sound sensor 12.

[0036] 1.3. Arrangement of sound sensors: Sound sensors are distributed around the transmission towers to form a three-dimensional positioning network.

[0037] 1.4. Sound source location and object type identification: When the acceleration sensor initially determines that an object has fallen, the sound sensor array starts working to capture the sound signal of the object falling. The time difference method (TDOA) positioning algorithm is used to calculate the time difference Δt between multiple sensors receiving the sound signal. ij , through the speed of sound v sound Calculate the distance difference Δd ij , and then determine the three-dimensional coordinates of the sound source (x s ,y s , z s ). In addition, the type of object is further determined based on the frequency and sound pressure level of the sound, such as light objects (such as plastic bags, etc.) and heavy objects (iron tools, etc.).

[0038] Based on the data collection of the above sensors, the processor further performs high-altitude falling object event verification based on the data fusion algorithm, including:

[0039] 2.1. Data reception and preprocessing: The processor receives raw data from the acceleration sensor and sound sensor, and removes noise interference through signal processing algorithms in the processor, such as filtering, to ensure the purity and reliability of the data.

[0040] 2.2 Data fusion: The processor performs a comprehensive analysis of the pre-processed acceleration data and sound data to correct the initially determined falling object event. In an optional implementation, the acceleration sensor data a(t) and the sound sensor data Lp (i.e., the above-mentioned sound pressure level) can be assigned corresponding weights according to the corresponding actual conditions, and data fusion can be performed through weighted averaging to ensure balanced use of the data from the two sensors.

[0041] 2.3 Event verification and alarm: Based on the fused data, it is compared with the preset value. If the fused data is greater than the preset value, it is determined that a high-altitude falling object event has occurred. Furthermore, the processor sends the relevant information of the time (time, location, object type, etc.) to the designated terminal through the communication connection, and records the event data in the system for subsequent statistics and analysis.

[0042] Based on the alarm of the above processor, further, the alarm module performs an alarm including:

[0043] 3.1. Alarm of the alarm module: The alarm module of this application is provided with one or more alarm signal generators and wireless communication devices. The above alarm signal generator is used to generate alarm sound or light signal, and the wireless communication device supports 4G, 5G and satellite communication at the same time to ensure fast and reliable alarm.

[0044] 3.2. Alarm logic: The alarm module is connected to the processor for communication. After the processor determines that a falling object event has occurred through a data fusion algorithm, it sends a signal including the falling time, the target falling object type and the falling location to the alarm module to drive the alarm module to sound an alarm.

[0045] Based on the above embodiments, in an optional real-time mode, the power module of the present application comprises: a solar panel, a battery management system (BMS) and a backup battery. The solar panel is used to collect energy under lighting conditions and convert solar energy into electrical energy. The BMS is responsible for monitoring the power status to ensure efficient use of energy and the health of the battery. The backup battery provides stable power at night or in rainy weather to ensure continuous operation of the system.

[0046] In the above embodiment, in order to ensure the accuracy of the alarm, in an optional implementation manner, the system may perform further algorithm optimization during use, including:

[0047] 4.1 Sensitivity adjustment: To adapt to different environmental conditions, the system has a sensitivity adjustment function. In a noisy environment, the threshold of the sound sensor can be increased to reduce false alarms; in areas with a high risk of falling objects, the threshold of the acceleration sensor can be appropriately lowered to ensure that small falling objects can also be detected in time.

[0048] 4.2 False Alarm Control: By continuously optimizing the data fusion algorithm and sensor threshold setting, the system can effectively control the false alarm rate and avoid unnecessary alarms. For example, a machine learning model is used to predict the ambient noise level and dynamically adjust the sensor threshold to improve the detection accuracy in noisy environments.

[0049] Through the above-mentioned embodiments, the accuracy and reliability of high-altitude falling object detection are improved by combining the data of the acceleration sensor and the sound sensor. The acceleration sensor can quickly respond to falling object events, while the sound sensor can locate the sound source. The combination of the two can not only detect high-altitude falling objects in time, but also accurately determine the type and location of the falling objects, effectively avoiding the misjudgment that may be caused by a single sensor. In addition, verification through the data fusion algorithm further improves the accuracy of detection, providing strong technical support for the safe management of high-altitude falling objects. In practical applications, the system can significantly improve public safety and reduce potential risks and losses caused by high-altitude falling objects. The above embodiments solve the problem of low accuracy of object falling detection using a single type of sensor in the prior art.

[0050] In this embodiment, a method for detecting falling objects from a high altitude using multi-source data fusion running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0051] Figure 2 1 is a flow chart of a high-altitude falling object detection method based on multi-source data fusion according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0052] Step S201, controlling the acceleration sensor to monitor the acceleration change of the target falling object, obtaining a first target signal, and determining whether there is a falling object event based on the first target signal;

[0053] In the specific implementation, first, when the system starts, the processor activates the acceleration sensor to put it in a real-time monitoring state; then, the acceleration sensor collects data, continuously measures the acceleration changes in the target area, and obtains real-time acceleration data a(t). At the same time, according to the real-time acceleration data (the first target signal), by comparing it with the preset threshold a thresha Comparison is performed to determine whether there is a falling object event. That is, when the real-time acceleration data is greater than the preset threshold, it is determined that there is a falling object event and transmitted to the processor and the sound sensor.

[0054] Step S202, when the acceleration sensor determines that the falling object event exists, controlling each of the sound sensors to monitor the sound signal of the target falling object to obtain a plurality of second target signals;

[0055] In the specific implementation, first, when the acceleration sensor preliminarily determines that there is a falling object based on the acceleration data, the sound sensor array is controlled to start and capture the sound signal of the falling object; then, each sensor S1, S2, ..., S n Start recording the sound signal generated by the falling object, and obtain multiple time-domain sound signal data Lp(t) (i.e., the second target signal mentioned above); perform sound source location and object type identification based on the time-domain sound signal, and perform time-frequency analysis based on the second target signal to extract the frequency and intensity characteristics of the sound. Analyze the time difference Δt between different sensors receiving the sound signal through the time difference localization algorithm (TDOA) ij , calculate the sound source distance difference Δd ij , and determine the three-dimensional coordinates of the sound source (x s ,y s , z s ), to accurately locate the falling object. At the same time, the type of falling object is identified according to the frequency and intensity characteristics of the sound, and the result is transmitted to the above processor.

[0056] Step S203, controlling the processor to receive the first target signal and the second target signal, and performing data fusion based on the first target signal and the second target signal to obtain a third target signal, and verifying the first target signal based on the third target signal.

[0057] Specifically, the processor removes interference through signal preprocessing (such as filtering) based on the first target signal a(t) and multiple second target signals Lp(t) of the sound sensor to ensure data purity. The data fusion algorithm is run based on the preprocessed data to perform weighted fusion on the first target signal a(t) and the second target signal Lp(t) to form a third target signal F(t), which integrates the motion state and sound characteristics of the falling object. The falling object event is confirmed and verified based on the third target signal F(t) and the comprehensive threshold F thresh Compare and verify the existence of falling object event again. thresh The setting of needs to comprehensively consider the background noise level and event characteristics of acceleration and sound signals. thresh When the system finally confirms the falling object event, it sends out an alarm message through the alarm module.

[0058] In the above embodiment, to ensure safety, in an optional implementation, after the falling object event is confirmed, the system issues an alarm through the alarm module, and also sends detailed event information (including time, location, and type of falling object) to the remote terminal through the wireless communication module to ensure that the staff can respond quickly and take appropriate measures.

[0059] Through this embodiment, first, the acceleration sensor is controlled to monitor the acceleration change of the target falling object to obtain a first target signal, and based on the first target signal, it is determined whether there is a falling object event; then, when the acceleration sensor determines that there is a falling object event, each of the sound sensors is controlled to monitor the sound signal of the target falling object to obtain multiple second target signals; finally, the processor is controlled to receive the first target signal and the second target signal, and data fusion is performed based on the first target signal and the second target signal to obtain a third target signal, and the first target signal is verified based on the third target signal. The present application detects falling events by controlling the acceleration sensor, and locates the falling object by controlling the sound sensor, and on this basis, verifies the authenticity of the falling event by controlling the processor to perform data fusion on the monitoring signals of the acceleration sensor and the sound sensor, thereby ensuring the accuracy of falling event detection and solving the problem of low accuracy of object falling detection by a single type of sensor in the prior art.

[0060] In order to perform a preliminary diagnosis of a fall event, in an optional implementation, the above step S201 includes:

[0061] Step S2011, calculating the product of the gravitational acceleration and a preset ratio to obtain a first threshold;

[0062] In a specific implementation, the first threshold calculation includes: after the processor receives the acceleration data, it calculates the gravity acceleration g (about 9.8m / s 2 ) multiplied by the preset ratio α, the first threshold a is obtained thresh =g×α. The setting of α depends on the on-site environment and the type of object, and is generally between 0.8 and 1.2 to meet the detection requirements of different falling object events.

[0063] Step S2012: When the acceleration of the target falling object is greater than or equal to the first threshold, determine that the falling object event exists.

[0064] In a specific implementation, the processor compares the acceleration data a(t) collected in real time with the first threshold a thresh Based on the judgment results, the falling object event is diagnosed. If a(t)≥a thresh , that is, the acceleration change of the target object exceeds the preset falling object event judgment standard, and the processor immediately determines that there is a falling object event.

[0065] In the above embodiment, in order to ensure the accuracy of diagnosis, in an optional implementation, the present application sets the above acceleration sensor for environmental adaptation, including: threshold dynamic adjustment: the system has a threshold dynamic adjustment function, which can adjust the first threshold a according to the environmental changes (such as wind speed, temperature, etc.) of the monitoring area. thresh , ensuring that the system can accurately detect high-altitude falling object events under different conditions; Adaptive event detection: By real-time calculation and adjustment of the first threshold, the system can adaptively monitor various possible falling object events, whether it is the slow fall of a light object or the rapid fall of a heavy object, it can be discovered in time.

[0066] In order to perform multi-source data fusion, in an optional implementation, the above step S203 includes:

[0067] Step S2031, determining the falling acceleration of the target falling object based on the first target signal;

[0068] Specifically, fall acceleration calculation: When the system preliminarily determines that there may be a falling object from a high altitude, the processor calculates the real-time fall acceleration of the target falling object based on the first target signal a(t) collected by the acceleration sensor. Considering that the acceleration of an object in free fall should be close to the gravity acceleration g, the processor calculates the instantaneous fall acceleration of the object by monitoring the changes of a(t) in real time.

[0069] Step S2032, determining a target sound pressure level of the sound generated by the target falling object based on the second target signal;

[0070] Specifically, the target sound pressure level is obtained: At the same time, when the sound sensor detects a falling object event, it monitors and records the second target signal, that is, the sound signal generated when the object falls. The processor performs time-frequency analysis on the sound signal, extracts the intensity characteristics of the sound, and calculates the target sound pressure level Lp of the sound generated by the target falling object.

[0071] Step S2033, obtaining a first preset weight and a second preset weight, wherein the first preset weight and the second preset weight are weight parameters of the falling acceleration and the target sound pressure level, respectively;

[0072] Specifically, the weight parameter is obtained: the processor obtains the first preset weight w according to the preset weight parameter a and the second preset weight w s The two weight parameters reflect the importance of fall acceleration and target sound pressure level in event judgment and are determined through system training or on-site adjustment to balance the contribution of the two sensor data.

[0073] Step S2034, performing weighted calculation based on the falling acceleration, the target sound pressure level, the first preset weight, and the second preset weight to obtain the third target signal;

[0074] Specifically, the processor performs weighted calculation based on the falling acceleration, the target sound pressure level, and the first preset weight wa and the second preset weight ws. The specific formula is as follows: F(t)=w a ×a(t)+w s ×Lp(t), where F(t) is the third target signal, which is a real-time fusion signal of the comprehensive falling acceleration and sound intensity.

[0075] Step S2035: When the third target signal is greater than or equal to the second threshold, it is determined that the falling object event occurs.

[0076] Specifically, first, the processor sets a second threshold F thresh , as a comprehensive criterion for determining falling object events. thresh The setting is based on the false alarm and missed alarm rate of the system in different environments, which is optimized through a large number of training data sets to ensure the accuracy and reliability of the system in detecting falling objects. The processor compares the third target signal F(t) calculated in real time with the second threshold F thresh When F(t)≥F thresh , the system finally determined that a falling object event had occurred.

[0077] In the above embodiment, the accuracy of the processor verification is guaranteed. In an optional implementation, the present application sets the above processor to perform environmental adaptation, including: Environmental adaptability adjustment: The system has a dynamic adjustment mechanism for weight parameters, which can adjust the first preset weight w in real time according to factors such as environmental noise level and falling object risk area. a and the second preset weight w s , to meet the needs of falling object detection in different scenarios. System optimization and false alarm control: By continuously optimizing the weighted calculation model, the system can effectively reduce false alarms and missed alarms. For example, in a noisy environment, w can be appropriately reduced. s , increase w a The proportion of the sound signal to ensure that the interference will not cause false alarms; in areas with a higher risk of falling objects, the w s , in order to capture sound signals more sensitively and improve detection accuracy.

[0078] In order to locate the sound source and classify the falling object, in an optional implementation, after controlling each of the above-mentioned sound sensors to monitor the sound signal of the above-mentioned target falling object and obtaining a plurality of second target signals, the above-mentioned method further includes:

[0079] Step S301, determining the time when the sound sensor corresponding to each of the second target signals receives the second target signal, and obtaining multiple target times;

[0080] In the specific implementation, at the moment when the acceleration abnormality is detected, the system immediately controls each sound sensor to start monitoring the sound signal generated by the target falling object. The processor records the precise time t when each sound sensor receives the second target signal i (i=1,2,…,n), where n is the number of sound sensors.

[0081] Step S302, determining the time difference for each of the sound sensors to receive the second target signal based on the target time, and obtaining a target duration;

[0082] In the specific implementation, based on the recorded target time t i , calculate the time difference Δt between any two sound sensors receiving the signal ij =t i -t j , where i and j represent two different groups of sensors. The time difference reflects the time difference of sound waves reaching different sensors and is the core parameter for sound source localization.

[0083] Step S303, determining the distance difference between each of the sound sensors and the target falling object based on each of the target durations, and obtaining a first target distance;

[0084] In a specific implementation, the processor obtains the speed of sound waves propagating in the air vsound (about 343m / s), and uses the time difference Δt ij Calculate the distance difference Δd between each sound sensor and the sound source ij =vsound×Δt ij By calculating the distance difference, the relative position of the falling object and each sensor can be further determined.

[0085] Step S304, obtaining the position deviation between the sound sensors to obtain a second target distance, and locating the target falling object based on the first target distance and the second target distance to obtain target position information;

[0086] Specifically, the processor converts the distance difference Δd ij Combined with the position deviation between sensors (known distance between sensors), the precise position of the target falling object is determined by nonlinear solution methods (such as least squares method or iterative optimization algorithm). This process uses mathematical models and algorithms to determine the three-dimensional coordinate position (x s ,y s , z s ), realizing the spatial positioning of falling objects.

[0087] Step S305, determining the frequency of the sound generated by the target falling object according to the second target signal to obtain a target frequency.

[0088] Specifically, the sound sensor not only monitors the arrival time of the sound signal, but also records the frequency characteristics of the sound signal. Based on the second target signal Lp(t), the processor converts the time domain signal into a frequency domain signal through Fourier transform to obtain the spectrum characteristics of the sound.

[0089] In the above embodiments, in order to ensure the accuracy of data analysis, in an optional implementation, the present application sets the parameters of the time difference positioning algorithm and the frequency analysis model of the sound signal to continuously adjust to adapt to different monitoring environments and noise levels, thereby improving the detection accuracy and robustness of the system. By setting a reasonable sound source positioning threshold and frequency analysis range, the system can significantly reduce false alarms caused by environmental noise or other interference, ensuring that each alarm is based on accurate falling object event data. Through sound source positioning technology, the system can accurately determine the location of falling objects, which is suitable for high-rise residential areas, commercial complexes and other places that require precise location information, and provides an important basis for timely cleaning of falling objects and avoiding secondary injuries.

[0090] In order to evaluate the impact of falling objects from high altitude, in an optional implementation, after controlling the acceleration sensor to monitor the acceleration change of the target falling object and obtaining the first target signal, the method further includes:

[0091] Step S401, integrating based on the first target signal to determine the falling speed of the target falling object.

[0092] In a specific implementation, when the system preliminarily determines that there may be a falling object event, the processor starts to perform an integration operation on the first target signal a(t). The integral calculation formula is: v(t) = ∫a(t)dt + v0, where v(t) is the instantaneous velocity of the object at time t, and v0 is the initial velocity (in the case of free fall, the initial velocity is usually 0).

[0093] Based on the above embodiment, the processor can also compare the calculated instantaneous speed v(t) with the preset speed threshold v thresh Compare. If v(t)≥v thresh , further confirm the falling object event and incorporate the speed information into the event analysis to provide support for the subsequent sound source location and object type identification. Specifically, to ensure the accuracy and reliability of event judgment, the processor combines v(t) with the falling object position and sound signal intensity characteristics obtained based on the time difference positioning algorithm to conduct multi-dimensional verification of the falling object event.

[0094] In the above embodiments, in order to ensure the accuracy of data analysis, in an optional implementation, the present application is configured to adjust the detection and response strategy of the system by calculating the falling velocity v(t) in real time. For example, when the falling velocity is abnormally high, the system can automatically increase the alarm level to ensure that the emergency is handled in a timely manner. The falling velocity information v(t) is not only used for real-time monitoring and event verification, but also for generating event reports. The report contains the velocity data of the falling object, which helps managers assess the impact force and potential hazards of the falling object, and provides a quantitative basis for safety management and risk prevention. The calculation of the falling velocity helps to further judge the degree of danger of the falling object, and is suitable for infrastructure such as bridges and tunnels that need to assess the impact force of the falling object, and provides data support for maintaining structural safety.

[0095] To facilitate the handling of the fall event, in an optional implementation, after verifying the first target signal based on the third target signal, the method further includes:

[0096] Step S501, when the target frequency is greater than or equal to the third threshold and less than or equal to the fourth threshold, determining the type of the target falling object is a heavy object; when the target frequency is greater than the fourth threshold and less than or equal to the fifth threshold, determining the type of the target falling object is a light object;

[0097] In a specific implementation, the processor sets a third threshold F3 and a fourth threshold F4 to distinguish the frequency characteristics of heavy objects and light objects. The third threshold F3 and the fourth threshold F4 are set according to a preset object type acoustic model, usually F3 = 20 Hz, F4 = 10 kHz, to adapt to the falling frequency range of different objects. The processor performs frequency analysis based on the second target signal collected by the sound sensor and extracts the target frequency f. By comparing f with F3 and F4, the type of the falling object is determined. If the target frequency f is greater than or equal to F3 and less than or equal to F4, the processor identifies the type of the target falling object as a heavy object; if f is greater than F4 and less than or equal to the fifth threshold F5 (such as F5 = 20 kHz), the target object is classified as a light object. This process ensures that the system can intelligently identify the type of object based on the sound characteristics of the falling object, providing key information for event processing.

[0098] Step S502, determining the falling height of the target falling object based on the target position information, determining the falling time of the target falling object based on the falling height and the falling speed, and determining the falling position of the target falling object based on the target position information, the falling speed and the falling time;

[0099] Specifically, the processor calculates the target position information (x s ,y s , z s ) Determine the falling height h of the target falling object. The falling height h can be calculated by the vertical coordinate difference in the target position information, that is, h = Hz s , where H is the height of the tower or monitoring area. Using the free fall motion formula and the known fall height h, the processor can infer the fall time t. By calculating the fall time, the system can more accurately determine the entire process of the object falling. The processor combines the fall velocity v(t), fall time t and target position information to perform three-dimensional spatial positioning and determine the precise fall position of the target falling object. This process ensures that the system can provide detailed spatial information of the falling object event, providing support for subsequent event analysis and processing.

[0100] Step S503: Generate alarm information based on the type, falling time and falling location of the falling object.

[0101] In a specific implementation, the processor constructs alarm information content based on information such as the type of falling object, falling time and falling location, including key parameters such as falling time, object type, falling height, and precise falling location.

[0102] In the above embodiment, the system automatically records detailed information of each falling object incident, including alarm information, type of falling object, time and location of falling, etc., and generates an event report. These data are not only used for immediate response, but also for subsequent event analysis, helping managers identify falling object risk trends and formulate effective risk prevention strategies. This type identification and detailed information recording function enables the system to provide comprehensive information after detecting falling objects. It is suitable for scenarios such as high-rise building management and urban safety monitoring that require detailed accident reports, and provides strong evidence for accident investigation and responsibility determination.

[0103] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0104] The embodiment of the present application also provides a high-altitude falling object detection device with multi-source data fusion. It should be noted that the high-altitude falling object detection device with multi-source data fusion in the embodiment of the present application can be used to execute the high-altitude falling object detection method for multi-source data fusion provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0105] The following is an introduction to the high-altitude falling object detection device with multi-source data fusion provided in an embodiment of the present application.

[0106] Figure 3 : is a structural block diagram of a high-altitude falling object detection device based on multi-source data fusion according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0107] A first control unit 10 is used to control the acceleration sensor to monitor the acceleration change of the target falling object, obtain a first target signal, and determine whether there is a falling object event based on the first target signal;

[0108] In the specific implementation, first, when the system starts, the processor activates the acceleration sensor to put it in a real-time monitoring state; then, the acceleration sensor collects data, continuously measures the acceleration changes in the target area, and obtains real-time acceleration data a(t). At the same time, according to the real-time acceleration data (the first target signal), by comparing it with the preset threshold a threshaComparison is performed to determine whether there is a falling object event. That is, when the real-time acceleration data is greater than the preset threshold, it is determined that there is a falling object event and transmitted to the processor and the sound sensor.

[0109] The second control unit 20 is used to control each of the sound sensors to monitor the sound signal of the target falling object to obtain a plurality of second target signals when the acceleration sensor determines that the falling object event exists;

[0110] In the specific implementation, first, when the acceleration sensor preliminarily determines that there is a falling object based on the acceleration data, the sound sensor array is controlled to start and capture the sound signal of the falling object; then, each sensor S1, S2, ..., S n Start recording the sound signal generated by the falling object, and obtain multiple time-domain sound signal data Lp(t) (i.e., the second target signal mentioned above); perform sound source location and object type identification based on the time-domain sound signal, and perform time-frequency analysis based on the second target signal to extract the frequency and intensity characteristics of the sound. Analyze the time difference Δt between different sensors receiving the sound signal through the time difference localization algorithm (TDOA) ij , calculate the sound source distance difference Δd ij , and determine the three-dimensional coordinates of the sound source (x s ,y s , z s ), to accurately locate the falling object. At the same time, the type of falling object is identified according to the frequency and intensity characteristics of the sound, and the result is transmitted to the above processor.

[0111] The third control unit 30 is used to control the processor to receive the first target signal and the second target signal, perform data fusion based on the first target signal and the second target signal to obtain a third target signal, and verify the first target signal based on the third target signal.

[0112] Specifically, the processor removes interference through signal preprocessing (such as filtering) based on the first target signal a(t) and multiple second target signals Lp(t) of the sound sensor to ensure data purity. The data fusion algorithm is run based on the preprocessed data to perform weighted fusion on the first target signal a(t) and the second target signal Lp(t) to form a third target signal F(t), which integrates the motion state and sound characteristics of the falling object. The falling object event is confirmed and verified based on the third target signal F(t) and the comprehensive threshold F thresh Compare and verify the existence of falling object event again. thresh The setting of needs to comprehensively consider the background noise level and event characteristics of acceleration and sound signals. threshWhen the system finally confirms the falling object event, it sends out an alarm message through the alarm module.

[0113] In the above embodiment, to ensure safety, in an optional implementation, after the falling object event is confirmed, the system issues an alarm through the alarm module, and also sends detailed event information (including time, location, and type of falling object) to the remote terminal through the wireless communication module to ensure that the staff can respond quickly and take appropriate measures.

[0114] Through this embodiment, the first control unit controls the acceleration sensor to monitor the acceleration change of the target falling object, obtains the first target signal, and determines whether there is a falling object event based on the first target signal; the second control unit controls each of the sound sensors to monitor the sound signal of the target falling object when the acceleration sensor determines that there is a falling object event, and obtains multiple second target signals; the third control unit controls the processor to receive the first target signal and the second target signal, and performs data fusion based on the first target signal and the second target signal to obtain the third target signal, and verifies the first target signal based on the third target signal. The present application detects falling events by controlling the acceleration sensor, and locates the falling object by controlling the sound sensor, and on this basis verifies the authenticity of the falling event by controlling the processor to perform data fusion on the monitoring signals of the acceleration sensor and the sound sensor, thereby ensuring the accuracy of falling event detection and solving the problem of low accuracy of object falling detection by a single type of sensor in the prior art.

[0115] In order to perform a preliminary diagnosis of a fall event, in an optional implementation, the first control unit includes:

[0116] A first calculation module, used for calculating the product of the gravitational acceleration and a preset ratio to obtain a first threshold;

[0117] In a specific implementation, the first threshold calculation includes: after the processor receives the acceleration data, it calculates the gravity acceleration g (about 9.8m / s 2 ) multiplied by the preset ratio α, the first threshold a is obtained thresh =g×α. The setting of α depends on the on-site environment and the type of object, and is generally between 0.8 and 1.2 to meet the detection requirements of different falling object events.

[0118] The first determination module is used to determine the existence of the falling object event when the acceleration of the target falling object is greater than or equal to the first threshold.

[0119] In a specific implementation, the processor compares the acceleration data a(t) collected in real time with the first threshold a threshBased on the judgment results, the falling object event is diagnosed. If a(t)≥a thresh , that is, the acceleration change of the target object exceeds the preset falling object event judgment standard, and the processor immediately determines that there is a falling object event.

[0120] In the above embodiment, in order to ensure the accuracy of diagnosis, in an optional implementation, the present application sets the above acceleration sensor for environmental adaptation, including: threshold dynamic adjustment: the system has a threshold dynamic adjustment function, which can adjust the first threshold a according to the environmental changes (such as wind speed, temperature, etc.) of the monitoring area. thresh , ensuring that the system can accurately detect high-altitude falling object events under different conditions; Adaptive event detection: By real-time calculation and adjustment of the first threshold, the system can adaptively monitor various possible falling object events, whether it is the slow fall of a light object or the rapid fall of a heavy object, it can be discovered in time.

[0121] In order to perform multi-source data fusion, in an optional implementation, the third control unit includes:

[0122] A second determination module, configured to determine the falling acceleration of the target falling object based on the first target signal;

[0123] Specifically, fall acceleration calculation: When the system preliminarily determines that there may be a falling object from a high altitude, the processor calculates the real-time fall acceleration of the target falling object based on the first target signal a(t) collected by the acceleration sensor. Considering that the acceleration of an object in free fall should be close to the gravity acceleration g, the processor calculates the instantaneous fall acceleration of the object by monitoring the changes of a(t) in real time.

[0124] A third determination module is used to determine a target sound pressure level of the sound generated by the target falling object based on the second target signal;

[0125] Specifically, the target sound pressure level is obtained: At the same time, when the sound sensor detects a falling object event, it monitors and records the second target signal, that is, the sound signal generated when the object falls. The processor performs time-frequency analysis on the sound signal, extracts the intensity characteristics of the sound, and calculates the target sound pressure level Lp of the sound generated by the target falling object.

[0126] A first acquisition module is used to acquire a first preset weight and a second preset weight, where the first preset weight and the second preset weight are weight parameters of the falling acceleration and the target sound pressure level, respectively;

[0127] Specifically, the weight parameter is obtained: the processor obtains the first preset weight w according to the preset weight parameter a and the second preset weight w sThe two weight parameters reflect the importance of fall acceleration and target sound pressure level in event judgment and are determined through system training or on-site adjustment to balance the contribution of the two sensor data.

[0128] a second calculation module, configured to perform weighted calculation based on the falling acceleration, the target sound pressure level, the first preset weight, and the second preset weight to obtain the third target signal;

[0129] Specifically, the processor performs weighted calculation based on the falling acceleration, the target sound pressure level, and the first preset weight wa and the second preset weight ws. The specific formula is as follows: F(t)=w a ×a(t)+w s ×Lp(t), where F(t) is the third target signal, which is a real-time fusion signal of the comprehensive falling acceleration and sound intensity.

[0130] The fourth determination module is used to determine the existence of the falling object event when the third target signal is greater than or equal to the second threshold.

[0131] Specifically, first, the processor sets a second threshold F thresh , as a comprehensive criterion for determining falling object events. thresh The setting is based on the false alarm and missed alarm rate of the system in different environments, which is optimized through a large number of training data sets to ensure the accuracy and reliability of the system in detecting falling objects. The processor compares the third target signal F(t) calculated in real time with the second threshold F thresh When F(t)≥F thresh , the system finally determined that a falling object event had occurred.

[0132] In the above embodiment, the accuracy of the processor verification is guaranteed. In an optional implementation, the present application sets the above processor to perform environmental adaptation, including: Environmental adaptability adjustment: The system has a dynamic adjustment mechanism for weight parameters, which can adjust the first preset weight w in real time according to factors such as environmental noise level and falling object risk area. a and the second preset weight w s , to meet the needs of falling object detection in different scenarios. System optimization and false alarm control: By continuously optimizing the weighted calculation model, the system can effectively reduce false alarms and missed alarms. For example, in a noisy environment, w can be appropriately reduced. s , increase w a The proportion of the sound signal to ensure that the interference will not cause false alarms; in areas with a higher risk of falling objects, the w s , in order to capture sound signals more sensitively and improve detection accuracy.

[0133] In order to locate the sound source and classify the falling objects, in an optional embodiment, the above device further includes:

[0134] A first determining unit is used to control each of the sound sensors to monitor the sound signal of the target falling object to obtain a plurality of second target signals, and then determine the time when the sound sensor corresponding to each of the second target signals receives the second target signal to obtain a plurality of target times;

[0135] In the specific implementation, at the moment when the acceleration abnormality is detected, the system immediately controls each sound sensor to start monitoring the sound signal generated by the target falling object. The processor records the precise time t when each sound sensor receives the second target signal i (i=1,2,…,n), where n is the number of sound sensors.

[0136] A second determining unit is used to determine the time difference between each of the sound sensors receiving the second target signal based on the target time to obtain a target duration;

[0137] In the specific implementation, based on the recorded target time t i , calculate the time difference Δt between any two sound sensors receiving the signal ij =t i -t j , where i and j represent two different groups of sensors. The time difference reflects the time difference of sound waves reaching different sensors and is the core parameter for sound source localization.

[0138] A third determining unit is used to determine the distance difference between each of the sound sensors and the target falling object based on each of the target durations to obtain a first target distance;

[0139] In a specific implementation, the processor obtains the speed of sound waves propagating in the air vsound (about 343m / s), and uses the time difference Δt ij Calculate the distance difference Δd between each sound sensor and the sound source ij =vsound×Δt ij By calculating the distance difference, the relative position of the falling object and each sensor can be further determined.

[0140] a fourth determination unit, configured to obtain a position deviation between the sound sensors to obtain a second target distance, and locate the target falling object based on the first target distance and the second target distance to obtain target position information;

[0141] Specifically, the processor converts the distance difference Δd ijCombined with the position deviation between sensors (known distance between sensors), the precise position of the target falling object is determined by nonlinear solution methods (such as least squares method or iterative optimization algorithm). This process uses mathematical models and algorithms to determine the three-dimensional coordinate position (x s ,y s , z s ), realizing the spatial positioning of falling objects.

[0142] The fifth determination unit is used to determine the frequency of the sound generated by the target falling object according to the second target signal to obtain a target frequency.

[0143] Specifically, the sound sensor not only monitors the arrival time of the sound signal, but also records the frequency characteristics of the sound signal. Based on the second target signal Lp(t), the processor converts the time domain signal into a frequency domain signal through Fourier transform to obtain the spectrum characteristics of the sound.

[0144] In the above embodiments, in order to ensure the accuracy of data analysis, in an optional implementation, the present application sets the parameters of the time difference positioning algorithm and the frequency analysis model of the sound signal to continuously adjust to adapt to different monitoring environments and noise levels, thereby improving the detection accuracy and robustness of the system. By setting a reasonable sound source positioning threshold and frequency analysis range, the system can significantly reduce false alarms caused by environmental noise or other interference, ensuring that each alarm is based on accurate falling object event data. Through sound source positioning technology, the system can accurately determine the location of falling objects, which is suitable for high-rise residential areas, commercial complexes and other places that require precise location information, and provides an important basis for timely cleaning of falling objects and avoiding secondary injuries.

[0145] In order to evaluate the impact of falling objects from high altitude, in an optional embodiment, the above device further includes:

[0146] The first calculation unit is used to control the acceleration sensor to monitor the acceleration change of the target falling object, obtain a first target signal, and then perform integration based on the first target signal to determine the falling speed of the target falling object.

[0147] In a specific implementation, when the system preliminarily determines that there may be a falling object event, the processor starts to perform an integration operation on the first target signal a(t). The integral calculation formula is: v(t) = ∫a(t)dt + v0, where v(t) is the instantaneous velocity of the object at time t, and v0 is the initial velocity (in the case of free fall, the initial velocity is usually 0).

[0148] Based on the above embodiment, the processor can also compare the calculated instantaneous speed v(t) with the preset speed threshold v thresh Compare. If v(t)≥v thresh, further confirm the falling object event and incorporate the speed information into the event analysis to provide support for the subsequent sound source location and object type identification. Specifically, to ensure the accuracy and reliability of event judgment, the processor combines v(t) with the falling object position and sound signal intensity characteristics obtained based on the time difference positioning algorithm to conduct multi-dimensional verification of the falling object event.

[0149] In the above embodiments, in order to ensure the accuracy of data analysis, in an optional implementation, the present application is configured to adjust the detection and response strategy of the system by calculating the falling velocity v(t) in real time. For example, when the falling velocity is abnormally high, the system can automatically increase the alarm level to ensure that the emergency is handled in a timely manner. The falling velocity information v(t) is not only used for real-time monitoring and event verification, but also for generating event reports. The report contains the velocity data of the falling object, which helps managers assess the impact force and potential hazards of the falling object, and provides a quantitative basis for safety management and risk prevention. The calculation of the falling velocity helps to further judge the degree of danger of the falling object, and is suitable for infrastructure such as bridges and tunnels that need to assess the impact force of the falling object, and provides data support for maintaining structural safety.

[0150] In order to facilitate the handling of a fall event, in an optional embodiment, the above device further includes:

[0151] a sixth determination unit, configured to, after verifying the first target signal based on the third target signal, determine that the type of the target falling object is a heavy object when the target frequency is greater than or equal to the third threshold and less than or equal to the fourth threshold, and determine that the type of the target falling object is a light object when the target frequency is greater than the fourth threshold and less than or equal to the fifth threshold;

[0152] In a specific implementation, the processor sets a third threshold F3 and a fourth threshold F4 to distinguish the frequency characteristics of heavy objects and light objects. The third threshold F3 and the fourth threshold F4 are set according to a preset object type acoustic model, usually F3 = 20 Hz, F4 = 10 kHz, to adapt to the falling frequency range of different objects. The processor performs frequency analysis based on the second target signal collected by the sound sensor and extracts the target frequency f. By comparing f with F3 and F4, the type of the falling object is determined. If the target frequency f is greater than or equal to F3 and less than or equal to F4, the processor identifies the type of the target falling object as a heavy object; if f is greater than F4 and less than or equal to the fifth threshold F5 (such as F5 = 20 kHz), the target object is classified as a light object. This process ensures that the system can intelligently identify the type of object based on the sound characteristics of the falling object, providing key information for event processing.

[0153] a second calculation unit, configured to determine a falling height of the target falling object based on the target position information, determine a falling time of the target falling object based on the falling height and the falling speed, and determine a falling position of the target falling object based on the target position information, the falling speed and the falling time;

[0154] Specifically, the processor calculates the target position information (x s ,y s , z s ) Determine the falling height h of the target falling object. The falling height h can be calculated by the vertical coordinate difference in the target position information, that is, h = Hz s , where H is the height of the tower or monitoring area. Using the free fall motion formula and the known fall height h, the processor can infer the fall time t. By calculating the fall time, the system can more accurately determine the entire process of the object falling. The processor combines the fall velocity v(t), fall time t and target position information to perform three-dimensional spatial positioning and determine the precise fall position of the target falling object. This process ensures that the system can provide detailed spatial information of the falling object event, providing support for subsequent event analysis and processing.

[0155] A generating unit is used to generate alarm information based on the type of the falling object, the falling time and the falling location.

[0156] In a specific implementation, the processor constructs alarm information content based on information such as the type of falling object, falling time and falling location, including key parameters such as falling time, object type, falling height, and precise falling location.

[0157] In the above embodiment, the system automatically records detailed information of each falling object incident, including alarm information, type of falling object, time and location of falling, etc., and generates an event report. These data are not only used for immediate response, but also for subsequent event analysis, helping managers identify falling object risk trends and formulate effective risk prevention strategies. This type identification and detailed information recording function enables the system to provide comprehensive information after detecting falling objects. It is suitable for scenarios such as high-rise building management and urban safety monitoring that require detailed accident reports, and provides strong evidence for accident investigation and responsibility determination.

[0158] The multi-source data fusion high-altitude falling object detection device includes a processor and a memory. The first control unit, the second control unit, and the third control unit are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0159] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the accuracy of high-altitude falling object diagnosis can be improved by adjusting kernel parameters.

[0160] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0161] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is running, the device where the computer-readable storage medium is located is controlled to execute the high-altitude falling object detection method using multi-source data fusion.

[0162] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes at least the steps of the high-altitude falling object detection method with the above-mentioned multi-source data fusion.

[0163] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0164] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0165] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0166] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0168] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0169] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0170] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0171] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0172] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0173] 1) The high-altitude falling object detection system of the present application with multi-source data fusion combines the data of the acceleration sensor and the sound sensor to improve the accuracy and reliability of high-altitude falling object detection. The acceleration sensor can quickly respond to falling object events, while the sound sensor can locate the sound source. The combination of the two can not only detect high-altitude falling objects in time, but also accurately determine the type and location of the falling objects, effectively avoiding the misjudgment that may be caused by a single sensor. In addition, verification through the data fusion algorithm further improves the accuracy of detection, providing strong technical support for the safe management of high-altitude falling objects. In practical applications, the system can significantly improve public safety and reduce potential risks and losses caused by falling objects. The above embodiments solve the problem of low accuracy of object fall detection using a single type of sensor in the prior art.

[0174] 2) The high-altitude falling object detection method of the multi-source data fusion of the present application, first, controls the above-mentioned acceleration sensor to monitor the acceleration change of the target falling object, obtains the first target signal, and determines whether there is a falling object event based on the above-mentioned first target signal; then, when the above-mentioned acceleration sensor determines that there is the above-mentioned falling object event, controls each of the above-mentioned sound sensors to monitor the sound signal of the above-mentioned target falling object, and obtains multiple second target signals; finally, controls the above-mentioned processor to receive the above-mentioned first target signal and the above-mentioned second target signal, and performs data fusion based on the above-mentioned first target signal and the above-mentioned second target signal to obtain a third target signal, and verifies the above-mentioned first target signal based on the third target signal. The present application detects falling events by controlling the acceleration sensor, and locates the falling object by controlling the sound sensor, and on this basis verifies the authenticity of the falling event by controlling the processor to perform data fusion on the monitoring signals of the acceleration sensor and the sound sensor, thereby ensuring the accuracy of falling event detection and solving the problem of low accuracy of object falling detection by a single type of sensor in the prior art.

[0175] 2) The high-altitude falling object detection device with multi-source data fusion of the present application, the first control unit controls the above-mentioned acceleration sensor to monitor the acceleration change of the target falling object, obtains the first target signal, and determines whether there is a falling object event based on the above-mentioned first target signal; the second control unit controls each of the above-mentioned sound sensors to monitor the sound signal of the above-mentioned target falling object when the above-mentioned acceleration sensor determines that there is the above-mentioned falling object event, and obtains multiple second target signals; the third control unit controls the above-mentioned processor to receive the above-mentioned first target signal and the above-mentioned second target signal, and performs data fusion based on the above-mentioned first target signal and the above-mentioned second target signal to obtain the third target signal, and verifies the above-mentioned first target signal based on the third target signal. The present application detects falling events by controlling the acceleration sensor, and locates the falling object by controlling the sound sensor, and on this basis verifies the authenticity of the falling event by controlling the processor to perform data fusion on the monitoring signals of the acceleration sensor and the sound sensor, thereby ensuring the accuracy of falling event detection and solving the problem of low accuracy of object falling detection by a single type of sensor in the prior art.

[0176] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A high-altitude falling object detection system with multi-source data fusion, characterized in that: include: Acceleration sensor, used to monitor the acceleration change of the target falling object and preliminarily determine whether there is a falling object event; A sound sensor, which is in communication with the acceleration sensor and is used to monitor the sound signal generated by the target falling object when the falling object event is detected, so as to locate the sound source and the type of the target falling object; The processor is communicatively connected with the acceleration sensor and the sound sensor, and is used for receiving sensor data and re-verifying the falling object event through a data fusion algorithm to avoid misjudgment.

2. The system according to claim 1, characterized in that The high-altitude falling object detection system also includes: an alarm module, which is in communication with the processor and, when the processor determines that the falling object event has occurred, issues an alarm message to prompt a staff member to handle the event, wherein the alarm message includes at least the falling time, the type of the target falling object, and the falling location; A power supply module is electrically connected to the acceleration sensor, the sound sensor, the processor and the alarm module, and is used to supply power to the acceleration sensor, the sound sensor, the processor and the alarm module.

3. A high-altitude falling object detection method based on multi-source data fusion, characterized in that: The high-altitude falling object detection method is used in the high-altitude falling object detection system according to claim 1 or 2, and the method comprises: Controlling the acceleration sensor to monitor the acceleration change of the target falling object, obtaining a first target signal, and determining whether there is a falling object event based on the first target signal; When the acceleration sensor determines that the falling object event exists, controlling each of the sound sensors to monitor the sound signal of the target falling object to obtain a plurality of second target signals; The processor is controlled to receive the first target signal and the second target signal, and to perform data fusion based on the first target signal and the second target signal to obtain a third target signal, and the first target signal is verified based on the third target signal.

4. The method according to claim 3, characterized in that Determining whether there is a falling object event based on the first target signal includes: Calculate the product of the gravitational acceleration and the preset ratio to obtain a first threshold; When the acceleration of the target falling object is greater than or equal to the first threshold, it is determined that the falling object event exists.

5. The method according to claim 3, characterized in that: Performing data fusion based on the first target signal and the second target signal to obtain a third target signal includes: determining a falling acceleration of the target falling object based on the first target signal; determining a target sound pressure level of a sound generated by the target falling object based on the second target signal; Obtaining a first preset weight and a second preset weight, wherein the first preset weight and the second preset weight are weight parameters of the falling acceleration and the target sound pressure level, respectively; Performing weighted calculation based on the falling acceleration, the target sound pressure level, the first preset weight, and the second preset weight to obtain the third target signal; When the third target signal is greater than or equal to the second threshold, it is determined that the falling object event exists.

6. The method according to claim 3, characterized in that: After controlling each of the sound sensors to monitor the sound signal of the target falling object to obtain a plurality of second target signals, the method further includes: Determine the time when the sound sensor corresponding to each of the second target signals receives the second target signal to obtain multiple target times; Determine the time difference between each of the sound sensors receiving the second target signal based on the target time, and obtain a target duration; Determine the distance difference between each of the sound sensors and the target falling object based on each of the target durations to obtain a first target distance; Acquire the position deviation between each of the sound sensors to obtain a second target distance, and locate the target falling object based on the first target distance and the second target distance to obtain target position information; The frequency of the sound generated by the target falling object is determined according to the second target signal to obtain a target frequency.

7. The method according to claim 6, characterized in that After controlling the acceleration sensor to monitor the acceleration change of the target falling object and obtaining the first target signal, the method further includes: Integration is performed based on the first target signal to determine the falling speed of the target falling object.

8. The method according to claim 7, characterized in that After verifying the first target signal based on the third target signal, the method further includes: When the target frequency is greater than or equal to the third threshold and less than or equal to the fourth threshold, the type of the target falling object is determined to be a heavy object; when the target frequency is greater than the fourth threshold and less than or equal to the fifth threshold, the type of the target falling object is determined to be a light object; Determine the falling height of the target falling object based on the target position information, determine the falling time of the target falling object based on the falling height and the falling speed, and determine the falling position of the target falling object based on the target position information, the falling speed and the falling time; Alarm information is generated based on the type of the falling object, the falling time and the falling location.

9. A high-altitude falling object detection device with multi-source data fusion, characterized in that: The high-altitude falling object detection device is used in the high-altitude falling object detection system according to claim 1 or 2, and the device comprises: a first control unit, configured to control the acceleration sensor to monitor the acceleration change of the target falling object, obtain a first target signal, and determine whether there is a falling object event based on the first target signal; A second control unit is used to control each of the sound sensors to monitor the sound signal of the target falling object to obtain a plurality of second target signals when the acceleration sensor determines that the falling object event exists; The third control unit is used to control the processor to receive the first target signal and the second target signal, perform data fusion based on the first target signal and the second target signal to obtain a third target signal, and verify the first target signal based on the third target signal.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 3 to 7.

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