Excavating equipment operation intelligent safety early warning method and system based on environment perception

Abstract

The invention discloses an intelligent safety early warning method and system for excavation equipment operation based on environmental perception, and relates to the technical field of intelligent early warning, and the method comprises the steps: carrying out the initial detection of an operation environment based on an environment monitoring module, and carrying out the adjustment of equipment parameters according to an initial environment state, a matching target early warning mode and a reference target early warning mode, monitoring the working environment according to the adjusted environment monitoring module, obtaining real-time environment data, and performing safety risk identification in combination with a static safety boundary and a dynamic safety boundary; and according to the risk identification result, generating a safety early warning instruction, and performing path dynamic optimization of the mining equipment. The technical problems that in the prior art, a traditional excavation equipment operation early warning method is poor in risk sensing and early warning capacity and high in safety accident rate in a complex environment are solved, and the technical effects that the all-weather risk sensing and early warning capacity of excavation equipment in the complex environment is improved, and the safety accident rate is reduced are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent early warning technology, and in particular to an intelligent safety early warning method and system for excavation equipment operations based on environmental perception. Background Art

[0002] In the field of engineering machinery operations, especially in the application scenarios of excavation equipment, the operating environment is often complex and changeable, involving a variety of potential risk factors, such as undulating terrain, obstacle distribution, the presence of other moving vehicles or pedestrians, etc. These risk factors pose a severe challenge to the operating efficiency and safety of excavation equipment. The traditional operating method mainly relies on the operator's experience and intuition to judge and deal with potential risks, but this method has the disadvantages of strong subjectivity, slow response speed, and easy omission of risk points. Summary of the invention

[0003] The present application provides an intelligent safety warning method and system for excavation equipment operations based on environmental perception, which is used to solve the technical problems in the prior art that traditional excavation equipment operation warning methods have weak risk perception and warning capabilities in complex environments and a high incidence of safety accidents.

[0004] The first aspect of the present application provides an intelligent safety warning method for excavation equipment operations based on environmental perception, the method comprising: based on an environmental monitoring module, performing an initial inspection of the operating environment, obtaining an initial environmental state, and matching a target warning mode according to the initial environmental state; adjusting the equipment parameters of the environmental monitoring module with reference to the target warning mode, and monitoring the operating environment according to the adjusted environmental monitoring module to obtain real-time environmental data; based on the real-time environmental data, combining static safety boundaries and dynamic safety boundaries, performing safety risk identification, and obtaining risk identification results, the risk identification results including equipment collision risk and predicted intrusion risk; based on the risk identification results, generating safety warning instructions, and dynamically optimizing the path of the excavation equipment.

[0005] The second aspect of the present application provides an intelligent safety warning system for excavation equipment operations based on environmental perception, the system comprising: a target warning pattern matching module, the target warning pattern matching module is used to perform an initial inspection of the operating environment based on the environmental monitoring module, obtain the initial environmental state, and match the target warning pattern according to the initial environmental state; an operating environment monitoring module, the operating environment monitoring module is used to refer to the target warning pattern, adjust the equipment parameters of the environmental monitoring module, and monitor the operating environment according to the adjusted environmental monitoring module to obtain real-time environmental data; a safety risk identification module, the safety risk identification module is used to identify safety risks according to the real-time environmental data in combination with static safety boundaries and dynamic safety boundaries, and obtain risk identification results, the risk identification results including equipment collision risks and predicted intrusion risks; an equipment path optimization module, the equipment path optimization module is used to generate safety warning instructions according to the risk identification results, and dynamically optimize the path of the excavation equipment.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The intelligent safety warning method for excavation equipment operation based on environmental perception provided in the present application relates to the field of intelligent warning technology. An initial inspection of the operating environment is carried out through an environmental monitoring module, and a target warning mode is matched. Equipment parameters are adjusted with reference to the target warning mode, and the operating environment is monitored according to the adjusted environmental monitoring module. Safety risks are identified in combination with static safety boundaries and dynamic safety boundaries, and the path of the excavation equipment is dynamically optimized based on the risk identification results. The technical problems of weak risk perception and warning capabilities and high incidence of safety accidents in traditional excavation equipment operation warning methods in the prior art are solved, and the technical effect of improving the risk perception and warning capabilities of excavation equipment in complex environments and reducing the incidence of safety accidents is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of a process flow of an intelligent safety warning method for mining equipment operation based on environmental perception provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the structure of an intelligent safety warning system for excavation equipment operations based on environmental perception provided in an embodiment of the present application.

[0011] Explanation of the accompanying drawings: target warning pattern matching module 11, working environment monitoring module 12, safety risk identification module 13, equipment path optimization module 14. DETAILED DESCRIPTION

[0012] The present application provides an intelligent safety warning method and system for excavation equipment operations based on environmental perception, which is used to solve the technical problems in the prior art that traditional excavation equipment operation warning methods have weak risk perception and warning capabilities in complex environments and a high incidence of safety accidents.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely 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 are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification 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 sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides an intelligent safety warning method for mining equipment operation based on environmental perception, the method comprising:

[0016] P10: Based on the environmental monitoring module, perform an initial inspection of the working environment, obtain the initial environmental status, and match the target early warning mode according to the initial environmental status.

[0017] Furthermore, step P10 of the embodiment of the present application further includes:

[0018] P11: Based on the target working area, an environmental monitoring module is deployed, and the environmental monitoring module includes multiple monitoring devices, and the multiple monitoring devices include but are not limited to lidar, image acquisition device, thermal imaging sensor and ultrasonic sensor; P12: According to the environmental monitoring module, an initial inspection of the working environment is performed to obtain the initial environmental state, and the initial environmental state includes but is not limited to initial obstacle distribution, initial personnel distribution, and complexity of the working site; P13: Using the initial environmental state, traverse the early warning mode library for matching to obtain the target early warning mode, wherein the early warning mode library contains multiple early warning modes, and the target early warning mode contains multiple initial environmental indicator values.

[0019] It should be understood that the construction of an initial inspection process for the working environment based on the environmental monitoring module is intended to comprehensively and accurately obtain the initial environmental status of the working area and match the most suitable target early warning mode accordingly.

[0020] First, according to the specific characteristics and needs of the target operation area, an environmental monitoring module is deployed. This module integrates a variety of monitoring equipment, including but not limited to laser radar (for high-precision ranging and three-dimensional modeling), image acquisition devices (such as high-definition cameras to capture environmental visual information), thermal imaging sensors (to detect heat sources such as human bodies or high-temperature equipment at night or in bad weather), and ultrasonic sensors (to detect close-range obstacles and environmental changes). The coordinated work of these devices ensures the comprehensiveness and accuracy of environmental monitoring.

[0021] Subsequently, the deployed environmental monitoring module is used to conduct a preliminary inspection of the working environment. Through the scanning of the laser radar, a three-dimensional obstacle distribution map of the working area is quickly constructed; the image acquisition device captures the on-site image and combines image processing technology to identify the initial personnel distribution; at the same time, thermal imaging sensors and ultrasonic sensors provide additional environmental information, such as temperature distribution and close-range obstacles. Combining this information, a detailed initial environmental status report is generated, including but not limited to the initial obstacle distribution (such as fixed obstacles such as rocks and trees), the initial personnel distribution (staff location), and the complexity assessment of the working site (such as undulating terrain, narrow passages, etc.).

[0022] After obtaining the initial environmental state, the matching stage of the early warning mode library is entered. The early warning mode library is a pre-built database that contains a variety of early warning modes for different environmental conditions. Each early warning mode sets a series of initial environmental indicator values, such as light intensity threshold, temperature range, dust concentration upper limit, etc., as well as corresponding early warning strategies and countermeasures. The system traverses the early warning mode library, compares the indicator values ​​in the initial environmental state with the indicator values ​​of each mode in the library, and searches for the most matching early warning mode. Once a match is found, it is determined as the target early warning mode. This mode will guide subsequent safety risk assessments and early warning instruction generation to ensure that the mining equipment can operate safely and efficiently in a complex and changeable working environment.

[0023] P20: Referring to the target early warning mode, the equipment parameters of the environmental monitoring module are adjusted, and the operating environment is monitored according to the adjusted environmental monitoring module to obtain real-time environmental data.

[0024] Furthermore, step P20 of the embodiment of the present application further includes:

[0025] P21: Based on the target early warning mode, extract key environmental monitoring indicators and corresponding multiple initial environmental indicator values; P22: Establish a mapping relationship between the key environmental monitoring indicators and the environmental monitoring equipment, and adjust the equipment parameters according to the mapping relationship and the multiple initial environmental indicator values ​​to obtain an initialized environmental monitoring module; P23: Based on the initialized environmental monitoring module, perform operating environment monitoring to obtain real-time environmental data, which includes static scene data and dynamic target data.

[0026] Optionally, adjust the parameters of the environmental monitoring module based on the target warning mode and obtain real-time environmental data. First, extract the key environmental monitoring indicators that are most relevant to the current working environment based on the determined target warning mode. These indicators may include light intensity, temperature, humidity, dust concentration, obstacle distance, etc., which are directly related to the safety and efficiency of the operation. At the same time, obtain multiple initial environmental indicator values ​​corresponding to these key indicators from the target warning mode, which define the threshold range or specific conditions for safe operation.

[0027] Next, establish a mapping relationship between key environmental monitoring indicators and environmental monitoring equipment. This mapping relationship clarifies which indicators each monitoring device (such as lidar, image acquisition device, thermal imaging sensor, etc.) should be responsible for monitoring, and specifies how to adjust the parameters of the device according to these indicators. For example, if light intensity is one of the key indicators and the current ambient light is insufficient, the system may increase the exposure time of the image acquisition device or turn on the night vision mode. According to the mapping relationship and the initial environmental indicator value, the system makes fine parameter adjustments to the environmental monitoring module to ensure that each device can work in the optimal state, thereby obtaining more accurate and comprehensive environmental information. This adjustment process is called initialization, and the initialized environmental monitoring module is obtained after completion.

[0028] Furthermore, based on the initialized environmental monitoring module, continuous operating environment monitoring is carried out. During this process, various monitoring devices work together to capture and process environmental data in real time. These data include static scene data (such as topography, fixed obstacle locations, etc.) and dynamic target data (such as moving personnel, vehicles, changing weather conditions, etc.). Static scene data provides background information of the operating area for the mining equipment, which helps to plan a safe path; while dynamic target data reflects changes in the operating environment in real time, which is crucial for timely detection and response to potential risks. Through advanced data processing algorithms, the system converts the collected raw data into structured, easy-to-understand real-time environmental data, providing strong support for subsequent safety risk assessment and early warning instruction generation.

[0029] P30: Based on the real-time environmental data, combined with static security boundaries and dynamic security boundaries, security risk identification is performed to obtain risk identification results, where the risk identification results include equipment collision risk and predicted intrusion risk.

[0030] Furthermore, step P30 of the embodiment of the present application further includes:

[0031] P31: According to the operating range and operating path of the target mining equipment, static safety boundaries and dynamic safety boundaries are set and embedded in the risk identification network, and the risk identification network includes a collision analysis identification channel and an intrusion risk prediction channel; P32: Static scene data and dynamic target data are extracted based on the real-time environmental data, and the static scene data includes multiple fixed obstacle information, and the dynamic target data includes multiple motion obstacle information; P33: According to the collision analysis identification channel, combined with the multiple fixed obstacle information, collision risk identification is performed to obtain equipment collision risk; P34: According to the intrusion risk prediction channel, combined with the multiple motion obstacle information, intrusion risk prediction is performed to obtain predicted intrusion risk; P35: The equipment collision risk and the predicted intrusion risk are integrated to obtain the risk identification result.

[0032] In a possible embodiment of the present application, accurate security risk identification is performed based on real-time environmental data in combination with static security boundaries and dynamic security boundaries.

[0033] First, static safety boundaries and dynamic safety boundaries are set according to the operating range and operating path of the target mining equipment. The static safety boundary is a fixed safety range set around the operating area to prevent the equipment from colliding with fixed obstacles (such as buildings, trees, etc.). The dynamic safety boundary is dynamically adjusted according to real-time environmental changes during the operation process to deal with potential threats from mobile obstacles (such as personnel, vehicles, etc.). These safety boundaries are embedded in the risk identification network, which consists of two core channels: the collision analysis and identification channel and the intrusion risk prediction channel. These two channels are responsible for processing different types of risk information to ensure the comprehensiveness and accuracy of risk identification.

[0034] Next, based on the real-time environmental data, data processing technology is used to extract static scene data and dynamic target data. Static scene data mainly includes the location, shape, size and other information of multiple fixed obstacles, which constitute the static background of the working environment. Dynamic target data covers the real-time location, speed, direction and other dynamic information of multiple moving obstacles, which is crucial for predicting potential intrusion risks.

[0035] In the collision analysis and identification channel, the collision detection algorithm is used to simulate and analyze the working path of the excavation equipment in combination with the extracted information of multiple fixed obstacles. The magnitude of the collision risk is evaluated by calculating the minimum distance, relative speed and other parameters between the equipment and the fixed obstacles. Once a potential collision risk is detected, the system will immediately generate a corresponding risk warning signal and may trigger safety measures such as emergency braking or path adjustment.

[0036] In the intrusion risk prediction channel, the system uses prediction models to predict the future movement trajectory of obstacles based on the motion obstacle information in the dynamic target data. These prediction models can be built based on machine learning or deep learning algorithms, and can consider multiple factors (such as obstacle speed, acceleration, direction change, etc.) to improve the accuracy of predictions. By predicting the potential intersection of the future position of obstacles and mining equipment, intrusion risks can be identified in advance and corresponding preventive measures can be taken.

[0037] Finally, the collision risk identification results and intrusion risk prediction results are fused to form the final risk identification result. This result not only includes detailed information on the equipment collision risk (such as risk level, specific location, etc.), but also covers the evaluation of predicted intrusion risk (such as intrusion time, intrusion path, etc.). By comprehensively considering these two types of risks, the system can provide more comprehensive and accurate safety guidance for excavation equipment, ensuring the safety and efficiency of the operation process.

[0038] Furthermore, step P34 of the embodiment of the present application also includes:

[0039] P34-1: The intrusion risk prediction channel includes a motion trajectory prediction unit and an intrusion probability calculation unit, and the intrusion probability calculation unit is embedded with the dynamic safety boundary; P34-2: Through the motion trajectory prediction unit, combined with the multiple motion obstacle information, the target motion trajectory is predicted to obtain multiple predicted motion trajectories; P34-3: Through the intrusion probability calculation unit, based on the multiple predicted motion trajectories, the intrusion probability calculation is performed to obtain multiple target intrusion probabilities, and the predicted intrusion risks of multiple moving targets are evaluated.

[0040] Specifically, according to the intrusion risk prediction channel, combined with the multiple motion obstacle information, the specific process of intrusion risk prediction can be: first, the intrusion risk prediction channel is composed of two core units: a motion trajectory prediction unit and an intrusion probability calculation unit. The motion trajectory prediction unit is responsible for predicting the future motion trajectory of these obstacles based on the dynamic target data acquired in real time (such as the position, speed, acceleration, etc. of the moving obstacles) using a prediction algorithm (such as Kalman filtering, particle filtering, or a prediction model based on machine learning). The intrusion probability calculation unit has a built-in concept of a dynamic safety boundary, which is used to evaluate the potential intersection between the predicted trajectory and the operating area of ​​the excavation equipment, that is, the intrusion risk.

[0041] In the motion trajectory prediction unit, the system first analyzes each moving obstacle individually, and uses its historical motion data and current state information (such as position, speed, acceleration, etc.) to simulate the future motion trajectory through the prediction algorithm. This process may involve the generation of multiple prediction trajectories to cover the possible uncertainty range. The system will select appropriate algorithm parameters and iteration times based on the complexity and accuracy requirements of the prediction algorithm to ensure the accuracy and reliability of the prediction results.

[0042] After obtaining multiple predicted motion trajectories, these trajectories are input into the intrusion probability calculation unit. The unit first determines the protection area and potential intrusion area of ​​the mining equipment according to the definition of the dynamic safety boundary. Then, for each predicted trajectory, key parameters such as the minimum distance and intersection time between it and the protection area are calculated to evaluate the intrusion probability corresponding to the trajectory. This process involves complex geometric calculations and probabilistic statistical methods to ensure the accuracy and objectivity of the evaluation results.

[0043] Finally, the predicted intrusion risk of multiple moving targets is comprehensively evaluated based on the predicted intrusion probability of each moving obstacle. These risk information not only includes the numerical representation of the intrusion probability, but also may include detailed information such as intrusion time and intrusion path, providing comprehensive safety warning and decision support for excavation equipment. By timely identifying and evaluating potential intrusion risks, preventive measures can be taken in advance to ensure the safety and efficiency of the operation process.

[0044] Furthermore, step P34-2 of the embodiment of the present application also includes:

[0045] P34-21: Lock the first moving target, and extract the first motion obstacle information in combination with the multiple motion obstacle information; P34-22: Perform motion state analysis on the first motion obstacle information to obtain the first target motion speed, the first target motion direction, and the first target motion position; P34-23: Acquire the road network of the target operating area, and perform target trajectory prediction in combination with the first target motion speed, the first target motion direction, and the first target motion position to obtain the predicted motion trajectory.

[0046] Optionally, the specific process of predicting the motion trajectory of a moving target may be that, first, the system locks the first moving target as the current analysis object from a large amount of motion obstacle information. This locking process may be automatically completed based on the significant features of the target (such as size, speed, trajectory stability, etc.), or it may be manually specified by the operator. After locking, all motion obstacle information related to the target is extracted, that is, the first motion obstacle information. This information includes but is not limited to dynamic parameters such as the current position, speed, acceleration, etc. of the target.

[0047] Next, the first motion obstacle information is subjected to an in-depth motion state analysis. By calculating and analyzing the historical motion data of the target (such as position sequence, speed change, etc.), the motion speed (i.e., the first target motion speed), motion direction (i.e., the first target motion direction) and precise motion position (i.e., the first target motion position) of the first target at the current moment are obtained. These parameters provide key inputs for subsequent trajectory prediction.

[0048] After obtaining the motion state parameters of the first target, the trajectory is further predicted in combination with the road network information of the target operation area. Road network information may include static information such as road layout, intersection location, and traffic rules, which are essential for understanding the potential motion path of the target. These static information are combined with dynamic motion state parameters to simulate the future motion trajectory of the target through trajectory prediction algorithms (such as prediction based on physical models, prediction based on machine learning, etc.). This process takes into account multiple factors such as the target's motion inertia and road restrictions to generate a more accurate and reliable predicted motion trajectory.

[0049] In summary, by locking the first moving target, extracting information, analyzing the motion state, and predicting the trajectory based on the road network, the system can accurately predict the future trajectory of a single moving target. This prediction result is of great significance for assessing potential intrusion risks and formulating security warning strategies.

[0050] Furthermore, step P34-3 of the embodiment of the present application also includes:

[0051] P34-31: Through the intrusion probability calculation unit, combined with the multiple predicted motion trajectories, multiple target motion speeds, multiple target current target positions and the dynamic safety boundary, target movement prediction is performed to obtain multiple predicted arrival times; P34-32: The multiple predicted arrival times are traversed, compared and analyzed with the intrusion arrival time threshold to obtain multiple dynamic target intrusion probabilities; P34-33: Based on the multiple dynamic target intrusion probabilities, predicted intrusion risk assessments are performed on multiple moving targets respectively to obtain multiple predicted intrusion risks.

[0052] It should be understood that the predicted intrusion risk of multiple moving targets is evaluated by the intrusion probability calculation unit. First, the intrusion probability calculation unit receives multiple predicted motion trajectories from the trajectory prediction unit, and combines the current speed (i.e., multiple target motion speeds) and current position (i.e., multiple target current target positions) of each moving target, as well as the pre-set dynamic safety boundary, to perform target movement prediction. This prediction process takes into account the motion characteristics of the target, road conditions, and safety boundary restrictions to simulate various locations that the target may reach in the future. By calculating the time required for the target to move from its current position along the predicted trajectory to the safety boundary (or a more dangerous area), that is, the predicted arrival time, the system can quantify the speed at which the target approaches the potential danger area.

[0053] Next, all predicted arrival times are traversed and compared with the preset intrusion arrival time threshold. The intrusion arrival time threshold is a key parameter used to determine whether the target is likely to enter the danger zone within an unacceptable time. If a predicted arrival time is less than or equal to the intrusion arrival time threshold, the target is considered to have a high intrusion risk and the corresponding dynamic target intrusion probability is calculated. This probability value reflects the probability that the target may invade the operation area in the future.

[0054] Finally, based on the calculated intrusion probabilities of multiple dynamic targets, a predicted intrusion risk assessment is performed for each moving target. This assessment process not only takes into account the numerical value of the intrusion probability, but also comprehensively considers multiple factors such as the type, importance, and historical behavior records of the target. Through the assessment, the system can generate multiple predicted intrusion risk reports, each of which describes in detail the intrusion risk level, potential impact range, recommended preventive measures, and other information of the corresponding target. This information provides important decision-making support for mining equipment operators, helping them to take necessary measures in a timely manner to reduce or eliminate potential intrusion risks.

[0055] P40: Based on the risk identification results, generate safety warning instructions and dynamically optimize the path of the excavation equipment.

[0056] Specifically, once the risk identification system has completed a comprehensive assessment of the equipment collision risk and predicted intrusion risk, and generated the corresponding risk identification results, these results will become an important basis for generating safety warning instructions. The system will first analyze the various data in the risk identification results, including key information such as risk type (collision or intrusion), risk level, specific location, and impact range. Based on this information, the system can intelligently determine the potential threats in the current operating environment and generate safety warning instructions accordingly.

[0057] The generation of safety warning instructions can include multiple aspects. On the one hand, the system will determine the urgency of the warning instructions based on the risk level. For example, high risk may trigger emergency braking or immediate evacuation instructions, while low risk may only require operators to pay more attention or adjust the operation speed. On the other hand, the instructions will also include specific risk descriptions, location information, and response measures, so that operators can quickly and accurately understand the current situation and take appropriate actions.

[0058] While generating safety warning instructions, the system will also start a dynamic path optimization mechanism. The core of this mechanism is to dynamically adjust the operation path of the excavation equipment based on real-time risk information and the current status of the equipment to avoid potential dangerous areas or reduce the risk of collision and intrusion. The path optimization algorithm will comprehensively consider a variety of factors, including equipment performance, operating efficiency, safety boundary restrictions, and changes in real-time environmental data, and calculate the optimal or suboptimal alternative path.

[0059] During the path optimization process, the system may communicate with the control system of the excavation equipment in real time, transmit the optimized path information to the equipment, and guide it to make corresponding path adjustments. At the same time, the system will continue to monitor environmental changes and risk conditions so that the path can be optimized or adjusted again when necessary. Through this dynamic optimization method, the excavation equipment can minimize safety risks while ensuring operating efficiency, ensuring the safety and stability of the operation process.

[0060] In summary, the embodiments of the present application have at least the following technical effects:

[0061] This application collects various data in the working environment in real time, including terrain information, obstacle location, speed and direction of moving targets, etc., through a sensor network deployed on the excavation equipment and its surrounding environment, and transmits it to the central processing unit to identify potential collision risks and intrusion risks. Once the risk is identified, it immediately generates corresponding safety warning instructions and dynamically adjusts the operating path of the excavation equipment according to the risk status and the current status of the equipment to avoid dangerous areas or reduce the risk level.

[0062] The technical effect of improving the risk perception and early warning capabilities of excavation equipment in complex environments and reducing the occurrence rate of safety accidents has been achieved.

[0063] Embodiment 2 is based on the same inventive concept as the intelligent safety warning method for mining equipment operation based on environment perception in the above embodiment. Figure 2 As shown, the present application provides an intelligent safety warning system for mining equipment operation based on environmental perception. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0064] The target warning pattern matching module 11 is used to perform an initial inspection of the operating environment based on the environment monitoring module, obtain the initial environment state, and match the target warning pattern according to the initial environment state.

[0065] The operating environment monitoring module 12 is used to refer to the target early warning mode, adjust the equipment parameters of the environmental monitoring module, and monitor the operating environment according to the adjusted environmental monitoring module to obtain real-time environmental data.

[0066] The security risk identification module 13 is used to identify security risks based on the real-time environmental data in combination with static security boundaries and dynamic security boundaries, and obtain risk identification results, wherein the risk identification results include equipment collision risks and predicted intrusion risks.

[0067] The equipment path optimization module 14 is used to generate a safety warning instruction according to the risk identification result, and dynamically optimize the path of the mining equipment.

[0068] Furthermore, the target warning pattern matching module 11 is also used to perform the following steps:

[0069] Based on the target working area, an environmental monitoring module is deployed, and the environmental monitoring module includes multiple monitoring devices, and the multiple monitoring devices include but are not limited to laser radar, image acquisition device, thermal imaging sensor and ultrasonic sensor; according to the environmental monitoring module, an initial inspection of the working environment is carried out to obtain the initial environmental state, and the initial environmental state includes but is not limited to the initial obstacle distribution, the initial personnel distribution, and the complexity of the working site; using the initial environmental state, the early warning mode library is traversed for matching to obtain the target early warning mode, wherein the early warning mode library contains multiple early warning modes, and the target early warning mode contains multiple initial environmental indicator values.

[0070] Furthermore, the working environment monitoring module 12 is also used to perform the following steps:

[0071] Based on the target early warning mode, key environmental monitoring indicators and corresponding multiple initial environmental indicator values ​​are extracted; a mapping relationship between the key environmental monitoring indicators and environmental monitoring equipment is established, and equipment parameters are adjusted according to the mapping relationship and the multiple initial environmental indicator values ​​to obtain an initialized environmental monitoring module; based on the initialized environmental monitoring module, operating environment monitoring is performed to obtain real-time environmental data, wherein the real-time environmental data includes static scene data and dynamic target data.

[0072] Furthermore, the security risk identification module 13 is also used to perform the following steps:

[0073] According to the operating range and operating path of the target mining equipment, static safety boundaries and dynamic safety boundaries are set and embedded in the risk identification network, wherein the risk identification network includes a collision analysis and identification channel and an intrusion risk prediction channel; static scene data and dynamic target data are extracted based on the real-time environmental data, wherein the static scene data includes multiple fixed obstacle information, and the dynamic target data includes multiple motion obstacle information; according to the collision analysis and identification channel, collision risk identification is performed in combination with the multiple fixed obstacle information to obtain equipment collision risk; according to the intrusion risk prediction channel, intrusion risk prediction is performed in combination with the multiple motion obstacle information to obtain predicted intrusion risk; the equipment collision risk and the predicted intrusion risk are integrated to obtain the risk identification result.

[0074] Furthermore, the security risk identification module 13 is also used to perform the following steps:

[0075] The intrusion risk prediction channel includes a motion trajectory prediction unit and an intrusion probability calculation unit, wherein the dynamic safety boundary is embedded in the intrusion probability calculation unit; the motion trajectory prediction unit is used to predict the target motion trajectory in combination with the multiple motion obstacle information to obtain multiple predicted motion trajectories; the intrusion probability calculation unit is used to calculate the intrusion probability based on the multiple predicted motion trajectories to obtain multiple target intrusion probabilities, and the predicted intrusion risks of multiple moving targets are evaluated and obtained.

[0076] Furthermore, the security risk identification module 13 is also used to perform the following steps:

[0077] Lock the first moving target, and extract the first motion obstacle information in combination with the multiple motion obstacle information; perform motion state analysis on the first motion obstacle information to obtain the first target motion speed, the first target motion direction, and the first target motion position; obtain the road network of the target operating area, and perform target trajectory prediction in combination with the first target motion speed, the first target motion direction, and the first target motion position to obtain a predicted motion trajectory.

[0078] Furthermore, the security risk identification module 13 is also used to perform the following steps:

[0079] Through the intrusion probability calculation unit, target movement prediction is performed in combination with the multiple predicted motion trajectories, multiple target motion speeds, multiple target current target positions and the dynamic safety boundary to obtain multiple predicted arrival times; the multiple predicted arrival times are traversed and compared with the intrusion arrival time threshold to obtain multiple dynamic target intrusion probabilities; based on the multiple dynamic target intrusion probabilities, predicted intrusion risk assessments are performed on multiple moving targets respectively to obtain multiple predicted intrusion risks.

[0080] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0082] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent safety warning method for mining equipment operation based on environmental perception, characterized in that: The method comprises: Based on the environmental monitoring module, perform an initial inspection of the operating environment, obtain the initial environmental status, and match the target early warning mode according to the initial environmental status; Referring to the target early warning mode, adjusting the equipment parameters of the environmental monitoring module, and performing operating environment monitoring according to the adjusted environmental monitoring module to obtain real-time environmental data; According to the real-time environmental data, combined with the static security boundary and the dynamic security boundary, security risk identification is performed to obtain risk identification results, wherein the risk identification results include equipment collision risk and predicted intrusion risk; According to the risk identification results, a safety warning instruction is generated, and the path of the excavation equipment is dynamically optimized.

2. The intelligent safety early warning method for mining equipment operation based on environmental perception according to claim 1 is characterized in that: Based on the environmental monitoring module, an initial inspection of the operating environment is performed to obtain the initial environmental status, and the target early warning mode is matched according to the initial environmental status, including: Based on the target operation area, an environmental monitoring module is deployed, wherein the environmental monitoring module includes multiple monitoring devices, including but not limited to laser radar, image acquisition device, thermal imaging sensor and ultrasonic sensor; Performing an initial inspection of the working environment according to the environmental monitoring module to obtain an initial environmental state, wherein the initial environmental state includes but is not limited to initial obstacle distribution, initial personnel distribution, and complexity of the working site; Using the initial environmental state, the early warning mode library is traversed for matching to obtain a target early warning mode, wherein the early warning mode library contains multiple early warning modes, and the target early warning mode contains multiple initial environmental indicator values.

3. The method for intelligent safety early warning of excavation equipment operation based on environmental perception according to claim 2, characterized in that: Referring to the target early warning mode, adjusting the equipment parameters of the environment monitoring module includes: Based on the target early warning mode, extract key environmental monitoring indicators and corresponding multiple initial environmental indicator values; Establishing a mapping relationship between the key environmental monitoring indicators and the environmental monitoring equipment, and adjusting equipment parameters according to the mapping relationship and the multiple initial environmental indicator values ​​to obtain an initialized environmental monitoring module; Based on the initialized environment monitoring module, the operating environment is monitored to obtain real-time environment data, which includes static scene data and dynamic target data.

4. The method for intelligent safety early warning of excavation equipment operation based on environmental perception according to claim 3, characterized in that: Based on the real-time environment data, combined with static security boundaries and dynamic security boundaries, security risk identification is performed, including: According to the operation range and operation path of the target mining equipment, static safety boundaries and dynamic safety boundaries are set and embedded in the risk identification network, which includes a collision analysis identification channel and an intrusion risk prediction channel; Extracting static scene data and dynamic target data based on the real-time environment data, wherein the static scene data includes a plurality of fixed obstacle information, and the dynamic target data includes a plurality of motion obstacle information; According to the collision analysis and identification channel, collision risk identification is performed in combination with the plurality of fixed obstacle information to obtain equipment collision risk; According to the invasion risk prediction channel, combined with the plurality of movement disorder information, invasion risk prediction is performed to obtain a predicted invasion risk; The device collision risk and the predicted intrusion risk are integrated to obtain the risk identification result.

5. The method for intelligent safety early warning of excavation equipment operation based on environmental perception according to claim 4, characterized in that: According to the invasion risk prediction channel, combined with the plurality of movement disorder information, invasion risk prediction is performed to obtain predicted invasion risk, including: The intrusion risk prediction channel includes a motion trajectory prediction unit and an intrusion probability calculation unit, and the intrusion probability calculation unit is embedded with the dynamic security boundary; By combining the plurality of movement obstacle information with the movement trajectory prediction unit, target movement trajectory prediction is performed to obtain a plurality of predicted movement trajectories; The intrusion probability calculation unit performs intrusion probability calculation according to the multiple predicted motion trajectories, obtains multiple target intrusion probabilities, and evaluates and obtains the predicted intrusion risks of the multiple moving targets.

6. The method for intelligent safety early warning of excavation equipment operation based on environmental perception according to claim 5, characterized in that: The target motion trajectory is predicted by the motion trajectory prediction unit in combination with the plurality of motion obstacle information to obtain a plurality of predicted motion trajectories, including: Locking the first moving target, and extracting the first movement disorder information in combination with the plurality of movement disorder information; Performing motion state analysis on the first motion obstacle information to obtain a first target motion speed, a first target motion direction, and a first target motion position; The road network of the target operation area is obtained, and the target trajectory is predicted based on the first target movement speed, the first target movement direction, and the first target movement position to obtain a predicted movement trajectory.

7. The method for intelligent safety early warning of excavation equipment operation based on environmental perception according to claim 5, characterized in that: The intrusion probability calculation unit calculates the intrusion probability according to the multiple predicted motion trajectories, obtains the intrusion probabilities of multiple targets, and evaluates the predicted intrusion risks of the multiple moving targets, including: By combining the multiple predicted motion trajectories, multiple target motion speeds, multiple target current target positions and the dynamic safety boundary, the intrusion probability calculation unit performs target movement prediction to obtain multiple predicted arrival times; Traversing the multiple predicted arrival times, comparing and analyzing them with the intrusion arrival time threshold, and obtaining multiple dynamic target intrusion probabilities; Based on the multiple dynamic target intrusion probabilities, predicted intrusion risk assessments are performed on multiple moving targets respectively to obtain multiple predicted intrusion risks.

8. Intelligent safety warning system for mining equipment operation based on environmental perception, characterized in that: The system comprises: A target early warning pattern matching module, which is used to perform an initial inspection of the operating environment based on the environment monitoring module, obtain an initial environment state, and match a target early warning pattern according to the initial environment state; An operating environment monitoring module, the operating environment monitoring module is used to adjust the equipment parameters of the environmental monitoring module with reference to the target early warning mode, and to monitor the operating environment according to the adjusted environmental monitoring module to obtain real-time environmental data; A security risk identification module, the security risk identification module is used to identify security risks according to the real-time environmental data, in combination with static security boundaries and dynamic security boundaries, and obtain risk identification results, the risk identification results including equipment collision risk and predicted intrusion risk; The device path optimization module is used to generate a safety warning instruction according to the risk identification result and dynamically optimize the path of the mining equipment.

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