Multi-parameter fusion intelligent double-hook safety early warning method and system

Abstract

The invention provides a multi-parameter fusion intelligent double-hook safety early warning method and system, and relates to the technical field of safety protection, and the method comprises the steps: obtaining multi-source detection information of a target hook; the multi-source detection information comprises an infrared signal, three-axis attitude information and displacement track information; determining an infrared risk coefficient, a posture risk coefficient and a displacement risk coefficient based on the infrared signal, the three-axis posture information and the relationship between the displacement track information and corresponding threshold values, and querying a preset weight mapping list in combination with a current detection scene, obtaining weights corresponding to the infrared risk coefficient, the attitude risk coefficient and the displacement risk coefficient, and generating a corresponding comprehensive risk value through weighted fusion; the preset weight mapping list comprises a mapping relationship between each risk coefficient and a corresponding weight under different scene categories; and determining a current alarm level according to a threshold interval in which the comprehensive risk value is located.

Description

Technical Field

[0001] This invention relates to the field of safety protection technology, and in particular to a multi-parameter fusion intelligent double-hook safety early warning method and system. Background Technology

[0002] Traditional high-altitude operations in power grids generally employ mechanical double-hook protection devices, relying on the experience of operators to judge the opening and closing status of the hooks and the effectiveness of the connection. This approach carries risks such as the hooks closing before penetrating the tower material and the failure of the anti-detachment structure, and lacks real-time status monitoring methods. Existing technologies mostly use a single sensor to detect hook closure, which cannot identify invalid connections, resulting in a high false alarm rate. Monitoring data is stored locally, lacking remote early warning capabilities and making it difficult to intervene in risks in a timely manner.

[0003] Although some smart safety hooks incorporate IoT technology, their parameter collection dimensions are limited and they do not integrate parameters such as hook posture, displacement trajectory, and environmental interference. This makes it difficult to address the problem of false triggering caused by weak signals in mountainous areas and complex scenarios, and it is not possible to effectively detect safety hazards in a timely manner and ensure personnel safety. Summary of the Invention

[0004] In view of this, the present invention proposes a multi-parameter fusion intelligent double-hook safety early warning method and system.

[0005] The technical solution of this invention is implemented as follows: The first aspect of this invention provides a multi-parameter fusion-based intelligent double-hook safety early warning method, comprising: Acquire multi-source detection information of the target hook; the multi-source detection information includes infrared signals, three-axis attitude information, and displacement trajectory information; Based on the relationship between the infrared signal, the three-axis attitude information, the displacement trajectory information, and the corresponding thresholds, the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient are determined. Then, in conjunction with the current detection scenario, a preset weight mapping list is queried to obtain the weights corresponding to the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, and the corresponding comprehensive risk value is generated through weighted fusion. The preset weight mapping list includes the mapping relationship between each risk coefficient and its corresponding weight under different scenario categories. The current alarm level is determined based on the threshold range in which the comprehensive risk value falls.

[0006] Based on the above technical solutions, preferably, the acquisition of multi-source detection information for the target hook includes: The signal strength difference corresponding to the target hook is obtained using an infrared photodetector unit. The acceleration, angular velocity, and tilt angle of the target hook are obtained using a three-axis attitude sensor. The displacement trajectory corresponding to the target hook is obtained using a photoelectric displacement encoder.

[0007] Based on the above technical solutions, preferably, the step of determining the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient based on the relationship between the infrared signal, the three-axis attitude information, the displacement trajectory information, and the corresponding thresholds includes: The location information of the target hook is determined based on the relationship between the signal strength difference and the first threshold, and the infrared risk coefficient is obtained based on the location information. The position information of the target hook is determined based on the relationship between the acceleration and the angular velocity and the second and third thresholds, and the attitude risk coefficient is obtained based on the position information. The attitude stability of the target hook is determined based on the relationship between the displacement trajectory and the fourth threshold, and the displacement risk coefficient is obtained based on the attitude stability.

[0008] Based on the above technical solutions, preferably, the step of querying a preset weight mapping list in conjunction with the current detection scenario to obtain the weights corresponding to the infrared risk coefficient, the attitude risk coefficient, and the displacement risk coefficient, and generating a corresponding comprehensive risk value through weighted fusion, includes: In motion-based operation scenarios, the weight of the infrared risk coefficient is less than that of the normal infrared risk coefficient, while the weights of the posture risk coefficient and the displacement risk coefficient are greater than those of the normal posture and normal displacement risk coefficients. In a static operation scenario, the weights of the infrared risk coefficient and the attitude risk coefficient are greater than the weights of the normal infrared and normal attitude, while the weight of the displacement risk coefficient is less than the weight of the normal displacement.

[0009] Based on the above technical solutions, preferably, the step of querying a preset weight mapping list in conjunction with the current detection scenario to obtain the weights corresponding to the infrared risk coefficient, the attitude risk coefficient, and the displacement risk coefficient, and generating the corresponding comprehensive risk value through weighted fusion, further includes: If any one of the infrared risk coefficient, the attitude risk coefficient, and the displacement risk coefficient is greater than a preset risk threshold, the other two risk coefficients within a preset historical time period are obtained. If the other two risk coefficients within the preset historical period do not exceed the corresponding preset risk thresholds, the current situation is determined to be normal.

[0010] Based on the above technical solutions, preferably, the step of determining the current alarm level according to the threshold range of the comprehensive risk value includes: Based on historical alarm data, the prior probability of the target being linked to an anomaly under different historical comprehensive risk values ​​is determined. Using Bayes' theorem and the prior probability, determine the confidence level of the current comprehensive risk value; The current alarm level is determined based on the confidence level.

[0011] Based on the above technical solutions, preferably, the step of determining the current alarm level according to the threshold range of the comprehensive risk value includes: When the overall risk value is within the first threshold range, the current situation is determined to be normal; the maximum value of the first threshold range is the upper limit of the safety threshold. When the comprehensive risk value is within the second threshold range, a level one warning is triggered; the minimum value of the second threshold range is greater than the upper limit of the safety threshold. When the comprehensive risk value is within the third threshold range, a level two warning is triggered; the minimum value of the third threshold range is greater than the maximum value of the second threshold range.

[0012] Furthermore, a second aspect of the present invention provides a multi-parameter fusion intelligent double-hook safety early warning system, comprising: an information acquisition module, a weighted fusion module, and an alarm determination module; wherein, The information acquisition module is configured to acquire multi-source detection information of the target hook; the multi-source detection information includes infrared signals, three-axis attitude information, and displacement trajectory information. The weighted fusion module is configured to determine the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient based on the relationship between the infrared signal, the three-axis attitude information, the displacement trajectory information, and the corresponding thresholds. It then queries a preset weight mapping list in conjunction with the current detection scenario to obtain the weights corresponding to each of the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, and generates a corresponding comprehensive risk value through weighted fusion. The preset weight mapping list includes the mapping relationship between each risk coefficient and its corresponding weight under different scenario categories. The alarm determination module is configured to determine the current alarm level based on the threshold range in which the comprehensive risk value falls.

[0013] More preferably, a third aspect of the present invention provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the multi-parameter fusion intelligent double-hook safety early warning method described in the first aspect.

[0014] More preferably, in a fourth aspect of the present invention, a computer storage medium is provided on which a computer program is stored, wherein the computer program, when executed by a processor, implements the intelligent double-hook safety early warning method with multi-parameter fusion as described in the first aspect.

[0015] The intelligent double-hook safety early warning method and system based on multi-parameter fusion of the present invention have the following advantages over the prior art: 1. By comprehensively utilizing three different types of detection signals—infrared signals, three-axis attitude information, and displacement trajectory information—to complement each other, a more comprehensive understanding of the target hook's operational status can be obtained. This avoids the limitations and misjudgments that may exist with a single information source, thereby more accurately assessing the target hook's risk status. Combined with a scene-adaptive weighted fusion method, various risk factors can be more rationally considered, further improving the accuracy of risk assessment.

[0016] 2. By comprehensively considering multiple risk coefficients, when one risk coefficient becomes abnormal, the impact of a single point of failure on detection and judgment can be reduced by referring to the historical situation of other risk coefficients and based on the other two risk coefficients within the preset historical period. This improves the stability and reliability of detection, better adapts to complex and ever-changing environments, accurately judges the true state of the target hook, and enhances the reliability and stability of detection results in complex environments.

[0017] 3. Different alarm levels correspond to different degrees of risk. The alarm level determined by the confidence level can guide the rational allocation of human and material resources. For high-level alarms, due to the greater risk, more manpower is needed immediately for emergency handling and investigation to prevent the occurrence or escalation of accidents; while for low-level alarms, fewer personnel can be assigned to conduct regular observation and monitoring to avoid excessive waste of resources. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a multi-parameter fusion-based intelligent double-hook safety early warning method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a multi-parameter fusion intelligent double-hook safety early warning system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] In some embodiments, such as Figure 1 As shown, Figure 1 This is a flowchart illustrating a multi-parameter fusion-based intelligent double-hook safety early warning method provided by an embodiment of the present invention; the multi-parameter fusion-based intelligent double-hook safety early warning method provided by the present invention includes: S110, acquire multi-source detection information of the target hook; the multi-source detection information includes infrared signal, three-axis attitude information and displacement trajectory information.

[0022] Here, the controller (STM32H743VI) synchronously acquires data from three types of sensors via the I2C / SPI interface, with a uniform sampling frequency of 100Hz. A 1ms precision timestamp is added to each data frame to ensure that the infrared (1ms response), attitude (5ms update), and displacement (10ms sampling) data are aligned in the time dimension.

[0023] In some embodiments, S110, acquiring multi-source detection information for the target hook includes: The signal strength difference corresponding to the target hook is obtained using an infrared pair detection unit; The acceleration, angular velocity, and tilt angle of the target hook are obtained using a three-axis attitude sensor. The displacement trajectory corresponding to the target hook is obtained by using a photoelectric displacement encoder.

[0024] In this embodiment, a multi-sensor array built into the hook, including an infrared phototransistor detection unit, a three-axis attitude sensor, and a photoelectric displacement encoder, collaboratively collects hook-up status data. The infrared phototransistor detection unit accurately locates the hook's position and status by measuring the difference in infrared signal intensity at specific locations on the target hook. The three-axis attitude sensor can simultaneously measure the target hook's acceleration, angular velocity, and tilt angle in three axes, comprehensively monitoring the hook's motion state. The photoelectric displacement encoder has high-precision displacement measurement capabilities, accurately acquiring the target hook's displacement trajectory in real time. Analysis of the displacement trajectory reveals the target hook's motion patterns and characteristics. Here, the infrared phototransistor detection unit can be an MJT-GP2Y0E03 (Meijiate) infrared phototransistor module, the three-axis attitude sensor can be an SC7A20 model attitude sensor, which can acquire tilt angles (accuracy ±0.5°) and vibration frequencies (sampling rate 100Hz), and the photoelectric displacement encoder can record the displacement trajectory (resolution 0.1mm).

[0025] When the signal strength difference between the infrared transmitter and receiver is ≥30%, the hook is considered to have effectively penetrated the tower material. The specific principle is as follows: if the hook does not penetrate the tower material, such as when it is attached to the surface or not fully closed, the infrared light will be reflected or blocked by the metal surface of the tower material, resulting in a small difference between the signal strength at the receiver and transmitter. If the hook penetrates the tower material, the infrared light propagates through the gaps in the tower material, and the signal strength attenuation at the receiver meets a preset threshold. When the difference is ≥30%, it is considered a valid connection. The controller uses a database of standard transmission tower component spacing models, such as standard dimensions of angle steel and crossarms, to match and analyze the displacement trajectory collected by the photoelectric displacement encoder. The displacement encoder records the coordinates of the movement trajectory after the hook is attached. The edge computing unit compares the trajectory data with the standard component spacing in the database, such as adjacent tower material spacing of 0.8m or 1.2m. If a continuous unattached movement distance exceeds 0.5m in the trajectory without a corresponding standard spacing match, it is considered a "risk of unattached movement."

[0026] S120: Based on the relationship between infrared signals, three-axis attitude information, and displacement trajectory information and corresponding thresholds, determine the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient. Combined with the current detection scenario, query the preset weight mapping list to obtain the weights corresponding to the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, and generate the corresponding comprehensive risk value through weighted fusion. The preset weight mapping list includes the mapping relationship between each risk coefficient and its corresponding weight under different scenario categories.

[0027] By comparing infrared signals, three-axis attitude information, and displacement trajectory information with corresponding thresholds, the system determines the corresponding risk coefficients, achieving precise quantification of different types of risks. Since the weights of each risk coefficient can be adjusted according to actual conditions, when a certain risk factor becomes more prominent in the current scenario, the system can promptly increase the weight of its corresponding risk coefficient, thereby more effectively responding to risk changes.

[0028] In some embodiments, the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient are determined based on the relationship between infrared signals, three-axis attitude information, and displacement trajectory information and corresponding thresholds, including: The location information of the target hook is determined based on the relationship between the signal strength difference and the first threshold, and the infrared risk coefficient is obtained based on the location information. The attitude stability of the target hook is determined based on the relationship between acceleration and angular velocity and the second and third thresholds, and the attitude risk coefficient is obtained based on the attitude stability. The trajectory error of the target hook is determined based on the relationship between the displacement trajectory and the fourth threshold, and the displacement risk coefficient is obtained based on the trajectory error.

[0029] In this embodiment, risk coefficients are determined through threshold comparisons, thus quantifying risk. For example, the infrared risk coefficient can be set to 0 when the signal strength difference is greater than 30%, indicating no risk; the infrared risk coefficient can be set to 1 when the signal strength difference is within a certain range of the first threshold, indicating low risk; and the infrared risk coefficient can be set to 2 when the difference exceeds this range, indicating high risk. Similarly, similar quantification methods can be used for attitude risk coefficients and displacement risk coefficients. In addition, other quantification methods can be used in combination.

[0030] In one optional embodiment, before obtaining the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, normalization is performed in advance to ensure that the scale of each risk coefficient is consistent and to avoid the effect of some risk coefficients being ignored. For the infrared risk coefficient, the distance between the target hook and the device is detected by an infrared sensor. Combined with preset safe distance threshold and dynamic adjustment coefficient Calculate the infrared risk coefficient : ; For attitude risk factors, the angular velocities of the target hook along the X, Y, and Z axes can be obtained using a three-axis attitude sensor. , , ) and acceleration ( , , ), calculate attitude stability index Then mapped to attitude risk coefficient .

[0031] Attitude stability index : ; Attitude risk coefficient : ; here, and These are the low and high risk thresholds, respectively.

[0032] For the displacement risk coefficient, the displacement trajectory of the target hook can be obtained through a photoelectric displacement encoder, and the error of the trajectory deviating from the preset path can be calculated. and rate of change of velocity Generate a comprehensive risk value : ; in, For path error weights, For the rate of change, and These represent the maximum permissible error and the maximum rate of change of speed.

[0033] Finally, a corresponding comprehensive risk value is generated through weighted fusion. : ; in, , , These are the weights corresponding to the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, respectively. These weights can be adaptively adjusted according to the current scenario and can be obtained by querying the preset weight mapping list in conjunction with the current detection scenario.

[0034] In some embodiments, a preset weight mapping list is queried in conjunction with the current detection scenario to obtain the weights corresponding to the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, and a corresponding comprehensive risk value is generated through weighted fusion, including: In motion-based operation scenarios, the weight of infrared risk coefficient is less than that of normal infrared risk coefficient, while the weights of attitude risk coefficient and displacement risk coefficient are greater than those of normal attitude and normal displacement risk coefficient. In static operation scenarios, the weights of infrared risk coefficient and attitude risk coefficient are greater than those of normal infrared weight and normal attitude weight, while the weight of displacement risk coefficient is less than that of normal displacement weight.

[0035] In dynamic operation scenarios, the target hook is in a state of constant change, with more frequent shifts in displacement and attitude. Infrared signals are more susceptible to interference, such as rapid movement of surrounding objects and changes in lighting conditions, leading to unstable signal strength differences. Reducing the weight of the infrared risk coefficient can prevent misjudgments of risk due to occasional fluctuations in the infrared signal. In static operation scenarios, the hook's displacement is relatively fixed, and the main risks are concentrated on abnormal infrared signals, such as obstruction or equipment malfunction, and unstable attitude, such as the hook tilting causing the goods to slip. Increasing the weight of the infrared and attitude risk coefficients helps to more accurately assess these static risks. Because displacement changes are small in a static state, their impact on overall risk is relatively reduced. Reducing the weight of the displacement risk coefficient can prevent the system from overemphasizing this relatively unimportant factor, improving the relevance and accuracy of risk assessment.

[0036] In one example, in a climbing scenario (displacement change rate > 0.2 m / s): the linkage of "attitude sensor + displacement encoder" is prioritized. When both abnormal attitude (tilt angle > 15° and vibration frequency > 5 Hz) and excessive displacement (continuous unattached movement > 0.3 m) are triggered simultaneously, it is judged as a risk (to avoid misjudgment of climbing action). At this time, the infrared weight is reduced to 40%. In a static operation scenario (displacement change rate < 0.05 m / s): dual verification of "infrared penetration + attitude stability" is initiated. If the infrared signal difference is < 30% and the tilt angle fluctuation of the attitude sensor is > 5° within 30 seconds, it is judged as "surface attachment risk". At this time, the displacement weight is reduced to 5%.

[0037] In some embodiments, the method involves querying a preset weight mapping list in conjunction with the current detection scenario to obtain the weights corresponding to the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, and generating a corresponding comprehensive risk value through weighted fusion. The method further includes: If any one of the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient is greater than a preset risk threshold, obtain the other two risk coefficients within a preset historical period. If the other two risk coefficients within the preset historical period do not exceed the corresponding preset risk thresholds, the current situation is considered normal.

[0038] In complex operating environments, a single risk coefficient exceeding a threshold may be an isolated event caused by accidental factors and does not necessarily indicate a serious problem with the overall system. For example, in a motion-based operation scenario, the infrared risk coefficient may suddenly increase due to a brief obstruction, while the posture and displacement risk coefficients remain normal. By obtaining two other risk coefficients within a preset historical period for comprehensive judgment, it is possible to distinguish between such isolated risks and true systemic risks. If the other two risk coefficients have not exceeded their corresponding thresholds within the preset historical period, it indicates that only the current risk indicator is abnormal, which may be a localized and temporary problem rather than a system-wide failure, thus avoiding the misjudgment of an isolated event as a systemic risk.

[0039] In one example, when a risk warning is triggered by data from a certain sensor, the system automatically backtracks historical data from two other types of sensors within the previous 5 seconds. For instance, if the infrared sensor indicates "non-penetration," and a simultaneous displacement trajectory of "0.5m continuous unattached movement" with a stable tilt angle (<5°) is detected, the risk is confirmed. If the attitude data shows simultaneous climbing vibration (frequency 8Hz), it is determined to be a "false alarm by infrared under vibration interference," and the warning is canceled. Here, 5 seconds is merely an example; it could also be 10 seconds or 20 seconds, without specific limitations.

[0040] S130, determine the current alarm level based on the threshold range of the comprehensive risk value.

[0041] Different alarm levels correspond to different response measures. By implementing tiered alarms, resources can be allocated more rationally. For low-risk levels, less resources can be allocated for monitoring and handling; for high-risk levels, resources can be concentrated on emergency rescue and troubleshooting, improving resource utilization efficiency.

[0042] In some embodiments, S130 determines the current alarm level based on the threshold range of the comprehensive risk value, including: Based on historical alarm data, we can determine the prior probability of the target being linked to an anomaly under different historical comprehensive risk values. Using Bayes' theorem and prior probabilities, the confidence level of the current comprehensive risk value is determined. The current alarm level is determined based on the confidence level.

[0043] In this embodiment, the collected historical data is cleaned to remove duplicate, erroneous, or incomplete records. The range of the comprehensive risk value is then divided into several intervals. For example, if the comprehensive risk value range is 0-100, it can be divided into five intervals: 0-20, 21-40, 41-60, 61-80, and 81-100. For each comprehensive risk value interval, the number of anomalies occurring within that interval and the total number of records in that interval are counted. The frequency of anomalies within that interval, i.e., the prior probability, is obtained by dividing the number of anomalies by the total number of records. This is then applied using Bayes' theorem. Here, A represents an abnormal event, and B represents the current comprehensive risk value. The confidence level of the current comprehensive risk value can be determined, thereby determining the current alarm level, improving the accuracy of the assessment of the current comprehensive risk value, and reducing false alarms and missed alarms.

[0044] In some embodiments, S130 determines the current alarm level based on the threshold range in which the comprehensive risk value falls, including: When the overall risk value is within the first threshold range, the current situation is determined to be normal; the maximum value of the first threshold range is the upper limit of the safety threshold. A Level 1 warning is triggered when the overall risk value is within the second threshold range; the minimum value of the second threshold range is greater than the upper limit of the safety threshold. A level-two warning is triggered when the comprehensive risk value is within the third threshold range; the minimum value of the third threshold range is greater than the maximum value of the second threshold range.

[0045] In one example, if the overall risk value is less than 0.7, it indicates that the current situation is normal and no warning is triggered; if the overall risk value is greater than 0.7 but less than 0.9, a level one warning is triggered; if the overall risk value is ≥0.9, a level two warning is triggered.

[0046] In another example, a level one alarm is triggered when the infrared signal strength is less than the corresponding threshold and the displacement encoder detects a continuous unhooked movement distance > 0.5m. If simultaneously a hook tilt angle > 15° and a vibration frequency exceeding the safe range (2-5Hz) are detected, it is determined to be a complex risk scenario, and a level two warning is initiated. Here, the warning signal can be uploaded to the server and ground monitoring center via a LoRa module installed on the hook. Additionally, when the hook's current altitude is high, the controller on the hook can switch the LoRa module's spreading factor to SF to increase the signal transmission range.

[0047] In some embodiments, please refer to Figure 2 , Figure 2This is a schematic diagram of the structure of a multi-parameter fusion intelligent double-hook safety early warning system provided in an embodiment of the present invention. The present invention provides a multi-parameter fusion intelligent double-hook safety early warning system 200, including: an information acquisition module 210, a weighted fusion module 220, and an alarm determination module 230; wherein, Information acquisition module 210 is configured to acquire multi-source detection information of the target hook; the multi-source detection information includes infrared signals, three-axis attitude information and displacement trajectory information; The weighted fusion module 220 is configured to determine the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient based on the relationship between infrared signals, three-axis attitude information, and displacement trajectory information and corresponding thresholds. It also queries a preset weight mapping list in conjunction with the current detection scenario to obtain the weights corresponding to the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, and generates the corresponding comprehensive risk value through weighted fusion. The preset weight mapping list includes the mapping relationship between each risk coefficient and its corresponding weight under different scenario categories. The alarm determination module 230 is configured to determine the current alarm level based on the threshold range of the comprehensive risk value.

[0048] In some embodiments, the information acquisition module 210 is specifically configured as follows: The signal strength difference corresponding to the target hook is obtained using an infrared pair detection unit; The acceleration, angular velocity, and tilt angle of the target hook are obtained using a three-axis attitude sensor. The displacement trajectory corresponding to the target hook is obtained by using a photoelectric displacement encoder.

[0049] In some embodiments, the weighted fusion module 220 is specifically configured as follows: The location information of the target hook is determined based on the relationship between the signal strength difference and the first threshold, and the infrared risk coefficient is obtained based on the location information. The attitude stability of the target hook is determined based on the relationship between acceleration and angular velocity and the second and third thresholds, and the attitude risk coefficient is obtained based on the attitude stability. The trajectory error of the target hook is determined based on the relationship between the displacement trajectory and the fourth threshold, and the displacement risk coefficient is obtained based on the trajectory error.

[0050] In some embodiments, the weighted fusion module 220 is specifically configured as follows: In motion-based operation scenarios, the weight of infrared risk coefficient is less than that of normal infrared risk coefficient, while the weights of attitude risk coefficient and displacement risk coefficient are greater than those of normal attitude and normal displacement risk coefficient. In static operation scenarios, the weights of infrared risk coefficient and attitude risk coefficient are greater than those of normal infrared weight and normal attitude weight, while the weight of displacement risk coefficient is less than that of normal displacement weight.

[0051] In some embodiments, the weighted fusion module 220 is specifically configured as follows: If any one of the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient is greater than a preset risk threshold, obtain the other two risk coefficients within a preset historical period. If the other two risk coefficients within the preset historical period do not exceed the corresponding preset risk thresholds, the current situation is considered normal.

[0052] In some embodiments, the alarm determination module 230 is specifically configured as follows: Based on historical alarm data, we can determine the prior probability of the target being linked to an anomaly under different historical comprehensive risk values. Using Bayes' theorem and prior probabilities, the confidence level of the current comprehensive risk value is determined. The current alarm level is determined based on the confidence level.

[0053] In some embodiments, the alarm determination module 230 is specifically configured as follows: When the overall risk value is within the first threshold range, the current situation is determined to be normal; the maximum value of the first threshold range is the upper limit of the safety threshold. A Level 1 warning is triggered when the overall risk value is within the second threshold range; the minimum value of the second threshold range is greater than the upper limit of the safety threshold. A level-two warning is triggered when the comprehensive risk value is within the third threshold range; the minimum value of the third threshold range is greater than the maximum value of the second threshold range.

[0054] It should be noted that the multi-parameter fusion intelligent double-hook safety early warning system provided in this application embodiment and the multi-parameter fusion intelligent double-hook safety early warning method provided in this application embodiment are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned multi-parameter fusion intelligent double-hook safety early warning method, and the repeated parts will not be described again.

[0055] In some embodiments, please refer to Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 300 provided in this embodiment includes a processor 310 and a memory 320; the memory 320 stores a computer program, wherein the computer program, when executed by the processor, implements the aforementioned multi-parameter fusion intelligent double-hook safety early warning method.

[0056] Specifically, processor 310 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. Processor 310 may also include onboard memory for caching purposes. Processor 310 may be a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.

[0057] The memory 320 may be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, the memory 320 may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, apparatuses, or propagation media. Specific examples of the memory 320 include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); and may also be random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0058] This application also provides a computer-readable medium storing a computer program thereon, which, when executed by a processor, implements the above-described intelligent double-hook safety warning method based on multi-parameter fusion. This computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into that device / apparatus / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0059] According to embodiments of this application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., or any suitable combination thereof.

[0060] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the protection scope of this invention.

Claims

1. A multi-parameter fusion intelligent double-hook safety early warning method, characterized in that, include: Acquire multi-source detection information of the target hook; the multi-source detection information includes infrared signals, three-axis attitude information, and displacement trajectory information; Based on the relationship between the infrared signal, the three-axis attitude information, the displacement trajectory information, and the corresponding thresholds, the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient are determined. Then, in conjunction with the current detection scenario, a preset weight mapping list is queried to obtain the weights corresponding to the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, and the corresponding comprehensive risk value is generated through weighted fusion. The preset weight mapping list includes the mapping relationship between each risk coefficient and its corresponding weight under different scenario categories. The current alarm level is determined based on the threshold range in which the comprehensive risk value falls.

2. The intelligent double-hook safety early warning method based on multi-parameter fusion as described in claim 1, characterized in that, The acquisition of multi-source detection information for the target hook includes: The signal strength difference corresponding to the target hook is obtained using an infrared photodetector unit. The acceleration, angular velocity, and tilt angle of the target hook are obtained using a three-axis attitude sensor. The displacement trajectory corresponding to the target hook is obtained using a photoelectric displacement encoder.

3. The intelligent double-hook safety early warning method based on multi-parameter fusion as described in claim 2, characterized in that, The determination of the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient based on the relationship between the infrared signal, the three-axis attitude information, the displacement trajectory information, and corresponding thresholds includes: The location information of the target hook is determined based on the relationship between the signal strength difference and the first threshold, and the infrared risk coefficient is obtained based on the location information. The attitude stability of the target hook is determined based on the relationship between the acceleration and the angular velocity and the second and third thresholds, and the attitude risk coefficient is obtained based on the attitude stability. The trajectory error of the target hook is determined based on the relationship between the displacement trajectory and the fourth threshold, and the displacement risk coefficient is obtained based on the trajectory error.

4. The intelligent double-hook safety early warning method based on multi-parameter fusion as described in claim 1, characterized in that, The process involves querying a preset weight mapping list based on the current detection scenario to obtain the weights corresponding to the infrared risk coefficient, the attitude risk coefficient, and the displacement risk coefficient, and then generating a corresponding comprehensive risk value through weighted fusion, including: In motion-based operation scenarios, the weight of the infrared risk coefficient is less than that of the normal infrared risk coefficient, while the weights of the posture risk coefficient and the displacement risk coefficient are greater than those of the normal posture and normal displacement risk coefficients. In a static operation scenario, the weights of the infrared risk coefficient and the attitude risk coefficient are greater than the weights of the normal infrared and normal attitude, while the weight of the displacement risk coefficient is less than the weight of the normal displacement.

5. The intelligent double-hook safety early warning method based on multi-parameter fusion as described in claim 1, characterized in that, The step of querying a preset weight mapping list in conjunction with the current detection scenario to obtain the weights corresponding to the infrared risk coefficient, the attitude risk coefficient, and the displacement risk coefficient, and generating a corresponding comprehensive risk value through weighted fusion, further includes: If any one of the infrared risk coefficient, the attitude risk coefficient, and the displacement risk coefficient is greater than a preset risk threshold, the other two risk coefficients within a preset historical time period are obtained. If the other two risk coefficients within the preset historical period do not exceed the corresponding preset risk thresholds, the current situation is determined to be normal.

6. The intelligent double-hook safety early warning method based on multi-parameter fusion as described in claim 1, characterized in that, Determining the current alarm level based on the threshold range of the comprehensive risk value includes: Based on historical alarm data, the prior probability of the target being linked to an anomaly under different historical comprehensive risk values ​​is determined. Using Bayes' theorem and the prior probability, determine the confidence level of the current comprehensive risk value; The current alarm level is determined based on the confidence level.

7. The intelligent double-hook safety early warning method based on multi-parameter fusion as described in claim 6, characterized in that, Determining the current alarm level based on the threshold range of the comprehensive risk value includes: When the overall risk value is within the first threshold range, the current situation is determined to be normal; the maximum value of the first threshold range is the upper limit of the safety threshold. When the comprehensive risk value is within the second threshold range, a level one warning is triggered; the minimum value of the second threshold range is greater than the upper limit of the safety threshold. When the comprehensive risk value is within the third threshold range, a level two warning is triggered; the minimum value of the third threshold range is greater than the maximum value of the second threshold range.

8. A multi-parameter fusion intelligent double-hook safety early warning system, characterized in that, include: The module consists of an information acquisition module, a weighted fusion module, and an alarm determination module; among them, The information acquisition module is configured to acquire multi-source detection information of the target hook; the multi-source detection information includes infrared signals, three-axis attitude information, and displacement trajectory information. The weighted fusion module is configured to determine the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient based on the relationship between the infrared signal, the three-axis attitude information, the displacement trajectory information, and the corresponding thresholds. It then queries a preset weight mapping list in conjunction with the current detection scenario to obtain the weights corresponding to each of the infrared risk coefficient, attitude risk coefficient, and displacement risk coefficient, and generates a corresponding comprehensive risk value through weighted fusion. The preset weight mapping list includes the mapping relationship between each risk coefficient and its corresponding weight under different scenario categories. The alarm determination module is configured to determine the current alarm level based on the threshold range in which the comprehensive risk value falls.

9. An electronic device comprising a processor and a memory; said memory having a storage for a computer program, wherein, When the computer program is executed by the processor, it implements the intelligent double-hook safety early warning method with multi-parameter fusion as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, It stores a computer program, wherein when the computer program is executed by a processor, it implements the intelligent double-hook safety early warning method with multi-parameter fusion as described in any one of claims 1 to 7.

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