Method and System for Detecting the Electrical Status of Construction Machinery Based on Electric Field Induction

Abstract

This invention relates to the field of electric field measurement technology, specifically to a method and system for detecting the energized state of construction machinery based on electric field induction. Addressing the problems of fixed safety distances being unable to adapt to real-time changes in electric field distribution and the uncertainty of the energized state of the robotic arm's location leading to the risk of discharge accidents, the method first determines the electric field stability by analyzing the real-time fluctuations of the electric field intensity of the discharge equipment near the construction site. Then, based on the real-time positions of the robotic arm and the discharge equipment and the characteristics of the electric field influence, the real-time energized state is assessed, and the optimal real-time safety distance is determined. The energized state of the construction machinery is then detected based on this optimal safety distance, thereby improving the overall safety of the construction site and reducing the risk of discharge accidents during construction.

Description

Technical Field

[0001] This invention relates to the field of electric field measurement technology, and specifically to a method and system for detecting the energized state of construction machinery based on electric field induction. Background Technology

[0002] During power construction, equipment such as cranes and aerial work platforms are prone to accidental contact with live equipment or induced current, leading to discharge accidents. To avoid discharge accidents, existing technologies generally set a fixed safety distance to prevent the robotic arm from coming too close to the live equipment. However, since the electric field distribution is dynamic, the energized state of the robotic arm is also dynamic. The fixed safety distance threshold set by existing technologies cannot take into account the dynamic changes in the energized state of the robotic arm, resulting in a high risk of discharge accidents during construction. Summary of the Invention

[0003] To address the technical problem that existing technologies, by setting fixed safety distance thresholds, cannot account for the dynamic changes in the energized state of robotic arms, leading to a high risk of discharge accidents during construction, this application aims to provide a method and system for detecting the energized state of construction machinery based on electric field induction. The specific technical solution adopted is as follows:

[0004] The first aspect of this application provides a method for detecting the energized state of construction machinery based on electric field induction, including:

[0005] In the construction environment of a robotic arm with live equipment, a three-dimensional field strength distribution map and the measured current value of each robotic arm segment were collected at each sampling time.

[0006] Based on the chaotic spatial distribution of electric field strength and the temporal fluctuation of electric field strength in the three-dimensional field strength distribution map, the disorder of electric field strength at each sampling time is determined; based on the measured current value, electric field strength and charge conduction between each robotic arm at each sampling time, the corresponding electric risk value of the robotic arm is determined.

[0007] Based on the electric field strength disorder and the electric risk value of the robotic arm, a safe distance adjustment factor is determined for each sampling time; based on the safe distance adjustment factor, the optimal safe distance of the robotic arm at each sampling time is determined; and the electric status detection of the construction machinery is performed based on the optimal safe distance.

[0008] Furthermore, the process of obtaining the disorder of the electric field strength includes:

[0009] On the three-dimensional field strength distribution map, the electric field strength of each monitoring point in the neighborhood of the energized equipment is obtained; wherein, the distance between all monitoring points and the center of the energized equipment is equal to the preset standard distance, and each monitoring point is located at each vertex of a cube centered on the energized equipment;

[0010] The difference between the electric field strength of the charged equipment and the electric field strength of each monitoring point is used as the field strength deviation value of each monitoring point; a reference deviation value is determined based on the ratio between the field strength deviation value and the preset standard distance; and the spatial disorder of the field strength at each sampling time is determined based on the standard deviation of the reference deviation values ​​of all monitoring points corresponding to the charged equipment.

[0011] The instantaneous field strength change value is determined based on the difference between the electric field strength at each monitoring point at each sampling time and the electric field strength at the previous sampling time; the temporal fluctuation of the field strength is determined based on the mean of the instantaneous field strength change values ​​of all monitoring points at each sampling time.

[0012] The electric field strength disorder at each sampling time is determined by the product of the spatial disorder of the electric field strength and the temporal fluctuation of the electric field strength.

[0013] Furthermore, the process of obtaining the electric risk value of the robotic arm includes:

[0014] The corresponding induced charge coefficient is determined by multiplying the electric field strength of each robotic arm segment with the measured current value at each sampling time; the self-induced charge intensity of each robotic arm segment at each sampling time is determined by the ratio between the induced charge coefficient and the insulation resistance of each robotic arm segment.

[0015] Each robotic arm segment is designated as the target robotic arm; the other robotic arm segments are designated as reference robotic arms; based on the overall magnitude of the contact resistance between the target robotic arm and each reference robotic arm, as well as the relative distance, the reference influence coefficient of each reference robotic arm on the target robotic arm is determined.

[0016] The overall influence coefficient of the target robotic arm is determined by summing the reference influence coefficients between the target robotic arm and all reference robotic arms; the relative influence of each reference robotic arm on the target robotic arm is determined by the ratio between the reference influence coefficients and the overall influence coefficient.

[0017] The self-induced charge intensity of the target robot arm is corrected based on the self-induced charge intensity of each reference robot arm and the corresponding relative influence, and the corrected charge intensity of the target robot arm is determined. Based on the corrected charge intensity of each robot arm at each sampling time, the corresponding robot arm charge risk value is determined.

[0018] Furthermore, the process of obtaining the reference influence coefficient includes:

[0019] The cumulative contact resistance at all robotic arm connections between the target robotic arm and each reference robotic arm is used as the resistance impedance coefficient. The distance impedance coefficient is determined based on the Euclidean distance between the center of the target robotic arm and the center of each reference robotic arm. The product of the resistance impedance coefficient and the distance impedance coefficient is negatively correlated to determine the reference influence coefficient of each reference robotic arm on the target robotic arm.

[0020] Furthermore, the process of obtaining the corrected charge strength includes:

[0021] The local charge conduction coefficient of each reference manipulator to the target manipulator is determined based on the product of the relative influence degree, the self-induced charge intensity of each reference manipulator, and the conductivity of the manipulator material; the corrected charge intensity of the target manipulator is determined based on the sum of the local charge conduction coefficients of all reference manipulators to the target manipulator and the sum of the self-induced charge intensity of the target manipulator.

[0022] Furthermore, the process of determining the corresponding robotic arm's charge risk value based on the corrected charge strength of each robotic arm at each sampling time includes:

[0023] Based on the range of the corrected charge strength of all robotic arms, the charge risk value of the robotic arm at each sampling time is determined.

[0024] Furthermore, the process of obtaining the safety distance adjustment factor includes:

[0025] By performing a positive correlation mapping between the product of the electric field strength disorder and the electric risk value of the robotic arm, a safe distance adjustment factor is determined for each sampling time.

[0026] Furthermore, the process of obtaining the optimal safe distance includes:

[0027] The optimal safe distance for the robotic arm at the current moment is determined by multiplying the current safe distance adjustment factor with the preset safe distance.

[0028] Furthermore, the process of detecting the energized state of construction machinery based on the optimal safety distance includes:

[0029] At each sampling time, when the minimum distance between all sections of the robotic arm and the energized equipment is less than the corresponding optimal safe distance, the robotic arm is determined to be in a dangerous energized state, and an energized danger warning is issued; when the minimum distance between all sections of the robotic arm and the energized equipment is greater than or equal to the corresponding optimal safe distance, the robotic arm is determined to be in a safe state, and no energized danger warning is issued.

[0030] Secondly, this application provides a system for detecting the energized state of construction machinery based on electric field induction, the system comprising:

[0031] The data acquisition module is used to collect a three-dimensional field strength distribution map and the measured current value of each section of the robotic arm at each sampling time in the construction environment of the robotic arm with live equipment.

[0032] The parameter determination module is used to determine the disorder of the electric field strength at each sampling moment based on the disorder of the spatial distribution of the electric field strength and the temporal fluctuation of the electric field strength in the three-dimensional field strength distribution map; and to determine the corresponding electric risk value of the robotic arm based on the measured current value, electric field strength and charge conduction between the robotic arms at each sampling moment.

[0033] The safety distance correction module is used to determine a safety distance adjustment factor at each sampling time based on the electric field strength disorder and the electric risk value of the robotic arm; determine the optimal safety distance of the robotic arm at each sampling time based on the safety distance adjustment factor; and perform electric status detection of the construction machinery based on the optimal safety distance.

[0034] Thirdly, this application provides a computer device including a memory and a processor. The memory is used to store computer program code, and the processor is used to call and run the computer program code from the memory to perform the method as described in the first aspect of this application or any embodiment of the first aspect.

[0035] Fourthly, this application provides a computer program product comprising computer program code, which, when executed, performs the method as described in the first aspect of this application or any embodiment thereof.

[0036] Fifthly, this application provides a computer-readable storage medium that stores computer program code, which, when executed, performs the method as described in the first aspect of this application or any embodiment thereof.

[0037] This application has the following beneficial effects:

[0038] This application addresses the problem that fixed safety distances cannot adapt to real-time changes in electric field distribution and the risk of discharge accidents caused by the uncertainty of the charged state of the robotic arm's location. First, it determines the electric field stability by analyzing the fluctuations in the real-time electric field intensity of the discharge equipment near the construction site. Then, it assesses the real-time charged state based on the real-time position of the robotic arm and the discharge equipment and the characteristics of the electric field influence, and determines the optimal real-time safety distance. Based on the optimal safety distance, it detects the charged state of the construction machinery, thereby improving the overall safety of the construction site and reducing the risk of discharge accidents during construction. Attached Figure Description

[0039] To more clearly illustrate the technical solutions and advantages 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.

[0040] Figure 1 A flowchart illustrating a method for detecting the energized state of construction machinery based on electric field induction, provided in one embodiment of the present invention;

[0041] Figure 2 A schematic diagram of the overall construction environment of a robotic arm for a method of detecting the energized state of construction machinery based on electric field induction, provided in an embodiment of the present invention.

[0042] Figure 3 The diagram shows a structural diagram of a construction machinery energization detection system based on electric field induction, according to an embodiment of the present invention.

[0043] Figure 4 This is a schematic diagram of a computer device structure provided in one embodiment of the present invention. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and system for detecting the energized state of construction machinery based on electric field induction proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment, and specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0046] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method and system for detecting the energized state of construction machinery based on electric field induction provided by this invention.

[0047] This application provides a method for detecting the energized state of construction machinery based on electric field induction. Please refer to [link to relevant documentation]. Figure 1The diagram illustrates a flowchart of a method for detecting the energized state of construction machinery based on electric field induction, according to an embodiment of the present invention. The method includes:

[0048] Step S101: In the construction environment of the robotic arm with live equipment, collect the three-dimensional field strength distribution map and the measured current value of each section of the robotic arm at each sampling time.

[0049] Please see Figure 2 It shows an overall schematic diagram of the construction environment of a robotic arm using a method for detecting the energized state of construction machinery based on electric field induction, according to an embodiment of the present invention; in Figure 2 In this system, an electric field scanning probe is installed on the lifting base to monitor the energized equipment in real time, obtaining a three-dimensional electric field distribution map at each sampling moment. This map determines the magnitude of the electric field at each location within the robotic arm's operating environment, including the electric field strength of the energized equipment and the electric field strength at the center of each robotic arm segment. An integrated sensor is installed at the center of each robotic arm segment, embedded within the arm to avoid affecting its mechanical structure and degrees of freedom. This integrated sensor includes a current sensor and a distance sensor. The current sensor collects the measured current value of each robotic arm segment at each sampling moment. The relative distance between the distance sensors determines the Euclidean distance between the centers of each robotic arm segment and the centers of other robotic arm segments at each sampling moment.

[0050] Furthermore, in this embodiment of the invention, the contact resistance at the connection points of all adjacent robotic arm segments and the insulation resistance of each robotic arm segment are measured in advance using a micro-ohmmeter, and the corresponding conductivity is determined in advance based on the robotic arm material for subsequent calculation and analysis. In one specific implementation of this embodiment, the sampling frequency is set to once per minute, which can be adjusted according to the specific implementation environment, and will not be further elaborated here. It should be noted that this embodiment of the invention is only applicable to implementation environments with a live electrical device, which can be a high-voltage electrical box, generator, or other facility that provides power, and will not be further elaborated here.

[0051] Step S102: Based on the chaotic spatial distribution of electric field strength and the temporal fluctuation of electric field strength in the three-dimensional field strength distribution map, determine the chaotic nature of electric field strength at each sampling time; based on the measured current value, electric field strength, and charge conduction between each robotic arm at each sampling time, determine the corresponding electric risk value of the robotic arm.

[0052] During the operation of the robotic arm, if there are live electrical devices nearby, the electric field they generate may trigger a discharge accident. Fluctuations in the power system of these devices cause instability in their electric field strength both spatially and temporally, leading to real-time changes in the safe distance. Therefore, to determine the optimal safe distance in real time, this step assesses the real-time electric field strength disorder based on the chaotic distribution and temporal fluctuations of the electric field strength of the live devices; and then adaptively adjusts the safe distance according to this disorder.

[0053] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the disorder of the electric field strength includes:

[0054] On the three-dimensional field strength distribution map, the electric field strength of each monitoring point in the neighborhood of the energized equipment is obtained; wherein, the distance between all monitoring points and the center of the energized equipment is equal to the preset standard distance, and each monitoring point is located at each vertex of a cube centered on the energized equipment; in a specific implementation of this invention, the preset standard distance is set to 2m, which can be adjusted according to the specific implementation environment. In addition to setting the monitoring points through the cube method, other methods can also be used to set the monitoring points. The cube is only used to accurately determine the position of the monitoring points, and will not be further elaborated here.

[0055] The difference between the electric field strength of the energized equipment and the electric field strength at each monitoring point is used as the field strength deviation value for each monitoring point. The reference deviation value is determined based on the ratio of the field strength deviation value to a preset standard distance. Using the preset standard distance as the denominator reduces the impact of different preset standard distances set under different implementation environments on the calculation process of the reference deviation value. For energized equipment, the closer the deviation distribution between its corresponding electric field strength and the electric field strength at each monitoring point, the more uniform the spatial distribution of the electric field strength. Since the standard deviation characterizes the dispersion of a set of data, the spatial disorder of the field strength at each sampling time is determined based on the standard deviation of the reference deviation values ​​for all monitoring points corresponding to the energized equipment. A greater spatial disorder of the field strength indicates a more non-uniform spatial distribution of the electric field strength.

[0056] The instantaneous field strength change is determined by comparing the electric field strength at each monitoring point at each sampling time with that at the previous sampling time. The temporal fluctuation of the electric field strength is determined by the average of the instantaneous field strength changes at all monitoring points at each sampling time. The greater the instantaneous field strength change at each monitoring point at each sampling time, the stronger the temporal fluctuation of the electric field strength, indicating a more significant temporal fluctuation characteristic.

[0057] The spatial disorder and temporal fluctuation of electric field strength characterize the instability of electric field strength at each sampling moment in the spatial and temporal dimensions, respectively. The more unstable the distribution of electric field strength, the greater the safety distance is required to avoid discharge accidents in order to ensure construction safety. Therefore, the electric field strength disorder at each sampling moment is further determined by the product of the spatial disorder and temporal fluctuation of electric field strength, so that the greater the electric field strength disorder, the greater the safety distance should be at the corresponding sampling moment.

[0058] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the disorder of the electric field strength is expressed by the following formula: ;in, Sampling time The disorder of electric field strength under these conditions; Sampling time The standard deviation of the reference deviation values ​​of all monitoring points corresponding to the live equipment, that is, the spatial disorder of the electric field strength; Number of monitoring points; Sampling time The electric field strength; Sampling time The electric field strength at the previous sampling time; Sampling time The instantaneous change in field strength; Sampling time The temporal fluctuation of the field strength; wherein, the temporal fluctuation of the field strength at the first sampling time is set to 0 to ensure the completeness of the embodiment.

[0059] The disorder of electric field strength can evaluate the instability of the electric field of the discharge equipment itself. However, during construction, the robotic arm needs to move continuously to adapt to the progress of high-altitude operations. Therefore, the charging state of the robotic arm itself can represent the degree of influence of the electric field, thereby affecting the risk of electric shock accidents. Generally, the charging state of the robotic arm mainly comes from the electromagnetic induction of the electric field itself and the charge conduction of other robotic arms. Therefore, this embodiment of the invention further determines the corresponding robotic arm charging risk value based on the measured current value, electric field strength, and charge conduction between robotic arms at each sampling time. This makes the higher the robotic arm charging risk value, the higher the risk of electric shock accidents, and the greater the safe distance at the corresponding sampling time should be.

[0060] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the electric risk value of the robotic arm includes:

[0061] The induced charge coefficient is determined by multiplying the electric field strength of each robotic arm segment by the measured current value at each sampling moment. The self-induced charge intensity of each robotic arm segment at each sampling moment is determined by the ratio of the induced charge coefficient to the insulation resistance of each segment. For each robotic arm segment, a stronger electric field and a larger measured current value indicate a greater influence of electromagnetic induction and more charge accumulation. Conversely, a lower insulation resistance indicates lower insulation performance. Therefore, a higher electric field strength, a larger measured current value, and a lower insulation resistance should result in a higher charge intensity of the robotic arm at the corresponding sampling moment.

[0062] In one specific implementation of this invention, the process of obtaining the self-induced charge intensity is expressed by the formula: ;in, Sampling time Next The self-induced charge strength of the robotic arm; Sampling time Next The electric field strength of the robotic arm; Sampling time Next The measured current value of the robotic arm; Sampling time Next The induced charge coefficient of the robotic arm; Sampling time Next Insulation resistance of the robotic arm.

[0063] Each robotic arm segment is sequentially designated as the target robotic arm; other robotic arm segments besides the target robotic arm are designated as reference robotic arms; based on the overall magnitude of the contact resistance between the target robotic arm and each reference robotic arm, and the relative distance, a reference influence coefficient for each reference robotic arm on the target robotic arm is determined; in a specific implementation of this invention, the process of obtaining the reference influence coefficient includes: using the cumulative value of the contact resistance at all robotic arm connections between the target robotic arm and each reference robotic arm as the resistance impedance coefficient; determining the distance impedance coefficient based on the Euclidean distance between the center of the target robotic arm and the center of each reference robotic arm; and performing a negative correlation mapping between the product of the resistance impedance coefficient and the distance impedance coefficient to determine the reference influence coefficient for each reference robotic arm on the target robotic arm.

[0064] For each reference robotic arm, the greater the distance between it and the target robotic arm, and the greater the overall contact resistance at all the connection points between the robotic arms, the more severe the attenuation of charge when it is conducted from the reference robotic arm to the target robotic arm in both the spatial and resistance dimensions. Consequently, the influence of the self-induced charge intensity of the reference robotic arm on the target robotic arm is smaller, and therefore the corresponding reference influence coefficient should be smaller.

[0065] The overall influence coefficient of the target robotic arm is determined by summing the reference influence coefficients between the target robotic arm and all reference robotic arms. The relative influence of each reference robotic arm on the target robotic arm is determined by the ratio between the reference influence coefficients and the overall influence coefficient. By using the overall influence coefficient as the denominator to restrict the reference influence coefficient of each reference robotic arm, the sum of the relative influence of the target robotic arm on all reference robotic arms is made to be 1, which better characterizes the charge influence of each reference robotic arm in the dimension of relative analysis.

[0066] Since the attenuation of the self-induced charge intensity of each reference robotic arm affects the charge intensity of the target robotic arm, the self-induced charge intensity of the target robotic arm is further corrected based on the self-induced charge intensity of each reference robotic arm and its corresponding relative influence, thus determining the corrected charge intensity of the target robotic arm. In a specific implementation of this invention, the process of obtaining the corrected charge intensity includes:

[0067] The local charge conduction coefficient of each reference robotic arm to the target robotic arm is determined by multiplying the relative influence, the self-induced charge intensity of each reference robotic arm, and the conductivity of the robotic arm material. Considering that the robotic arm material may differ under different implementation environments, resulting in varying conductivity, the conductivity of the robotic arm material must also be considered when calculating the local charge conduction coefficient of each reference robotic arm to the target robotic arm. For each reference robotic arm, a higher self-induced charge intensity, higher conductivity, and a greater relative influence indicate less attenuation of the charge in the reference robotic arm during conduction to the target robotic arm, resulting in more charge being conducted to the target robotic arm; therefore, the corresponding local charge conduction coefficient should be larger. Finally, combining the local charge conduction coefficients of all reference robotic arms to the target robotic arm, and the sum of the sum of the local charge conduction coefficients of all reference robotic arms to the target robotic arm and the self-induced charge intensity of the target robotic arm, a corrected charge intensity of the target robotic arm is determined. A larger corrected charge intensity indicates that the target robotic arm carries more charge and has a higher potential.

[0068] In one specific implementation of this invention, the process of obtaining the corrected charge strength is expressed by the following formula: ;in, Sampling time Lower target robotic arm Corrected charge strength; Sampling time Lower target robotic arm The self-induced charge intensity; For target robotic arm The reference number of robotic arms; The electrical conductivity of the robotic arm material; Sampling time Lower target robotic arm The corresponding number Section reference robot arm to target robot arm The relative degree of influence; Sampling time Lower target robotic arm The corresponding number The section references the self-induced charge strength of the robotic arm; Sampling time Lower target robotic arm The corresponding number The section references the local charge conduction coefficient of the robotic arm.

[0069] Finally, based on the corrected charge intensity of each robotic arm at each sampling time, the corresponding robotic arm charge risk value is determined. For each robotic arm segment, the calculated corrected charge intensity characterizes its own charge characteristics in terms of both self-induction and external conduction. The larger the corresponding corrected charge intensity, the higher the potential caused by the charge should be. Since all robotic arm segments are integrated, the larger the overall potential difference between robotic arms, the higher the probability of a discharge accident, and the greater the need to increase the safety distance. Therefore, this embodiment of the invention determines the robotic arm charge risk value at each sampling time based on the range of the corrected charge intensity of all robotic arm segments. The larger the range of the corresponding corrected charge intensity, the larger the maximum potential difference it represents, and the higher the risk of a discharge accident. Therefore, a larger robotic arm charge risk value is assigned to correct the safety distance.

[0070] Step S103: Determine the safety distance adjustment factor for each sampling time based on the electric field strength disorder and the risk value of the robotic arm being charged; determine the optimal safety distance of the robotic arm at each sampling time based on the safety distance adjustment factor; and perform electrical status detection of the construction machinery based on the optimal safety distance.

[0071] As can be seen from step S102, the greater the disorder of the electric field strength and the greater the risk of the robotic arm becoming charged, the higher the risk of a discharge accident, and the greater the required safety distance should be. Therefore, this embodiment of the invention combines the disorder of the electric field strength and the risk of the robotic arm becoming charged to determine the safety distance adjustment factor for each sampling moment. Preferably, in some possible implementations of this embodiment, the process of obtaining the safety distance adjustment factor includes: performing a positive correlation mapping between the product of the disorder of the electric field strength and the risk of the robotic arm becoming charged to determine the safety distance adjustment factor at each sampling moment; and fusing the disorder of the electric field strength and the risk of the robotic arm becoming charged through a product, so that the larger the safety distance adjustment factor obtained after analyzing the correlation, the farther the subsequent safety distance should be.

[0072] In one specific implementation of this invention, the process of obtaining the safety distance adjustment factor is expressed by the formula: ;in, Sampling time The safety distance adjustment factor below; Sampling time The disorder of electric field strength under these conditions; Sampling time The risk value of the robotic arm being electrified; The function is a linear normalization function. It should be noted that, unless otherwise specified, the normalization method in the embodiments of the present invention is linear normalization, which will not be elaborated further here.

[0073] Further, based on the obtained safety distance adjustment factor, the distance is adjusted at each sampling time to determine the optimal safety distance of the robotic arm at each sampling time. Preferably, in some possible implementations of this invention, the process of obtaining the optimal safety distance includes:

[0074] The optimal safe distance for the robotic arm at the current moment is determined by multiplying the current safe distance adjustment factor by the preset safe distance. In one specific implementation of this invention, the preset safe distance is set to 2 meters, which can be adjusted according to the specific implementation environment, and will not be elaborated further here. By weighting the preset safe distance with the safe distance adjustment factor, the safe distance can increase to different degrees at different sampling times, minimizing the impact on the robotic arm's operation while reducing the risk of discharge accidents.

[0075] Finally, the energized state of the construction machinery is detected based on the optimal safety distance. Specifically: at each sampling moment, if the minimum distance between all sections of the robotic arm and the energized equipment is less than the corresponding optimal safety distance, the robotic arm is determined to be in a dangerous energized state, and an energized hazard warning is issued; if the minimum distance between all sections of the robotic arm and the energized equipment is greater than or equal to the corresponding optimal safety distance, the robotic arm is determined to be in a safe state, and no energized hazard warning is issued. In a specific implementation of this invention, after issuing an energized hazard warning, the operator is reminded to manually intervene to maintain the optimal safety distance, so as to ensure the safe operation of the construction machinery as much as possible.

[0076] In summary, this application proposes a method for detecting the energized state of construction machinery based on electric field induction. This method addresses the problems of fixed safety distances being unable to adapt to real-time changes in electric field distribution and the uncertainty of the energized state of the robotic arm's location, leading to the risk of discharge accidents. First, it determines the electric field stability by analyzing the fluctuations in the real-time electric field intensity of the discharge equipment near the construction site. Then, based on the real-time positions of the robotic arm and the discharge equipment, and the characteristics of the electric field influence, it assesses the real-time energized state and determines the optimal real-time safety distance. Based on this optimal safety distance, it detects the energized state of the construction machinery, thereby improving the overall safety of the construction site and reducing the risk of discharge accidents during construction.

[0077] This application also provides a system for detecting the live state of construction machinery based on electric field induction. Please refer to [link to relevant documentation]. Figure 3 The diagram shows a structural diagram of a construction machinery energization detection system based on electric field induction according to an embodiment of the present invention. The system includes: a data acquisition module 301, a parameter determination module 302, and a safety distance correction module 303.

[0078] The data acquisition module 301 is used to acquire a three-dimensional field strength distribution map and the measured current value of each section of the robotic arm at each sampling time in a construction environment with live equipment.

[0079] The parameter determination module 302 is used to determine the disorder of the electric field strength at each sampling time based on the disorder of the spatial distribution of the electric field strength and the temporal fluctuation of the electric field strength in the three-dimensional field strength distribution map; and to determine the corresponding electric risk value of the robotic arm based on the measured current value, electric field strength and charge conduction between the robotic arms at each sampling time.

[0080] The safety distance correction module 303 is used to determine the safety distance adjustment factor at each sampling time based on the electric field strength disorder and the electric risk value of the robotic arm; determine the optimal safety distance of the robotic arm at each sampling time based on the safety distance adjustment factor; and perform electric status detection of the construction machinery based on the optimal safety distance.

[0081] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the electric field induction-based construction machinery live state detection system and the electric field induction-based construction machinery live state detection method embodiment provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiment, which will not be repeated here.

[0082] This application also provides a computer device; please refer to [link / reference]. Figure 4 The diagram illustrates a computer device structure according to an embodiment of the present invention. The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the computer device can execute any of the aforementioned methods for detecting the energized state of construction machinery based on electric field induction.

[0083] This application also provides a computer program product that, when run on a computer device, enables the computer device to execute any of the aforementioned methods for detecting the energized state of construction machinery based on electric field induction.

[0084] This application also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer device, the computer device can execute any of the aforementioned methods for detecting the energized state of construction machinery based on electric field induction.

[0085] In the embodiments provided in this application, it should be understood that the computer device, computer program product and computer-readable storage medium provided are all used to perform the corresponding methods provided above, and therefore the beneficial effects they can achieve can be referred to the beneficial effects of the methods provided above, which will not be repeated here.

[0086] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting the energized state of construction machinery based on electric field induction, characterized in that, The method includes: In the construction environment of a robotic arm with live equipment, a three-dimensional field strength distribution map and the measured current value of each robotic arm segment were collected at each sampling time. Based on the chaotic spatial distribution of electric field strength and the temporal fluctuation of electric field strength in the three-dimensional field strength distribution map, the disorder of electric field strength at each sampling time is determined; based on the measured current value, electric field strength and charge conduction between each robotic arm at each sampling time, the corresponding electric risk value of the robotic arm is determined. Based on the electric field strength disorder and the electric risk value of the robotic arm, a safe distance adjustment factor is determined for each sampling time; based on the safe distance adjustment factor, the optimal safe distance of the robotic arm at each sampling time is determined; and the electric status detection of the construction machinery is performed based on the optimal safe distance. The process of obtaining the disorder of the electric field strength includes: On the three-dimensional field strength distribution map, the electric field strength of each monitoring point in the neighborhood of the energized equipment is obtained; wherein, the distance between all monitoring points and the center of the energized equipment is equal to the preset standard distance, and each monitoring point is located at each vertex of a cube centered on the energized equipment; The difference between the electric field strength of the charged equipment and the electric field strength of each monitoring point is used as the field strength deviation value of each monitoring point; a reference deviation value is determined based on the ratio between the field strength deviation value and the preset standard distance; and the spatial disorder of the field strength at each sampling time is determined based on the standard deviation of the reference deviation values ​​of all monitoring points corresponding to the charged equipment. The instantaneous field strength change value is determined based on the difference between the electric field strength at each monitoring point at each sampling time and the electric field strength at the previous sampling time; the temporal fluctuation of the field strength is determined based on the mean of the instantaneous field strength change values ​​of all monitoring points at each sampling time. The electric field strength disorder at each sampling time is determined by the product of the spatial disorder of the electric field strength and the temporal fluctuation of the electric field strength. The process of obtaining the electric risk value of the robotic arm includes: The corresponding induced charge coefficient is determined by multiplying the electric field strength of each robotic arm segment with the measured current value at each sampling time; the self-induced charge intensity of each robotic arm segment at each sampling time is determined by the ratio between the induced charge coefficient and the insulation resistance of each robotic arm segment. Each robotic arm segment is designated as the target robotic arm; the other robotic arm segments are designated as reference robotic arms; based on the overall magnitude of the contact resistance between the target robotic arm and each reference robotic arm, as well as the relative distance, the reference influence coefficient of each reference robotic arm on the target robotic arm is determined. The overall influence coefficient of the target robotic arm is determined by summing the reference influence coefficients between the target robotic arm and all reference robotic arms; the relative influence of each reference robotic arm on the target robotic arm is determined by the ratio between the reference influence coefficients and the overall influence coefficient. The self-induced charge intensity of the target robot arm is corrected based on the self-induced charge intensity of each reference robot arm and the corresponding relative influence, and the corrected charge intensity of the target robot arm is determined; based on the corrected charge intensity of each robot arm at each sampling time, the corresponding robot arm charge risk value is determined. The process of obtaining the reference influence coefficient includes: The cumulative contact resistance at all robotic arm connections between the target robotic arm and each reference robotic arm is used as the resistance impedance coefficient. The distance impedance coefficient is determined based on the Euclidean distance between the center of the target robotic arm and the center of each reference robotic arm. The product of the resistance impedance coefficient and the distance impedance coefficient is negatively correlated to determine the reference influence coefficient of each reference robotic arm on the target robotic arm.

2. The method for detecting the energized state of construction machinery based on electric field induction according to claim 1, characterized in that, The process of obtaining the corrected charge strength includes: The local charge conduction coefficient of each reference manipulator to the target manipulator is determined based on the product of the relative influence degree, the self-induced charge intensity of each reference manipulator, and the conductivity of the manipulator material; the corrected charge intensity of the target manipulator is determined based on the sum of the local charge conduction coefficients of all reference manipulators to the target manipulator and the sum of the self-induced charge intensity of the target manipulator.

3. The method for detecting the energized state of construction machinery based on electric field induction according to claim 1, characterized in that, The process of determining the corresponding electric risk value of each robotic arm based on the corrected electric charge intensity of each robotic arm at each sampling time includes: Based on the range of the corrected charge strength of all robotic arms, the charge risk value of the robotic arm at each sampling time is determined.

4. The method for detecting the energized state of construction machinery based on electric field induction according to claim 1, characterized in that, The process of obtaining the safety distance adjustment factor includes: By performing a positive correlation mapping between the product of the electric field strength disorder and the electric risk value of the robotic arm, a safe distance adjustment factor is determined for each sampling time.

5. The method for detecting the energized state of construction machinery based on electric field induction according to claim 1, characterized in that, The process of obtaining the optimal safe distance includes: The optimal safe distance for the robotic arm at the current moment is determined by multiplying the current safe distance adjustment factor with the preset safe distance.

6. The method for detecting the energized state of construction machinery based on electric field induction according to claim 1, characterized in that, The process of detecting the energized state of construction machinery based on the optimal safety distance includes: At each sampling time, when the minimum distance between all sections of the robotic arm and the energized equipment is less than the corresponding optimal safe distance, the robotic arm is determined to be in a dangerous energized state, and an energized danger warning is issued; when the minimum distance between all sections of the robotic arm and the energized equipment is greater than or equal to the corresponding optimal safe distance, the robotic arm is determined to be in a safe state, and no energized danger warning is issued.

7. A system for detecting the energized state of construction machinery based on electric field induction, characterized in that, The system includes: The data acquisition module is used to collect a three-dimensional field strength distribution map and the measured current value of each section of the robotic arm at each sampling time in the construction environment of the robotic arm with live equipment. The parameter determination module is used to determine the disorder of the electric field strength at each sampling moment based on the disorder of the spatial distribution of the electric field strength and the temporal fluctuation of the electric field strength in the three-dimensional field strength distribution map; and to determine the corresponding electric risk value of the robotic arm based on the measured current value, electric field strength and charge conduction between the robotic arms at each sampling moment. The safety distance correction module is used to determine a safety distance adjustment factor at each sampling time based on the electric field strength disorder and the electric risk value of the robotic arm; determine the optimal safety distance of the robotic arm at each sampling time based on the safety distance adjustment factor; and perform electric status detection of the construction machinery based on the optimal safety distance. The process of obtaining the disorder of the electric field strength includes: On the three-dimensional field strength distribution map, the electric field strength of each monitoring point in the neighborhood of the energized equipment is obtained; wherein, the distance between all monitoring points and the center of the energized equipment is equal to the preset standard distance, and each monitoring point is located at each vertex of a cube centered on the energized equipment; The difference between the electric field strength of the charged equipment and the electric field strength of each monitoring point is used as the field strength deviation value of each monitoring point; a reference deviation value is determined based on the ratio between the field strength deviation value and the preset standard distance; and the spatial disorder of the field strength at each sampling time is determined based on the standard deviation of the reference deviation values ​​of all monitoring points corresponding to the charged equipment. The instantaneous field strength change value is determined based on the difference between the electric field strength at each monitoring point at each sampling time and the electric field strength at the previous sampling time; the temporal fluctuation of the field strength is determined based on the mean of the instantaneous field strength change values ​​of all monitoring points at each sampling time. The electric field strength disorder at each sampling time is determined by the product of the spatial disorder of the electric field strength and the temporal fluctuation of the electric field strength. The process of obtaining the electric risk value of the robotic arm includes: The corresponding induced charge coefficient is determined by multiplying the electric field strength of each robotic arm segment with the measured current value at each sampling time; the self-induced charge intensity of each robotic arm segment at each sampling time is determined by the ratio between the induced charge coefficient and the insulation resistance of each robotic arm segment. Each robotic arm segment is designated as the target robotic arm; the other robotic arm segments are designated as reference robotic arms; based on the overall magnitude of the contact resistance between the target robotic arm and each reference robotic arm, as well as the relative distance, the reference influence coefficient of each reference robotic arm on the target robotic arm is determined. The overall influence coefficient of the target robotic arm is determined by summing the reference influence coefficients between the target robotic arm and all reference robotic arms; the relative influence of each reference robotic arm on the target robotic arm is determined by the ratio between the reference influence coefficients and the overall influence coefficient. The self-induced charge intensity of the target robot arm is corrected based on the self-induced charge intensity of each reference robot arm and the corresponding relative influence, and the corrected charge intensity of the target robot arm is determined; based on the corrected charge intensity of each robot arm at each sampling time, the corresponding robot arm charge risk value is determined. The process of obtaining the reference influence coefficient includes: The cumulative contact resistance at all robotic arm connections between the target robotic arm and each reference robotic arm is used as the resistance impedance coefficient. The distance impedance coefficient is determined based on the Euclidean distance between the center of the target robotic arm and the center of each reference robotic arm. The product of the resistance impedance coefficient and the distance impedance coefficient is negatively correlated to determine the reference influence coefficient of each reference robotic arm on the target robotic arm.

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