A method, apparatus, and medium for remote monitoring of construction equipment data
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA RAILWAY NO 10 ENG GRP CO LTD
- Filing Date
- 2026-03-05
- Publication Date
- 2026-06-19
Smart Images
Figure CN121785376B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of industrial control. In particular, it relates to a method, device, and medium for remote monitoring of data from engineering machinery and equipment. Background Technology
[0002] In the field of remote monitoring and collaborative scheduling of engineering machinery cluster operations, existing technologies mainly rely on rule-based priority ranking or weighted cost functions based on distance and equipment status for task allocation and path planning.
[0003] The methods described above typically linearly weight equipment status (such as remaining fuel level and engine load) with the target distance to form a total cost, and then select the target with the minimum cost. For multi-machine collaboration, an artificial potential field method is often used, which constructs a repulsive potential for obstacles or working boundaries and an attractive potential for the target point, with the equipment moving along the negative gradient direction of the composite potential field.
[0004] However, it has significant shortcomings in handling complex engineering constraints and dynamic interactions among multiple agents. Engineering machinery is an entity composed of multiple interconnected components such as energy, heat, and mechanical sub-equipment. Its operational capacity exhibits a significant bottleneck effect; the failure of any critical factor will lead to the termination of the entire operation. Such rigid constraints cannot be accurately adjusted through weight adjustment.
[0005] Secondly, the traditional artificial potential field method only considers the current static obstacles and lacks the ability to predict the future movement intentions of neighboring devices. This makes it easy for devices to be attracted by high-value targets at the same time, forming trajectory intersections and causing congestion or even collisions.
[0006] Finally, if control commands are generated directly based on the instantaneous potential field gradient, decision oscillations can easily occur in dynamic environments due to small fluctuations in potential energy values, leading to high-frequency switching of equipment between different targets, which seriously damages the stability and operational efficiency of the actuator.
[0007] Therefore, in scenarios such as large-scale earthwork projects and mining, how to achieve intelligent collaborative navigation that not only conforms to the inherent constraints of engineering physics, but also anticipates and avoids multi-machine conflicts, while ensuring the coherence and stability of decision-making commands, has become a core technical problem that urgently needs to be solved. Summary of the Invention
[0008] To address the aforementioned technical problems, this application provides solutions in the following aspects.
[0009] In a first aspect, this application provides a method for remote monitoring of data from engineering machinery and equipment, comprising:
[0010] Acquire and process real-time operating data and spatial location data of each piece of construction machinery within the target work area;
[0011] Based on the real-time operating data, the intrinsic operating capability potential energy factor of each device is calculated by multiplying the key state factors by forcibly returning them to zero, and the global navigation potential energy surface of the device's comprehensive efficiency is constructed by combining the target gravity and distance loss.
[0012] It receives motion state prediction data from other devices in the neighborhood, calculates the future spatiotemporal congestion risk, and generates a modulation factor to correct the global navigation potential energy surface, forming an effective interactive potential energy field for collaborative obstacle avoidance.
[0013] Based on the gradient of the effective interactive potential energy field, a preliminary control command is generated. At the same time, the cumulative path input integral value of the device to the current target is calculated, and a dynamic decision-making switching resistance is constructed based on the input and the remaining distance. The preliminary control command is locked or switched through hysteresis comparison logic, and finally a rigid control command is output.
[0014] Preferably, the acquisition and processing of real-time operating condition data and spatial location data of each piece of construction machinery within the target operating area includes: acquiring energy data, hydraulic and thermal status data, and engine operating condition data of the equipment through the vehicle bus, and acquiring the real-time spatial coordinates of the equipment through the positioning module; and normalizing the energy data to obtain the energy self-sufficiency index of the equipment's range.
[0015] Preferably, the calculation of the intrinsic operating capability potential energy factor of each device includes the following steps: preprocessing the real-time operating condition data of each piece of construction machinery in the target operating area to obtain the baseline value of the real-time operating condition data of each piece of construction machinery in the target operating area, and multiplying the baseline values of the real-time operating condition data of each piece of construction machinery in the target operating area; wherein, when any of the key state factors approaches the preset failure threshold, the intrinsic operating capability potential energy factor is forcibly reduced to zero.
[0016] Preferably, the construction of the global navigation potential energy surface includes the following steps: multiplying the intrinsic operational capability potential energy factor by the target gravitational potential energy value preset based on the digital map of the target operational area, and then subtracting the distance loss term determined by the distance between the current position of the device and the target position and the preset transmission loss coefficient.
[0017] Preferably, receiving motion state prediction data of other devices in the neighborhood includes the steps of: periodically broadcasting and receiving state data packets containing trajectory prediction envelopes of each neighboring device over a future period of time through a vehicle network communication module.
[0018] Preferably, the calculation of future spatiotemporal congestion risk includes the following steps: based on the trajectory prediction envelope, determining the probability of each device occupying a specific spatial area at a future time, and after normalizing the occupancy probability, calculating the spatiotemporal congestion entropy of the area at a future time according to the information entropy formula, and using the spatiotemporal congestion entropy as the quantitative value of congestion risk.
[0019] Preferably, the formation of an effective interactive potential energy field for collaborative obstacle avoidance includes the step of multiplying the global navigation potential energy surface by a decay factor with the spatiotemporal congestion entropy as a variable, so that the potential energy value of the global navigation potential energy surface is significantly decayed in areas with high congestion risk.
[0020] Preferably, the computing device calculates the cumulative path input integral value for the current target by the following steps: integrating the product of the chassis traction force and the instantaneous motion rate of the device since it locks onto the current target over time to obtain the cumulative input quantification value for the physical work consumed.
[0021] Secondly, this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor loads the computer program, it executes the aforementioned method for remote monitoring of engineering machinery equipment data.
[0022] Thirdly, this application provides a computer-readable storage medium storing a computer program, which, when loaded by a processor, executes the aforementioned method for remote monitoring of engineering machinery equipment data.
[0023] This application has the following effects:
[0024] 1. This application calculates the intrinsic operational capability potential energy factor so that the overall potential energy automatically returns to zero when any critical state factor approaches the failure threshold, thereby preventing the assignment of tasks to unavailable equipment from the decision-making source and breaking through the technical bottleneck of the inability of linear weighted reliability to be connected.
[0025] 2. This application receives the motion prediction of neighboring devices and calculates the future spatiotemporal congestion risk. It uses the congestion risk as a modulation factor to correct the global navigation potential energy surface, transforming future conflicts into instantaneous repulsive forces, guiding devices to detour in advance, and solving the fundamental defect of reactive obstacle avoidance that cannot predict trajectory intersections.
[0026] 3. This application constructs a dynamic decision-making switching damping barrier based on the cumulative path input integral value and the remaining distance, and locks or switches control commands through hysteresis comparison logic, so that the decision has physical inertia that increases with the progress of the task, effectively suppressing the target oscillation caused by instantaneous potential energy fluctuations, and ensuring the continuity of operation and the stability of the actuator. Attached Figure Description
[0027] Figure 1 This is a flowchart of steps S1-S4 in a remote monitoring method for engineering machinery equipment data according to an embodiment of this application. Detailed Implementation
[0028] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.
[0029] Reference Figure 1 A method for remote monitoring of data from engineering machinery equipment includes steps S1-S4, as detailed below:
[0030] S1: Acquire and process real-time operating condition data and spatial location data of each piece of engineering machinery within the target work area.
[0031] In the remote monitoring of engineering machinery cluster operations, the directly collected sensor data suffers from problems such as inconsistent dimensions, noise interference, and mixed physical meanings, making it difficult to directly use for subsequent potential energy field modeling and collaborative decision-making.
[0032] Simply filtering the data and directly using it as the cost function ignores the physical constraints and correlations inherent in the data itself, leading to a disconnect between scheduling decisions and the actual state of the equipment. Therefore, it is essential to first process heterogeneous data in a unified manner.
[0033] The equipment is considered as an energy conversion entity composed of energy, heat, and mechanical sub-equipment connected in series, and its overall operational efficiency is limited by the weakest link.
[0034] By normalization and safety margin calculation, the physical quantity is transformed into a dimensionless quantity. The state index within the interval can eliminate the influence of dimensions and intuitively reflect the margin of the equipment from the failure threshold.
[0035] Firstly, the remaining fuel or electricity data of the energy equipment is collected in real time via the vehicle-mounted controller local area network bus deployed on the construction machinery. Current operating temperature of hydraulic equipment And engine speed and load parameters, used to calculate effective thermal efficiency. .
[0036] Simultaneously, the device's current precise spatial coordinates are obtained through a global navigation satellite receiver, and then the planar projected coordinates are obtained after coordinate transformation. .
[0037] The data collection period is set as follows: To ensure real-time response to device dynamics, the data collection cycle can be adjusted by the implementer according to the specific implementation scenario.
[0038] The collected raw data is preprocessed, and a sliding window filtering algorithm is used to remove sensor noise. The window width is set to... Each sampling point can be adjusted by the implementer according to the specific implementation scenario.
[0039] Then, normalization is performed: defining the energy self-sufficiency index. ,in The rated energy capacity of the equipment, Reflects current driving range; defines hydraulic thermal safety margin. ,in The maximum permissible operating temperature for hydraulic systems; the maximum operating temperature for a single working device is... This information can be obtained from the equipment's factory specifications. The closer This indicates a higher risk of overheating, when it is less than [a certain value]. Then force to set This indicates that the temperature has exceeded the limit and the device has failed.
[0040] For engine data, the effective thermal efficiency baseline value under the current operating conditions is obtained by looking up a table using a pre-calibrated universal characteristic curve. This value has been normalized to Interval.
[0041] Finally, the processed standardized data , , and location coordinates Encapsulate it into a unified data structure.
[0042] S2: Based on real-time operating data, the intrinsic operational capability potential energy factor of each device is calculated by multiplying the key state factors by forcibly returning them to zero, and the overall navigation potential energy surface of the device is constructed by combining the target gravity and distance loss.
[0043] In the scheduling of engineering machinery clusters, a linear weighted method is used to integrate equipment status and target distance to construct a cost function for task allocation.
[0044] However, this linear model is inherently flawed because the overall operational capability of a device consisting of energy, heat, and mechanical sub-equipment connected in series is limited by the weakest link. Once the fuel runs out or the hydraulic system overheats, the operation will be forced to stop no matter how good the performance of other sub-equipment is.
[0045] Linear weighting cannot reflect this rigid constraint of veto power, which can easily lead to scheduling instructions contradicting the actual state of the equipment.
[0046] Furthermore, the intrinsic operational capability of the equipment is modeled as the product of various key state factors, such that if any key state factor approaches zero, the entire product result will be forced to zero, thereby eliminating the possibility of assigning new tasks to failed equipment from the source of decision-making.
[0047] Meanwhile, target attraction and distance loss are incorporated into the same potential energy framework. By simulating the decay of gains with distance through spatial transmission loss, a continuous and differentiable global navigation potential energy surface is ultimately formed, providing a unified gain metric for subsequent cooperative navigation.
[0048] First, based on the standardized state data obtained in step S1, calculate the device's state at time [time]. Intrinsic working potential factor The following Cobb-Douglas type product coupling formula is adopted:
[0049]
[0050] in, It is the energy self-sufficiency index, which is obtained by normalizing the ratio of remaining energy to rated capacity, and is dimensionless.
[0051] Hydraulic thermal safety margin is defined as follows: ,when Forced take .
[0052] The effective thermal efficiency of the engine is the baseline value, normalized to... .
[0053] This is the global operating condition gain coefficient, used to adjust for the influence of environmental factors. The reference value is taken as... On sunny days, you can make slight adjustments. Rainy or snowy days can drop to It can be adjusted by the implementer according to the specific implementation scenario.
[0054] and As a sensitivity index, the penalties for energy shortages and overheating risks are controlled separately in mining operation scenarios. , It can be adjusted by the implementer according to the specific implementation scenario to highlight the importance of thermal constraint.
[0055] Ensure that or When approaching zero, It rapidly decays to near zero, achieving a rigid constraint circuit break.
[0056] By combining the digital map information of the target work area, obtain each grid point. Preset target gravitational potential energy value .
[0057] The gravitational potential energy value is generated based on the construction design drawings. For example, in earthwork excavation tasks, it is positively correlated with the density of the soil to be excavated, and normalized to... For the current location Equipment, for use in Specific target point at a given time Global navigation potential energy surface Calculated using the following formula:
[0058]
[0059] in, This is the Euclidean distance from the current location of the device to the target location, in meters.
[0060] The distance transmission loss coefficient is a proportionality constant that converts geometric distance into potential energy attenuation, with dimensions of . The energy consumption needs to be calibrated based on the size of the site and the mobility of the equipment.
[0061] In a typical quarry scenario, if the radius of the operating area is approximately ,suggestion ,at this time Distance correspondence Unit potential energy loss, and Theoretical maximum value In comparison, it can produce a sufficiently significant decay gradient.
[0062] In actual navigation optimization, all [devices] are scanned first. Target point, calculate the corresponding Value, and select to make The point with the highest return is taken as the initial optimal profit target.
[0063] Then, a continuous local potential field is generated centered on the target point using bilinear interpolation, which is used for subsequent gradient calculations.
[0064] Each control cycle will be recalculated based on the latest equipment status. and update This allows for real-time reflection of the impact of equipment status changes on operational revenue.
[0065] S3: Receive motion state prediction data of other devices in the neighborhood, calculate the future spatiotemporal congestion risk, and generate modulation factors to correct the global navigation potential energy surface, forming an effective interactive potential energy field for collaborative obstacle avoidance.
[0066] The device's navigation decisions rely solely on static obstacles or instantaneous location information at the current moment, lacking the ability to perceive the future movement intentions of other devices in the vicinity.
[0067] When multiple machines work together, they may be attracted to the same high-value target at the same time, calculate similar navigation paths, and thus their trajectories may intersect at a future point in time and space, causing congestion or even collisions.
[0068] By treating the future motion state of neighboring devices as exchangeable information, and sharing trajectory prediction data through vehicle-to-everything (V2X) networks, the uncertainty of conflict between the trajectories of multiple devices in the future spatiotemporal domain is quantified as congestion entropy, which is then used as a modulation factor to correct the constructed global navigation potential energy surface.
[0069] Firstly, through the vehicle-to-everything (V2X) communication modules deployed on construction machinery, with a periodicity... It interacts with other devices in the neighborhood at a frequency. Each device broadcasts status data packets. Include: Position vector at time Velocity vector acceleration vector And the covariance matrix describing the uncertainty of prediction. .
[0070] Divide the target work area into areas of size [size missing]. The uniform grid is used, and the size of the grid for dividing the target work area can be adjusted by the implementer according to the specific implementation scenario.
[0071] For the Each grid and its center coordinates Calculate its future moments spatiotemporal projection density ,in, Pick These are empirical values and can be adjusted by the implementer based on the specific implementation scenario.
[0072] This density is obtained by summing the occupancy probabilities of all neighboring devices on this grid:
[0073]
[0074] in, It is a device based on constant acceleration motion model prediction. The location at a future moment. This represents the probability density function of a multivariate Gaussian distribution. Indicates the first The center coordinates of each grid. The covariance matrix is the time-varying variance matrix, which is usually taken as... , Indicates the initial The covariance matrix at time step (TMT), provided by the state data packets broadcast by the device, is used to describe the uncertainty of the prediction. Represents the identity matrix. Let be the velocity uncertainty coefficient, and take an empirical value of . It can be adjusted by the implementer according to the specific implementation scenario.
[0075] To calculate the conflict uncertainty inherent in the distribution, the first... Each grid corresponds to a local spatiotemporal congestion entropy. .
[0076] Normalize the density values to a discrete probability distribution: ,in Given the set of all grid cells within the sensing range, the entropy of the discrete probability distribution is then calculated using the Shannon entropy formula. , as the spacetime congestion entropy.
[0077] The unit is nanometers. The higher the value, the more chaotic the probability distribution of the grid being occupied by multiple devices at the same time in the future, that is, the greater the risk of conflict.
[0078] Thus utilizing the spatiotemporal congestion entropy Constructing an entropy increase decay factor An exponential decay form is employed to enhance sensitivity to high-entropy regions, where is the entropy decay coefficient, an adjustable hyperparameter used to control the response strength to congestion entropy. In mining or large-scale earthmoving engineering scenarios, it is recommended to take . This is an empirical value and can be adjusted by the implementer according to the specific implementation scenario.
[0079] Using the attenuation factor for the global navigation potential energy surface Perform grid-by-grid dot product correction to form an effective interactive potential energy field. This adjustment affects areas that previously had higher returns but also higher future congestion risks. large and The potential energy value is significantly lowered, which is equivalent to dynamically generating a virtual obstacle zone driven by future conflict predictions on the entire potential energy surface. Based on... During navigation, it can detour in advance before physical distance conflicts occur, achieving predictive cooperative obstacle avoidance.
[0080] S4: Generate preliminary control commands based on the gradient of the effective interactive potential energy field, calculate the cumulative path input integral value of the device to the current target, construct dynamic decision switching resistance based on input and remaining distance, lock or switch the preliminary control commands through hysteresis comparison logic, and finally output rigid control commands.
[0081] When the potential energy values of multiple target work areas are close, even small potential energy fluctuations can cause the equipment to repeatedly switch the desired path between different targets. This high-frequency decision oscillation will subject the massive actuators of the construction machinery to alternating stress, causing abnormal wear and fuel waste. It is an unstable mode that must be suppressed in control.
[0082] Furthermore, the cumulative path input integral value is introduced as the sunk cost that the equipment has paid for the current goal; and a dynamically growing decision-making switching damping barrier is constructed based on this integral and the remaining distance.
[0083] This barrier increases dramatically with increasing investment and decreasing distance. Switching is only permitted when the profit advantage of the new target is sufficient to overcome this barrier, thereby suppressing ineffective oscillations.
[0084] Based on the obtained effective interactive potential field and its spatial gradient This generates initial control commands. These commands are expressed as the desired velocity vector. Its direction is determined by the negative gradient direction, which is the direction of slowest potential energy increase. The magnitude is related to the gradient magnitude and the maximum allowable speed of the device.
[0085]
[0086] In the formula, This is the device's current position. The velocity gain coefficient is a proportional constant that converts the potential energy gradient into a velocity command, measured in units of... For example, it can be taken from mining dump trucks. It only reflects the optimal direction of the instantaneous field and can be determined by the implementer according to the specific implementation scenario.
[0087] At the same time, parallel computing devices are working on the currently selected target. Cumulative path input integral value The points are awarded when the device locks onto the target. The moment From now time The physical work expended to reach that goal.
[0088] By collecting the traction force of the chassis drive in real time (unit ) and the instantaneous speed of the device along the current path (unit Integral value is obtained by integrating over time. :
[0089]
[0090] in, The unit is joule (J). In engineering, it is often expressed in kilojoules (kJ). For example, when a fully loaded dump truck climbs a hill, the product of its traction force and speed is relatively large. The accumulation was rapid, and the costs already incurred for the current task were objectively calculated.
[0091] based on From current device location to target The remaining Euclidean distance (unit Construct a dynamic decision-making switching damping barrier. Its value simulates the logical resistance that needs to be overcome in the switching decision: ;in, Let be the barrier gain constant, a dimensionless hyperparameter used to globally adjust the suppression strength of decision switching. In earthmoving scenarios requiring high operational continuity, it is recommended to take . It can be adjusted by the implementer according to the specific implementation scenario. For example, it is a very small positive number. This is used to prevent the denominator from being zero.
[0092] Therefore, when the equipment is first set off Small, big, Very low, allowing for flexible switching; when approaching the target big, Approaching zero Approaching infinity, locking onto the current target, ensuring task completion.
[0093] In each control cycle, evaluate except All potential targets effective potential energy value It then executes hysteresis comparison logic to determine whether to switch.
[0094] Set the current goal The corresponding effective potential energy is The switching condition is the existence of a This allows its potential energy advantage to exceed the dynamic potential barrier:
[0095]
[0096] When this condition is met, switch the current target to and based on the new Field Regeneration Output as a rigid control command; otherwise, maintain the original target and... It is directly issued as a rigid control command to the chassis actuators. This ensures the continuity and stability of decision-making, enabling construction machinery to complete complex tasks smoothly and efficiently.
[0097] This application discloses a terminal device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The processor executes the computer program using a remote monitoring method for engineering machinery equipment as described in the above-described embodiments.
[0098] Terminal devices include computer devices such as desktop computers, laptops, or cloud servers, and include, but are not limited to, processors and memory. For example, terminal devices may also include input / output devices, network access devices, and buses.
[0099] The processor can be a central processing unit (CPU). Of course, depending on the actual use, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc. This application does not limit this.
[0100] The memory can be an internal storage unit of the terminal device, such as a hard disk or RAM of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the terminal device. Furthermore, the memory can also be a combination of internal storage units and external storage devices of the terminal device. The memory is used to store computer programs and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.
[0101] In this terminal device, the remote monitoring method for engineering machinery equipment data of the above embodiment is stored in the memory of the terminal device and loaded and executed on the processor of the terminal device for user convenience.
[0102] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.
[0103] The above-described method for remote monitoring of engineering machinery equipment data is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the method.
[0104] It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this patent application shall be determined by the appended claims.
Claims
1. A method of remote monitoring of construction equipment data, characterized by, include: Acquire and process real-time operating data and spatial location data of each piece of construction machinery within the target work area; Based on the real-time operating data, the intrinsic operating capability potential energy factor of each device is calculated by multiplying the key state factors by forcibly returning them to zero, and the global navigation potential energy surface of the device's comprehensive efficiency is constructed by combining the target gravity and distance loss. The calculation of the intrinsic operating capability potential factor of each device includes the following steps: The real-time operating data of each piece of construction machinery in the target work area is preprocessed to obtain the baseline value of the real-time operating data of each piece of construction machinery in the target work area, and the baseline values of the real-time operating data of each piece of construction machinery in the target work area are multiplied together. Wherein, when any of the key state factors approaches the preset failure threshold, the intrinsic operational capability potential energy factor is forced to zero. It receives motion state prediction data from other devices in the neighborhood, calculates the future spatiotemporal congestion risk, and generates a modulation factor to correct the global navigation potential energy surface, forming an effective interactive potential energy field for collaborative obstacle avoidance. Based on the gradient of the effective interactive potential energy field, a preliminary control command is generated. At the same time, the cumulative path input integral value of the device to the current target is calculated, and a dynamic decision-making switching resistance is constructed based on the input and the remaining distance. The preliminary control command is locked or switched through hysteresis comparison logic, and finally a rigid control command is output.
2. The method of claim 1, wherein, The acquisition and processing of real-time operating condition data and spatial location data of each piece of construction machinery within the target work area includes: The device's energy data, hydraulic and thermal status data, and engine operating condition data are obtained through the vehicle bus, and the device's real-time spatial coordinates are obtained through the positioning module. The energy data is normalized to obtain the energy self-sufficiency index of the device's endurance.
3. The method of claim 1, wherein, The construction of the global navigation potential energy surface includes the following steps: Multiply the intrinsic operational capability potential energy factor by the target gravitational potential energy value preset based on the digital map of the target operational area, and then subtract the distance loss term, which is determined by the distance between the current position of the equipment and the target position and the preset transmission loss coefficient.
4. The method for remote monitoring of engineering machinery equipment data according to claim 1, characterized in that, Receiving motion state prediction data from other devices in the neighborhood includes the following steps: The vehicle-to-everything (V2X) communication module periodically broadcasts and receives status data packets containing the trajectory prediction envelopes of each neighboring device over a future period of time.
5. The method for remote monitoring of engineering machinery equipment data according to claim 4, characterized in that, The calculation of future spatiotemporal congestion risk includes the following steps: Based on the trajectory prediction envelope, the probability of each device occupying a specific spatial area at a future time is determined. After normalizing the occupancy probability, the spatiotemporal congestion entropy of the area at a future time is calculated according to the information entropy formula. The spatiotemporal congestion entropy is used as the quantitative value of congestion risk.
6. The method for remote monitoring of engineering machinery equipment data according to claim 5, characterized in that, The formation of an effective interactive potential energy field for collaborative obstacle avoidance includes the following steps: Multiplying the global navigation potential energy surface by a decay factor with the spatiotemporal congestion entropy as the variable results in a significant decay of the potential energy value of the global navigation potential energy surface in areas with high congestion risk.
7. The method for remote monitoring of engineering machinery equipment data according to claim 1, characterized in that, The computing device assigns an integral value to the cumulative path of the current target, including the following steps: By integrating the product of the chassis traction force and the instantaneous speed of motion of the equipment from the moment it locks onto the current target over time, the cumulative input quantification value for the physical work consumed is obtained.
8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads the computer program, it executes the remote monitoring method for engineering machinery equipment data as described in claims 1-7.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded by the processor, it executes the remote monitoring method for engineering machinery equipment data as described in claims 1-7.