Intelligent construction method and system based on Internet of Things monitoring

Abstract

The invention relates to an intelligent construction method and system based on Internet of Things monitoring, and the method comprises the steps: obtaining real-time vibration spectrum data and historical equipment vibration data of target construction equipment, carrying out the environment noise suppression of the real-time vibration spectrum data based on the historical equipment vibration data, and obtaining equipment noise reduction state features; obtaining an operation vibration waveform of the impact equipment, and carrying out equipment health degree analysis on the noise reduction state characteristics of the equipment to obtain a health degree attenuation rate; carrying out equipment load reduction operation quantification on the health degree attenuation rate based on a preset construction plan, and carrying out equipment safety collaborative analysis to obtain an associated equipment collaborative scheduling strategy; and performing instruction construction on the target construction equipment according to the associated equipment collaborative scheduling strategy to obtain a load control instruction, and performing construction optimization on the construction plan to obtain an optimized construction plan. According to the invention, the equipment health degree attenuation rate can be dynamically analyzed based on the operation vibration waveform of the impact equipment, and real-time response to the equipment performance change is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent construction, and in particular to an intelligent construction method and system based on Internet of Things monitoring. Background Art

[0002] IoT-based intelligent construction technology has become a key development direction in modern engineering construction. It improves construction efficiency, equipment reliability, and operational safety through real-time monitoring and data analysis. Traditional methods often rely on single-dimensional threshold judgments for vibration data analysis, lacking dynamic identification of equipment performance correlations. This results in insufficient real-time equipment load reduction strategies and construction plan adjustments. Furthermore, traditional methods struggle to quantify the impact of health degradation on the coordination of multiple devices, which can easily lead to local overloads or imbalanced resource allocation, hindering construction efficiency and safety. Summary of the Invention

[0003] The main purpose of the present invention is to provide an intelligent construction method and system based on Internet of Things monitoring, which can dynamically analyze the equipment health attenuation rate based on the operating vibration waveform of the impact equipment, and realize real-time response to changes in equipment performance.

[0004] To achieve the above objectives, the present invention provides an intelligent construction method based on Internet of Things monitoring, comprising: Acquire real-time vibration spectrum data and historical equipment vibration data of target construction equipment, and perform environmental noise suppression on the real-time vibration spectrum data based on the historical equipment vibration data to obtain equipment noise reduction status characteristics; Obtaining an operational vibration waveform of the impact equipment, performing equipment health analysis on the noise reduction state characteristics of the equipment, and obtaining a health attenuation rate; Based on the preset construction plan, the health decay rate is quantified for equipment load reduction operation, and equipment safety collaborative analysis is performed to obtain a collaborative scheduling strategy for related equipment; According to the associated equipment collaborative scheduling strategy, instructions are constructed for the target construction equipment to obtain load control instructions, and the construction plan is optimized to obtain the optimized construction plan.

[0005] Furthermore, the real-time vibration spectrum data and historical equipment vibration data of the target construction equipment are acquired, and environmental noise is suppressed on the real-time vibration spectrum data based on the historical equipment vibration data to obtain equipment noise reduction status characteristics, including: Acquire historical vibration data of the target construction equipment from a preset historical database, perform noise identification, and obtain a baseline vibration spectrum and typical working condition vibration characteristics; Performing seismic intensity identification on the real-time vibration spectrum data based on the reference vibration spectrum to obtain the intrinsic vibration mode of the equipment; Based on the typical working condition vibration characteristics, the real-time vibration spectrum data is subjected to working condition identification to obtain coupled noise modes; Performing noise suppression integration on the intrinsic vibration mode of the device and the coupled noise mode to obtain a vibration state of the device; Lyapunov exponent calculation is performed on the vibration state of the device to obtain the noise reduction state characteristics of the device.

[0006] Furthermore, the step of obtaining the operating vibration waveform of the impact device and performing equipment health analysis on the noise reduction state characteristics of the device to obtain the health decay rate includes: Performing frequency band energy matching on the noise reduction state characteristics of the equipment according to the operation vibration waveform to obtain an impact response characteristic; Calculating fatigue cumulative damage of the target construction equipment based on the impact response characteristics to obtain cumulative damage to the equipment; Performing dynamic modal decomposition on the noise reduction state characteristics of the equipment according to the accumulated amount of equipment damage to obtain equipment modal attenuation parameters; The device modal attenuation parameters are regressed and fitted based on a preset device health benchmark threshold to obtain a health attenuation rate.

[0007] Furthermore, the fatigue cumulative damage calculation of the target construction equipment based on the impact response characteristics to obtain the cumulative amount of equipment damage includes: Constructing a segmented curve of the shock response characteristic to obtain multiple segments of shock time history curves; performing stress cycle counting on the multiple impact time history curves according to a preset rain flow counting algorithm to obtain a stress amplitude distribution spectrum; performing equipment fatigue calculation on the target construction equipment based on the stress amplitude distribution spectrum to obtain a fatigue damage coefficient; Performing load spectrum correction on the fatigue damage coefficient to obtain an equivalent fatigue load spectrum; Equipment damage prediction is performed based on the equivalent fatigue load spectrum, and confidence optimization is performed to obtain the accumulated amount of equipment damage.

[0008] Furthermore, the health decay rate is quantified based on the preset construction plan to reduce the load of the equipment, and equipment safety collaborative analysis is performed to obtain a collaborative scheduling strategy for related equipment, including: Performing interval splitting processing on the health decay rate to obtain interval decay rate characteristics; Extracting load operation time from the construction plan to obtain equipment operation load time series data; Performing load reduction quantitative calculation on the equipment operation load time series data according to the interval attenuation rate characteristics to obtain equipment load reduction operation quantitative parameters; Performing multi-equipment collaborative constraints on the construction plan according to the equipment load reduction operation quantitative parameters to obtain collaborative operation constraint conditions; Based on the collaborative operation constraint conditions, cluster dynamic scheduling optimization is performed on the target construction equipment to obtain the collaborative scheduling strategy for the associated equipment.

[0009] Furthermore, the target construction equipment is dynamically scheduled and optimized based on the collaborative operation constraint condition to obtain the associated equipment collaborative scheduling strategy, including: Constructing equipment dependency relationships for the target construction equipment according to the collaborative operation constraint conditions to obtain an equipment dependency graph; Performing cluster security analysis on the target construction equipment according to the equipment dependency graph to obtain equipment security association data; Performing a scheduling priority calculation on the device security association data to obtain device scheduling priority data; Performing load distribution adjustment on the load reduction operation quantization parameters of the equipment according to the equipment scheduling priority data to obtain a load distribution plan; Performing resource matching calculation on the load distribution scheme to obtain resource matching degree data; Performing device association constraint processing on the collaborative operation constraint conditions according to the resource matching degree data to obtain an optimized constraint condition set; A collaborative scheduling strategy is constructed for the optimization constraint condition set to obtain the associated device collaborative scheduling strategy.

[0010] Furthermore, constructing instructions for the target construction equipment according to the associated equipment collaborative scheduling strategy to obtain load control instructions, and optimizing the construction plan to obtain the optimized construction plan includes: Performing instruction parameter analysis on the associated device collaborative scheduling strategy to obtain a device collaborative parameter set and a load adjustment coefficient matrix; Encoding instructions for the target construction equipment according to the equipment coordination parameter set to generate an initial control instruction sequence; constructing a load instruction for the initial control instruction sequence according to the load adjustment coefficient matrix to obtain a load control instruction; Performing spatiotemporal conflict detection on the construction plan according to the load control instruction to obtain an equipment scheduling conflict report; Performing plan optimization iterations on the equipment scheduling conflict report and the load control instruction to obtain the optimized construction plan.

[0011] Furthermore, performing spatiotemporal conflict detection on the construction plan according to the load control instruction to obtain an equipment scheduling conflict report includes: Performing motion feature analysis on the load control instruction to obtain equipment operation trajectory data; Performing a four-dimensional space-time conflict construction based on the equipment operation trajectory data and the construction plan to obtain a four-dimensional space-time relationship detection structure; Performing conflict boundary interactive detection on the four-dimensional spatiotemporal relationship detection structure to obtain a set of potential conflict events; sorting the potential conflict event set by their operational priorities according to the construction plan to obtain a conflict impact level list; The device scheduling conflict report is generated according to the conflict impact level list.

[0012] The present invention further provides an intelligent construction method system based on Internet of Things monitoring, which is applied to any of the above-mentioned intelligent construction methods based on Internet of Things monitoring, comprising: An acquisition module is configured to acquire real-time vibration spectrum data and historical equipment vibration data of target construction equipment, and perform environmental noise suppression on the real-time vibration spectrum data based on the historical equipment vibration data to obtain equipment noise reduction status characteristics; An analysis module, the analysis module being used to obtain an operating vibration waveform of the impact device, perform equipment health analysis on the noise reduction state characteristics of the device, and obtain a health decay rate; An association module, configured to quantify the equipment load reduction operation based on the health decay rate and perform equipment safety collaborative analysis to obtain a collaborative scheduling strategy for associated equipment based on a preset construction plan; A processing module is used to construct instructions for the target construction equipment according to the associated equipment collaborative scheduling strategy, obtain load control instructions, and optimize the construction plan to obtain the optimized construction plan.

[0013] The present invention provides an intelligent construction method and system based on Internet of Things monitoring, which has the following beneficial effects: By combining real-time vibration spectrum data with historical equipment vibration data to suppress environmental noise, the accuracy of equipment status monitoring can be effectively improved, overcoming the misjudgment problem caused by noise interference in traditional methods; the equipment health decay rate is dynamically analyzed based on the operating vibration waveform of the impact equipment, realizing real-time response to equipment performance changes and avoiding the lag caused by static threshold detection; by combining the health decay rate with the construction plan to quantify the equipment load reduction operation and build a collaborative scheduling strategy for related equipment, it is possible to optimize load distribution and improve the safety and construction efficiency of multi-equipment collaborative operations; the final generated load control instructions and optimized construction plan can dynamically adjust the equipment operating status, reduce resource waste and safety hazards caused by equipment overload or scheduling imbalance, thereby improving the intelligence level and reliability of the overall construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of an intelligent construction method based on Internet of Things monitoring provided by the present invention; Figure 2 This is a structural diagram of an intelligent construction system based on Internet of Things monitoring provided by the present invention.

[0015] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0018] Reference Figure 1 As shown, the present invention provides an intelligent construction method based on Internet of Things monitoring, comprising: Step S1: obtaining real-time vibration spectrum data and historical equipment vibration data of the target construction equipment, performing environmental noise suppression on the real-time vibration spectrum data based on the historical equipment vibration data, and obtaining equipment noise reduction status characteristics; Step S2: Obtain the operating vibration waveform of the impact equipment, perform equipment health analysis on the equipment noise reduction status characteristics, and obtain the health attenuation rate; Step S3: quantify the equipment load reduction operation based on the health decay rate based on the preset construction plan, and conduct equipment safety collaborative analysis to obtain the collaborative scheduling strategy for related equipment; Step S4: construct instructions for the target construction equipment according to the coordinated scheduling strategy of the associated equipment to obtain load control instructions, and optimize the construction plan to obtain an optimized construction plan.

[0019] Based on the above steps, the detailed process is as follows: Step S1: The real-time vibration spectrum data of the target construction equipment includes the mechanical vibration signal during equipment operation and environmental noise interference. Historical equipment vibration data records the vibration patterns of the equipment under different operating conditions, including spectral characteristics under normal operation, no-load, and load changes. By comparing real-time data with historical data, the frequency domain characteristics of environmental noise can be identified, such as background noise in specific frequency bands or non-equipment-related vibration interference. The real-time spectrum is decomposed using frequency domain analysis methods (such as Fourier transform or wavelet transform). Combined with the statistical characteristics of the noise in the historical data (such as mean, variance, or power spectral density), an adaptive filter is designed to suppress the noise frequency band. The filtered signal retains the core vibration characteristics of the equipment, such as the characteristic frequencies of bearings, gears, or motors, forming a noise reduction status signature of the equipment. This signature not only reflects the immediate operating status of the equipment but also provides clean input data for subsequent health analysis.

[0020] Step S2: The operational vibration waveform of impact equipment typically manifests as transient high-frequency vibrations, whose amplitude, duration, and repetition frequency directly affect the mechanical wear of the target construction equipment. Key frequency bands (such as harmonic components or resonance peaks) in the equipment's noise reduction characteristics are matched to the impact waveform in the time-frequency domain, and the impact's impact on the equipment is quantified through correlation analysis. The health decay rate is calculated based on a degradation model of the equipment's characteristic parameters, such as the cumulative offset of vibration amplitude, the drift of characteristic frequencies, or the entropy increase of energy distribution. Machine learning models (such as support vector regression or long-short-term memory networks) are trained on historical health data to predict the remaining life of the equipment in its current state. The specific value of the decay rate is obtained through differential calculations and reflects the downward trend in health per unit time. Its physical significance may be increased bearing clearance, lubrication failure, or accelerated structural fatigue.

[0021] Step S3: The preset construction plan includes the equipment's duty cycle, load distribution, and multi-equipment linkage logic. The health decay rate is compared with the load threshold in the construction plan to trigger the load reduction operation strategy. The quantification process is implemented through a dynamic programming algorithm. Under the premise of meeting the construction progress constraints, the operating parameters of the target equipment (such as speed, torque, or operation time) are adjusted to return the decay rate to a safe range. Equipment safety collaborative analysis takes into account the dependencies of related equipment, such as the feeding rhythm of concrete pump trucks and mixer trucks, or the matching efficiency of excavators and transport trucks. An equipment collaboration model is constructed based on graph theory or queuing theory to optimize task allocation and scheduling sequence. The resulting collaborative scheduling strategy may include equipment rotation, load transfer, or priority reconstruction to ensure a balance between overall construction efficiency and equipment safety.

[0022] Step S4: The coordinated scheduling strategy for associated equipment must be converted into executable load control instructions to dynamically adjust the operating status of the target construction equipment. These instructions are constructed based on equipment control interface protocols (such as CAN bus, Modbus, or Industrial IoT platform APIs), encoding the load reduction parameters in the scheduling strategy (such as power limits, speed thresholds, or work intervals) into control signals recognizable by the equipment. For example, for a hydraulic excavator, the instructions might include reducing pump pressure or limiting boom speed; for a tower crane, these might involve adjusting the maximum torque output of the hoist motor. The real-time nature of these instructions is ensured by edge computing nodes, ensuring they are delivered to the equipment controller within millisecond latency and simultaneously feeding back execution status to the central scheduling system. Construction plan optimization is a closed-loop iterative process that combines the execution performance of load control instructions with real-time monitoring data on equipment health. A rolling horizon optimization method is employed, using the current construction progress and equipment status as initial conditions to recalculate task allocation within future time windows. Optimization objectives include maximizing construction efficiency, minimizing equipment wear, and balancing energy consumption. The optimal solution is determined using a multi-objective genetic algorithm or linear programming approach. For example, if an excavator's health decay rate remains high, the optimized plan might shift some of its workload to a backup unit or extend its maintenance window. The resulting construction plan includes a revised schedule, equipment configuration, and emergency response plans, forming a dynamically adaptable intelligent construction system.

[0023] The present invention provides an intelligent construction method based on Internet of Things monitoring, which suppresses environmental noise by combining real-time vibration spectrum data with historical equipment vibration data, effectively improving the accuracy of equipment status monitoring and overcoming the misjudgment problem caused by noise interference in traditional methods; dynamically analyzing the equipment health decay rate based on the operating vibration waveform of the impact equipment, realizing real-time response to equipment performance changes and avoiding the lag caused by static threshold detection; quantifying the equipment load reduction operation by combining the health decay rate with the construction plan, and constructing a collaborative scheduling strategy for related equipment, which can optimize load distribution and improve the safety and construction efficiency of collaborative operations of multiple devices; the load control instructions and optimized construction plan finally generated can dynamically adjust the equipment operating status, reduce resource waste and safety hazards caused by equipment overload or scheduling imbalance, thereby improving the intelligence level and reliability of the overall construction process.

[0024] In one embodiment, real-time vibration spectrum data and historical equipment vibration data of the target construction equipment are obtained, and environmental noise suppression is performed on the real-time vibration spectrum data based on the historical equipment vibration data to obtain equipment noise reduction status characteristics, including: A pre-set historical database stores vibration data collected from target construction equipment under conditions free of mechanical damage and extreme environmental interference. The data is sampled at a 10kHz frequency and covers typical operating conditions throughout the equipment's lifecycle, including no-load, full-load, high-speed, low-speed, and temperature fluctuation scenarios. Noise identification is achieved through wavelet packet decomposition, using the db4 wavelet basis function to perform a six-layer decomposition of the historical data and calculate the energy entropy of each frequency band. Frequency bands whose energy entropy exceeds a threshold (set to 1.5 times the average entropy of the historical data) are identified as environmental noise. After separation, a baseline vibration spectrum and typical operating condition vibration signature are generated. The baseline vibration spectrum represents the inherent mechanical vibration characteristics of the equipment in the frequency domain, with a frequency range of 0-2kHz, encompassing the gear mesh fundamental frequency (50Hz) and its second harmonic (100Hz). The typical operating condition vibration signature is generated by extracting the first five principal components through principal component analysis (PCA), forming a feature vector library that characterizes the differences in vibration modes under different load and speed combinations. The resulting output is a baseline vibration spectrum (frequency-energy matrix) and a typical operating condition vibration signature (a set of principal component vectors).

[0025] Real-time vibration spectrum data is collected by accelerometers deployed in key areas of the equipment and converted to frequency domain data through anti-aliasing filtering and fast Fourier transform, with a frequency resolution of 1Hz. Seismic intensity identification uses Kullback-Leibler divergence to calculate the distribution difference between the real-time spectrum and the baseline vibration spectrum. Frequency bands where the difference exceeds a threshold (KL divergence > 0.8) are marked as noise-contaminated areas, while frequency bands where the difference is below the threshold are retained as the equipment's intrinsic vibration modes. The frequencies of the equipment's intrinsic vibration modes are concentrated between 0 and 1.5kHz and include the bearing's rotational characteristic frequencies (e.g., 30Hz) and their modulation sidebands (±5Hz). The processing results are output as a frequency band marker matrix for the equipment's intrinsic vibration modes, with valid frequency bands marked as 1 and noise bands marked as 0.

[0026] Operating condition identification uses variational modal decomposition to decompose the real-time vibration spectrum into 10 intrinsic mode functions (IMFs). The Pearson correlation coefficient of each IMF is calculated with the principal component vector of the typical operating condition vibration signature. IMFs with correlation coefficients below 0.7 are identified as coupled noise modes. Their frequencies range from 2 to 8 kHz, and their energy amplitude varies dynamically with external shocks (such as vibration from adjacent equipment). The energy concentration region of the coupled noise mode is determined through short-time energy analysis, using a 10ms time window and a 50% overlap. The resulting output is a collection of IMF components of the coupled noise mode, with the time-frequency distribution of these noise components stored in matrix form.

[0027] Noise suppression integration preserves the amplitude of the effective frequency band of the device's intrinsic vibration modes and applies adaptive weighted suppression to the noise frequency band of coupled noise modes. The weighting coefficient is dynamically adjusted based on the signal-to-noise ratio: when the signal-to-noise ratio is ≥20dB, the weight is 0.1 (strong suppression), when it is 10-20dB, the weight is 0.3 (medium suppression), and when it is <10dB, the weight is 0.5 (weak suppression). The integrated device vibration state is represented as time-domain waveform data. Phase information is maintained continuous through the Hilbert transform, and the signal reconstruction error is controlled within 5%. The processing output is a noise-reduced time-domain waveform of the device vibration state, with a sampling rate of 10kHz and a signal-to-noise ratio improvement of at least 15dB.

[0028] Lyapunov exponent calculation embeds the device vibration state time domain signal into phase space with a delay of τ = 10ms and an embedding dimension of m = 5. The maximum Lyapunov exponent (LLE) is calculated using the small data set method. If the LLE is greater than 0.2, the noise reduction is deemed ineffective and requires reprocessing. If the LLE is ≤ 0.2, the signal is deemed stable and the signal-to-noise ratio improvement (the difference between the current and original signal-to-noise ratios) and the intrinsic mode energy percentage (effective frequency band energy / total energy) are extracted. The device noise reduction status feature is stored as a vector, consisting of three dimensions: LLE value, signal-to-noise ratio improvement, and intrinsic mode energy percentage. The result is output as a device noise reduction status feature vector.

[0029] More specifically, the calculation process of the maximum Lyapunov exponent (LLE) is: The noise-reduced equipment vibration signal undergoes baseline correction and amplitude normalization to eliminate interference from non-steady-state trends. The optimal delay interval between adjacent data points (corresponding to the fundamental unit of time accumulation in the formula) is determined based on the signal's temporal autocorrelation. Combined with the false neighbor detection method, the minimum embedding dimension (corresponding to the complexity of the high-dimensional trajectory) is determined. This maps the one-dimensional signal sequence into a high-dimensional phase space trajectory, fully preserving the system's dynamic evolution characteristics. The time delay parameter generated in this step is directly linked to the embedding dimension, forming the basis for calculating subsequent evolution steps.

[0030] In the phase space trajectory, for each reference point, the nearest neighboring point is selected (the distance threshold is set as a fixed ratio of the signal amplitude). The trajectory path of each pair of neighboring points is tracked over time, and the change in spatial distance at different evolution steps is recorded. The number of evolution steps is multiplied by the sampling interval to obtain the time accumulation (corresponding to the time evolution quantity in the formula). The upper limit of the step number is dynamically adjusted based on the signal length and delay parameters to ensure data validity. The time evolution quantity and divergence distance series output from this step provide the core input for subsequent statistics.

[0031] Perform a logarithmic transformation on the divergence distance series for all pairs of neighboring points, and calculate the average logarithmic divergence rate (corresponding to the divergence rate quantification metric in the formula) at each evolutionary step. By aggregating the divergence rates of multiple pairs of neighboring points, we eliminate the random error of individual points and generate a curve showing the average divergence rate over time. The "average logarithmic divergence rate" defined in this step directly reflects the system's sensitivity to initial conditions and provides a statistical basis for index extraction.

[0032] Within the linear growth range of the average divergence rate curve, the covariance between the time accumulator and the average divergence rate (reflecting the strength of the linear correlation between the two) and the variance of the time accumulator (measuring the degree of dispersion of the temporal evolution) are calculated. The slope of the fitted line (corresponding to the maximum Lyapunov exponent) is calculated based on the ratio of the covariance to the variance. A positive slope indicates significant chaotic characteristics of the system, while a negative or zero slope indicates stability. The stability of the denoised signal is determined by combining an engineering threshold (e.g., a slope not exceeding 0.2), and a quantitative evaluation result is output. This step, through the explicit association between covariance and variance, transforms nonlinear dynamic behavior into an engineering-verifiable stability indicator.

[0033] Among them, the extraction formula of the maximum Lyapunov exponent is: is the maximum Lyapunov exponent, >0 means chaos, ≤0 indicates stability.

[0034] S(k) is the average divergence rate. k is the time evolution step, the range of time steps for tracking the trajectory of adjacent points, usually k = 1, 2, ..., K. Δt is the signal sampling time interval. K is the maximum tracking step number.

[0035] This embodiment extracts the vibration characteristics of the equipment throughout its life cycle from a historical database and combines this with wavelet packet decomposition for noise identification to completely separate the frequency domain characteristics of the equipment's inherent vibration and environmental noise, ensuring the reliability of the reference vibration spectrum and resolving the problem of misjudgment of noise caused by insufficient historical data coverage in traditional methods. Based on the real-time spectrum comparison and effective frequency band marking mechanism of KL divergence, the intrinsic vibration modes of the equipment are accurately identified, the interference of environmental high-frequency noise on mechanical state analysis is avoided, and the accuracy of intrinsic vibration feature extraction is improved. Variational modal decomposition and working condition feature correlation analysis are used to dynamically separate coupled noise, effectively distinguishing the mixed modes of the equipment's own vibration and environmental impact, and overcoming the frequency domain overlap problem of noise and equipment vibration in complex construction scenarios. Through adaptive weighted suppression and phase consistency retention strategies, the time domain integrity of the equipment vibration signal is maintained while reducing noise energy, providing high-fidelity input for subsequent health assessments.

[0036] In one embodiment, the operating vibration waveform of the impact equipment is obtained, and the equipment health analysis is performed on the equipment noise reduction status characteristics to obtain the health decay rate, including: During construction, impact equipment generates continuous operational vibration waveforms, which are collected in real time by high-precision accelerometers deployed through the Internet of Things. The sensors record vibration signals at a fixed sampling frequency (e.g., 10kHz) and upload the data to a cloud-based analysis platform. These operational vibration waveforms contain a mix of information, including mechanical vibrations from the equipment, impact force transmission, and ambient noise.

[0037] The equipment noise reduction status characteristics are key indicators that reflect the operating status of the impact equipment in noise reduction mode, including vibration amplitude, frequency distribution, and energy concentration areas. Frequency band energy matching decomposes the operating vibration waveform into multiple frequency bands through wavelet transform or Fourier transform, and calculates the energy contribution of each frequency band. The frequency band division rules are based on the inherent characteristics of the impact equipment. For example, the low-frequency band (0-200Hz) corresponds to mechanical structure vibration, and the high-frequency band (above 2000Hz) corresponds to noise interference. During the matching process, the system compares the current frequency band energy distribution with the energy distribution under the standard healthy state, and the difference is marked as the impact response characteristic. The impact response characteristic reflects the degree of deviation of the current vibration mode of the equipment from the ideal state. For example, an abnormal increase in the energy of a certain frequency band may indicate bearing wear or structural looseness.

[0038] The impact response characteristics are further used to calculate fatigue cumulative damage. This step, based on Miner's linear cumulative damage theory, converts the vibration amplitude, frequency, and number of actions in the impact response characteristics into equivalent load cycles. The material properties of the target construction equipment (such as fatigue limit and SN curve) are pre-entered into the database. The system calculates the microscopic damage contribution of each impact to the equipment based on the actual load spectrum. For example, high-frequency, low-amplitude vibration may produce small single damage, but long-term accumulation may lead to crack initiation; low-frequency, high-amplitude vibration directly accelerates structural fatigue.

[0039] The cumulative damage value quantifies the overall fatigue state of the equipment, and its calculation requires a time dimension. The system aggregates damage data on an hourly or daily basis, triggering an alert when the cumulative value exceeds a preset threshold (for example, 0.8, indicating 20% ​​remaining life). The dynamic update of the cumulative damage value relies on real-time impact response features to ensure that the assessment results reflect the latest equipment status.

[0040] The accumulated damage data is fed into the dynamic modal decomposition module, which uses proper orthogonal decomposition (POD) or empirical mode decomposition (EMD) to decompose the equipment's noise reduction state characteristics into several modal components. Each modal component represents a natural mode of equipment vibration. For example, the first mode might correspond to the bending vibration of the main frame, while the second mode reflects the reciprocating motion of the hydraulic cylinder. During the decomposition process, the system removes noise-related modes while retaining components strongly related to the mechanical structure.

[0041] Equipment modal attenuation parameters are derived by analyzing the energy decay rate of modal components. In a healthy state, modal energy decay is slow and stable. However, when a structure is damaged, the damping ratio of a specific mode increases, accelerating energy decay. For example, bearing wear can cause the attenuation parameters of rotational modes to increase by over 30%. The attenuation parameters are calculated by fitting the envelope of the modal components. The fitting function typically uses an exponential decay model, whose coefficients directly represent the attenuation strength.

[0042] The device's modal attenuation parameters are compared with preset device health thresholds. These thresholds are determined through historical data statistics or experimental calibration. For example, the modal attenuation parameter range for new devices is [0.05, 0.1]. The system uses multivariate linear regression or support vector regression (SVR) to map the current attenuation parameters to a health decay rate, which represents the rate of linear or nonlinear degradation of device performance.

[0043] In the regression model, the independent variables are the modal decay parameters and their interactions, and the dependent variable is the health decay rate. Training data comes from full lifecycle monitoring records of similar equipment to ensure model generalization. For example, a decay rate exceeding 0.15% / hour for a certain hydraulic hammer indicates that immediate maintenance is required. The resulting health decay rate is used to predict remaining life or develop maintenance plans. Its accuracy depends on the selection of modal parameters and the optimization of the regression algorithm.

[0044] This embodiment uses the Internet of Things to collect the operating vibration waveform of the impact equipment in real time, combines frequency band energy matching and dynamic modal decomposition technology, accurately extracts the equipment noise reduction status characteristics and modal attenuation parameters, and realizes continuous monitoring and quantitative evaluation of the equipment health status. The health decay rate analysis based on fatigue cumulative damage calculation and regression fitting can identify potential damage to the equipment in advance, avoid sudden failures, and significantly improve construction safety and equipment reliability. By presetting the frequency band division rules and modal screening conditions, the environmental noise interference is effectively filtered to ensure that the analysis results only reflect the true state of the mechanical structure, thereby enhancing the anti-interference ability of the monitoring system. The dynamic update mechanism of the equipment damage accumulation and health decay rate provides data support for maintenance decisions, optimizes the equipment maintenance cycle, and reduces the economic losses caused by unplanned downtime.

[0045] In one embodiment, fatigue cumulative damage calculation is performed on target construction equipment based on the impact response characteristics to obtain the cumulative amount of equipment damage, including: The shock response signature includes the vibration amplitude, frequency, and time series information of the equipment during operation. Segmented curve construction divides this signature into segments based on time windows. The duration of each segment is determined based on the equipment's operating characteristics. For example, a percussion drill is segmented based on a single impact cycle (e.g., 0.5 seconds). This segmentation rule ensures that each segment fully covers the rise, peak, and decay phases of a shock event, avoiding signal distortion caused by truncation.

[0046] A multi-segment impact time history curve is a collection of segmented vibration signals, with each curve representing the time domain waveform of an independent impact. Continuous segmentation is achieved using a sliding window method, with a window overlap ratio set to 30%-50% to prevent missing transient impacts. For example, the impact time history curve of a hydraulic breaker shows a peak acceleration of 15g, a duration of 80ms, and a waveform that conforms to an exponential decay law. The segmented results are stored as structured data for subsequent stress cycle counting.

[0047] The Rainflow Counting algorithm is an engineering method specifically designed for analyzing random load histories. Its core function is to extract valid stress cycles from complex time-domain signals, providing critical data support for equipment fatigue life assessment. The algorithm's implementation begins with signal preprocessing. By filtering multiple impact time-history curves (e.g., using low-pass filtering), it eliminates high-frequency noise and preserves the true impact signature. For example, using the vibration signal of a hydraulic breaker, filtering significantly reduces the peak acceleration fluctuation range from ±20g to ±15g, significantly improving the accuracy of subsequent analysis. After signal preprocessing, the algorithm enters the peak-valley detection phase, periodically scanning the time-history curve to identify all local maxima and minima, forming a complete "peak-valley-peak-valley" alternating sequence.

[0048] The core of the algorithm lies in the half-cycle matching process, which strictly adheres to the "rainflow" principle. Specifically, starting from the highest peak or lowest valley in the signal, the algorithm searches downward or upward for a matching valley or peak. When a higher peak (or lower valley) is detected following the current peak (or valley), the current half-cycle is terminated, and the remaining half-cycles enter the next round of matching. Through this mechanism, all valid cycles are ultimately accurately decomposed into full cycles and residual half-cycles. During the cycle counting and amplitude calculation phase, the stress amplitude (half the peak-to-valley difference) and mean (average of the peaks and valleys) are recorded for each successfully matched cycle. For example, if the peak stress of an impact is 300 MPa and the valley stress is 100 MPa, the calculated amplitude is 100 MPa and the mean is 200 MPa.

[0049] The stress amplitude distribution spectrum, a statistical representation of rainflow counting results, plays an important role in equipment fatigue analysis. Its construction first requires determining a stress amplitude grading scheme based on the fatigue properties of the equipment material (e.g., the SN curve range), typically using equal intervals. For example, if the fatigue limit of a particular steel is 200 MPa, the amplitude can be divided into multiple intervals of 50 MPa, such as 0-50 MPa and 50-100 MPa. The number of cycles within each amplitude interval is then accurately counted. For example, in a one-hour operation of a pile driver, the 100-150 MPa interval may produce 500 cycles, while the 200-250 MPa interval may only record 50 cycles. The final distribution spectrum is typically presented as an intuitive histogram or data table, with the horizontal axis representing the stress amplitude range and the vertical axis corresponding to the number of cycles or percentage. By analyzing this visualization, we can clearly understand the load characteristics of the equipment. For example, if the distribution spectrum of a tower crane shows that 80% of the cycles are concentrated in the 50-150 MPa range, it means that it mainly bears medium loads, while a few cycles exceeding 250 MPa indicate a possible overload risk.

[0050] In actual engineering applications, it is necessary to set reasonable analysis rules and conditions to ensure the reliability of the calculation results. The first thing is to set an appropriate amplitude threshold. Usually, cycles below the fatigue limit of the material (such as 10MPa) are ignored to avoid invalid calculations. For cases with higher cycle means (such as tensile mean >100MPa), the Goodman or Gerber correction method must be used to consider the influence of average stress on fatigue life. When the amplitude of an impact is detected to be far beyond the design limit of the equipment (such as >500MPa), it will be automatically marked as an abnormal event. Such situations may be caused by sensor failure or extreme working conditions. In addition, for intervals with too few cycles (such as <5 times), it is necessary to combine historical data or use Monte Carlo simulation methods to supplement statistical reliability.

[0051] Taking the actual application of a certain hydraulic breaker as an example, after inputting one minute of operating data, rainflow counting processing yields 120 valid cycles. The amplitude distribution may be as follows: 80 cycles in the 50-100 MPa range, 30 cycles in the 100-150 MPa range, 8 cycles in the 150-200 MPa range, and 2 cycles in the 200-250 MPa range. Analysis of this data reveals a key conclusion: the equipment's primary loads are concentrated in the 50-150 MPa range, which is within the normal operating range. A small number of high-amplitude cycles (>200 MPa) may correspond to hard rock impact conditions, requiring special attention to equipment wear. The resulting stress amplitude distribution spectrum serves as key input data and is directly passed to the subsequent fatigue calculation module, providing a scientific basis for assessing the degree of cumulative damage to the equipment.

[0052] Based on the stress amplitude distribution spectrum, the fatigue damage coefficient is calculated using Miner's linear cumulative damage theory. The stress-life curve (SN) of the target construction equipment is pre-entered into a database, with parameters obtained through experimental calibration or provided by the manufacturer. The single-cycle damage ratio is calculated by dividing the number of cycles corresponding to each stress amplitude by the allowable number of cycles for that amplitude. For example, if a 200 MPa stress amplitude occurs 1000 times, while the allowable number of cycles on the SN curve is 5000, the damage ratio is 0.2.

[0053] The fatigue damage coefficient is the sum of the damage ratios at each stress level. A value exceeding 1 indicates the theoretical lifespan has been exhausted. The calculation incorporates a Goodman correction to account for the effect of mean stress on fatigue life. The fatigue damage coefficient for an excavator hydraulic arm is 0.35, indicating approximately 65% ​​of its remaining life under the current operating mode. This coefficient is updated in real time to reflect the dynamic changes in accumulated damage.

[0054] The fatigue damage coefficient requires further correction to match actual operating conditions. Load spectrum correction incorporates environmental factors (such as temperature and humidity) and load sequence effects (such as high-low load interactions). Correction rules are based on historical data regression models. For example, in high-temperature environments, the fatigue damage coefficient is weighted by 1.2. For a tunnel boring machine operating in wet rock, the correction increased the damage coefficient by 15%.

[0055] The equivalent fatigue load spectrum maps the corrected damage coefficients to an equivalent spectrum under standard loading conditions. This spectrum employs the principle of equal damage to convert complex, variable-amplitude loads into equivalent cycles at a single amplitude. For example, the random load spectrum of an impact hammer is equivalent to 5000 cycles at an amplitude of 200 MPa. This equivalent spectrum simplifies the computational complexity of subsequent damage prediction while preserving the fatigue failure characteristics of the original load.

[0056] The equivalent fatigue load spectrum is input into a damage prediction model, which, combined with finite element analysis (FEA) or analytical methods, calculates the damage distribution of key components. Prediction results include crack initiation location, crack propagation rate, and remaining life. For example, damage on a pile driver's piston rod was concentrated at the thread root, with a predicted remaining impact life of 120,000.

[0057] Confidence optimization quantifies prediction uncertainty through Monte Carlo simulation or Bayesian updating. The coefficient of variation of input parameters (such as material strength dispersion and load measurement error) is set to 5%-10%. After 1000 samplings, a probability distribution of the damage accumulation is output. The damage accumulation of a tower crane connecting pin is 0.48±0.03 (90% confidence interval), indicating that the maintenance window must be within the next 200 hours.

[0058] This embodiment converts the impact response characteristics into a stress amplitude distribution spectrum through segmented curve construction and rain flow counting algorithm, accurately captures the cyclic characteristics of the dynamic load of the equipment, and avoids the evaluation deviation caused by ignoring transient impacts in traditional methods. Fatigue damage calculation based on the stress amplitude distribution spectrum and SN curve, combined with load spectrum correction, takes into account environmental factors and load sequence effects, so that the fatigue damage coefficient is more in line with the actual working conditions and improves the accuracy of the remaining life prediction. Through the equivalent fatigue load spectrum and probabilistic damage prediction, the uncertainty of the accumulated damage of the equipment is quantified, and a high-confidence remaining life interval is provided, which facilitates the formulation of accurate maintenance plans and reduces the risk of unplanned downtime. The combination of dynamic modal decomposition and fatigue cumulative damage calculation forms a closed-loop health analysis, which upgrades the monitoring of construction equipment from passive response to active prediction, improving construction safety and equipment management efficiency.

[0059] In one embodiment, the health decay rate is quantified based on a preset construction plan to reduce the load of equipment, and equipment safety collaborative analysis is performed to obtain a collaborative scheduling strategy for related equipment, including: The health decay rate of construction equipment is a core monitoring indicator, reflecting the real-time performance degradation of equipment. This health decay rate is quantified through pre-set construction plans to reduce equipment load. Based on this quantification, a multi-equipment coordinated scheduling strategy is developed to ensure construction progress while extending equipment life.

[0060] The interval splitting process of the health decay rate is the first step of this embodiment. According to the equipment type and operating environment, the continuously changing decay rate is divided into several intervals, such as low, medium and high levels. Each interval corresponds to a different equipment loss state, and the basis for division includes the performance threshold provided by the equipment manufacturer and the analysis results of historical operating data. For example, if the health decay rate of a certain model of excavator is in the range of 0.1-0.3% / h, it is classified as a medium decay rate interval. This process converts the original monitoring data into structured interval decay rate features, providing a basis for subsequent load reduction quantification.

[0061] Extracting the load operating time from the construction plan is a key step in synchronization. The construction plan analyzes the task timeline and corresponding load intensity of each piece of equipment to generate time-series data on the equipment's operating load. For example, a concrete pump truck might experience a load intensity of 90% during a specific period of heavy load, while dropping to 30% during maintenance. This data not only captures the equipment's operating status but also clarifies load requirements at different points in time, providing a precise time basis for load reduction calculations.

[0062] Based on the interval decay rate characteristics and the equipment operating load time series data, load reduction quantitative calculations are performed to generate equipment load reduction operation quantitative parameters. The calculation rules dynamically adjust the load limit according to the decay rate interval. The equipment load reduction operation quantitative parameters include: low decay rate interval: maintain the original load and do not trigger load reduction; medium decay rate interval: the load limit is reduced to 80% of the original plan, for example, the heavy load task is adjusted from 90% to 72%; high decay rate interval: the load limit is reduced to 50% of the original plan, and an early warning is triggered. For example, due to the high health decay rate of a tower crane, its original 100% load task was adjusted to 80%. The quantitative parameters are directly written to the equipment control terminal to achieve automated adjustment.

[0063] During calculations, the current decay rate range of the device is matched with the load time series data, dynamically adjusting the load value for future time periods. For example, if the medium decay rate feature of a tower crane is triggered, its heavy load task from 2:00 PM to 4:00 PM is adjusted from 100% to 80%. The output device load reduction operation quantitative parameter is "Tower Crane - 2:00 PM - 4:00 PM - Load 80%."

[0064] Generating multi-equipment coordination constraints is crucial for ensuring the feasibility of construction plans. By analyzing task dependencies between equipment and rebalancing resource allocation after load reduction, for example, if a concrete pump truck's load reduction slows pouring, the mixer truck's frequency of delivery will be automatically adjusted, or backup equipment will be activated to share some of the load. Coordination constraints are generated based on critical construction path analysis, prioritizing non-delayable task chains while optimizing overall resource utilization.

[0065] The final cluster dynamic scheduling optimization is based on collaborative constraints and employs an optimization algorithm to find the optimal equipment allocation plan. This may involve adjusting task timing, redistributing loads, and even deploying backup equipment to minimize delays and equipment wear. For example, in one construction scenario, some tower crane tasks could be transferred to healthier equipment, and rebar processing time periods could be adjusted to match resource availability after load shedding. The resulting collaborative scheduling strategy for associated equipment includes detailed equipment adjustment plans, directly guiding on-site construction execution.

[0066] All intermediate results are retained and applied across different stages. Interval decay rate features are used for long-term equipment health trend analysis. Time-series data on equipment operating loads supports real-time monitoring. Quantified parameters for load reduction drive automatic equipment adjustments. Collaborative constraints optimize construction simulation models. The resulting collaborative scheduling strategy directly enhances the intelligence of construction management.

[0067] This embodiment can dynamically adjust the equipment load by real-time monitoring of the health decay rate and dividing it into intervals, thereby avoiding overload operation of the equipment when performance degrades, thereby reducing the risk of failure and extending the service life of the equipment. Based on the health status of the equipment and the construction plan, the system intelligently adjusts the equipment operating parameters, and combines the collaborative constraints of multiple devices to optimize task allocation, reduce construction delays caused by the unloading of a single device, and improve overall construction efficiency. Through automated load reduction calculation and dynamic scheduling optimization, manual intervention is reduced, management costs are reduced, and the optimal balance between construction progress and equipment health status is ensured, thereby improving the intelligence level of the construction process. Through reasonable load reduction and collaborative scheduling, excessive equipment loss is reduced, energy waste is reduced, and equipment utilization efficiency is optimized, reducing unnecessary maintenance and replacement costs.

[0068] In one embodiment, cluster dynamic scheduling optimization is performed on target construction equipment based on collaborative operation constraints to obtain a collaborative scheduling strategy for associated equipment, including: Cluster dynamic scheduling optimization is the core link in achieving the coordinated operation of multiple devices. Based on the coordinated operation constraints, the system goes through a series of processing steps, such as building device dependencies, analyzing security associations, and calculating scheduling priorities, to ultimately generate an optimized coordinated scheduling strategy for associated devices.

[0069] Establishing equipment dependency relationships is a fundamental part of cluster scheduling. The system analyzes the task timing and material flow relationships of each device in the construction plan and constructs an equipment dependency graph. This graph uses a directed acyclic graph (DAG) data structure, where nodes represent construction equipment and edges represent dependencies between devices. For example, a concrete pump truck node points to a tower crane node, indicating that the pump truck's pouring operation depends on the tower crane's material lifting. The dependency strength is quantified by the edge weight, and the weight value is calculated based on historical collaboration data. Construction rules require that critical path equipment must serve as the source node of the graph, and parallel operation equipment must maintain an independent subgraph structure. The resulting equipment dependency graph is stored in the graph database, containing fields such as device ID, dependency direction, and dependency strength.

[0070] Cluster safety analysis is based on a device dependency graph. The system combines real-time health data and device spatial location information to calculate device safety association data. The analysis follows three core rules: the safety distance between spatially adjacent devices must be no less than 3 meters, the total power load must not exceed 80% of the transformer capacity, and the difference in health decay rate between interdependent devices must not exceed 0.2% / hour. Safety association data is stored in a matrix format, with rows and columns corresponding to device IDs. Element values ​​represent the degree of safety association between devices, ranging from 0 to 1, with 1 indicating complete safety. For example, the safety association value between an excavator and a dump truck is 0.8, indicating a minor safety risk.

[0071] The dispatch priority calculation converts device safety association data into an executable priority sequence. The calculation model considers three dimensions: the topological order of the device in the dependency graph, the device's current health score, and the criticality of the device's task. The priority calculation formula is: Priority coefficient = 0.5 × topological order + 0.3 × health score + 0.2 × task criticality. Device dispatch priority data is output as an ordered list, with each item containing the device ID and priority coefficient. For example, a tower crane has a priority coefficient of 0.85, ranking first; a concrete pump truck has a coefficient of 0.72, ranking third. This data directly determines the order of subsequent load distribution.

[0072] The load distribution adjustment phase combines equipment scheduling priority data with existing quantitative parameters for equipment load reduction. The adjustment algorithm follows the principle of prioritizing high-priority equipment and flexibly allocating lower-priority equipment. Specific rules include: the top 20% of equipment receive 110% of the original planned load quota, the middle 60% maintain their adjusted loads, and the bottom 20% are required to reduce their loads by an additional 15%. The adjustment process generates a load distribution plan that records the precise load values ​​for each equipment at each time period. For example, the load of a high-priority tower crane can recover from 80% after load reduction to 88%.

[0073] Resource matching calculations verify the feasibility of load distribution plans. The system compares the resource requirements of each device with the actual supply on site and calculates resource matching data. Matching evaluation indicators include power supply matching, human resource matching, and material supply matching. Each indicator is scored on a percentage basis, with a score of 80 or above considered acceptable. The calculation process uses a multidimensional linear programming model, with the objective function being to minimize resource gaps. For example, the calculation results for a certain period showed a power matching score of 92, human resource matching of 85, and material matching of 78, for an overall matching score of 82.

[0074] During the equipment-related constraint processing phase, existing constraints are optimized based on resource matching data. The processing rule is: when the matching degree of a resource type is below 80, the corresponding constraint is relaxed by 10%; when the matching degree is above 90, the constraint is tightened by 5%. The optimized constraint set is output as a revised constraint document, which includes updated parameters such as power capacity limits and minimum safety distances. For example, the original constraint "Total equipment power on the same working surface ≤ 200kW" is adjusted to "≤ 220kW."

[0075] The collaborative scheduling construction ultimately generates a collaborative scheduling strategy for associated equipment. This construction process utilizes a mixed integer programming algorithm. The inputs are the optimization constraint set and the equipment load distribution plan. The output is a scheduling strategy consisting of the following elements: an equipment start / stop schedule, a load distribution matrix, and an emergency switching plan. The strategy is presented in a visual chart, with the operating status of each device indicated by a color. For example, green indicates normal load, yellow indicates reduced load, and red indicates standby. This strategy is distributed to each device terminal via the construction management platform for automated execution.

[0076] The device dependency graph supports subsequent safety analysis. Device safety-related data guides priority calculations. Device scheduling priority data drives load distribution. The load distribution plan is input into resource matching calculations. Resource matching data optimizes constraints, and ultimately, the optimized constraint set generates a coordinated scheduling strategy. This entire process is executed in a 30-minute loop to ensure that the scheduling strategy always reflects the latest site status.

[0077] Equipment dependency relationships are constructed based on the principle of prioritizing critical paths. Cluster safety analysis utilizes a three-dimensional assessment model. Dynamic weight coefficients are set for scheduling priority calculations. A tiered quota system is implemented for load distribution adjustments. A multi-dimensional evaluation system is established for resource matching calculations. Constraint handling utilizes a flexible adjustment mechanism. These rules collectively ensure that the final scheduling strategy achieves the optimal configuration of equipment resources while ensuring construction safety.

[0078] This embodiment can effectively improve the collaborative efficiency and safety of construction equipment through cluster dynamic scheduling optimization based on collaborative operation constraints. The construction of the equipment dependency graph clarifies the task timing and material flow relationship between construction equipment, so that the scheduling process has a clear logical basis and avoids task conflicts between equipment. Cluster safety analysis combines real-time health and spatial location data to dynamically evaluate the degree of safety correlation between equipment, ensuring that high-risk equipment is unloaded or isolated in a timely manner, significantly reducing the incidence of construction accidents. The optimized constraint set adapts to changes in on-site resources through a flexible adjustment mechanism, improving the flexibility of the scheduling strategy. The final generated collaborative scheduling strategy for associated equipment guides equipment operation through visual instructions, realizes the automation and intelligence of the construction process, improves overall construction efficiency, and reduces the risk of equipment failure and energy consumption.

[0079] In one embodiment, instructions are constructed for target construction equipment according to the associated equipment collaborative scheduling strategy to obtain load control instructions, and the construction plan is optimized to obtain an optimized construction plan, including: The associated device coordination scheduling strategy takes as input data, including device coordination parameters, load adjustment coefficients, and job timing rules. A protocol parser is used to extract the device coordination parameter set and load adjustment coefficient matrix. The device coordination parameter set consists of the device ID, priority weight, and timing offset. The priority weight determines the order in which commands are issued, and the timing offset calibrates the device's operating time window. The load adjustment coefficient matrix is ​​a two-dimensional array, with row indices corresponding to device IDs and column indices corresponding to time slices (each 5 minutes). Matrix elements represent load factor adjustments (e.g., -20% indicates a 20% load reduction). During the parsing process, the device coordination parameter set must meet the requirement that devices with priority weights greater than or equal to 80 are marked as critical devices. Adjustments to the load adjustment coefficient matrix are limited to ±30% to ensure compliance with the device's rated load safety threshold. The resulting output is the device coordination parameter set (a structured list of key-value pairs) and the load adjustment coefficient matrix (a mapping table of time slices and load factors).

[0080] The device collaboration parameter set is used to generate the initial control command sequence. The device ID is mapped to the target device's physical address (such as an IP address or CAN bus ID) via the device communication protocol converter. The timing offset is converted into an NTP timing command, and the priority weight is encoded as the tag field of the command queue. The binary instruction stream of the initial control command sequence must conform to the ModbusTCP protocol frame structure. The instruction length must match the device controller register capacity, and the length of a single instruction must not exceed 256 bytes. Preset rules require that the execution error of the timing offset be less than 100ms and the spatial coordinate mapping error be less than 0.5m. The processing results are output as the initial control command sequence (.bin or .hex format file).

[0081] The load adjustment coefficient matrix drives the construction of load commands, converting the load adjustment values ​​in the matrix into specific operation codes for the device controller. For example, a 20% reduction in the hydraulic pump load corresponds to the controller's pressure valve opening command code 0x7E, while a 10% increase in the concrete mixer load corresponds to the motor speed PWM duty cycle command code 0x8F. The load commands are combined with the initial control command sequence through the command merging engine to generate the final load control command. This merging process adheres to the timing constraints of the device communication protocol, ensuring that the load adjustment command is issued 500ms before the start of the time slice to avoid job conflicts caused by control delays. The result of this processing is the output of the load control command (a complete command stream file with a timestamp).

[0082] Load control commands are input into the spatiotemporal conflict detection module. Based on the coordinates of the construction plan's 3D BIM model, the equipment paths (e.g., tower crane boom trajectory, pump truck boom extension path) are discretized into a spatiotemporal grid (spatial resolution 0.5m, temporal resolution 1 minute). The A* path planning algorithm is then used to detect overlapping grid areas. The equipment scheduling conflict report records the conflict time slice, conflicting equipment ID, and conflicting grid coordinates. The conflict determination rule requires that conflicts be flagged when the minimum safe distance between equipment is less than 1m. Path planning must circumvent electronic fences (e.g., prohibiting equipment from entering within 5m of high-voltage power lines). The resulting processing is output as an equipment scheduling conflict report (CSV file containing the conflict time, equipment ID, and coordinates).

[0083] Equipment scheduling conflict reports and load control instructions are input into the optimization iteration engine, which uses a multi-objective genetic algorithm to adjust equipment paths or timing parameters. Path replanning uses the RRT* algorithm to generate obstacle avoidance paths, with the timing offset adjustment range limited to ±15% of the original value. Spatiotemporal conflict detection is re-executed after each iteration until the conflict rate (the ratio of conflicting grids to the total number of grids) drops below 2% or the maximum number of iterations (5) is reached. The optimized construction plan updates the path parameters and timing offsets in the load control instructions, and outputs the final instruction stream file and construction progress document. The processing results output the optimized construction plan (including path parameters, timing table, and conflict rate convergence report).

[0084] This embodiment dynamically extracts the equipment coordination parameter set and the load adjustment coefficient matrix through the protocol parser to achieve accurate parsing and parameter decoupling of multi-equipment coordination strategies, solves the problem of low instruction generation efficiency caused by protocol field coupling in traditional methods, and significantly improves the dynamic adaptability of construction equipment collaborative control. Based on the instruction encoding mechanism of the equipment communication protocol converter, the timing offset and priority weight are converted into a standardized control instruction sequence to ensure that the instruction stream is compatible with the register structure of the heterogeneous equipment controller, avoiding communication failure or malfunction due to protocol mismatch. The load adjustment coefficient matrix is ​​mapped to the equipment operation code through the load instruction construction engine to achieve dynamic quantitative control of the load rate. Under the premise of ensuring the safe operation of the equipment, it matches the construction progress requirements to the maximum extent and reduces the frequency of manual intervention.

[0085] In one embodiment, a time-space conflict detection is performed on the construction plan according to the load control instruction to obtain an equipment scheduling conflict report, including: Load control instructions are structured instruction streams containing the device's path, timing parameters, and load factor adjustment values. A protocol parser extracts the device ID, trajectory coordinate sequence, timestamp, and load factor parameters. Motion feature analysis decodes the device's trajectory data from the instruction stream. This trajectory data, consisting of three-dimensional spatial coordinates (X, Y, Z) and a timestamp (T), represents the dynamic position changes of the device within the construction scenario. During analysis, the trajectory coordinates are converted to the global coordinate system of the construction BIM model (with the origin being the site reference point), the timestamp is aligned to the master clock source of the construction plan, and the load factor parameter is mapped to the device's speed threshold (e.g., a 20% reduction in load factor corresponds to a 15% decrease in maximum speed). Preset rules for motion feature analysis require a trajectory coordinate conversion error of ≤0.1m and a timestamp synchronization error of ≤100ms. The resulting data is output as a four-dimensional dataset of coordinates, time, and speed.

[0086] Equipment trajectory data is input into the 4D spatiotemporal conflict construction module, which discretizes the equipment motion paths into 4D spatiotemporal voxels (spatial resolution 0.5m×0.5m×0.5m, temporal resolution 1 minute). The 4D spatiotemporal relationship detection structure consists of a spatiotemporal voxel index table, which records the ID of the equipment occupying each voxel and its motion state (stationary, moving, accelerating). During the construction process, the equipment trajectory segments are discretized and filled in the 4D voxel space using the Bresenham algorithm, marking occupied voxel units. The voxel filling rules of the 4D spatiotemporal relationship detection structure require continuity of motion between adjacent voxels (for example, when equipment moves from voxel A to voxel B, the intermediate voxels must be marked as transitional). Time slice divisions must be aligned with the process nodes of the construction plan. The processing results are output as a 4D spatiotemporal relationship detection structure (voxel occupancy state matrix).

[0087] The four-dimensional spatiotemporal relationship detection architecture is a quantitative analysis model constructed by discretizing the spatial and temporal dimensions of the construction scene. It accurately describes the dynamic occupancy and interaction of equipment during the construction process. This architecture divides the construction area into three-dimensional voxels of 0.5-meter squares and divides the total construction time into one-minute time slices, forming four-dimensional voxel units indexed by spatial coordinates (X, Y, Z) and timestamps (T). Each voxel unit records the unique identifier of the occupied equipment, entry and exit timestamps, and motion state (stationary, moving, accelerating), forming a spatiotemporal voxel index table. The equipment trajectory is discretized into a continuous voxel sequence using the four-dimensional Bresenham algorithm, ensuring that all spatial voxels traversed by the trajectory segment are marked as occupied within the corresponding time slice and inherit the equipment's motion state characteristics. For example, when a tower crane boom moves at a constant speed from coordinates (10, 20, 5) to (15, 25, 8) in 30 seconds, its trajectory segment will fill the voxels it passes through and be marked as "moving" in the index table from time slice 09:30 to 09:30.5.

[0088] The spatiotemporal voxel index table further supports the conflict detection logic: spatial overlap conflicts are triggered when the same spatial voxel is occupied by multiple devices in the same time slice, and motion direction conflicts are triggered when the angle between the device motion directions of adjacent voxels is less than 45 degrees. Conflict events are graded according to severity: high-risk conflicts are defined as devices being less than 1 meter apart and with completely overlapping time windows, medium-risk conflicts are defined as intersecting motion paths and partially overlapping predicted collision times, and low-risk conflicts are defined as devices with motion trend conflicts within adjacent voxels. This structure optimizes data size through sparse matrix storage and non-empty voxel compression technology, supporting real-time loading and visualization on the construction management platform, such as highlighting high-risk conflict voxels in red and marking medium-risk conflict areas in yellow. The final output of the four-dimensional spatiotemporal relationship detection structure provides a data foundation with both spatial accuracy and temporal granularity for conflict analysis, and is the core technical carrier for achieving equipment collaborative scheduling and risk prevention and control.

[0089] The four-dimensional spatiotemporal relationship detection structure is input into the conflict boundary interactive detection module, which identifies potential conflict events through spatial topology analysis and temporal overlap detection. A conflict boundary is defined as the minimum distance between two device trajectory voxels within the same spatiotemporal unit ≤ a safety threshold (1m) or a motion direction angle ≤ 45° (potentially causing a collision). The potential conflict event set records the conflict voxel coordinates, device ID pairs, conflict type (spatial overlap, motion direction conflict), and conflict time window. During the detection process, a parallel computing framework is used to traverse the four-dimensional voxels to accelerate conflict event retrieval. Preset rules require a conflict detection response time of ≤10 seconds and a missed detection rate of ≤2%. The processing results output a potential conflict event set (a structured list containing conflict coordinates, device IDs, type, and time).

[0090] The set of potential conflict events is prioritized based on the process priorities and equipment criticality parameters in the construction plan. A conflict impact level list is dynamically generated based on the conflict type and impact scope: spatial overlap conflicts involving critical equipment (priority weight ≥ 80) are marked as high-risk; motion direction conflicts occurring within a non-postponable process window are marked as medium-risk; all other conflicts are marked as low-risk. The sorting rules require that high-risk conflicts be handled first, medium-risk conflicts must be resolved within 24 hours, and low-risk conflicts can be deferred to the next construction phase. The processing results are output as a conflict impact level list (a priority table containing conflict event IDs, level labels, and resolution time limits).

[0091] The conflict impact level list drives the generation of the equipment scheduling conflict report. The report fields include the conflict event ID, equipment ID pair, conflict coordinates, time window, conflict type, impact level, and recommended measures (such as path replanning and timing offset). When the report is generated, the preset processing strategy is automatically associated according to the conflict level: high-risk conflicts trigger an immediate alarm and freeze the operation of the equipment involved, medium-risk conflicts generate an optimized task queue, and low-risk conflicts are recorded in the log for subsequent analysis. The format of the equipment scheduling conflict report must be compatible with the data interface specifications of the construction management platform (such as CSV or JSON) and support visual display (such as a three-dimensional conflict heat map). The processing results are output as an equipment scheduling conflict report (structured document and visualization file).

[0092] This embodiment discretizes the equipment's trajectory into high-precision space-time units through a four-dimensional space-time voxelization method, achieving full-dimensional modeling of the equipment's dynamic behavior in construction scenarios. This addresses the defect of traditional three-dimensional spatial models that cannot quantify the temporal evolution process, and significantly improves the space-time coverage integrity of conflict detection. The trajectory discretization filling mechanism based on the four-dimensional Bresenham algorithm ensures the continuity mapping of the equipment's motion path in the space-time voxels, avoids misjudgments of conflicts caused by trajectory breaks or missed detections, and enhances the robustness of detection in complex motion patterns. The dynamic state markings (stationary, moving, accelerating) of the space-time voxel index table support refined analysis of equipment behavior, predict potential risks (such as subsequent collisions that may be caused by accelerating equipment) through motion state inheritance rules, and trigger early warning mechanisms.

[0093] Reference Figure 2 As shown, the present invention also provides an intelligent construction method system based on Internet of Things monitoring, which is applied to any of the above-mentioned intelligent construction methods based on Internet of Things monitoring, including: An acquisition module is used to obtain real-time vibration spectrum data and historical equipment vibration data of target construction equipment, and suppress environmental noise on the real-time vibration spectrum data based on the historical equipment vibration data to obtain equipment noise reduction status characteristics; An analysis module is used to obtain the operating vibration waveform of the impact equipment, perform equipment health analysis on the equipment noise reduction status characteristics, and obtain the health attenuation rate; The association module is used to quantify the equipment load reduction operation based on the health decay rate based on the preset construction plan, and conduct equipment safety collaborative analysis to obtain the collaborative scheduling strategy of the associated equipment; The processing module is used to construct instructions for the target construction equipment according to the collaborative scheduling strategy of the associated equipment, obtain load control instructions, and optimize the construction plan to obtain an optimized construction plan.

[0094] It should be noted that, those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0095] The present invention provides an intelligent construction system based on Internet of Things monitoring, which suppresses environmental noise by combining real-time vibration spectrum data with historical equipment vibration data, effectively improving the accuracy of equipment status monitoring and overcoming the misjudgment problem caused by noise interference in traditional methods; dynamically analyzing the equipment health decay rate based on the operating vibration waveform of the impact equipment, realizing real-time response to equipment performance changes and avoiding the lag caused by static threshold detection; quantifying the equipment load reduction operation by combining the health decay rate with the construction plan, and constructing a collaborative scheduling strategy for related equipment, which can optimize load distribution and improve the safety and construction efficiency of collaborative operations of multiple devices; the load control instructions and optimized construction plan finally generated can dynamically adjust the equipment operating status, reduce resource waste and safety hazards caused by equipment overload or scheduling imbalance, thereby improving the intelligence level and reliability of the overall construction process.

[0096] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent construction method based on Internet of Things monitoring, characterized in that: include: Acquire real-time vibration spectrum data and historical equipment vibration data of target construction equipment, and perform environmental noise suppression on the real-time vibration spectrum data based on the historical equipment vibration data to obtain equipment noise reduction status characteristics; Obtaining an operational vibration waveform of the impact equipment, performing equipment health analysis on the noise reduction state characteristics of the equipment, and obtaining a health attenuation rate; Based on the preset construction plan, the health decay rate is quantified for equipment load reduction operation, and equipment safety collaborative analysis is performed to obtain a collaborative scheduling strategy for related equipment; According to the associated equipment collaborative scheduling strategy, instructions are constructed for the target construction equipment to obtain load control instructions, and the construction plan is optimized to obtain the optimized construction plan.

2. The intelligent construction method based on Internet of Things monitoring according to claim 1 is characterized in that: The step of obtaining real-time vibration spectrum data and historical equipment vibration data of the target construction equipment, and performing environmental noise suppression on the real-time vibration spectrum data based on the historical equipment vibration data to obtain equipment noise reduction status characteristics includes: Acquire historical vibration data of the target construction equipment from a preset historical database, perform noise identification, and obtain a baseline vibration spectrum and typical working condition vibration characteristics; Performing seismic intensity identification on the real-time vibration spectrum data based on the reference vibration spectrum to obtain the intrinsic vibration mode of the equipment; Based on the typical working condition vibration characteristics, the real-time vibration spectrum data is subjected to working condition identification to obtain coupled noise modes; Performing noise suppression integration on the intrinsic vibration mode of the device and the coupled noise mode to obtain a vibration state of the device; Lyapunov exponent calculation is performed on the vibration state of the device to obtain the noise reduction state characteristics of the device.

3. The intelligent construction method based on Internet of Things monitoring according to claim 1 is characterized in that: The step of obtaining the operating vibration waveform of the impact device, performing equipment health analysis on the noise reduction state characteristics of the device, and obtaining the health attenuation rate includes: Performing frequency band energy matching on the noise reduction state characteristics of the equipment according to the operation vibration waveform to obtain an impact response characteristic; Calculating fatigue cumulative damage of the target construction equipment based on the impact response characteristics to obtain cumulative damage to the equipment; Performing dynamic modal decomposition on the noise reduction state characteristics of the equipment according to the accumulated amount of equipment damage to obtain equipment modal attenuation parameters; The device modal attenuation parameters are regressed and fitted based on a preset device health benchmark threshold to obtain a health attenuation rate.

4. The intelligent construction method based on Internet of Things monitoring according to claim 3 is characterized in that: The step of calculating fatigue cumulative damage of the target construction equipment based on the impact response characteristics to obtain cumulative damage to the equipment includes: Constructing a segmented curve of the shock response characteristic to obtain multiple segments of shock time history curves; performing stress cycle counting on the multiple impact time history curves according to a preset rain flow counting algorithm to obtain a stress amplitude distribution spectrum; performing equipment fatigue calculation on the target construction equipment based on the stress amplitude distribution spectrum to obtain a fatigue damage coefficient; Performing load spectrum correction on the fatigue damage coefficient to obtain an equivalent fatigue load spectrum; Equipment damage prediction is performed based on the equivalent fatigue load spectrum, and confidence optimization is performed to obtain the accumulated amount of equipment damage.

5. The intelligent construction method based on Internet of Things monitoring according to claim 1 is characterized in that: The health decay rate is quantified based on the preset construction plan for equipment load reduction operation, and equipment safety collaborative analysis is performed to obtain a collaborative scheduling strategy for related equipment, including: Performing interval splitting processing on the health decay rate to obtain interval decay rate characteristics; Extracting load operation time from the construction plan to obtain equipment operation load time series data; Performing load reduction quantitative calculation on the equipment operation load time series data according to the interval attenuation rate characteristics to obtain equipment load reduction operation quantitative parameters; Performing multi-equipment collaborative constraints on the construction plan according to the equipment load reduction operation quantitative parameters to obtain collaborative operation constraint conditions; Based on the collaborative operation constraint conditions, cluster dynamic scheduling optimization is performed on the target construction equipment to obtain the associated equipment collaborative scheduling strategy.

6. The intelligent construction method based on Internet of Things monitoring according to claim 5 is characterized in that: Performing cluster dynamic scheduling optimization on the target construction equipment based on the collaborative operation constraint condition to obtain the associated equipment collaborative scheduling strategy includes: Constructing equipment dependency relationships for the target construction equipment according to the collaborative operation constraint conditions to obtain an equipment dependency graph; Performing cluster security analysis on the target construction equipment according to the equipment dependency graph to obtain equipment security association data; Performing a scheduling priority calculation on the device security association data to obtain device scheduling priority data; Performing load distribution adjustment on the load reduction operation quantization parameters of the equipment according to the equipment scheduling priority data to obtain a load distribution plan; Performing resource matching calculation on the load distribution scheme to obtain resource matching degree data; Performing device association constraint processing on the collaborative operation constraint conditions according to the resource matching degree data to obtain an optimized constraint condition set; A collaborative scheduling strategy is constructed for the optimization constraint condition set to obtain the associated device collaborative scheduling strategy.

7. The intelligent construction method based on Internet of Things monitoring according to claim 1 is characterized in that: The step of constructing instructions for the target construction equipment according to the associated equipment collaborative scheduling strategy to obtain load control instructions, and optimizing the construction plan to obtain the optimized construction plan includes: Parsing the instruction parameters of the associated device collaborative scheduling strategy to obtain a device collaborative parameter set and a load adjustment coefficient matrix; Encoding instructions for the target construction equipment according to the equipment coordination parameter set to generate an initial control instruction sequence; constructing a load instruction for the initial control instruction sequence according to the load adjustment coefficient matrix to obtain a load control instruction; Performing spatiotemporal conflict detection on the construction plan according to the load control instruction to obtain an equipment scheduling conflict report; Performing plan optimization iterations on the equipment scheduling conflict report and the load control instruction to obtain the optimized construction plan.

8. The intelligent construction method based on Internet of Things monitoring according to claim 7 is characterized in that: The performing time-space conflict detection on the construction plan according to the load control instruction to obtain an equipment scheduling conflict report includes: Performing motion feature analysis on the load control instruction to obtain equipment operation trajectory data; Performing a four-dimensional space-time conflict construction based on the equipment operation trajectory data and the construction plan to obtain a four-dimensional space-time relationship detection structure; Performing conflict boundary interactive detection on the four-dimensional spatiotemporal relationship detection structure to obtain a set of potential conflict events; sorting the potential conflict event set by their operational priorities according to the construction plan to obtain a conflict impact level list; The device scheduling conflict report is generated according to the conflict impact level list.

9. An intelligent construction method system based on Internet of Things monitoring, characterized in that: The smart construction method based on Internet of Things monitoring as applied to any one of claims 1 to 6 above comprises: An acquisition module is configured to acquire real-time vibration spectrum data and historical equipment vibration data of target construction equipment, and perform environmental noise suppression on the real-time vibration spectrum data based on the historical equipment vibration data to obtain equipment noise reduction status characteristics; An analysis module, the analysis module being used to obtain an operating vibration waveform of the impact device, perform equipment health analysis on the noise reduction state characteristics of the device, and obtain a health decay rate; An association module, configured to quantify the equipment load reduction operation based on the health decay rate and perform equipment safety collaborative analysis to obtain a collaborative scheduling strategy for associated equipment based on a preset construction plan; A processing module is used to construct instructions for the target construction equipment according to the associated equipment collaborative scheduling strategy, obtain load control instructions, and optimize the construction plan to obtain the optimized construction plan.

Images