A method and system for monitoring the progress of road construction sites

Abstract

This invention relates to the field of progress monitoring technology, specifically to a method and system for monitoring the progress of road construction sites. The method includes the following steps: collecting temperature and humidity data and constructing environmental impact monitoring data through differential calculations; using a Kalman filter to predict and label anomalies to generate construction environmental risk assessment results; analyzing the progress and calculating and solidifying impact factors and connection factors; using fuzzy evaluation to correct equipment compatibility and generate construction progress monitoring adjustment instructions; and updating the progress table to form real-time monitoring results of road construction progress. In this invention, by forming a sequence of temperature and humidity changes around construction nodes and performing correlation analysis on fluctuation trends and deviations, environmental disturbances are quantified into construction condition parameters. These parameters are then mapped to material curing cycles and process connection windows through anomaly identification, driving dynamic correction of progress parameters. Furthermore, by adapting and calibrating execution conditions through evaluation, progress adjustments are made based on environmental response, improving the coordination of construction rhythm and the level of process controllability.

Description

Technical Field

[0001] This invention relates to the field of progress monitoring technology, and in particular to a method and system for monitoring the progress of road construction sites. Background Technology

[0002] The technology of progress monitoring involves the acquisition, recording, and comparative analysis of the time progress of construction stages in engineering construction activities. Core aspects include the formulation and breakdown of construction plans, data collection of on-site operations, comparison of progress nodes, recording of construction status, and identification of progress deviations. Focusing on infrastructure construction scenarios such as roads, bridges, and buildings, it systematically manages the start time, completion time, operation continuity, and resource input of construction processes through methods such as manual recording, equipment collection, and information aggregation, in order to support engineering management personnel in grasping and coordinating the construction rhythm.

[0003] The traditional method for monitoring the progress of road construction sites involves setting up manual inspection points at the construction site, filling out construction logs, comparing them with the progress schedule in the construction organization design, and combining them with construction drawings and itemized project lists to record the actual completion status of specific processes such as roadbed excavation, pavement laying, and pipeline installation. This relies on on-site management personnel to regularly visit the site to observe the construction status, record the start and end times of each process, take on-site photos, and compile them into daily or weekly reports to reflect the phased progress of the road construction site.

[0004] Current road construction progress monitoring mainly relies on manual inspections and log summaries. Progress information acquisition depends on intermittent observations, and environmental conditions are only included in the records as background descriptions. There is a lack of continuous data support corresponding to construction nodes. When changes in temperature and humidity affect material performance and process rhythm, it is difficult to reflect them in a timely manner. Progress deviation identification is mostly limited to result comparison, and adjustments are based on experience judgment. Risk exposure is delayed, and plan revisions lack pertinence, which can easily lead to node imbalance and resource scheduling disorder. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for monitoring the progress of road construction sites.

[0006] To achieve the above objectives, the present invention employs a method for monitoring the progress of road construction sites, comprising the following steps: S1: Environmental data is acquired through temperature and humidity sensors. The temperature and humidity numerical sequences are then interpolated to obtain the fluctuation sequence. This sequence is mapped based on construction node timestamps to construct environmental impact monitoring data. S2: Input the environmental impact monitoring data into a Kalman filter to predict environmental trends, calculate the deviation between the preset environmental stability threshold and the fluctuation change sequence, mark the values ​​exceeding the threshold as abnormal, and generate the construction environmental risk assessment results. S3: Based on the construction environment risk assessment results, adjust and analyze the construction node schedule plan, calculate the material curing time influence factor according to the temperature gradient anomaly mark, calculate the material connection window adaptation factor according to the humidity fluctuation anomaly mark, and generate construction schedule deviation correction parameters. S4: Input the construction progress deviation correction parameters into the fuzzy controller for adaptability evaluation, adjust the operating conditions of the construction equipment for parameters with a matching degree lower than the matching threshold, and generate construction progress monitoring and adjustment instructions. S5: Invoke the construction progress monitoring adjustment command to update the node execution time and progress completion configuration in the construction operation progress table, and generate real-time monitoring results of road construction progress.

[0007] As a further aspect of the present invention, the environmental impact monitoring data includes environmental change magnitude indicators, node environmental adaptation characteristics, and environmental temporal correlation attributes; the construction environmental risk assessment results include environmental risk level classification, risk impact scope definition, and risk sensitivity identification; the construction progress deviation correction parameters include progress adjustment magnitude, construction period fluctuation time, and process connection correction amount; the construction progress monitoring adjustment instructions include equipment adaptation adjustment items, work rhythm correction amount, and resource allocation instruction set; and the real-time monitoring results of road construction progress include a summary of node execution status, an overall progress deviation index, and a progress anomaly warning indicator.

[0008] As a further aspect of the present invention, the step of acquiring the environmental impact monitoring data is as follows: S11: Acquire environmental data through on-site temperature and humidity sensors, perform consistency verification on the output time stamps, sort and reorganize the continuous time point data frames, perform index alignment on the temperature and humidity fields, and perform labeling on the missing time point data frames to generate raw environmental perception data. S12: Based on the original environmental sensing data, perform difference calculation on the temperature numerical sequence according to adjacent time indices, perform difference calculation on the humidity numerical sequence according to the same time index, rearrange the difference records in chronological order, and generate a temperature and humidity fluctuation change sequence. S13: Call the temperature and humidity fluctuation sequence, perform a one-to-one mapping registration between the timestamp and the corresponding differential index according to the construction node timestamp sequence, perform unified time axis encoding and organization on the mapping results, and establish environmental impact monitoring data.

[0009] As a further aspect of the present invention, the steps for obtaining the construction environment risk assessment results are as follows: S21: Obtain the environmental impact monitoring data, use the Kalman filter state equation to perform recursive estimation on the temperature fluctuation time series, perform state update on the humidity fluctuation time series, perform iterative correction on the estimation error covariance matrix, and generate an environmental trend prediction vector. S22: Call the environmental trend prediction vector and the temperature and humidity fluctuation sequence, perform point-by-point difference calculation on the predicted value and the measured value, calculate the comprehensive environmental deviation index, perform index mapping on the comprehensive environmental deviation index according to the construction time stamp sequence, and generate the environmental deviation quantification sequence. S23: Call the environmental deviation quantification sequence, compare the values ​​in the environmental deviation quantification sequence with the environmental stability threshold item by item, register the abnormality for values ​​that exceed the preset environmental stability threshold, and establish the construction environment risk assessment result.

[0010] As a further aspect of the present invention, the preset environmental stability threshold is statistically determined based on environmental impact monitoring data collected during the original construction period. The statistical determination process includes dividing the time series of temperature fluctuations and the time series of humidity fluctuations into time windows, and calculating the mean and dispersion of the corresponding measured values ​​of temperature gradient and humidity fluctuations within each time window. The threshold corresponding to the temperature gradient benchmark value is determined by a weighted combination of the mean and dispersion, and the threshold corresponding to the humidity fluctuation benchmark value is determined by a weighted combination of the mean and dispersion.

[0011] As a further aspect of the present invention, the step of obtaining the construction progress deviation correction parameter is as follows: S31: Based on the construction environment risk assessment results, extract the time node sequence and task dependency relationship chain, map the temperature gradient anomaly markers to the time nodes, calculate the time distribution density of the anomaly markers, and generate the node time offset risk index. S32: Call the node time offset risk index to extract the temperature change rate and temperature fluctuation amplitude, using the formula: ; Calculate the material curing time influence factor, extract the predetermined curing time window, and calculate the difference between the material curing time influence factor and the predetermined curing time window to obtain the curing time deviation correction amount. in, Factors affecting the curing time of representative materials This represents the total number of temperature gradient anomaly markers. Representing the The temperature deviation corresponding to each anomaly marker Representing the The curing rate sensitivity coefficient corresponding to each abnormal marker Representing the The temperature fluctuation amplitude corresponding to each anomaly marker. Representing the The time delay coefficient corresponding to each anomaly marker Representing the The weighting factor corresponding to each anomaly marker; S33: Call the curing time deviation correction amount, extract the humidity change rate and humidity fluctuation amplitude, determine the humidity disturbance influence coefficient through the humidity change rate, modulate the humidity disturbance influence coefficient based on the bonding strength coefficient of the material connection interface, and perform weighted calculation with the curing time deviation correction amount and the material connection window adaptation factor to generate the construction progress deviation correction parameters.

[0012] As a further aspect of the present invention, the step of obtaining the construction progress monitoring and adjustment instruction is as follows: S41: Based on the construction progress deviation correction parameter input fuzzy controller, collect the parameter membership matrix and rule weight matrix, compare the membership output value with the adaptation evaluation benchmark threshold value item by item, mark the parameter index that exceeds the threshold interval and serialize it to generate a set of working condition consistency evaluation factors. S42: Based on the set of working condition consistency evaluation factors, filter the parameter index below the consistency threshold, obtain the corresponding set of working condition parameters for construction equipment, perform consistency judgment on the mapping relationship between the equipment operating condition constraint interval and the construction progress deviation correction parameters, and implement work rhythm rearrangement and equipment operating status switching for inconsistent parameters to form a set of working condition adjustment parameters for construction equipment. S43: Based on the set of operating condition adjustment parameters for the construction equipment, call the node identifier of the construction progress deviation correction parameter, perform the corresponding mapping between parameter status and node control item, perform instruction encoding rule conversion and time sequence arrangement processing on the mapping result, and generate construction progress monitoring and adjustment instructions.

[0013] As a further aspect of the present invention, the adaptation evaluation benchmark threshold is determined by the effective value range of the membership output value in the parameter membership matrix within the corresponding interface constraint interval, and the upper and lower boundaries of the effective value range are respectively used as the lower limit threshold and the upper limit threshold of the adaptation evaluation benchmark threshold. The consistency threshold is determined by the consistency evaluation interval formed by weighting the weight values ​​of the rule weight matrix in the working condition consistency evaluation factor set and the corresponding membership output values.

[0014] As a further aspect of the present invention, the steps for obtaining the real-time monitoring results of road construction progress are as follows: S51: Call the construction progress monitoring and adjustment instruction, obtain the node identifier sequence and time adjustment parameter set, retrieve the corresponding node in the construction operation progress table, calculate the difference between the original execution time and the time adjustment parameter and write it into the node field, and generate the node execution time update sequence. S52: Based on the node record items corresponding to the node execution time update sequence, extract the node construction status identifier and task quantity configuration parameters, calculate the ratio of the current completed amount of the node to the task quantity, judge and map the ratio result with the status code interval, and generate a node progress completion configuration set. S53: Call the node progress completion configuration set and node execution time update sequence, perform consistency verification and structural reorganization on the execution time field and progress completion field according to the node sequence index of the construction operation schedule, and aggregate them in time order to generate real-time monitoring results of road construction progress.

[0015] The road construction site progress monitoring system is used to execute the above-mentioned road construction site progress monitoring method. The system includes: The environmental perception module acquires environmental data through temperature and humidity sensors, performs difference calculations on the temperature and humidity numerical sequences to obtain the fluctuation change sequence, maps it based on construction node timestamps, and constructs environmental impact monitoring data. The trend assessment module inputs the environmental impact monitoring data into a Kalman filter to predict environmental trends, calculates the deviation between a preset environmental stability threshold and the fluctuation sequence, marks values ​​exceeding the threshold as abnormal, and generates construction environmental risk assessment results. The progress analysis module adjusts and analyzes the construction node progress plan based on the construction environment risk assessment results, calculates the material curing time influence factor based on the temperature gradient anomaly marker, calculates the material connection window adaptation factor based on the humidity fluctuation anomaly marker, and generates construction progress deviation correction parameters. The parameter adaptation module inputs the construction progress deviation correction parameters into the fuzzy controller for adaptation evaluation, adjusts the operating conditions of the construction equipment for parameters with a matching degree lower than the matching threshold, and generates construction progress monitoring and adjustment instructions. The progress update module calls the construction progress monitoring adjustment command to update the node execution time and progress completion configuration in the construction operation progress table, and generates real-time monitoring results of road construction progress.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by constructing a sequence of temperature and humidity changes around construction nodes and performing correlation analysis on fluctuation trends and deviation states, environmental disturbances are quantitatively expressed in relation to construction conditions. Through anomaly identification, environmental risks are directly mapped to material curing cycles and process connection windows, guiding the dynamic correction of progress parameters. At the same time, through adaptation assessment, the execution conditions are calibrated for consistency, so that progress adjustments have an environmental response basis, reducing the probability of deviation accumulation and enhancing the coordination ability of construction rhythm and the level of process control. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a flowchart illustrating the acquisition of environmental impact monitoring data in this invention. Figure 3 This is a flowchart illustrating the process of obtaining the construction environment risk assessment results in this invention. Figure 4 This is a flowchart illustrating the process of obtaining the construction progress deviation correction parameters in this invention. Figure 5 This is a flowchart illustrating the process of obtaining construction progress monitoring and adjustment instructions in this invention. Figure 6 This is a flowchart illustrating the process of obtaining real-time monitoring results of road construction progress in this invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0019] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] Example 1: Please refer to Figure 1 This invention provides a technical solution: a method for monitoring the progress of road construction sites, comprising the following steps: S1: Environmental data is acquired through temperature and humidity sensors. The temperature and humidity numerical sequences are then interpolated to obtain the fluctuation sequence. This sequence is mapped based on construction node timestamps to construct environmental impact monitoring data. S2: Input environmental impact monitoring data into a Kalman filter to predict environmental trends, calculate the deviation between the preset environmental stability threshold and the fluctuation sequence, mark values ​​exceeding the threshold as abnormal, and generate construction environmental risk assessment results. S3: Based on the construction environment risk assessment results, adjust and analyze the construction node schedule plan, calculate the material curing time influence factor according to the temperature gradient anomaly mark, calculate the material connection window adaptation factor according to the humidity fluctuation anomaly mark, and generate construction schedule deviation correction parameters. S4: Input the construction progress deviation correction parameters into the fuzzy controller for adaptability evaluation, adjust the operating conditions of the construction equipment for parameters with a matching degree lower than the matching threshold, and generate construction progress monitoring and adjustment instructions. S5: Call the construction progress monitoring adjustment command to update the node execution time and progress completion status configuration in the construction operation schedule table, and generate real-time monitoring results of road construction progress; Environmental impact monitoring data includes environmental change magnitude indicators, node environmental adaptation characteristics, and environmental temporal correlation attributes. Construction environmental risk assessment results include environmental risk level classification, risk impact scope definition, and risk sensitivity indicators. Construction progress deviation correction parameters include progress adjustment magnitude, construction period fluctuation time, and process connection correction amount. Construction progress monitoring adjustment instructions include equipment adaptation adjustment items, work rhythm correction amount, and resource allocation instruction set. Real-time monitoring results of road construction progress include node execution status summary, overall progress deviation index, and progress anomaly warning indicators.

[0021] Please see Figure 2 The specific steps for obtaining environmental impact monitoring data are as follows: S11: Acquire environmental data through on-site temperature and humidity sensors, perform consistency verification on the output time stamps, sort and reorganize the continuous time point data frames, perform index alignment on the temperature and humidity fields, and perform labeling on the missing time point data frames to generate raw environmental perception data. Environmental data is acquired using on-site temperature and humidity sensors. First, a consistency check is performed on the timestamp field of the received data packets. A standard time check window is set, for example, 5 seconds. If the absolute value of the difference between the arrival time of a data packet and the timestamp recorded within the data packet is greater than 5 seconds, the timestamp of the data packet is considered abnormal and marked as "invalid timestamp." For example, a data packet received on 2025-12-15-14:30:10 with an internal timestamp of 14:30:16 has a difference of 6 seconds, exceeding the validity period. A 5-second verification window is used to mark the data packet. Conversely, if the received time is 14:30:10 and the internal timestamp is 14:30:08, the difference is 2 seconds, and the verification passes. Then, the consecutive time-point data frames that pass verification are sorted and reassembled. The data frames are arranged in ascending order according to their timestamp field. For example, if the collected data frame sequence is {frame A (14:31), frame C (14:33), frame B (14:32)}, after reassembly, it becomes {frame A (14:31), frame B (14:32), frame C (14:30:08)}. 33)} Subsequently, index alignment is performed on the temperature and humidity fields. A data record is created for each unique timestamp, and the temperature and humidity values ​​corresponding to that timestamp are filled into the corresponding fields of the record. This ensures that each time point has readings for both temperature and humidity. For example, for time point 14:32, the temperature value (e.g., 12.5℃) and humidity value (e.g., 68%RH) from frame B are stored together in the record for that time point. Finally, missing time point data frames are marked. An expected data collection frequency is set, such as once per minute. The sorted data frame sequence is traversed, and the time difference between two adjacent timestamps is checked. If the time difference is greater than the set collection interval, such as jumping directly from 14:33 to 14:35, the data frame for 14:34 is determined to be missing. An empty record is inserted at that time point, and its numerical fields, including temperature and humidity, are marked as "missing". For example, in the record for time point 14:34, the temperature field is marked as "missing", and the humidity field is also marked as "missing". After processing the data in this way, the raw environmental perception data is generated.

[0022] S12: Based on the original environmental sensing data, perform difference calculations on the temperature numerical sequence according to adjacent time indices, and perform difference calculations on the humidity numerical sequence according to the same time index. Rearrange the difference records in chronological order to generate a temperature and humidity fluctuation sequence.

[0023] Extract the temperature values ​​that are not marked as "missing" and arrange them in chronological order. Then, subtract the temperature value of the previous time point from the temperature value of the next time point to obtain the temperature difference. For example, if the temperature values ​​at 14:31, 14:32, and 14:33 are extracted from the original environmental sensing data as 12.3℃, 12.5℃, and 12.4℃ respectively, then the temperature difference at 14:32 is calculated to be 0.2℃, and the temperature difference at 14:33 is -0.1℃. This difference is then correlated with the next time point. Next, perform the difference operation on the humidity value sequence according to the same time index. This difference operation does not refer to time difference, but rather to comparing the actual measured humidity value at the same time point with a baseline humidity value. This baseline humidity value is set based on the average daily humidity of the same month (December) in the construction area over the past five years. Assuming that the average daily humidity of the area in December is calculated to be 65%RH based on the original meteorological data, this value is set as the baseline humidity value. A humidity baseline value is defined. For example, at three time points, 14:31, 14:32, and 14:33, the humidity values ​​in the original environmental sensing data are 67%RH, 68%RH, and 66%RH, respectively. The differences from the baseline value of 65%RH are calculated, resulting in a humidity difference of 2%RH at 14:31, 3%RH at 14:32, and 1%RH at 14:33. The difference records are then rearranged chronologically. The calculated temperature and humidity differences are merged and sorted according to their corresponding timestamps to create a new time series data structure. Each time point contains a temperature difference and a humidity difference. For example, for time point 14:32, the record is {temperature difference: 0.2, humidity difference: 3}, and for 14:33, the record is {temperature difference: -0.1, humidity difference: 1}. Finally, the records of each time point are combined to generate a temperature and humidity fluctuation sequence.

[0024] S13: Call the temperature and humidity fluctuation sequence, perform one-to-one mapping registration between the timestamp and the corresponding differential index according to the construction node timestamp sequence, perform unified time axis encoding and organization on the mapping results, and establish environmental impact monitoring data; The generated temperature and humidity fluctuation sequence is called and combined with a pre-set construction node timestamp sequence. This sequence represents the start and end times of key processes determined according to the construction plan. For example, the concrete pouring starts at 15:00 on December 15, 2025, and ends at 17:00 on the same day. First, a one-to-one mapping registration is performed between the timestamps and their corresponding difference indices. Specifically, each time point in the construction node timestamp sequence is traversed, and the timetamp index that perfectly matches or is closest in time to the time point is found in the temperature and humidity fluctuation sequence. The temperature and humidity difference record under that index is then associated with the corresponding construction node. For example, if the timestamp of the construction node "Concrete Pouring Start" is 15:00, the index for 15:00 is found in the temperature and humidity fluctuation sequence, and its corresponding record is {Temperature Difference: 0.5, Humidity Difference: -4}. The record is mapped to the "Concrete Pouring Start" node. The mapping results are then uniformly coded and organized along a timeline, assigning a unique code to each construction node, such as "GJ-001" representing "Concrete Pouring Start". The mapped nodes and their associated temperature and humidity difference data are arranged chronologically according to the construction process, forming a structured dataset. For example, the dataset might look like this: [{Node Code: "GJ-001", Node Name: "Concrete Pouring Start", Time: "15:00", Temperature Difference: 0.5, Humidity Difference: -4}, {Node Code: "GJ-002", Node Name: "Initial Setting Vibration", Time: "16:00", Temperature Difference: -0.2, Humidity Difference: -3}]. Finally, this coded and organized dataset is stored to establish environmental impact monitoring data.

[0025] Please see Figure 3 The specific steps for obtaining the construction environment risk assessment results are as follows: S21: Obtain environmental impact monitoring data, use the Kalman filter state equation to perform recursive estimation on the temperature fluctuation time series, perform state update on the humidity fluctuation time series, perform iterative correction on the estimation error covariance matrix, and generate an environmental trend prediction vector; To acquire environmental impact monitoring data, the temperature fluctuation time series is first processed using a recursive estimation method. Specifically, the state vector is initialized by setting the temperature difference at the first time point, such as 15:00, to 0.5 as the initial state estimate. And set an initial estimation error covariance. For example, 0.1. Then, based on the state estimate from the previous time step, the state at the current time step is predicted using the following formula: ,in Let be the state transition matrix, set to 1, to indicate that temperature changes have inertia. To control the input matrix, set it to 0. The control vector is also set to 0 because there is no external control input. For example, predicting the state at 15:01 is equivalent to estimating the state at 15:00 as 0.5, and then updating the prediction error covariance. ,in This is the process noise covariance, representing the uncertainty of the prediction model itself. Let's set it to 0.01. Then, a status update is performed on the humidity fluctuation time series, when the measured humidity fluctuation value at the current time 15:01 is obtained. When, for example, -3.5, calculate the Kalman gain. ,in Let the observation matrix be 1. To observe the noise covariance, representing the sensor measurement error, we set it to 0.04 and calculate the Kalman gain. ,in Let the observation matrix be 1. To observe the noise covariance, representing the sensor measurement error, we set it to 0.04. The calculation logic here treats large fluctuations in humidity as signals affecting sudden temperature changes. Finally, we perform iterative correction on the estimated error covariance matrix, updating the formula as follows: ,in It is the identity matrix. Since the state variables are one-dimensional scalars, they are taken as 1 in the calculation. The updated version and This process is repeated iteratively, using the revised state estimate obtained at each time point as input for the next round of calculation (15:02). Combined, they generate an environmental trend prediction vector.

[0026] S22: Call the environmental trend prediction vector and the temperature and humidity fluctuation sequence, perform point-by-point difference calculation between the predicted and measured values, using the following formula: ; Calculate the comprehensive environmental deviation index, perform index mapping on the comprehensive environmental deviation index according to the construction time stamp sequence, and generate a quantitative environmental deviation sequence. in, Represents a comprehensive indicator of environmental deviation. Represents the total number of time points. Representing the Predicted temperature gradient values ​​at specific time points Representing the Measured values ​​of temperature gradient at different time points Representing the Predicted humidity fluctuations over time. Representing the Measured values ​​of humidity fluctuations over time. Represents the baseline value of the temperature gradient. Represents the baseline value for humidity fluctuations; Extract the predicted temperature gradient value for each time point from the environmental trend prediction vector. And extract the measured values ​​of the temperature gradient at corresponding time points from the temperature and humidity fluctuation sequence. Then calculate the absolute value of the difference between the two. Perform the same operation on humidity. For example, at time 15:01, the predicted temperature difference is -2.432℃, and the measured temperature difference is -0.2℃. Then the absolute value of the temperature deviation at that point is... The predicted humidity fluctuation value is -3.5%RH, and the measured value is -3%RH. Therefore, the absolute value of the humidity deviation at that point is... Next, the comprehensive environmental deviation index is calculated using the following formula: ; This formula first calculates the total average relative deviation of temperature and humidity over a period of time, then combines the two by taking the square root of the sum of their squares to obtain a comprehensive deviation measure. The parameters in the formula... The comprehensive environmental deviation index is a dimensionless numerical value that comprehensively reflects the degree of difference between the prediction model and actual environmental changes. This represents the total number of time points used for calculation. and From 1 to The counting index, and Representing the first and The temperature and humidity values ​​in the prediction vector generated at each time point and These are the measured values ​​from the generated temperature and humidity fluctuation sequence. This is the temperature gradient benchmark value, set with reference to the requirements for daily average temperature in the "Code for Acceptance of Construction Quality of Concrete Structures" (GB50204). The daily temperature difference should not exceed 15℃. Considering hourly variations, a conservative gradient benchmark value of 2℃ / hour is set, i.e. , This is the baseline value for humidity fluctuations. Based on original meteorological statistics, during winter construction, the daily relative humidity fluctuation range is within 20%RH. Half of this range is taken as the baseline. .

[0027] Table 1 Comparison of Predicted and Measured Values ; As shown in Table 1, data from three consecutive time points were selected for calculation, and the total number of time points is... The sum of the absolute values ​​of the total temperature deviations is: ; The sum of the absolute values ​​of the total deviations in the humidity section is: ; Substituting the above values ​​into the formula yields... ; The results show that the comprehensive environmental deviation index was 0.420 during the period from 15:01 to 15:03. This value represents the overall deviation between the prediction model and actual environmental changes. Subsequently, an index mapping was performed on this comprehensive environmental deviation index according to the construction time stamp sequence, and the calculated... The value 0.420 is correlated with the construction node corresponding to this calculation period, such as "concrete initial setting vibration," to generate a quantitative sequence of environmental deviations. The advantage of this formula is that by normalizing and square-rooting the deviations of temperature and humidity, the deviations of two different physical quantities are integrated into a unified, dimensionless index, allowing the environmental stability of different time periods or different processes to be quantitatively compared.

[0028] S23: Call the environmental deviation quantification sequence, compare the values ​​in the environmental deviation quantification sequence with the environmental stability threshold item by item, register the abnormality of values ​​that exceed the environmental stability threshold, and establish the construction environmental risk assessment results; The comprehensive environmental deviation index value of 0.420 associated with the construction node "initial setting vibration of concrete" was extracted and compared with the preset environmental stability threshold. The environmental stability threshold was determined based on the environmental impact monitoring data collected within the original construction cycle, such as one week before the project started. The specific determination process was as follows: first, the time series of temperature fluctuations and humidity fluctuations were divided into time windows, and the data of one week was divided into one time window per hour. Within each time window, the mean and dispersion of the corresponding measured temperature gradient and humidity fluctuation values ​​are calculated. The dispersion is obtained by calculating the standard deviation. For example, for temperature, the mean and standard deviation sequences for 168 windows are calculated, and then the overall average of these two sequences is calculated. Assuming the overall average of the temperature gradient mean is 0.1℃ / hour and the overall average of the standard deviation is 0.5℃ / hour, the threshold corresponding to the temperature gradient benchmark value is then determined by a weighted combination of the mean and dispersion. and Based on experience regarding the environmental sensitivity of construction, temperature stability is more important for concrete construction; therefore, the following settings are provided. , Then the threshold component of the temperature part is Similarly, assuming the total average of humidity fluctuations is 1.2%RH and the total average of standard deviations is 2.5%RH, the weights are set as follows: Then the threshold component of the humidity part is Finally, these two components are connected through... Combine formulas with similar structures and divide by the base value. and The final environmental stability threshold is obtained. The calculated comprehensive environmental deviation index value of 0.420 was compared with the threshold value of 0.251. If the value exceeds the environmental stability threshold, an anomaly identification registration is performed on the value exceeding the environmental stability threshold. The construction node "concrete initial setting vibration" associated with the value 0.420 and its timestamp are marked as "environmental anomaly". The specific value, threshold, and excess amount are recorded together. Finally, the comparison and identification results of the construction nodes are combined to establish the construction environmental risk assessment results.

[0029] Please see Figure 4 The specific steps for obtaining the construction progress deviation correction parameters are as follows: S31: Based on the construction environment risk assessment results, extract the time node sequence and task dependency relationship chain, map the temperature gradient anomaly markers to the time nodes, calculate the time distribution density of the anomaly markers, and generate the node time offset risk index. From the risk assessment results, construction nodes marked as "environmentally abnormal" and their corresponding timestamps were identified. For example, the "concrete initial setting vibration" node was marked between 15:00 and 16:00, and its subsequent task, "secondary finishing," depended on the completion of "concrete initial setting vibration." Next, the temperature gradient anomaly markers were mapped to time nodes. For the "concrete initial setting vibration" node, the measured temperature gradient values ​​for the corresponding time period (15:00-16:00) were retrieved from the environmental impact monitoring data, and their values ​​were compared with the baseline temperature gradient value. The deviation (℃ / hour) is calculated. For example, between 15:00 and 16:00, the measured temperature gradient values ​​at three time points are 2.5℃ / hour, 2.8℃ / hour, and -2.2℃ / hour, respectively, all exceeding the baseline value. Therefore, these three time points are considered as temperature gradient anomalies, and the anomaly markers are associated with the "concrete initial setting vibration" node. Then, the temporal distribution density of the anomaly markers is calculated to determine the frequency of anomaly markers within the duration of a specific construction node. In the 1 hour (60 minutes) of "concrete initial setting vibration," assuming data is recorded every 10 minutes, there are a total of 6 data points. If 3 of these points are marked as anomalies, then the temporal distribution density is... This density value is initially defined as the risk quantification value of the node. The risk quantification values ​​of the nodes are arranged in chronological order to generate the node time offset risk index.

[0030] S32: Call the node time offset risk indicator to extract the temperature change rate and temperature fluctuation amplitude using the following formula: ; Calculate the material curing time influence factor, extract the predetermined curing time window, and calculate the difference between the material curing time influence factor and the predetermined curing time window to obtain the curing time deviation correction amount. in, Factors affecting the curing time of representative materials This represents the total number of temperature gradient anomaly markers. Representing the The temperature deviation corresponding to each anomaly marker, in °C / h. Representing the The curing rate sensitivity coefficient corresponding to each abnormal marker, in h² / ℃. Representing the The temperature fluctuation amplitude corresponding to each anomaly marker, in °C. Representing the The time delay factor corresponding to each anomaly marker, in h / ℃. Representing the The weighting factor corresponding to each anomaly marker; First, extract the temperature change rate and temperature fluctuation amplitude associated with the "environmental anomaly" marker. The temperature change rate is the calculated temperature difference, also known as the measured temperature gradient. The temperature fluctuation amplitude is the difference between the highest and lowest temperatures within a specific time window (e.g., 1 hour). Then, calculate the material curing time influence factor using the following formula: ; This formula quantifies the combined effect of ambient temperature changes on material curing time by comprehensively considering the cumulative effect of temperature deviation and the direct impact of temperature fluctuation amplitude. The curing time factor represents the material's curing time, expressed in hours (h), indicating the expected deviation in curing time. This represents the total number of temperature gradient anomaly markers, with the subscript... Indicates the first Anomaly marker, It is the first The temperature deviation corresponding to each anomaly marker is equal to the measured temperature gradient value at that point minus the baseline temperature gradient value. , This is the curing rate sensitivity coefficient, which represents the degree of influence of unit temperature deviation on curing time. Its value is determined based on test data of specific materials (such as C30 concrete). It is a weighting factor, set according to the importance of the construction stage where the anomaly marker is located. It is the temperature fluctuation amplitude within the time period corresponding to the anomaly marker. It is the time delay factor, which represents the curing time delay caused by a unit temperature fluctuation amplitude.

[0031] Table 2 Anomaly Marking Parameters ; As shown in Table 2, three abnormal markers were identified for the "initial setting and vibration of concrete" stage. ), perform parameter assignment and calculation: The value was calculated based on the measured temperature gradient of 2.5℃ / h and the baseline value of 2℃ / h. ℃ / h, similarly ℃ / h, Calculated based on -2.2℃ / h, but since temperature decreases also affect curing, the absolute value of the difference from the baseline value is used here. For the sake of simplicity, we assume here that the third anomaly is caused by a slight increase in temperature, with an actual measured value of 2.2℃ / h. ℃ / h, Curing rate sensitivity coefficient Based on the material properties of C30 concrete, a weighting factor of 0.2 h² / ℃ is set. Based on the importance of each construction stage, the first two anomalies occurred in the core pouring area and were set at 0.8, while the third was in the edge area and set at 0.6. Temperature fluctuation amplitude... The temperature was measured within one hour and was uniformly set at 1.2℃, with a time delay factor. Based on experience, it is set to 0.1 h / ℃. Substituting the value into the formula: Part 1: ; Part Two: ; h; The results indicate that the material curing time is expected to increase by 0.228 hours due to abnormal fluctuations in ambient temperature. A predetermined curing time window was then extracted; for example, according to the construction plan, the initial setting time window for C30 concrete is 4 to 6 hours after pouring. The calculated material curing time impact factor of 0.228 hours was added to the starting time of this window (4 hours) to obtain a corrected curing start time of 4.228 hours. This 0.228-hour difference is the correction amount for the curing time deviation. The advantage of this formula is that it distinguishes between the different impact mechanisms of temperature change rate (gradient) and temperature fluctuation amplitude on curing time, and comprehensively evaluates the cumulative effect of multiple short-term environmental anomalies on construction progress through a weighted average.

[0032] S33: Call the curing time deviation correction amount, extract the humidity change rate and humidity fluctuation amplitude, determine the humidity disturbance influence coefficient through the humidity change rate, modulate the humidity disturbance influence coefficient based on the bonding strength coefficient of the material connection interface, and perform weighted calculation with the curing time deviation correction amount and the material connection window adaptation factor to generate the construction progress deviation correction parameters. From the temperature and humidity fluctuation sequence, the measured environmental humidity value sequence corresponding to the "concrete initial setting vibration" construction node is extracted. The time interval corresponding to this construction node is 15:00–16:00. Assuming that humidity is sampled at fixed time intervals within this time interval, with a sampling interval of Δt (unit: hour), the measured humidity value sequence is obtained as follows: The humidity changes corresponding to adjacent sampling points were -3.0, -2.5, and -2.7 (unit: %RH), respectively.

[0033] Based on the measured humidity value sequence, the rate of humidity change between adjacent sampling times is calculated using a differential method, specifically as follows: ; The humidity change rate was averaged to obtain the average humidity change rate within the time interval corresponding to the construction node. For example, when Δt is taken as 1 hour, the average rate of change of humidity can be obtained as: ; The average humidity change rate is used to characterize the overall trend of environmental humidity change over time during the initial setting and vibration stage of concrete. Based on experimental data, a critical humidity disturbance rate parameter is pre-determined to characterize the threshold at which humidity changes significantly affect the interfacial bond performance between new and old concrete. This critical rate parameter has the same dimensions as the humidity change rate and is assumed to be −5%RH / h, where a negative value indicates that the direction of humidity decrease has a favorable effect on interfacial bonding. The average humidity change rate and the critical humidity disturbance rate parameter are normalized and mapped to obtain the humidity disturbance influence coefficient, for example: ; The humidity disturbance influence coefficient is a dimensionless parameter used to quantify the relative degree to which interfacial adhesion performance is affected by environmental disturbances under current humidity changes. Simultaneously, the curing time deviation correction is applied, and weighted by the material bonding window adaptation factor. The material bonding window adaptation factor is a dimensionless parameter used to characterize the tolerance of subsequent construction processes to changes in the curing time of the preceding process, with a value ranging from 0 to 1. Assuming that the subsequent "secondary finishing" process is highly dependent on the initial setting time window, its material bonding window adaptation factor is set to 0.3; when the curing time deviation correction is 0.228 hours, the weighted time deviation is: ; Finally, the humidity disturbance influence coefficient and the weighted time deviation are fused through a preset combination model to generate a construction progress deviation correction parameter. For example, through a linear combination model: ; where and are weight coefficients, taking 0.4 and 0.6 respectively, then we can get: ; Taking as the construction progress deviation correction parameter, it is used to dynamically adjust the rhythm and time arrangement of subsequent construction processes.

[0034] Please refer to Figure 5 , the specific steps for obtaining the construction progress monitoring and adjustment instruction are as follows: S41: Input the construction progress deviation correction parameter into the fuzzy controller, collect the parameter membership matrix and the rule weight matrix, compare the membership output value and the adaptation evaluation benchmark threshold value item by item numerically, mark the parameter indexes exceeding the threshold interval and serialize and organize them to generate a working condition consistency evaluation factor set; Taking the obtained construction progress deviation correction parameter value 0.25968 as the domain of discourse of this parameter, that is, the range of its values, it is divided into three fuzzy sets: {low, medium, high}, and each fuzzy set corresponds to a membership function. For example, "low" corresponds to a triangular membership function with parameters (0, 0, 0.4), which means that when the input value is less than 0.4, its degree of belonging to "low" gradually decreases, "medium" also corresponds to a triangular function (0.2, 0.5, 0.8), and "high" corresponds to (0.6, 1, 1). Substituting the input value 0.25968 into the calculation, the membership degree to "low" is , the membership degree to "medium" is The membership degree for "high" is 0, and the membership output values ​​{0.3508, 0.1989, 0} form one row of the membership degree matrix. Then, the membership output values ​​are compared item by item with the adaptation evaluation benchmark threshold. The adaptation evaluation benchmark threshold is determined by the effective range of the membership output values ​​in the parameter membership matrix within the corresponding interface constraint interval. Specifically, for each fuzzy set, such as "low," the non-zero value range of its membership function output in the universe of discourse is (0,1], which is the effective value range. Therefore, the lower limit of the adaptation evaluation benchmark threshold is 0, and the upper limit is 1. The calculated membership value is 0.3508. 0.8 and 0.1989 are compared with the threshold interval [0,1] and found to be within the interval. If a membership degree calculation result is -0.1 due to model error, then the value exceeds the lower limit threshold 0. Subsequently, the parameter index that exceeds the threshold interval is marked and serialized. If the membership degree value -0.1 is associated with the fuzzy set "extremely low" of the input parameter "humidity influence ratio", then the parameter index "humidity influence ratio" and its associated fuzzy set index "extremely low" are recorded. The marked indexes are arranged according to their processing order in the fuzzy controller to form an ordered list, and finally the working condition consistency evaluation factor set is generated.

[0035] S42: Based on the set of evaluation factors for consistent operating conditions, filter the index of parameters below the consistency threshold, obtain the corresponding set of operating condition parameters for construction equipment, perform consistency judgment on the mapping relationship between the equipment operating condition constraint interval and the construction progress deviation correction parameters, and implement work rhythm rearrangement and equipment operating status switching for inconsistent parameters to form a set of adjustment parameters for operating conditions of construction equipment. The setting of the working condition consistency threshold is based on the consistency evaluation value range formed by weighting and summing the weight values ​​of the rule weight matrix in the working condition consistency evaluation factor set with the corresponding membership output values. The specific calculation process is as follows: A fuzzy rule library is called, for example, rule 1: "If the schedule deviation is 'low,' then the adjustment amount is 'small'"; rule 2: "If the schedule deviation is 'medium,' then the adjustment amount is 'medium'." Each rule has a weight; for example, rule 1 has a weight of 0.9, and rule 2 has a weight of 0.7. The input membership values ​​0.3508 (corresponding to 'low') and 0.1989 (corresponding to 'medium') are multiplied by the rule weights to obtain the rule activation intensity, which is respectively... and , these two values constitute the evaluation value of working condition consistency, and its value range is theoretically [0, 1]. According to the requirements of construction adjustment accuracy and equipment operation stability, the threshold of working condition consistency is set to 0.2. When the evaluation value of working condition consistency corresponding to a certain rule is lower than the threshold of working condition consistency, the construction progress deviation correction parameter index associated with the input end of this rule is screened out. In this example, the activation intensity of Rule 2, 0.13923, is lower than 0.2, so the parameter index related to the input end of Rule 2, that is, the fuzzy set "medium" associated with the "construction progress deviation correction parameter", is screened out. Then, the corresponding construction equipment operation working condition parameter set is obtained. For example, this working condition parameter set corresponds to the operation rotation speed parameter of a concrete mixer, and its current operation rotation speed is 15 rpm, and the corresponding equipment operation working condition constraint interval is [10 rpm, 25 rpm]. The target adjustment amount output by the fuzzy controller after defuzzification is to adjust the rotation speed of the mixer to 9 rpm. The consistency between the target adjustment amount and the equipment operation working condition constraint interval is judged, and it is found that 9 rpm is less than the lower limit 10 rpm of the constraint interval, and it is judged that the working condition is inconsistent. For the working condition parameters judged to be inconsistent, the operation rhythm adjustment and equipment operation state switching processing are performed on them. The rotation speed adjustment task is adjusted from "execute immediately" to "pending confirmation" state, and the target rotation speed is corrected to the lower limit value 10 rpm of the constraint interval. Finally, the corrected construction equipment operation working condition parameters are organized to form a construction equipment operation working condition adjustment parameter set, which is used to generate subsequent construction progress monitoring and adjustment instructions.

[0036] S43: Based on the construction equipment operation working condition adjustment parameter set, call the construction progress deviation correction parameter node identifier, perform the corresponding mapping of the parameter state and the node control item, and perform the instruction coding rule conversion and time sequence arrangement processing on the mapping result to generate the construction progress monitoring and adjustment instruction; The process begins by acquiring the construction node identifier related to the current calculation, namely "concrete initial setting vibration." Next, a mapping is performed between parameter status and node control items. The parameters {Equipment ID: 'Mixer-01', Parameter Name: 'Speed', Correction Value: '10rpm', Status: 'Pending Confirmation'} from the construction equipment operating condition adjustment parameter set are mapped to the node control items. Since the operating speed of the concrete mixer directly affects the concrete material supply rate, the corrected mixer speed parameter is mapped to the "Material Supply Rate" control item, inheriting its "Pending Confirmation" parameter status identifier. After completing the parameter-control item mapping, the mapping result is converted into construction equipment operation control instructions according to preset instruction generation rules. Based on the timing constraints of the construction process and the parameter status identifiers, the control instructions are then ordered according to their timing. Control instructions with the status identifier "Pending Confirmation" are assigned a lower execution priority than "Immediate Execution" control instructions and are placed later in the control instruction queue. Finally, the control instructions that have completed mapping, status identifier inheritance, and timing sorting are combined to form construction progress monitoring and adjustment instructions, which are used to guide the adjustment of equipment operating conditions under the corresponding construction nodes.

[0037] Please see Figure 6 The specific steps for obtaining real-time monitoring results of road construction progress are as follows: S51: Call the construction progress monitoring adjustment instruction, obtain the node identifier sequence and time adjustment parameter set, retrieve the corresponding node in the construction operation progress table, calculate the difference between the original execution time and the time adjustment parameter and write it into the node field, and generate the node execution time update sequence. The time-related adjustment information is parsed from the generated instruction sequence. For example, the instruction sequence may contain an instruction for the subsequent task "secondary finishing" after "initial concrete setting and vibration". The instruction content is {node identifier: 'secondary finishing', time adjustment parameter: +0.228 hours}. This time adjustment parameter is the curing time deviation correction amount of 0.228 hours calculated by S32. Then, the corresponding node in the construction work schedule is retrieved. In the project's digital schedule (e.g., a Gantt chart database), the query is performed by the node identifier "secondary finishing". Locate the corresponding record, which contains the fields {Task Name:'Secondary Finishing', Original Planned Start Time:'2025-12-15-19:00', Original Planned End Time:'2025-12-15-20:00', Dependency:'Initial Concrete Vibration'}. Then, calculate the difference between the original execution time and the time adjustment parameter and write it to the node field. Add the original planned start time "2025-12-15-19:00" to the time adjustment parameter + 0.228 hours. First, convert the 0.228 hours to minutes. The new start time is "2025-12-15-19:14". Similarly, the original planned end time is also postponed by 14 minutes to "2025-12-15-20:14". These two new time values ​​are updated to the fields of the corresponding nodes in the progress table. At the same time, since the start time of the "secondary finishing" task is postponed, the planned time of subsequent tasks (such as "coverage maintenance") also needs to be recursively updated in sequence. The node records with updated times are collected, arranged in chronological order, and a node execution time update sequence is generated.

[0038] S52: Based on the node record items corresponding to the node execution time update sequence, extract the node construction status identifier and task quantity configuration parameters, calculate the ratio of the current completed quantity of the node to the task quantity, judge and map the ratio result with the status code interval, and generate a node progress completion configuration set. The currently active node is selected from the sequence, such as "concrete initial setting vibration". Its status identifier ("in progress") and task quantity configuration parameters are extracted from its records. For example, the task quantity of this node is to complete the vibration of 50 cubic meters of concrete, i.e., the task quantity is 50. Then, the current completed amount is obtained through on-site IoT sensors (such as counters and GPS modules installed on the vibrator) or manually reported data. Assuming 40 cubic meters have been completed, the ratio of the node's current completed amount to the total task quantity is calculated using the formula: Completion Ratio = Current Completed Amount / Total Task Quantity. Substituting the values... This means that 80% of the task has been completed. The proportional result is then compared with the status coding intervals and mapped accordingly. A pre-defined mapping relationship between the status coding intervals and the status descriptions is established. For example, the interval [0, 0.01) is mapped to "Not Started," coded as 00; the interval [0.01, 0.30) is mapped to "Initial Progress," coded as 01; the interval [0.30, 0.85) is mapped to "Steady Progress," coded as 02; the interval [0.85, 1.0) is mapped to "Final Stage," coded as 03; and the interval {1.0} is mapped to "Completed," coded as 04. The calculated proportional result of 0.8 is compared with each interval, and it is found that... Therefore, it falls into the "steady progress" interval and is mapped to status code 02. The node's identifier "concrete initial setting vibration", the calculated completion rate of 80%, and the mapped status code 02 are combined into a data unit. The same operation is performed on the ongoing node to generate a node progress completion configuration set.

[0039] S53: Call the node progress completion configuration set and node execution time update sequence, perform consistency verification and structural reorganization on the execution time field and progress completion field according to the node sequence index of the construction operation schedule, and aggregate them in time order to generate real-time monitoring results of road construction progress; Obtain the standard construction node sequence index from the project management plan, for example: 1-"Foundation Excavation", 2-"Subbase Construction", 3-"Reinforcement Binding", 4-"Concrete Pouring", 5-"Initial Concrete Vibration", 6-"Secondary Finishing". Then, extract data from the update sequence and configuration set to verify node "Initial Concrete Vibration" (index 5). Its updated execution time window is [15:00, 19:14] (assuming the original end time is 19:00), and its progress completion rate is 80%, which is logically consistent because the task is in progress and has not yet reached the updated end time. However, for node "Secondary Finishing" (index 6), its updated start time is 19:14, while the current time is 18:00, and its progress completion rate should be 0. If read from the configuration set... If the completion rate is 5%, it is considered inconsistent. In this case, the progress completion rate of the node is forcibly reset to 0, and a "data anomaly" mark is added. After verification, the identifier of each node, the updated start and end time, the calculated progress completion percentage, and the status code are integrated into a standardized data structure. For example, {Node: 'Concrete initial setting vibration', Planned start: '2025-12-15-15:00', Planned end: '2025-12-15-19:14', Actual progress: '80%', Status: 'Steady progress'}. Finally, the node data structure is aggregated according to the node order index of the construction operation schedule to form a complete data list arranged in chronological and logical order, generating real-time monitoring results of road construction progress.

[0040] The road construction site progress monitoring system is used to implement the above-mentioned road construction site progress monitoring method. The system includes: The environmental perception module acquires environmental data through temperature and humidity sensors, performs difference calculations on the temperature and humidity numerical sequences to obtain the fluctuation change sequence, maps it based on construction node timestamps, and constructs environmental impact monitoring data. The trend assessment module inputs environmental impact monitoring data into a Kalman filter to predict environmental trends, calculates the deviation between a preset environmental stability threshold and the fluctuation sequence, marks values ​​exceeding the threshold as abnormal, and generates construction environmental risk assessment results. The progress analysis module adjusts and analyzes the construction node progress plan based on the construction environment risk assessment results, calculates the material curing time impact factor based on the temperature gradient anomaly marker, calculates the material connection window adaptation factor based on the humidity fluctuation anomaly marker, and generates construction progress deviation correction parameters. The parameter adaptation module inputs the construction progress deviation correction parameters into the fuzzy controller for adaptation evaluation. For parameters with a matching degree lower than the matching threshold, it adjusts the operating conditions of the construction equipment and generates construction progress monitoring and adjustment instructions. The progress update module calls the construction progress monitoring adjustment command to update the execution time and progress completion status configuration of nodes in the construction operation progress table, and generates real-time monitoring results of road construction progress.

[0041] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for monitoring the progress of road construction sites, characterized in that, Includes the following steps: S1: Environmental data is acquired through temperature and humidity sensors. The temperature and humidity numerical sequences are then interpolated to obtain the fluctuation sequence. This sequence is mapped based on construction node timestamps to construct environmental impact monitoring data. S2: Input the environmental impact monitoring data into a Kalman filter to predict environmental trends, calculate the deviation between the preset environmental stability threshold and the fluctuation change sequence, mark the values ​​exceeding the threshold as abnormal, and generate the construction environmental risk assessment results. S3: Based on the construction environment risk assessment results, adjust and analyze the construction node schedule plan, calculate the material curing time influence factor according to the temperature gradient anomaly mark, calculate the material connection window adaptation factor according to the humidity fluctuation anomaly mark, and generate construction schedule deviation correction parameters. S4: Input the construction progress deviation correction parameters into the fuzzy controller for adaptability evaluation, adjust the operating conditions of the construction equipment for parameters with a matching degree lower than the matching threshold, and generate construction progress monitoring and adjustment instructions. S5: Invoke the construction progress monitoring adjustment command to update the node execution time and progress completion configuration in the construction operation progress table, and generate real-time monitoring results of road construction progress.

2. The method for monitoring the progress of road construction sites according to claim 1, characterized in that, The environmental impact monitoring data includes environmental change magnitude indicators, node environmental adaptation characteristics, and environmental temporal correlation attributes. The construction environmental risk assessment results include environmental risk level classification, risk impact scope definition, and risk sensitivity identification. The construction progress deviation correction parameters include progress adjustment magnitude, construction period fluctuation time, and process connection correction amount. The construction progress monitoring adjustment instructions include equipment adaptation adjustment items, work rhythm correction amount, and resource allocation instruction set. The real-time monitoring results of road construction progress include node execution status summary, overall progress deviation index, and progress anomaly warning indicator.

3. The method for monitoring the progress of road construction sites according to claim 1, characterized in that, The steps for obtaining the environmental impact monitoring data are as follows: S11: Acquire environmental data through on-site temperature and humidity sensors, perform consistency verification on the output time stamps, sort and reorganize the continuous time point data frames, perform index alignment on the temperature and humidity fields, and perform labeling on the missing time point data frames to generate raw environmental perception data. S12: Based on the original environmental sensing data, perform difference calculation on the temperature numerical sequence according to adjacent time indices, perform difference calculation on the humidity numerical sequence according to the same time index, rearrange the difference records in chronological order, and generate a temperature and humidity fluctuation change sequence. S13: Call the temperature and humidity fluctuation sequence, perform a one-to-one mapping registration between the timestamp and the corresponding differential index according to the construction node timestamp sequence, perform unified time axis encoding and organization on the mapping results, and establish environmental impact monitoring data.

4. The method for monitoring the progress of road construction sites according to claim 3, characterized in that, The steps for obtaining the construction environment risk assessment results are as follows: S21: Obtain the environmental impact monitoring data, use the Kalman filter state equation to perform recursive estimation on the temperature fluctuation time series, perform state update on the humidity fluctuation time series, perform iterative correction on the estimation error covariance matrix, and generate an environmental trend prediction vector. S22: Call the environmental trend prediction vector and the temperature and humidity fluctuation sequence, perform point-by-point difference calculation on the predicted value and the measured value, calculate the comprehensive environmental deviation index, perform index mapping on the comprehensive environmental deviation index according to the construction time stamp sequence, and generate the environmental deviation quantification sequence. S23: Call the environmental deviation quantification sequence, compare the values ​​in the environmental deviation quantification sequence with the preset environmental stability threshold item by item, register the abnormality for values ​​exceeding the environmental stability threshold, and establish the construction environment risk assessment result.

5. The method for monitoring the progress of road construction sites according to claim 4, characterized in that, The preset environmental stability threshold is determined statistically based on environmental impact monitoring data collected during the original construction period. The statistical determination process includes dividing the time series of temperature fluctuations and humidity fluctuations into time windows, and calculating the mean and dispersion of the corresponding measured values ​​of temperature gradient and humidity fluctuations within each time window. The threshold corresponding to the temperature gradient benchmark value is determined by a weighted combination of the mean and dispersion, and the threshold corresponding to the humidity fluctuation benchmark value is determined by a weighted combination of the mean and dispersion.

6. The method for monitoring the progress of road construction sites according to claim 4, characterized in that, The steps for obtaining the construction progress deviation correction parameters are as follows: S31: Based on the construction environment risk assessment results, extract the time node sequence and task dependency relationship chain, map the temperature gradient anomaly markers to the time nodes, calculate the time distribution density of the anomaly markers, and generate the node time offset risk index. S32: Call the node time offset risk index to extract the temperature change rate and temperature fluctuation amplitude, using the formula: ； Calculate the material curing time influence factor, extract the predetermined curing time window, and calculate the difference between the material curing time influence factor and the predetermined curing time window to obtain the curing time deviation correction amount. in, Factors affecting the curing time of representative materials This represents the total number of temperature gradient anomaly markers. Representing the The temperature deviation corresponding to each anomaly marker Representing the The curing rate sensitivity coefficient corresponding to each abnormal marker Representing the The temperature fluctuation amplitude corresponding to each anomaly marker. Representing the The time delay coefficient corresponding to each anomaly marker Representing the The weighting factor corresponding to each anomaly marker; S33: Call the curing time deviation correction amount, extract the humidity change rate and humidity fluctuation amplitude, determine the humidity disturbance influence coefficient through the humidity change rate, modulate the humidity disturbance influence coefficient based on the bonding strength coefficient of the material connection interface, and perform weighted calculation with the curing time deviation correction amount and the material connection window adaptation factor to generate the construction progress deviation correction parameters.

7. The method for monitoring the progress of road construction sites according to claim 6, characterized in that, The steps for obtaining the construction progress monitoring and adjustment instructions are as follows: S41: Based on the construction progress deviation correction parameter input fuzzy controller, collect the parameter membership matrix and rule weight matrix, compare the membership output value with the adaptation evaluation benchmark threshold value item by item, mark the parameter index that exceeds the threshold interval and serialize it to generate a set of working condition consistency evaluation factors. S42: Based on the set of working condition consistency evaluation factors, filter the parameter index below the consistency threshold, obtain the corresponding set of working condition parameters for construction equipment, perform consistency judgment on the mapping relationship between the equipment operating condition constraint interval and the construction progress deviation correction parameters, and implement work rhythm rearrangement and equipment operating status switching for inconsistent parameters to form a set of working condition adjustment parameters for construction equipment. S43: Based on the set of operating condition adjustment parameters for the construction equipment, call the node identifier of the construction progress deviation correction parameter, perform the corresponding mapping between parameter status and node control item, perform instruction encoding rule conversion and time sequence arrangement processing on the mapping result, and generate construction progress monitoring and adjustment instructions.

8. The method for monitoring the progress of road construction sites according to claim 7, characterized in that, The adaptation evaluation benchmark threshold is determined by the effective value range of the membership output value in the parameter membership matrix within the corresponding interface constraint interval. The upper and lower boundaries of the effective value range are respectively used as the lower and upper limits of the adaptation evaluation benchmark threshold. The consistency threshold is determined by the consistency evaluation interval formed by weighting the weight values ​​of the rule weight matrix in the working condition consistency evaluation factor set and the corresponding membership output values.

9. The method for monitoring the progress of road construction sites according to claim 7, characterized in that, The steps for obtaining the real-time monitoring results of road construction progress are as follows: S51: Call the construction progress monitoring and adjustment instruction, obtain the node identifier sequence and time adjustment parameter set, retrieve the corresponding node in the construction operation progress table, calculate the difference between the original execution time and the time adjustment parameter and write it into the node field, and generate the node execution time update sequence. S52: Based on the node record items corresponding to the node execution time update sequence, extract the node construction status identifier and task quantity configuration parameters, calculate the ratio of the current completed amount of the node to the task quantity, judge and map the ratio result with the status code interval, and generate a node progress completion configuration set. S53: Call the node progress completion configuration set and node execution time update sequence, perform consistency verification and structural reorganization on the execution time field and progress completion field according to the node sequence index of the construction operation schedule, and aggregate them in time order to generate real-time monitoring results of road construction progress.

10. A road construction site progress monitoring system, characterized in that, The system is used to implement the road construction site progress monitoring method according to any one of claims 1-9, the system comprising: The environmental perception module acquires environmental data through temperature and humidity sensors, performs difference calculations on the temperature and humidity numerical sequences to obtain the fluctuation change sequence, maps it based on construction node timestamps, and constructs environmental impact monitoring data. The trend assessment module inputs the environmental impact monitoring data into a Kalman filter to predict environmental trends, calculates the deviation between a preset environmental stability threshold and the fluctuation sequence, marks values ​​exceeding the threshold as abnormal, and generates construction environmental risk assessment results. The progress analysis module adjusts and analyzes the construction node progress plan based on the construction environment risk assessment results, calculates the material curing time influence factor based on the temperature gradient anomaly marker, calculates the material connection window adaptation factor based on the humidity fluctuation anomaly marker, and generates construction progress deviation correction parameters. The parameter adaptation module inputs the construction progress deviation correction parameters into the fuzzy controller for adaptation evaluation, adjusts the operating conditions of the construction equipment for parameters with a matching degree lower than the matching threshold, and generates construction progress monitoring and adjustment instructions. The progress update module calls the construction progress monitoring adjustment command to update the node execution time and progress completion configuration in the construction operation progress table, and generates real-time monitoring results of road construction progress.

Images