A cable engineering construction progress intelligent prediction and management system

Abstract

The application relates to the technical field of cable engineering construction progress management, and discloses an intelligent cable engineering construction progress prediction and management system, which comprises the following: a multi-dimensional constraint data acquisition module acquires multi-source heterogeneous data of a working space; a construction impedance mapping module constructs a dynamic space-time impedance distribution model and calculates a construction impedance coefficient; a resource saturation threshold calculation module calculates a maximum effective resource carrying capacity by using an impedance-efficiency nonlinear decay model; and a scheduling instruction generation and numerical clamping module executes nonlinear clamping when resource demand is over the limit, locks input and generates a process adjustment strategy. The application introduces an impedance-efficiency coupling mechanism to correct the calculation distortion problem of a linear progress model in a limited space from the algorithm level, ensures that resource scheduling instructions conform to physical bearing rules, and avoids the efficiency collapse caused by congestion effect.

Description

TECHNICAL FIELD

[0001] The application relates to an intelligent cable engineering construction progress prediction and management system and belongs to the technical field of cable engineering construction progress management. BACKGROUND

[0002] In the current linear infrastructure construction of cable engineering, computer system progress deduction and resource planning are used as a conventional means to ensure project delivery. The existing technology adopts the critical path method or the planning review technique to construct a network planning model, set the process logical relationship and duration, calculate the engineering progress parameters to generate a resource allocation plan. The data processing system is based on the linear resource gain assumption, and the algorithm level defaults that the job output rate and the resource input amount are positively correlated. When the actual progress lags behind the planned time, the standard processing logic generates an increase in personnel or equipment input scheduling instructions to compress the subsequent construction period.

[0003] In addition to the above-mentioned congenital defects of traditional mathematical models, the existing progress management system based on computer software also generally ignores the physical laws when dealing with complex restricted space operations. For example, the Chinese invention patent with the authorization publication number CN114580754B discloses a construction engineering progress management system and method based on machine learning. Although this scheme introduces a machine learning model to associate the weather, personnel input and daily construction quantity for training, it tries to predict the subsequent progress by fitting historical data. However, this kind of pure data-driven algorithm lacks underlying modeling of the physical properties of the operation space, and essentially still falls into the logic limitation of monotonic increasing input-output. When facing high impedance restricted environments such as underground pipe corridors, the system is difficult to identify the marginal effect of decreasing or even negative growth caused by resource input saturation, and is prone to generate scheduling instructions that exceed the physical space carrying limit, thereby causing efficiency collapse due to site congestion. In the restricted operation space of cable engineering underground pipe corridors, deep foundation pits or complex urban road sections, the above-mentioned linear assumption has a blind area of calculation that does not conform to physical facts. The space carrying threshold of the restricted operation surface has a nonlinear saturation characteristic. The construction environment resistance increases, leading to construction period delay. The data processing system forcibly adds resources following the linear compensation logic, causing congestion effects in the site operation unit, leading to marginal utility decreasing or even negative growth, and generating inaccurate scheduling instructions. The existing prediction model uses discrete state judgment logic, only focuses on the occurrence and end time of external constraint events, and does not quantify the hysteresis characteristics of the environment state recovery process. For example, after the rain stops or the administrative ban is lifted, the operation environment is still in the high impedance physical recovery period. Ignoring the process inertia data processing method leads to systematic deviation in the completion time prediction.

[0004] Therefore, how to construct a precise and quantitative data processing logic for the saturation characteristics of resources in the restricted space and the hysteresis effect of the environment, correct the calculation distortion of the linear model under complex constraints, and output progress prediction and resource management instructions that conform to the physical objective laws, has become a technical problem to be solved by the present application. SUMMARY

[0005] To solve the problems presented in the background art, the technical solutions of the present application are as follows: An intelligent cable engineering construction progress prediction and management system, the system comprising:

[0006] A multi-dimensional constraint data acquisition module for acquiring geographic space data, meteorological environment data and administrative approval state data of a target cable engineering work space within a current time window;

[0007] A construction impedance mapping module for calling a preset impedance quantization function, mapping the geographic space data, meteorological environment data and administrative approval state data into normalized construction impedance coefficients, and constructing a dynamic spatio-temporal impedance distribution model based on a discretized work space grid; the construction impedance coefficients are used to represent the resource consumption rate resistance value required to advance a unit of engineering quantity within a unit of work space;

[0008] A resource saturation threshold calculation module for calculating the maximum effective resource carrying capacity of the work space under the current time window according to the construction impedance coefficient through a preset impedance-efficiency nonlinear decay model; the impedance-efficiency nonlinear decay model defines a data relationship in which the maximum effective resource carrying capacity decreases nonlinearly with the increase of the construction impedance coefficient;

[0009] A scheduling instruction generation and numerical clamping module for executing resource effectiveness determination logic when a progress lag signal is received, calculating the numerical value of the proposed input resources required to theoretically eliminate the lag, and numerically comparing the numerical value of the proposed input resources with the maximum effective resource carrying capacity; when the numerical value of the proposed input resources is greater than the maximum effective resource carrying capacity, triggering a nonlinear clamping logic to forcibly set the resource numerical value in the final output resource scheduling instruction to the maximum effective resource carrying capacity, and simultaneously generating process timing adjustment strategy data to replace the excessive resource augmentation instruction.

[0010] Preferably, the impedance-efficiency nonlinear decay model preset in the resource saturation threshold calculation module comprises a hyperbolic numerical limit function, and the calculation logic of the hyperbolic numerical limit function satisfies: wherein, is the maximum effective resource carrying capacity, is a spatial geometric constant based on the physical dimension of the work space, is the construction impedance coefficient under the current time window, is a numerical correction factor, and the resource saturation threshold calculation module uses the function to calculate the upper limit boundary value of resource input under a high impedance state.

[0011] Preferably, the construction impedance mapping module further comprises an impedance recovery delay correction unit, which is configured to introduce the actual construction impedance value of the previous time window as a basis for calculation when calculating the construction impedance coefficient of the current time window; when the theoretical impedance value calculated based on the current multidimensional constraint data is less than the actual construction impedance value of the previous time window, the impedance recovery delay correction unit calculates the actual construction impedance coefficient containing a residual resistance component based on a preset delay attenuation coefficient, so as to quantify the process time difference of the work space node from a high constraint state to a low constraint state.

[0012] Preferably, the multidimensional constraint data acquisition module comprises a geological parameter analysis unit, which is configured to convert the obtained geological survey report data into structured stratum hardness indicators and underground water level indicators; the construction impedance mapping module retrieves the preconfigured geological-impedance reference database according to the stratum hardness indicators and underground water level indicators, and generates the basic geological impedance component for the underground concealed engineering work surface.

[0013] Preferably, the process timing adjustment strategy data generated by the scheduling instruction generation and numerical clamping module comprises work shift rearrangement instructions and process serialization instructions; the work shift rearrangement instructions are used to discretely allocate the excess resources originally planned to be put into a single time window to subsequent continuous multiple time windows; the process serialization instructions are used to adjust the originally parallel non-critical path processes to a serial execution sequence under the condition of limited resource input to reduce the instantaneous resource density.

[0014] Preferably, the system further comprises an engineering progress deduction module, which is configured to perform cumulative calculation of virtual construction flow in the dynamic space-time impedance distribution model; the engineering progress deduction module calculates the time consumption value of the virtual construction flow flowing through each work space grid according to the construction impedance coefficient, and accumulatively generates the total duration prediction value of the critical path; the progress lag signal is generated by the engineering progress deduction module after comparing the total duration prediction value with the preset target duration value.

[0015] Preferably, the construction impedance mapping module constructs the construction impedance coefficient by using a multi-layer weighted superposition logic, which comprises: mapping the geographic space data into a basic physical impedance layer, mapping the meteorological environment data into a dynamic environmental impedance layer, and mapping the administrative approval state data into a conditional constraint impedance layer; the construction impedance coefficient is the weighted sum of the values of the basic physical impedance layer, the dynamic environmental impedance layer and the conditional constraint impedance layer in the corresponding work space grid.

[0016] Preferably, the scheduling instruction generation and numerical clamping module is further configured to generate resource matching degree early warning data, the resource matching degree early warning data comprising an impedance-resource ratio index; the impedance-resource ratio index is used to quantify the closeness between the resource input amount in the current work space and the maximum effective resource carrying capacity, and a resource locking instruction for the work space is generated when the index exceeds a preset safety threshold.

[0017] Preferably, the system further comprises a model parameter calibration module, which is configured to collect project actual completion data and corresponding historical impedance records; the model parameter calibration module adjusts the decay rate parameter in the impedance-efficiency nonlinear decay model through regression analysis calculation, so as to minimize the numerical deviation between the historical predicted construction period and the actual completion period.

[0018] Preferably, the system is deployed in a computing device comprising a processor and a memory, the multi-dimensional constraint data collection module, the construction impedance mapping module, the resource saturation threshold calculation module, and the scheduling instruction generation and numerical clamping module are computer program instruction codes stored in the memory and executed by the processor, and the computing device establishes a data connection channel with an external environment monitoring server and an administrative management database through a network interface.

[0019] Compared with the prior art, the present application has the following advantages:

[0020] 1. In the progress of cable engineering construction, by constructing a resource saturation threshold dynamic clamping logic based on construction impedance, the deviation of the traditional progress prediction model in which resource input and output present linear gain algorithm is corrected; the system calls the preset impedance-efficiency mapping rule according to the real-time comprehensive construction impedance coefficient of the work space node, calculates the maximum resource carrying capacity under the current state of the node, and when the deduced calculation shows that the amount of resources to be input exceeds the carrying capacity, performs a numerical clamping operation to limit the resource instruction within the threshold value. This processing logic identifies and avoids the congestion effect caused by excessive resource stacking under high impedance constraints at the data level, ensures that the generated resource scheduling instruction conforms to the physical carrying law of the limited space, and avoids the resource allocation failure and the reduction of on-site work efficiency caused by the linear compensation logic in the prior art.

[0021] 2. The introduction of asymmetric impedance hysteresis decay logic in the impedance model eliminates the systematic optimistic bias in the progress deduction process due to the neglect of environmental state recovery inertia. When calculating the time-varying impedance data, the system combines the multi-dimensional constraint state data at the current time, introduces the actual construction impedance value at the previous time step as a decay term, and calculates the actual impedance containing residual resistance components. This iterative operation contains historical state memory and realizes mathematical representation of the hysteresis characteristics of physical or management processes such as soil drainage and administrative resumption of work approval flow. The progress prediction result covers the implicit time required for the recovery from high constraint state to normal state, and improves the stability and credibility of the prediction data in the dynamic mutation environment.

[0022] 3. The system utilizes unified dynamic space-time impedance field mapping to realize the normalization processing and decoupling operation of multi-source heterogeneous constraint data such as geology, meteorology and administration, abstracts the discrete operation space unit as a field with specific impedance properties, and quantifies the complex external environment constraint as a comprehensive construction impedance coefficient which has a retarding effect on the construction flow. This data processing method converts the nonlinear characteristics of multi-dimensional physical environment into a standardized numerical calculation boundary, so that the progress deduction algorithm can generate schedule prediction and critical path analysis directly based on objective data flow without relying on subjective qualitative judgment, solving the problem of calculation model distortion caused by non-uniform data dimensions in traditional methods when dealing with multi-factor coupling interference. BRIEF DESCRIPTION OF DRAWINGS

[0023] Fig. 1 Fig. 1 is a schematic diagram of the system data processing logic and resource clamping control process of the present application;

[0024] Fig. 2 Fig. 4 is a comparison curve diagram of the time-varying dynamic characteristics and model of the construction impedance coefficient of the present application;

[0025] Fig. 3 Fig. 6 is a function architecture decomposition and multi-dimensional constraint logic fishbone diagram of the present application. DETAILED DESCRIPTION

[0026] The following examples are further explanations and illustrations of the present application and do not constitute any limitation on the present application.

[0027] The embodiment of the present application provides a cable engineering construction progress intelligent prediction and management system, which comprises a multi-dimensional constraint data acquisition module, a construction impedance mapping module, a resource saturation threshold calculation module, a scheduling instruction generation and numerical clamping module, and an engineering progress deduction module. The above modules are stored in the memory of a computing device and are executed by a processor to realize corresponding data processing functions. The computing device establishes data connection with an external geographic information system, an environment monitoring server and an administrative database through a network interface. The multi-dimensional constraint data acquisition module is used to acquire geographic space data, meteorological environment data and administrative approval state data of a target cable engineering operation space within a current time window. The geographic space data includes unstructured text in a geological survey report, which is parsed into structured stratum hardness indicators and underground water level indicators by a keyword extraction algorithm. The meteorological environment data includes real-time rainfall, wind speed and environmental temperature. The administrative approval state data is a Boolean value or a state code representing the current construction permit state. The construction impedance mapping module is used to construct a dynamic space-time impedance distribution model, discretize the operation space into grid units, and perform weighted superposition operation on the geographic space data, meteorological environment data and administrative approval state data to generate normalized construction impedance coefficients . The construction impedance mapping module is used to construct a dynamic space-time impedance distribution model, discretize the operation space into grid units, and perform weighted superposition operation on the geographic space data, meteorological environment data and administrative approval state data to generate normalized construction impedance coefficients . The resource consumption rate resistance value required for representing the unit work space to promote the unit engineering quantity, the calculation logic satisfies the following relationship: , is the basic physical impedance component determined based on the stratum hardness index and the underground water level index; is the dynamic environmental impedance component determined based on meteorological data such as rainfall; is the conditional constraint impedance component determined based on the administrative approval state; , , respectively, the numerical values are determined by regression analysis of historical engineering data.

[0028] The construction impedance mapping module further includes an impedance recovery delay correction unit for quantifying the inertia characteristics of the environmental state recovery process, which introduces the actual construction impedance value of the previous time window as a decay term when calculating the actual construction impedance coefficient of the current time window , the unit compares the theoretical impedance value calculated based on the multi-dimensional constraint data at the current time with the actual construction impedance value of the previous time window , when is less than , the unit uses a preset delay decay coefficient to calculate the actual construction impedance coefficient containing residual resistance component according to the following formula : , is a constant related to soil permeability or administrative process flow time consumption, used to represent the process time difference of the work space node from high constraint state to low constraint state; resource saturation threshold calculation module, for calculating the maximum effective resource carrying capacity of the work space under the current time window according to the construction impedance coefficient , the module calls the preset impedance-efficiency nonlinear decay model, which defines the inverse proportional constraint relationship between resource capacity and impedance, the specific calculation logic satisfies the following equation: , is the maximum effective resource carrying capacity allowed to be put into the current work space node; is a spatial geometric constant based on the physical dimension of the work space, the value depends on the ratio of the work surface cross-sectional area to the minimum free activity area required by the single work unit; is a numerical correction factor, used to prevent the denominator from being zero and to calibrate the reference capacity under low impedance state.

[0029] The scheduling instruction generation and numerical clamping module is used to execute resource availability determination logic when a progress lag signal is received. This module calculates the theoretically required resource values ​​to eliminate the lag. And this value is compared with the maximum effective resource carrying capacity. When a comparison is performed, Greater than At this time, the module triggers non-linear clamping logic, forcibly setting the resource value in the final output resource scheduling instruction to a certain value. Start the digital comparator circuit and monitor The arithmetic logic unit outputs a status register; once an overflow flag is detected... or sign bit An abnormal flip-flop triggers the highest priority hardware interrupt. Suspend the current instruction pipeline and call the pre-loaded read-only memory. The clamping subroutine in the code extracts the maximum effective resource capacity through addressing. The binary two's complement is used to directly overwrite the general-purpose register. The proposed resource values ​​are synchronously written to the status control register using a lock mask. To prevent subsequent speculative execution branches from illegally modifying clamped values, write permissions to the data bus are disabled, utilizing direct memory access. The channel will be corrected. The burst transmission is sent to the scheduling instruction output buffer queue, ending the resource allocation calculation for the current clock cycle. Simultaneously, this module generates process timing adjustment strategy data to replace excess resource allocation instructions. This data includes shift rescheduling instructions and process serialization instructions. Shift rescheduling instructions are used to discretely allocate excess resources across multiple consecutive time windows, while process serialization instructions are used to adjust originally parallel non-critical path processes into a serial execution sequence, constructing a dual-code network diagram. adjacency matrix Calculate the earliest start time of all nodes. With the latest start time Filter by total time difference Non-critical process set Establish based on resource intensity A priority queue with weights is used to execute a resource smoothing algorithm, where... For resource intensity, The total amount of resources planned to be invested in this specific process. The planned duration value for this specific process. Chinese elements Sort in descending order and search subsequent time windows in turn. Remaining resource capacity ,like , then insert the time slot into the node and update the dependency edge in , if the traversal window exceeds the maximum allowed delay , trigger the process splitting logic to split the single process node into a serial sub-node sequence , re-execute the critical path method forward calculation until the total duration resource histogram peak , and the project total duration deviation converges to the preset threshold . .

[0030] The project progress deduction module is used to perform cumulative calculation of the virtual construction flow in the dynamic space-time impedance distribution model, which calculates the time consumption value of the virtual construction flow flowing through each work space grid according to the construction impedance coefficient , and accumulates to generate the total duration prediction value of the critical path, and generates a progress lag signal by comparing the total duration prediction value with the preset target duration value; the system also includes a model parameter calibration module for calibrating the parameters in the above-mentioned model by using the actual project completion data and the corresponding historical impedance record, which adjusts the weight coefficient 、 、 , the spatial geometric constant , the numerical correction factor and the delay attenuation coefficient by least square regression analysis to minimize the numerical deviation between the historical predicted duration and the actual completion duration.

[0031] Example 1: In the underground comprehensive pipe gallery cable laying project located in the core area of the city, the work space is limited to the narrow deep foundation pit environment, and the geological conditions involve high viscosity soft soil layer; when the project is in the foundation pit excavation and support installation stage on the critical path, the continuous 48-hour heavy rainfall on site causes the actual construction progress to lag behind the preset target duration by 3 days; after the rainfall ends and the administrative restart order is issued, the rainfall reading in the meteorological environment data collected by the multi-dimensional constraint data acquisition module is zero, and the administrative approval state data changes to allow construction; the impedance recovery delay correction unit in the construction impedance mapping module compares the theoretical impedance value calculated based on the current meteorological data with the actual construction impedance value during the rainfall, and determines that the environment is in a non-transient recovery process; the unit calls the delay attenuation coefficient matching the drainage characteristics of high viscosity soil , and calculates the comprehensive construction impedance coefficient under the current time window according to the formula ​, the value is higher than the benchmark level due to the physical state of soil oversaturation and water accumulation at the bottom of the pit.

[0032] The engineering progress deduction module calculates the required input resource value to eliminate the lagging period according to linear logic , the value corresponds to the simultaneous input of double operation teams in the foundation pit; the resource saturation threshold calculation module calculates the comprehensive construction impedance coefficient according to the previous step , combined with the spatial geometric constant representing the narrow cross-section attribute of the deep foundation pit , executes the operation ; the calculation result shows that under the current high impedance constraint, the maximum effective resource carrying capacity of the operation surface is lower than the conventional working condition capacity; the scheduling instruction generation and numerical clamping module performs resource effectiveness determination, and the comparison result shows that the proposed input resource value is greater than the maximum effective resource carrying capacity , and the system determines that forcibly executing the linear increment strategy will lead to resource congestion in the restricted space; the scheduling instruction generation and numerical clamping module triggers the nonlinear clamping logic, locks the resource value in the final output resource scheduling instruction as , and generates process timing adjustment strategy data; the driving scheduling engine adjusts the originally parallel arranged support welding process to serial execution, and generates operation shift rearrangement instructions to discretely allocate the uninput daytime excess resources to the newly added night shift; this processing flow modifies the resource input through physical impedance constraints, avoiding on-site congestion effects while realizing the catch-up of the lagging period.

[0033] Example 2: This example builds a digital test platform based on real engineering data and discrete event simulation (DES), quantitatively verifies the effectiveness and stability of the impedance-efficiency nonlinear decay model and impedance recovery delay correction logic proposed in the invention in complex restricted space cable engineering progress prediction. The test is particularly aimed at the typical restricted space scenario of underground pipe gallery cable laying, by introducing multi-source environmental disturbances and random events, and comparing and verifying the performance advantages of the invention scheme compared with the traditional linear critical path method (CPM) in dealing with nonlinear constraints and dynamic environmental changes; the test selects a length of 2.5 kilometers of underground pipe gallery cable laying engineering as the simulation object. The geological survey data of this section shows that the stratum hardness index fluctuate between 4 to 6, belonging to typical medium-hard rock strata, and along the line there are two narrow nodes that need to cross existing municipal pipelines, constituting high impedance bottlenecks in physical space, the test platform is built based on a discrete event simulation engine, which is configured to receive and analyze meteorological environmental data streams and administrative approval instruction streams with timing characteristics, in order to ensure that the test results can truly reflect the complexity of the engineering site, the input data is not idealized steady-state value, but actively injects noise and disturbance in line with engineering practice, the signal-to-noise ratio of the random wind speed fluctuation in the meteorological data sequence is 15dB, and the rainfall data (mm / h) follows a Poisson distribution; the administrative approval state data simulates the random delay of 1 to 3 days commonly seen in the approval process, the sampling period of the test is set to 1 hour, which is determined based on the Nyquist sampling theorem analysis of the minimum time granularity of the construction process (usually 0.5 workdays, i.e. 4 hours) and the frequency of meteorological changes (hourly).

[0034] Three parallel test groups are set up in this embodiment for comparison and verification, the control group 1 uses the traditional CPM linear model, which assumes a fixed proportion between resource input and progress output, and does not have the delay correction function of environmental impedance, the control group 2 is a partially missing control group, although it introduces the concept of construction impedance , but removes the nonlinear clamping logic in the resource saturation threshold calculation module, i.e. its maximum resource carrying capacity is set to infinity, the inventive group enables the full-function system including the nonlinear constraint model and the impedance recovery delay correction unit; the test process simulates a continuous rainfall scenario starting from the 10th day of the construction period, from the 10th to the 12th day, the simulated rainfall maintains at 20mm / h or above, triggering a high impedance state, on the 13th day the rainfall stops, but the soil humidity is still in a supersaturated state, during this period, each test group detects progress lag and attempts to generate catch-up instructions; the test results show differences, for the control group 1, as soon as the rainfall stops on the 13th day, the system issues instructions to resume full-speed construction and invest double resources, resulting in a significant advance in the predicted completion time, however, the actual simulation does not take into account the reduced work efficiency due to slippery soil, causing prediction bias, for the control group 2, although a higher impedance value is calculated, due to the lack of resource clamping, the system issues aggressive instructions to invest 3 times the resources on the 13th day, which causes resource congestion in the simulation environment, resulting in a 45% decrease in output rate per unit of time, in contrast, the inventive group does not resume full-speed construction on the 13th day, but based on the impedance delay correction logic, the value calculated by the inventive group is exponentially decaying, truly reflecting the recovery process of the environment, while being limited by The nonlinear clamping mechanism of this invention ensures that the resource scheduling instructions generated by the sample group control the input amount within the physical carrying capacity limit of the working surface, thus avoiding congestion. Table 1 shows the comparison of key performance indicators of each test group on the 15th day.

[0035] Table 1: Comparison of performance indicators of each experimental group on day 15

[0036]

[0037] Referring to Table 1, the data shows that within the same environmental recovery period (0% rainfall, but soil moisture as high as 85%), the sample group of this invention, by limiting resource input to 120 person-hours (lower than the control group), controlled the congestion coefficient at a low level of 0.15, thereby achieving a resource utilization rate (92%) and project duration prediction (deviation of only ±0.5 days). To further verify the rationality of the numerical range, the experiment was also designed to target spatial geometric constants. gradient control, when When the value is set to 50% of the baseline value (simulating an extremely confined space), the system frequently triggers clamping, leading to project delays; while when When the value is set to 150% of the baseline value (simulating a spacious area), the clamping mechanism fails, resulting in excessive resource investment.

[0038] Example 3: This example combines Figs. 1 to 3 This document describes an intelligent prediction and management system for cable engineering construction progress, such as... Fig. 1 As shown, the logical operation flow of this system begins with the input layer receiving geospatial data, meteorological environmental data, and administrative approval status data. The multi-dimensional constraint data acquisition module is responsible for acquiring multi-source heterogeneous data of the work space. Through mapping operations, the data is transmitted to the construction impedance mapping module to construct a dynamic spatiotemporal impedance distribution model. Then, the resource saturation threshold calculation module uses the impedance efficiency nonlinear attenuation model to perform calculation tasks. When the system receives an external input progress lag signal, the scheduling instruction generation and numerical clamping module is immediately started, performing resource validity determination and nonlinear clamping logic. Finally, at the output end, resource scheduling instructions with numerical clamping are generated in parallel, as well as process timing adjustment strategies containing serialization or shift rescheduling content.

[0039] like Fig. 2 As shown, regarding the construction impedance coefficient In the data comparison graph over time, the horizontal axis marks the time points from day 10 to day 15, and the vertical axis quantifies the construction resistance coefficient. The numerical size of the control group 1 linear model, the control group 2 without clamping, and the three independent trend lines of the sample group of the application in the chart are all between 0.8 and 0.9 in the interval from the 10th day to the 12th day, and after the 13th day, the numerical value of the control group 1 drops sharply to 0.2 and remains stable, the numerical value of the control group 2 slowly decreases, and by the 15th day, it is 0.75, while the numerical value of the sample group of the application presents a smooth slope from about 0.7 on the 13th day to about 0.3 on the 15th day, as shown in Fig. 3 As shown, the functional elements of the overall architecture of the system are hierarchically displayed in the form of a fishbone diagram, the head of the fish points to the overall goal of progress intelligent prediction and management, the branches above the main trunk correspond to the multi-dimensional constraint data acquisition module and the resource saturation threshold calculation module in turn, wherein the multi-dimensional constraint data acquisition module is subordinate to the geographic spatial data containing geological and hardness indicators and the meteorological environmental data containing rainfall and temperature indicators, the resource saturation threshold calculation module is associated with the impedance effectiveness nonlinear decay model and the maximum effective resource carrying capacity, the branches below the main trunk correspond to the construction impedance mapping module and the scheduling instruction generation and numerical clamping module in turn, wherein the construction impedance mapping module covers the dynamic spatiotemporal impedance distribution model, the normalized construction impedance coefficient, and the impedance recovery delay correction element with inertia characteristics, and the scheduling instruction generation and numerical clamping module integrates resource effectiveness judgment, process timing adjustment strategy, and nonlinear clamping logic with forced locking function.

[0040] Embodiment 4: To solve the stability problem of the system when the multi-dimensional constraint data is incomplete or has abnormal values, i.e., the parameter / threshold black box and the algorithm path black box, this embodiment provides a construction impedance mapping mechanism based on adaptive confidence weighting, which eliminates the impedance calculation deviation caused by input data defects at the source level by introducing data quality evaluation and dynamic weight adjustment logic, ensuring the engineering usability of the model in a non-ideal data environment. For the geographic spatial data, meteorological environmental data, and administrative approval status data obtained by the multi-dimensional constraint data acquisition module, the system does not directly substitute them into the impedance calculation formula, but first executes a data quality preprocessing process, which defines the validity boundary and confidence score rules for each type of data. For the real-time rainfall of the meteorological environmental data, the system sets a physically reasonable interval based on historical extreme values ; if the collected value exceeds this interval, the system determines that the sensor is faulty, reduces its confidence to 0, and automatically calls the interpolation data of the adjacent weather station as a substitute input. For the administrative approval status data, the system calculates the consistency coefficient as the confidence by cross-comparing the records of different levels of administrative platforms.

[0041] In constructing the dynamic spatiotemporal impedance distribution model, the construction impedance mapping module adopts an improved weighted superposition logic. The traditional static weight coefficient 、 、 is replaced by a dynamic weight based on real-time data confidence. The specific calculation procedure is as follows: the system calculates the normalized confidence weight of each data source wherein , is the normalized weight of the th data source after confidence correction, is the initial static weight coefficient of the th data source, is the current real-time confidence score of the th data source, is a summation symbol for performing summation operation on the data source set covered by the subscript , is the initial static weight coefficient of the th data source, is the current real-time confidence score of the th data source, and iterates the values in the three dimensions of geography (geo), meteorological environment (env), and administrative approval (admin), and calculates the comprehensive construction impedance coefficient wherein is the comprehensive construction impedance coefficient under the current time window, 、 、 are the corrected normalized weights of the geo, env, and admin dimensions, respectively, 、 、 are the basic impedance components of the corresponding dimensions determined based on the geo data, env data, and admin state data, respectively. This algorithm ensures that when the input data quality of a certain dimension decreases, the influence of the dimension on the final impedance result automatically attenuates, thereby maintaining the stability of the system output. In addition, the spatial geometry constant in the resource saturation threshold calculation module is introduced. This embodiment introduces a dynamic calibration mechanism based on the real-time state of the work surface. Considering the dynamic change of the effective work area in a limited space such as a deep foundation pit due to the erection of supporting structures and the entry of equipment during the construction process, the system no longer regards as a fixed value. This mechanism analyzes the on-site monitoring video stream and uses computer vision algorithms to extract the passable area contour of the work surface in real time, calculates the current effective free activity area , and dynamically updates the spatial geometry constant at the current time according to the formula wherein The updated standard footprint for a single resource The actual impedance-efficiency nonlinear decay model is entered .

[0042] Embodiment 5: This embodiment provides a resource carrying capacity baseline calibration procedure for new engineering site deployment, before the system is formally put into operation, the target work space is fully covered by three-dimensional scanning using unmanned aerial vehicles or ground laser scanning equipment, a digital twin model is constructed, the cross section geometric parameters of each work node are automatically extracted through the model and the initial space geometric constant is generated The system performs a simulated load test, injects a standardized virtual work unit flow into the model, gradually increases the flow until a virtual collision or path deadlock event is monitored, at this time the flow value is recorded as the physical limit carrying capacity of the node, the system accordingly reverses the calibration of the correction factor in the impedance-efficiency nonlinear decay model, ensuring The calculated reference capacity is consistent with the physical limit, where is the theoretical minimum construction impedance.

[0043] And for the risk of technical black box, this embodiment further elaborates the offline construction and verification method of the weight coefficient , , in the impedance quantification function, based on a multi-dimensional orthogonal test covering different geological conditions, meteorological environment and administrative management strength, several groups of standardized construction scenes are designed, by changing only the rainfall or only the approval state, the actual work efficiency loss rate under each scene is recorded, using multiple linear regression analysis, the system establishes a quantitative mapping relationship between each dimension constraint and work efficiency loss, and accordingly determines the initial value of each weight coefficient, by introducing a group of independent historical engineering data set for cross-validation, the correlation coefficient between the predicted impedance and the actual work efficiency resistance is calculated, when the coefficient is more than 0.85, the relevant weight parameter is only solidified to the parameter library of the formal system.

[0044] Embodiment 6: In order to ensure that the system can capture the real constraints of the physical space when it is first deployed, and solve the problem of missing basis for setting physical / geometric parameters, this embodiment provides a space geometric constant standardization calibration procedure based on physical simulation and digital inversion, in the standardized test site, a physical model with a 1:1 scale of the target underground pipe gallery cross section size and internal pipeline layout is constructed, a hierarchical personnel filling pressure test is performed, the number of workers is gradually increased by a preset step, at the same time, a distributed displacement sensor network is used to monitor the average free activity area of the individual work unit in real time, when the monitored When the number of people reaches a preset safe limit, such as 1.5 square meters per person, the current maximum number of people is recorded The system calculates the reference value of the space geometry constant under the specific cross-section configuration according to the formula , wherein is the effective passage area of the cross-section. By repeating the above procedure for different cross-section types, such as circular, rectangular, and obstacle density levels, a standardized value lookup table is constructed for the system to directly call when deployed in the field.

[0045] To address the problem of insufficient quantification of logical / judgment conditions, the embodiment further specifies the specific algorithmic logic of resource effectiveness determination, converting the fuzzy congestion judgment into deterministic numerical comparison. The system sets the resource congestion determination threshold , such as 0.95. At the start of each scheduling period, the ratio of the amount of resources to be input to the current maximum effective resource carrying capacity , i.e., the resource saturation index , is calculated. When , the non-linear clamping logic is triggered by the hardware interrupt signal, performing the assignment operation of and simultaneously activating the alarm flag. If , the pass-through operation of is performed.

[0046] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An intelligent prediction and management system for cable engineering construction progress, characterized in that, The system comprises: A multi-dimensional constraint data acquisition module for acquiring geospatial data, meteorological environment data and administrative approval state data of a target cable engineering work space within a current time window; A construction impedance mapping module for calling a preset impedance quantification function, mapping the geospatial data, meteorological environment data and administrative approval state data into normalized construction impedance coefficients, and constructing a dynamic spatio-temporal impedance distribution model based on a discretized work space grid; the construction impedance coefficients are used to represent the resource consumption rate resistance value required for advancing a unit of engineering quantity within a unit of work space; A resource saturation threshold calculation module for calculating the maximum effective resource carrying capacity of the work space under the current time window according to the construction impedance coefficients through a preset impedance-efficiency nonlinear decay model; the impedance-efficiency nonlinear decay model defines a data relationship in which the maximum effective resource carrying capacity decreases nonlinearly with the increase of the construction impedance coefficient; A scheduling instruction generation and numerical clamping module for executing resource effectiveness determination logic when a progress lag signal is received, calculating the numerical value of the proposed input resources required to theoretically eliminate the lag, and numerically comparing the numerical value of the proposed input resources with the maximum effective resource carrying capacity; when the numerical value of the proposed input resources is greater than the maximum effective resource carrying capacity, a nonlinear clamping logic is triggered to forcibly set the resource numerical value in the final output resource scheduling instruction to the maximum effective resource carrying capacity, and a process timing adjustment strategy data is generated simultaneously to replace the excessive resource augmentation instruction.

2. The intelligent prediction and management system for cable engineering construction progress according to claim 1, characterized in that, The preset impedance-performance nonlinear attenuation model in the resource saturation threshold calculation module comprises a hyperbolic numerical limiting function, and the calculation logic of the hyperbolic numerical limiting function satisfies: wherein, is the maximum effective resource carrying capacity, is a space geometry constant based on the physical dimension of the work space, is a construction impedance coefficient under a current time window, is a numerical correction factor, and the resource saturation threshold calculation module uses the function to calculate the upper limit boundary value of resource input under a high impedance state.

3. The intelligent prediction and management system for cable engineering construction progress according to claim 1, characterized in that, The construction impedance mapping module further comprises an impedance recovery delay correction unit for introducing the actual construction impedance value of the previous time window as a basis for calculation when calculating the construction impedance coefficient of the current time window; when the theoretical impedance value calculated based on the current multi-dimensional constraint data is less than the actual construction impedance value of the previous time window, the impedance recovery delay correction unit calculates the actual construction impedance coefficient containing a residual resistance component based on a preset delay decay coefficient to quantify the process time difference of the work space node from a high constraint state to a low constraint state.

4. The intelligent prediction and management system for cable engineering construction progress according to claim 1, characterized in that, The multi-dimensional constraint data acquisition module comprises a geological parameter analysis unit for converting the obtained geological survey report data into structured stratum hardness indicators and underground water level indicators; the construction impedance mapping module retrieves a preset geological-impedance reference database based on the stratum hardness indicators and underground water level indicators to generate a basic geological impedance component for the underground concealed engineering work surface.

5. The intelligent prediction and management system for cable engineering construction progress according to claim 1, characterized in that, The process timing adjustment strategy data generated by the scheduling instruction generation and numerical clamping module includes work shift rearrangement instructions and process serialization instructions; the work shift rearrangement instructions are used to discretely allocate the excess resources originally planned to be input within a single time window to subsequent continuous multiple time windows; the process serialization instructions are used to adjust the originally parallel non-critical path processes to a serial execution sequence under the condition of limited resource input to reduce the instantaneous resource density.

6. The intelligent prediction and management system for cable engineering construction progress according to claim 1, characterized in that, The system further comprises an engineering progress deduction module, which is configured to perform cumulative calculation of the virtual construction flow in the dynamic space-time impedance distribution model; the engineering progress deduction module calculates the time consumption value of the virtual construction flow flowing through each work space grid according to the construction impedance coefficient, and accumulatively generates the total duration prediction value of the critical path; the progress lag signal is generated by the engineering progress deduction module after comparing the total duration prediction value with the preset target duration value.

7. The intelligent prediction and management system for cable engineering construction progress according to claim 1, characterized in that, The construction impedance mapping module adopts a multi-layer weighted superposition logic to construct the construction impedance coefficient, which includes: mapping the geographic space data into a basic physical impedance layer, mapping the meteorological environment data into a dynamic environment impedance layer, and mapping the administrative approval state data into a conditional constraint impedance layer; the construction impedance coefficient is the weighted sum of the values of the basic physical impedance layer, the dynamic environment impedance layer and the conditional constraint impedance layer in the corresponding work space grid.

8. The intelligent prediction and management system for cable engineering construction progress according to claim 1, characterized in that, The scheduling instruction generation and numerical clamping module is further configured to generate resource matching degree early warning data, which contains an impedance-resource ratio index; the impedance-resource ratio index is used to quantify the closeness between the resource input quantity and the maximum effective resource carrying capacity in the current work space, and generate a resource locking instruction for the work space when the index exceeds the preset safety threshold.

9. The intelligent prediction and management system for cable engineering construction progress according to claim 1, characterized in that, The system further comprises a model parameter calibration module, which is configured to collect actual project completion data and corresponding historical impedance records; The model parameter calibration module adjusts the decay rate parameter in the impedance-efficiency nonlinear decay model through regression analysis calculation, so as to minimize the numerical deviation between the historical predicted duration and the actual completion duration.

10. The intelligent prediction and management system for cable engineering construction progress according to claim 1, characterized in that, The system is deployed in a computing device comprising a processor and a memory, the multi-dimensional constraint data acquisition module, the construction impedance mapping module, the resource saturation threshold calculation module and the scheduling instruction generation and numerical clamping module are computer program instruction codes stored in the memory and executed by the processor, and the computing device establishes a data connection channel with an external environment monitoring server and an administrative management database through a network interface.

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