CN117541004A - Visual management method and system for static pressure pipe pile construction - Google Patents
Visual management method and system for static pressure pipe pile construction Download PDFInfo
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Abstract
The invention relates to the technical field of construction management, in particular to a visual management method and system for static pressure pipe pile construction, wherein the method comprises the following steps: establishing a three-dimensional resource view, and highly displaying construction resource response time and construction resource available volume according to the construction resource surplus position and the construction resource idle position through a bar graph; aiming at the construction resource demand position, highly displaying the construction resource demand shortage through a bar graph; revising construction resource response time and construction resource available volume based on analysis results of personnel and equipment capabilities; and analyzing the revised three-dimensional resource view to obtain a construction resource scheduling scheme. The invention can help to know the requirements, surplus and idle conditions of construction resources, response time, available volume and other indexes; through predictive analysis and resource capacity analysis, the conditions of resource demand and supply can be evaluated more accurately, resource allocation and scheduling decisions can be optimized, construction efficiency and quality can be improved, and cost and risk can be reduced.
Description
Technical Field
The invention relates to the technical field of construction management, in particular to a visual management method and system for static pressure pipe pile construction.
Background
Static pressure pipe pile construction is a common foundation treatment method for increasing the soil bearing capacity and improving the foundation stability, and the current static pressure pipe pile construction has some challenges and problems, including the current situations of unbalanced resource allocation, long response time, idle resources and the like.
Specifically, in the conventional static pressure pipe pile construction, owners of using resources, such as construction companies, suppliers, etc., or users of the construction resources, such as construction teams, project managers, etc., often rely on experience and manual decisions for allocation and scheduling of the construction resources, and lack scientific and systematic management methods, which results in waste and inefficient use of a large amount of high-cost construction resources, and also increases risks and costs in the construction process. In addition, because the information between the resource demand and the supply is asymmetric, the response time of the construction resource is longer, the construction demand is difficult to meet in time, and the construction progress and quality are affected.
Disclosure of Invention
The invention provides a visual management method and a visual management system for static pressure pipe pile construction, thereby effectively solving the problems pointed out in the background technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
A visual management method for static pressure pipe pile construction comprises the following steps:
establishing a three-dimensional resource view and performing visual display, wherein the three-dimensional resource view displays a construction resource demand position, a construction resource surplus position and a construction resource idle position through a plane, and highly displays construction resource response time and construction resource available volume according to the construction resource surplus position and the construction resource idle position through a bar graph;
based on a set time range, traversing the construction resource demand position to predict construction resources, generating a construction resource demand shortage, and highly displaying the construction resource demand shortage according to the construction resource demand position through a bar graph;
analyzing personnel and equipment capacities of the construction resource surplus position and the construction resource idle position, and revising the displayed construction resource response time and the displayed construction resource available volume based on analysis results;
and analyzing the revised three-dimensional resource view to obtain a construction resource scheduling scheme.
Further, based on the set time range, the construction resource demand position is searched for construction resource demand prediction, and the construction resource demand shortage is generated, including:
Collecting construction project historical data and preprocessing, wherein the historical data comprises construction resource demand positions, time ranges and actual used resource amounts;
extracting relevant features from the collected data, wherein the relevant features are factors influencing resource requirements;
dividing the historical data into a training set and a testing set;
selecting a neural network model and determining the structure of the neural network model;
training the neural network model by using the training set, updating the weight and bias of the neural network model by a back propagation algorithm to minimize a loss function, and optimizing the performance and generalization capability of the neural network model by different super-parameter setting and regularization techniques;
and predicting the construction resource demand by using the trained model, inputting the related characteristics, and outputting the construction resource demand shortage.
Further, based on the set time range, the construction resource demand position is searched for construction resource demand prediction, and the construction resource demand shortage is generated, including:
collecting construction project historical data and preprocessing, wherein the historical data comprises construction resource demand positions, time ranges and actual used resource amounts;
Extracting relevant features from the collected data, the relevant features including location information, time information, historical resource demand and seasonal information;
dividing the historical data into a training set and a testing set, wherein the dividing is performed according to a time sequence;
selecting a long-period memory network model, and determining the number, the layer number, the input dimension and the output dimension of neurons of the long-period memory network model;
training the neural network model by using the training set, updating the weight and bias of the neural network model by a back propagation algorithm to minimize a loss function, and optimizing the performance and generalization capability of the neural network model by different super-parameter setting and regularization techniques;
and predicting the construction resource demand by using the trained model, inputting the position and the time range to be predicted, and outputting the construction resource demand shortage.
Further, training the neural network model using the training set, including:
extracting input features at the current moment from a training set, and inputting the input features into an input gate of a long-short-term memory network model, wherein the input gate adjusts the influence of the input features by using weights and biases;
Multiplying the output value of the input gate with the input characteristic, screening and weighting the influence of the input, and transmitting the influence to a candidate memory unit;
the candidate memory unit calculates a new candidate memory value according to the input characteristic at the current moment and the memory state at the previous moment, takes the new candidate memory value as an input, and transmits the new candidate memory value and the input characteristic to the forgetting gate;
the forgetting gate carries out weight distribution on each element in the memory state, determines the retention degree of each element in the memory state at the previous moment, and multiplies the retention degree by the candidate memory value element by element to obtain a final memory value;
integrating the outputs of the candidate memory units and the forgetting gate, updating the memory units to obtain the memory state at the current moment, and transmitting the memory state to the output gate;
and the output gate determines the weight of the output memory information through the input characteristics and the memory state at the current moment.
Further, before training the neural network model, attention mechanisms are introduced, including:
taking the extracted relevant features as input of an attention mechanism;
the attention mechanism calculates attention weight distribution of the features according to the importance of the related features;
Multiplying the attention weight with the relevant feature to obtain a weighted feature representation;
the weighted feature representation is input as an input feature into the neural network model.
Further, the method further comprises the following steps: applying the feature weight calculated by the attention mechanism to the display of the construction resource surplus position and the construction resource idle position, wherein the feature weight comprises the following components:
preparing a data set containing the construction resource surplus position and the construction resource idle position, and extracting corresponding characteristics of construction resource response time and construction resource available volume;
weighting the corresponding features by using an attention mechanism, and calculating attention weight distribution of the features;
and displaying the calculated attention weight as a bar graph color at the construction resource surplus position and the construction resource idle position.
Further, the personnel and equipment capacities of the construction resource surplus position and the construction resource idle position are analyzed, and the displayed construction resource response time and the displayed construction resource available volume are revised based on the analysis result, including:
collecting personnel and equipment capability data related to the construction resource surplus position and the construction resource idle position;
analyzing the collected data and performing the steps of:
Re-evaluating construction resource response time according to availability and response capacity of personnel and equipment; and re-evaluating the available volume of the construction resource according to the availability and the number of the personnel and the equipment;
and updating the display of the three-dimensional resource view according to the revised response time of the construction resource and the available volume of the construction resource.
Further, analyzing the revised three-dimensional resource view to obtain a construction resource scheduling scheme, including:
determining a target and constraint conditions of resource scheduling according to the revised stereoscopic resource view;
defining an adaptability function for evaluating the advantages and disadvantages of the scheme based on the targets and the constraint conditions, and measuring the quality of the construction resource scheduling scheme;
randomly generating a group of initial scheduling schemes as a population, wherein each individual in the population represents a construction resource scheduling scheme, and repeatedly executing the following steps, wherein the termination condition is that the maximum iteration number is reached or a satisfactory solution is found:
calculating an fitness value for each individual in the group, and evaluating the quality degree of a resource scheduling scheme according to the definition of a fitness function;
selecting a part of individuals from the population as parents by using a selection operator, and performing cross operation on the selected parent individuals to generate offspring individuals;
Carrying out mutation operation on offspring individuals and introducing certain randomness; combining the parent individuals and the variant child individuals into a new population, and updating the state of the population;
after the termination condition is met, the individual with the optimal fitness value is selected from the final population as the optimal solution.
A visual management system for static pressure pipe pile construction comprises:
the resource view establishing module establishes a three-dimensional resource view and performs visual display, the three-dimensional resource view displays a construction resource demand position, a construction resource surplus position and a construction resource idle position through a plane, and the construction resource response time and the construction resource available volume are displayed at the construction resource surplus position and the construction resource idle position through a column diagram;
the resource prediction module is used for performing construction resource prediction by traversing the construction resource demand position based on a set time range, generating a construction resource demand shortage, and the resource view establishment module is used for displaying the construction resource demand shortage according to the construction resource demand position through a bar chart;
the resource analysis module is used for analyzing the spare positions of the construction resources and personnel and equipment capacity of the spare positions of the construction resources and revising the displayed response time of the construction resources and the displayed available volume of the construction resources based on analysis results;
And the resource scheduling analysis module is used for analyzing the revised three-dimensional resource view to obtain a construction resource scheduling scheme.
Further, the resource prediction module includes:
the historical data collection and preprocessing module is used for collecting and preprocessing the historical data of the construction project, wherein the historical data comprises construction resource demand positions, time ranges and actual used resource amounts;
the feature extraction module is used for extracting relevant features from the collected historical data, wherein the relevant features are factors influencing resource requirements;
the data set dividing module divides the preprocessed historical data into a training set and a testing set for model training and evaluation;
the neural network model selection and structure determination module selects a neural network model and determines the structure of the neural network model;
the neural network model training module is used for training the neural network model by using the training set, updating the weight and the bias of the neural network model through a back propagation algorithm so as to minimize a loss function, and optimizing the performance and the generalization capability of the neural network model through different super-parameter setting and regularization technologies;
And the demand prediction module is used for predicting the demand of construction resources by using the trained model, inputting the related characteristics and outputting the demand shortage of the construction resources.
By the technical scheme of the invention, the following technical effects can be realized:
the visual management method for the static pressure pipe pile construction helps a user to intuitively know indexes such as requirements, surplus and idle conditions of construction resources, response time, available volume and the like through visual display of the three-dimensional resource view and the column diagram; by means of predictive analysis and resource capacity analysis, the conditions of resource demand and supply can be evaluated more accurately, and corresponding construction resource scheduling schemes are formulated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
FIG. 1 is a flow chart of a visual management method for static pressure pipe pile construction;
FIG. 2 is a flow chart for predicting construction resource demand and generating a construction resource demand shortage by traversing construction resource demand locations based on a set time horizon;
FIG. 3 is an optimized flow chart for construction resource demand prediction by traversing construction resource demand locations based on a set timeframe to generate a construction resource demand shortage;
FIG. 4 is a flow chart for training a neural network model using a training set;
FIG. 5 is a flow chart of an attention mechanism introduced prior to training a neural network model;
FIG. 6 is a flow chart of feature weights calculated by the attention mechanism applied to construction resource surplus position and construction resource idle position display;
FIG. 7 is a flow chart for analyzing the personnel and equipment capabilities of the construction resource surplus and idle positions, revising the displayed construction resource response time and construction resource availability based on the analysis results;
fig. 8 is a flowchart of a construction resource scheduling scheme obtained by analyzing the revised perspective resource view.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Example 1
As shown in fig. 1, a visual management method for static pressure pipe pile construction includes:
s10: establishing a three-dimensional resource view and performing visual display, wherein the three-dimensional resource view displays a construction resource demand position, a construction resource surplus position and a construction resource idle position through a plane, and highly displays construction resource response time and construction resource available volume according to the construction resource surplus position and the construction resource idle position through a column diagram, so that a user and an owner of the construction resource can intuitively know the distribution condition and the availability of the resource;
s20: based on the set time range, construction resource prediction is carried out by traversing the construction resource demand position, the construction resource demand shortage is generated, and the construction resource demand shortage is highly displayed through a bar graph aiming at the construction resource demand position, so that a user and an owner of the construction resource are helped to know the difference between the demand and the supply in time;
S30: analyzing personnel and equipment capacities of the surplus position and the idle position of the construction resource, revising the displayed response time of the construction resource and the displayed available volume of the construction resource based on the analysis result, and ensuring that users and owners of the construction resource can adjust the resource scheduling plan to be matched with the actual resource situation;
s40: and analyzing the revised three-dimensional resource view to obtain a construction resource scheduling scheme.
The construction resource demand position in the invention refers to a specific position or a construction unit which needs to use specific resources (such as manpower, equipment, materials and the like) in the construction process, the construction resource demand position can be a specific region of a construction site, a specific procedure or task of a construction stage or other specific positions which need resource support, and by determining the construction resource demand position, the position where appropriate resources need to be allocated to complete corresponding construction work can be known. The construction resource surplus position refers to a position or an area where surplus resources exist in the construction process, and the position or the area may have resources exceeding the current demand, which may be caused by unbalanced resource allocation, construction progress change or other factors. The construction resource idle position refers to the position or the area of resources which are not fully utilized or not used in the construction process, and the position or the area may have resources which are not scheduled or allocated, so that the resources are wasted and are not utilized effectively.
The visual management method for the static pressure pipe pile construction helps a user to intuitively know indexes such as requirements, surplus and idle conditions of construction resources, response time, available volume and the like through visual display of the three-dimensional resource view and the column diagram; by means of predictive analysis and resource capacity analysis, the conditions of resource demand and supply can be evaluated more accurately, and corresponding construction resource scheduling schemes are formulated.
As a preferred embodiment, as shown in fig. 2, the construction resource demand prediction is performed by traversing the construction resource demand location based on the set time range, and the construction resource demand shortage is generated, including:
s210: collecting construction project historical data and preprocessing, wherein the historical data comprises construction resource demand positions, time ranges and actual used resource amounts;
s220: extracting relevant features from the collected data, wherein the relevant features are factors influencing resource requirements;
s230: dividing the historical data into a training set and a testing set; the preprocessing comprises the steps of sorting and cleaning data, processing missing values, abnormal values and the like, dividing the data into a training set and a testing set, and training and evaluating a model;
S240: selecting a neural network model and determining the structure of the neural network model;
s250: training the neural network model by using a training set, updating the weight and bias of the neural network model by using a back propagation algorithm to minimize a loss function, and optimizing the performance and generalization capability of the neural network model by using different super-parameter settings such as a learning rate, a batch size and the like;
s260: and predicting the construction resource demand by using the trained model, inputting relevant characteristics, and outputting the construction resource demand shortage.
In the optimization scheme, the adopted neural network model is excellent in terms of processing complex nonlinear relations, potential modes and trends of construction resource demands can be learned and captured, and compared with a traditional statistical method, the neural network model can more accurately predict the resource demands and improve the prediction accuracy; the neural network model can simultaneously consider a plurality of factors affecting the construction resource requirement, such as the position, the time range and other related factors, learn and discover the complex relation between the factors and the resource requirement from the related characteristics, and provide a more comprehensive prediction result; the types which can be processed comprise quantitative data and qualitative data, can adapt to different construction projects and scenes, and further improves the performance and adaptability of the model by adjusting the model structure and super parameters.
In conclusion, the neural network model is adopted to predict the construction resource demand, so that the method has the advantages of high accuracy, strong learning ability, consideration of multiple factors, flexibility, adaptability and instantaneity, and is beneficial to improving the management and scheduling of the construction resource and improving the construction efficiency and the resource utilization effect.
As a further optimization of the above embodiment, the construction resource demand prediction is performed by traversing the construction resource demand location based on the set time range, and the construction resource demand shortage is generated, as shown in fig. 3, more specifically including:
a210: collecting construction project historical data and preprocessing, wherein the historical data comprises construction resource demand positions, time ranges and actual used resource amounts;
a220: extracting relevant features from the collected data, wherein the relevant features comprise position information, time information, historical resource demand and season information;
a230: dividing the historical data into a training set and a testing set, wherein the dividing is performed according to a time sequence;
specifically, a future period of time is taken as a test set and a past period of time is taken as a training set, which is to simulate the situation in an actual application scene, namely, training a model by using historical data, and predicting future data by using the model; because in the context of construction resource demand prediction, it is better to train the model using past resource demand data and evaluate the prediction performance of the model in the future time period, by ensuring that the data in the test set is the future time period, it is possible to better simulate the situation in actual application, evaluate the accuracy and reliability of the model in the face of the future data, ensure that the model has practicability in actual application, and enable accurate construction resource demand prediction in the future time period.
A240: selecting a long-period memory network model, and determining the number, the layer number, the input dimension and the output dimension of neurons of the long-period memory network model;
a250: training the neural network model by using a training set, updating the weight and bias of the neural network model by using a back propagation algorithm to minimize a loss function, and optimizing the performance and generalization capability of the neural network model by using different super-parameter setting and regularization technologies;
a260: and predicting the construction resource demand by using the trained model, inputting the position and the time range to be predicted, and outputting the construction resource demand shortage.
In the optimization scheme, a long-term and short-term memory network model (LSTM model) is adopted to effectively treat long-term dependency, and the long-term and long-term memory network model has better memory capacity for remote historical information in time series data, which is very important for construction resource demand prediction, because past resource demand conditions have larger influence on current and future resource demands, and the LSTM model can selectively reserve and forget information through memory units and gating mechanisms in the network, so that time sequence characteristics of the resource demands, such as seasonal, periodic and trend changes, are better captured. The time range of construction resource requirements may be different, variable-length sequence data is generated, the LSTM model can process variable-length sequences, the LSTM model is suitable for time windows with different lengths, the length of an input sequence is not required to be fixed, and the flexibility and the adaptability of the model are improved.
For the step a250, training the neural network model by using the training set, as shown in fig. 4, specifically includes:
a251: extracting input features at the current moment from the training set, inputting the input features into an input gate of the long-short-period memory network model, and adjusting the influence of the input features by using weights and biases through the input gate;
specifically, the input gate determines the influence degree of the input feature at the current moment on the memory unit, and the output of the input gate is a value between 0 and 1, which indicates the importance or forgetting degree of the corresponding feature;
a252: multiplying the output value of the input gate with the input characteristic, screening and weighting the influence of the input, and transmitting the influence to the candidate memory unit;
the larger the output value of the input gate is, the larger the influence of the corresponding characteristic is, and the candidate memory unit decides the degree of memory reservation and update according to the output value of the input gate;
a253: the candidate memory unit calculates a new candidate memory value according to the input characteristic at the current moment and the memory state at the previous moment, and takes the new candidate memory value as an input, and transmits the new candidate memory value and the input characteristic to the forgetting gate;
the input characteristics provide relevant information at the current moment, such as position information, time information, historical resource demand and season information, the memory state at the previous moment comprises useful information memorized in the previous time step by the network, and the process of calculating the new candidate memory value utilizes parameters such as weight, activation function and the like in the neural network and the internal state of the memory unit; the candidate memory unit can generate a new memory value adapting to the current input characteristic through modes of operation, nonlinear conversion and the like according to a specific network structure and parameter setting.
A254: the forgetting gate carries out weight distribution on each element in the memory state, determines the retention degree of each element in the memory state at the previous moment, and multiplies the retention degree by the candidate memory value element by element to obtain a final memory value;
the multiplication operation is to control the retention degree of each element in the memory state, the weight in the forgetting gate determines the importance of each element, the lower weight indicates that the information of the corresponding element is forgotten more, the higher weight indicates that the information of the corresponding element is retained more, the memory state can be updated by multiplying the candidate memory value, the important information is retained and the unimportant information is discarded, so that the obtained final memory value is transferred to the next time step for processing and calculation at the next moment.
A255: synthesizing the outputs of the candidate memory unit and the forgetting gate, updating the memory unit to obtain the memory state at the current moment, and transmitting the memory state to the output gate;
a256: the output gate determines the weight of the output memory information according to the input characteristics and the memory state at the current moment.
In this step, the memory state contains the memory information of the previous moment, the input feature of the current moment provides information about the current situation, the output gate is used for deciding the output memory information according to the information, controlling which memory information is transferred to the next moment, and weighting the memory information. In conclusion, the input characteristics are combined with the memory state in different steps to jointly influence the calculation of candidate memory values, the updating of the memory state and the generation of output, so that the prediction and management of the construction resource requirements of the static pressure pipe pile by the long-term memory network are realized.
In the step S60, the task is to predict the construction resource demand by using a trained model, input the position and time range to be predicted, and generate the predicted construction resource demand shortage by using the memory information given with weight and the input characteristics of the current time by using the LSTM model; the memory state serves to store history information and provide context in this step, which is a state inside the long-short-term memory network, and the memory information is specific information extracted from the memory state after being selected and weighted through the output gate.
The method has the advantages that the history data is fully utilized, related characteristics are introduced, information flow is flexibly controlled, and memory information is outputted in a weighted mode, so that the accuracy and the adaptability of the prediction of the construction resource requirements in the visualized management of the static pressure tubular pile construction are improved. In the data preprocessing stage, season information is extracted as one of the relevant characteristics, so that the model can know the influence of different seasons on the construction resource requirement, for example, more heat insulation materials and construction equipment may be required in winter, and more cooling equipment may be required in summer. The influence of the seasonal information on the memory state and the candidate memory value can be adaptively adjusted by the model through a gating mechanism; the forgetting gate can determine the retention degree of each element in the previous time memory state according to the seasonal variation, and the input gate can adjust the weight of the current time input characteristic according to the seasonal variation, so that the model can better adapt to the construction resource demand variation in different seasons; the output gate can consider the season information at the current moment when deciding the output memory information, which means that the model can selectively emphasize the memory information related to the season according to the seasons, thereby providing more accurate and consistent actual construction resource demand prediction. The method has the advantages of improving the prediction capability, generalization capability and understanding of time correlation of the model aiming at the seasonal information in the long-term and short-term memory network model, so that the method is better suitable for the seasonal change of the visualized management of the static pressure tubular pile construction.
In construction management, different projects may have different time spans, and the related season information and the length of the historical resource demand data may also be different, so that, as a preference of the above embodiment, attention mechanisms are introduced before training the neural network model, as shown in fig. 5, including:
b010: taking the extracted relevant features as input of an attention mechanism;
b020: the attention mechanism calculates the attention weight distribution of the features according to the importance of the related features;
b030: multiplying the attention weight with the relevant feature to obtain a weighted feature representation;
b040: the weighted feature representation is input as an input feature into the neural network model.
By multiplying the attention weights with the corresponding features, the locations of the resources that are considered critical in the model can be highlighted, so that the stereoscopic resource view can be revised according to the weighted feature representation of the attention mechanism to better reflect the distribution and importance of the resources, thereby providing more accurate and targeted information and helping to optimize the construction resource scheduling scheme.
For example, historical construction data is collected, a data set containing construction resource spare locations and construction resource spare locations is prepared, and the following features are extracted: construction resource response time: representing the time from receiving a demand to putting it into use for a certain type of resource. Construction resource availability volume: indicating the amount or capacity of a certain class of resources available in a certain time frame.
The attention mechanism can automatically learn and decide the importance of each feature to resource management according to the historical data and the current situation; the three-dimensional resource view is adopted to display the distribution condition of the resources, and the surplus position of the construction resources and the idle position of the construction resources are marked on the plan view in the view, so that the distribution condition of the resources can be intuitively seen. To highlight the importance of the resource feature at each location, the attention weight is converted to a color representation and then applied to the color of the bar graph, e.g., for bar graphs of construction resource rich and construction resource idle locations, the color of the bar will be adjusted according to the attention weight of the corresponding feature, and if the attention weight of a feature is greater, the corresponding bar color will be more saturated or brighter, and conversely, the color will be lighter or darker.
As a preference of the above embodiment, further including applying the feature weight calculated by the attention mechanism to the presentation of the construction resource surplus position and the construction resource idle position, as shown in fig. 6, including:
s70: preparing a data set containing the construction resource surplus position and the construction resource idle position, and extracting corresponding characteristics of construction resource response time and construction resource available volume;
S80: weighting the corresponding features by using an attention mechanism, and calculating attention weight distribution of the features;
s90: and displaying the calculated attention weight as a bar graph color at the construction resource surplus position and the construction resource idle position.
By applying the attention mechanism and visualizing the effect, the scheme can provide more accurate and visual resource management information, help project management personnel to make targeted decisions, and improve the utilization efficiency of construction resources and the overall management level of projects.
As a preferable aspect of the above embodiment, analyzing the spare positions of construction resources and the personnel and equipment capacities of the spare positions of construction resources, revising the displayed response time of construction resources and the displayed available volume of construction resources based on the analysis results, as shown in fig. 7, includes:
s310: collecting personnel and equipment capacity data related to the construction resource surplus position and the construction resource idle position, wherein the personnel and equipment capacity data comprises information such as personnel number, skill level, equipment type, quantity and service life;
s320: analyzing the collected data and performing the steps of:
s321: re-evaluating construction resource response time according to availability and response capacity of personnel and equipment; if personnel and equipment capabilities are low, the response time may be prolonged, and if capabilities are high, the response time may be shortened;
And, S322: re-evaluating the available volume of the construction resource according to the availability and the number of personnel and equipment; if personnel and equipment capacity is low, the available volume may decrease and if capacity is high, the available volume may increase;
s330: and updating the display of the three-dimensional resource view according to the revised response time of the construction resource and the available volume of the construction resource.
There will of course be cases where no revision is required, depending on the degree; by analyzing and revising the personnel and equipment capacities of the surplus position of the construction resources and the idle position of the construction resources, the actual condition of the resources can be reflected more accurately, and a targeted resource scheduling scheme is provided to optimize the utilization and response capacity of the construction resources.
In the implementation process, the revised three-dimensional resource view is analyzed to obtain a construction resource scheduling scheme, as shown in fig. 8, which includes:
s410: determining a target and constraint conditions of resource scheduling according to the revised stereoscopic resource view; for example, minimizing resource idling, reducing locations of excessive response time, or optimizing overall resource utilization, etc.; for the goal of minimizing resource idling, a goal value of maximum utilization or minimum utilization of resources can be defined; for the goal of reducing the position with overlong response time, a threshold value of the response time can be determined, and the position exceeding the threshold value needs to be improved; in the implementation process, specific definition of targets and constraint conditions is ensured, and quantification and measurement can be realized;
S420: defining an adaptability function for evaluating the advantages and disadvantages of the scheme based on the targets and the constraint conditions, and measuring the quality of the construction resource scheduling scheme; for example, indexes such as resource utilization rate, response time and the like can be included in a fitness function, and an appropriate weight is allocated to each index, so that the fitness function can accurately measure the quality of each individual, namely the resource scheduling scheme;
s430: randomly generating a group of initial scheduling schemes as a population, wherein each individual in the population represents a construction resource scheduling scheme, and repeatedly executing the steps S441-S443, wherein the termination condition is that the maximum iteration number is reached or a satisfactory solution is found:
s441: calculating an fitness value for each individual in the population, and evaluating the quality degree of the resource scheduling scheme according to the definition of the fitness function;
s442: selecting a part of individuals from the population as parents by using a selection operator, and performing cross operation on the selected parent individuals to generate offspring individuals; crossover operations simulate crossover inheritance of genes, creating new individuals by exchanging certain parts of individuals, selection strategies of selection operators such as roulette selection, competitive selection, etc.;
s443: carrying out mutation operation on offspring individuals and introducing certain randomness; combining the parent individuals and the variant child individuals into a new population, and updating the state of the population; mutation operations are similar to mutation of genes, and diversity is created by changing certain genes or attributes of an individual, for example, mutation operations can increase diversity of a population by randomly changing values or attributes of certain genes in an individual, introducing certain randomness;
S450: after the termination condition is met, selecting an individual with the optimal fitness value from the final population as an optimal solution, namely an optimal resource scheduling scheme, for example, directly selecting an individual with the highest fitness value as the optimal solution, or selecting a plurality of preferred individuals from the population as the optimal solution according to a certain rule.
Example two
A visual management system for static pressure pipe pile construction comprises:
the resource view establishing module is used for establishing a three-dimensional resource view and carrying out visual display, wherein the three-dimensional resource view displays the construction resource demand position, the construction resource surplus position and the construction resource idle position through a plane, and highly displays the construction resource response time and the construction resource available volume according to the construction resource surplus position and the construction resource idle position through a bar graph;
the resource prediction module is used for performing construction resource prediction by traversing the construction resource demand position based on the set time range, generating a construction resource demand shortage, and the resource view establishment module is used for highly displaying the construction resource demand shortage through a bar graph according to the construction resource demand position;
the resource analysis module is used for analyzing the spare positions of the construction resources and the personnel and equipment capacity of the spare positions of the construction resources, and revising the displayed response time of the construction resources and the displayed available volume of the construction resources based on analysis results;
And the resource scheduling analysis module is used for analyzing the revised three-dimensional resource view to obtain a construction resource scheduling scheme.
As a preference of the above embodiment, the resource prediction module includes:
the historical data collection and preprocessing module is used for collecting and preprocessing the historical data of the construction project, wherein the historical data comprises construction resource demand positions, time ranges and actual used resource amounts;
the feature extraction module is used for extracting relevant features from the collected historical data, wherein the relevant features are factors influencing resource requirements;
the data set dividing module divides the preprocessed historical data into a training set and a testing set for model training and evaluation;
the neural network model selection and structure determination module selects a neural network model and determines the structure of the neural network model;
the neural network model training module is used for training the neural network model by using a training set, updating the weight and bias of the neural network model through a back propagation algorithm so as to minimize a loss function, and optimizing the performance and generalization capability of the neural network model through different super-parameter setting and regularization technologies;
and the demand prediction module is used for predicting the demand of construction resources by using the trained model, inputting relevant characteristics and outputting the demand shortage of the construction resources.
The technical effects that can be achieved in this embodiment are the same as those in the above embodiment, and will not be described here again.
The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. The visualized management method for the construction of the static pressure pipe pile is characterized by comprising the following steps of:
establishing a three-dimensional resource view and performing visual display, wherein the three-dimensional resource view displays a construction resource demand position, a construction resource surplus position and a construction resource idle position through a plane, and highly displays construction resource response time and construction resource available volume according to the construction resource surplus position and the construction resource idle position through a bar graph;
based on a set time range, traversing the construction resource demand position to predict construction resources, generating a construction resource demand shortage, and highly displaying the construction resource demand shortage according to the construction resource demand position through a bar graph;
Analyzing personnel and equipment capacities of the construction resource surplus position and the construction resource idle position, and revising the displayed construction resource response time and the displayed construction resource available volume based on analysis results;
and analyzing the revised three-dimensional resource view to obtain a construction resource scheduling scheme.
2. The method for visual management of static pressure pipe pile construction according to claim 1, wherein the step of traversing the construction resource demand location based on a set time range to predict the construction resource demand and generating a construction resource demand shortage comprises:
collecting construction project historical data and preprocessing, wherein the historical data comprises construction resource demand positions, time ranges and actual used resource amounts;
extracting relevant features from the collected data, wherein the relevant features are factors influencing resource requirements;
dividing the historical data into a training set and a testing set;
selecting a neural network model and determining the structure of the neural network model;
training the neural network model by using the training set, updating the weight and bias of the neural network model by a back propagation algorithm to minimize a loss function, and optimizing the performance and generalization capability of the neural network model by different super-parameter setting and regularization techniques;
And predicting the construction resource demand by using the trained model, inputting the related characteristics, and outputting the construction resource demand shortage.
3. The method for visual management of static pressure pipe pile construction according to claim 2, wherein the step of traversing the construction resource demand location based on the set time range to predict the construction resource demand and generating the construction resource demand shortage comprises:
collecting construction project historical data and preprocessing, wherein the historical data comprises construction resource demand positions, time ranges and actual used resource amounts;
extracting relevant features from the collected data, the relevant features including location information, time information, historical resource demand and seasonal information;
dividing the historical data into a training set and a testing set, wherein the dividing is performed according to a time sequence;
selecting a long-period memory network model, and determining the number, the layer number, the input dimension and the output dimension of neurons of the long-period memory network model;
training the neural network model by using the training set, updating the weight and bias of the neural network model by a back propagation algorithm to minimize a loss function, and optimizing the performance and generalization capability of the neural network model by different super-parameter setting and regularization techniques;
And predicting the construction resource demand by using the trained model, inputting the position and the time range to be predicted, and outputting the construction resource demand shortage.
4. A static pressure pipe pile construction visualization management method as defined in claim 3, wherein training the neural network model using the training set comprises:
extracting input features at the current moment from a training set, and inputting the input features into an input gate of a long-short-term memory network model, wherein the input gate adjusts the influence of the input features by using weights and biases;
multiplying the output value of the input gate with the input characteristic, screening and weighting the influence of the input, and transmitting the influence to a candidate memory unit;
the candidate memory unit calculates a new candidate memory value according to the input characteristic at the current moment and the memory state at the previous moment, takes the new candidate memory value as an input, and transmits the new candidate memory value and the input characteristic to the forgetting gate;
the forgetting gate carries out weight distribution on each element in the memory state, determines the retention degree of each element in the memory state at the previous moment, and multiplies the retention degree by the candidate memory value element by element to obtain a final memory value;
Integrating the outputs of the candidate memory units and the forgetting gate, updating the memory units to obtain the memory state at the current moment, and transmitting the memory state to the output gate;
and the output gate determines the weight of the output memory information through the input characteristics and the memory state at the current moment.
5. The method for visual management of static pressure pipe pile construction according to claim 2, wherein before training the neural network model, introducing an attention mechanism comprises:
taking the extracted relevant features as input of an attention mechanism;
the attention mechanism calculates attention weight distribution of the features according to the importance of the related features;
multiplying the attention weight with the relevant feature to obtain a weighted feature representation;
the weighted feature representation is input as an input feature into the neural network model.
6. The visual management method for static pressure pipe pile construction according to claim 1, further comprising: applying the feature weight calculated by the attention mechanism to the display of the construction resource surplus position and the construction resource idle position, wherein the feature weight comprises the following components:
preparing a data set containing the construction resource surplus position and the construction resource idle position, and extracting corresponding characteristics of construction resource response time and construction resource available volume;
Weighting the corresponding features by using an attention mechanism, and calculating attention weight distribution of the features;
and displaying the calculated attention weight as a bar graph color at the construction resource surplus position and the construction resource idle position.
7. The visual management method for static-pressure pipe pile construction according to claim 1, wherein the analysis of the personnel and equipment capacities of the construction resource surplus position and the construction resource idle position, and the revision of the displayed construction resource response time and the displayed construction resource usable volume based on the analysis result, comprises:
collecting personnel and equipment capability data related to the construction resource surplus position and the construction resource idle position;
analyzing the collected data and performing the steps of:
re-evaluating construction resource response time according to availability and response capacity of personnel and equipment; and re-evaluating the available volume of the construction resource according to the availability and the number of the personnel and the equipment;
and updating the display of the three-dimensional resource view according to the revised response time of the construction resource and the available volume of the construction resource.
8. The visual management method for static pressure pipe pile construction according to claim 1, wherein analyzing the revised three-dimensional resource view to obtain a construction resource scheduling scheme comprises:
Determining a target and constraint conditions of resource scheduling according to the revised stereoscopic resource view;
defining an adaptability function for evaluating the advantages and disadvantages of the scheme based on the targets and the constraint conditions, and measuring the quality of the construction resource scheduling scheme;
randomly generating a group of initial scheduling schemes as a population, wherein each individual in the population represents a construction resource scheduling scheme, and repeatedly executing the following steps, wherein the termination condition is that the maximum iteration number is reached or a satisfactory solution is found:
calculating an fitness value for each individual in the group, and evaluating the quality degree of a resource scheduling scheme according to the definition of a fitness function;
selecting a part of individuals from the population as parents by using a selection operator, and performing cross operation on the selected parent individuals to generate offspring individuals;
carrying out mutation operation on offspring individuals and introducing certain randomness; combining the parent individuals and the variant child individuals into a new population, and updating the state of the population;
after the termination condition is met, the individual with the optimal fitness value is selected from the final population as the optimal solution.
9. The utility model provides a visual management system of static pressure tubular pile construction which characterized in that includes:
The resource view establishing module establishes a three-dimensional resource view and performs visual display, the three-dimensional resource view displays a construction resource demand position, a construction resource surplus position and a construction resource idle position through a plane, and the construction resource response time and the construction resource available volume are displayed at the construction resource surplus position and the construction resource idle position through a column diagram;
the resource prediction module is used for performing construction resource prediction by traversing the construction resource demand position based on a set time range, generating a construction resource demand shortage, and the resource view establishment module is used for displaying the construction resource demand shortage according to the construction resource demand position through a bar chart;
the resource analysis module is used for analyzing the spare positions of the construction resources and personnel and equipment capacity of the spare positions of the construction resources and revising the displayed response time of the construction resources and the displayed available volume of the construction resources based on analysis results;
and the resource scheduling analysis module is used for analyzing the revised three-dimensional resource view to obtain a construction resource scheduling scheme.
10. The visual management system for static pressure pipe pile construction according to claim 1, wherein the resource prediction module comprises:
The historical data collection and preprocessing module is used for collecting and preprocessing the historical data of the construction project, wherein the historical data comprises construction resource demand positions, time ranges and actual used resource amounts;
the feature extraction module is used for extracting relevant features from the collected historical data, wherein the relevant features are factors influencing resource requirements;
the data set dividing module divides the preprocessed historical data into a training set and a testing set for model training and evaluation;
the neural network model selection and structure determination module selects a neural network model and determines the structure of the neural network model;
the neural network model training module is used for training the neural network model by using the training set, updating the weight and the bias of the neural network model through a back propagation algorithm so as to minimize a loss function, and optimizing the performance and the generalization capability of the neural network model through different super-parameter setting and regularization technologies;
and the demand prediction module is used for predicting the demand of construction resources by using the trained model, inputting the related characteristics and outputting the demand shortage of the construction resources.
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