A construction plan planning system for earthwork excavation of deep foundation pits in soft soil

Abstract

The present invention discloses a construction plan planning system for earthwork excavation of deep soft soil foundation pits, which relates to the technical field of construction plan planning. The present invention aims to effectively solve the problem that the strategies formulated based on the prediction results in the existing implementation methods lack a certain degree of perfection. By converting the prediction results of the existing prediction model into the first quantitative score in the form of the confidence level output by it, and then performing a series of data-driven compensation processes on the first quantitative score. Specifically, by effectively quantifying the uncertain factors in the process of earthwork excavation of deep soft soil foundation pits to be converted into the third quantitative score, and then combining the third quantitative score with the first quantitative score of the prediction result, so as to obtain the comprehensive quantitative score of the prediction result. Finally, based on the comprehensive quantitative score of the prediction result, a construction plan planning strategy is formulated to effectively solve the problem that the existing strategy lacks perfection, and thus can effectively improve the reliability of the project quality.

Description

Technical Field

[0001] The present invention relates to the technical field of construction plan planning, and particularly to a construction plan planning system for earthwork excavation of deep soft soil foundation pits. Background Art

[0002] With the acceleration of the urbanization process, especially in coastal, riverine and lake surrounding areas, deep foundation pit projects under soft soil geological conditions are increasing day by day. Due to the characteristics of soft soil such as high natural water content, large compressibility and low shear strength, the construction of deep foundation pits in such areas faces many challenges. The existing construction plan planning for earthwork excavation of deep soft soil foundation pits usually adopts the following technical means: First, advanced geophysical exploration techniques and drilling sampling are used to obtain high-resolution geological structure information data, and environmental information data is collected, including experience data of previous similar projects. Subsequently, the above-mentioned collected data is preprocessed, where the preprocessing includes removing noise, missing value processing, etc. to ensure data quality, and at the same time, feature engineering operations are performed to extract key features helpful for the prediction model, such as soil physical property parameters, groundwater level changes, etc. Then, a deep learning model is selected, usually a neural network model is used as the prediction model, and after a series of conventional trainings on the prediction model using the collected data, it is put into prediction use. Finally, based on the prediction results of the prediction model, where the prediction results include construction risks such as earthwork excavation of deep soft soil foundation pits, and corresponding construction plan planning strategies are formulated based on the prediction results;

[0003] However, during the prediction process of the existing prediction models, since they usually combine practical data, such as the geological structure information data, environmental information data and experience data of previous similar projects mentioned above, they often ignore the uncertain factors during the earthwork excavation construction process of deep soft soil foundation pits, such as changes in geological conditions, impacts of extreme weather events and sudden changes in groundwater levels, etc., resulting in the strategies finally formulated based on the prediction results lacking a certain degree of perfection, and thus easily having a certain impact on the reliability of project quality;

[0004] Therefore, there is an urgent need in the prior art for a technical solution for a construction plan planning system for earth excavation in deep soft soil foundation pits. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a construction plan planning system for earthwork excavation of deep soft soil foundation pits, which specifically includes the following modules:

[0006] A data collection module: used to collect geological and environmental information data and historical project experience data during the earthwork excavation of deep soft soil foundation pits, and integrate them into a first data set;

[0007] The first quantization score acquisition module: connected to the data collection module, used to preprocess the collected first dataset, input the preprocessed first dataset into a pre-trained prediction model, and determine the first quantization score of the prediction result based on the confidence level output by the prediction model;

[0008] The comprehensive quantization score calculation module: connected to the first quantization score acquisition module, used to perform compensation processing on the first quantization score to obtain the comprehensive quantization score of the prediction result;

[0009] The uncertain factor collection and classification unit: used to collect uncertain factors by analyzing the historical project experience data in the first dataset, and classify the collected uncertain factors according to the nature of the uncertain factors;

[0010] The impact range value and occurrence frequency calculation unit: used to calculate the impact range value and occurrence frequency of each type of uncertain factor;

[0011] The impact area definition subunit: used to define the area affected by each type of uncertain factor by using geographic information system technology;

[0012] The measurement subunit: used to measure the total area of the area affected by each type of uncertain factor;

[0013] The average slope factor acquisition subunit: used to calculate the slope of each point in the area affected by each type of uncertain factor by using a digital elevation model, and take the average value to obtain the average slope factor of each point in the area affected by each type of uncertain factor;

[0014] The building density factor acquisition subunit: used to use remote sensing images to count the number of buildings in the area affected by each type of uncertain factor, and calculate the number of buildings per unit area to obtain the building density factor in the area affected by each type of uncertain factor;

[0015] The impact range value calculation subunit: used to calculate the impact range value of each type of uncertain factor according to the total area of the area affected by each type of uncertain factor, the average slope factor of each point in the area affected by each type of uncertain factor, and the building density factor in the area affected by each type of uncertain factor;

[0016] The calculation formula for the impact range value of each type of uncertain factor is: In the formula, represents the impact range value of the i-th type of uncertain factor; represents the total area of the area affected by the i-th type of uncertain factor; represents the average slope factor of each point in the area affected by the i-th type of uncertain factor; represents the building density factor in the area affected by the i-th type of uncertain factor; , represent the adjustment coefficients of the average slope factor and the building density factor respectively;

[0017] Statistical subunit: used to count the actual occurrences of each type of uncertain factor based on historical project experience data;

[0018] Segmentation subunit: used to segment the actual occurrences of each type of uncertain factor according to the time period;

[0019] Occurrence frequency calculation subunit: used to calculate the occurrence frequency of each type of uncertain factor based on the actual occurrences of each type of uncertain factor within each time period;

[0020] The calculation formula for the occurrence frequency of each type of uncertain factor is:

[0021] In the formula, represents the occurrence frequency of the i-th type of uncertain factor; represents the actual occurrences of the i-th type of uncertain factor within the j-th time period; represents the length of the j-th time period; represents the total number of time periods;

[0022] Second quantization score calculation unit: used to calculate the second quantization score of each type of uncertain factor by integrating the influence range value and the occurrence frequency of each type of uncertain factor;

[0023] The calculation formula for the second quantization score of each type of uncertain factor is: In the formula, represents the second quantization score of the i-th type of uncertain factor; represents the influence range value of the i-th type of uncertain factor; represents the occurrence frequency of the i-th type of uncertain factor; , represent the weight coefficients of the influence range value and the occurrence frequency respectively;

[0024] Third quantization score calculation unit: used to calculate the third quantization score of all uncertain factors by integrating the second quantization scores of each type of uncertain factor;

[0025] The calculation formula for the third quantization score of all uncertain factors is: In the formula, represents the third quantization score of all uncertain factors; represents the second quantization score of the i-th type of uncertain factor; represents the total number of uncertain factor categories;

[0026] Comprehensive quantitative scoring calculation unit: used to combine the third quantitative scores of all uncertain factors with the first quantitative score of the prediction result to obtain the comprehensive quantitative score of the prediction result;

[0027] The calculation formula for the comprehensive quantitative score of the prediction result is: In the formula, represents the comprehensive quantitative score of the prediction result; represents the first quantitative score of the prediction result; represents the third quantitative scores of all uncertain factors; and respectively represent the weight coefficients of the first quantitative score and the third quantitative score;

[0028] Strategy formulation module: connected to the comprehensive quantitative scoring calculation module, used to formulate a construction plan planning strategy based on the comprehensive quantitative score of the prediction result.

[0029] The embodiments of the present invention have the following technical effects:

[0030] The present invention aims to effectively solve the problem that the strategies formulated based on the prediction results in the existing implementation methods lack a certain degree of perfection. By converting the prediction results of the existing prediction model into the first quantitative scores in the form of their output confidence levels, and then performing a series of data-driven compensation processes on the first quantitative scores. Specifically, by effectively quantifying the uncertain factors in the construction process of soft soil deep foundation pit earth excavation to convert them into the third quantitative scores, and then combining the third quantitative scores with the first quantitative scores of the prediction results, so as to obtain the comprehensive quantitative score of the prediction result. Finally, formulate a construction plan planning strategy based on the comprehensive quantitative score of the prediction result to effectively solve the problem of the lack of perfection of the existing strategies, and thus can effectively improve the reliability of the project quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0032] Figure 1 is a framework diagram of a construction plan planning system for soft soil deep foundation pit earth excavation provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope protected by the present invention.

[0034] Embodiment 1: As Figure 1 shown, the present invention provides a construction plan planning system for earthwork excavation of deep soft soil foundation pits, including the following modules:

[0035] Data collection module: used to collect geological and environmental information data and historical project experience data during the earthwork excavation of deep soft soil foundation pits, and integrate them into a first data set;

[0036] It should be noted that the geological information data mainly relates to the underground structure and characteristics of the foundation pit and its surrounding areas, specifically including but not limited to the following types of data: geotechnical parameter data, such as the distribution of different types of soils such as silt, clay, and silty sand at the foundation pit, and soil moisture content data, and also includes the proportion of pore space in the soil, that is, the void ratio; the environmental information data covers the natural and social environmental characteristics of the construction area and its surrounding areas, specifically including but not limited to the following types of data: meteorological data, such as rainfall, temperature changes, wind speed, and wind direction; hydrological data, such as river and lake water levels; ecological and environmental protection data, such as the vegetation type and coverage rate around the foundation pit.

[0037] First quantitative score acquisition module: connected to the data collection module, used to preprocess the collected first data set, input the preprocessed first data set into a pre-trained prediction model, and determine the first quantitative score of the prediction result based on the confidence level output by the prediction model;

[0038] It should be noted that since the prediction model not only outputs specific prediction results but also provides the confidence level for each prediction result, which is mainly used to measure the confidence level of the model in the prediction result, and the confidence interval is usually between 0 and 1. For example, when the confidence level of the prediction result is in the interval greater than or equal to 0 and less than 0.5, it indicates that the model is skeptical about the prediction result, and the prediction results in this interval are usually considered unreliable. Therefore, usually such prediction results are directly ignored. When the confidence level of the prediction result is in the interval greater than or equal to 0.5 and less than 0.7, it indicates that the model has a certain confidence in the prediction result, but there is still a large degree of uncertainty. These results can be used as a reference, but should be treated with caution in practical applications. When the confidence level of the prediction result is in the interval greater than or equal to 0.7 and less than 0.9, it indicates that the model has a relatively high confidence in the prediction result. These prediction results are usually considered to be relatively reliable and can be used for strategy or decision support, but still need to be treated with caution. When the confidence level of the prediction result is in the interval greater than or equal to 0.9 and less than or equal to 1, it indicates that the model has great confidence in the prediction result, and these prediction results are usually considered to be very reliable and can be directly used for strategy or decision support; For this reason, regarding the first quantitative score for determining the prediction result based on the confidence level output by the prediction model, the confidence level output by the prediction model can be directly used as the first quantitative score;

[0039] It is further worth noting that the process of obtaining the pre-trained prediction model includes: First, collect a large amount of geological, environmental information and various parameters during the construction process from multiple completed similar projects. These data should be as comprehensive as possible, including but not limited to geological data during the excavation of soft soil deep foundation pits, etc. Subsequently, label the collected historical data to clarify the success or failure of each project, risk level, and other key indicators. The quality of the labeling directly affects the effect of model training. Then, perform preprocessing on the collected historical data, including noise removal, missing value filling, and feature extraction, etc. Subsequently, select a neural network model as the model for prediction, such as a convolutional neural network, a recurrent neural network model, etc. Immediately afterwards, divide the labeled historical data into a training set, a validation set, and a test set, generally in the ratio of 70% training set, 15% validation set, and 15% test set, and use the training set among them to train the selected model. During the training process, continuously adjust the model parameters through the backpropagation algorithm to minimize the loss function and improve the prediction accuracy. Then, use the validation set and the test set to evaluate the model performance, mainly focusing on indicators such as accuracy, recall rate, and F1 score. When the model is fully trained and reaches the expected performance, it is saved for subsequent prediction applications.

[0040] Comprehensive quantitative score calculation module: Connected to the first quantitative score acquisition module, used to perform compensation processing on the first quantitative score to obtain the comprehensive quantitative score of the prediction result;

[0041] Uncertainty factor collection and classification unit: used to collect uncertainty factors by analyzing historical project experience data in the first dataset, and classify the collected uncertainty factors according to the nature of the uncertainty factors;

[0042] It should be noted that the nature of uncertainty factors can be human and non-human factors, etc., and uncertainty factors include but are not limited to: changes in geological conditions, impacts of extreme weather events, and sudden changes in groundwater levels, etc. Among them, changes in geological conditions can include soil layer settlement and soil compression; impacts of extreme weather events can include heavy rain and typhoons; sudden changes in groundwater levels can include rising groundwater levels and falling groundwater levels;

[0043] It should be further noted that by classifying uncertainty factors, different types of uncertainty factors can be more carefully identified and distinguished. Each type has different influence mechanisms and characteristics. After classification, more precise analysis can be carried out for specific types;

[0044] Influence range value and occurrence frequency calculation unit: used to calculate the influence range value and occurrence frequency of each type of uncertainty factor;

[0045] Influence area definition subunit: used to define the area affected by each type of uncertainty factor by using geographic information system technology;

[0046] Measurement subunit: used to measure the total area of the area affected by each type of uncertainty factor;

[0047] Average slope factor acquisition subunit: used to calculate the slope of each point in the area affected by each type of uncertainty factor by using a digital elevation model, and take the average value to obtain the average slope factor of each point in the area affected by each type of uncertainty factor;

[0048] It should be noted that a digital elevation model is a dataset representing ground elevation, usually stored in the form of a regular grid. Each grid cell contains a value representing the surface height at that location. In geographic information systems, digital elevation models are widely used in terrain analysis, flood simulation, visibility analysis, etc. Among them, the center of each grid cell in the digital elevation model is called a grid node, which has clear coordinates and corresponding elevation values; for the slope calculation method, the slope can be estimated by the height difference of its surrounding neighboring nodes. Common slope calculation methods include the neighborhood method and the gradient method. Among them, the neighborhood method is to calculate the slope by using the central node and its surrounding eight neighbor nodes, while the gradient method is based on the differential principle and directly extracts the slope value from the digital elevation model data. This slope calculation method is relatively conventional and will not be elaborated here one by one.

[0049] Building density factor acquisition subunit: used to utilize remote sensing images to count the number of buildings within the area affected by each type of uncertainty factor, calculate the number of buildings per unit area, and obtain the building density factor within the area affected by each type of uncertainty factor;

[0050] Influence range value calculation subunit: used to calculate the influence range value of each type of uncertainty factor based on the total area of the area affected by each type of uncertainty factor, the average slope factor of each point within the area affected by each type of uncertainty factor, and the building density factor within the area affected by each type of uncertainty factor;

[0051] Among them, the calculation formula for obtaining the influence range value of each type of uncertainty factor is: In the formula, represents the influence range value of the i-th type of uncertainty factor; represents the total area of the area affected by the i-th type of uncertainty factor; represents the average slope factor of each point within the area affected by the i-th type of uncertainty factor; represents the building density factor within the area affected by the i-th type of uncertainty factor; , respectively represent the adjustment coefficients of the average slope factor and the building density factor; Statistical subunit: used to count the actual occurrence times of each type of uncertainty factor based on historical project experience data;

[0052] Segmentation subunit: used to segment the actual occurrence times of each type of uncertainty factor according to the time period;

[0053] Occurrence frequency calculation subunit: used to calculate the occurrence frequency of each type of uncertainty factor according to the actual occurrence times of each type of uncertainty factor within each time period;

[0054] Among them, the calculation formula for obtaining the occurrence frequency of each type of uncertainty factor is: In the formula, represents the occurrence frequency of the i-th type of uncertainty factor; represents the actual occurrence times of the i-th type of uncertainty factor within the j-th time period; represents the length of the j-th time period; represents the total number of time periods;

[0055] Second quantization score calculation unit: used to comprehensively calculate the second quantization score of each type of uncertainty factor based on the influence range value and occurrence frequency of each type of uncertainty factor;

[0056] Among them, the calculation formula for obtaining the second quantization score of each type of uncertainty factor is: In the formula, The second quantization score representing the i-th type of uncertainty factor; The influence range value representing the i-th type of uncertainty factor; The occurrence frequency representing the i-th type of uncertainty factor; 、 The weight coefficients representing the influence range value and the occurrence frequency respectively;

[0057] The third quantization score calculation unit: used to comprehensively calculate the second quantization scores of each type of uncertainty factor to obtain the third quantization score of all uncertainty factors;

[0058] Among them, the calculation formula for obtaining the third quantization score of all uncertainty factors is: In the formula, Represents the occurrence frequency of the i-th type of uncertainty factor; Represents the actual number of occurrences of the i-th type of uncertainty factor in the j-th time period; Represents the length of the j-th time period; Represents the total number of time periods;

[0059] The second quantization score calculation unit: used to comprehensively calculate the influence range value and the occurrence frequency of each type of uncertainty factor to obtain the second quantization score of each type of uncertainty factor;

[0060] Among them, the calculation formula for obtaining the second quantization score of each type of uncertainty factor is: In the formula, Represents the second quantization score of the i-th type of uncertainty factor; Represents the influence range value of the i-th type of uncertainty factor; Represents the occurrence frequency of the i-th type of uncertainty factor; 、 The weight coefficients representing the influence range value and the occurrence frequency respectively;

[0061] The third quantization score calculation unit: used to comprehensively calculate the second quantization scores of each type of uncertainty factor to obtain the third quantization score of all uncertainty factors;

[0062] Among them, the calculation formula for obtaining the third quantization score of all uncertainty factors is: In the formula, Represents the third quantization score of all uncertainty factors; Represents the second quantization score of the i-th type of uncertainty factor; Represents the total number of uncertainty factor categories;

[0063] It should be noted that by classifying uncertain factors, more accurate analysis can be carried out for specific types, which can effectively ensure the accuracy of the second quantitative score and reduce calculation errors. This enables the accuracy and reliability of the third quantitative score obtained from the final calculation to be effectively guaranteed.

[0064] Comprehensive Quantitative Score Calculation Unit: It is used to combine the third quantitative scores of all uncertain factors with the first quantitative score of the prediction result to obtain the comprehensive quantitative score of the prediction result.

[0065] Among them, the calculation formula for obtaining the comprehensive quantitative score of the prediction result is: In the formula, represents the comprehensive quantitative score of the prediction result; represents the first quantitative score of the prediction result; represents the third quantitative scores of all uncertain factors; 、 respectively represent the weight coefficients of the first quantitative score and the third quantitative score;

[0066] Strategy Formulation Module: It is connected to the comprehensive quantitative score calculation module and is used to formulate a construction plan planning strategy based on the comprehensive quantitative score of the prediction result.

[0067] Exemplarily, when the prediction result shows that there is a thick layer of silty clay layer at the bottom of the foundation pit, with low shear strength, and based on the above calculation process, the comprehensive quantitative score of this prediction result is 0.71. Then, considering the comprehensive quantitative score of this prediction result, it indicates that the construction of this foundation pit project faces relatively high risks. Therefore, a more cautious and comprehensive construction plan planning strategy needs to be adopted. The corresponding strategies that can be taken are: considering the low shear strength of the silty clay layer at the bottom of the foundation pit, a more robust support structure needs to be adopted, such as increasing the number of anchor rods or using steel sheet piles for reinforcement to ensure the stability of the slope. And at the design stage, invite geological experts and structural engineers to participate together to optimize the support plan to ensure that it can withstand possible geological changes.

[0068] It should be noted that the terms used in this invention are only for describing specific embodiments and do not limit the scope of this application. As shown in the specification of this invention, unless the context clearly indicates an exceptional situation, words such as "a", "an", "one", and / or "the" do not specifically refer to the singular and may also include the plural. The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method or device comprising a series of elements not only includes those elements but also other elements not expressly listed, or elements inherent to such a process, method or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method or device comprising said element.

[0069] It should also be noted that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing this invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of this invention. Unless otherwise clearly specified and defined, terms such as "installed", "connected", "joined" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in this invention can be understood according to specific circumstances.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this invention and not to limit them; although this invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of this invention.

Claims

1. A soft soil deep foundation pit earthwork excavation construction plan planning system, characterized in that: Includes the following modules: Data collection module: used to collect geological and environmental information data and historical project experience data during the excavation of soft soil deep foundation pits, and integrate them into the first data set; A first quantitative score acquisition module: connected to the data collection module, used to preprocess the collected first data set, input the preprocessed first data set into the pre-trained prediction model, and determine the first quantitative score of the prediction result based on the confidence output by the prediction model; Comprehensive quantitative score calculation module: connected to the first quantitative score acquisition module, used to perform compensation processing on the first quantitative score to obtain a comprehensive quantitative score of the prediction result, including: Uncertainty factor collection and classification unit: used to collect uncertainties by analyzing the historical project experience data in the first data set, and classify the collected uncertainties according to the nature of the uncertainties; Influence range value and occurrence frequency calculation unit: used to calculate the influence range value and occurrence frequency of each type of uncertain factors; Impact area definition subunit: used to define the area affected by each type of uncertainty factor using geographic information system technology; Measurement subunit: used to measure the total area of ​​the region affected by each type of uncertainty factor; Average slope factor acquisition subunit: used to calculate the slope of each point in the area affected by each type of uncertain factors using the digital elevation model, and take the average to obtain the average slope factor of each point in the area affected by each type of uncertain factors; Building density factor acquisition subunit: used to use remote sensing images to count the number of buildings in the area affected by each type of uncertain factors, and calculate the number of buildings per unit area to obtain the building density factor in the area affected by each type of uncertain factors; Impact range value calculation subunit: used to calculate the impact range value of each type of uncertain factor according to the total area of ​​the area affected by each type of uncertain factor, the average slope factor of each point in the area affected by each type of uncertain factor, and the building density factor in the area affected by each type of uncertain factor; The second quantitative score calculation unit is used to comprehensively calculate the impact range value and occurrence frequency of each type of uncertainty factor to obtain the second quantitative score of each type of uncertainty factor; A third quantitative score calculation unit: used for synthesizing the second quantitative scores of each type of uncertain factors to calculate the third quantitative scores of all uncertain factors; Uncertainties include changes in geological conditions, the impact of extreme weather events and sudden changes in groundwater levels; Comprehensive quantitative score calculation unit: used for combining the third quantitative scores of all uncertain factors with the first quantitative scores of the prediction results to obtain a comprehensive quantitative score of the prediction results; Strategy formulation module: connected with the comprehensive quantitative scoring calculation module, it is used to formulate construction plan planning strategies based on the comprehensive quantitative scoring of the prediction results.

2. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 1, characterized in that: The calculation of the occurrence frequency of each type of uncertainty factor includes: Statistics subunit: used to count the actual number of occurrences of each type of uncertainty factor based on historical project experience data; Segmentation subunit: used to segment the actual number of occurrences of each type of uncertainty factor according to the time period; Occurrence frequency calculation subunit: used to calculate the occurrence frequency of each type of uncertainty factor according to the actual number of occurrences of each type of uncertainty factor in each time period.

3. The soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 1 is characterized in that: The calculation formula for the influence range value of each type of uncertainty factor is: In the formula, Represents the impact range value of the i-th type of uncertainty factor; Represents the total area of ​​the region affected by the i-th type of uncertainty factor; Represents the average slope factor of each point in the area affected by the i-th type of uncertainty factor; Represents the building density factor in the area affected by the i-th type of uncertainty factor; , They represent the adjustment coefficients of average slope factor and building density factor respectively.

4. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 2, characterized in that: The calculation formula for the occurrence frequency of each type of uncertainty factor is: In the formula, Represents the occurrence frequency of the i-th type of uncertainty factor; represents the actual number of occurrences of the i-th type of uncertainty factor in the j-th time period; represents the length of the jth time period; Represents the total number of time periods.

5. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 4, characterized in that: The calculation formula of the second quantitative score of each type of uncertainty factor is: In the formula, The second quantitative score representing the uncertainty factor of the i-th category; Represents the impact range value of the i-th type of uncertainty factor; Represents the occurrence frequency of the i-th type of uncertainty factor; , They represent the weight coefficients of the impact range value and the occurrence frequency respectively.

6. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 5, characterized in that: The calculation formula of the third quantitative score of all uncertain factors is: In the formula, a third quantitative score representing all uncertainties; The second quantitative score representing the uncertainty factor of the i-th category; Represents the total number of uncertainty categories.

7. A soft soil deep foundation pit earthwork excavation construction plan planning system according to claim 6, characterized in that: The calculation formula for the comprehensive quantitative score of the prediction result is: In the formula, Represents the comprehensive quantitative score of the prediction results; The first quantitative score representing the prediction result; Represents the third quantitative score of all uncertain factors; ɑ and β represent the weight coefficients of the first quantitative score and the third quantitative score, respectively.

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