Building engineering management system and management method thereof

Abstract

The invention relates to the technical field of engineering management, and particularly discloses a building engineering management system and a management method thereof, and the method comprises the steps: carrying out the semantic analysis of electronic data of a target labor service worker through employing a data processing technology based on deep learning, so as to extract the identity information feature of the labor service worker, and meanwhile, in combination with pre-formulated construction requirement rules, semantic query response coding is performed on the identity information of the labor service personnel and a large number of construction requirement rules, so that a potential matching relationship between the labor service personnel and the construction project is mined, and intelligent assessment of the adaptability of the labor service personnel is realized. According to the method, whether the labor service personnel meet the specific requirements of the construction project or not can be evaluated more comprehensively and accurately, and the construction problem caused by insufficient skills or lack of experience of the labor service personnel is effectively avoided.

Description

Technical Field

[0001] The present application relates to the technical field of engineering management, and more specifically, to a construction engineering management system and a management method thereof. Background Art

[0002] In the field of construction engineering, effective management of construction progress, personnel and materials is the key to ensuring the smooth progress of the project. With the rapid development of the construction industry and technological progress, traditional manual management methods have gradually exposed problems such as low efficiency, untimely data processing and difficulty in comprehensive monitoring. In this regard, the invention patent with publication number CN118840072A proposes a management system for construction projects, which is equipped with a project management module, a personnel information module, a project material module and a supervision module. It can not only supervise the progress of the project, the construction status of the construction personnel, the consumables of the construction materials and the construction environment status, but also realize the risk assessment of the labor construction personnel, ensure that the personnel involved in the construction meet the construction requirements, and ensure that the project can proceed smoothly.

[0003] However, existing technologies mainly determine whether laborers meet the requirements of construction projects by judging whether their identity documents are valid, checking whether they are within the legal working age, and confirming whether they are monitored personnel, while ignoring important factors such as laborers' professional skills, work experience, and adaptability to specific construction projects. Therefore, in actual applications, there may be situations where laborers meet the basic identity requirements but are unable to effectively complete construction tasks due to their lack of skills or experience, thereby affecting the overall progress and quality of the project.

[0004] Therefore, an optimized construction project management system and management method thereof are expected. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a construction project management system and a management method thereof, which uses deep learning-based data processing technology to perform semantic analysis on the electronic data of target labor personnel to extract the identity information characteristics of the labor personnel, and at the same time, in combination with pre-established construction requirement rules, the identity information of the labor personnel is semantically queried and coded with a large number of construction requirement rules to dig out the potential matching relationship between the labor personnel and the construction project, thereby realizing intelligent evaluation of the adaptability of the labor personnel. In this way, it is possible to more comprehensively and accurately evaluate whether the labor personnel meet the specific requirements of the construction project, and effectively avoid construction problems caused by insufficient skills or lack of experience of the labor personnel.

[0006] Accordingly, according to one aspect of the present application, a construction project management system is provided, which includes: a central processing unit module for two-way data interaction with other modules; a storage module for storing various data information of the construction project; a control module for receiving user operation instructions through a connected touch screen to control and manage other modules; a project management module for recording and managing various information of the project; a personnel information module for judging and evaluating labor personnel information, creating files for them and conducting daily attendance management; an engineering material module for collecting, classifying and managing material information required for construction; and a supervision module for judging the degree to which the current environmental status is suitable for construction based on the construction site environmental data.

[0007] In the above-mentioned construction project management system, the personnel information module includes: A labor personnel information acquisition unit is used to acquire the electronic information of target labor personnel; A construction requirement rule acquisition unit, used to extract a set of construction requirement rules from a construction project rule library; An identity information structured coding unit, used for performing structured coding on the electronic data to obtain a comprehensive structured coding vector of the identity information of the target service personnel; A construction requirement rule semantic understanding unit, used for extracting semantic features of each construction requirement rule in the set of construction requirement rules to obtain a set of construction requirement rule semantic embedding coding vectors; A semantic search verification unit, used for performing identity-rule semantic search verification coding based on dynamic weight regulation on the set of the target labor personnel identity information comprehensive structured coding vector and the construction requirement rule semantic embedded coding vector to obtain an identity-construction rule semantic level query response coding vector; The labor suitability assessment unit is used to determine whether the target labor meets the requirements of the construction project based on the identity-construction rule semantic level query response encoding vector.

[0008] According to another aspect of the present application, a construction project management method is provided, comprising: Obtain the electronic data of target labor personnel; Extracting a set of construction requirement rules from a construction project rule base; Performing structured coding on the electronic data to obtain a comprehensive structured coding vector of the target labor personnel's identity information; Extracting semantic features of each construction requirement rule in the set of construction requirement rules to obtain a set of semantic embedding coding vectors of construction requirement rules; Performing identity-rule semantic search verification coding based on dynamic weight regulation on the set of the target labor personnel identity information comprehensive structured coding vector and the construction requirement rule semantic embedded coding vector to obtain an identity-construction rule semantic level query response coding vector; Based on the identity-construction rule semantic level query response encoding vector, it is determined whether the target labor personnel meet the requirements of the construction project.

[0009] Beneficial effect: Compared with the prior art, the construction project management system and management method provided by the present application use deep learning-based data processing technology to perform semantic analysis on the electronic data of target labor personnel to extract the identity information characteristics of the labor personnel, and at the same time combine the pre-established construction requirement rules, by semantically querying and responding the labor personnel's identity information with a large number of construction requirement rules to dig out the potential matching relationship between the labor personnel and the construction project, thereby realizing intelligent evaluation of the adaptability of the labor personnel, and can more comprehensively and accurately evaluate whether the labor personnel meet the specific requirements of the construction project, and effectively avoid construction problems caused by insufficient skills or lack of experience of the labor personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0011] Figure 1 It is a block diagram of the personnel information module in the construction project management system according to an embodiment of the present application.

[0012] Figure 2 Schematic diagram of data flow of a personnel information module in a construction project management system according to an embodiment of the present application.

[0013] Figure 3 It is a block diagram of a semantic search verification unit in a construction project management system according to an embodiment of the present application.

[0014] Figure 4 A block diagram of a feature contribution modeling and evaluation subunit in a construction project management system according to an embodiment of the present application.

[0015] Figure 5 It is a flowchart of a construction project management method according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0018] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0019] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0020] Figure 1 It is a block diagram of the personnel information module in the construction project management system according to an embodiment of the present application. Figure 2 Schematic diagram of data flow in the personnel information module of the construction project management system according to the embodiment of the present application. Figure 1 and Figure 2As shown, the personnel information module 100 includes: a labor information acquisition unit 110, which is used to acquire the electronic data of the target labor personnel; a construction requirement rule acquisition unit 120, which is used to extract a set of construction requirement rules from the construction project rule library; an identity information structured coding unit 130, which is used to perform structured coding on the electronic data to obtain a comprehensive structured coding vector of the target labor personnel's identity information; a construction requirement rule semantic understanding unit 140, which is used to extract the semantic features of each construction requirement rule in the set of construction requirement rules to obtain a set of construction requirement rule semantic embedding coding vectors; a semantic search verification unit 150, which is used to perform identity-rule semantic search verification coding based on dynamic weight control on the set of the comprehensive structured coding vector of the target labor personnel's identity information and the set of the construction requirement rule semantic embedding coding vector to obtain an identity-construction rule semantic level query response coding vector; a labor adaptability evaluation unit 160, which is used to determine whether the target labor personnel meets the construction project requirements based on the identity-construction rule semantic level query response coding vector.

[0021] In the above-mentioned construction project management system, the labor information acquisition unit 110 is used to obtain the electronic data of the target labor. In a specific example of the present application, the electronic data includes an ID card, a special operation operation certificate, a physical examination report in the last three months, a safety training certificate, and a resume of past projects. It should be understood that the ID card is a key credential for confirming the identity of the laborer, and the information contained therein, such as name, gender, date of birth, address, ID card number, etc., is the basis for establishing personnel files and conducting personnel management. The special operation operation certificate proves that the laborer has the qualifications and ability to engage in specific types of construction work, which is particularly important for construction projects involving high risks or special skills (such as welding, lifting, high-altitude operations, etc.). By confirming the special operation operation certificate held by the laborer, the safe conduct of special operations during the construction process can be ensured, and safety accidents and violations caused by inconsistent personnel qualifications can be avoided. At the same time, the intensity of construction work is high and the environment is complex, which places certain requirements on the physical health of laborers. The physical examination report records in detail the various physical examination indicators such as blood routine, urine routine, liver function, kidney function, electrocardiogram, blood pressure, etc., which can help managers understand the physical functions and health status of laborers. On the one hand, it can ensure that they have enough physical strength and energy to complete their work tasks. On the other hand, it can prevent sudden illness at work due to physical reasons and reduce the health risks and potential losses of the project. The safety training certificate shows that the laborers have received necessary safety education and training, and have mastered basic safety knowledge and operating skills. Laborers holding safety training certificates can better comply with safety regulations during the construction process, have safety awareness and emergency response capabilities, which will help reduce safety accidents at the construction site and ensure a safe construction environment for the project. The past project resume records in detail the construction projects that the laborers have participated in in the past, the positions they have held, their work performance, their skill levels, and the evaluations and rewards they have received. It can fully reflect the work experience and ability level of the laborers, help evaluate whether they have the experience and ability to participate in the current project, and provide a strong reference for personnel job allocation and project team formation. Based on this, this application provides comprehensive and accurate data support by comprehensively collecting information such as the basic situation, skill qualifications, physical condition, safety awareness, and work experience of the target labor personnel, which will help avoid project delays, quality risks, and other problems caused by improper personnel selection.

[0022] In the above-mentioned construction project management system, the construction requirement rule acquisition unit 120 is used to extract a set of construction requirement rules from the construction project rule library. Specifically, since different construction projects have different requirements and standards in terms of technology, safety, progress, etc. In order to accurately evaluate whether the labor personnel meet the needs of a specific construction project, this application extracts a set of construction requirement rules related to the current project from the construction project rule library. By matching and analyzing these construction requirement rules with the electronic data of the target labor personnel, the adaptability of the labor personnel can be more accurately evaluated to ensure that the selected personnel are competent for the construction task, thereby improving the execution efficiency and quality of the project.

[0023] In the above-mentioned construction engineering management system, the identity information structured coding unit 130 is used to perform structured coding on the electronic data to obtain a comprehensive structured coding vector of the target labor personnel's identity information. In a specific example of the present application, the identity information structured coding unit 130 is further used to: perform structured coding on each item in the electronic data to obtain an identity information structured coding vector, a special operation operation certificate structured coding vector, a physical examination report structured coding vector, a safety training certificate structured coding vector and a past project resume structured coding vector. It should be understood that the present application takes into account that the original electronic data of labor personnel, such as identity card information, certificate text, physical examination data, etc., usually exist in an unstructured text format. Therefore, in order to convert the original text information into a computer-recognizable structured data form for subsequent data processing and analysis, the present application further performs structured coding on each item in the electronic data. In an embodiment of the present application, firstly, named entity recognition is performed on text information such as identity card information, certificate text, physical examination report and past project resume by natural language processing technology to extract key information of each item. Then, a pre-trained word embedding model (such as the BERT model) is used to vectorize the extracted information. The rich language knowledge learned by the word embedding model during the pre-training process is used to map the information into a dense vector form in a high-dimensional semantic space, thereby obtaining structured coding vectors for identity information, special operation certificate, physical examination report, safety training certificate, and past project resume.

[0024] In a specific example of the present application, the identity information structured coding unit 130 is also used to: splice the identity information structured coding vector, the special operation operation certificate structured coding vector, the physical examination report structured coding vector, the safety training certificate structured coding vector and the past project resume structured coding vector to obtain the target labor personnel identity information comprehensive structured coding vector. Specifically, since the single-dimensional labor personnel data information can only reflect the characteristics of a certain aspect of the labor personnel, it is impossible to fully reflect their comprehensive capabilities and adaptability. Therefore, the present application further splices the identity information structured coding vector, the special operation operation certificate structured coding vector, the physical examination report structured coding vector, the safety training certificate structured coding vector and the past project resume structured coding vector to integrate multi-dimensional information and form a complete labor personnel feature description, thereby obtaining the target labor personnel identity information comprehensive structured coding vector, so as to facilitate comprehensive and overall matching with the construction requirements and rules, and improve the accuracy and comprehensiveness of the matching.

[0025] In the above-mentioned construction project management system, the construction requirement rule semantic understanding unit 140 is used to extract the semantic features of each construction requirement rule in the set of construction requirement rules to obtain a set of semantic embedding coding vectors of construction requirement rules. It should be understood that the construction requirement rules are generally presented in natural language text. In order to convert them into a computable and comparable vectorized form, the present application also uses a pre-trained word embedding model to semantically encode each construction requirement rule in the set of construction requirement rules to capture the key semantic information of each construction requirement rule and generate a set of semantic embedding coding vectors of construction requirement rules. A person of ordinary skill in the art should know that during the pre-training process, the word embedding model can map the natural language text description into a continuous vector space by learning the vocabulary co-occurrence relationship and context information in a large amount of text data, so that the semantically similar text information is closer in the vector space, so that the semantic relevance and matching degree between the construction requirement rules and the identity information of the labor personnel can be measured based on the vector distance or similarity in the high-dimensional semantic space, which provides strong support for subsequent personnel screening and adaptation analysis.

[0026] In the above-mentioned construction project management system, the semantic search verification unit 150 is used to perform identity-rule semantic search verification encoding based on dynamic weight control on the set of the target labor personnel identity information comprehensive structured encoding vector and the construction requirement rule semantic embedding encoding vector to obtain the identity-construction rule semantic level query response encoding vector. Specifically, by further performing semantic query response encoding on the set of the target labor personnel identity information comprehensive structured encoding vector and the construction requirement rule semantic embedding encoding vector, the association matching relationship between the target labor personnel and the construction requirement rules is captured. Specifically, in order to improve the accuracy and efficiency of matching, this application proposes an identity-rule semantic search verification coding method based on dynamic weight regulation, which first captures the dynamic response relationship between the semantic features of the target labor personnel's identity information and each construction requirement rule by means of semantic anchoring, and then adaptively learns the importance weights of semantic interaction information of different dimensions in the global context based on an adaptive splicing mechanism to generate a set of weighted codes, and fuses the correlation between the comprehensive information of the target labor personnel and the local fine-grained interaction features between each construction requirement rule to generate a highly correlated semantic matching representation between the target labor personnel and the construction requirement rules, namely, the identity-construction rule semantic-level query response coding vector. Among them, Figure 3 FIG. 1 is a block diagram of a semantic search verification unit in a construction project management system according to an embodiment of the present application. Figure 3 As shown, the semantic search verification unit 150 includes: a semantic response anchor coding subunit 151, which is used to perform semantic response anchor coding on the target labor personnel identity information comprehensive structured coding vector and each construction requirement rule semantic embedding coding vector in the set of the construction requirement rule semantic embedding coding vector to obtain a set of identity-construction rule semantic response anchor coding matrices; a feature contribution modeling evaluation subunit 152, which is used to perform feature contribution modeling evaluation on each identity-construction rule semantic response anchor coding matrix in the set of the identity-construction rule semantic response anchor coding matrix to obtain a set of identity-construction rule decision anchor adaptive splicing weight factors; a feature fusion subunit 153, which is used to fuse the set of identity-construction rule semantic response anchor coding matrices based on the set of identity-construction rule decision anchor adaptive splicing weight factors to obtain the identity-construction rule semantic level query response coding vector.

[0027] Specifically, in a specific example of the present application, the semantic response anchor coding subunit 151 is used to: perform deep implicit feature extraction based on fully connected coding on the comprehensive structured coding vector of the target labor personnel identity information to obtain the deep implicit coding vector of the semantic features of the target labor personnel identity information; perform deep implicit feature extraction based on fully connected coding on each construction requirement rule semantic embedding coding vector in the set of construction requirement rule semantic embedding coding vectors to obtain a set of deep implicit coding vectors of construction requirement rule semantic features, which is expressed by the formula: in, represents the set of semantic embedding encoding vectors of construction requirement rules, , , and They represent the first, second, and third elements in the set of semantic embedding encoding vectors of construction requirement rules. and construction requirement rule semantic embedding encoding vector, is the number of vectors in the set of semantic embedding encoding vectors of the construction requirement rules, represents the identity information semantic feature weight matrix, represents the construction rule semantic feature weight matrix, represents the semantic feature bias of identity information, represents the construction rule semantic feature bias, Represents the comprehensive structured coding vector of the target labor personnel’s identity information, express Activation function, Represents the deep implicit coding vector of the semantic features of the target labor personnel’s identity information, express The corresponding construction requirement rule semantic feature deep implicit encoding vector.

[0028] Specifically, in order to enhance the semantic expression ability of the target labor personnel identity information and construction requirement rules, the present application first performs deep implicit feature extraction based on fully connected coding on the comprehensive structured coding vector of the target labor personnel identity information and the semantic embedded coding vector of each construction requirement rule, so as to learn the global nonlinear interaction relationship within the original features through the nonlinear mapping of the fully connected neural network, generate a deeper semantic feature representation, and obtain a set of deep implicit coding vectors of the semantic features of the target labor personnel identity information and deep implicit coding vectors of the semantic features of the construction requirement rules.

[0029] Specifically, in a specific example of the present application, the semantic response anchor coding subunit 151 is further used to: input each construction requirement rule semantic feature deep implicit coding vector in the set of the target labor personnel identity information semantic feature deep implicit coding vector and the construction requirement rule semantic feature deep implicit coding vector into the semantic response decision anchor component to obtain the set of the identity-construction rule semantic response anchor coding matrix, which is expressed by the formula: in, represents the transpose of a vector, represents vector multiplication, represents the feature scale scaling factor, express and Identity-construction rules between semantic responses anchor encoding matrices.

[0030] Specifically, the semantic response decision anchoring component is further used to perform semantic interaction encoding between the deep implicit coding vector of the semantic features of the target labor personnel's identity information and the deep implicit coding vector of the semantic features of each construction requirement rule. The outer product calculation between the vectors is used to capture the semantic association response relationship between the target labor personnel's identity information and each construction requirement rule, thereby generating a set of identity-construction rule semantic response anchoring coding matrices.

[0031] Figure 4 FIG. 1 is a block diagram of a feature contribution modeling and evaluation subunit in a construction project management system according to an embodiment of the present application. Figure 4 As shown, the feature contribution modeling evaluation subunit 152 includes: a decision anchor adaptive splicing factor calculation secondary subunit 1521, which is used to determine the decision anchor adaptive splicing factor of each identity-construction rule semantic response anchor coding matrix in the set of identity-construction rule semantic response anchor coding matrices based on the feature distribution of each identity-construction rule semantic response anchor coding matrix to obtain a set of identity-construction rule decision anchor adaptive splicing factors; a weighting secondary subunit 1522, which is used to perform weighting processing on the set of identity-construction rule decision anchor adaptive splicing factors based on the Softmax function to obtain a set of identity-construction rule decision anchor adaptive splicing weight factors.

[0032] In a specific example of the present application, the decision anchor adaptive splicing factor calculation secondary subunit 1521 is used to: based on the statistical eigenvalues ​​of each identity-construction rule semantic response anchor coding matrix in the set of the identity-construction rule semantic response anchor coding matrix, calculate the decision anchor adaptive splicing factor of each identity-construction rule semantic response anchor coding matrix to obtain the set of identity-construction rule decision anchor adaptive splicing factors, wherein the statistical eigenvalues ​​include the maximum eigenvalue, the characteristic variance, the characteristic mean and the number of eigenvalues. More specifically, the sum of the characteristic variance and the drift coefficient of the identity-construction rule semantic response anchor coding matrix is ​​used as the numerator, and the square of the difference between the maximum eigenvalue and the characteristic mean of the identity-construction rule semantic response anchor coding matrix is ​​calculated and multiplied by the number of its eigenvalues, and the drift coefficient and twice the characteristic variance are added as the denominator to obtain the identity-construction rule decision anchor adaptive splicing factor, wherein the drift coefficient is used to smooth the characteristic distribution fluctuation of the identity-construction rule semantic response anchor coding matrix, and is expressed by the formula: in, Indicates the number of elements in the calculation matrix, represents the difference amplification factor, i.e., the number of eigenvalues ​​of the identity-construction rule semantic response anchor encoding matrix, represents the feature variance of the identity-construction rule semantic response anchor encoding matrix, represents the drift coefficient of the identity-construction rule semantic response anchor encoding matrix, represents the feature mean of the identity-construction rule semantic response anchor encoding matrix, To obtain the maximum value function, express The corresponding identity-construction rule decision anchors the adaptive splicing factor.

[0033] Specifically, in order to more accurately measure the importance of semantic interaction information of different dimensions, this application introduces an adaptive weight allocation mechanism, which calculates the adaptive splicing factor of the decision anchor by learning the feature distribution of each identity-construction rule semantic response anchor encoding matrix, and generates a set of identity-construction rule decision anchor adaptive splicing factors. Here, the identity-construction rule decision anchor adaptive splicing factor is used to reflect the dominance of semantic interaction information of different dimensions in the global context, and is the basis for weights in subsequent feature fusion.

[0034] In particular, in a preferred example of the present application, for the identity-construction rule decision anchor adaptive splicing factor drift coefficient , the distribution of the eigenvalue set of the identity-construction rule semantic response anchor coding matrix transitions from the state of weak overall interpretability of the mean to strong local interpretability of the maximum value. This application introduces the drift coefficient The weak to strong interpretable generalization of the identity-construction rule semantic response anchor encoding matrix is ​​enhanced by the global dominance basis, which is expressed as: in, is the intermediate transition representation value of the feature distribution equilibrium state of the identity-construction rule semantic response anchor encoding matrix, is a matrix No. eigenvalues, is a natural constant.

[0035] Specifically, As an intermediate state transition representation from weak interpretability to strong interpretability, each eigenvalue of the encoding matrix is ​​anchored for the identity-construction rule semantic response , which is used as the importance score of the identity-construction rule semantic response anchor encoding matrix for the global smooth state transition to analyze the intermediate state transition The importance score weights relative to the global state transition are globally controlled to achieve interpretable generalized inference of the weights of the identity-construction rule decision anchor adaptive splicing factors.

[0036] More specifically, the weighted secondary subunit 1522 is expressed as follows: in, represents the normalized exponential function, Representation Matrix The identity-construction rule decides the anchor adaptive splicing weight factor.

[0037] Specifically, the set of identity-construction rule decision anchor adaptive splicing factors is weighted based on the Softmax function, that is, the set of identity-construction rule decision anchor adaptive splicing factors is normalized into a weight set with a probability distribution property using the Softmax function, and through the characteristics of the exponential function, the significant differences between the identity-construction rule semantic response anchor encoding matrices are further amplified to enhance the distribution distinction ability of the features.

[0038] Specifically, the feature fusion subunit 153 is expressed by the formula: in, Represents identity-construction rule semantic response anchor encoding fusion matrix, represents the characteristic shape reshaping function, Representing identity-construction rules in semantic-level query response encoding vectors.

[0039] Specifically, based on the generated weight distribution, the set of identity-construction rule semantic response anchor coding matrices is weighted and fused to fuse the semantic interaction information of different dimensions, and restored to vector form through feature shape reshaping to generate the final identity-construction rule semantic level query response coding vector. In this way, the identity-construction rule semantic level query response coding vector, as the core basis for subsequent personnel screening and adaptation analysis, not only contains the comprehensive semantic matching information between the target labor personnel and the construction requirement rules, but also emphasizes the importance of key semantic interaction information through the adaptive weight control mechanism, thereby improving the accuracy and efficiency of matching.

[0040] In the above-mentioned construction project management system, the labor suitability evaluation unit 160 is used to determine whether the target labor meets the requirements of the construction project based on the identity-construction rule semantic level query response encoding vector. In a specific example of the present application, the labor suitability evaluation unit 160 is used to: input the identity-construction rule semantic level query response encoding vector into an intelligent decision module based on a classifier to obtain an intelligent decision result, and the intelligent decision result is used to indicate whether the target labor meets the requirements of the construction project. Specifically, the identity-construction rule semantic level query response encoding vector is fully connected using the fully connected layer of the intelligent decision module to obtain the identity-construction rule semantic level query response fully connected encoding vector; the identity-construction rule semantic level query response fully connected encoding vector is input into the Softmax classification function of the intelligent decision module to obtain the probability value of the identity-construction rule semantic level query response encoding vector belonging to each classification label, wherein the classification label includes the target labor meets the construction project requirements and the target labor does not meet the construction project requirements; the classification label corresponding to the largest of the probability values ​​is determined as the decision result. Here, the classifier-based intelligent decision-making module is based on a neural network architecture. By performing multi-layer feature perception on the identity-construction rule semantic-level query response encoding vector, it learns the semantic matching relationship between the target labor personnel and the construction requirement rules contained in the identity-construction rule semantic-level query response encoding vector, and performs intelligent decision processing based on this, and outputs an intelligent decision result on whether the target labor personnel meets the requirements of the construction project.

[0041] In the technical solution of the present application, considering that the set of the target labor personnel identity information comprehensive structured coding vector and the set of the construction requirement rule semantic embedding coding vector respectively represent the set of target labor personnel information semantic coding features and the set of construction requirement rule semantic coding features, when aligning for semantic-level feature dynamic query adaptive response splicing, the different semantic feature dimensions of each construction requirement rule semantic embedding coding vector in the set of the target labor personnel identity information comprehensive structured coding vector and the construction requirement rule semantic embedding coding vector and the semantic feature coding difference between each construction requirement rule semantic embedding coding vector in the set of the construction requirement rule semantic embedding coding vector will cause the obtained identity-construction rule semantic-level query response coding vector to have cross-sample semantic difference imbalance and insufficient semantic consistency within the sample, and thus have cross-domain dynamic matching differences. Therefore, it is expected to improve the detail semantic aggregation response expression effect of the identity-construction rule semantic-level query response coding vector.

[0042] In a preferred example, inputting the identity-construction rule semantic level query response encoding vector into a classifier-based intelligent decision module to obtain an intelligent decision result includes: Based on the feature value information of each position of the identity-construction rule semantic level query response encoding vector, a first identity-construction rule semantic level query response semantic evolution strength index and a second identity-construction rule semantic level query response semantic evolution strength index are constructed, which are expressed as: in, represents the identity-construction rule semantic-level query response encoding vector, The first encoding vector of the identity-construction rule semantic level query response is represented by The eigenvalues ​​at the positions, The total number of eigenvalues ​​representing the identity-construction rule semantic level query response encoding vector, Represents the first identity-construction rule semantic level query response semantic evolution strength index, Represents the second identity - construction rule semantic level query response semantic evolution strength index; The number of eigenvalues ​​of the identity-construction rule semantic level query response encoding vector is extracted, and based on the number of eigenvalues, the first identity-construction rule semantic level query response semantic evolution strength index and the second identity-construction rule semantic level query response semantic evolution strength index, the eigenvalues ​​of each position in the identity-construction rule semantic level query response encoding vector are low-order phase reconstructed to obtain the first identity-construction rule semantic level query response phase reconstruction vector, which is expressed as: in, It means point multiplication by position. It means subtracting by position. represents the position-wise inverse of a vector, Represents the first identity-construction rule semantic level query response phase reconstruction vector; Based on the number of eigenvalues, the first identity-construction rule semantic level query response semantic evolution strength index and the second identity-construction rule semantic level query response semantic evolution strength index, the eigenvalues ​​at each position in the identity-construction rule semantic level query response encoding vector are subjected to high-order phase reconstruction to obtain a second identity-construction rule semantic level query response phase reconstruction vector, which is expressed as: in, Represents the second identity-construction rule semantic level query response phase reconstruction vector; Linearly dynamically fusing the first identity-construction rule semantic level query response phase reconstruction vector and the second identity-construction rule semantic level query response phase reconstruction vector to obtain an optimized identity-construction rule semantic level query response encoding vector; The optimized identity-construction rule semantic-level query response encoding vector is input into a classifier-based intelligent decision module to obtain an intelligent decision result.

[0043] Here, the identity-construction rule semantic level query response encoding vector is denoted as The optimization expression is: in, and represents the weight hyperparameter, represents vector addition, Representing optimized identity-construction rules for semantic-level query response encoding vectors.

[0044] Accordingly, in the preferred example, the difference between the attribute parameters of each position of the identity-construction rule semantic level query response coding vector and the feature set of the vector as a whole is compared to construct a semantic evolution intensity index, and the position-related mimetic phase mapping is realized through the control paradigm generated by the difference comparison. Furthermore, based on the multi-dimensional tensor equalization framework, through the displacement transformation operation under the stacked alternating architecture, the fusion enhancement strategy driven by the phase-sensitive mechanism can expand the axial perception range along the feature aggregation direction, so as to effectively enhance the recognition efficiency of the aggregated representation of the identity-construction rule semantic level query response feature vector for the detailed semantic changes, thereby improving the perception effect of the aggregated semantics of the identity-construction rule semantic level query response coding vector for the detailed semantic changes, so as to improve the expression effect of the identity-construction rule semantic level query response coding vector, and improve the accuracy of the intelligent decision results obtained by passing it through the classifier-based intelligent decision module.

[0045] In summary, the construction project management system based on the embodiment of the present application is explained, which uses deep learning-based data processing technology to perform semantic analysis on the electronic data of the target labor personnel to extract the identity information characteristics of the labor personnel, and at the same time, combined with the pre-established construction requirement rules, the identity information of the labor personnel is semantically queried and coded with a large number of construction requirement rules to dig out the potential matching relationship between the labor personnel and the construction project, thereby realizing intelligent evaluation of the adaptability of the labor personnel. In this way, it is possible to more comprehensively and accurately evaluate whether the labor personnel meet the specific requirements of the construction project, and effectively avoid construction problems caused by insufficient skills or lack of experience of the labor personnel.

[0046] Furthermore, a construction project management method is also provided.

[0047] Figure 5 Flowchart of the construction project management method according to the embodiment of the present application. Figure 5 As shown, the construction project management method includes the following steps: S1, obtaining the electronic data of the target labor personnel; S2, extracting a set of construction requirement rules from a construction project rule library; S3, performing structured coding on the electronic data to obtain a comprehensive structured coding vector of the target labor personnel's identity information; S4, extracting the semantic features of each construction requirement rule in the set of construction requirement rules to obtain a set of semantic embedded coding vectors of construction requirement rules; S5, performing identity-rule semantic search verification coding based on dynamic weight regulation on the comprehensive structured coding vector of the target labor personnel's identity information and the set of semantic embedded coding vectors of the construction requirement rules to obtain an identity-construction rule semantic-level query response coding vector; S6, determining whether the target labor personnel meet the construction project requirements based on the identity-construction rule semantic-level query response coding vector.

[0048] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0049] In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0050] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0051] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units stated in the system claims can also be implemented by one unit through software or hardware.

[0052] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A construction project management system, comprising: The central processing unit module is used for two-way data interaction with other modules; Storage module, used to store various data information of construction projects; The control module is used to receive user operation instructions through the connected touch screen to control and manage other modules; the project management module is used to record and manage various project information; The personnel information module is used to judge and evaluate the information of labor personnel, create files for them and conduct daily attendance management; the engineering material module is used to collect, classify and manage the material information required for construction; The supervision module is used to determine the degree to which the current environmental state is suitable for construction based on the construction site environmental data, and is characterized in that the personnel information module includes: A labor personnel information acquisition unit is used to acquire the electronic information of target labor personnel; A construction requirement rule acquisition unit, used to extract a set of construction requirement rules from a construction project rule library; An identity information structured coding unit, used for performing structured coding on the electronic data to obtain a comprehensive structured coding vector of the identity information of the target service personnel; A construction requirement rule semantic understanding unit, used for extracting semantic features of each construction requirement rule in the set of construction requirement rules to obtain a set of construction requirement rule semantic embedding coding vectors; A semantic search verification unit, used for performing identity-rule semantic search verification coding based on dynamic weight regulation on the set of the target labor personnel identity information comprehensive structured coding vector and the construction requirement rule semantic embedded coding vector to obtain an identity-construction rule semantic level query response coding vector; The labor suitability assessment unit is used to determine whether the target labor meets the requirements of the construction project based on the identity-construction rule semantic level query response encoding vector.

2. The construction project management system according to claim 1, characterized in that: The electronic data include identity card, special operation operation certificate, physical examination report within the last three months, safety training certificate and past project history.

3. The construction project management system according to claim 2, characterized in that: The identity information structured encoding unit is used to: Performing structured coding on each item of the electronic data to obtain a structured coding vector of identity information, a structured coding vector of a special operation operation certificate, a structured coding vector of a physical examination report, a structured coding vector of a safety training certificate, and a structured coding vector of past project resumes; The identity information structured coding vector, the special operation operation certificate structured coding vector, the physical examination report structured coding vector, the safety training certificate structured coding vector and the past project resume structured coding vector are spliced ​​to obtain a comprehensive structured coding vector of the target labor personnel's identity information.

4. The construction project management system according to claim 3, characterized in that: The semantic search verification unit includes: The semantic response anchor coding subunit is used to perform semantic response anchor coding on the target labor personnel identity information comprehensive structured coding vector and each construction requirement rule semantic embedding coding vector in the set of the construction requirement rule semantic embedding coding vector to obtain a set of identity-construction rule semantic response anchor coding matrices; A feature contribution modeling evaluation subunit, used for performing feature contribution modeling evaluation on each identity-construction rule semantic response anchor coding matrix in the set of identity-construction rule semantic response anchor coding matrices to obtain a set of identity-construction rule decision anchor adaptive splicing weight factors; A feature fusion subunit is used to fuse the set of identity-construction rule semantic response anchor encoding matrices based on the set of identity-construction rule decision anchor adaptive splicing weight factors to obtain the identity-construction rule semantic level query response encoding vector.

5. The construction project management system according to claim 4, characterized in that: The semantic response anchor encoding subunit is used to: Performing deep implicit feature extraction based on fully connected coding on the comprehensive structured coding vector of the target labor personnel's identity information to obtain a deep implicit coding vector of semantic features of the target labor personnel's identity information; Performing deep implicit feature extraction based on fully connected coding on each construction requirement rule semantic embedding coding vector in the set of construction requirement rule semantic embedding coding vectors to obtain a set of construction requirement rule semantic feature deep implicit coding vectors; Each construction requirement rule semantic feature deep implicit coding vector in the set of the target labor personnel identity information semantic feature deep implicit coding vector and the construction requirement rule semantic feature deep implicit coding vector is input into the semantic response decision anchoring component respectively to obtain a set of identity-construction rule semantic response anchoring coding matrices.

6. The construction project management system according to claim 5, characterized in that: The feature contribution modeling evaluation subunit includes: A decision anchor adaptive splicing factor calculation secondary subunit is used to determine the decision anchor adaptive splicing factor of each identity-construction rule semantic response anchor coding matrix in the set of identity-construction rule semantic response anchor coding matrices based on the feature distribution of each identity-construction rule semantic response anchor coding matrix to obtain a set of identity-construction rule decision anchor adaptive splicing factors; The weighted secondary subunit is used to perform weighted processing on the set of identity-construction rule decision anchor adaptive splicing factors based on the Softmax function to obtain the set of identity-construction rule decision anchor adaptive splicing weight factors.

7. The construction project management system according to claim 6, characterized in that: The decision anchor adaptive splicing factor calculation secondary subunit is used to: Based on the statistical eigenvalues ​​of each identity-construction rule semantic response anchor coding matrix in the set of identity-construction rule semantic response anchor coding matrices, the decision anchor adaptive splicing factors of each identity-construction rule semantic response anchor coding matrix are calculated to obtain the set of identity-construction rule decision anchor adaptive splicing factors, and the statistical eigenvalues ​​include the maximum eigenvalue, eigenvariance, eigenmean and eigenvalue number.

8. The construction project management system according to claim 7, characterized in that: The decision anchor adaptive splicing factor calculation secondary subunit is used to: The sum of the feature variance and the drift coefficient of the identity-construction rule semantic response anchor coding matrix is ​​used as the numerator, and the square of the difference between the maximum eigenvalue and the feature mean of the identity-construction rule semantic response anchor coding matrix is ​​multiplied by the number of its eigenvalues, and then the drift coefficient and twice the feature variance are added as the denominator to obtain the identity-construction rule decision anchor adaptive splicing factor, wherein the drift coefficient is used to smooth the feature distribution fluctuations of the identity-construction rule semantic response anchor coding matrix.

9. The construction project management system according to claim 8, characterized in that: The labor suitability assessment unit is used to: The identity-construction rule semantic level query response encoding vector is input into a classifier-based intelligent decision module to obtain an intelligent decision result, and the intelligent decision result is used to indicate whether the target labor personnel meet the requirements of the construction project.

10. A construction project management method, characterized in that: include: Obtain the electronic data of target labor personnel; Extracting a set of construction requirement rules from a construction project rule base; Performing structured coding on the electronic data to obtain a comprehensive structured coding vector of the target labor personnel's identity information; Extracting semantic features of each construction requirement rule in the set of construction requirement rules to obtain a set of semantic embedding coding vectors of construction requirement rules; Performing identity-rule semantic search verification coding based on dynamic weight regulation on the set of the target labor personnel identity information comprehensive structured coding vector and the construction requirement rule semantic embedded coding vector to obtain an identity-construction rule semantic level query response coding vector; Based on the identity-construction rule semantic level query response encoding vector, it is determined whether the target labor personnel meet the requirements of the construction project.

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