Construction material supervision method, system and equipment based on data analysis and medium
By introducing an intelligent matching mechanism based on machine learning algorithms into the construction material management system, the material distribution is automated, and the problem of inefficient construction material management in the existing technology is solved, achieving more efficient and safer material supervision and processing.
Patent Information
- Application Number
- CN202411320965.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-23
- Publication Date
- 2025-01-10
AI Technical Summary
The existing construction material management system relies on manual recording, is inefficient and error-prone, and cannot achieve real-time dynamic tracking and early warning, resulting in insufficient management efficiency and security.
Using an intelligent matching mechanism based on machine learning algorithms, the material allocation is automatically completed through the analysis of material characteristics and work information of the person in charge, ensuring that the material is allocated to the most suitable person in charge, avoiding work overload or idleness, and improving supervision and processing efficiency and accuracy sex.
It improves the efficiency and accuracy of supervision and treatment of construction materials, reduces human errors and prejudice, ensures efficient and fair distribution of materials, and ensures construction safety.
Smart Images
Figure CN119294978A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of construction material management, and in particular to a construction material supervision method, system, equipment and medium based on data analysis. Background Art
[0002] In today's rapidly developing environment, construction sites are facing increasing challenges in construction material management. As the scale of construction projects expands and grows, efficient construction material management has always been a key link.
[0003] At present, construction site material management mainly relies on manual recording and inspection, which is inefficient and prone to errors. Although there are some intelligent material management systems, most of them can only achieve static data recording and cannot track and warn materials in real time. Therefore, there is room for improvement. Summary of the invention
[0004] In order to improve the monitoring and management efficiency of construction materials and ensure construction safety, the present application provides a construction material supervision method, system, equipment and medium based on data analysis.
[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A construction material supervision method based on data analysis, the construction material supervision method based on data analysis comprising the steps of: Receiving a material task request, extracting material characteristics according to the material task request, and obtaining material characteristics of the material task request; Traverse the built-in responsible person database to obtain the work information of each responsible person; wherein the work information includes work skills and current material management load; Based on the machine learning algorithm, the best person in charge is determined according to the material characteristics and the work information of each person in charge; According to the material task request and the best person in charge, generate materials to be managed and assign them to the best person in charge; Receive the order confirmation request from the best person in charge, obtain the progress status and completion status of the material to be managed, and upload them to the material management library.
[0006] By adopting the above technical solutions and introducing an intelligent matching mechanism based on machine learning algorithms, the system can intelligently match the best person in charge through the analysis of material characteristics and the person in charge's work information, and automatically complete the material allocation process, thereby improving the efficiency and accuracy of construction material supervision and processing. At the same time, considering the current workload of the person in charge, it can more reasonably allocate the material management work to avoid overload or idleness, effectively solving the problems existing in the traditional material management system, improving the efficiency of material supervision and processing, and ensuring construction safety.
[0007] In a preferred example, the present application may be further configured as follows: extracting material characteristics according to the material task request specifically includes: Obtaining basic information of the material task request; wherein the basic information includes material description and expected management time; Perform natural language processing analysis based on the material description of the material task request to extract key features; Determine the material type based on the extracted key features and classification algorithm; Determine the urgency based on the expected management time of the material task request; The material characteristics of the material task request are obtained according to the material type and the urgency.
[0008] By adopting the above technical solutions, it is ensured that the material type and the urgency of the material are properly considered, which helps to classify materials and give priority to urgent materials, thereby improving the efficiency of material management response.
[0009] In a preferred example, the present application can be further configured as follows: the method of determining the best person in charge based on the material characteristics and the work information of each person in charge based on the machine learning algorithm specifically includes: Input the material characteristics and the work information of each person in charge into a pre-trained material matching model to match and obtain the best person in charge corresponding to the material characteristics; The material matching model is pre-trained based on historical material data, and the historical material data includes historical material characteristics, work information of historical persons in charge, and the label of the best person in charge.
[0010] By adopting the above technical solution, the model trained with historical data can accurately and quickly match the person most suitable for current material management. This not only improves the accuracy of matching, but also makes the material allocation process more intelligent and automated, reduces human errors and biases, and ensures that construction materials can be efficiently and fairly allocated to the most suitable person in charge.
[0011] In a preferred example, the present application may be further configured as follows: before inputting the material characteristics and the work information of each person in charge into a pre-trained material matching model to match the best person in charge corresponding to the material characteristics, the construction material supervision method based on data analysis also includes: Acquire a sample data set; wherein the sample data set includes historical material characteristics, work information of historical persons in charge, and a label of the best person in charge in the historical material data; Performing data preprocessing on the sample data set and dividing it into a training set and a test set; Training a pre-built neural network model based on the historical material data in the training set, and optimizing model parameters to obtain a material matching model; The material matching model is tested and model parameters are corrected based on the test set until the loss function of the model meets a preset condition or the number of model iterations reaches a preset number, thereby obtaining a trained material matching model.
[0012] By adopting the above technical solution, the trained model can learn how to predict the best person in charge based on material characteristics and person in charge information. The optimized model parameters enable the model to match more accurately, thereby improving the efficiency and accuracy of material management allocation.
[0013] In a preferred example, the present application may be further configured as follows: the testing of the material matching model and the correction of model parameters based on the test set specifically include: A test model is established according to the historical material data in the test set, and the performance index of the material matching model is evaluated and the model parameters are corrected based on the test model; wherein the test indicators of the test model include accuracy, recall rate and F1 value.
[0014] By adopting the above technical solution, after test set verification and model tuning, a material matching model with high accuracy, high recall rate and high F1 value can be obtained, which can be used in actual construction material management to achieve efficient and accurate material allocation.
[0015] In a preferred example, the present application may be further configured as follows: generating the material information to be managed according to the material task request and the best person in charge, specifically including: Based on the material characteristics of the material task request, traverse a preset template library to determine a material allocation template corresponding to the material task request; The basic information of the material task request and the best person in charge are automatically filled in the material allocation template to obtain the information of the materials to be managed.
[0016] By adopting the above technical solution, the preset template library is traversed and the material allocation information is automatically filled in, which speeds up the generation of information on materials to be managed, reduces the workload and error probability of manual input, and improves the efficiency and accuracy of material allocation.
[0017] The second object of the invention is achieved by the following technical solutions: A construction material supervision system based on data analysis, the construction material supervision system based on data analysis comprising: A material property extraction module is used to receive a material task request, extract material properties according to the material task request, and obtain the material properties of the material task request; A manager analysis module is used to traverse the built-in person in charge database to obtain the work information of each person in charge; wherein the work information includes work skills and current material management load; A material allocation module is used to determine the best person in charge based on the material characteristics and the work information of each person in charge based on a machine learning algorithm; generate materials to be managed and allocate them to the best person in charge based on the material task request and the best person in charge; The information uploading module is used to receive the order confirmation request from the best person in charge, obtain the progress status and completion status of the material to be managed, and upload them to the material management library.
[0018] By adopting the above technical solutions and introducing an intelligent matching mechanism based on machine learning algorithms, the system can intelligently match the best person in charge through the analysis of material characteristics and the person in charge's work information, and automatically complete the material allocation process, thereby improving the efficiency and accuracy of construction material supervision and processing. At the same time, considering the current workload of the person in charge, it can more reasonably allocate the material management work to avoid overload or idleness, effectively solving the problems existing in the traditional material management system, improving the efficiency of material supervision and processing, and ensuring construction safety.
[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned construction material supervision method based on data analysis when executing the computer program.
[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned construction material supervision method based on data analysis.
[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. Introducing an intelligent matching mechanism based on machine learning algorithms. By analyzing the material characteristics and the work information of the person in charge, the system can intelligently match the best person in charge and automatically complete the material allocation process, thereby improving the efficiency and accuracy of construction material supervision and processing. At the same time, considering the current workload of the person in charge, it can more reasonably allocate material management work to avoid overload or idleness, effectively solving the problems existing in traditional material management systems, improving the efficiency of material supervision and processing, and ensuring construction safety; 2. The model trained with historical data can accurately and quickly match the person in charge who is most suitable for current material management. This not only improves the accuracy of matching, but also makes the material allocation process more intelligent and automated, reduces human errors and biases, and ensures that construction materials can be efficiently and fairly allocated to the most suitable person in charge. 3. The trained model can learn how to predict the best person in charge based on material characteristics and person in charge information. The optimized model parameters enable the model to match more accurately, improving the efficiency and accuracy of material management allocation; 4. Traverse the preset template library and automatically fill in the material allocation information, which speeds up the generation of information on materials to be managed, reduces the workload and error probability of manual input, and improves the efficiency and accuracy of material allocation. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1 is a flow chart of a construction material supervision method based on data analysis in one embodiment of the present application; Figure 2 It is a flowchart for implementing step S10 in the construction material supervision method based on data analysis in one embodiment of the present application; Figure 3 It is a flowchart for implementing step S30 in the construction material supervision method based on data analysis in one embodiment of the present application; Figure 4 is another implementation flow chart of the construction material supervision method based on data analysis in one embodiment of the present application; Figure 5 It is a flowchart for implementing step S304 in the construction material supervision method based on data analysis in one embodiment of the present application; Figure 6 It is a flowchart for implementing step S40 in the construction material supervision method based on data analysis in one embodiment of the present application; Figure 7 It is a principle block diagram of a construction material supervision system based on data analysis in one embodiment of the present application; Figure 8It is a schematic diagram of a computer device in one embodiment of the present application. DETAILED DESCRIPTION
[0023] The present application is further described in detail below in conjunction with the accompanying drawings.
[0024] In one embodiment, if Figure 1 As shown, the present application discloses a construction material supervision method based on data analysis, which specifically includes the following steps: S10: receiving a material task request, extracting material characteristics according to the material task request, and obtaining material characteristics of the material task request.
[0025] Specifically, it receives material task requests from engineering management. The material task requests are the basis for all subsequent material allocation operations, ensuring that all materials to be allocated and processed can be captured and recorded by the system, providing a data basis for subsequent processing.
[0026] Furthermore, by analyzing the content of the material task request, key material characteristics are extracted, such as material type, urgency, estimated management time, etc., in order to clarify the specific needs and characteristics of the materials and provide a basis for subsequent matching of responsible persons.
[0027] S20: Traverse the built-in person in charge database to obtain work information of each person in charge; wherein the work information includes work skills and current material management load.
[0028] Specifically, the system has a built-in database of responsible persons, which records the work skills and current material load of each responsible person. The system can obtain detailed information of each responsible person by traversing this database, thereby providing the necessary information for subsequent intelligent matching of the best responsible person, ensuring that construction materials can be allocated to the most suitable person.
[0029] S30: Based on a machine learning algorithm, the best person in charge is determined according to the material characteristics and the work information of each person in charge.
[0030] Specifically, the matching process is optimized through algorithms, and intelligent matching is performed based on material characteristics and the work information of the person in charge to determine the best person in charge, thereby improving the efficiency and accuracy of construction material allocation and ensuring that construction materials can be quickly and accurately allocated to the appropriate person in charge.
[0031] S40: Generate materials to be managed according to the material task request and the best person in charge and assign them to the best person in charge.
[0032] S50: receiving the order confirmation request from the best person in charge, obtaining the progress status and completion status of the material to be managed and uploading them to the material management library.
[0033] Specifically, by introducing an intelligent matching mechanism based on machine learning algorithms, the system can intelligently match the best person in charge through analysis of material characteristics and the person in charge's work information, automatically complete the material allocation process, and thus improve the efficiency and accuracy of construction material supervision and processing. At the same time, considering the current workload of the person in charge, it can more reasonably allocate material management work to avoid overload or idleness, effectively solving the problems existing in traditional material management systems, improving the efficiency of material supervision and processing, and ensuring construction safety.
[0034] In this embodiment, an intelligent matching mechanism based on a machine learning algorithm is introduced. By analyzing the material characteristics and the work information of the person in charge, the system can intelligently match the best person in charge and automatically complete the material allocation process, thereby improving the efficiency and accuracy of construction material supervision and processing. At the same time, considering the current workload of the person in charge, the material management work can be more reasonably allocated to avoid overload or idleness, effectively solving the problems existing in the traditional material management system, improving the efficiency of material supervision and processing, and ensuring construction safety.
[0035] In one embodiment, if Figure 2 As shown, in step S10, material characteristics are extracted according to the material task request, specifically including: S11: Obtain basic information of the material task request; wherein the basic information includes material description and expected management time.
[0036] S12: Perform natural language processing analysis based on the material description of the material task request to extract key features.
[0037] S13: Determine the material type based on the extracted key features and classification algorithm.
[0038] S14: Determine the urgency level according to the expected management time of the material task request.
[0039] S15: Obtaining material characteristics of the material task request according to the material type and the urgency.
[0040] Specifically, material description and expected management time are key to understanding material allocation requirements and urgency, ensuring that subsequent processing steps have sufficient information for detailed analysis, laying the foundation for accurate extraction of material characteristics.
[0041] Natural language processing (NLP) technology is used to analyze material descriptions and extract key features. Technologies such as part-of-speech tagging, named entity recognition, and keyword extraction are used to accurately extract key information reflecting the demand and nature of construction materials from material descriptions in order to understand the essential needs of construction material allocation and improve the accuracy of construction material classification and matching. The extracted key features are input into classification algorithms (such as support vector machines, decision trees, etc.) for processing to determine the specific types of construction materials. The system learns how to distinguish different types of construction materials through labeled training data, so that it can automatically identify the types of construction materials, providing an important classification basis for subsequent matching of the best person in charge. By comparing and analyzing the expected management time with the current time, the system evaluates the urgency of construction material allocation, ensuring that the material type and the urgency of construction materials are properly considered, which is helpful for the classification of construction materials and the priority processing of urgent construction materials, thereby improving response efficiency.
[0042] In one embodiment, if Figure 3 As shown, in step S30, based on the machine learning algorithm, the best person in charge is determined according to the material characteristics and the work information of each person in charge, specifically including: S31: Input the material characteristics and the work information of each person in charge into a pre-trained material matching model to match and obtain the best person in charge corresponding to the material characteristics.
[0043] S32: wherein the material matching model is pre-trained based on historical material data, wherein the historical material data includes historical material characteristics, work information of historical persons in charge, and a label of the best person in charge.
[0044] In this embodiment, the standard highway 3D point cloud data refers to data obtained after performing abnormal elimination processing, filtering processing and data calibration processing on the initially collected highway 3D point cloud data.
[0045] Specifically, in an actual highway construction site, the data collection environment is complex. Trees, houses, people and vehicles at the highway construction site will be initially collected into the highway three-dimensional point cloud data. According to step S211, the initially collected highway three-dimensional point cloud data is subjected to abnormality elimination, filtering and data calibration processing to form standard highway three-dimensional point cloud data. Based on the obtained standard highway three-dimensional point cloud data, accurate highway construction pavement information data is obtained to improve the accuracy of highway construction data.
[0046] In one embodiment, if Figure 4 As shown, before step S31, that is, before the material properties and the work information of each person in charge are input into the pre-trained material matching model to match the best person in charge corresponding to the material properties, the construction material supervision method based on data analysis also includes: S301: Acquire a sample data set; wherein the sample data set includes historical material characteristics, work information of historical persons in charge, and a label of the best person in charge in the historical material data.
[0047] S302: Perform data preprocessing on the sample data set and divide it into a training set and a test set.
[0048] S303: Training a pre-built neural network model based on the historical material data in the training set, and optimizing model parameters to obtain a material matching model.
[0049] S304: Testing the material matching model and calibrating model parameters based on the test set until the loss function of the model meets a preset condition or the number of model iterations reaches a preset number, thereby obtaining a trained material matching model.
[0050] Specifically, the trained model can learn how to predict the best person in charge based on material characteristics and person in charge information. The optimized model parameters enable the model to match more accurately, improving the efficiency and accuracy of material management allocation.
[0051] In one embodiment, if Figure 5 As shown, in step S304, the material matching model is tested and model parameters are calibrated based on the test set, specifically including: S3041: Establishing a test model according to the historical material data in the test set, evaluating the performance index of the material matching model based on the test model and correcting model parameters; S3042: Among them, the test indicators of the test model include accuracy, recall rate and F1 value.
[0052] Specifically, after test set verification and model tuning, a material matching model with high accuracy, high recall rate and high F1 value can be obtained, which can be used in actual construction material management to achieve efficient and accurate material allocation.
[0053] In one embodiment, if Figure 6 As shown, in step S40, the material information to be managed is generated according to the material task request and the best person in charge, specifically including: S41: Based on the material characteristics of the material task request, traverse a preset template library to determine a material allocation template corresponding to the material task request.
[0054] S42: Automatically fill in the basic information of the material task request and the best person in charge in the material allocation template to obtain the material information to be managed.
[0055] Specifically, traversing the preset template library and automatically filling in material allocation information speeds up the generation of information on materials to be managed, reduces the workload and error probability of manual input, and improves the efficiency and accuracy of material allocation.
[0056] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0057] In one embodiment, a construction material supervision system based on data analysis is provided, and the construction material supervision system based on data analysis corresponds one-to-one to the construction material supervision method based on data analysis in the above embodiment. Figure 7 As shown in the figure, the construction material supervision system based on data analysis includes a material characteristic extraction module, a management personnel analysis module, a material allocation module and an information upload module. The detailed description of each functional module is as follows: A material property extraction module is used to receive a material task request, extract material properties according to the material task request, and obtain the material properties of the material task request; A manager analysis module is used to traverse the built-in person in charge database to obtain the work information of each person in charge; wherein the work information includes work skills and current material management load; A material allocation module is used to determine the best person in charge based on the material characteristics and the work information of each person in charge based on a machine learning algorithm; generate materials to be managed and allocate them to the best person in charge based on the material task request and the best person in charge; The information uploading module is used to receive the order confirmation request from the best person in charge, obtain the progress status and completion status of the material to be managed, and upload them to the material management library.
[0058] Optionally, the material characteristics extraction module includes: A basic information acquisition submodule is used to acquire basic information of the material task request; wherein the basic information includes material description and expected management time; A key feature extraction submodule is used to perform natural language processing analysis based on the material description of the material task request to extract key features; The material type determination submodule is used to determine the material type based on the extracted key features and the classification algorithm.
[0059] For the specific limitations of the construction material supervision system based on data analysis, please refer to the limitations of the construction material supervision method based on data analysis above, which will not be repeated here. Each module in the above-mentioned construction material supervision system based on data analysis can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0060] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store highway construction data, a constructed target BIM model and a highway construction disease knowledge base. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a construction material supervision method based on data analysis is implemented.
[0061] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Receiving a material task request, extracting material characteristics according to the material task request, and obtaining material characteristics of the material task request; Traverse the built-in responsible person database to obtain the work information of each responsible person; wherein the work information includes work skills and current material management load; Based on the machine learning algorithm, the best person in charge is determined according to the material characteristics and the work information of each person in charge; According to the material task request and the best person in charge, generate materials to be managed and assign them to the best person in charge; Receive the order confirmation request from the best person in charge, obtain the progress status and completion status of the material to be managed, and upload them to the material management library.
[0062] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Receiving a material task request, extracting material characteristics according to the material task request, and obtaining material characteristics of the material task request; Traverse the built-in responsible person database to obtain the work information of each responsible person; wherein the work information includes work skills and current material management load; Based on the machine learning algorithm, the best person in charge is determined according to the material characteristics and the work information of each person in charge; According to the material task request and the best person in charge, generate materials to be managed and assign them to the best person in charge; Receive the order confirmation request from the best person in charge, obtain the progress status and completion status of the material to be managed, and upload them to the material management library.
[0063] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0064] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
[0065] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.
Claims
1. A construction material supervision method based on data analysis, characterized in that: The construction material supervision method based on data analysis comprises the following steps: Receiving a material task request, extracting material characteristics according to the material task request, and obtaining material characteristics of the material task request; Traverse the built-in responsible person database to obtain the work information of each responsible person; wherein the work information includes work skills and current material management load; Based on the machine learning algorithm, the best person in charge is determined according to the material characteristics and the work information of each person in charge; According to the material task request and the best person in charge, generate materials to be managed and assign them to the best person in charge; Receive the order confirmation request from the best person in charge, obtain the progress status and completion status of the material to be managed, and upload them to the material management library.
2. The construction material supervision method based on data analysis according to claim 1 is characterized in that: The extracting of material characteristics according to the material task request specifically includes: Obtaining basic information of the material task request; wherein the basic information includes material description and expected management time; Perform natural language processing analysis based on the material description of the material task request to extract key features; Determine the material type based on the extracted key features and classification algorithm; Determine the urgency based on the expected management time of the material task request; The material characteristics of the material task request are obtained according to the material type and the urgency.
3. The construction material supervision method based on data analysis according to claim 1 is characterized in that: The method based on the machine learning algorithm, determining the best person in charge according to the material characteristics and the work information of each person in charge, specifically includes: Input the material characteristics and the work information of each person in charge into a pre-trained material matching model to match and obtain the best person in charge corresponding to the material characteristics; The material matching model is pre-trained based on historical material data, and the historical material data includes historical material characteristics, work information of historical persons in charge, and the label of the best person in charge.
4. The construction material supervision method based on data analysis according to claim 3 is characterized in that: Before inputting the material characteristics and the work information of each person in charge into the pre-trained material matching model to match the best person in charge corresponding to the material characteristics, the construction material supervision method based on data analysis also includes: Acquire a sample data set; wherein the sample data set includes historical material characteristics, work information of historical persons in charge, and a label of the best person in charge in the historical material data; Performing data preprocessing on the sample data set and dividing it into a training set and a test set; Training a pre-built neural network model based on the historical material data in the training set, and optimizing model parameters to obtain a material matching model; The material matching model is tested and model parameters are corrected based on the test set until the loss function of the model meets a preset condition or the number of model iterations reaches a preset number, thereby obtaining a trained material matching model.
5. The construction material supervision method based on data analysis according to claim 4 is characterized in that: The testing of the material matching model and the correction of model parameters based on the test set specifically include: A test model is established according to the historical material data in the test set, and the performance index of the material matching model is evaluated and the model parameters are corrected based on the test model; wherein the test indicators of the test model include accuracy, recall rate and F1 value.
6. The construction material supervision method based on data analysis according to claim 1 is characterized in that: The generating of the material information to be managed according to the material task request and the best person in charge specifically includes: Based on the material characteristics of the material task request, traverse a preset template library to determine a material allocation template corresponding to the material task request; The basic information of the material task request and the best person in charge are automatically filled in the material allocation template to obtain the information of the materials to be managed.
7. A construction material supervision system based on data analysis, characterized in that: The construction material supervision system based on data analysis includes: A material property extraction module is used to receive a material task request, extract material properties according to the material task request, and obtain the material properties of the material task request; A manager analysis module is used to traverse the built-in person in charge database to obtain the work information of each person in charge; wherein the work information includes work skills and current material management load; A material allocation module is used to determine the best person in charge based on the material characteristics and the work information of each person in charge based on a machine learning algorithm; generate materials to be managed and allocate them to the best person in charge based on the material task request and the best person in charge; The information uploading module is used to receive the order confirmation request from the best person in charge, obtain the progress status and completion status of the material to be managed, and upload them to the material management library.
8. The construction material supervision system based on data analysis according to claim 7 is characterized in that: The material characteristics extraction module includes: A basic information acquisition submodule is used to acquire basic information of the material task request; wherein the basic information includes material description and expected management time; A key feature extraction submodule is used to perform natural language processing analysis based on the material description of the material task request to extract key features; The material type determination submodule is used to determine the material type based on the extracted key features and the classification algorithm.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the construction material supervision method based on data analysis as described in any one of claims 1 to 6 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a construction material supervision method based on data analysis as described in any one of claims 1 to 6 are implemented.