Digital residence construction service fusion method and system based on one-network unified management

Abstract

The invention relates to the technical field of data fusion, in particular to a digital residence construction business fusion method and system based on one-network unified management, and the method comprises the following steps: carrying out the synchronous analysis of project time nodes based on the full life cycle data of an engineering project, associating engineering approval and construction data, and generating the basic information of engineering construction. According to the method, through synchronous analysis of the full-life-cycle data of the engineering project, engineering approval and construction data are associated, and the timeliness and accuracy of the information flow are remarkably improved. Through deep correlation analysis of the transaction chain and the accumulation fund application, more accurate housing and accumulation fund information is formed, and the checking burden in the approval process is reduced. Based on dynamic updating of geographic data and a building information model, real-time monitoring of the operation state of the urban construction project is achieved, and resource allocation and approval paths are more accurate. The prediction of the approval time node and the flow optimization enable the approval process to be more efficient, and can flexibly cope with the resource demand difference in the project progress.

Description

Technical Field

[0001] The present invention relates to the technical field of data fusion, and in particular to a method and system for integrating digital housing and construction services based on one-network unified management. Background Art

[0002] The approval process of various housing and construction business systems has undergone several rounds of iterations. Some systems with Guijianyun as the core have completed data interconnection, but most business systems are still in independent operation, and there are still major differences in the content and direction of various approval flows.

[0003] Existing technical solutions fail to solve the difficulties in the process of information exchange and sharing. Especially in the vertical serial approval process, there are certain problems in the approval process of grassroots departments at the municipal, county and district levels, resulting in the inability of information flow to be in place in a timely manner. When making an application, it is often necessary to prepare the original and copy of the party’s ID card, the original and copy of the company’s business certificate, etc. The approval personnel need to further verify the authenticity of the documents. There are certain difficulties in retrieving information across platforms, and it is difficult to use it quickly in the approval process. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a digital housing construction business integration method and system based on one-network unified management.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a digital housing construction service integration method based on one network management, comprising the following steps:

[0006] S1: Based on the data of the entire life cycle of the project, synchronous analysis of project time nodes is carried out, linking project approval and construction data to generate basic information of project construction;

[0007] S2: By collecting provident fund payment information, housing property rights and transaction status data, we conduct corresponding analysis of the transaction chain and participating roles, link the application of provident funds in real estate transactions, and generate basic housing and provident fund information;

[0008] S3: Based on GIS urban geographic data, combined with BIM, the data is modeled and dynamically updated to evaluate the operational status of urban construction projects and generate basic information on urban construction;

[0009] S4: Based on the basic information of engineering construction, basic information of housing and provident fund, and basic information of urban construction, predict the time node distribution of approval, adjust the approval path and node sequence, and generate the approval process adjustment result;

[0010] S5: Based on the approval process adjustment result, analyze the resource demand differences in each time period, adjust the use of human and computing resources according to current needs, and generate resource adjustment configuration;

[0011] S6: Based on the approval process adjustment results and resource adjustment configuration, analyze the deviation between approval progress and resource allocation, adjust approval nodes and resource allocation, and generate approval and resource optimization results.

[0012] The present invention has the following improvements: the basic information of engineering construction includes project category information, project location coordinates and construction permit information, construction unit qualifications, project investment scale and project progress; the basic information of housing and provident fund includes provident fund payment amount, provident fund usage record and house transaction process status, identity information of buyer and seller, loan approval status and related mortgage information; the basic information of urban construction includes the operation status of urban infrastructure, new urbanization project data and the progress of renovation of old residential areas, location of urban facilities, service life of facilities and maintenance records; the adjustment result of the approval process includes the node sequence after the approval path is adjusted, the approval time prediction and resource allocation for each link; the resource adjustment configuration includes the human resource deployment in each time period, the calculation resource usage and resource idle rate analysis; the approval and resource optimization results include the adjustment of real-time approval nodes, dynamic monitoring data of resource usage and deviation analysis of approval progress.

[0013] The present invention is improved in that, based on the full life cycle data of the engineering project, the specific steps of performing synchronous analysis of the project time nodes, associating the engineering approval and construction data, and generating the basic information of the engineering construction are as follows:

[0014] S101: Based on the entire life cycle of the project, preliminary sorting of project information is performed to obtain project classification, location and license information, and relevant licenses are checked with project content to generate a project information data set;

[0015] S102: Based on the project information data set, extract the time node information of the project, perform time comparison according to the order of project progress, mark the project progress deviation, and generate project time node analysis data;

[0016] S103: Based on the project time node analysis data, dynamically adjust the priority order of each project, reallocate approval and construction resources, optimize the order of project execution and resource arrangement, and generate basic information for engineering construction.

[0017] The present invention is improved in that, by collecting provident fund payment information, house property rights and transaction status data, corresponding analysis of transaction chains and participating roles is performed, and the application of provident funds in real estate transactions is associated. The specific steps of generating basic information on housing and provident funds are as follows:

[0018] S201: Based on the provident fund payment information and the house property data, classify the data, collect the associated information with the roles in the transaction chain, match the provident fund data with the house property data, and generate a transaction chain data set;

[0019] S202: Based on the transaction chain data set, analyze the provident fund usage of the role in the transaction process, match the transaction status with the role operation behavior, and generate provident fund transaction related data;

[0020] S203: Based on the provident fund transaction-related data, verify the circulation and use of the provident fund of the transaction node, collect the provident fund data of all transaction roles, and generate basic information on housing and provident fund.

[0021] The present invention has the following improvements: based on GIS urban geographic data, combined with BIM, the data is modeled and dynamically updated, the operation status of urban construction projects is evaluated, and the specific steps of generating basic information of urban construction are as follows:

[0022] S301: Based on GIS urban geographic data, collect the geographical location and attribute information of urban infrastructure, extract information on urbanization projects and renovation of old communities, and generate urban infrastructure data sets through standardized processing of data;

[0023] S302: Based on the urban infrastructure data set, the project geographic information is associated with the facility attributes, and the BIM model is constructed in combination with the current operation status of the project to generate urban project operation data;

[0024] S303: Based on the urban project operation data, monitor the operation status of facilities, continuously update the geographic information and operation status of the project, process the dynamic changes of the model, and generate basic information for urban construction.

[0025] The present invention is improved in that, based on the basic information of engineering construction, basic information of housing and provident fund, and basic information of urban construction, the specific steps of predicting the time node distribution of approval, adjusting the approval path and node sequence, and generating the approval process adjustment result are as follows:

[0026] S401: Based on the basic information of engineering construction, basic information of housing and provident fund, and basic information of urban construction, collect the time, results, and data of participants in the approval process, organize the data of each approval node, classify the processing time of the approval link, and generate approval time node distribution data;

[0027] S402: Based on the approval time node distribution data, analyze the time of the approval nodes, mark the time distribution of key nodes, which are nodes reviewed by multiple departments, nodes that rely on external information, and approval links that require signatures, predict the processing time of each approval link, adjust the delays in the approval process, build a time analysis model, and obtain approval time prediction data;

[0028] S403: Based on the approval time prediction data, optimize resource allocation according to the importance and time requirements of the approval nodes, and generate an approval process adjustment result.

[0029] The present invention has the following improvements: for predicting the processing time of each approval link, the formula is used:

[0030]

[0031] Calculate the predicted processing time T' p ;

[0032] Among them, T' i represents the actual processing time of the i-th similar approval link in the historical data, W i Represents the importance weight of each approval link, L i Represents the influencing factor of the approval process complexity, E i It represents the degree of dependence on external factors in the approval process, and n represents the total number of approval nodes.

[0033] The present invention is improved in that, based on the approval process adjustment result, the resource demand difference in each time period is analyzed, and the use of human and computing resources is adjusted according to the current demand. The specific steps of generating resource adjustment configuration are as follows:

[0034] S501: Based on the approval process adjustment result, the resource usage of past approval nodes is called, the consumption data of manpower and computing resources of each approval node is collected, node resource usage analysis is performed, and approval node resource usage data is generated;

[0035] S502: Analyze the resource demand in each time period based on the approval node resource usage data, divide the time periods with intensive resource usage and sparse resource usage, mark the resource demand intensity of each time period, and generate resource demand distribution data;

[0036] S503: Based on the resource demand distribution data, adjust the use of manpower and resources according to the resource demand of the approval node, optimize the resource allocation plan, and generate a resource adjustment configuration.

[0037] The present invention is improved in that the specific steps of analyzing the deviation between approval progress and resource allocation, adjusting approval nodes and resource allocation, and generating approval and resource optimization results based on the approval process adjustment results and resource adjustment configuration are as follows:

[0038] S601: Based on the approval process adjustment result and resource adjustment configuration, monitor the resource consumption of nodes in the approval process in real time, collect real-time data on approval progress and resource usage, mark the difference between resource allocation and usage, and generate approval resource monitoring data;

[0039] S602: Based on the approval resource monitoring data, analyzing the deviation between the approval progress and resource allocation, locating nodes with insufficient or excessive resource allocation, performing resource and process matching analysis, adjusting resource allocation, and generating resource allocation adjustment data;

[0040] S603: Based on the resource allocation adjustment data, the progress of the approval process and resource usage are regulated in real time to generate approval and resource optimization results.

[0041] A digital housing and construction business integration system based on one-network unified management, the system comprising:

[0042] The data collection module collects project time nodes based on the data of the entire life cycle of the project, associates the project approval and construction data, integrates the provident fund payment information, house property rights and transaction status data, analyzes the corresponding relationship between the transaction chain and the participating roles, and generates basic information on the project and real estate;

[0043] The project management module integrates GIS urban geographic data based on the basic data of the project and real estate, and combines BIM to build a three-dimensional city model, binds the status information of each type of project and facility with the geographical location, and generates basic information for urban construction;

[0044] The approval optimization module, based on the basic information of urban construction, calls the information of engineering construction and real estate transactions, analyzes the time nodes of the approval process, determines the processing time of the approval link, sorts them according to the node priority, and generates approval process adjustment data;

[0045] The resource scheduling module adjusts the approval process data based on the approval process, calls the resource usage data of the past approval nodes, analyzes the resource demand in each time period, optimizes the resource control strategy, and generates resource adjustment configuration;

[0046] The dynamic monitoring module adjusts the approval process data and resource adjustment configuration based on the approval process, records the deviation between the approval progress and resource allocation, adjusts the matching of approval nodes and resources, and generates approval and resource optimization results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by synchronously analyzing the data of the entire life cycle of the project, the project approval and construction data are associated, which significantly improves the timeliness and accuracy of the information flow and avoids the problem of information delay. Through in-depth correlation analysis of the transaction chain and the application of provident fund, more accurate housing and provident fund information is formed, reducing the burden of verification during the approval process. Based on the dynamic update of geographic data and building information model, real-time monitoring of the operating status of urban construction projects is realized, making resource allocation and approval paths more accurate. The prediction and process optimization of approval time nodes make the approval process more efficient, and can flexibly respond to differences in resource requirements during project progress, ensuring that the approval progress is consistent with resource allocation. The real-time adjustment of approval process and resource allocation effectively reduces the delays and waste caused by resource mismatch or unreasonable approval node settings, thereby improving the overall approval efficiency and shortening the cycle of project approval. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention proposes a flow chart of a digital housing construction business integration method based on one-network unified management;

[0050] Figure 2 This is a detailed flow chart of step S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of step S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of step S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of step S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of step S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of step S6 of the present invention;

[0056] Figure 8 The present invention proposes a module diagram of a digital housing and construction business integration system based on one-network management. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution: a digital housing construction service integration method based on one-network unified management, comprising the following steps:

[0060] S1: Based on the data of the entire life cycle of the project, synchronous analysis of project time nodes is carried out, linking project approval and construction data to generate basic information of project construction;

[0061] S2: By collecting provident fund payment information, housing property rights and transaction status data, we conduct corresponding analysis of the transaction chain and participating roles, link the application of provident funds in real estate transactions, and generate basic housing and provident fund information;

[0062] S3: Based on GIS urban geographic data, combined with BIM, the data is modeled and dynamically updated to evaluate the operational status of urban construction projects and generate basic information on urban construction;

[0063] S4: Based on the basic information of engineering construction, housing and provident fund, and urban construction, predict the time node distribution of approval, adjust the approval path and node sequence, and generate the approval process adjustment results;

[0064] S5: Based on the approval process adjustment results, analyze the resource demand differences in each time period, adjust the use of human and computing resources according to current needs, and generate resource adjustment configuration;

[0065] S6: Based on the approval process adjustment results and resource adjustment configuration, analyze the deviation between approval progress and resource allocation, adjust approval nodes and resource allocation, and generate approval and resource optimization results.

[0066] The basic information of engineering construction includes project category information, project location coordinates and construction permit information, construction unit qualifications, project investment scale and project progress. The basic information of housing and provident fund includes provident fund contribution amount, provident fund usage record and house transaction process status, identity information of buyer and seller, loan approval status and related mortgage information. The basic information of urban construction includes the operating status of urban infrastructure, new urbanization project data and the progress of old community renovation, urban facility location, facility service life and maintenance records. The results of approval process adjustment include the node sequence after the adjustment of approval path, approval time prediction and resource allocation for each link. Resource adjustment configuration includes the human resource allocation in each time period, calculation of resource usage and resource idle rate analysis. The results of approval and resource optimization include the adjustment of real-time approval nodes, dynamic monitoring data of resource usage and deviation analysis of approval progress.

[0067] See also Figure 2 Based on the data of the entire life cycle of the project, the specific steps for synchronous analysis of project time nodes, linking project approval and construction data, and generating basic information of engineering construction are as follows:

[0068] S101: Based on the entire life cycle of the project, preliminary sorting of project information is performed to obtain project classification, location and license information, and relevant licenses are checked with project content to generate a project information data set;

[0069] Collect and summarize relevant basic data of various projects, including project classification, geographical location and project-related license information, extract this information from different sources and unify the format to ensure data integrity and consistency, especially check whether the project license information matches the actual situation of the project, further screen out missing or inconsistent data, merge data from different sources through rule matching algorithms, mark projects with problems, cross-check license information with project content, find out licensing matters that are inconsistent with the project execution plan, use fuzzy matching methods to correct some incompletely matched data, adjust project files that do not meet the requirements, ensure that all projects operate within the scope of the license and make corrected updates.

[0070] S102: Based on the project information data set, extract the time node information of the project, perform time comparison according to the order of project progress, mark the project progress deviation, and generate project time node analysis data;

[0071] Through the automatically recorded progress data, the actual duration of the project is compared with the planned duration one by one, the progress difference of each project is calculated, and the projects that are completed in excess or ahead of schedule are marked. The projects that exceed the time are put into the delay processing list, and the deviation days are recorded. For calculating the project progress deviation value, according to the formula D = T a -Tp , where D represents the project progress deviation value, T a Represents the actual completion time of the project, T p Represents the planned completion time of the project.

[0072] Obtain T through the duration data in the project plan file p , actual completion time T a It is obtained through the actual data records in the project management system. Substitute the value into the formula to calculate the project deviation value. The planned completion time of a project is 100 days, and the actual completion time is 110 days. The deviation value is D = 110-100 = 10, indicating that the project has a delay of 10 days.

[0073] S103: Based on the project time node analysis data, dynamically adjust the priority order of each project, reallocate approval and construction resources, optimize the order and resource arrangement of project execution, and generate basic information for engineering construction;

[0074] Divide projects into different processing levels according to priority, give priority to projects with higher resource requirements and shorter time delays, mark key projects, and allocate resources to these projects first, adjust the construction and approval processes to ensure maximum resource utilization, and dynamically adjust the priority order of each project according to the formula: Calculate project priorities and reallocate approval and construction resources. In the formula, P represents project priority, R represents project resource requirements, and T s Represents the planned end time of the project, T e Represents the actual completion time of the project.

[0075] The project priority is calculated based on the actual resource requirements of the project and the project plan and actual completion time. The resource requirements R are obtained through the resource input in the project management system, and the planned end time T s and the actual end time T e Obtained through project information records. If a project's resource requirement is 100, the planned end time is 120 days, and the actual completion time is 130 days, the priority calculation is The results show that lower priority projects will be delayed.

[0076] See also Figure 3 By collecting provident fund payment information, housing property rights and transaction status data, conducting corresponding analysis of transaction chains and participating roles, and associating the application of provident funds in real estate transactions, the specific steps for generating basic housing and provident fund information are as follows:

[0077] S201: Based on the provident fund payment information and the house property data, classify the data, collect the associated information with the roles in the transaction chain, match the provident fund data with the house property data, and generate a transaction chain data set;

[0078] Provident fund payment information and house property data are extracted from different source systems to ensure that each data set contains complete user information, payment information and house transaction information. The format of the extracted data is unified, and missing and abnormal data are removed using a data cleaning algorithm. Provident fund payment records and house property information are mapped and matched through user IDs to ensure that each record has a unique user ID. The roles involved in the transaction chain are then associated, and the operation information of different roles related to house transactions is obtained through a user behavior matching algorithm. The operation behavior is combined with the user's provident fund payment status to ensure that the behavior of each role corresponds to the corresponding provident fund record in the transaction chain. The provident fund information corresponding to each role in the transaction chain is recorded, and a mapping relationship is formed between the house property data and the provident fund data, and the role behaviors are associated.

[0079] S202: Based on the transaction chain data set, analyze the role's housing fund usage during the transaction process, match the transaction status with the role's operation behavior, and generate housing fund transaction related data;

[0080] Through the provident fund usage records of each transaction node in the system, the number of times the provident fund is used in each node is counted, and the usage frequency of all provident funds is distributed and analyzed by transaction nodes. The more frequently used nodes are, the greater their importance in the transaction chain. Nodes with a provident fund usage frequency of more than 50% are marked as key nodes, and the frequency distribution model is used to conduct an overall analysis of the use of provident funds. For calculating the usage frequency of provident funds in transaction nodes, according to the formula In the formula, F represents the application frequency of the provident fund, U represents the number of times the provident fund is used, and N represents the total number of transaction nodes. The number of times the provident fund is used is obtained through the records of provident fund withdrawal or loan use recorded in the system, and the total number of transaction nodes N is obtained by counting the number of nodes involved in the entire transaction process. Assuming that the total number of nodes involved in a transaction chain is 50 times and the number of times the provident fund is used is 30 times, the application frequency of the provident fund can be calculated by substituting it into the formula: The results show that 60% of the nodes used provident funds during the transaction process.

[0081] S203: Based on the provident fund transaction-related data, verify the circulation and use of the provident fund of the transaction node, collect the provident fund data of all transaction roles, and generate basic information on housing and provident funds;

[0082] According to the provident fund usage records in each transaction node, the provident fund circulation data of each node is extracted, the circulation data is arranged in chronological order of the nodes, and the time series algorithm is used to compare the abnormal situations in the provident fund circulation process, especially the cases of repeated use or unauthorized use. The circulation data of each transaction node is checked with the actual usage record, and the transaction nodes that may have problems are marked. Further verification is carried out on the problem nodes to ensure that each use of the provident fund has legal authorization and corresponding housing transaction behavior. Then, the provident fund usage data of all transaction roles are collected, and the total amount of provident fund usage of each role in the entire transaction process is summarized to verify the circulation path and usage node of each fund.

[0083] See also Figure 4 Based on GIS urban geographic data, combined with BIM, the data model is constructed and dynamically updated to evaluate the operating status of urban construction projects and generate basic information on urban construction. The specific steps are as follows:

[0084] S301: Based on GIS urban geographic data, collect the geographical location and attribute information of urban infrastructure, extract information on urbanization projects and renovation of old communities, and generate urban infrastructure data sets through standardized processing of data;

[0085] Extract the coordinate information and related attribute data of each infrastructure, perform preliminary data cleaning, remove incomplete or duplicate data, and use the standardized algorithm of the geographic information system to process the data to ensure the consistency of the format of all geographic location and attribute information. Then extract the urbanization projects and old community renovation information related to the facilities, associate this information with the attribute information of the facilities according to the geographic coordinates, and use the spatial data matching algorithm to match the project location with the facility information one by one to ensure the completeness of the geographic location of each project and the information of the related facilities. Finally, organize all the processed geographic and attribute data into a standardized database.

[0086] S302: Based on the urban infrastructure dataset, the project geographic information is associated with the facility attributes, and the BIM model is constructed in combination with the current operation status of the project to generate urban project operation data;

[0087] Use coordinates to calculate the distance between the facility and the project, combine these geographic locations with facility attribute information, and then use the BIM model to perform three-dimensional modeling of the attributes and location of each facility. Update the model status based on the current operation of the facility, record the operating parameters of the facility, and associate the project geographic information with the facility attributes according to the formula Calculate the distance between the facility and the project location and establish a geographical association, where R represents the distance between the facility and the project, and x 1 ,y 1Represents the coordinates of the facility, x 2 ,y 2 Represents the coordinates of the project. Use the geographic coordinates of each facility and project to calculate the distance between them. The coordinates of the facility are x 1 ,y 1 and the coordinates x of the item 2 ,y 2 The spatial distance between the project and the facility is calculated by GIS system extraction. If the coordinates of a facility are (10,20) and the coordinates of the project are (15,25), the distance calculation is: The results show that the distance between the facility and the project is 7.07 units.

[0088] S303: Based on the urban project operation data, monitor the operation status of facilities, continuously update the geographic information and operation status of the project, process the dynamic changes of the model, and generate basic information for urban construction;

[0089] Conduct status monitoring on the completed BIM model, extract the current operating parameters of the facility, including power consumption, water use and maintenance, collect these real-time data through the sensor system and store them in the operation database, use the time series analysis algorithm to monitor and warn the operation data of the facility, mark the abnormal operation status of the equipment or exceed the preset threshold, and then dynamically adjust the operation status of the facility according to the abnormal situation, and ensure that the continuous changes of the model are consistent with the current status of the facility by updating the geographic and operational information in the BIM model in real time. Finally, store all monitored and updated data in the database.

[0090] See also Figure 5 , based on the basic information of engineering construction, basic information of housing and provident fund, and basic information of urban construction, the specific steps of predicting the time node distribution of approval, adjusting the approval path and node sequence, and generating the approval process adjustment results are as follows:

[0091] S401: Based on the basic information of engineering construction, basic information of housing and provident fund, and basic information of urban construction, collect the time, results, and data of participants in the approval process, organize the data of each approval node, classify the processing time of the approval link, and generate the approval time node distribution data;

[0092] Extract data from each system on each link involved in each approval process, including the approval start time, end time, approval or failure, and operation records of the participants. Then classify the extracted data. According to the specific link type of each approval node, classify the processing time and results of different nodes. Use the approval node classification algorithm to identify and label the type of each link, focus on distinguishing between ordinary approval nodes and key approval nodes, sort the processing time of each approval node, and finally summarize and organize the processing time of all approval links.

[0093] S402: Based on the approval time node distribution data, analyze the time of the approval nodes, mark the time distribution of key nodes, which are nodes reviewed by multiple departments, nodes that rely on external information, or approval links that require signatures, predict the processing time of the approval links, adjust the delays in the approval process, build a time analysis model, and obtain approval time prediction data;

[0094] According to the processing time of historical approval links, combined with the weights of different nodes, process complexity and external dependence, the processing time of each node is predicted. Nodes that require the participation of multiple departments and external information support are given higher weights and influence factors, so as to obtain more accurate time prediction results. The formula for predicting the processing time of each approval link is used:

[0095]

[0096] Calculate the predicted processing time T' p ;

[0097] Among them, T' i represents the actual processing time of the ith similar approval link in the historical data, which is usually obtained through the approval history database. These data are usually collected by the automatic recording function of the approval system and reflect the past processing time of each node; W i Represents the importance weight of each approval link, indicating the priority and influence of this link in the approval process. The importance weight W i It can be set according to the complexity of the node's responsibilities, the level of the reviewer, or the criticality of the approval node's process, usually determined through historical data analysis and expert judgment; i Represents the influencing factor of the complexity of the approval process, measuring the complexity of the approval process. The complexity influencing factor L i It is usually determined by analyzing whether the node involves multi-department collaboration, whether additional documents or procedures are required, and the difficulty of the approval process. Complex processes will have higher L i Value, E iRepresents the degree of reliance on external factors in the approval process. It is mainly used to quantify the degree of reliance of a node on external information, systems or personnel, especially for approval links that require signatures and third-party information. The external dependency factor E i By analyzing the external dependencies of similar links in the past, it is determined that nodes with high external information dependence will have higher E i Value, n represents the total number of approval nodes, which is the total number of nodes involved in the approval process. This value is obtained through the approval flowchart or approval node list.

[0098] There are three historical approval stages, with actual processing times of 20 minutes, 30 minutes, and 40 minutes respectively. The corresponding weights are W i Set the complexity factor L to 1, 2, and 1 i is 1.1, 1.2 and 1, the external dependency factor E i is 1.0, 1.5 and 1.0, then the formula is:

[0099]

[0100] The results show that the predicted processing time is 37.78 minutes.

[0101] S403: Based on the approval time prediction data, optimize resource allocation according to the importance and time requirements of the approval nodes, and generate approval process adjustment results;

[0102] Based on the predicted processing time of the approval nodes, the key nodes that need to be focused on in the approval process are identified, and the order of the approval process is optimized using the critical path algorithm. The priority of each node is sorted according to its importance, and the nodes with long approval time and requiring collaboration from multiple people are marked as high-priority nodes. Then, resources are allocated based on the time requirements of the approval nodes. Combined with the resource usage of the nodes, more manpower and computing resources are allocated to key nodes to ensure that these nodes can be processed first, and the allocation plan of approval resources is adjusted. Finally, the order of the approval process is readjusted based on the optimized resource configuration.

[0103] See also Figure 6 ,Based on the results of the approval process adjustment, analyze the differences in resource requirements in each time period, adjust the use of human and computing resources according to current needs, and generate the specific steps of resource adjustment configuration as follows:

[0104] S501: Based on the approval process adjustment result, the resource usage of past approval nodes is called, the consumption data of manpower and computing resources of each approval node is collected, the node resource usage is analyzed, and the approval node resource usage data is generated;

[0105] The human resource and computing resource consumption data of each approval node are extracted from the approval system. The human working hours and system load records in the resource monitoring records are used as the data source. The consumption data of each node is cleaned, and incomplete or duplicate data are eliminated. The human resource consumption and computing resource usage are quantified separately. Combined with the approval type and workload of each node, the resource usage is preliminarily analyzed through the K-means clustering algorithm. The algorithm divides the approval nodes into different groups according to their resource consumption characteristics, marks the nodes with higher consumption, and classifies them according to the type of resource usage to ensure that the approval nodes with similar resource requirements are allocated to the appropriate resource groups.

[0106] S502: Analyze resource demand in each time period based on the approval node resource usage data, divide the time periods with intensive resource usage and sparse resource usage, mark the resource demand intensity of each time period, and generate resource demand distribution data;

[0107] To analyze the resource requirements in each time period, according to the formula Calculate the resource demand intensity for each time period, where: Q f represents the resource demand intensity, indicating the level of demand for resources in a specified period of time, j represents the total amount of resources used by the jth approval node, including the sum of human resources and computing resources. This value is extracted from the system through historical resource usage data; C j Represents the priority of each approval node, reflecting the importance of the node in the approval process. This value can be determined based on the node's function, complexity, and past processing time; D j ' represents the workload of the node, which measures the number of tasks actually processed by the node during the approval process. This value is obtained through the number of tasks or approval workload data recorded by the approval system. Z represents the duration of the time period, usually in minutes or hours.

[0108] The resource usage of the three approval nodes in a certain period of time is 100, 150 and 200 respectively, the total duration is 8 hours, the respective priorities are 1, 2 and 1, and the workloads are 3, 2 and 4 respectively. Substitute the formula into the calculation to get:

[0109]

[0110] The results show that the resource demand intensity is 212.5 units. In order to determine the intensive or sparse state of demand, Q is usually calculated in different time periods. f If the resource demand intensity of a certain time period is higher than that of other time periods, then this time period is considered as the peak time period of resource usage, while the time period below the average level is considered as the sparse time period.

[0111] S503: Based on the resource demand distribution data, adjust the use of manpower and resources according to the resource demand of the approval node, optimize the resource allocation plan, and generate resource adjustment configuration;

[0112] Marked resource-intensive and sparse time periods are extracted from the system, and the resource demand data is analyzed through a genetic algorithm combined with the priority and task load of the approval node. The algorithm simulates the natural selection process, generates multiple possible resource allocation schemes, and gradually optimizes resource allocation through "selection, crossover, and mutation" operations. Then, according to the resource demand intensity of each time period, human and computing resources are allocated to time periods with higher demand intensity in priority. In particular, for periods of intensive resource use, a resource balancing and allocation algorithm is used to reallocate resources to reduce the occurrence of resource bottlenecks.

[0113] See also Figure 7 , based on the approval process adjustment results and resource adjustment configuration, analyze the deviation between approval progress and resource allocation, adjust approval nodes and resource allocation, and generate approval and resource optimization results. The specific steps are as follows:

[0114] S601: Based on the approval process adjustment results and resource adjustment configuration, monitor the resource consumption of nodes in the approval process in real time, collect real-time data on approval progress and resource usage, mark the differences between resource allocation and usage, and generate approval resource monitoring data;

[0115] Through the monitoring tools and logs in the system, the real-time usage data of human resources, computing resources and other key resources of each approval node are collected. A sliding window is used to capture the resource consumption of each node at set time intervals. The resource consumption in continuous time periods is recorded and aggregated. These data are used to compare resource allocation plans, analyze the actual resource usage of nodes, and mark nodes with excessive or insufficient resources.

[0116] S602: Based on the approval resource monitoring data, analyze the deviation between the approval progress and resource allocation, locate the nodes with insufficient or excessive resource allocation, perform resource and process matching analysis, adjust resource allocation, and generate resource allocation adjustment data;

[0117] To analyze the deviation of approval progress and resource allocation, according to the formula Calculate the average resource deviation of all nodes in the approval process, where: avg Represents the average deviation of resource allocation in the approval process, O' k represents the actual resource usage of the kth approval node, obtained from the real-time monitoring data in the system, G k It represents the planned resource allocation of the kth approval node, extracted from the allocation plan, and n' is the total number of approval nodes.

[0118] There are three approval nodes with actual usage of 110, 130 and 160, and planned resources of 100, 140 and 170. The calculation is as follows:

[0119]

[0120] The results show that the average resource deviation is 10 units, which means that there is a certain difference between the actual use of resources and the planned allocation.

[0121] S603: Based on the resource allocation adjustment data, the progress of the approval process and resource usage are regulated in real time to generate approval and resource optimization results;

[0122] The priority scheduling algorithm is used to allocate resources. Based on the priority and resource requirements of each approval node, the algorithm allocates resources to high-priority nodes with scarce resources first, and reduces the resource allocation of low-priority nodes to ensure that critical task nodes can obtain sufficient resources. The system makes dynamic adjustments based on real-time resource usage and node priority, and combines historical resource usage data.

[0123] See also Figure 8 , a digital housing and construction business integration system based on one network management, the system includes:

[0124] The data collection module collects project time nodes based on the data of the entire life cycle of the project, associates the project approval and construction data, integrates the provident fund payment information, house property rights and transaction status data, analyzes the corresponding relationship between the transaction chain and the participating roles, and generates basic information on the project and real estate;

[0125] The project management module is based on engineering and real estate basic data, integrates GIS urban geographic data, and combines BIM to build a three-dimensional city model, binds the status information of each type of project and facility with the geographical location, and generates basic information for urban construction;

[0126] The approval optimization module is based on the basic information of urban construction, calls the information of engineering construction and real estate transactions, analyzes the time nodes of the approval process, determines the processing time of the approval link, sorts them according to the node priority, and generates approval process adjustment data;

[0127] The resource scheduling module adjusts data based on the approval process, calls resource usage data of past approval nodes, analyzes resource demand in each time period, optimizes resource regulation strategies, and generates resource adjustment configurations;

[0128] The dynamic monitoring module adjusts data and resource configuration based on the approval process, records the deviation between approval progress and resource allocation, adjusts the matching of approval nodes and resources, and generates approval and resource optimization results.

[0129] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A digital housing construction service integration method based on one network management, characterized in that: The following steps are involved: Based on the data of the entire life cycle of the project, synchronous analysis of project time nodes is carried out, linking project approval and construction data to generate basic information for project construction; By collecting provident fund payment information, housing property rights and transaction status data, we can conduct corresponding analysis of transaction chains and participating roles, link the application of provident funds in real estate transactions, and generate basic housing and provident fund information; Based on GIS urban geographic data, combined with BIM, the data is modeled and dynamically updated to evaluate the operational status of urban construction projects and generate basic information on urban construction; Based on the basic information of engineering construction, basic information of housing and provident fund, and basic information of urban construction, predict the time node distribution of approval, adjust the approval path and node sequence, and generate the approval process adjustment result; Based on the approval process adjustment results, analyze the resource demand differences in each time period, adjust the use of human and computing resources according to current needs, and generate resource adjustment configurations; Based on the approval process adjustment results and resource adjustment configuration, analyze the deviation between approval progress and resource allocation, adjust approval nodes and resource allocation, and generate approval and resource optimization results.

2. The digital housing construction service integration method based on one-network management according to claim 1 is characterized by: The basic information on engineering construction includes project category information, project location coordinates and construction permit information, construction unit qualifications, project investment scale and project progress; the basic information on housing and provident fund includes provident fund payment amount, provident fund usage record and house transaction process status, identity information of buyer and seller, loan approval status and related mortgage information; the basic information on urban construction includes the operating status of urban infrastructure, new urbanization project data and the progress of old community renovation, urban facility location, facility service life and maintenance records; the approval process adjustment results include the node sequence after the approval path is adjusted, the approval time prediction and the resource allocation for each link; the resource adjustment configuration includes the human resource deployment in each time period, the computing resource usage and resource idle rate analysis; the approval and resource optimization results include the adjustment of real-time approval nodes, dynamic monitoring data of resource usage and deviation analysis of approval progress.

3. The digital housing construction service integration method based on one-network management according to claim 1 is characterized by: Based on the data of the entire life cycle of the project, the specific steps for synchronous analysis of project time nodes, linking project approval and construction data, and generating basic information of project construction are as follows: Based on the entire life cycle of the project, preliminary sorting of project information is carried out to obtain project classification, location and license information, and to check the relevant licenses with the project content to generate a project information data set; Based on the project information data set, extract the time node information of the project, perform time comparison according to the order of project progress, mark the project progress deviation, and generate project time node analysis data; Based on the project time node analysis data, the priority order of each project is dynamically adjusted, approval and construction resources are reallocated, the order of project execution and resource arrangement are optimized, and basic information on engineering construction is generated.

4. The digital housing construction service integration method based on one-network management according to claim 1 is characterized by: By collecting provident fund payment information, housing property rights and transaction status data, conducting corresponding analysis of transaction chains and participating roles, and linking the application of provident funds in real estate transactions, the specific steps for generating basic housing and provident fund information are as follows: Based on the provident fund payment information and house property data, data is classified, and the associated information with the roles in the transaction chain is collected. The provident fund data and house property data are matched to generate a transaction chain data set; Based on the transaction chain data set, analyze the role's use of provident fund during the transaction process, match the transaction status with the role's operation behavior, and generate provident fund transaction related data; Based on the provident fund transaction-related data, the circulation and use of the provident fund of the transaction node are verified, the provident fund data of all transaction roles are collected, and the basic information of housing and provident fund is generated.

5. The digital housing construction service integration method based on one-network management according to claim 1 is characterized by: Based on GIS urban geographic data, combined with BIM, the specific steps for constructing and dynamically updating data models, evaluating the operating status of urban construction projects, and generating basic information on urban construction are as follows: Based on GIS urban geographic data, the geographical location and attribute information of urban infrastructure are collected, and information on urbanization projects and renovation of old communities is extracted. Through standardized processing of the data, an urban infrastructure data set is generated; Based on the urban infrastructure dataset, the project geographic information is associated with the facility attributes, and the BIM model is constructed in combination with the current operation status of the project to generate urban project operation data; Based on the urban project operation data, the operation status of the facilities is monitored, the geographic information and operation status of the project are continuously updated, the dynamic changes of the model are processed, and the basic information of urban construction is generated.

6. The digital housing construction service integration method based on one-network management according to claim 1 is characterized by: Based on the basic information of engineering construction, basic information of housing and provident fund, and basic information of urban construction, the specific steps of predicting the time node distribution of approval, adjusting the approval path and node sequence, and generating the approval process adjustment result are as follows: Based on the basic information of engineering construction, basic information of housing and provident fund and basic information of urban construction, the time, results and data of participants in the approval process are collected, the data of each approval node is sorted, the processing time of the approval link is classified, and the distribution data of approval time nodes is generated; Based on the approval time node distribution data, the time of the approval nodes is analyzed, and the time distribution of key nodes is marked. The key nodes are nodes reviewed by multiple departments, nodes that rely on external information, and approval links that require signatures. The processing time of each approval link is predicted, and the delays in the approval process are adjusted. A time analysis model is constructed to obtain approval time prediction data; Based on the approval time prediction data, resource allocation is optimized according to the importance and time requirements of the approval nodes, and an approval process adjustment result is generated.

7. The digital housing construction service integration method based on one-network management according to claim 6 is characterized by: To predict the processing time of each approval link, the formula is used: Calculate the predicted processing time T' p ; Among them, T' i represents the actual processing time of the i-th similar approval link in the historical data, W i Represents the importance weight of each approval link, L i Represents the influencing factor of the approval process complexity, E i It represents the degree of dependence on external factors in the approval process, and n represents the total number of approval nodes.

8. The digital housing construction service integration method based on one-network management according to claim 1 is characterized by: Based on the adjustment results of the approval process, analyze the differences in resource requirements in each time period, adjust the use of human and computing resources according to current needs, and generate the specific steps of resource adjustment configuration as follows: Based on the approval process adjustment result, the resource usage of past approval nodes is called, the consumption data of manpower and computing resources of each approval node is collected, the node resource usage is analyzed, and the approval node resource usage data is generated; Based on the resource usage data of the approval node, the resource demand in each time period is analyzed, the time periods with intensive resource usage and sparse resource usage are divided, the resource demand intensity of each time period is marked, and resource demand distribution data is generated; Based on the resource demand distribution data, the use of manpower and resources is adjusted according to the resource demand of the approval node, the resource allocation plan is optimized, and the resource adjustment configuration is generated.

9. The digital housing construction service integration method based on one-network management according to claim 1 is characterized by: Based on the approval process adjustment results and resource adjustment configuration, analyze the deviation between approval progress and resource allocation, adjust approval nodes and resource allocation, and generate approval and resource optimization results. The specific steps are as follows: Based on the approval process adjustment results and resource adjustment configuration, real-time monitoring of resource consumption of nodes in the approval process is performed, real-time data on approval progress and resource usage is collected, differences in resource allocation and usage are marked, and approval resource monitoring data is generated; Based on the approval resource monitoring data, analyzing the deviation between approval progress and resource allocation, locating nodes with insufficient or excessive resource allocation, performing resource and process matching analysis, adjusting resource allocation, and generating resource allocation adjustment data; Based on the resource allocation adjustment data, the progress of the approval process and resource usage are regulated in real time to generate approval and resource optimization results.

10. A digital housing and construction business integration system based on one network management, characterized in that: According to any one of claims 1 to 9, the digital housing construction business integration method based on one-network unified management is implemented, and the system includes: The data collection module collects project time nodes based on the data of the entire life cycle of the project, associates the project approval and construction data, integrates the provident fund payment information, house property rights and transaction status data, analyzes the corresponding relationship between the transaction chain and the participating roles, and generates basic information on the project and real estate; The project management module integrates GIS urban geographic data based on the basic data of the project and real estate, and combines BIM to build a three-dimensional city model, binds the status information of each type of project and facility with the geographical location, and generates basic information for urban construction; The approval optimization module, based on the basic information of urban construction, calls the information of engineering construction and real estate transactions, analyzes the time nodes of the approval process, determines the processing time of the approval link, sorts them according to the node priority, and generates approval process adjustment data; The resource scheduling module adjusts the approval process data based on the approval process, calls the resource usage data of the past approval nodes, analyzes the resource demand in each time period, optimizes the resource control strategy, and generates resource adjustment configuration; The dynamic monitoring module adjusts the approval process data and resource adjustment configuration based on the approval process, records the deviation between the approval progress and resource allocation, adjusts the matching of approval nodes and resources, and generates approval and resource optimization results.

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