Intelligent optimization method for infrastructure management platform and mobile application

Abstract

The invention discloses an intelligent optimization method for an infrastructure management platform and a mobile application. The intelligent optimization method comprises the following steps: collecting requirements, resource information and workflow data of each service domain, analyzing an information flow path, and determining nodes and bottlenecks; establishing a mathematical model of resource allocation and information circulation, and defining an optimization target; an optimization algorithm is selected, and a lightweight algorithm with high real-time performance is designed to run on a mobile platform; processing the real-time data, testing different resource allocation schemes and information flow paths by utilizing a simulation technology, and evaluating the performance of the resource allocation schemes and the information flow paths under actual conditions; based on a simulation result, optimizing a resource allocation and information flow path, and adjusting through an iterative algorithm; an optimization algorithm is integrated into an infrastructure management platform and a mobile application, and a visual resource configuration scheme and an information circulation scheme are provided. The method has the advantage of being capable of optimizing information circulation and resource allocation of business domains such as construction, supervision and design.

Description

Technical Field

[0001] The present invention belongs to the field of digital management, and specifically is an intelligent optimization method for infrastructure management platform and mobile applications. Background Art

[0002] In order to improve the level of digital management, realize the digitization of the entire process of engineering construction, and promote the transformation of traditional infrastructure to modern intelligent construction, it is necessary to build a vertical three-level linkage and horizontal professional collaboration management and control mechanism to improve the overall digital management level. However, the existing management system and mobile system are difficult to meet the needs of information management.

[0003] To solve this problem, it is necessary to expand the system functions, cover more business scenarios, connect the information management chain, and promote the closed-loop processing of management processes. At the same time, it is planned to deepen the functions of the construction management unit module, expand the mobile terminal construction, build an information management model, and realize the linkage between the issuance of management information and the on-site execution reporting. This move aims to improve the project progress, risk performance, and subcontracting team assessment capabilities of key management personnel and front-line personnel, and improve data collection and reporting methods to provide strong support for management decisions.

[0004] Although the existing management system is similar to the self-built system in some functions, it does not cover the application of business domains such as construction, supervision, and design. The information flow path between business domains is unclear, and there are nodes and bottlenecks. The resource allocation method may not be optimal, which affects the business efficiency. There are also large differences in management methods and focus points. Therefore, it is necessary to design an intelligent optimization algorithm for infrastructure management platforms and mobile applications that can optimize the information flow and resource allocation of business domains such as construction, supervision, and design. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an intelligent optimization method for infrastructure management platforms and mobile applications, which has the advantage of being able to optimize information flow and resource allocation in business domains such as construction, supervision, and design.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] An intelligent optimization method for an infrastructure management platform and mobile applications, comprising the following steps:

[0008] S1: Collect the requirements, resource information and workflow data of each business domain, analyze the information flow path, and identify nodes and bottlenecks;

[0009] Preferably, S1 further includes collecting the needs, resource information and workflow data of each business domain, and obtaining the task requirements, construction period constraints, staffing, equipment resources, material allocation and usage of the construction, supervision, design and other departments; at the same time, recording the interaction methods between each business, the time nodes of information flow, the approval links and the frequency and sequence of task delivery, analyzing these information flow paths, and finding out the bottlenecks that cause delays or inefficiency.

[0010] S2: Establish a mathematical model of resource allocation and information flow and define the optimization goal;

[0011] Preferably, S2 further includes establishing a mathematical model of resource allocation and information flow, which requires converting various types of resources into variables and constructing relevant constraints, such as limited resources, dependencies between tasks, and construction period and budget constraints. The optimization goals include minimizing construction period, minimizing resource costs, and maximizing resource utilization, and the overall optimization effect is measured by the objective function.

[0012] S3: Select optimization algorithms and design lightweight and real-time algorithms to run on mobile platforms;

[0013] Preferably, S3 further includes selecting an optimization algorithm, based on the complexity and real-time requirements of the problem, giving priority to an algorithm suitable for processing large-scale resource allocation and dynamic information flow;

[0014] Evaluate the quality of each solution, expressed as a maximization or minimization objective, as follows:

[0015]

[0016] Among them, x is the resource allocation scheme to be optimized, n is the number of resources or tasks involved in the problem, and c is the number of tasks involved in the problem. i is the weight of the i-th resource or task, r i (x) is the utility of the ith resource or task under scenario x;

[0017] Select individuals with higher fitness from the current population. The formula is as follows:

[0018]

[0019] Among them, P(x i ) is individual x i The probability of being selected, f(x i ) is individual x i The fitness value of m is the total number of individuals in the current population.

[0020] S4: Process real-time data and use simulation technology to test different resource allocation schemes and information flow paths to evaluate their performance under actual conditions;

[0021] Preferably, S4 further includes collecting real-time status data from each business domain and preprocessing the data when processing real-time data, using simulation technology to build a virtual environment, inputting different resource allocation schemes and information flow paths, running simulations through simulation tools, testing performance under different working conditions, such as resource utilization, construction period, task completion, delay indicators, considering the dynamic changes of external conditions, and evaluating the feasibility and stability of each scheme in actual operation.

[0022] S5: Based on the simulation results, optimize resource allocation and information flow paths through iterative algorithm adjustments;

[0023] Preferably, S5 further includes generating an initial resource allocation scheme and information flow path based on the simulation results, defining an initial optimization target, using an iterative algorithm to gradually adjust the parameters in the scheme, and after each round of iteration, evaluating the performance of the new scheme and comparing it with the indicators of the previous scheme, setting a termination condition, and stopping the iteration;

[0024] Optimize resource allocation and information flow path. The formula is as follows:

[0025]

[0026] Among them, x t is the resource allocation and information flow path plan at the tth iteration, η is the learning rate or step size, which controls the adjustment range of the plan in each iteration, is the objective function f(x t ) for the current solution (x t ) indicates the improvement direction of the current solution;

[0027] The objective function is as follows:

[0028]

[0029] Among them, u i (x) is the resource utilization of the ith task under configuration x, c j (x) is the cost of the jth task under solution x.

[0030] S6: Integrate optimization algorithms into infrastructure management platforms and mobile applications to provide visual resource allocation and information flow solutions.

[0031] To integrate the optimization algorithm into the infrastructure management platform and mobile applications, it is necessary to first develop an interface so that the algorithm can receive real-time data input from the platform, such as project progress, resource status, and task dependencies, and seamlessly connect with the backend database. A user-friendly front-end interface should be designed to intuitively display the optimization results of resource allocation plans and information flow paths through charts, Gantt charts, and heat maps. Users can dynamically adjust parameters and view the optimized effects in real time.

[0032] The present invention includes a server, a display, a security architecture and non-functional requirements of the system. The server uses springboot as the basis to build a framework, integrates a unified authority authentication framework, introduces kafka message queues into the important message system of the platform, and adopts docker containerization technology and Jenkins integrated automated operation and maintenance in terms of platform operation and maintenance. Containerized operation and maintenance enables the system to be ported to other platforms. The display adopts the mainstream vue framework. The corresponding tool library is used in the project. The front-end routing and access control system is built. The i18n internationalization solution is adopted. The project UI uses element-ui as the basis, and two sets of themes are developed based on it. The project adopts npm scripts to uniformly control package dependencies and version releases, and uses gitea to manage code quality. The security architecture includes boundary security , transmission security, data security and architecture security. The boundary security is used to distinguish internal data from external application access, establish a two-way authorization mechanism, and filter sensitive data. In the transmission security, all data transmission needs to pass through software and hardware firewalls. Internal data transmission adopts symmetric encryption technology, and external data access adopts digital certificate encryption technology. In the data security, data storage adopts a hierarchical mechanism, sensitive data is encrypted and stored, and a signature mechanism is used to ensure data integrity. The architecture security focuses on protection from two aspects: infrastructure security and application system security. The non-functional requirements of the system need to have concurrency capabilities. The system response time does not exceed 2 seconds under 10 users, and the system response time does not exceed 8 seconds under 100 users. It can support access by 1,000 users at the same time. All access records need to be logged, and the log records must be kept for three months.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention collects the needs, resource information and workflow data of each business domain, analyzes the information flow path, and determines the nodes and bottlenecks; establishes a mathematical model of resource allocation and information flow, and defines the optimization goal; selects the optimization algorithm, and designs a lightweight and real-time algorithm to run on the mobile platform; processes the real-time data, uses simulation technology to test different resource allocation schemes and information flow paths, and evaluates their performance under actual conditions; based on the simulation results, optimizes the resource allocation and information flow path, and adjusts them through iterative algorithms; integrates the optimization algorithm into the infrastructure management platform and mobile applications, and provides a visualized resource allocation scheme and information flow scheme, which can solve the problem of insufficient application of the State Grid unified promotion system in business domains such as construction, supervision, and design, optimize information flow and resource allocation, and improve overall business efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Example 1

[0038] An intelligent optimization method for infrastructure management platform and mobile application, including server, display, security architecture and system non-functional requirements. The server uses springboot as the basis to build a framework, integrates a unified authority authentication framework, introduces kafka message queues into the important message system of the platform, and uses docker containerization technology and Jenkins integrated automated operation and maintenance for platform operation and maintenance. Containerized operation and maintenance enables the system to be ported to other platforms. The display uses the mainstream vue framework, and the corresponding tool library is used in the project. A front-end routing and access control system is built, and the i18n internationalization solution is adopted. The project UI uses element-ui as the basis, and two sets of themes are developed based on it. The project uses npm scripts to uniformly control package dependencies and version releases, and uses gitea to manage code quality. The security architecture includes boundary security, transmission security, data security and architecture security. The boundary security is used to distinguish internal data from external application access, establish a two-way authorization mechanism, and filter sensitive data. In the transmission security, all data transmission needs to pass through software and hardware firewalls. Internal data transmission adopts symmetric encryption technology, and external data access adopts digital certificate encryption technology. In the data security, data storage adopts a hierarchical mechanism, sensitive data is encrypted and stored, and a signature mechanism is used to ensure data integrity. The architecture security focuses on protection from two aspects: infrastructure security and application system security. The non-functional requirements of the system need to have concurrency capabilities. The system response time does not exceed 2 seconds under 10 users, and the system response time does not exceed 8 seconds under 100 users. It can support access by 1,000 users at the same time. All access records need to be logged, and the log records are required to be kept for three months.

[0039] An intelligent optimization method for an infrastructure management platform and mobile applications, comprising the following steps:

[0040] S1: Collect the requirements, resource information and workflow data of each business domain, analyze the information flow path, and identify nodes and bottlenecks;

[0041] When collecting the needs, resource information and workflow data of each business domain, it is necessary to obtain detailed information on the task requirements, construction period restrictions, staffing, equipment resources, material allocation and usage of the construction, supervision, design and other departments; at the same time, record the interaction methods between each business, the time nodes of information flow, the approval links and the frequency and sequence of task delivery, analyze these information flow paths, and find out the bottlenecks that may cause delays or inefficiency, such as nodes where the approval time is too long, the task delivery link is not smooth, or the resource scheduling is unreasonable, so as to provide a basis for subsequent optimization.

[0042] S2: Establish a mathematical model of resource allocation and information flow and define the optimization goal;

[0043] When establishing a mathematical model for resource allocation and information flow, it is necessary to quantify various resources, such as manpower, equipment, materials, and factors such as task time, cost, priority, etc. into variables, and construct relevant constraints, such as limited resources, dependencies between tasks, duration and budget constraints. Optimization goals may include minimizing duration, minimizing resource costs, maximizing resource utilization, or improving the efficiency of information transmission. The model should also consider the priority of each task, the possibility of parallel processing, and the ability to respond to unforeseen events, and measure the overall optimization effect through the objective function.

[0044] S3: Select optimization algorithms and design lightweight and real-time algorithms to run on mobile platforms;

[0045] When selecting an optimization algorithm, it is necessary to give priority to algorithms that are suitable for handling large-scale resource allocation and dynamic information flow based on the complexity and real-time requirements of the problem, such as genetic algorithms for global optimization, ant colony algorithms for path optimization, or dynamic programming for solving multi-stage decision-making problems. During design, the algorithm should be simplified to reduce computational complexity, and efficiency should be improved by cutting unnecessary steps, introducing heuristic methods or distributed computing. In order to adapt to the performance limitations of mobile platforms, the algorithm's memory and processor usage should be optimized to ensure real-time response in low-latency and multi-tasking environments, and combined with caching mechanisms or incremental computing technologies to improve execution speed.

[0046] S4: Process real-time data and use simulation technology to test different resource allocation schemes and information flow paths to evaluate their performance under actual conditions;

[0047] When selecting an optimization algorithm, you should give priority to using genetic algorithms, ant colony algorithms, or dynamic programming, which are suitable for handling large-scale resource allocation and path optimization problems based on the characteristics of the problem. When designing lightweight, real-time algorithms, you need to optimize the time and space complexity of the algorithm, simplify the calculation steps, use incremental calculations or heuristic methods to reduce the amount of calculations, and introduce parallel processing to improve execution efficiency. To adapt to the hardware performance of the mobile platform, you should use low memory usage and efficient data caching technology to ensure that the algorithm can run stably in a low-latency, multi-tasking environment and quickly respond to user needs.

[0048] S5: Based on the simulation results, optimize resource allocation and information flow paths through iterative algorithm adjustments;

[0049] When optimizing resource allocation and information flow paths based on simulation results, first analyze the performance indicators of the simulation feedback, such as resource utilization, construction period, and cost, find out the bottlenecks or inefficiencies in the current solution, and use iterative algorithms, such as gradient descent or genetic algorithms, to gradually adjust the key parameters in resource allocation and information flow paths to generate new configuration solutions. After each iteration, re-simulate and evaluate the performance of the new solution until the performance indicators tend to be optimized. The entire process requires setting optimization goals, such as minimizing construction period or maximizing resource utilization, and termination conditions, such as achieving the expected effect or an upper limit on the number of iterations, to ensure the effectiveness and practicality of the solution.

[0050] S6: Integrate optimization algorithms into infrastructure management platforms and mobile applications to provide visual resource allocation and information flow solutions.

[0051] Integrating the optimization algorithm into the infrastructure management platform and mobile applications requires the development of an interface to enable the algorithm to receive real-time data input from the platform, such as project progress, resource status, and task dependencies, and seamlessly connect with the backend database. Next, a user-friendly front-end interface is designed to intuitively display the optimization results of resource allocation plans and information flow paths through visualization tools such as charts, Gantt charts, and heat maps. Users can dynamically adjust parameters and view the optimized effects in real time, ensuring that the algorithm runs efficiently on mobile devices, providing real-time feedback and suggestions, and enabling users to make quick decisions and optimize resource scheduling and task execution.

[0052] To illustrate the implementation process of the solution in more detail, here are some specific case data:

[0053] 1. Business domain requirements

[0054] Construction business domain: It is necessary to update the construction progress in real time, optimize the material supply chain, and reduce construction delays.

[0055] Supervision business domain: It is necessary to obtain construction progress and quality problem reports in a timely manner in order to respond quickly.

[0056] Design business domain: An efficient feedback mechanism is needed to adjust the design plan and reduce design changes.

[0057] 2. Data Collection

[0058] Construction data: Progress data, material usage records, and staffing information for 100 construction projects were collected.

[0059] Supervision data: collected progress reports, quality problem records, and rectification feedback from 50 supervision projects.

[0060] Design data: collected change records, design approval processes, and design feedback from 30 design projects.

[0061] 3. Analysis of information flow paths

[0062] From construction to supervision: the information flow path includes construction progress reports, quality issue feedback, and rectification notices.

[0063] From design to construction: The information flow path includes design plan, design change notification, and construction drawing approval.

[0064] 4. Mathematical Model

[0065] A model based on queuing theory was established to simulate the information flow, where nodes represent the various stages of information processing and the bottleneck is the stage with the longest processing time in the information flow.

[0066] 5. Choose an optimization algorithm

[0067] Algorithm selection: Genetic algorithm was selected to optimize resource allocation because it is suitable for solving multi-objective optimization problems.

[0068] 6. Real-time data processing and simulation testing

[0069] The progress data of 10 construction projects and the quality problem reports of 5 supervision projects were collected in real time.

[0070] The following scenarios were simulated and tested:

[0071] Plan A: Increase the frequency of information flow from construction to supervision and reduce information processing time.

[0072] Plan B: Adjust the information flow path from design to construction and directly notify the construction manager of design changes.

[0073] 7. Iterative Optimization

[0074] Simulation results:

[0075] Plan A: Improves the supervision response speed, but increases the workload of information processing.

[0076] Option B: Reduces the average processing time for design changes, but requires more frequent communication.

[0077] Iterative Adjustment:

[0078] Based on the simulation results, the parameters of the genetic algorithm were adjusted and the simulation was repeated until a better resource allocation and information flow path was found.

[0079] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0080] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent optimization method for infrastructure management platform and mobile application, characterized in that: The following steps are involved: S1: Collect the requirements, resource information and workflow data of each business domain, analyze the information flow path, and identify nodes and bottlenecks; S2: Establish a mathematical model of resource allocation and information flow and define the optimization goal; S3: Select optimization algorithms and design lightweight and real-time algorithms to run on mobile platforms; S4: Process real-time data and use simulation technology to test different resource allocation schemes and information flow paths to evaluate their performance under actual conditions; S5: Based on the simulation results, optimize resource allocation and information flow paths through iterative algorithm adjustments; S6: Integrate optimization algorithms into infrastructure management platforms and mobile applications to provide visual resource allocation and information flow solutions.

2. According to claim 1, an intelligent optimization method for infrastructure management platform and mobile application is characterized in that: In S1, when collecting the needs, resource information and workflow data of each business domain, it is necessary to obtain the task requirements, construction period constraints, staffing, equipment resources, material allocation and usage of the construction, supervision and design departments; at the same time, record the interaction methods between each business, the time nodes of information flow, the approval links and the frequency and sequence of task delivery, analyze these information flow paths, and find out the bottlenecks that cause delays or inefficiency.

3. The intelligent optimization method for infrastructure management platform and mobile application according to claim 1, characterized in that: In S2, when establishing a mathematical model for resource allocation and information flow, it is necessary to transform various types of resources into variables and construct relevant constraints. The optimization goals include minimizing construction period, minimizing resource cost, and maximizing resource utilization. The overall optimization effect is measured through the objective function.

4. The intelligent optimization method for infrastructure management platform and mobile application according to claim 1, characterized in that: In S3, when selecting optimization algorithms, it is necessary to give priority to algorithms suitable for handling large-scale resource allocation and dynamic information flow based on the complexity and real-time requirements of the problem; Evaluate the quality of each solution, expressed as a maximization or minimization objective, as follows: Among them, x is the resource allocation scheme to be optimized, n is the number of resources or tasks involved in the problem, and c is the number of tasks involved in the problem. i is the weight of the i-th resource or task, r i (x) is the utility of the ith resource or task under scenario x; Select individuals with higher fitness from the current population. The formula is as follows: Among them, P(x i ) is individual x i The probability of being selected, f(x i ) is individual x i The fitness value of m is the total number of individuals in the current population.

5. The intelligent optimization method for infrastructure management platform and mobile application according to claim 1, characterized in that: In S4, when processing real-time data, real-time status data from various business domains is collected and pre-processed. A virtual environment is constructed using simulation technology, different resource allocation schemes and information flow paths are input, and simulations are run through simulation tools to test performance under different working conditions. The dynamic changes of external conditions are considered to evaluate the feasibility and stability of each scheme in actual operations.

6. The intelligent optimization method for infrastructure management platform and mobile application according to claim 1, characterized in that: In S5, the initial resource allocation scheme and information flow path are generated based on the simulation results, the initial optimization goal is defined, and the parameters in the scheme are gradually adjusted using an iterative algorithm. After each round of iteration, the performance of the new scheme is evaluated and compared with the indicators of the previous scheme, and the termination condition is set to stop the iteration; Optimize resource allocation and information flow path. The formula is as follows: Among them, x t is the resource allocation and information flow path plan at the tth iteration, η is the learning rate or step size, which controls the adjustment range of the plan in each iteration, is the objective function f(x t ) for the current solution (x t ) indicates the improvement direction of the current solution; The objective function is as follows: Among them, u i (x) is the resource utilization of the ith task under configuration x, c j (x) is the cost of the jth task under solution x.

7. The intelligent optimization method for infrastructure management platform and mobile application according to claim 1, characterized in that: In S6, the optimization algorithm is integrated into the infrastructure management platform and mobile applications. First, an interface is developed to enable the algorithm to receive real-time data input from the platform and seamlessly connect with the backend database. A user-friendly front-end interface is designed to intuitively display the optimization results of resource allocation plans and information flow paths through charts, Gantt charts, and heat maps. Users can dynamically adjust parameters and view the optimized effects in real time.

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