Multi-service scheduling optimization algorithm for infrastructure safety management

Abstract

The invention discloses a multi-service scheduling optimization method for infrastructure safety management. The multi-service scheduling optimization method comprises the following steps: intelligently acquiring structured and unstructured data from a plurality of service systems; cleaning, filtering and formatting the collected data, analyzing by using a data fusion technology, and extracting comprehensive information useful for safety and quality management; based on multi-objective optimization, a self-adaptive scheduling algorithm is designed, and resources, tasks and manpower are automatically allocated; the method comprises the following steps: constructing a digital twin model of the infrastructure, performing omnibearing real-time monitoring on the infrastructure through a synchronous virtual and physical system, and simulating risk points and potential problems; an intelligent monitoring feedback loop is used for detecting abnormal behaviors and potential risks; on the basis of real-time monitoring and feedback, the scheduling process is continuously optimized, and the adaptability of a scheduling algorithm is improved by applying machine learning and a prediction model. The method has the advantages that data of multiple service systems can be integrated, and data sharing and fusion analysis are realized.

Description

Technical Field

[0001] The present invention belongs to the field of security management, and in particular is a multi-service scheduling optimization algorithm for infrastructure security management. Background Art

[0002] With the upgrade of infrastructure safety and quality control management model, we need to not only pay attention to our own data but also broaden our horizons. Through digital applications, we can more comprehensively grasp the various indicator data across business domains, comprehensively analyze potential business problems, track problem solving, and improve the safety and quality control closed loop. Frontline safety and quality inspections are no longer limited to simply viewing report data. Through digital systems, massive amounts of integrated data can be effectively utilized to assist in completing business activities. Superiors also need to focus on safety and quality control, automatically display data applications of various relevant cross-business integrated indicators to assist in decision-making and management. Through the display of different data themes, they can understand the true situation of the business from a full perspective, and drill down to the root details for risks and problems.

[0003] At present, the infrastructure profession has built multiple business systems built around different core business needs, but the data has not been connected, forming data islands and cannot be effectively integrated with other business modules. The safety quality control business needs to collect and integrate data from multiple systems to effectively control and manage the overall safety quality. At present, there is no dedicated monitoring, analysis, dispatching and command application for safety quality, and there is no inspection and management application for front-line safety and quality inspection staff.

[0004] The existing infrastructure security management has the following problems: the infrastructure professional has built multiple business systems, but lacks an effective resource, task and manpower allocation mechanism, and is unable to prioritize responses to high-risk events and balance system loads; data between different business systems is not connected, resulting in information being unable to be shared, affecting the efficiency and quality of the overall business process; there is a lack of comprehensive real-time monitoring and simulation of infrastructure, and it is impossible to discover risk points and potential problems in a timely manner; the existing scheduling process lacks adaptability and cannot be self-corrected and optimized based on real-time monitoring data.

[0005] Therefore, it is necessary to design a multi-business scheduling optimization algorithm for infrastructure safety management that can integrate multi-business system data, realize data sharing and fusion analysis, and optimize the infrastructure safety quality management process through intelligent scheduling and real-time monitoring. Summary of the invention

[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a multi-service scheduling optimization algorithm for infrastructure security management, which has the advantages of being able to integrate multi-service system data and realize data sharing and fusion analysis.

[0007] The objective of the present invention is achieved through the following technical solutions:

[0008] A multi-service scheduling optimization algorithm for infrastructure security management includes the following steps:

[0009] S1: Intelligently collect structured and unstructured data from multiple business systems;

[0010] S2: Clean, filter and format the collected data, apply data fusion technology to analyze it, and extract comprehensive information useful for safety and quality management;

[0011] Preferably, S2 further includes cleaning the collected data, removing noise, missing values, outliers and redundant data, using filtering rules and algorithms to screen out data points with low relevance or poor quality, applying data fusion technology, matching and integrating multi-source data from different systems and sensors through time, space, and feature dimensions, performing cross-dimensional correlation analysis, extracting core information and trend data sensitive to safety risks and quality control through dimensionality reduction, feature engineering, and pattern recognition, and generating comprehensive information that is available in real time.

[0012] S3: Based on multi-objective optimization, an adaptive scheduling algorithm is designed to automatically allocate resources, tasks, and manpower, prioritize high-risk events, and balance system loads;

[0013] Preferably, S3 further includes defining multiple scheduling objectives based on multi-objective optimization theory, establishing a mathematical model, converting these objectives into constraints and optimization objective functions, using an adaptive scheduling algorithm, and automatically adjusting resource and task allocation strategies by real-time monitoring of system status. The algorithm dynamically allocates manpower, equipment and computing resources through iterative optimization, responds to high-risk events first, and adjusts task priorities according to risk levels. The scheduling algorithm balances system load;

[0014] The calculation formula of scheduling optimization target is as follows:

[0015]

[0016] Among them, C i is the computing resource consumption of the ith task, R i is the total amount of resources allocated to the ith task, w i is the weight of the ith task, representing the priority of the task, which is determined based on the urgency, importance or risk level of the task. λ is the risk factor, which is used to balance the preferred treatment of high-risk events. The larger the value, the higher the priority of the response to high-risk events. highrisk is the total time required to respond to high-risk events, T totalis the total processing time of all tasks, μ is the load balancing coefficient, which is used to control the balance of the system load. The larger the value, the higher the requirement for load balancing. u is the current system load of the ith node, is the average load of the entire system, and n is the number of summary points currently participating in scheduling.

[0017] S4: Build a digital twin model of the infrastructure to monitor the infrastructure in real time and simulate risk points and potential problems by synchronizing virtual and physical systems;

[0018] Preferably, S4 further includes collecting physical data of the infrastructure, building a high-precision digital twin model corresponding to the actual infrastructure, and continuously updating the digital twin model through real-time data stream input. The system monitors and warns of abnormal situations in real time by comparing the differences between the virtual model and the physical system, and combines algorithms to predict possible future failures and risk points.

[0019] S5: Use intelligent monitoring feedback loops to detect abnormal behaviors and potential risks, and automatically adjust scheduling plans and digital twin models to self-correct after identifying problems;

[0020] Preferably, S5 further includes collecting data of physical equipment and digital twin models in real time through an intelligent monitoring system, applying an anomaly detection algorithm, and after detecting an anomaly, the system automatically triggers a feedback loop to analyze the cause of the anomaly and match the optimal response strategy. The system automatically adjusts the scheduling plan according to the nature of the anomaly, reallocates resources and tasks, and prioritizes solving urgent problems. The digital twin model is synchronously updated, the mapping between the virtual and physical systems is adjusted, the new status is reflected, and the prediction model is corrected.

[0021] S6: Based on real-time monitoring and feedback, continuously optimize the scheduling process, apply machine learning and predictive models, and improve the adaptability of the scheduling algorithm.

[0022] Preferably, S6 further includes collecting key data in the scheduling process through a real-time monitoring system, and using this data as input for training the machine learning model, applying supervised learning or reinforcement learning algorithms, analyzing the relationship between historical scheduling behavior and system performance, and using predictive models to estimate future task loads, resource requirements, and potential risk points. Based on the prediction results, the scheduling algorithm dynamically adjusts task priorities and resource allocation plans.

[0023] The present invention adopts the MVC mode, including a large-screen display layer, a data storage layer, a data interface layer, a permission control layer, a data calculation layer and a PC management layer. The large-screen display layer is responsible for obtaining data from the data interface layer for display, and is implemented using Vue3+Vite+TypeScript technology. The data storage layer is responsible for storing the data collected by the system, and uses Mysql database to perform relational storage on the data. The data interface layer is responsible for providing data for data service display, and the underlying framework uses Spring The Boot framework can create independent Spring applications, and based on its Maven plug-in, it can quickly package them into JARs and WARs required for deployment. The default embedded Tomcat container is convenient for development, debugging and deployment, and does not require complex XML configuration. Data encryption uses AES encryption to encrypt interface data. The authority control layer is responsible for the overall control of users and user authorities, data authorities, and single sign-on functions. Spring+Redis is used as the underlying development framework to implement identity authentication, authorization, password, session management, single sign-on, and data authorization functions. The data computing layer includes data collection, data preprocessing, and data analysis stages. At the same time, the data source must be standardized and uniformly processed. The PC management layer uses Vue3+SpringBoot as the front-end and back-end development frameworks, and manages the large screen as the management end of the large screen.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention designs an adaptive scheduling algorithm based on multi-objective optimization, automatically allocates resources, tasks and manpower, prioritizes responses to high-risk events and balances system loads; builds a digital twin model of the infrastructure, synchronizes virtual and physical systems, monitors the infrastructure in all directions in real time, and simulates risk points and potential problems; utilizes an intelligent monitoring feedback loop to detect abnormal behaviors and potential risks, and automatically adjusts the scheduling plan and digital twin model for self-correction after identifying problems; based on real-time monitoring and feedback, continuously optimizes the scheduling process, applies machine learning and predictive models, and improves the adaptability of the scheduling algorithm.

[0026] The present invention not only optimizes the allocation of resources, tasks and manpower, but also realizes data sharing and fusion analysis, and improves the efficiency and quality of the infrastructure safety quality management process through intelligent scheduling and real-time monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0028] 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.

[0029] Example 1

[0030] A multi-service scheduling optimization algorithm for infrastructure security management includes the following steps:

[0031] S1: Intelligently collect structured and unstructured data from multiple business systems;

[0032] By establishing connections with various business systems, such as sensor networks, databases, and monitoring system interfaces, and using data collection tools or APIs, it automatically obtains structured data, such as tables and log records in relational databases, and unstructured data, such as video streams, images, and text files. The intelligent collection system has adaptive capabilities and can automatically identify the type and format of data sources and dynamically adjust collection strategies. The system pre-processes the collected unstructured data, such as image recognition, text parsing, and video frame extraction, and converts it into an analyzable format. During the collection process, the system also performs real-time data verification and compression to ensure the integrity and efficiency of data transmission, and ultimately aggregates multi-source heterogeneous data into a unified data warehouse or processing platform for subsequent analysis and processing.

[0033] S2: Clean, filter and format the collected data, apply data fusion technology to analyze it, and extract comprehensive information useful for safety and quality management;

[0034] Clean the collected data to remove noise, missing values, outliers and redundant data; format the data according to business needs and standards, unify the formats of timestamps, units, classification labels, etc., use filtering rules and algorithms to filter out data points with low relevance or poor quality, apply data fusion technology, match and integrate multi-source data from different systems and sensors through time, space, and feature dimensions, conduct cross-dimensional correlation analysis, extract core information and trend data sensitive to safety risks and quality control through dimensionality reduction, feature engineering and pattern recognition, and generate real-time comprehensive information for further optimization of monitoring and decision-making.

[0035] S3: Based on multi-objective optimization, an adaptive scheduling algorithm is designed to automatically allocate resources, tasks, and manpower, prioritize high-risk events, and balance system loads;

[0036] Based on multi-objective optimization theory, multiple scheduling objectives are defined, such as minimizing safety risks, maximizing resource utilization, and minimizing task completion time. A mathematical model is established to transform these objectives into constraints and optimization objective functions. An adaptive scheduling algorithm is used to automatically adjust resource and task allocation strategies by real-time monitoring of system status, such as task queues, resource utilization, and risk levels. The algorithm dynamically allocates manpower, equipment, and computing resources through iterative optimization, prioritizes responses to high-risk events, and adjusts task priorities according to risk levels. The scheduling algorithm balances the system load to avoid excessive concentration in a certain part, ensuring the stability and efficiency of the overall system. The algorithm's adaptability is reflected in the self-adjustment of parameters based on actual operating conditions to adapt to changes in system requirements and ensure continuous optimization and efficient execution of scheduling.

[0037] S4: Build a digital twin model of the infrastructure to monitor the infrastructure in real time and simulate risk points and potential problems by synchronizing virtual and physical systems;

[0038] The physical data of the infrastructure, such as structure, sensor data, and equipment operating status, is collected and digitized to build a high-precision digital twin model corresponding to the actual infrastructure; the model integrates various types of information, including equipment parameters, environmental conditions, historical data, etc., to ensure that it can be synchronized with the physical system. Then, through real-time data stream input, the digital twin model is continuously updated to maintain state consistency with the physical system. On this basis, simulation technology is used to simulate key parts and high-risk points in the infrastructure, and potential problems and failure modes are analyzed. The system compares the differences between the virtual model and the physical system, monitors and warns of abnormal situations in real time, and combines algorithms to predict possible future failures and risk points to provide a basis for decision-making. The digital twin model can support all-round dynamic monitoring and analysis, and continuously optimize the safety and operation management of infrastructure.

[0039] S5: Use intelligent monitoring feedback loops to detect abnormal behaviors and potential risks, and automatically adjust scheduling plans and digital twin models to self-correct after identifying problems;

[0040] Through the intelligent monitoring system, data from physical devices and digital twin models are collected in real time, and anomaly detection algorithms such as machine learning or rule engines are applied to identify abnormal behaviors or potential risks, such as equipment failures, abnormal data fluctuations, or situations that exceed safety thresholds. After detecting the anomaly, the system automatically triggers the feedback loop, analyzes the cause of the anomaly and matches the optimal response strategy. The system automatically adjusts the scheduling plan according to the nature of the anomaly, reallocates resources and tasks, and prioritizes solving urgent problems. The digital twin model is updated synchronously, adjusting the mapping between the virtual and physical systems to reflect the new status and correct the prediction model. The self-correction process includes the optimization of scheduling parameters and the iteration of the digital twin model to ensure that the problem is effectively solved and enhance the system's ability to respond to similar problems in the future.

[0041] S6: Based on real-time monitoring and feedback, continuously optimize the scheduling process, apply machine learning and predictive models, and improve the adaptability of the scheduling algorithm.

[0042] Through the real-time monitoring system, key data of the scheduling process, such as task completion time, resource utilization, and response speed, are collected, and these data are used as input for training machine learning models. Supervised learning or reinforcement learning algorithms are applied to analyze the relationship between historical scheduling behavior and system performance, identify key factors affecting scheduling efficiency, and use predictive models, such as time series prediction or neural networks, to estimate future task loads, resource requirements, and potential risk points. Based on the prediction results, the scheduling algorithm dynamically adjusts task priorities and resource allocation plans to prevent possible bottlenecks or conflicts in advance. The scheduling algorithm continuously optimizes itself and corrects strategies through feedback loops to improve adaptability to adapt to changes in system environment, task types, and resource status, ultimately achieving continuous improvement and efficient execution of the scheduling process.

[0043] To illustrate the implementation process of the solution in more detail, here are some specific case data:

[0044] 1. Adaptive Scheduling Algorithm Implementation

[0045] Project name: Urban subway expansion project

[0046] Project scale: involving 10 sites and 20 construction sections

[0047] Before resource optimization: There were an average of 10 workers in each construction section and the material delivery delay rate was 15%.

[0048] After resource optimization: The adaptive scheduling algorithm dynamically adjusts resource allocation based on project progress and risk level.

[0049] Manpower allocation: Increase to 15 workers in high-risk construction sections and reduce to 8 workers in low-risk construction sections.

[0050] Material distribution: The delivery route is optimized through algorithms, and the delay rate is reduced to 5%.

[0051] Result: The overall project schedule was advanced by 10% and the cost was saved by 8%.

[0052] 2. Digital Twin Model Construction

[0053] Project name: Large bridge construction project

[0054] Project features: A bridge across a river, with high construction difficulty and high safety risks

[0055] Model construction: A digital twin model of the bridge was created, including factors such as structure, materials, and environment.

[0056] Risk simulation: The model simulates construction risks under different weather conditions.

[0057] Risk point identification: Three high-risk operation areas were identified, including two high-altitude operation points and one underwater operation point.

[0058] Results: Through the simulation, risk response measures were formulated in advance and two potential safety accidents were avoided.

[0059] 3. Intelligent monitoring feedback loop

[0060] Monitoring scenario: real-time monitoring of construction sites

[0061] Monitoring object: Safety behavior and equipment status at the construction site

[0062] Anomaly detection: The monitoring system detected 30 abnormal behaviors within a month, including not wearing a safety helmet and operating machinery in violation of regulations.

[0063] Automatic adjustment: The system automatically adjusted the scheduling plan 20 times, including increasing the frequency of safety inspections and adjusting operating hours.

[0064] Results: The time for handling abnormal events was shortened by 50% and the incidence of safety accidents at construction sites was reduced by 20%.

[0065] 4. Scheduling process optimization

[0066] Business system integration: Integrates business systems such as construction management, material management, and personnel management

[0067] Data silo problem: Previously, data from various systems were not interoperable, resulting in inefficient decision-making

[0068] Data sharing: Real-time sharing of data among various business systems is achieved through API interfaces and data centers.

[0069] Fusion analysis: Use big data analysis technology to conduct in-depth analysis of the fused data.

[0070] Resource utilization: Analysis shows that through data fusion, resource utilization has increased from 70% to 85%.

[0071] Results: Scheduling process efficiency increased by 30% and project management costs decreased by 12%.

[0072] 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.

[0073] 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. A multi-service scheduling optimization method for infrastructure security management, characterized in that: The following steps are involved: S1: Intelligently collect structured and unstructured data from multiple business systems; S2: Clean, filter and format the collected data, apply data fusion technology to analyze it, and extract comprehensive information useful for safety and quality management; S3: Based on multi-objective optimization, an adaptive scheduling algorithm is designed to automatically allocate resources, tasks, and manpower, prioritize high-risk events, and balance system loads; S4: Build a digital twin model of the infrastructure to monitor the infrastructure in real time and simulate risk points and potential problems by synchronizing virtual and physical systems; S5: Use intelligent monitoring feedback loops to detect abnormal behaviors and potential risks, and automatically adjust scheduling plans and digital twin models to self-correct after identifying problems; S6: Based on real-time monitoring and feedback, continuously optimize the scheduling process, apply machine learning and predictive models, and improve the adaptability of the scheduling algorithm.

2. A multi-service scheduling optimization method for infrastructure security management according to claim 1, characterized in that: In S2, the collected data is cleaned to remove noise, missing values, outliers and redundant data. Filtering rules and algorithms are used to screen out data points with low relevance or poor quality. Data fusion technology is applied to match and integrate multi-source data from different systems and sensors through time, space and feature dimensions, and cross-dimensional correlation analysis is performed. Through dimensionality reduction, feature engineering and pattern recognition, core information and trend data sensitive to safety risks and quality control are extracted to generate comprehensive information that is available in real time.

3. A multi-service scheduling optimization method for infrastructure security management according to claim 1, characterized in that: In S3, based on multi-objective optimization theory, multiple scheduling objectives are defined, mathematical models are established, these objectives are converted into constraints and optimization objective functions, and adaptive scheduling algorithms are used to automatically adjust resource and task allocation strategies by real-time monitoring of system status. The algorithm dynamically allocates manpower, equipment, and computing resources through iterative optimization, responds to high-risk events first, and adjusts task priorities according to risk levels. The scheduling algorithm balances the system load; The scheduling target optimization calculation formula is as follows: Among them, C i is the computing resource consumption of the ith task, R i is the total amount of resources allocated to the ith task, w i is the weight of the ith task, representing the priority of the task, which is determined based on the urgency, importance or risk level of the task. λ is the risk factor, which is used to balance the preferred treatment of high-risk events. The larger the value, the higher the priority of the response to high-risk events. highrisk is the total time required to respond to high-risk events, T total is the total processing time of all tasks, μ is the load balancing coefficient, which is used to control the balance of the system load. The larger the value, the higher the requirement for load balancing. i is the current system load of the ith node, is the average load of the entire system, and n is the number of summary points currently participating in scheduling.

4. A multi-service scheduling optimization method for infrastructure security management according to claim 1, characterized in that: In S4, the physical data of the infrastructure is collected and a high-precision digital twin model corresponding to the actual infrastructure is constructed. The digital twin model is continuously updated through real-time data stream input. The system monitors and warns of abnormal situations in real time by comparing the differences between the virtual model and the physical system, and combines algorithms to predict possible future failures and risk points.

5. A multi-service scheduling optimization method for infrastructure security management according to claim 1, characterized in that: In S5, the intelligent monitoring system is used to collect data from physical devices and digital twin models in real time, and the anomaly detection algorithm is applied. Once an anomaly is detected, the system automatically triggers a feedback loop to analyze the cause of the anomaly and match the optimal response strategy. The system automatically adjusts the scheduling plan based on the nature of the anomaly, reallocates resources and tasks, and prioritizes solving urgent problems. The digital twin model is updated synchronously, adjusting the mapping between the virtual and physical systems to reflect the new status and correct the prediction model.

6. A multi-service scheduling optimization method for infrastructure security management according to claim 1, characterized in that: In S6, key data in the scheduling process is collected through a real-time monitoring system, and this data is used as input for training machine learning models. Supervised learning or reinforcement learning algorithms are applied to analyze the relationship between historical scheduling behavior and system performance. The prediction model is used to estimate future task loads, resource requirements, and potential risk points. Based on the prediction results, the scheduling algorithm dynamically adjusts task priorities and resource allocation plans.

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