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CN-122022154-A - Urban infrastructure system collaborative state dynamic monitoring system and method

CN122022154ACN 122022154 ACN122022154 ACN 122022154ACN-122022154-A

Abstract

The invention relates to the technical field of monitoring of smart cities and complex systems, in particular to a system and a method for dynamically monitoring the collaborative state of an urban infrastructure system, wherein the system comprises a multi-source data convergence and treatment module, an index quantification and weight calculation module, a system collaborative state dynamic monitoring engine, a collaborative monitoring evaluation and grading partition module and a visual decision support module; firstly, processing multi-source heterogeneous data through a self-adaptive space-time filtering fusion algorithm, secondly, determining system state index weight by adopting a CRITIC-game weighting method, and finally, dynamically and quantitatively monitoring and evaluating the coordination level of physical bearing capacity PCC, real-time running load ROL and energy efficiency environment state EES three-dimension by adopting a dynamic interaction coordination evaluation method based on toughness adjustment. The invention provides a monitoring analysis framework with dynamic property, robustness and explanatory power for the collaborative operation and maintenance of the urban infrastructure system, and also provides scientific basis for realizing systematic scheduling and toughness improvement.

Inventors

  • WANG YUAN
  • NIU YAWEI
  • YANG YIFAN
  • Ding Zimen

Assignees

  • 天津大学

Dates

Publication Date
20260512
Application Date
20260127

Claims (10)

  1. 1. The system for dynamically monitoring the collaborative state of the urban infrastructure system is characterized by comprising a multi-source data aggregation and treatment module, an index quantification and weight calculation module, a system collaborative state dynamic monitoring engine, a collaborative monitoring evaluation and grading partition module and a visual decision support module; The multi-source data aggregation and treatment module is used for acquiring original observation data of a target area, including urban and rural construction physical bearing capacity dimension PCC, real-time operation load dimension ROL and energy efficiency environment state dimension EES, in a preset time period, and carrying out cleaning and interpolation pretreatment on the original observation data to form a basic index data set; The index quantization and weight calculation module is used as a post module of the multi-source data convergence and treatment module, and adopts a self-adaptive space-time filtering fusion calculation method to process the basic index data set so as to construct a standardized index matrix Based on a matrix The index weight vectors in PCC, ROL, EES are calculated respectively by CRITIC-game weighting method and output as , , Wherein In order to be a time sample, In correspondence with the position PCC, ROL, EES of the first embodiment, For the index of the index in the dimension, 、 、 Index weight vectors of PCC, ROL, EES respectively; The system collaborative state dynamic monitoring engine comprises a multidimensional development index synthesizing unit and a collaborative state analyzing unit, and is used as a post module of an index quantization and weight calculation module for receiving , , The engine carries out linear weighted aggregation on the standardized dimension indexes according to the received weight by a multidimensional development index synthesizing unit to respectively generate time sequence indexes representing the dimension levels of PCC, ROL and EES 、 、 The collaborative state analysis unit invokes a built-in dynamic interaction collaborative assessment method based on toughness adjustment to 、 、 As input, calculate dynamic coupling strength Toughness perception integrated development level System coordination degree ; The collaborative monitoring evaluation and grading partition module comprises a collaborative grade monitoring evaluation unit, a system unbalance mode dividing unit and a key limiting factor identifying unit, and is used as a post module of a system collaborative state dynamic monitoring engine for receiving 、 、 System coordination degree The module monitors and evaluates the unit through the cooperative level firstly, and pairs according to the preset cooperative development level spectrum The value is mapped to output qualitative cooperative level of the target area, and then the system unbalance mode dividing unit is based on 、 、 The relative size relationship among the three Finally, a key limiting factor identification unit identifies and outputs a key limiting factor set which restricts the cooperative operation of the system by calculating the obstacle degree of each index aiming at the area judged to be in an unbalanced mode to form a multi-level monitoring evaluation result; The module calls the built-in visual template based on the received collaborative development level, the type of the system unbalance mode area, the key limiting factor set and the three-dimensional development index data to automatically generate a comprehensive report comprising a PCC-ROL-EES system state radar comparison graph, a development index time sequence evolution graph and a structural monitoring evaluation conclusion panel.
  2. 2. The system for dynamically monitoring the collaborative state of an urban infrastructure system according to claim 1, wherein in a multi-source data aggregation and governance module, the raw observation data comprises statistics of annual notices, spatial data of a geographic information system, remote sensing image data and internet of things sensor data; The space-time resolution of the original observation data is that the time granularity is divided into units or regular grid units according to the year, quarter or month, and the space granularity is divided into units or regular grid units according to the administrative division; the observation data of the physical bearing capacity dimension PCC comprises road network density and connectivity, critical section traffic capacity, pipe network system design capacity, power supply network maximum load and communication base station coverage density; The observed data of the real-time operation load dimension ROL comprises traffic real-time flow, regional instantaneous energy consumption, water supply capacity in peak hours, data center calculation load and public traffic passenger flow; The observation data of the energy efficiency environment state dimension EES comprise comprehensive pipe network leakage rate, unit production value energy consumption, noise standard reaching area coverage rate, important area air quality index and greening coverage rate.
  3. 3. The system for dynamically monitoring the collaborative state of an urban infrastructure system according to claim 1, wherein in a multi-source data aggregation and management module, the data cleaning comprises the steps of eliminating duplicate records and correcting obvious error data; and the data interpolation adopts a mean value interpolation, regression interpolation or space interpolation method to complement the missing data.
  4. 4. The system for dynamically monitoring the collaborative state of an urban infrastructure system according to claim 1, wherein the method comprises the following steps: S1, preprocessing a basic index data set by adopting a self-adaptive space-time filtering fusion calculation method, and specifically adopting an exponential weighting sliding filtering and dynamic standardization fusion algorithm, wherein the formula is as follows: ; Wherein, the , As a dynamic standard deviation within the current time window, For a dynamic average calculated by exponentially weighted moving average, For a predetermined length of time period, For time samples The interval from the reference point in time, For a predetermined time-decay factor, Is the time sample interval; s2, CRITIC-game weighting method is used for calculating index weight vectors in dimensions, and specifically comprises the following steps: s21, CRITIC weighting formula: ; ; Wherein, the Is of dimension Lower (th) The initial weight of the individual indicators is set, Representing dimensions Lower (th) The information amount of the individual indicators is calculated, In order to be a dynamic standard deviation of the standard, Representing dimensions Lower (th) Index number and first The correlation coefficient between the individual indices is such that, Traversing all indexes of the dimension for sum variables; S22, game theory equalization: ; Wherein, the Is of dimension Lower (th) Final weight of each index, constraint condition of the equalization is that 。
  5. 5. The system for dynamically monitoring the collaborative state of an urban infrastructure system according to claim 4, wherein the system collaborative state dynamic monitoring engine comprises the following steps: s3, the multidimensional development index synthesis unit generates a time sequence index representing three dimensional levels 、 、 : For dimensions of At time sample Index of the upper The method comprises the following steps: ; Wherein, the Is of dimension The total number of indicators to be used in the process, Is normalized index value At the time of The corresponding value is down; S4, calculating the system coordination degree by the coordination state analysis unit through a dynamic interaction coordination evaluation method based on toughness adjustment The method specifically comprises the following steps: S41, calculating dynamic coupling strength : ; Wherein, the In order to control the adjustment parameters of the steepness of the Logistic function curve, For dimensions calculated within a time window And dimension of Time-varying correlation coefficients of (2); s42, calculating the toughness perception integrated state level : ; Wherein, the Is of dimension A corresponding toughness adjustment factor is used to adjust the toughness of the steel sheet, Is of dimension Is of the complex index of (2) Time series variance of (2); s43, calculating the system collaboration degree : ; Wherein, the To adjust the parameters, the results are mapped to Interval.
  6. 6. The system for dynamically monitoring the collaborative state of an urban infrastructure system according to claim 1, wherein in the collaborative monitoring assessment and grading partition module, a collaborative development grade spectrum preset in the collaborative grade monitoring assessment unit is specifically: the collaborative development level spectrum CDGS is based on the system co-ordination The value range of (2) is divided into five grades: When (when) When the system is in a detuning stage; When (when) Early warning the attention level; When (when) The basic cooperative level; When (when) When the normal level is operated; When (when) Gao Xiaoxie peer.
  7. 7. The system according to claim 1, wherein in the collaborative monitoring assessment and classification partitioning module, the preset rule for classifying the area into the specific type of system imbalance pattern area by the system imbalance pattern classification unit is: presetting a cooperative threshold And comparing and judging And (3) with Is of the size of (2): When (when) When (1): If it is And is also provided with Judging that the physical bearing capacity is lagged out of balance area; If it is And is also provided with Judging that the load hysteresis type unbalance area is operated in real time; If it is And is also provided with Judging that the energy efficiency environment state is lag type unbalanced area; When (when) When the operation is performed, judging that the operation is in a normal region; is based on the actual application scene And (5) internal selection.
  8. 8. The system for dynamically monitoring the collaborative status of an urban infrastructure system according to claim 1, wherein the step of the key limiting factor identifying unit identifying and outputting key limiting factor sets in the collaborative monitoring assessment and classification partitioning module is specifically as follows: calculating index obstacle degree: for the region judged to be the unbalance mode, the dimension of the region is Each index below Calculating the obstacle degree : ; Wherein, the For the area The normalized mean value of the index is used to determine, Is of dimension Lower (th) Final weights of the individual indicators; Screening key factors: According to the degree of obstruction And sorting from large to small, selecting indexes of N bits before ranking to form a key limiting factor set, wherein N is a preset positive integer.
  9. 9. The system according to claim 1, wherein the structured monitoring assessment conclusion panel integrates a collaborative development level, a system imbalance pattern zone type and a list of key limiting factors in the visual decision support module.
  10. 10. A method for dynamic monitoring of co-operating states of an urban infrastructure system using a system for dynamic monitoring of co-operating states of an urban infrastructure system according to any of claims 1-9, comprising the steps of: S1, data acquisition and standardization, namely acquiring multi-source original observation data of a target area under a physical bearing capacity dimension PCC, a real-time operation load dimension ROL and an energy efficiency environment state dimension EES, preprocessing including cleaning and interpolation, and processing by adopting a self-adaptive space-time filtering fusion calculation method to generate a standardized index matrix; s2, weight calculation and index synthesis, namely based on the standardized index matrix, adopting CRITIC-game weighting method to respectively determine PCC, ROL, EES three-dimensional internal index weight vectors, and carrying out weight calculation according to the internal index weight vectors to generate a time sequence index representing the development level of each dimension 、 、 ; S3, dynamically evaluating the system coordination degree, namely, indexing the three time sequences 、 、 Inputting a dynamic interaction cooperative evaluation model based on toughness adjustment, and sequentially calculating the dynamic coupling strength of the dynamic interaction cooperative evaluation model Toughness perception integrated status level Final system co-ordination ; S4, collaborative monitoring evaluation and mode discrimination, namely according to the system collaborative degree Determining a synergy level according to the value comparison synergy development level spectrum 、 、 Judging a specific system unbalance mode region by combining a preset threshold value according to the relative magnitude relation of the three, and screening out a key limiting factor set by calculating the obstacle degree of each index aiming at the region judged to be the unbalance mode; And S5, generating and visualizing a result, namely generating a visual report comprising multi-dimensional comparison analysis and monitoring evaluation conclusion based on the collaborative development grade, the type of a system unbalance mode region, the key limiting factor set and the three-dimensional development index.

Description

Urban infrastructure system collaborative state dynamic monitoring system and method Technical Field The invention relates to the technical field of monitoring of smart cities and complex systems, in particular to a system and a method for dynamically monitoring the collaborative state of an urban infrastructure system. Background Modern urban infrastructure systems are a highly coupled complex running entity. The intelligent city management system realizes safe, efficient and tough collaborative operation, is a core target of intelligent city management, and is characterized in that the whole operation state of the system is monitored and estimated accurately in real time. However, the existing monitoring and evaluating methods have two remarkable limitations, namely, visual angle fragmentation, lack of overall collaborative perspective on an infrastructure system from three dimensions of physical bearing capacity, real-time operation load and energy efficiency environment state only aiming at single subsystem or performance indexes such as traffic flow, energy consumption and water quality, and have the defect of low applicability of the method, and the current few methods related to multi-index collaborative analysis depend on clear physical connection or topological relation data and are analyzed by constructing a network model. The method is difficult to be suitable for a fusion analysis scene of multisource asynchronous space-time data which exists in a large amount in an infrastructure system and is based on the sensors of the Internet of things, the space geographic information, the operation statistical report and the like. The method is particularly limited in that firstly, dynamic matching degree and harmony of running states in different dimensions in time sequence are difficult to directly quantify, and secondly, the interaction relation between the system running level and the internal cooperation degree cannot be effectively analyzed, so that overall efficiency reduction or linkage fault risk caused by internal mismatch of the system is difficult to early warn. Therefore, a need has arisen to break through the prior art framework, and to propose a monitoring method and a system capable of deeply fusing multi-source heterogeneous spatio-temporal data, directly quantifying the dynamic collaboration level between the multi-dimensional states in the infrastructure system, and performing comprehensive evaluation by organically coupling the "running state" and the "collaboration relationship", so as to overcome the above limitations and provide a reliable technical tool for collaborative scheduling, preventive maintenance and toughness promotion of urban infrastructure. Disclosure of Invention The invention aims to quantitatively evaluate the system cooperative operation level of the three-dimensional capacity of physical bearing capacity, real-time operation load and energy efficiency environment state related to urban and rural construction of a region, thereby providing a quantitative basis for scientific monitoring and evaluation of the cooperative state of urban and rural construction and optimization of related schemes. The invention provides a system for dynamically monitoring the collaborative state of an urban infrastructure system, which comprises a multi-source data convergence and treatment module, an index quantification and weight calculation module, a system collaborative state dynamic monitoring engine, a collaborative monitoring evaluation and grading partition module and a visual decision support module; The multi-source data aggregation and treatment module is used for acquiring original observation data of a target area, including urban and rural construction physical bearing capacity dimension PCC, real-time operation load dimension ROL and energy efficiency environment state dimension EES, in a preset time period, and carrying out cleaning and interpolation pretreatment on the original observation data to form a basic index data set; The index quantization and weight calculation module is used as a post module of the multi-source data convergence and treatment module, and adopts a self-adaptive space-time filtering fusion calculation method to process the basic index data set so as to construct a standardized index matrix Based on a matrixThe index weight vectors in PCC, ROL, EES are calculated respectively by CRITIC-game weighting method and output as,,WhereinIn order to be a time sample,In correspondence with the position PCC, ROL, EES of the first embodiment,For the index of the index in the dimension,、、Index weight vectors of PCC, ROL, EES respectively; The system collaborative state dynamic monitoring engine comprises a multidimensional development index synthesizing unit and a collaborative state analyzing unit, and is used as a post module of an index quantization and weight calculation module for receiving ,,The engine carries out linear weighted aggregation on the sta