Transport hub maintenance scheduling efficiency optimization method based on variable topology super network deconstruction

Abstract

The invention discloses a transport hub maintenance scheduling efficiency optimization method based on variable topology super-network deconstruction. The method comprises the following steps: step 1, carrying out mapping modeling on related maintenance elements in a current real-time maintenance scheduling scene of a transport hub and an association relationship between the maintenance elements into a super-graph network model; 2, representing the basic uncertain features of the topological structure of the hypergraph network CNN; 3, according to the hypergraph network model, calculating to obtain a measurement index evaluation value; 4, comparing the calculated three indexes with a reasonable value interval range of a historical scheduling result, and judging the rationality of a current scheduling state; and 5, based on the measurement index, improving the maintenance scheduling efficiency of the transit hub. The method has the beneficial effects that the execution efficiency, the resource cooperation degree and the dynamic robustness of maintenance scheduling can be effectively improved through super-network modeling and quantitative analysis, and bottleneck positioning and optimization are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to a maintenance scheduling optimization method, more particularly to a transfer hub maintenance scheduling efficiency optimization method based on variable topology super network decomposition. BACKGROUND

[0002] Airport, port and other transfer hubs as the core nodes of regional logistics network, its efficient operation is highly dependent on timely and reliable maintenance support. The daily maintenance of the transfer hub not only contains the field infrastructure, conventional equipment, special equipment, but also involves the temporary parking of transport carriers (aircraft, ships, etc.). Maintenance tasks usually need to be arranged periodically according to the actual needs of maintenance, such as daily, weekly, monthly, seasonal and annual time windows, or immediate dynamic response scheduling (equipment inspection, facility maintenance, equipment repair, fault finding, emergency repair, etc.). Maintenance work not only involves routine equipment repair, but also a series of highly specialized and technical complex tasks, covering mechanical, electrical, structural, energy, safety and other fields, so it needs to involve different maintenance resource scheduling, such as different professional technicians (different job responsibilities, professional fields, technical proficiency, professional qualifications, etc.), different professional maintenance tools (such as oscilloscope, logic analyzer, etc.).

[0003] In the daily operation of the transfer hub, maintenance scheduling has the characteristics of dynamic, multi-concurrent and high linkage. At present, although intelligent operation and maintenance monitoring and control, large model driven auxiliary decision making, special AI wisdom assistant and other means have been introduced to help improve the maintenance response and scheduling service capability, there are still problems such as low efficiency of multi-element cooperation, poor global allocation of resources, and insufficient response to dynamic disturbance, which have not been effectively solved. In view of the dynamic and uncertain maintenance requirements and task environment in the daily operation of the transfer hub, it is of great significance to reasonably model the multi-collaboration elements such as maintenance tasks and maintenance resources, identify and analyze the coupling relationship between elements in multiple dimensions, and quantitatively measure the expected execution efficiency and robustness of immediate maintenance scheduling arrangement from the perspective of global optimization, in order to optimize the maintenance process and scheduling arrangement, maximize the release of limited maintenance resources, and improve the maintenance service capability under the constraint of maintenance cost, and to ensure and improve the efficiency of daily operation of the transfer hub. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a transfer hub maintenance scheduling efficiency optimization method based on variable topology super network decomposition, which solves the problems of insufficient processing capacity, low scheduling efficiency, poor resource cooperation and weak robustness of the existing maintenance scheduling method in dynamic and uncertain task environment. The present application realizes real-time optimization of transfer hub maintenance scheduling through variable topology super network decomposition and quantitative analysis, and improves the overall efficiency.

[0005] To achieve the above object, the present application provides the following technical solutions: A transport hub maintenance scheduling performance optimization method based on variable topology super network decomposition, comprising the following steps: step one, mapping and modeling the maintenance elements involved in the current real-time maintenance scheduling scene of the transport hub and the correlation therebetween into a supergraph network model; step two, representing two basic uncertain features of the supergraph network CNN topology structure under a dynamic and uncertain maintenance task environment; step three, based on the variable topology super network model mapped and constructed for the current real-time maintenance scheduling scene in steps one and two, fusing network feature quantitative analysis, and combining the multi-coupling maintenance task concurrent collaborative response scheduling situation characteristics of the transport hub, calculating the expected execution performance, resource collaboration degree and dynamic robustness of the current real-time maintenance scheduling in three aspects, and outputting the quantitative measurement analysis results; step four, judging the rationality of the current scheduling state by comparing the evaluation values of the expected execution performance, resource collaboration degree and dynamic robustness calculated for the current real-time maintenance scheduling with the reasonable value interval range of the historical scheduling results; step five, based on the expected execution performance, resource collaboration degree and dynamic robustness obtained in step four, first positioning the maintenance scheduling bottleneck, then optimizing through dynamic scheduling, an integrated information platform and a hierarchical emergency plan, and finally iterating and verifying to realize the maintenance scheduling performance improvement of the transport hub.

[0006] As a further improvement of the present application, the supergraph network model in step one comprises: a maintenance resource sub-network for integrating the current schedulable maintenance resources, explicitly historical collaboration correlation between resources, and providing a resource pool and collaboration basis for resource optimization configuration; a maintenance task sub-network for sorting out the internal relationship of the current concurrent maintenance tasks, explicitly task importance, dependency order and execution mode, and providing decision rules for task scheduling and resource inclination; and an interlayer correlation network for connecting the maintenance resource sub-network and the maintenance task sub-network to explicitly the boundary and reliability of the task undertaken by the resources, and providing a basis for accurate matching of resources and tasks.

[0007] As a further improvement of the present application, the specific steps of representing the two basic uncertain features of the supergraph network CNN topology structure under a dynamic and uncertain maintenance task environment in step two are as follows: step two one, modeling and representing the uncertainty feature of the node-to-node edge weight disturbance; and step two two, modeling and representing the uncertainty feature of the task node probability failure.

[0008] As a further improvement of the present application, the specific way of modeling and representing the uncertainty feature of the node-to-node edge weight disturbance in step two one is as follows: considering the dynamic and uncertain maintenance task environment and the fuzziness of the quantitative evaluation value, for an associated edge s m×m , Da m×m , and Ea n×m in the edge weight matrix Qa ijThe attribute value of the edge weight is defined as [ , ], use estimation and empirical methods to determine the range of this edge weight disturbance.

[0009] As a further improvement of the present invention, the specific method of modeling and characterizing the uncertainty characteristics of the probability failure of the task node in step 22 is as follows: Considering the uncertainty of the probability failure of the task node in the dynamic and uncertain maintenance task environment, the characterization definition is as follows: l Undertake and participate in maintenance tasks tn i When the task node tn i The failure probability is tfp i .

[0010] As a further improvement of the present invention, in step 3, the three measurement indicators of expected execution efficiency, resource collaboration, and dynamic robustness of the current real-time maintenance scheduling are calculated, and the specific steps of outputting the quantitative measurement analysis results are as follows: step 31, quantitative measurement of expected execution efficiency, which is measured by comprehensively measuring the probabilistic rework impact coefficient and the execution efficiency of the critical chain in the current maintenance task subnetwork; step 32, quantitative measurement of resource collaboration, which is measured by comprehensively measuring the corrected weighted degree and expected reliability of the maintenance resource subnetwork; step 33, quantitative measurement of dynamic robustness, which is measured by comprehensively measuring the topology change response performance under super network CNN node failure and the attribute change response performance under edge weight disturbance.

[0011] Beneficial effects of the present invention: The present invention addresses the problems that existing technical solutions are insufficient in addressing the dynamic uncertainty, multi-concurrency, high linkage and other characteristics in the daily maintenance and scheduling of transfer hubs, and lack of quantitative evaluation methods for global scheduling efficiency. It has the advantages of supporting the real mapping of maintenance and scheduling elements to variable topology super network modeling representation, supporting the expected execution efficiency and robustness of real-time maintenance and scheduling arrangements from a global optimization perspective, etc., which can effectively help optimize maintenance processes and scheduling arrangements, support the maximization of limited maintenance resources, and maintenance service capabilities under maintenance cost constraints, and ensure and improve the daily operation efficiency of transfer hubs. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of the CCN network structure of the super network model; Figure 2 Schematic diagram of the network topology uncertainty of the super network model CCN. DETAILED DESCRIPTION

[0013] Draft specific implementation methods according to the claims, and the content format of the specific implementation methods is as follows: The transport hub maintenance scheduling efficiency optimization method based on variable topology super network decomposition contains four main steps: (1) The maintenance elements and their associated relationships in the current real-time maintenance scheduling scene of the transport hub are mapped and modeled as a supergraph network model: The maintenance elements and their associated relationships in the current real-time maintenance scheduling scene of the transport hub are characterized and modeled as a multi-mode multi-layer super network model: CCN=(RN, TN, NRR), wherein: RN=(Rn, Sc), representing the maintenance resource subnetwork, wherein the subnetwork node set Rn=(rn1, rn2, …, rn n ) is the n maintenance resource nodes in the current scheduling; the edge weight matrix Sc n×n The value of sc ij in the edge weight matrix Sc i is the historical collaboration association edge attribute value between rn j and rn ij , which is taken as sc ij =1 when the associated edge exists, and sc m =0 when the associated edge does not exist.

[0014] TN=(Tn, Qa, Da, Es), representing the maintenance task subnetwork, wherein the subnetwork node set Tn=(tn1, tn2, …, tn m ) is the m concurrent maintenance task nodes in the current scheduling; the edge weight matrix Qa m×m , Da m×m The values of qa ij and da ij in the edge weight matrix Qa i and Da j are the quality association edge attribute value and execution dependency association edge attribute value between task nodes tn ij and tn ij , respectively, which can be evaluated by judging the common influence degree of the two tasks on the normal operation related links of the transport hub and the interaction dependency association degree in execution; the edge weight matrix Es m×m The value of es ij in the edge weight matrix Es ij is the execution series-parallel relationship association edge attribute value between tasks, which is taken as es i =1 when there is a series-parallel execution relationship between tn j and tn i and tn j is the task immediately before tn ij , and sc =0 when there is no series-parallel association.

[0015] NRR=(Ea, Ir), representing the node association relationship between the subnetwork RN and TN layers, wherein the edge weight matrix Ean×m The value of ea in the resource node rn ij i The value of ea in the resource node rn j The attribute value of the execution reliability associated edge between the resource node rn n×m and the task node tn ij may be obtained by comprehensively evaluating its competence in executing similar tasks in the historical record; the edge weight matrix Ir i The value of ea in the resource node rn j The attribute value of the execution reliability associated edge between the resource node rn ij and the task node tn ij is sc=1 when the associated edge exists, and sc =0 when the associated edge does not exist.

[0016] (2) Two basic uncertain characteristics of the CNN supergraph network topology in a dynamic and uncertain maintenance task environment are represented: The uncertain characteristic of the edge weight disturbance between nodes is modeled and represented: considering the dynamic and uncertain maintenance task environment and the fuzziness of the quantitative evaluation value, for the attribute value of a certain associated edge s m×m in the edge weight matrix Qa m×m , Da n×m , and Ea ij , the edge weight change interval defined by the representation is [ , ], and the range of such edge weight disturbance can be determined by estimation, experience, etc.

[0017] The uncertain characteristic of the task node probability failure is modeled and represented: considering the uncertainty of the task node probability failure in a dynamic and uncertain maintenance task environment, the representation defines that when the maintenance resource node rn l participates in the maintenance task task tn i , the failure probability of the task node tn i is tfp i , which can be calculated as follows: [1] [2] where Sm i,l is the skill requirement matching satisfaction degree of the resource node rn l and the task node tn i ; Qsp i,l is the comprehensive completion probability of the similar task experienced by the resource node rn l in the historical task record of the task tn i ; e is the number of similar task experiences included in the evaluation statistics; is the task tn iThe similarity with the kth task among e similar tasks; is the completion satisfaction of the kth task among e similar tasks; Ev T For the task tn i Achieve the satisfaction threshold corresponding to the requirements.

[0018] (3) Based on the variable topology super network model mapped and constructed for the current real-time maintenance scheduling scenario in steps (1) and (2), the network characteristics quantitative analysis is integrated, and combined with the characteristics of the multi-coupling maintenance tasks concurrent collaborative response scheduling scenario of the transfer hub, the expected execution efficiency of the current real-time maintenance scheduling is calculated. , resource collaboration , dynamic robustness 3 aspects of measurement indicators, output quantitative measurement analysis results.

[0019] (3-1) Expected execution performance Quantitative measurement Expected execution performance By integrating the probability rework impact coefficient in the current maintenance task subnetwork and critical chain execution efficiency To measure, calculate by the following formula: [3] [4] [5] in: To be affected by upstream tasks tn i Probabilistic iterative rework affects associated downstream tasks tn j Average expected rework time; Tet j Irr is the expected task cycle time without rework; j For the affected downstream tasks tn j The rework impact ratio is ;In j For downstream tasks tn j The total number of upstream tasks with execution dependencies; TCT TIN Tct is the number of task closures in the current task sequence; i is the number of tasks in the i-th task closed loop; Iif i is the execution dependency strength evaluation value of the i-th task closed loop; c is the number of tasks included in the critical task chain with the longest expected average completion time; tw i For task node tn i The comprehensive importance in the current task network is determined by calculate; The node importance degree of the node from the perspective of the Da associated edge is measured by PageRank analysis; The node importance degree of the node from the perspective of the Qa associated edge is measured by calculating the network node degree centrality; i The independent pre-determination value of the task node importance degree.

[0020] (3-2) Resource cooperation degree Quantitative measurement Resource cooperation degree The correction weighting degree of the maintenance resource sub-network is measured by comprehensive integration and the resource expected reliability Drr RN , which is calculated by the following formula: [6] [7] [8] wherein, rw i is the initial weight of the resource node, which takes the weight tw i of the task node participated by the resource node; is the correction weighting degree of the resource node rn i , which takes .

[0021] (3-3) Dynamic robustness Quantitative measurement Dynamic robustness The dynamic robustness is measured by comprehensive integration of the topology change response performance under the failure of the super network CNN node and the attribute change response performance under the edge weight disturbance , which is calculated by the following formula: [9]

[10]

[11] wherein, is the expected average value of the expected execution performance of the current task network under different edge weight value combinations in the edge weight attribute value disturbance change interval [ , ], and is the variance thereof, both of which can be calculated by means of random simulation.

[0022] (4) According to the expected execution performance , resource cooperation degree , and dynamic robustness The evaluation value of the three aspects of the metric index is compared with the reasonable value interval range of the historical scheduling result to determine the rationality of the current scheduling state.

[0023] (5) Based on the expected execution performance obtained above , resource cooperation degree and dynamic robustness , first locate the maintenance scheduling bottleneck, then optimize through dynamic scheduling, integrated information platform and hierarchical emergency plan, and finally iterate and verify to realize the optimization and improvement of the maintenance scheduling efficiency of the transfer hub.

[0024] The following examples are provided in this embodiment: For a transfer hub A, analyze the real-time maintenance scheduling scene at time t, sort out the maintenance elements such as maintenance resources and maintenance tasks involved and their correlation, map and convert, and model to represent the corresponding variable topology super network model: CCN=(RN,TN,NRR), wherein the maintenance task subnetwork contains 6 maintenance task nodes Tn=(tn1,tn2,tn3,tn4,tn5,tn6), the maintenance resource subnetwork contains 9 maintenance resource nodes RN=(rn1,rn2,rn3,rn4,rn5,rn6,rn7,rn8,rn9), and the network node edge weight matrix Sc 9×9 , Qa 6×6 , Da 6×6 , Es 6×6 , Ea 9×6 , Ir 9×6 is obtained by quantitative conversion and evaluation, wherein the uncertain topology edge weight disturbance is considered, and the attribute values in the matrix Qa 6×6 , Da 6×6 , Ea 9×6 are interval range values.

[0025] According to the skill requirement matching satisfaction degree of each resource node and maintenance task node with execution correlation edge, a quantitative matrix Sm 9×6 is obtained by evaluation, and the first 10 (e=10) similar task experiences in the historical record of each resource node with greater similarity to the maintenance task content participated by the resource node are selected as the evaluation basis. The similar task comprehensive completion probability matrix Qsp 9×6Then, the failure probability values of each task node under the current scheduling configuration are calculated by formula 1 as tfp1=0.13, tfp2=0.21, tfp3=0.22, tfp4=0.16, tfp5=0.18, and tfp6=0.25.

[0026] The pre-judgment values of the importance of each task node are wtt1, wtt2, wtt3, wtt4, wtt5, and wtt6. The importance of each task node in Da 6×6 Node importance value from the perspective of associated edges , , , , , The importance of each task node in Qa 6×6 Node importance value from the perspective of associated edges , , , , , Then, the comprehensive importance of each task node in the current task network is calculated by formula as tw1=0.19, tw2=0.25, tw3=0.18, tw4=0.15, tw5=0.11, and tw6=0.12.

[0027] The expected execution cycle time values of each task under the condition of no rework event are evaluated as Tet1, Tet2, Tet3, Tet4, Tet5, and Tet6. According to the quality-efficiency correlation / degree, execution dependency / degree, and task inter-serial relationship information in matrices Qa 6×6 , Da 6×6 , and Es 6×6 , the average expected value matrix of the rework time of each task is obtained Then, the rework influence ratio values of each task are calculated by formula as Irr1, Irr2, Irr3, Irr4, Irr5, and Irr6. Combined with the information in Es 6×6 and , the task item value c contained in the critical task chain with the longest sum of expected average completion time is determined, and the execution efficiency value of the critical chain is calculated by formula 4 Combined with the information in Da 6×6 and Es 6×6 , the task closed loop value TCT TINAnd the number of tasks in each task closed loop and the execution dependency strength evaluation value, then the probability rework impact coefficient in the current maintenance task subnetwork is calculated according to Formula 5 Then, according to Formula 3, we can calculate the expected execution efficiency quantitative measurement value under the current maintenance scheduling configuration: =36.84.

[0028] Let the initial importance value of each resource node be equal to the weight of the task node that the resource node participates in (take the maximum value if participating in multiple tasks), and get the initial weight value rw1, rw2, rw3, rw4, rw5, rw6 of each resource node. According to the formula Calculate the modified weighted degree of each resource node and use it into formula 7 to calculate the modified weighted degree of the maintenance resource subnetwork. , then according to Ea 9×6 The expected resource reliability Drr is calculated using formula 8 RN , put it into formula 6 to calculate the resource collaboration metric value =0.34.

[0029] Substitute the data values ​​of tw1, tw2, tw3, tw4, tw5, tw6 and tfp1, tfp2, tfp3, tfp4, tfp5, tfp6 and calculate the topology change response performance metric under the failure of the hypernetwork CNN node according to formula 10 According to the edge weight matrix Qa 6×6 、Da 6×6 、Ea 9×6 The range of each attribute in the range is calculated by random simulation method and The value is brought into formula 11 to calculate the attribute change response performance metric under edge weight disturbance Then and Substitute the value into formula 9 to calculate the dynamic robustness quantitative metric value =15.73.

[0030] Combined with the expected execution performance calculated for the current real-time maintenance scheduling conversion , resource collaboration , dynamic robustness The evaluation values ​​of the three measurement indicators can be used to judge the rationality of the current scheduling status based on the reasonable value range of historical scheduling results, or after adjusting the maintenance task execution sequence or maintenance resource allocation plan, the three quantitative indicators can be evaluated and calculated again for comparative judgment, and then the maintenance task can be optimized based on the judgment results.

[0031] To sum up, the transport hub maintenance scheduling efficiency optimization method based on variable topology super network decomposition can effectively help optimize the maintenance process and scheduling arrangement through evaluation.

[0032] The above only describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments. Any technical solution falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall also be considered as the protection scope of the present application.

Claims

1. A method for optimizing the performance of a transport hub based on variable topology hypernetwork deconstruction, characterized in that: Comprising the following steps: Step one, map the involved maintenance elements and their inter-relationships in the current real-time maintenance scheduling scenario of the transfer hub to a hypergraph network model; Step two, characterize two basic uncertain features of the hypergraph network CNN topology structure under dynamic and uncertain maintenance task environment; Step three, based on the variable topology hypernetwork model mapped and constructed in step one and step two for the current real-time maintenance scheduling scenario, fuse network feature quantitative analysis, and combined with the characteristics of concurrent collaborative response scheduling of multi-coupling maintenance tasks in the transfer hub, calculate the expected execution efficiency, resource collaboration degree, and dynamic robustness of the current real-time maintenance scheduling in three aspects of the measurement index, and output the quantitative measurement analysis result; Step four, according to the expected execution efficiency, resource collaboration degree, and dynamic robustness of the three aspects of the measurement index evaluation value calculated for the current real-time maintenance scheduling, compare with the reasonable value interval range of the historical scheduling result, judge the rationality of the current scheduling state; Step five, based on the expected execution efficiency, resource collaboration degree, and dynamic robustness obtained in step four, first locate the maintenance scheduling bottleneck, then optimize through dynamic scheduling, integrated information platform, and hierarchical emergency plan, and finally iterate and verify to realize the efficiency improvement of the maintenance scheduling of the transfer hub.

2. The method of claim 1, wherein the method is based on the deconstruction of a variable topology supernetwork. The hypergraph network model in step one comprises: A maintenance resource subnetwork for integrating current schedulable maintenance resources, clarifying historical collaboration relationships between resources, and providing a "resource pool" and collaboration basis for resource optimization configuration; A maintenance task subnetwork for clarifying the internal relationship of current concurrent maintenance tasks, clarifying task importance, dependency order, and execution mode, and providing decision rules for task scheduling and resource inclination; An interlayer association network for connecting the maintenance resource subnetwork and the maintenance task subnetwork to clarify the boundary and reliability of resource task acceptance, and provide basis for resource-task precise matching.

3. The method of claim 1 or 2, wherein the method is characterized by: The specific steps of characterizing two basic uncertain features of the hypergraph network CNN topology structure under dynamic and uncertain maintenance task environment in step two are as follows: Step two one, model and characterize the uncertainty feature of the edge weight disturbance between nodes; Step two two, model and characterize the uncertainty feature of the task node probability failure.

4. The method of claim 3, wherein the method is characterized by: The specific way of modeling the uncertainty characteristics of the inter-node edge weight perturbation in step two one is as follows: considering the dynamic, uncertain maintenance task environment and the fuzziness of the quantitative evaluation value, for the edge weight matrix Qa m×m , Da m×m , Ea n×m The attribute value of a certain associated edge s ij The edge weight change interval defined by the attribute value is [ , ] The range of this edge weight perturbation is determined by the estimation value and the experience value.

5. The method of claim 3, wherein the method is characterized by: The specific way of modeling the uncertainty feature of the task node probability failure in step two is as follows: considering the uncertainty of the task node probability failure under the dynamic and uncertain maintenance task environment, the definition is that when the maintenance resource node rn l participates in the maintenance task task tn i , the failure probability of the task node tn i is tfp i .

6. The method of claim 1 or 2, wherein the method is based on the deconstruction of a variable topology supernetwork. The specific steps of calculating the expected execution efficiency, resource collaboration degree, and dynamic robustness of the current real-time maintenance scheduling in three aspects of the measurement index, and outputting the quantitative measurement analysis result in step three are as follows: Step three one, expected execution efficiency quantitative measurement, which measures by comprehensively considering the probability rework influence coefficient in the current maintenance task subnetwork and the execution efficiency of the critical chain; Step three two, resource collaboration quantitative measurement, which measures by comprehensively considering the revised weighted degree of the maintenance resource subnetwork and the expected reliability of the resource; Step three three, dynamic robustness quantitative measurement, which measures by comprehensively considering the topology change response performance under hypernetwork CNN node failure and the attribute change response performance under edge weight disturbance.

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