CN115392796B

CN115392796B - Relation map-based rapid intelligent diagnosis method for operating tunnel structure diseases

Info

Publication number: CN115392796B
Application number: CN202211326224.2A
Authority: CN
Inventors: 韩玉珍; 张连卫; 华福才; 张雷; 何纪忠; 潘毫; 聂小凡; 雷刚; 宋菲
Original assignee: Beijing Urban Construction Design and Development Group Co Ltd
Current assignee: Beijing Urban Construction Design and Development Group Co Ltd
Priority date: 2022-10-27
Filing date: 2022-10-27
Publication date: 2023-02-03
Anticipated expiration: 2042-10-27
Also published as: CN115392796A

Abstract

The invention discloses a relation map-based rapid intelligent diagnosis method for operating tunnel structure diseases, which comprises the following steps: the method comprises the following steps: building an operation tunnel disease-cause-measure relation map; step two: assigning values to nodes and edges of the operation tunnel disease-cause-measure relation map; step three: acquiring an operation tunnel defect case, and inputting basic characteristic data of tunnel defects; step four: deducing the cause of the operation tunnel disease according to the operation tunnel disease-cause-measure relation map; step five: presume the disease development trend of the operation tunnel according to the disease-cause-measure relation map of the operation tunnel; step six: and recommending disease treatment measures according to the disease-cause-measure relation map of the operation tunnel. The method and the system can realize intelligent diagnosis and treatment of the urban rail transit operation tunnel diseases by utilizing the operation tunnel disease-cause-measure relation map, improve the diagnosis and treatment efficiency of the operation tunnel diseases and reduce the operation cost of rail transit.

Description

Relation map-based rapid intelligent diagnosis method for operating tunnel structure diseases

Technical Field

The invention relates to the technical field of rail transit, in particular to a rapid and intelligent diagnosis method for operating tunnel structure diseases based on a relational graph.

Background

The service life of the rail transit design is usually very long, the line operation frequency is high, the requirement on the line operation reliability is high, but the requirements are limited by various factors, and the tunnel structure may suffer from diseases of different degrees and different types in the tunnel operation process, including uneven settlement, transverse convergence deformation, cracks, water leakage, shield segment damage, material degradation and the like. For example, since operation of a certain subway line is started, a plurality of sections have large-range uneven settlement, and the tunnel structure damage directly threatens the operation safety of the rail transit line.

The high-density operation of the existing rail transit line provides high requirements for rapid diagnosis and treatment of tunnel structure diseases. In order to ensure the operation safety of rail transit lines, the current measures are to monitor and detect regularly, find diseases in time and treat the diseases. Because the time window is short, usually only a few hours, the existing rail transit diagnosis and treatment technology is slow in time, and at least a few days are needed to make an accurate diagnosis.

Disclosure of Invention

In view of the defects of the prior art, the invention mainly aims to provide a relation map-based method for quickly and intelligently diagnosing the operating tunnel structure diseases, so as to solve the problems that the tunnel diseases are mainly diagnosed in a manual mode and the diagnosis and treatment speed is low in the prior art.

The technical scheme of the invention is as follows:

a relation map-based rapid intelligent diagnosis method for operating tunnel structure diseases comprises the following steps:

the method comprises the following steps: building an operation tunnel disease-cause-measure relation map;

step two: assigning values to the nodes and edges of the operation tunnel disease-cause-measure relation map;

step three: acquiring an operation tunnel defect case, and inputting basic characteristic data of tunnel defects;

step four: deducing the cause of the operating tunnel disease according to an operating tunnel disease-cause-measure relation map;

step five: the disease development trend of the operation tunnel is presumed according to the disease-cause-measure relation map of the operation tunnel;

step six: and recommending disease treatment measures according to the disease-cause-measure relation map of the operation tunnel.

Preferably, in the first step, the building of the disease-cause-measure relationship map of the operating tunnel includes:

identifying operation tunnel disease-cause-measure key elements and associations between the key elements;

preliminarily building an operation tunnel disease-cause-measure relation map, taking three key elements of disease, cause and measure as nodes, and taking the association among the element nodes as edges;

and correcting the preliminarily established operating tunnel disease-cause-measure relation map by applying expert knowledge.

Preferably, a two-dimensional table is used for carrying out structured representation on the disease-cause-measure relation map of the operation tunnel.

Preferably, identifying the disease comprises identifying the disease type; identifying the disease cause comprises identifying an initial condition of the disease and a disease cause; identifying the disease treatment measures comprises determining the advantages and applicable conditions of the treatment measures.

Preferably, in the second step, assigning values to the nodes and edges of the relationship graph includes: counting the actual disease cases of the existing operation tunnel, modifying a disease-cause-measure relation map according to the statistical data, and adjusting the weight values of the element nodes.

Preferably, the third step specifically includes:

acquiring a current disease of an operation tunnel disease case, inquiring a disease element type two-dimensional table, and recording the disease type as ID0;

taking the disease type ID0 as a disease element ID (result) of a child node, inquiring the disease type ID of a related parent node, and marking as ID1 (cause); then, with the ID1 as the disease element ID (result) of the child node, inquiring the disease type ID of the associated parent node, and recording the disease type ID as ID2 (cause); by analogy, a disease element ID set IDSS = { ID0, ID1, ID2, \8230;, IDi, \8230;, IDn } is obtained.

Preferably, the termination condition of the disease element ID set IDSS = { ID0, ID1, ID2, \8230;, IDi, \8230;, IDn } includes:

(1) The disease type as a father node cannot be inquired and is associated with the father node;

(2) The disease type IDi found in the IDSS already appears.

Preferably, in step four, the step of inferring the cause of the operating tunnel disease includes:

finding the cause elements associated with the disease element IDs in the IDSS to obtain an initial disease cause ID set IDS1= { IDS1-1, IDS1-2, IDS1-3, \ 8230;, IDS1-i };

eliminating disease causes which cannot appear under the IDS1 actual condition to obtain a middle disease cause ID set IDS2= { IDS2-1, IDS2-2, IDS2-3, \ 8230;, IDS2-i };

sorting the disease causes in the IDS2 according to the disease cause element weight;

and (4) carrying out investigation and verification according to the actual situation in sequence, and removing the disease causes eliminated by verification to obtain a final disease cause ID set IDS3= { IDS3-1, IDS3-2, IDS3-3, \ 8230;, IDS3-i }.

Preferably, in step five, the presuming of the trend of the operating tunnel disease development includes:

inquiring a cause-disease path according to the disease cause of IDS 3;

obtaining a cause-disease path set;

obtaining a disease path set according to the cause-disease path set;

inquiring the weight of the disease elements to sort the diseases in the disease path set;

and (5) estimating the development trend of the diseases according to the disease sequencing sequence.

Preferably, the method specifically comprises the following steps:

inquiring and obtaining a cause-disease path caused by IDS3 according to the disease cause ID set of IDS 3: path (IDS 3_ i, IDS4_ j);

wherein IDS3_ i is a cause element ID in IDS3, IDS4_ j is a disease element ID pointed by the cause element ID, and Path represents an ordered sequence consisting of (IDS 3_ i, IDS4_ j);

eliminating cause-disease paths which cannot occur under actual conditions to obtain a cause-disease path set: s _ P = { Path (IDS 3_1, ids4 _1), \8230;

according to a cause-disease Path Path (IDS 3_ i, IDS4_ j) in the S _ P, using the IDS4_ j as a father node, inquiring to obtain a child node IDS4_ k, and expanding a propagation Path into a Path (IDS 3_ i, IDS4_ j, IDS4_ k, \8230;);

eliminating disease propagation paths which are causes and cannot occur under actual conditions;

marking the disease ID set on each propagation path as IDS5;

and sorting the disease element IDs in the IDS5 according to the disease element weight, and presuming the development trend of the operation tunnel diseases according to the sorting sequence.

Preferably, the termination conditions for all forward propagation path sets of IDS3 include:

(1) The disease type which can not be used as a fruit (child node) can not be inquired and associated with the disease type;

(2) The queried disease type IDS4_ n has appeared in Path (IDS 3_ i, IDS4_ j, IDS4_ k, \8230;).

Preferably, in step six, the treatment measures include:

checking and verifying the disease element ID in the IDS5, and eliminating the disease element ID which does not occur to obtain a disease element ID set IDS6;

inquiring and acquiring an initial treatment measure element ID set IDS7 corresponding to the disease element ID according to the disease element ID of the IDS6;

listing the use conditions corresponding to each initial treatment measure, screening according to actual conditions, and removing the limited treatment measures which cannot be implemented from the IDS7 to obtain a final treatment measure ID set IDS8;

and (5) inquiring the weight of the elements of the treatment measures to sort the treatment measures in the IDS8, and recommending the treatment measures in sequence.

Preferably, the method for rapidly and intelligently diagnosing the operating tunnel structure diseases based on the relational graph further comprises updating and expanding the operating tunnel disease-cause-measure relational graph according to the disease treatment tracking feedback condition.

Compared with the prior art, the invention has the beneficial effects that: the method for rapidly and intelligently diagnosing the operating tunnel structure diseases based on the relational graph can be used for diagnosing a new currently occurring operating tunnel disease case by utilizing the established operating tunnel disease-cause-measure relational graph, deducing possible disease causes by inputting disease types, conjecturing possible disease development trends and recommending treatment measures. The rapid intelligent diagnosis method for the operating tunnel structure diseases based on the relational graph realizes intelligent diagnosis and treatment of the urban rail transit operating tunnel diseases, improves the efficiency of diagnosis and treatment of the operating tunnel diseases, and reduces the operating cost of rail transit.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary and that other implementation drawings may be derived from the provided drawings by those of ordinary skill in the art without inventive effort.

The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so that those skilled in the art will understand and read the present invention, and do not limit the conditions for implementing the present invention, so that the present invention has no technical essence, and any modifications of the structures, changes of the ratio relationships, or adjustments of the sizes, should still fall within the scope covered by the technical contents disclosed in the present invention without affecting the efficacy and the achievable purpose of the present invention.

FIG. 1 is an overall flowchart of a method for rapidly and intelligently diagnosing a structural defect of an operating tunnel based on a relational graph according to an embodiment of the invention;

fig. 2 is a diagram illustrating a disease-cause-measure relationship map structure of an operating tunnel according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are described in further detail below with reference to the embodiments and the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.

It is to be understood that the terms "comprises/comprising," "consists of/8230; \8230"; "consists of," or any other variation, are intended to cover a non-exclusive inclusion, such that a product, device, process, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such product, device, process, or method as may be desired. Without further limitation, an element defined by the phrases "comprising/including" \8230; "comprising 8230;" \8230; and \8230; "comprises;" does not exclude the presence of additional like elements in a product, device, process, or method comprising the element.

Based on the frequent operation of the current and future urban rail transit lines, a method capable of rapidly solving the problem of tunnel disease diagnosis and treatment is urgently needed.

Therefore, a relation map for rapidly and intelligently diagnosing and treating the diseases of the urban rail transit operation tunnel structure is built, and the relation map is applied to disease diagnosis and treatment of the operation tunnel structure. The following describes the implementation of the present invention in detail with reference to preferred embodiments.

As shown in FIG. 1, the invention discloses a relation map-based rapid intelligent diagnosis method for operating tunnel structural diseases, which comprises the following steps:

step two: assigning values to nodes and edges of the operation tunnel disease-cause-measure relation map;

step four: deducing the cause of the operation tunnel disease according to the operation tunnel disease-cause-measure relation map;

step five: presume the disease development trend of the operation tunnel according to the disease-cause-measure relation map of the operation tunnel;

The invention applies the established disease-cause-measure relation map of the operation tunnel to disease diagnosis and treatment of the operation tunnel, improves the speed of disease diagnosis and treatment, compresses the work which can be completed only by a few days in the prior art to be within a few hours, and improves the speed of disease diagnosis by more than 10 times.

In the first step, the method identifies each element node such as the disease-cause-measure of the operation tunnel based on expert knowledge, identifies the association among the element nodes, takes the elements such as different types of diseases, causes and treatment measures as the nodes, and takes the association among the element nodes as the edges, and builds the element relation map structure of the disease-cause-measure of the operation tunnel.

According to the invention, the relation map can be used for timely deducing the cause of the diseases and inferring the development trend of the diseases according to the diseases and other diseases related to the current operation tunnel diseases, further recommending treatment measures and comprehensively, timely and effectively solving the problem of the diseases faced by the current operation tunnel. The relation graph also realizes the statistics, collection, analysis and other work which originally need to be completed by experts through a computer, and reduces the dependence on expert resources to a certain extent.

Specifically, on the basis of identifying the operation tunnel disease-cause-measure key elements and the correlation between the key elements, the operation tunnel disease-cause-measure relation map structure shown in fig. 2 is established.

Preferably, the type of the operation tunnel defect is identified. For example, for the shield tunnel operated by urban rail transit, the disease types include cracks, leakage water, segment dislocation, segment structural damage, uneven settlement, convergence deformation, material deterioration and the like.

And (4) identifying initial conditions, causes and the like of the diseases of the operation tunnel. For example, for an urban rail transit operation shield tunnel, the initial condition of the disease comprises regional stratum settlement, soft soil stratum, liquefied sand and the like, and the inducement of the disease comprises train vibration, upper part stacking, excavation of a near foundation pit, tunnel crossing, underground water level change and the like.

The method for treating the tunnel diseases is to define the advantages and applicable conditions of the treatment measures. For example, micro-disturbance double-liquid grouting is an effective technical means for treating uneven settlement of a shield tunnel, but is not suitable for shield segments with serious leakage.

On the basis of fully identifying the three key elements of the diseases, the causes and the treatment measures and the incidence relation among the elements, an operation tunnel disease-cause-measure relation map is built, the three key elements of the diseases, the causes and the measures are used as nodes, and the directed incidence among the element nodes is used as an edge. The direction of the edges represents causal associations between nodes. For example, the edge between the cause element and the disease element caused by the cause element is pointed to the disease element by the cause element; edges among the disease element classes point to secondary disease elements from primary disease elements; the edge between the treatment measure element and the disease element points to the disease element from the treatment measure element.

And correcting the preliminarily established operating tunnel disease-cause-measure relation map by applying expert knowledge. For example, no association is established between the uneven settlement and the segment dislocation in the original relation map, and experts judge that the dislocation between shield segments is likely to occur when the operating tunnel generates uneven settlement according to experience and logic of a large number of case diagnoses, so that the association can be established between two key elements of the uneven settlement and the segment dislocation.

In the second step, the assignment of the nodes and edges of the relational graph specifically comprises: and counting the actual disease cases of the existing operation tunnel, modifying the disease-cause-measure relation map according to the statistical data, and adjusting the weight values of the element nodes. It is easy to understand that the more the number of occurrences of the element node, the greater the weight.

Preferably, the invention uses the element nodes and the number of times the association occurs in the actual disease case as its weight. For example, if 34 cases of uneven settlement of the shield tunnel are counted, the weight of the critical element "uneven settlement" of the problem class is set to 34.

In some embodiments, the operating tunnel disease-cause-measure relationship map is structurally represented using a two-dimensional table. The method comprises an operation tunnel disease element type two-dimensional table, a cause element type two-dimensional table, a treatment measure element type two-dimensional table, a disease-cause correlation two-dimensional table, a disease-disease correlation two-dimensional table and a disease-treatment measure correlation two-dimensional table.

Specifically, as shown in table 1, five types of ID, disease name, index, control standard and weight are set in the two-dimensional table of the operating tunnel disease element type.

TABLE 1 two-dimensional table of disease element classes

ID	Disease and disease	Index (I)	Control standard	Weight of
					1	Sedimentation	Single point settlement	≤8mm	10
2	Sedimentation	Rate of sedimentation	≤81mm/d	7
					3	Differential settlement	Radius of curvature	≥15000m	23
4	Convergent distortion	Transverse convergence deformation	≤3mm	5
					5	Crack (crack)	Width of crack	≤1mm	64
8	Duct piece staggering platform			5
					9	Segment breakage			3
……	……	……	……

As shown in table 2, the disease cause two-dimensional table is provided with four types of ID, cause, classification, and weight.

TABLE 2 two-dimensional table of cause elements

ID	Cause of disease	Classification	Weight of
				1	Soft soil stratum	Basic conditions	25
2	Defect of construction	Basic conditions	12
				3	Liquefied soil layer	Basic conditions	9
4	Upper ground surface loading	Inducement	7
				5	Train load	Inducement	15
6	Near foundation pit excavation	Inducement	35
				7	Rise of ground water level	Inducement	6
8	Zone formation subsidence	Inducement	5
				……	……	……	……

As shown in table 3, four categories of ID, treatment measure, applicable condition, and weight are set in the two-dimensional table of the disease treatment measure.

TABLE 3 two-dimensional form of treatment measure element classes

ID	Treatment measures	Application conditions	Weight of
				1	Tunnel polyurethane grouting	During periods of outage	15
2	Micro-motion double-liquid grouting in tunnel	During periods of outage	43
				3	External micro-motion double-liquid grouting	Acting on both sides of the tunnel	12
4	Steel lining	During periods of outage	27
				5	Aramid cloth	During periods of outage	20
6	MJS grouting	During periods of outage	5
				7	Grouting after the wall	During periods of outage	15
……	……	……	……

It is easy to understand that in tables 1 to 3, the ID corresponds to the disease, cause, and type of the treatment measure, the disease, cause, and type of the treatment measure are obtained by actual case statistics of the operation tunnel disease, the index, the control standard, the classification, and the applicable condition are obtained by the expert according to the relevant standard and experience, and the weight is determined according to the frequency of occurrence of the disease, cause, and treatment measure in the actual case statistics.

As shown in table 4, the disease-cause correlation two-dimensional table of the operation tunnel is provided with three types of serial numbers, disease IDs and cause IDs;

TABLE 4 disease-cause correlation two-dimensional Table

As shown in table 5, the operation tunnel disease-disease association two-dimensional table is used to reflect a relationship chain between diseases, and is provided with three types of results of a serial number, a disease ID of a parent node, that is, a disease ID, and a disease ID of a child node, that is, a disease ID.

Wherein the disease ID of the parent node represents the disease element class ID as the antecedent, and the disease ID of the child node represents the disease element class ID as the result, i.e., the disease element class pointed to by the disease ID of the parent node may result in the disease element class pointed to by the disease ID of the child node. The disease IDs here are all from the two-dimensional table of disease element types in table 1.

TABLE 5 disease-disease Association two-dimensional table

Serial number	Disease ID of parent node	Disease ID of child node
			1	3 (differential sedimentation)	8 (segment staggered platform)
2	8 (segment staggered platform)	9 (segment damage)
			3	5 (crack)	10 (leakage water)
4	4 (convergent deformation)	5 (crack)
			……	……	……

As shown in table 6, the two-dimensional table relating the operation tunnel disease to the treatment measures is provided with three types of serial numbers, disease IDs and treatment measures IDs, wherein the disease IDs are from the two-dimensional table of the disease element types in table 1, and the treatment measures IDs are from the two-dimensional table of the treatment measure element types in table 3.

TABLE 6 disease-treatment measures correlation two-dimensional tables

Serial number	Disease ID	Treatment measures ID
			1	3 (differential settlement)	2 (inner tunnel micro-motion double liquid grouting)
2	4 (convergent deformation)	3 (external perturbation double liquid grouting)
			3	5 (crack)	5 (aramid cloth)
4	10 (leakage water)	1 (polyurethane grouting in tunnel)
			5	3 (differential sedimentation)	6 (MJS grouting))
6	8 (segment staggered platform)	2 (inner tunnel micro double liquid grouting)
			7	8 (segment staggered platform)	7 (grouting wall)
……	……	……

The invention sets ID for disease, cause and treatment, which can make the same disease correspond to different cause and treatment, to diagnose and treat the current disease effectively in all directions, improve the efficiency of diagnosing and treating the disease of the operation tunnel, and reduce the operation cost of rail transit.

In the third step, obtaining the operating tunnel defect case, and inputting the basic characteristic data of the tunnel defect specifically comprises:

acquiring the current diseases of an operation tunnel disease case, and inquiring the two-dimensional table of the disease element types in the table 1 to obtain the disease types, and recording the disease types as ID0;

the disease type is reversely inquired in the disease-disease association two-dimensional table in the table 5, the disease type ID0 is used as the disease element ID of the child node in the table 5, the disease type ID of the associated parent node is inquired and is marked as ID1; then, the disease element ID1 is used as the disease element ID of the child node, the disease type ID of the related parent node is inquired and is marked as ID2, and the like, so that a disease element ID set IDSS = { ID0, ID1, ID2, \8230;, IDi, \8230;, IDn } is obtained.

(1) The disease ID as the parent node cannot be found in the above table 5;

(2) The disease type IDi found in table 5 above has appeared in the IDSS.

The present invention makes a reverse query on the current disease type in the disease-disease association two-dimensional table of table 5, and searches other associated disease types to obtain a disease element ID set, so that the present disease can be solved by solving other disease problems.

And in the fourth step, the disease cause of the operation tunnel is deduced by using a relation map, and the reverse query is carried out according to the tables 2 and 4 in the relation map. It should be understood that the reverse query here refers to the "finding" of the cause by the "effect", i.e. the finding of the cause of the disease associated therewith from the disease result, as opposed to the direction of the arrow in fig. 2. Therefore, the bottom cause of the current dominant disease can be traced as far as possible, and further, the disease which is not found can be traced. The method specifically comprises the following steps:

inquiring the disease-cause correlation two-dimensional table in the table 4 according to the disease types, and searching the cause factors correlated with the ID of each disease factor in the IDSS to obtain an initial disease cause ID set IDS1= { IDS1-1, IDS1-2, IDS1-3, \ 8230;, IDS1-i };

screening according to actual disease conditions, and removing disease causes which cannot appear under the actual conditions from the IDS1 to obtain an intermediate disease cause ID set IDS2= { IDS2-1, IDS2-2, IDS2-3, \8230;, IDS2-i };

sorting the disease causes in IDS2 according to the disease cause element weights in the table 2;

and (4) carrying out investigation and verification according to the actual situation and sorting, and removing the disease cause eliminated by verification to obtain a final disease cause ID set IDS3= { IDS3-1, IDS3-2, IDS3-3, \8230;, IDS3-i }.

The disease element IDs are integrated in the disease-cause correlation two-dimensional table in the table 4 to perform reverse query, cause elements correlated with the disease element IDs in the IDSS are searched, more disease causes are obtained, a disease cause ID integration is formed, and then, the query and verification are performed according to actual conditions, so that the obtained disease causes are more comprehensive and closer to the actual disease conditions, and the treatment effect is more effective.

And in the fifth step, the relation map is used for estimating the development trend of the operating tunnel diseases, and forward query is required to be carried out according to tables 1 to 5 in the relation map on the basis of the fourth step. It should be understood that the forward query herein refers to the "cause" result, "i.e., the disease is presumed according to the cause of the disease, and the trend of the disease development is presumed according to the disease, which is the same as the direction of the arrow in fig. 2. Thus, the possible disease development caused by the current disease cause can be listed as completely as possible. The method specifically comprises the following steps:

inquiring the table 4 according to the disease cause element ID of the IDS3 to obtain a cause-disease path;

removing cause-disease paths which cannot occur according to actual conditions to obtain a cause-disease path set;

acquiring a disease path set according to the cause-disease path set;

inquiring the table 1, and sorting the diseases in the disease path set according to the weight of the disease elements;

Specifically, the table 4 is queried according to the disease cause ID set of IDS3 to obtain the cause-disease path caused by IDS 3: path (IDS 3_ i, IDS4_ j);

eliminating cause-disease paths which cannot occur under actual conditions to obtain a cause-disease path set: s _ P = { Path (IDS 3_1, IDS4 _1), path (IDS 3_2, IDS4 _2), path (IDS 3_3, IDS4 _3) \8230;);

according to the cause-disease Path in S _ P, taking Path (IDS 3_ i, IDS4_ j) as an example, inquiring the table 5, taking IDS4_ j as the disease ID of a parent node, obtaining the disease ID of a child node, marking the disease ID as IDS4_ k, expanding the propagation Path into Path (IDS 3_ i, IDS4_ j, IDS4_ k, \8230; \823030), and so on, obtaining all forward propagation Path sets of IDS 3;

eliminating disease propagation paths which are causes that actual conditions cannot occur;

marking the disease ID set on each propagation path as IDS5;

and sorting the disease element IDs in the IDS5 according to the corresponding disease element weights in the table 1, and presuming the development trend of the operation tunnel diseases according to the sorting sequence.

(1) The disease ID as the child node cannot be found in the above table 5;

(2) The disease type IDS4_ n queried in table 5 above has appeared in Path (IDS 3_ i, IDS4_ j, IDS4_ k, \8230;).

According to the invention, the cause-disease paths are obtained by inquiring the table 4, all disease path sets are further obtained, the diseases are sequenced, and the disease development trend is presumed according to the disease sequencing sequence in a forward inquiry mode.

And step six, recommending treatment measures by using the relation map, wherein reverse query needs to be carried out on the basis of step five according to the table 3 and the table 6 in the relation map. It should be understood that the reverse query here refers to the "finding" of the cause "by the" effect ", i.e. the finding of the treatment measure according to the disease, as opposed to the direction of the arrow in fig. 2. Thus, various treatment measures capable of treating the current diseases can be listed as fully as possible. The method specifically comprises the following steps:

firstly, disease element IDs in IDS5 are checked and verified, and disease element IDs which do not occur are removed to obtain a disease element ID set IDS6;

secondly, according to the disease element ID of the IDS6, inquiring the table 6, and acquiring an initial treatment measure element ID set IDS7 corresponding to the disease element ID;

then listing the use conditions corresponding to each initial treatment measure, screening according to actual conditions, and removing the limited treatment measures which cannot be implemented from the IDS7 to obtain a final treatment measure ID set IDS8;

and finally, inquiring the table 3, sequencing the treatment measures in the IDS8 according to the treatment measure element weight, and recommending the treatment measures in sequence.

According to the invention, the disease-treatment measure correlation two-dimensional table is inquired from the table 6, after screening is carried out according to the applicable conditions of the treatment measures, the treatment measures are recommended according to the disease-treatment measure correlation weights, and the current diseases can be rapidly and effectively solved by treating the actual operation tunnel disease cases according to the treatment measures recommended by the relation map.

In some embodiments, the method for rapidly and intelligently diagnosing the operating tunnel structure diseases based on the relation maps further comprises the step of updating and expanding the operating tunnel disease-cause-measure relation maps according to the disease treatment tracking feedback condition. The method specifically comprises the iteration of the relation map and the expansion of the relation map.

In the invention, the operation tunnel disease-cause-measure relation map has an autonomous learning mechanism, can be subjected to autonomous iterative evolution, and gradually reduces the dependence on expert resources.

It should be appreciated that the iterative approach to relational mapping includes: according to the relation map, a new diagnosis record is generated when the relation map is used for diagnosing the tunnel disease cases, the diagnosis record is used as a new case to correct each key element and associated weight in the original relation map after feedback and expert correction are carried out on site, and if the weight values of the key elements related to the new case are all accumulated to 1, the associated weight between the elements is updated according to the occurrence frequency.

The method for expanding the relation map comprises the following steps: according to the relation map, when deviation occurs in practical application, experts carry out thematic analysis on cases, and key elements and association among the elements which cannot be included in the existing relation map are confirmed and then added into the relation map.

Engineering applications

For example, if the disease type of a certain operation tunnel disease case is "uneven settlement", the disease type is first reversely queried in the disease-disease association two-dimensional table in table 5, and the disease type as a parent node is not associated with the disease type, then the disease element ID set IDSS = {3 (uneven settlement) }. Then, the disease-cause correlation two-dimensional table in the table 4 is inquired, and the disease cause ID correlated with the uneven settlement is searched, wherein the disease cause ID includes 4 items of train load, liquefied soil layer, soft soil stratum and regional stratum settlement, namely IDs1= {5 (train load), 3 (liquefied soil layer), 1 (soft soil stratum) and 8 (regional stratum settlement) }; since the actual condition of the disease case has no 'liquefied soil layer', the '3 (liquefied soil layer)' is removed from the IDS1, and IDS2= {5 (train load), 1 (soft soil layer), 8 (regional stratum settlement) } is obtained. And inquiring the two-dimensional table of the factor element class in the table 2 to obtain that the weights of all elements of IDS2 are respectively 5 (train load): 15,1 (soft soil formation): 25,8 (zone formation subsidence): 5, the above-mentioned associated causes are listed as IDS2= {1 (soft soil stratum, w = 25), 5 (train load, w = 15), 8 (regional stratum settlement, w = 5) }, from large to small, after being sorted. And then checking and verifying in sequence to obtain the IDS3.

Further, inspection and verification are performed in sequence, and accordingly IDS3= {1 (soft soil stratum), 5 (train load) }. The S _ P obtained by looking up the above tables 4 and 5 according to the above steps contains the following 2 possible cause-disease paths:

path _1 (1 (soft soil stratum), 3 (differential settlement), 8 (segment dislocation), 9 (segment breakage));

path _2 (5 (train load), 3 (differential settlement), 8 (segment dislocation), 9 (segment breakage)).

Therefore IDS5= {3 (differential settlement), 8 (segment staggering), 9 (segment breakage) }.

Further, referring to table 1 above, the respective weights are 3 (differential sedimentation): 23,8 (segment staggering): 5,9 (segment breakage): 3;

and after reordering according to the weight, IDS5= {3 (uneven settlement), 8 (segment dislocation), 9 (segment breakage) }, namely the possible diseases in the follow-up process.

Further, through examination and verification, the dislocation of the pipe pieces is already generated, the damage of the pipe pieces is not found, and IDS6= {3 (uneven settlement), 8 (pipe piece dislocation) } is adjusted. And querying the table 6 to obtain a treatment measure ID set IDS7= {7 (grouting after the wall), 2 (micro-motion double-liquid grouting in the tunnel), and 6 (MJS grouting) }. Because no MJS grouting condition exists on site, IDS8= {7 (wall postgrouting), 2 (in-tunnel micro-motion double-liquid grouting) }, the table 3 is inquired, the weights of the wall postgrouting and the in-tunnel micro-motion double-liquid grouting are respectively 15 and 43, and the reordered IDS8= {2 (in-tunnel micro-motion double-liquid grouting) and 7 (wall postgrouting) }. Therefore, the recommended sequence of treatment measures is as follows: 2 (micro-motion double-liquid grouting in the tunnel) > 7 (backfill grouting), so that the micro-motion double-liquid grouting in the tunnel is preferentially considered and then the backfill grouting is considered aiming at the treatment measure that the disease type is 'uneven settlement' in the project.

The method for rapidly and intelligently diagnosing the operating tunnel structure diseases based on the relational graph can utilize the operating tunnel disease-cause-measure relational graph to realize intelligent diagnosis and treatment of the urban rail transit operating tunnel diseases, compared with the traditional diagnosis method, the disease diagnosis and treatment time is calculated by days and is usually about 7 days, and by using the diagnosis method, the disease diagnosis and treatment time can be reduced to be within 4 hours, the disease diagnosis and treatment efficiency of the operating tunnel is improved, and the rail transit operation cost is reduced.

It will be readily appreciated by those skilled in the art that the above-described preferred embodiments may be freely combined, superimposed, without conflict.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims

1. A relation map-based rapid intelligent diagnosis method for operating tunnel structure diseases is characterized by comprising the following steps:

step three: acquiring an operation tunnel defect case, and inputting basic characteristic data of tunnel defects; the method specifically comprises the following steps:

taking the disease type ID0 as a disease element ID of a child node, inquiring the disease type ID of a related father node, and marking as ID1; then, using ID1 as the disease element ID of the child node, inquiring the disease type ID of the associated father node, and marking as ID2; by analogy, a disease element ID set IDSS = { ID0, ID1, ID2, \8230;, IDi, \8230;, IDn };

step four: deducing the cause of the operating tunnel disease according to an operating tunnel disease-cause-measure relation map; the method specifically comprises the following steps:

searching cause elements associated with all disease element IDs in the IDSS to obtain an initial disease cause ID set IDS1= { IDS1-1, IDS1-2, IDS1-3, \8230;, IDS1-i };

carrying out investigation and verification according to the actual situation and sorting, and removing the disease cause eliminated by verification to obtain a final disease cause ID set IDS3= { IDS3-1, IDS3-2, IDS3-3, \8230;, IDS3-i };

step five: presuming the development trend of the operating tunnel diseases according to an operating tunnel disease-cause-measure relation map; the method specifically comprises the following steps:

eliminating cause-disease paths which cannot occur under actual conditions to obtain a cause-disease path set: s _ P = { Path (IDS 3_1, IDS4 _1), \8230; path (IDS 3_ i, IDS4_ j) };

according to a cause-disease Path Path (IDS 3_ i, IDS4_ j) in the S _ P, taking IDS4_ j as a father node, inquiring to obtain a child node IDS4_ k thereof, expanding a propagation Path into a Path (IDS 3_ i, IDS4_ j, IDS4_ k, \8230; \ 8230), and so on, and acquiring all forward propagation Path sets of IDS 3;

disease IDs on each propagation path are collected and recorded as IDS5;

sorting the disease element IDs in the IDS5 according to the disease element weight, and presuming the development trend of the operation tunnel diseases according to the sorting sequence;

step six: recommending disease treatment measures according to the disease-cause-measure relation map of the operating tunnel; the method specifically comprises the following steps:

inquiring and acquiring an initial treatment measure element ID set IDS7 corresponding to the disease element ID according to the disease element ID of IDS6;

and inquiring the weight of the elements of the treatment measures to sort the treatment measures in the IDS8, and recommending the treatment measures in sequence.

2. The method for rapidly and intelligently diagnosing the operating tunnel structural diseases based on the relational graph as claimed in claim 1, wherein in the first step, the building of the operating tunnel disease-cause-measure relational graph comprises the following steps:

3. The relation-map-based rapid and intelligent diagnosis method for operating tunnel structure diseases according to claim 2, characterized in that identifying the diseases comprises identifying the disease types; identifying the disease cause comprises identifying an initial condition of the disease and a disease cause; and identifying the disease treatment measures comprises determining the advantages and applicable conditions of the treatment measures.

4. The method for rapidly and intelligently diagnosing the operating tunnel structure diseases based on the relational graph according to claim 1, wherein in the second step, the step of assigning the nodes and the edges of the relational graph comprises the following steps: counting the actual disease cases of the existing operation tunnel, modifying a disease-cause-measure relation map according to the statistical data, and adjusting the weight values of the element nodes.

5. The relation map-based rapid intelligent diagnosis method for operating tunnel structure diseases according to claim 1, characterized in that the termination conditions of all forward propagation path sets of the IDS3 include:

(1) The disease type serving as the child node cannot be inquired and associated with the child node;