A data processing method for expressway hazard source data correlation analysis

Abstract

The present application relates to a kind of data processing methods for expressway hazard source data correlation analysis, the method comprises the following steps: step S1, obtain expressway engineering construction demand data;Step S2, select the object to be evaluated and its belonging attribute set in combination with attribute topology theory, construct formal context by data classification and data description;Step S3, the formal context is preprocessed, and the adjacency matrix and the correlation matrix are obtained by conversion;Step S4, draw initial attribute topology graph using adjacency matrix, and based on initial attribute, constantly update until all causal relationships are analyzed, obtain the final data correlation visual inference result.Compared with prior art, the safety management target of expressway from construction period to operation period is considered as a whole in the present application, and the information silo in the safety management process is broken, which is convenient for the integrated management of expressway construction and maintenance.

Description

Technical Field

[0001] This invention relates to the field of highway safety management technology, and in particular to a data processing method for correlation analysis of highway hazard source data. Background Technology

[0002] Currently, domestic highway builders have not considered post-construction maintenance issues from the initial construction stage, and have not adequately considered the correlation between hazard source data. This has led to problems such as inconvenience in detection and maintenance during the maintenance period, significant traffic disruption, and frequent highway defects during the operation period.

[0003] Integrated construction and maintenance design, as one of the evaluation indicators, is of great significance to the safety management of expressways.

[0004] The integrated construction and maintenance management concept requires attention to the indivisibility of data and consideration of later maintenance issues from the initial construction stage. However, based on the current state of relevant research, existing highway construction projects have not yet addressed the correlation between data and lack methods for processing correlated data. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a data processing method for correlation analysis of hazard source data on highways.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] According to a first aspect of the present invention, a data processing method for correlation analysis of hazard source data on highways is provided, the method comprising the following steps:

[0008] Step S1: Obtain highway construction demand data;

[0009] Step S2: Combining attribute topology theory, select the object to be evaluated and its attribute set, and construct a formal background through data classification and data description;

[0010] Step S3: Preprocess the formal background and transform it to obtain the adjacency matrix and the incidence matrix;

[0011] Step S4: Use the adjacency matrix to draw the initial attribute topology graph, and continuously update it based on the initial attributes until all causal relationships are analyzed, and the final data correlation visualization inference result is obtained.

[0012] Preferably, the highway construction demand data in step S1 includes highway construction hazard data, construction quality control index data, highway building functional attribute data, and operation period hazard data.

[0013] Preferably, the attribute set in step S2 includes construction-phase hazard attributes, quality control indicators, building function attributes, and operation-phase hazard attributes.

[0014] Preferably, the preprocessing of the formal background in step S3 includes reducing global objects and global attributes.

[0015] Preferably, the mathematical expression of the formal background is K:=(G,M,I), where G is the set of objects in the formal background, M is the set of attributes, and I represents the relationship between G and M; the attributes of the formal background include compatibility, inclusion, and mutual exclusion relationships.

[0016] Preferably, step S4 specifically includes the following sub-steps:

[0017] Step S4.1: Construct an initial attribute topology graph based on the adjacency matrix, and label the corresponding object set of the attribute on the attribute in the form of weight;

[0018] Step S4.2: Select initial attributes, update data and formal background, and continuously update the attribute topology graph until all causal relationships are analyzed and the final data correlation visualization inference result is obtained.

[0019] Preferably, the initial attribute in step S4.2 is the attribute with the fewest corresponding object sets in the attribute topology graph.

[0020] Preferably, the compatibility, inclusion, and mutual exclusion relationships among the attributes of the formal background are represented in the attribute topology graph as bidirectional arrows, unidirectional arrows, and no connections, respectively.

[0021] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement any of the methods described above.

[0022] According to a fourth aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the methods described herein.

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] 1) This invention addresses the lack of integrated construction and maintenance of highways due to the failure to consider post-construction maintenance issues during the construction period. The data correlation analysis method adopted by this invention takes into account the safety management objectives of highways from the construction period to the operation period, breaking down information silos in the safety management process.

[0025] 2) The attribute topology graph analysis results obtained by the method of the present invention are convenient for highway decision-making and management, and are conducive to controlling construction hazards from the source. Attached Figure Description

[0026] Figure 1 This is a flowchart of the method of the present invention;

[0027] Figure 2 For attribute topology graph;

[0028] Figure 3 A diagram illustrating the inference of causal relationships;

[0029] Figure 4 This is the initial attribute topology graph in the embodiment;

[0030] Figure 5 This is the attribute topology graph updated for the first time in the embodiment;

[0031] Figure 6 This is the second updated attribute topology graph in the embodiment;

[0032] Figure 7 This is the final attribute topology diagram in the embodiment. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0034] Example

[0035] This embodiment provides a data processing method for correlation analysis of highway hazard source data, including: first, preprocessing the data to filter duplicate and redundant information; then, deriving the adjacency matrix and correlation matrix based on the purified formal background; and finally, drawing a topology graph based on the data co-occurrence relationship and causal inference principle. The initial topology graph is the most complex, requiring the selection of initial attributes to complete the data and formal background update process. Each update continuously updates the topology graph until all causal relationships are analyzed.

[0036] like Figure 1 As shown, the data processing method given in this embodiment specifically includes the following steps:

[0037] Step 1: Hazard data collection.

[0038] Collect relevant information about the highway project and conduct surveys of construction sites along the highway project route to identify the construction hazards of each unit project, such as engineering design data (geological survey data, design schemes and drawings, construction organization design and construction plans, etc.), legal and regulatory documents (mainly national and industry standard documents and technical regulations related to construction), systematic documents such as the company's own safety management methods, and other information.

[0039] Step 2: Construct a formal background through data classification and data description.

[0040] Based on modern safety science management theory, construction hazard data are divided into four categories: human factors, material factors, environmental factors, and management factors. Human factors mainly refer to improper human behavior that violates design principles and operating rules, leading to system malfunctions and accidents. These are primarily divided into two categories: random errors such as misinterpretation of information and signals, incorrect tool selection, and incorrect material use, as well as systemic errors. Material factors mainly refer to unsafe conditions caused by machinery, equipment, tools, and facilities not meeting safety requirements, as well as the failure state of machinery that prevents normal construction.

[0041] The data classification is based on standards and specifications, and the big data survey data support for the service nature during the operation period mainly refers to JTG F40-2004 Technical Specifications for Construction of Asphalt Pavement of Highway, JTG B01-2003 Technical Standards for Highway Engineering, JTG D62-2004 Design Specifications for Reinforced Concrete and Prestressed Concrete Bridges and Culverts of Highway, JTG E60-2008 On-site Testing Procedures for Highway Subgrade and Pavement, JTG F30-2003 Technical Specifications for Construction of Cement Concrete Pavement, JTGD50-2004 Design Specifications for Asphalt Pavement, JTGF80(1)-2004 Standards for Quality Inspection and Evaluation of Highway Engineering, and the "Basic Specifications for Risk Identification, Assessment and Control of Safety Production in Highway and Waterway Industry (Trial)", etc.

[0042] Formal background is the research object of formal concepts and also the data expression method of formal concept analysis. It can be represented by the mathematical expression of formal background as K:=(G,M,I), where G is the set of objects in formal background, M is the set of attributes, and I represents the relationship between G and M. The attributes of formal background include compatibility, inclusion and mutual exclusion relationships.

[0043] The formal background of the present invention will be explained below with reference to Tables 1 to 4.

[0044] Table 1 shows the background in a simplified form, where attributes a and d are mutually exclusive.

[0045] Table 1

[0046] a b c d 1 × × 2 × × 3 × ×

[0047] Table 2 shows the formal background 1, and Table 3 shows the formal background after preprocessing.

[0048] Table 2

[0049] a b c d 1 × × × 2 × × × × 3 × × × 4 × × ×

[0050] Table 3

[0051] b c d 1 × × 3 × × 4 × ×

[0052] Table 4 shows the formal background 2, with appendix. Figure 2 The topology diagram corresponding to the background in Table 4.

[0053] Table 4

[0054]

[0055]

[0056] Step 3: Data preprocessing.

[0057] Data preprocessing mainly involves reducing global attributes and global objects. A global object refers to an object to be evaluated where a specific attribute fully covers all four types of attributes.

[0058] The process of building an adjacency matrix involves clarifying the relationships between attributes, which can be categorized into compatible, mutually exclusive, and inclusive relationships.

[0059] The attribute topology diagram constructed from Table 4 is attached. Figure 2 As shown in the diagram, this diagram allows direct observation of the relationships between the attributes in the formal background. For example, the arrow between attributes a and b is bidirectional, indicating a compatibility relationship; the arrow between attributes b and d is unidirectional, indicating an inclusion relationship; and attributes c and d have no connection relationship, indicating a mutual exclusion relationship.

[0060] Step 4: Visualize and infer data correlations.

[0061] For the appendix Figure 2 Visual inferences of causal relationships are shown in the attached figure. Figure 2 The causal inference of the data correlation needs to be based on the assumptions of the formal background and to determine the causal relationship and associated attributes between them.

[0062] The object described by the attribute topology graph is the formal background, and the update process of the attribute topology graph will omit the update expression of the formal background.

[0063] Optionally, the selection of initial attributes needs to be explained: During the update of the attribute topology graph, the selection of initial attributes is random, but in order to prevent the loss of causal inference results and the increased complexity of traversal when deleting the "cause" attribute in the causal relationship, the attribute with the fewest corresponding object sets is selected as the initial attribute.

[0064] The following section uses specific examples to illustrate the data correlation analysis method.

[0065] In step one, data from five bridges in a certain highway project in a certain province were selected as the basis for analysis. This data was then combined with relevant engineering data from the project and reviewed in accordance with standards and literature to establish the data foundation.

[0066] In step two, for the bridge deck construction hazard data of a certain expressway project in Henan, hazard data during the construction and operation periods were collected in real time from the construction site. The corresponding safety construction standards and relevant information on the architectural function requirements of the expressway users during the operation period were retrieved. The classification basis of the hazard data is shown in Table 5 below. For the bridge deck paving construction process, the attribute topology of the hazard data of the five selected bridges is shown in Table 6.

[0067] Table 5

[0068]

[0069]

[0070] Table 6

[0071]

[0072] The specific meanings of the four types of attributes are as follows:

[0073] A: A 11 The construction workers lacked experience; A 12 The leveling machine malfunctioned and was not repaired or maintained in a timely manner;

[0074] B: B 11 Before construction, the top surface of the box girder was not checked to ensure it was flat, rough, dry, and clean; B 12 A leveling machine was not used for flattening and measurement; B 13 The track plane and elevation control points under the leveling machine's traveling wheels were not set at one point every 1.25m along the bridge direction, and the plane position and elevation of the leveling layer were not controlled; B 14 After the concrete is laid, the vibratory leveling speed of the screed is greater than 3m / min;

[0075] C:C 11 Insufficient load-bearing capacity; C 12 Insufficient stability; C 13Insufficient construction safety and traffic safety; D 11 Excessive cement usage leads to a violent hydration reaction, causing volume reduction.

[0076] D:D 12 Shrinkage cracks on the surface of concrete components caused by improper curing, resulting in water loss and drying shrinkage; D 13 Creep and support displacement.

[0077] The aforementioned formal background refers to the fact that, for randomly selected data from the bridge deck paving construction, the actual existing attributes are checked in Table 6, based on four categories of attribute characteristics: construction period hazard source attributes, quality control indicators, building function attributes, and operation period hazard source attributes.

[0078] Step 3 requires data preprocessing to reduce global objects and global attributes, but this step is not needed for the formal background in Table 6.

[0079] For step four, the adjacency matrix established based on the formal background is shown in Table 7.

[0080] Table 7

[0081]

[0082]

[0083] The purpose of establishing the adjacency matrix is ​​mainly to facilitate the construction of the topology graph. For the initial topology construction in step four, the corresponding object sets of attributes are labeled on the attributes in the form of weights. Based on the formal background in Table 6 and the adjacency matrix in Table 7, the original topology graph is constructed (see Appendix). Figures 4-7 .

[0084] For step five, the results of the visualized causal inference are shown below. The causal relationship basically satisfies the following: construction period hazard source attribute → quality attribute → building function attribute → operation period hazard source attribute, and is consistent with the engineering construction logic.

[0085] 1) Select A 12 A 12 Leveling machine malfunction, failure to repair and maintain in a timely manner → B 13 The track plane and elevation control points under the leveling machine's traveling wheels were not set at one point every 1.25m along the bridge direction, and the plane position and elevation of the leveling layer were not controlled; C 12 Insufficient stability, D 13 Creep and support displacement;

[0086] 2) Select A 11 A 11 Inexperienced construction workers → B 14 After the concrete is laid, the vibratory leveling speed of the screed should be greater than 3 m / min.13 Insufficient construction safety and traffic safety;

[0087] 3) Select A 11 :C 11 Insufficient load-bearing capacity, D 11 Excessive cement usage leads to a violent hydration reaction, causing volume shrinkage, D 12 Shrinkage cracks on the surface of concrete components caused by improper curing, resulting in water loss and drying shrinkage → A 11 The construction workers lacked experience;

[0088] 4) Select B 11 B 12 :C 11 Insufficient load-bearing capacity, D 11 Excessive cement usage leads to a violent hydration reaction, causing volume shrinkage, D 12 Shrinkage cracks on the surface of concrete components caused by improper curing, resulting in water loss and drying shrinkage → B 11 Before construction, the top surface of the box girder was not checked to ensure it was flat, rough, dry, and clean. 12 A leveling machine was not used for flattening and measurement;

[0089] 5) Select A 12 D 12 :C 11 Insufficient load-bearing capacity, D 11 Excessive cement usage leads to a violent hydration reaction, causing volume shrinkage → A 12 The leveling machine malfunctioned and was not repaired or maintained in a timely manner. 12 Shrinkage cracks on the surface of concrete components caused by improper curing, resulting in water loss and drying shrinkage.

[0090] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0091] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0092] The processing unit executes the various methods and processes described above, such as methods S1 to S4. For example, in some embodiments, methods S1 to S4 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S4 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S4 by any other suitable means (e.g., by means of firmware).

[0093] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0094] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0095] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0096] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A data processing method for correlation analysis of hazard source data on highways, characterized in that, The method includes the following steps: Step S1: Obtain highway construction demand data; the highway construction demand data includes highway construction hazard source data, construction quality control index data, highway building functional attribute data, and operation period hazard source data; Step S2: Combining attribute topology theory, select the object to be evaluated and its attribute set, and construct a formal background through data classification and data description; the attribute set includes construction-phase hazard attributes, quality control indicators, building functional attributes, and operation-phase hazard attributes; the mathematical expression of the formal background is: ,in, A collection of objects within a formal context. For a collection of attributes, express and The relationships between them; the attributes of the formal background include compatibility, inclusion, and mutual exclusion relationships; Step S3: Preprocess the formal background and transform it into an adjacency matrix and an incidence matrix; the preprocessing of the formal background includes reducing global objects and global attributes; Step S4: Use the adjacency matrix to draw the initial attribute topology graph, and continuously update it based on the initial attributes until all causal relationships are analyzed, and the final data correlation visualization inference result is obtained.

2. The data processing method for correlation analysis of highway hazard source data according to claim 1, characterized in that, Step S4 specifically includes the following sub-steps: Step S4.1: Construct an initial attribute topology graph based on the adjacency matrix, and label the corresponding object set of the attribute on the attribute in the form of weight; Step S4.2: Select initial attributes, update data and formal background, and continuously update the attribute topology graph until all causal relationships are analyzed and the final data correlation visualization inference result is obtained.

3. The data processing method for correlation analysis of highway hazard source data according to claim 2, characterized in that, The initial attribute in step S4.2 is the attribute with the fewest corresponding object sets in the attribute topology graph.

4. The data processing method for correlation analysis of highway hazard source data according to claim 2, characterized in that, The compatibility, inclusion, and mutual exclusion relationships among the attributes of the formal background are represented in the attribute topology graph as bidirectional arrows, unidirectional arrows, and no connections, respectively.

5. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

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