Geological disaster early warning method and system for drilling and blasting method tunnel construction

Abstract

The invention discloses a drilling and blasting method tunnel construction geological disaster early warning method and system, and the method comprises the steps: obtaining the construction information of a drilling and blasting method historical construction tunnel, and determining the risk level; constructing word bag models of the construction information under different risk levels, and constructing a common vocabulary bag model and an all vocabulary bag model; calculating the word frequency of each vocabulary in the word bag models of all vocabularies and the word frequency in each word bag model, and constructing a word frequency table of all vocabularies and a word frequency table of risk level vocabularies; calculating the risk level word frequency quotient of each vocabulary according to the risk level vocabulary frequency table, and determining the score of each vocabulary; vocabularies in the common vocabulary bag-of-word model are removed from the to-be-detected vocabulary bag-of-word model to obtain a feature vocabulary bag-of-word model, the word frequency of each vocabulary in the feature vocabulary bag-of-word model is calculated to obtain a to-be-detected vocabulary frequency table, the word frequency quotient of the to-be-detected vocabulary frequency table and all vocabulary frequency tables is calculated, and the disaster early warning risk level of the to-be-detected tunnel in the construction period is obtained. And timely disaster early warning is provided for the tunnel in the construction period.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel disaster early warning, and in particular to a method and system for early warning of geological disasters in tunnel construction using a drill and blast method. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] During tunnel construction, disasters such as water inrush, landslides, and large deformations frequently occur, bringing huge risks and serious threats to the project. In order to ensure the safety of tunnel construction and the safety of life and property of personnel, timely discover potential disaster hazards, take preventive measures, reduce the occurrence of safety accidents, improve work efficiency, and complete construction tasks with quality and quantity, disaster monitoring and early warning are particularly important.

[0004] At present, geological disaster warnings for tunnel construction using the drill-and-blast method often rely on experienced engineers to comprehensively analyze geological information, geophysical information, monitoring information, and drilling information to give disaster levels. Engineers need to analyze text reports that reflect the state of the disaster and the degree of harm. On the one hand, reading and searching is time-consuming and labor-intensive, and inefficient, making it difficult to cope with massive amounts of tunnel construction information; on the other hand, over-reliance on engineers' subjective experience may lead to differences in subjective factors among different engineers, which may lead to false alarms. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a geological disaster early warning method and system for tunnel construction using the drill and blast method, which is based on the TF-IDF (term frequency–inverse document frequency) keyword extraction technology to provide timely disaster warning for tunnels during construction. While assisting expert evaluation and reducing interference from human subjective factors, the method and system timely and accurately predict tunnel construction disasters through word frequency analysis of tunnel construction information keywords.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for early warning of geological disasters in tunnel construction using a drilling and blasting method, comprising:

[0008] Obtain construction information of historical tunnels constructed using the drill and blast method to determine the risk level;

[0009] Construct a bag-of-words model of construction information at different risk levels, and use it to construct a bag-of-common-words model and a bag-of-all-words model;

[0010] Calculate the frequency of each word in all word bag models and the frequency of each word in each word bag model, thereby constructing a frequency table of all words and a frequency table of risk level words; calculate the risk level frequency quotient of each word according to the risk level word frequency table, and use this to determine the score of each word;

[0011] A bag-of-words model to be tested is constructed according to the construction information of the tunnel to be tested, and the words in the common bag-of-words model are removed from the bag-of-words model to obtain a feature bag-of-words model, and the word frequency of each word in the feature bag-of-words model is calculated to obtain a frequency table of the words to be tested, and the word frequency quotient of the frequency table of the words to be tested and the frequency table of all words is calculated;

[0012] The disaster warning risk level of the tunnel to be tested during the construction period is obtained based on the word frequency quotient and score of each word.

[0013] As an optional implementation, construction information with the same risk level is merged to construct a bag-of-words model of construction information at different risk levels, and the words commonly contained in the bag-of-words model are combined into a common vocabulary bag-of-words model, and all words in the bag-of-words model are not removed to reconstruct it into a bag-of-all-vocabulary model.

[0014] As an optional implementation, the frequency F of each word in the risk level vocabulary frequency table in each word bag model Ai for:

[0015]

[0016] Among them, n 1i is the number of times each word appears in the i-th bag-of-words model; n 2i is the total number of words without deduplication in the i-th bag-of-words model; n 3i is the number of tunnels corresponding to each word in the i-th bag-of-words model; n 4i is the number of tunnels at the risk level corresponding to the i-th bag-of-words model.

[0017] As an optional implementation, the risk level frequency quotient P of each word Ai for:

[0018] The score K of each word is:

[0019] Among them, F Ai is the frequency of each word in the ith bag-of-words model, F Aj is the frequency of each word in the j-th bag-of-words model.

[0020] As an optional implementation, the frequency of each word in all vocabulary frequency tables is: the ratio of the total number of times each word appears in each bag-of-words model to the total number of words in all bag-of-words models without deduplication.

[0021] As an optional implementation, the word frequency of each word in the bag-of-features model is: the ratio of the number of times each word appears in the bag-of-features model to the total number of non-deduplicated words in the bag-of-features model.

[0022] As an optional implementation, the word frequency quotient of the word frequency table of the vocabulary to be tested and the word frequency table of all vocabulary is: the ratio of the word frequency of each word in the word frequency table of the vocabulary to be tested to its word frequency in all vocabulary frequency tables.

[0023] As an optional implementation method, the disaster warning score X of the tunnel to be tested during the construction period is obtained according to the word frequency quotient and score of each word, and the disaster warning risk level of the tunnel to be tested during the construction period is obtained according to the disaster warning score;

[0024]

[0025] Among them, n is the total number of words in the bag-of-words model to be tested, P i is the frequency quotient of the i-th word, K i is the score of the ith word.

[0026] In a second aspect, the present invention provides a geological disaster early warning system for tunnel construction using a drilling and blasting method, comprising:

[0027] An acquisition module is configured to acquire construction information of tunnels that have been historically constructed using the drill and blast method, thereby determining a risk level;

[0028] A bag-of-words model building module is configured to build a bag-of-words model of construction information at different risk levels, and thereby build a common bag-of-words model and a bag-of-words model of all words;

[0029] The first calculation module is configured to calculate the word frequency of each word in all word bag models and the word frequency in each word bag model, thereby constructing a word frequency table of all words and a word frequency table of risk level words; calculate the risk level word frequency quotient of each word according to the risk level word frequency table, and determine the score of each word accordingly;

[0030] The second calculation module is configured to construct a bag-of-words model to be tested according to the construction information of the tunnel to be tested, remove the words in the common bag-of-words model from the bag-of-words model to obtain a feature bag-of-words model, calculate the word frequency of each word in the feature bag-of-words model to obtain a frequency table of the vocabulary to be tested, and calculate the word frequency quotient of the frequency table of the vocabulary to be tested and the frequency table of all vocabulary;

[0031] The evaluation module is configured to obtain the disaster warning risk level of the tunnel to be tested during the construction period according to the word frequency quotient and score of each word.

[0032] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0034] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention proposes a geological disaster early warning method and system for tunnel construction using the drill and blast method, which is implemented based on the TF-IDF keyword extraction technology, and provides timely disaster warnings for tunnels during construction. While assisting expert evaluation and reducing interference from human subjective factors, it can timely and accurately predict tunnel construction disasters by analyzing the frequency of keywords in tunnel construction information without relying on manual experience for on-site decision-making.

[0037] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0039] Figure 1 A flow chart of a method for early warning of geological hazards in tunnel construction using the drill and blast method provided in Example 1 of the present invention;

[0040] Figure 2 A typical tunnel bag-of-words calculation flow chart provided in Example 1 of the present invention;

[0041] Figure 3 A flow chart of the calculation of the bag-of-words of the tunnel to be tested provided in Embodiment 1 of the present invention;

[0042] Figure 4This is a flow chart of tunnel risk assessment provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0044] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0045] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "comprise" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0046] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0047] Example 1

[0048] This embodiment provides a method for early warning of geological disasters in tunnel construction using the drill and blast method based on TF-IDF keyword extraction, which can make timely and accurate early warnings of tunnel construction disasters when it is impossible to make decisions on site based on human experience.

[0049] like Figure 1 As shown, specifically including:

[0050] Obtain construction information of historical tunnels constructed using the drill and blast method to determine the risk level;

[0051] Construct a bag-of-words model of construction information at different risk levels, and use it to construct a bag-of-common-words model and a bag-of-all-words model;

[0052] Calculate the frequency of each word in all word bag models and the frequency of each word in each word bag model, thereby constructing a frequency table of all words and a frequency table of risk level words; calculate the risk level frequency quotient of each word according to the risk level word frequency table, and use this to determine the score of each word;

[0053] A bag-of-words model to be tested is constructed according to the construction information of the tunnel to be tested, and the words in the common bag-of-words model are removed from the bag-of-words model to obtain a feature bag-of-words model, and the word frequency of each word in the feature bag-of-words model is calculated to obtain a frequency table of the words to be tested, and the word frequency quotient of the frequency table of the words to be tested and the frequency table of all words is calculated;

[0054] The disaster warning risk level of the tunnel to be tested during the construction period is obtained based on the word frequency quotient and score of each word.

[0055] In this embodiment, first, there is the expert evaluation system;

[0056] Obtain construction information from typical tunnel construction cases of different risk types using the drill and blast method in historical construction projects, including geological information, geophysical information, monitoring information, and drilling information;

[0057] According to the classification standards of geological factors, the evaluation indicators are determined, including the degree of rock mass structural development, rock mass integrity, groundwater level, tunnel burial depth, surrounding rock grade, water discharge from the tunnel face, and block loss from the tunnel face;

[0058] The analytic hierarchy process is used to rank the importance of the above seven evaluation indicators, and the weight of each evaluation indicator is calculated based on the ranking results. Each evaluation indicator in each case is weighted according to its corresponding weight, and the construction period disaster risk score of different tunnels is calculated, ranging from 1 to 4 points. The risk level of the tunnel is determined according to the construction period disaster risk score, including low, medium, high, and extremely high.

[0059] In this embodiment, if Figure 2 As shown, typical tunnel construction information with the same risk level is merged to construct bag-of-words models A1-A4 of typical tunnel construction information during construction periods at different risk levels, and the words commonly contained in the bag-of-words models A1-A4 are combined into a common vocabulary bag-of-words model C, and all words in the bag-of-words models A1-A4 are not removed and reconstructed into a bag-of-all-vocabulary model D.

[0060] Calculate the frequency of each word in the bag-of-words model D, thereby forming a frequency table of all words, where the frequency F1 of each word is:

[0061]

[0062] Among them, n1 is the total number of times each word appears in each bag-of-words model; n2 is the total number of words in all bag-of-words models without deduplication.

[0063] Calculate the frequency of each word in the word bag model A1-A4 in all word bag models D, thereby forming a risk level word frequency table, where the frequency of each word in the i-th word bag model F Ai for:

[0064]

[0065] Among them, n 1i is the number of times each word appears in the i-th bag-of-words model; n 2i is the total number of words without deduplication in the i-th bag-of-words model; n 3i is the number of tunnels corresponding to each word in the i-th bag-of-words model, then the total number of tunnels for this word is n 4i is the number of tunnels under the risk level corresponding to the i-th bag-of-words model, and the total number of tunnels

[0066] According to the risk level vocabulary frequency table, calculate the risk level frequency quotient P of each word Ai : And based on this word frequency quotient, the score K of each word is calculated: And K must be a real number [1, 4].

[0067] Among them, F Ai represents the frequency of each word in the ith bag-of-words model in the bag-of-words model A1-A4, F Aj represents the word frequency of each word in the j-th bag-of-words model in the bag-of-words model A1-A4, P Ai Represents the word frequency quotient of each word relative to the i-th word bag in the word bag models A1-A4.

[0068] In this embodiment, in the process of constructing the bag-of-words model of the tunnel under construction at four risk levels, the redundant retrieval results in the tunnel logs of the same risk level are removed, and each remaining retrieval result is preprocessed to obtain the bag-of-words model of different risk levels.

[0069] The preprocessing of each remaining search result includes: performing word segmentation and cleaning on each remaining search result in turn, unifying English letters into lowercase, screening keywords and high-frequency words as the basis of word bags, and not performing duplicate removal on these words, that is, ensuring that the number of times each word appears in a construction report is fully presented, and then obtaining the word bag model of tunnels during the construction period with four risk levels.

[0070] In this embodiment, if Figure 3 As shown in the figure, when tunnel construction that requires disaster warning during construction is carried out, the bag-of-words model E to be tested is obtained according to the construction information of the tunnel to be tested. After subtracting the words in the common word bag model C from the bag-of-words model E to be tested, the feature word bag model G is obtained. The word frequency of each word in the feature word bag model G is calculated to form a word frequency table of the words to be tested, in which the word frequency F2 of each word is:

[0071]

[0072] Among them, n5 is the number of times each word appears in the bag-of-features model G, and n6 is the total number of undeleted words in the bag-of-features model G.

[0073] Calculate the word frequency quotient P of the word frequency table F2 of the vocabulary to be tested and the word frequency table F1 of all vocabulary, that is, the quotient of the word frequency of each word in the word frequency table F2 of the vocabulary to be tested and its word frequency in the word frequency table F1 of all vocabulary;

[0074]

[0075] Among them, n7 is the total number of words after deduplication in the bag-of-words model G.

[0076] In this embodiment, it is determined whether the bag-of-words model E to be tested contains the same words as those in the bag-of-words model C. If all words are the same, there is no risk and the test is automatically exited.

[0077] In this embodiment, if Figure 4 As shown in the figure, according to the frequency quotient and score of each word, the disaster warning score X of the tunnel to be tested during the construction period is obtained:

[0078]

[0079] Among them, n is the total number of words in the bag-of-words model to be tested, P i is the frequency quotient of the i-th word, K i is the score of the ith word; single item P i *K i ∈[1,4]:When the single term P i *K i >4, its value = 4, single item P i *K i When <1, its value = 1.

[0080] According to the disaster warning score, the risk level (low, medium, high, and extremely high) of the tunnel to be tested is determined, so that a construction disaster warning evaluation can be conducted on the tunnel to be tested.

[0081] Verification Example

[0082] Step S1: Select typical tunnel construction cases of drilling and blasting method with different risk types from n4 historical construction projects, and obtain their geological information, geophysical information, monitoring information and drilling information (hereinafter referred to as construction information), as shown in Table 1; according to the classification standard of geological factors, determine seven indicators such as rock mass structure development degree, rock mass integrity, groundwater level, tunnel burial depth, surrounding rock level, face water discharge, face block drop, etc.; invite experts to rank the importance of the seven indicators according to the relevant rules of the hierarchical analysis method, and calculate the weight of each indicator according to the ranking, and calculate the disaster risk score of different tunnels during the construction period by weighted calculation based on the scores of various indicators, with an interval of 1-4 points, and determine the risk level according to the score, which is divided into four levels of "low, medium, high, and extremely high", and the number of tunnels of each of the four risk levels is n. 4i (i∈{1,2,3,4}), and

[0083] Table 1 Classification standards of geological factors

[0084]

[0085] Step S2: Figure 2 As shown, the tunnel construction information of the four tunnels with different risk levels in Table 1 is obtained respectively, the typical tunnel construction information with the same score is merged, and the redundant retrieval results in the tunnel logs of the same risk level are removed.

[0086] The search results are segmented and cleaned in turn, and the English letters are unified into lowercase. Keywords and high-frequency words are selected as the basis of word bags, and these words are not deduplicated, that is, to ensure that the number of times each word appears in all construction reports is fully presented.

[0087] The bag-of-words models A1-A4 of the construction period tunnels of four risk levels are obtained respectively. The words commonly contained in A1-A4 constitute the common vocabulary bag-of-words model C, and all the words in A1-A4 constitute the all vocabulary bag-of-words model D, that is, C = A1∩A2∩A3∩A4, and duplicates are removed; D = A1∪A2∪A3∪A4, without deduplication.

[0088] Calculate the word frequency, word frequency quotient and score of each word in all word bag-of-words models D that exist in word bag models A1-A4 with risk levels of 1-4.

[0089] Specifically:

[0090] Select each word in all the bag-of-words models D in turn and count the number of times it appears in the bag-of-words models A1-A4 with risk levels of 1-4 points n 1i (i∈{1,2,3,4}), and Count the total number of words n in the risk level bag-of-words model A1-A4 without deduplication 2i(i∈{1,2,3,4}), and Calculate and generate the word frequency table F1 of all words, where the word frequency of each word is

[0091] Count the number of tunnels n that each word exists in in the bag-of-words model A1-A4 with risk levels of 1-4 3i (i∈{1,2,3,4}), the total number of tunnels where the word exists is The frequency of each word in the 1-4 risk level bag-of-words model A1-A4 is calculated in turn to form a risk level vocabulary frequency table: Calculate the risk level frequency quotient P of each word Ai (P A1 -P A4 ):

[0092]

[0093] Calculate the score of each word in the bag-of-words model D as: And K must be a real number [1, 4].

[0094] Step S3: Figure 3 As shown in the figure, when tunnel construction that requires disaster warning during the construction period is carried out, the construction information of the tunnel to be inspected is obtained, and the key information in the tunnel construction, such as the degree of rock structure development, rock integrity, groundwater level, tunnel burial depth, surrounding rock level, water discharge from the face, and block falling from the face, is sorted out in the log. The log is segmented and cleaned, and the English letters are unified into lowercase. Keywords and high-frequency words are selected as the basis of word bags, and these words are not deduplicated, that is, the number of times each word appears in the current construction report is fully presented.

[0095] Get the bag-of-words model E to be tested, compare the bag-of-words model E with the bag-of-words model C of shared vocabulary, and determine whether the bag-of-words model to be tested contains the same vocabulary as that in the bag-of-words model C of shared vocabulary. If all are the same, there is no risk for the time being, and the test is automatically exited. The meaning of this operation is: when the vocabulary contained in the bag-of-words model to be tested are all the common vocabulary of the four risks (i.e., safe words), the risk level of this tunnel is extremely low, and no further testing is required.

[0096] Subtract the words in the common word bag model C from the word bag model E to get the feature word bag model G, and count the total number of undeduplicated words in the word bag n6, and the corresponding total number of deduplicated words n7. Select each word in the feature word bag model G in turn, and count the number of times it appears in the model G n5. Calculate and generate the word frequency table F2 of the word to be tested, where the word frequency of each word is

[0097] Select all the words in the feature bag-of-words model G, and calculate the word frequency quotient P of the corresponding word frequencies in the word frequency table F2 and the word frequency table F1 of all the words. The word frequency quotient is the quotient of the word frequency in the word frequency table F2 and the word frequency in the word frequency table F1, that is,

[0098] Step S4: Figure 4 As shown, the tunnel disaster warning score X during the construction period is calculated: Single P i *K i ∈[1,4]:When the single term P i *K i >4, its value = 4, single item P i *K i When <1, its value = 1, therefore: X∈{1,2,3,4} is an integer from 1 to 4, corresponding to the corresponding risk level.

[0099] According to the scores, the construction disaster early warning evaluation is carried out on the tunnel to be tested, that is, the X value of the integer in the score range of 1-4 is used to correspond the tunnel to the corresponding risk level.

[0100] Example 2

[0101] This embodiment provides a geological disaster early warning system for tunnel construction using a drill and blast method, comprising:

[0102] An acquisition module is configured to acquire construction information of tunnels that have been historically constructed using the drill and blast method, thereby determining a risk level;

[0103] A bag-of-words model building module is configured to build a bag-of-words model of construction information at different risk levels, and thereby build a common bag-of-words model and a bag-of-words model of all words;

[0104] The first calculation module is configured to calculate the word frequency of each word in all word bag models and the word frequency in each word bag model, thereby constructing a word frequency table of all words and a word frequency table of risk level words; calculate the risk level word frequency quotient of each word according to the risk level word frequency table, and determine the score of each word accordingly;

[0105] The second calculation module is configured to construct a bag-of-words model to be tested according to the construction information of the tunnel to be tested, remove the words in the common bag-of-words model from the bag-of-words model to obtain a feature bag-of-words model, calculate the word frequency of each word in the feature bag-of-words model to obtain a frequency table of the vocabulary to be tested, and calculate the word frequency quotient of the frequency table of the vocabulary to be tested and the frequency table of all vocabulary;

[0106] The evaluation module is configured to obtain the disaster warning risk level of the tunnel to be tested during the construction period according to the word frequency quotient and score of each word.

[0107] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0108] In further embodiments, there is also provided:

[0109] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, it will not be described in detail here.

[0110] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0111] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0112] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is completed.

[0113] The method in Example 1 can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0114] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in embodiment 1 is implemented.

[0115] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0116] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0117] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, etc. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0118] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0119] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for early warning of geological disasters in tunnel construction using a drilling and blasting method, characterized in that: include: Obtain construction information of historical tunnels constructed using the drill and blast method to determine the risk level; Construct a bag-of-words model of construction information at different risk levels, and use it to construct a bag-of-common-words model and a bag-of-all-words model; Calculate the frequency of each word in all word bag models and the frequency of each word in each word bag model, thereby constructing a frequency table of all words and a frequency table of risk level words; Calculate the risk level frequency quotient of each word according to the risk level word frequency table, and use this to determine the score of each word; A bag-of-words model to be tested is constructed according to the construction information of the tunnel to be tested, and the words in the common bag-of-words model are removed from the bag-of-words model to obtain a feature bag-of-words model, and the word frequency of each word in the feature bag-of-words model is calculated to obtain a frequency table of the words to be tested, and the word frequency quotient of the frequency table of the words to be tested and the frequency table of all words is calculated; The disaster warning risk level of the tunnel to be tested during the construction period is obtained based on the word frequency quotient and score of each word.

2. A method for early warning of geological disasters in tunnel construction using drill and blast method as claimed in claim 1, characterized in that: The construction information with the same risk level is merged to construct a bag-of-words model of construction information at different risk levels. The words commonly contained in the bag-of-words model are combined into a common bag-of-words model. All words in the bag-of-words model are not removed and reconstructed into a bag-of-all-words model.

3. A method for early warning of geological disasters in tunnel construction by drill and blast method as claimed in claim 1, characterized in that: The frequency of each word in the risk level vocabulary frequency table in each word bag model F Ai for: Among them, n 1i is the number of times each word appears in the i-th bag-of-words model; n 2i is the total number of words without deduplication in the i-th bag-of-words model; n 3i is the number of tunnels corresponding to each word in the i-th bag-of-words model; n 4i is the number of tunnels at the risk level corresponding to the i-th bag-of-words model.

4. A method for early warning of geological disasters in tunnel construction by drill and blast method as claimed in claim 1, characterized in that: The risk level of each word P Ai for: The score K of each word is: Among them, F Ai is the frequency of each word in the ith bag-of-words model, F Aj is the frequency of each word in the j-th bag-of-words model.

5. A method for early warning of geological disasters in tunnel construction by drill and blast method as claimed in claim 1, characterized in that: The frequency of each word in all word frequency tables is the ratio of the total number of times each word appears in each word bag model to the total number of words in all word bag models without deduplication. The frequency of each word in the bag-of-words model is the ratio of the number of times each word appears in the bag-of-words model to the total number of undeleted words in the bag-of-words model. The word frequency quotient of the word frequency table of the vocabulary to be tested and the word frequency table of all vocabulary is: the ratio of the word frequency of each word in the word frequency table of the vocabulary to be tested to its word frequency in the word frequency table of all vocabulary.

6. A method for early warning of geological disasters in tunnel construction by drill and blast method as claimed in claim 1, characterized in that: The disaster warning score X of the tunnel to be tested during the construction period is obtained according to the word frequency quotient and score of each word, and the disaster warning risk level of the tunnel to be tested during the construction period is obtained according to the disaster warning score; Among them, n is the total number of words in the bag-of-words model to be tested, P i is the frequency quotient of the i-th word, K i is the score of the ith word.

7. A geological disaster early warning system for tunnel construction using drill and blast method, characterized in that: include: An acquisition module is configured to acquire construction information of tunnels that have been historically constructed using the drill and blast method, thereby determining a risk level; A bag-of-words model building module is configured to build a bag-of-words model of construction information at different risk levels, and thereby build a common bag-of-words model and a bag-of-words model of all words; A first calculation module is configured to calculate the word frequency of each word in all word bag models and the word frequency in each word bag model, thereby constructing a word frequency table of all words and a word frequency table of risk level words; Calculate the risk level frequency quotient of each word according to the risk level word frequency table, and use this to determine the score of each word; The second calculation module is configured to construct a bag-of-words model to be tested according to the construction information of the tunnel to be tested, remove the words in the common bag-of-words model from the bag-of-words model to obtain a feature bag-of-words model, calculate the word frequency of each word in the feature bag-of-words model to obtain a frequency table of the vocabulary to be tested, and calculate the word frequency quotient of the frequency table of the vocabulary to be tested and the frequency table of all vocabulary; The evaluation module is configured to obtain the disaster warning risk level of the tunnel to be tested during the construction period according to the word frequency quotient and score of each word.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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