Coal mine hidden danger legal regulation recommendation method and device and electronic equipment

Abstract

The application provides a coal mine hidden danger legal regulation recommendation method and device and electronic equipment, and the method comprises the following steps: obtaining a coal mine proper noun text; constructing an FP tree of the coal mine proper noun text based on FPGrowth, wherein the FP tree comprises a frequent item set and a label set of the coal mine proper noun text; constructing a label library comprising a first level and a second level according to the label set and the FP tree; splitting the coal mine hidden danger according to the label library and PV-DBOW, and establishing a label comparison table of the coal mine legal regulation; and calculating the hit rate of the label comparison table of the coal mine hidden danger, and determining the recommended legal regulation. The embodiment of the application provides a reference for hidden danger management by recommending appropriate legal regulations for the coal mine hidden danger, and has important guiding significance for coal mine management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mine safety risk, in particular to a coal mine hidden danger legal regulation recommendation method and device and electronic equipment. BACKGROUND

[0002] The safety production accident hidden danger, also known as hidden danger, accident hidden danger or safety hidden danger, refers to the dangerous state of things, unsafe behavior of people and defects in management in the production and operation activities of the production and operation unit due to the violation of safety production laws, regulations, standards, rules and safety production management system, or other factors. Among the three manifestations of accident hidden danger, the dangerous state of things refers to the dangerous state of the material conditions in the production process or production area, the unsafe behavior of people refers to the operation, indication or other specific behavior of people in the work process that does not comply with safety regulations, and the defects in management refer to the defects in various organizations and coordination actions necessary for various production activities.

[0003] Coal mining enterprises belong to high-risk industries. The geological conditions of underground coal mining are complex, the working place is narrow, the environment is poor, and the five disasters of roof falling, water disaster, fire disaster, gas and coal dust bring great safety hidden danger in the actual production process of coal mines. The continuous production of mines, the continuous development of new levels, new mining areas and new working faces, and the production connection increase the complexity of risk management. With the increasing normalization of risk assessment and management of coal mine production process in coal mining enterprises, the enterprises generally assess the risk status of coal production by establishing a risk evaluation index system, but there is no appropriate correlation system between laws and regulations and hidden dangers. The corresponding responsible person does not know the corresponding laws and regulations after the hidden danger occurs, and there is no professional guidance for the action to eliminate the hidden danger. SUMMARY

[0004] Therefore, the present application provides a coal mine hidden danger legal regulation recommendation method, device and electronic equipment, which recommends appropriate laws and regulations for coal mine hidden dangers, provides a reference for hidden danger governance, and has important guiding significance for coal mine governance.

[0005] The present application provides a coal mine hidden danger legal regulation recommendation method, which comprises:

[0006] Obtaining coal mine specific terms and hidden danger data of coal mines;

[0007] Constructing an FP tree based on FPGrowth, wherein the FP tree comprises a frequent item set and a label set of the coal mine specific terms;

[0008] According to the label set and the FP tree, a label library of the classification of the coal mine specific terms is constructed;

[0009] According to the frequent item set, the hidden danger data is processed by word segmentation to obtain labels of the hidden danger data;

[0010] According to the label library and the PV-DBOW, a label list of the coal mine laws and regulations is constructed, and the label represents different coal mine laws and regulations;

[0011] In the label list of the coal mine laws and regulations, the label of the hidden danger data is searched to obtain the number of coincidences of the label, and the recommended laws and regulations are determined based on the number of coincidences of the label.

[0012] In an optional implementation, the method for recommending laws and regulations of the coal mine hidden danger further comprises:

[0013] A contrast rule table of full names, wrong characters and abbreviations of the coal mine proper nouns is established;

[0014] The label of the hidden danger data is corrected by using the contrast rule table.

[0015] In an optional implementation, the method for recommending laws and regulations of the coal mine hidden danger further comprises:

[0016] Based on the illegal laws and regulations and the law enforcement data, a similar model of the law enforcement data is trained;

[0017] In an optional implementation, the hidden danger data is input into the similar model to obtain IDs of the recommended laws and regulations.

[0018] After the step of inputting the hidden danger data into the similar model to obtain the IDs of the recommended laws and regulations, the method further comprises:

[0019] The sum of the similar ratios of the IDs of the recommended laws and regulations is calculated, and the IDs are sorted;

[0020] The IDs of the recommended laws and regulations ranked in the first two positions are selected, and the corresponding law and regulation nouns, clauses and contents are queried.

[0021] The embodiment of the application further provides a device for recommending laws and regulations of a coal mine hidden danger, and the device comprises:

[0022] An acquisition module is configured to acquire coal mine proper nouns and hidden danger data of a coal mine;

[0023] A first construction module is configured to construct an FP tree based on FPGrowth, and the FP tree comprises frequent item sets and a label set of the coal mine proper nouns;

[0024] The FP tree construction module is configured to construct a label library of the classification of the coal mine specific terms according to the label set and the FP tree construction.

[0025] The word segmentation module is configured to perform word segmentation processing on the hidden danger data according to the frequent item set, to obtain labels of the hidden danger data.

[0026] The label construction module is configured to construct a label list of the coal mine laws and regulations according to the label library and the PV-DBOW, and the labels represent different coal mine laws and regulations.

[0027] The recommendation module is configured to find the labels of the hidden danger data in the label list of the coal mine laws and regulations, to obtain a coincidence number of the labels, and to determine the recommended laws and regulations based on the coincidence number of the labels.

[0028] In an optional implementation, the device further includes a correction module configured to establish a comparison rule table of the full names, misspelt words and abbreviations of the coal mine specific terms.

[0029] The labels of the hidden danger data are corrected by using the comparison rule table.

[0030] In an optional implementation, the recommendation module is specifically configured to:

[0031] train a law enforcement data similarity model based on the laws and regulations and the law enforcement data that violate the laws;

[0032] input the hidden danger data into the similarity model to obtain IDs of the recommended laws and regulations.

[0033] Embodiments of the present application also provide an electronic device, including a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, the machine readable instructions are executed by the processor to execute the above-mentioned coal mine hidden danger law and regulation recommendation method, or the steps in any one of the possible implementation manners of the coal mine hidden danger law and regulation recommendation method.

[0034] Embodiments of the present application also provide a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by the processor to execute the above-mentioned coal mine hidden danger law and regulation recommendation method, or the steps in any one of the possible implementation manners of the coal mine hidden danger law and regulation recommendation method.

[0035] Embodiments of the present application also provide a computer program product, including a computer program / instruction, the computer program / instruction is executed by the processor to realize the steps in any one of the possible implementation manners of the above-mentioned coal mine hidden danger law and regulation recommendation method.

[0036] The present application provides a coal mine hidden danger legal regulation recommendation method and device, and electronic equipment, wherein the method comprises: obtaining a coal mine specific term text; constructing an FP tree of the coal mine specific term text based on an FPGrowth algorithm, the FP tree comprising a frequent item set and a label set of the coal mine specific term text; constructing a label library comprising a first level and a second level according to the label set and the FP tree; splitting the coal mine hidden danger according to the label library and a PV-DBOW algorithm, and establishing a label control table of the coal mine legal regulation; and calculating a hit rate of the coal mine hidden danger and the label control table to determine recommended legal regulations. The embodiment of the present application provides a reference for hidden danger management by recommending appropriate legal regulations for the coal mine hidden danger, which has important guiding significance for coal mine management.

[0037] In order to make the above objectives, characteristics and advantages of the present application more apparent, clear and easy to understand, a preferred embodiment is specifically described below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced below. The drawings are incorporated into the specification and form a part of the specification, which show the embodiments consistent with the present application and are used to illustrate the technical solutions of the present application together with the specification. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0039] Figure 1 A flowchart of a coal mine hidden danger legal regulation recommendation method provided by an embodiment of the present application is shown;

[0040] Figure 2 A flowchart of a specific method in a coal mine hidden danger legal regulation recommendation method provided by an embodiment of the present application is shown;

[0041] Figure 3 A flowchart of another coal mine hidden danger legal regulation recommendation method provided by an embodiment of the present application is shown;

[0042] Figure 4 A schematic diagram of a coal mine hidden danger legal regulation recommendation device provided by an embodiment of the present application is shown;

[0043] Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present application is shown;

[0044] Figure 6 An effect diagram of an embodiment of the present application is shown;

[0045] Figure 7 Another effect diagram of the embodiment of the present application is shown. DETAILED DESCRIPTION

[0046] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0047] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0048] The term "and / or" herein only describes an association relationship, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of multiple or any combination of at least two of multiple, for example, including at least one of A, B and C can mean including any one or more elements selected from the set consisting of A, B and C.

[0049] It is found through research that coal mining enterprises belong to high-risk industries, the geological conditions of underground mining in coal mines are complex, the working place is narrow, the environment is poor, and the five disasters of roof falling, flood, fire, gas and coal dust bring great safety hazards in the actual production process of coal mines. The continuous production of mines, the continuous development of new levels, new mining areas and new working faces, and the production connection increase the complexity of risk management work. With the increasing normalization of risk assessment and management work in the production process of coal mines in coal mining enterprises, the enterprise generally assesses the risk status of coal production by establishing a risk evaluation index system, but there is no appropriate legal and regulatory association system between hidden dangers, and the corresponding person in charge does not know the corresponding laws and regulations after the hidden danger occurs, so there is no professional guidance for the action to eliminate the hidden danger.

[0050] Based on the above research, the coal mine hidden danger legal regulation recommendation method, device and electronic equipment are provided, wherein the method comprises the following steps: obtaining a coal mine special term text; constructing an FP tree of the coal mine special term text based on FPGrowth, the FP tree comprising a frequent item set and a label set of the coal mine special term text; constructing a label library comprising a first level and a second level according to the label set and the FP tree; splitting the coal mine hidden danger according to the label library and PV-DBOW, and establishing a label comparison table of the coal mine legal regulation; and calculating the hit rate of the coal mine hidden danger and the label comparison table to determine the recommended legal regulation. The embodiment of the present application provides a reference for hidden danger management by recommending appropriate legal regulations for coal mine hidden dangers, which has important guiding significance for coal mine management.

[0051] In order to facilitate the understanding of the present embodiment, first, a coal mine hidden danger legal regulation recommendation method disclosed by the present embodiment is introduced in detail. The execution subject of the coal mine hidden danger legal regulation recommendation method provided by the present embodiment is generally a computer device with certain computing power, which may comprise a terminal device or a server or other processing device, and the terminal device may be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the coal mine hidden danger legal regulation recommendation method may be realized by a processor calling computer readable instructions stored in a memory.

[0052] Referring to Figure 1 and Figure 2 , a flowchart of the coal mine hidden danger legal regulation recommendation method provided by the present embodiment is shown, and the method comprises steps S101-S106, wherein:

[0053] Step S101: obtaining a coal mine special term and a hidden danger data of the coal mine;

[0054] In the present embodiment, the coal mine special term is taken from a coal mine special term dictionary, and the hidden danger data of the coal mine is taken from historical data and hidden danger events that may occur in real conditions.

[0055] Step S102: constructing an FP tree based on FPGrowth, and the FP tree comprises a frequent item set and a label set of the coal mine special term.

[0056] The minimum support degree of the coal mine specific term dictionary in step S101 is set to 3, and a frequent 3-item set is obtained. The frequent 3-item set is arranged in descending order according to each word, and words less than the minimum support degree are deleted to obtain a title table of the FP tree. After the title table is deleted, a word list obtained after the word frequency is obtained as a final label set to be obtained in the embodiment of the present application. Referring to Table 1, a label rule list is obtained.

[0057]

[0058] In step S103, a label library of classification of the coal mine specific term is constructed according to the label set and the FP tree.

[0059] According to the frequent item set obtained in step S102, a relationship list between words is generated according to the minimum support degree 3, the minimum confidence 0.6, and different lift degrees. According to the FP tree constructed based on the FPGrowth algorithm, the FP tree contains a label rule list. Starting labels with large association relationships with ending labels are filtered out according to the ending label, the support degree, the confidence, and the lift degree, as shown in Table 2. The ending label is a working face. The confidence is greater than 0.91 (the average value of the overall data is 0.91). Starting labels can be obtained. A preliminary label hierarchy is obtained. According to the division result, some actual deviations are manually corrected and deleted, for example, the starting labels "accident hidden danger" and "pressure table" in Table 2. Finally, a hierarchical label library with one or two levels is constructed.

[0060]

[0061] Optionally, the coal mine hidden danger legal regulation recommendation method further includes: establishing a full name, wrong word, and abbreviation contrast rule table of the coal mine specific term; and correcting the label of the hidden danger data by using the contrast rule table.

[0062] Because the label rule library in the embodiment of the present application may have the problems of wrong words and unclear abbreviations, the embodiment of the present application provides a contrast rule table to correct the hidden danger data and ensure the accuracy of the coal mine legal regulation recommendation.

[0063] In step S104, the hidden danger data is processed by using the frequent item set to obtain labels of a plurality of hidden danger data.

[0064] The hidden danger data is processed by using the label rule list and the label library to split the hidden danger data and assign corresponding labels.

[0065] In step S105, a label list of the coal mine legal regulation is constructed according to the label library and the PV-DBOW, and the label represents different coal mine legal regulations.

[0066] The coal mine special words are divided into a word dictionary, the stop words in the legal regulations are removed, the punctuation symbols are used hanlp.pretrained.tok.COARSE ELECTRA SMALL_ZH model of NLP, the legal regulations clauses are divided, the legal regulation corresponding word segmentation result is obtained, each word segmentation result is respectively brought into a label library for query, the queried label is used as the final label list corresponding to the legal regulations, as shown in Table 3, the label corresponding to each legal regulation, the id (database legal regulation id) corresponding to the legal regulation.

[0067]

[0068] Optionally, referring to the flowchart of Figure 3 In the label list of the coal mine legal regulations, the label of the hidden danger data is searched, the number of coincidences of the label is obtained, the recommended legal regulations are determined based on the number of coincidences of the label, including:

[0069] Based on the illegal legal regulations and the law enforcement data, a similar model of the law enforcement data is trained;

[0070] The hidden danger data is input into the similar model to obtain the IDs of the multiple recommended legal regulations.

[0071] Optionally, after the step of inputting the hidden danger data into the similar model to obtain the IDs of the multiple recommended legal regulations, including:

[0072] The sum of the similarity ratios of the IDs of the multiple recommended legal regulations is calculated and sorted;

[0073] The IDs of the recommended legal regulations ranked in the top two are selected and queried to obtain the corresponding legal regulation nouns, clauses and contents.

[0074] According to the law enforcement data ID, the law enforcement content level, the illegal legal regulation ID, the violated legal regulation name, the violated legal regulation version as a data unique ID, i.e. TaggededDocumentID, the law enforcement content description as a data set, using the self-owned coal mine noun special word library, the label library, the law enforcement content is divided, the divided result is trained by using PV-DBOW algorithm, the similar model formed by the TaggededDocumentID combined with the illegal legal regulations and the law enforcement data ID and the sentence vector obtained by training is obtained;

[0075] Then, input the hidden danger data, use the most_similar method of the similar model obtained by the above steps, take the TopN recommended by the algorithm, recommend TopN according to the similar model, cut TopN in the TaggededDocumentID with "&&", and according to the splicing mode of TaggededDocumentID, the third one of each TaggededDocumentID is the legal regulation ID, which forms the corresponding legal regulation set. And use the obtained legal regulation set to calculate the sum of the similar ratios corresponding to each legal regulation id in the set, sort, and take the top two legal regulation IDs.

[0076] Finally, according to the legal regulation ID obtained by the above steps, query the legal regulation table to obtain the corresponding legal regulation name, clause, and content.

[0077] Step S106, find the tag of hidden danger data in the tag list of coal mine legal regulations, get the number of coincidences of the tag, and determine the recommended legal regulations based on the number of coincidences of the tag.

[0078] The hit rate calculation is performed on the tag list of the hidden danger and the tag list of the legal regulations. The obtained hidden danger data tag is brought into the fourth step of the legal regulation corresponding tag library for query, the number of coincidences (hit number) of the hidden danger data tag and the coal mine legal regulation clause id is obtained, and the number of coincidences is divided by the number of tags in the hidden danger tag list to obtain the hit rate. The top two legal regulation IDs with high hit rate are recommended as the legal regulation clauses.

[0079] Optionally, referring to Figure 6 and Figure 7 , the coal mine legal regulation recommendation example of the embodiment of the present application is as follows, the corresponding legal name, specific clause and specific content of the hidden danger tag are obtained. Figure 6 The top line of the hidden danger content is hidden in the top line of the hidden danger content. According to the hidden danger content, the hidden danger tag is obtained, the corresponding legal regulations are obtained according to the tag, and the content description in the hidden danger is consistent, which has high accuracy. Figure 7 The top line of the hidden danger content is hidden in the top line of the hidden danger content. According to the hidden danger content, the hidden danger tag is obtained, the corresponding legal regulations are obtained according to the tag, and the content description in the hidden danger is consistent, which has high accuracy.

[0080] The coal mine hidden danger legal regulation recommendation method provided by the embodiment of the present application adopts obtaining coal mine special term text; constructing an FP tree of the coal mine special term text based on FPGrowth, the FP tree including a frequent item set and a label set of the coal mine special term text; constructing a label library including a first level and a second level according to the label set and the FP tree; segmenting the coal mine hidden danger according to the label library and PV-DBOW, establishing a control table of the coal mine legal regulation and the label; calculating the hit rate of the coal mine hidden danger and the control table of the label, and determining the recommended legal regulation. The embodiment of the present application provides a reference for hidden danger management by recommending appropriate legal regulations for the coal mine hidden danger, which has important guiding significance for coal mine management.

[0081] Those skilled in the art can understand that, in the above method of the specific embodiment, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process, and the specific execution order of each step should be determined by its function and possible internal logic.

[0082] Based on the same inventive concept, the embodiment of the present application also provides a coal mine hidden danger legal regulation recommendation device corresponding to the coal mine hidden danger legal regulation recommendation method. Since the principle of solving problems in the device of the embodiment of the present application is similar to the above-mentioned coal mine hidden danger legal regulation recommendation method of the embodiment of the present application, the implementation of the device can be referred to the implementation of the method, and the repeated parts will not be described here.

[0083] Please refer to Figure 4 , Figure 4 A schematic diagram of a coal mine hidden danger legal regulation recommendation device provided by the embodiment of the present application is shown in FIG. 4. Figure 4 As shown in FIG. 4, the coal mine hidden danger legal regulation recommendation device 400 provided by the embodiment of the present application includes an acquisition module for acquiring coal mine special terms and hidden danger data of the coal mine.

[0084] The acquisition module 401 is configured to acquire coal mine special terms and hidden danger data of the coal mine.

[0085] The first construction module 402 is configured to construct an FP tree based on FPGrowth, the FP tree including a frequent item set and a label set of the coal mine special term.

[0086] The FP tree construction module 403 is configured to construct a classified label library of the coal mine special term according to the label set and the FP tree.

[0087] The segmentation module 404 is configured to perform segmentation processing on the hidden danger data according to the frequent item set, to obtain labels of the plurality of hidden danger data.

[0088] The label construction module 405 is configured to construct a label list of the coal mine legal regulation according to the label library and PV-DBOW, the label representing different coal mine legal regulations.

[0089] The recommendation module 406 is configured to search for a label of the hidden danger data in a label list of coal mine laws and regulations, obtain a number of coincidences of the label, and determine the recommended laws and regulations based on the number of coincidences of the label.

[0090] Optionally, the coal mine hidden danger law and regulation recommendation device 400 further includes a correction module configured to establish a comparison rule table of full names, wrong characters and abbreviations of coal mine specific terms.

[0091] The label of the hidden danger data is corrected by using the comparison rule table.

[0092] Specifically, the recommendation module is specifically configured to:

[0093] train a similarity model of law enforcement data based on illegal laws and regulations and law enforcement data;

[0094] input the hidden danger data into the similarity model to obtain IDs of a plurality of recommended laws and regulations.

[0095] The processing procedure of each module in the device and the interaction procedure between the modules can refer to the related description in the above method embodiments, and will not be described in detail here.

[0096] The coal mine hidden danger law and regulation recommendation device provided in the embodiment of the application obtains coal mine specific term text, constructs an FP tree of the coal mine specific term text based on an FPGrowth algorithm, the FP tree includes a frequent item set and a label set of the coal mine specific term text, constructs a label library including a first level and a second level according to the label set and the FP tree, splits the coal mine hidden danger according to the label library and a PV-DBOW algorithm, establishes a comparison table of coal mine laws and regulations and labels, calculates a hit rate of the coal mine hidden danger and the comparison table of the labels, and determines recommended laws and regulations. The embodiment of the application provides a reference for hidden danger management by recommending appropriate laws and regulations for the coal mine hidden danger, and has important guiding significance for coal mine management.

[0097] Corresponding to the coal mine hidden danger law and regulation recommendation method in Figure 1 , the embodiment of the application further provides an electronic device 500, as shown in Figure 5 , a structural schematic diagram of the electronic device 500 provided in the embodiment of the application, which includes:

[0098] Referring to Figure 5 , the embodiment of the application further provides an electronic device 500, which includes a processor 504, a memory 501, a bus 502 and a communication interface 503, the processor 504, the communication interface 503 and the memory 501 are connected through the bus 502; the processor 504 is configured to execute executable modules stored in the memory 501, such as a computer program.

[0099] The memory 501 can include a random access memory (RAM) and can also include a non-volatile memory (NVM), such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 503 (which can be wired or wireless), and the Internet, a wide area network, a local area network, a metropolitan area network, etc. can be used. The bus 502 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5Only one bidirectional arrow is used to represent multiple buses and multiple types of buses. The storage 501 is configured to store programs, and the processor 504 executes the programs after receiving the execution instructions. The method performed by the device defined by the flowcharts disclosed in any of the embodiments of the present application can be applied to the processor 504 or implemented by the processor 504. The processor 504 can be an integrated circuit chip having a processing capability. In the implementation process, each step of the above method can be completed by the integrated logic circuit or the instruction in the software form of the hardware in the processor 504. The processor 504 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; or can be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in 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 in the art. The storage medium is located in the storage 501, and the processor 504 reads the information in the storage 501, and combines the hardware to complete the steps of the above method.

[0100] The embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is run by a processor to execute the seismic image noise suppression method in the foregoing method embodiment. The computer readable storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a RAM, a magnetic disk or an optical disk, and various storage program code mediums. In all the examples shown and described herein, any specific value should be interpreted as merely exemplary and not as a limitation, and thus, other examples of the exemplary embodiments can have different values. The flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the method and computer program product according to the embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code, which includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than those shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and the combination of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions. In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, and for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interfaces, devices or units, which can be electrical, mechanical or other forms.

[0101] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit.

[0102] Finally, it should be noted that the above-described embodiments are merely specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit the present application, and the protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily think of changes to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features therein, within the technical scope disclosed by the present application. Such modifications, changes or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of recommending laws and regulations for a coal mine hazard, characterized by, The method comprises the following steps: obtaining coal mine specific terms and coal mine hidden danger data; constructing an FP tree based on FPGrowth, wherein the FP tree comprises a frequent item set and a label set of the coal mine specific terms; constructing a label library of classifications of the coal mine specific terms according to the label set and the FP tree; the label library comprises a first level and a second level; performing word segmentation processing on the hidden danger data according to the frequent item set to obtain labels of the hidden danger data; segmenting the coal mine laws and regulations according to the label library and PV-DBOW to construct a label list of the coal mine laws and regulations, wherein a label represents different coal mine laws and regulations; finding the labels of the hidden danger data in the label list of the coal mine laws and regulations to obtain the number of coincidences of the labels, dividing the number of coincidences of the labels by the number of labels of the hidden danger data to obtain a hit rate of the labels, and determining recommended laws and regulations based on the hit rate; the step of determining the recommended laws and regulations based on the hit rate comprises: sorting the labels in descending order of the hit rate, and selecting the coal mine laws and regulations represented by the top two labels as the recommended laws and regulations.

2. The method of claim 1, wherein, The method for recommending laws and regulations for coal mine hidden dangers further comprises: establishing a comparison rule table of full names, wrong characters and abbreviations of the coal mine specific terms; correcting the labels of the hidden danger data by using the comparison rule table.

3. The coal mine hazard legal regulation recommendation method of claim 1, wherein, The step of finding the labels of the hidden danger data in the label list of the coal mine laws and regulations to obtain the number of coincidences of the labels, dividing the number of coincidences of the labels by the number of labels of the hidden danger data to obtain a hit rate of the labels, and determining recommended laws and regulations based on the hit rate comprises: training a similar model of law enforcement data based on illegal laws and regulations and law enforcement data; inputting the hidden danger data into the similar model to obtain IDs of the recommended laws and regulations.

4. The method of claim 3, wherein, After the step of inputting the hidden danger data into the similar model to obtain IDs of the recommended laws and regulations, the method further comprises: calculating the sum of similarity ratios of the IDs of the recommended laws and regulations and sorting them; selecting the IDs of the recommended laws and regulations ranked in the top two positions and querying to obtain corresponding law and regulation terms, clauses and contents.

5. A legal regulation recommendation device for a coal mine hazard, characterized by, The method comprises the following steps: an acquisition module for acquiring coal mine specific terms and coal mine hidden danger data; a first construction module for constructing an FP tree based on FPGrowth, wherein the FP tree comprises a frequent item set and a label set of the coal mine specific terms; an FP tree construction module for constructing a label library of classifications of the coal mine specific terms according to the label set and the FP tree; a word segmentation module for performing word segmentation processing on the hidden danger data according to the frequent item set to obtain labels of the hidden danger data; a label construction module for segmenting the coal mine laws and regulations according to the label library and PV-DBOW to construct a label list of the coal mine laws and regulations, wherein a label represents different coal mine laws and regulations; the label library comprises a first level and a second level; The recommendation module is configured to: find a label of the hidden danger data in a label list of the coal mine laws and regulations, obtain a coincidence number of the label, divide the coincidence number of the label by a number of labels of the hidden danger data to obtain a hit rate of the label, and determine the recommended laws and regulations based on the hit rate. The determination of the recommended laws and regulations based on the hit rate includes: sorting the labels in descending order of the hit rates, and selecting coal mine laws and regulations represented by the top two labels as the recommended laws and regulations.

6. The coal mine hazard legal regulation recommendation apparatus according to claim 5, characterized in that, The device further includes: The correction module is configured to establish a comparison rule table of full names, wrong characters and abbreviations of the coal mine proper nouns; The label of the hidden danger data is corrected by using the comparison rule table.

7. The coal mine hazard legal regulation recommendation apparatus according to claim 5, characterized by, The recommendation module is specifically configured to: train a similar model of law enforcement data based on the laws and regulations and the law enforcement data of illegal activities; input the hidden danger data into the similar model to obtain IDs of the multiple recommended laws and regulations.

8. An electronic device, comprising: It includes: A processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, the machine readable instructions are executed by the processor to execute the steps of the coal mine hidden danger law and regulation recommendation method in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, the computer program is executed by the processor to execute the steps of the coal mine hidden danger law and regulation recommendation method in any one of claims 1 to 5.

10. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to implement the steps of the coal mine hidden danger law and regulation recommendation method in any one of claims 1 to 5.

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