Method, system and equipment for determining running state of gas-fired boiler, medium and product

Abstract

The invention discloses a gas boiler operation state determination method, system and device, a medium and a product, and relates to the technical field of gas boiler state monitoring, the method comprises the following steps: carrying out word segmentation processing on a historical inspection report text set to obtain candidate new words; performing feature extraction on the candidate new words by using a TF-IDF weighting method to obtain a plurality of risk feature words; calculating a correlation coefficient between every two risk feature words, and combining the risk feature words of which the correlation coefficients exceed a first preset threshold value to obtain a risk expression statement; determining a first-level evaluation index, and determining a second-level evaluation index according to each risk expression statement; based on the first-level evaluation indexes, the second-level evaluation indexes and preset expert weights, determining the weight of each first-level evaluation index and the weight of each second-level evaluation index by adopting a group analytic hierarchy process; and determining the operation state of the gas boiler based on the weight of each primary evaluation index and the weight of each secondary evaluation index. According to the method, the operation state determination efficiency of the gas-fired boiler is improved.

Description

Technical Field

[0001] The present application relates to the technical field of gas boiler status monitoring, and in particular to a method, system, device, medium and product for determining the operating status of a gas boiler. Background Art

[0002] Due to the rapid product changes in the industry, the original method used to evaluate the operating status of coal-fired boilers is no longer fully applicable to gas-fired boilers. In order to standardize the operation and maintenance of gas-fired boilers and facilitate relevant practitioners to more objectively understand the safety level of the operating status of gas-fired boilers, a method for determining the operating status of gas-fired boilers is needed.

[0003] However, due to the differences in manufacturing materials and operating requirements for gas boilers at home and abroad, foreign related solutions cannot be directly provided for reference by domestic practitioners. At present, there is no method specifically used to determine the operating status of gas boilers in China, and there is a lack of complete research on the operation of gas boilers. There is a lack of methods for quantitatively evaluating the operating status of gas boilers, which leads to a complicated and inefficient process for determining the operating status of gas boilers.

[0004] Therefore, there is an urgent need for a method for determining the operating state of a gas boiler to solve the above problems. Summary of the invention

[0005] The purpose of the present application is to provide a method, system, device, medium and product for determining the operating state of a gas boiler, thereby improving the efficiency of determining the operating state of the gas boiler.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for determining an operating state of a gas boiler, the method for determining an operating state of a gas boiler comprising:

[0008] Obtain a text set of historical inspection reports of multiple gas boilers, and perform word segmentation on the text set of historical inspection reports to obtain candidate new words;

[0009] Using the TF-IDF weighted method, feature extraction is performed on candidate new words to obtain multiple risk feature words;

[0010] Calculate the correlation coefficients between each risk characteristic word, combine the risk characteristic words whose correlation coefficients exceed a first preset threshold, and obtain corresponding risk expression statements;

[0011] Determine the primary and secondary evaluation indicators for each risk performance statement; the primary evaluation indicator is a personnel risk factor, an equipment risk factor, or a management risk factor;

[0012] Based on the first-level evaluation indicators, second-level evaluation indicators and preset expert weights, the group hierarchical analysis method is used to determine the weights of each first-level evaluation indicator and each second-level evaluation indicator;

[0013] Based on the weights of the first-level evaluation indicators and the weights of the second-level evaluation indicators, the operating state of the gas boiler is determined; the operating state is safe operation or fault operation.

[0014] Optionally, the historical inspection report text set is segmented to obtain candidate new words, including:

[0015] Jieba word segmentation tool is used to perform preliminary word segmentation on the historical inspection report text set to obtain word sequences;

[0016] Determine the frequency of occurrence of each word in the word sequence in the historical inspection report text set and generate a word frequency table;

[0017] Filter words whose appearance frequency exceeds a second preset threshold in the word frequency table, and take all words whose appearance frequency exceeds the second preset threshold as the first words;

[0018] Combining the first words to generate a plurality of word pairs, and determining the frequency of occurrence of each word pair in the historical inspection report text set;

[0019] Based on the frequency of each first word in each word pair appearing alone and the frequency of each word pair appearing in the historical inspection report text set, the mutual information value between each first word in each word pair is calculated;

[0020] The word pairs whose mutual information values ​​exceed the third preset threshold are screened out, and the word pairs whose mutual information values ​​exceed the third preset threshold are used as candidate new words.

[0021] Optionally, the TF-IDF weighted method is used to extract features from candidate new words to obtain multiple risk feature words, including:

[0022] For any historical inspection report text set, use any historical inspection report text set as the current historical inspection report text set:

[0023] Determine the characteristic words of the candidate new words in all historical inspection report text sets, and calculate the occurrence frequency of each characteristic word in the current historical inspection report text set;

[0024] Based on the occurrence frequency of each feature word in the current historical inspection report text set, the TF-IDF weighting method is used to calculate the TF-IDF weight of each feature word in the current historical inspection report text set;

[0025] The TF-IDF weights of all feature words in all historical inspection report text sets are ranked, and the feature words ranked in the top 50% are taken as risk feature words.

[0026] Optionally, the calculation formula of the TF-IDF weight of each feature word is:

[0027]

[0028] Among them, T j (t) is the TF-IDF weight of the jth feature word in the current historical inspection report text set; TF(t ij ) is the frequency of occurrence of the jth feature word in the i-th historical inspection report text set; N is the total number of historical inspection report text sets; n j is the number of reports in which the jth feature word appears in the N historical inspection report text sets.

[0029] Optionally, based on the first-level evaluation indicators, the second-level evaluation indicators and the preset expert weights, the group hierarchy analysis method is used to determine the weights of each first-level evaluation indicator and each second-level evaluation indicator, specifically including:

[0030] Using the nine-level scaling method, multiple judgment matrices are determined based on the secondary evaluation indicators and the primary evaluation indicators;

[0031] Based on the preset expert weights, multiple judgment matrices are weighted averaged to obtain a comprehensive judgment matrix;

[0032] Using SPSSPRO software and based on the group hierarchical analysis method, the weight of the comprehensive judgment matrix was calculated to obtain the weight of each first-level evaluation indicator and the weight of each second-level evaluation indicator.

[0033] Optionally, based on the weights of the first-level evaluation indicators and the weights of the second-level evaluation indicators, determining the operating state of the gas boiler specifically includes:

[0034] Determine the frequency of occurrence of the risk characteristic words corresponding to each first-level evaluation indicator in the current historical inspection report text set and the frequency of occurrence of the risk characteristic words corresponding to each second-level evaluation indicator in the current historical inspection report text set;

[0035] The operating status of the gas boiler is determined based on the frequency of occurrence of the risk characteristic words corresponding to each first-level evaluation indicator in the current historical inspection report text set, the weight of each first-level evaluation indicator, the frequency of occurrence of the risk characteristic words corresponding to each second-level evaluation indicator in the current historical inspection report text set, and the weight of each second-level evaluation indicator.

[0036] In a second aspect, the present application provides a system for determining the operating state of a gas boiler, wherein the system for determining the operating state of a gas boiler is used to implement the method for determining the operating state of a gas boiler, and the system for determining the operating state of a gas boiler comprises:

[0037] A candidate new word determination unit is used to obtain a plurality of historical inspection report text sets of gas boilers, and perform word segmentation processing on the historical inspection report text sets to obtain candidate new words;

[0038] A risk characteristic word determination unit is used to extract features of candidate new words by using a TF-IDF weighting method to obtain multiple risk characteristic words;

[0039] A risk expression statement determination unit, used to calculate the correlation coefficients between each risk characteristic word, and combine the risk characteristic words whose correlation coefficients exceed a first preset threshold value to obtain a corresponding risk expression statement;

[0040] An evaluation index determination unit is used to determine the primary evaluation index and the secondary evaluation index of each risk performance statement; the primary evaluation index includes personnel risk factors, equipment risk factors and management risk factors;

[0041] A weight determination unit, for determining the weight of each first-level evaluation indicator and the weight of each second-level evaluation indicator by using a group hierarchical analysis method based on the first-level evaluation indicator, the second-level evaluation indicator and the preset expert weight;

[0042] The operating state determining unit is used to determine the operating state of the gas boiler based on the weights of each primary evaluation index and the weights of each secondary evaluation index; the operating state is safe operation or fault operation.

[0043] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for determining the operating status of a gas boiler.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for determining the operating status of a gas boiler.

[0045] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for determining the operating status of a gas boiler.

[0046] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0047] The present application discloses a method, system, device, medium and product for determining the operating status of a gas boiler. The method adopts text mining technology to perform word segmentation, feature extraction and related word mining on a text set of historical inspection reports of multiple gas boilers, thereby obtaining corresponding risk expression sentences, thereby improving the efficiency of feature extraction of the text set of historical inspection reports of gas boilers; at the same time, based on the first-level evaluation index, the second-level evaluation index and the preset expert weights, the group hierarchical analysis method is adopted to determine the weight of each first-level evaluation index and the weight of each second-level evaluation index, thereby determining the operating status of the gas boiler, thereby improving the objectivity of the operating status of the gas boiler. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 This is an application environment diagram of a method for determining an operating state of a gas boiler in an embodiment of the present application;

[0050] Figure 2 A schematic flow chart of a method for determining the operating state of a gas boiler provided in one embodiment of the present application;

[0051] Figure 3 A schematic diagram of functional modules of a system for determining the operating status of a gas boiler provided in an embodiment of the present application;

[0052] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application.

[0053] Reference numerals:

[0054] Candidate new word determination unit-1, risk characteristic word determination unit-2, risk expression statement determination unit-3, evaluation index determination unit-4, weight determination unit-5, operation status determination unit-6. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0057] The method for determining the operating state of a gas boiler provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the historical inspection report text set of the gas boiler to the server 104. After the server 104 receives the historical inspection report text set of the gas boiler, the server 104 determines the operating state of the gas boiler for the historical inspection report text set of the gas boiler. The server 104 can feedback the obtained operating state of the gas boiler to the terminal 102. In addition, in some embodiments, the operating state determination method of the gas boiler can also be implemented separately by the server 104 or the terminal 102, such as the terminal 102 can directly determine the operating state of the gas boiler for the historical inspection report text set of the gas boiler, or the server 104 can obtain the historical inspection report text set of the gas boiler from the data storage system, and determine the operating state of the gas boiler for the historical inspection report text set of the gas boiler.

[0058] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0059] In an exemplary embodiment, Figure 2 As shown, a method for determining the operating state of a gas boiler is provided, comprising the following steps S1 to S6. Among them:

[0060] Step S1, obtain a plurality of historical inspection report text sets of gas boilers, and perform word segmentation on the historical inspection report text sets to obtain candidate new words. The historical inspection reports include: internal inspection reports, external inspection reports, water quality inspections, and thermal efficiency test reports. Because the historical inspection report text set of gas boilers contains many new words, in order to accurately segment and obtain better word segmentation effects, for new words that are not in the word library, a mutual information-based method is used to segment the historical inspection report text set using Python and the word segmentation tool Jieba, and establish a boiler operation risk factor word library (i.e., a word library composed of candidate new words).

[0061] As an optional implementation, in step S1, the historical inspection report text set is segmented to obtain candidate new words, which specifically includes:

[0062] Step S11, using Jieba word segmentation tool to perform preliminary word segmentation on the historical inspection report text set to obtain a word sequence.

[0063] Step S12, determining the frequency of occurrence of each word in the word sequence in the historical verification report text set, and generating a word frequency table.

[0064] Step S13, filtering out words in the word frequency table whose appearance frequency exceeds the second preset threshold, and taking all words whose appearance frequency exceeds the second preset threshold as first words; wherein the first words, i.e., high-frequency words, serve as the basis of candidate new words.

[0065] Step S14, combining the first words to generate a plurality of word pairs, and determining the frequency of occurrence of each word pair in the historical inspection report text set.

[0066] Step S15, calculating the mutual information value between each first word in each word pair based on the frequency of each first word in each word pair appearing alone and the frequency of each word pair appearing in the historical verification report text set.

[0067] Specifically, calculate the marginal probability of each first word in each word pair, that is, the probability of the first word x and the first word y appearing separately: P(x) and P(y); calculate the joint probability P(x, y) of the appearance of the word pair in the historical verification report text set, that is, the frequency of the word pair (x, y) appearing in the historical verification report text set.

[0068] The formula for calculating the mutual information value I(x,y) is as follows:

[0069]

[0070] Step S16: Screen out the word pairs whose mutual information values exceed the third preset threshold, and use the word pairs whose mutual information values exceed the third preset threshold as candidate new words. In addition, establish a stop word library based on the historical inspection report text set. After tokenizing and removing stop words from the historical inspection report text set, conduct manual review on the screened candidate new words and stop words to ensure rationality and accuracy.

[0071] Step S2: Use the term frequency-inverse document frequency (TF-IDF) weighting method to extract features from the candidate new words, and obtain multiple risk feature words. Among them, using the TF-IDF weighting method to extract features from the candidate new words utilizes text mining technology.

[0072] As an optional implementation manner, Step S2 specifically includes:

[0073] Step S21: For any historical inspection report text set, use any historical inspection report text set as the current historical inspection report text set:

[0074] Determine the feature words of the candidate new words in all historical inspection report text sets, and calculate the occurrence frequency of each feature word in the current historical inspection report text set.

[0075] Specifically, assume that there are n historical inspection reports of various types in the historical inspection report library, and each historical inspection report text set has m different feature words, forming an m-dimensional vector space. Each inspection report b i (1 < i < n) can be represented by an m-dimensional feature vector:

[0076] b i = {c1, T1(t); c2, T2(t); …; c j , T j (t); …; c m , T m (t)}, 1 < j < m (2)

[0077] Among them, b i represents the i-th historical inspection report text set; i is the serial number of the historical inspection report text set; c j (1 < j < m) is the j-th feature word of the historical inspection report text set; j is the feature word serial number; T j (t) is the TF-IDF weight value of the j-th feature word in the current historical inspection report text set.

[0078] Step S22: Based on the occurrence frequency of each feature word in the current historical inspection report text set, use the TF-IDF weighting method to calculate the TF-IDF weight value of each feature word in the current historical inspection report text set.

[0079] Specifically, the calculation formula of the TF-IDF weight of each feature word is:

[0080]

[0081] Among them, T j (t) is the TF-IDF weight of the jth feature word in the current historical inspection report text set; TF(t ij ) is the frequency of occurrence of the jth feature word in the i-th historical inspection report text set; N is the total number of historical inspection report text sets; n j is the number of reports in which the j-th feature word appears in the N historical inspection report text sets (i.e., the j-th feature word has appeared in several historical inspection report text sets).

[0082] Step S23, sorting the TF-IDF weights of all feature words in all historical inspection report text sets, and taking the feature words ranked in the top 50% as risk feature words.

[0083] Step S3, calculate the correlation coefficients between each risk characteristic word, combine the risk characteristic words whose correlation coefficients exceed the first preset threshold, and obtain the corresponding risk expression statement. Among them, the risk characteristic words whose correlation coefficients exceed the first preset threshold are combined, and the text mining technology is applied to the mining of related words. The first preset threshold should be flexibly set according to the word segmentation results reported during actual use.

[0084] Specifically, since the same risk factor of boiler operation status has different expressions in different historical inspection reports, the correlation coefficient between each risk characteristic word and other risk characteristic words is calculated by calculating the correlation coefficient, and the risk characteristic words with related relationships are combined according to the calculation results of the correlation coefficient to obtain multiple boiler operation safety risk performance statements with complete meanings. Among them, the findAssocs function in the tm toolkit of R language is used to calculate the correlation coefficient.

[0085] Step S4, determine the primary evaluation indicators and secondary evaluation indicators of each risk expression statement; the primary evaluation indicators are personnel risk factors, equipment risk factors or management risk factors; the secondary evaluation indicators include risk characteristic words obtained by the above method. For example, the risk characteristic words are certificated and operator, and the correlation coefficient of the two words is 0.6 (exceeding the first preset threshold), then the risk expression statement is obtained: operators must be certified to work. Then a secondary evaluation indicator "whether the relevant operators are certified to work" under the primary indicator "personnel risk" is obtained.

[0086] Specifically, three first-level evaluation indicators are first established, namely, personnel risk factors, equipment risk factors, and management risk factors. Then, multiple risk performance statements are classified into different first-level evaluation indicators to form second-level evaluation indicators.

[0087] Step S5, based on the first-level evaluation indicators, the second-level evaluation indicators and the preset expert weights, the group hierarchy analysis method is used to determine the weights of each first-level evaluation indicator and the weights of each second-level evaluation indicator.

[0088] As an optional implementation, in step S5, based on the primary evaluation indicators, the secondary evaluation indicators and the preset expert weights, the group hierarchical analysis method is used to determine the weights of each primary evaluation indicator and each secondary evaluation indicator, specifically including:

[0089] Step S51, using a nine-level scaling method, based on the secondary evaluation index and the primary evaluation index, to determine multiple judgment matrices.

[0090] Specifically, the evaluation expert group consists of 6 types of personnel, each of which is represented by an expert, namely: boiler production design and installation personnel, factory equipment management personnel, equipment users, technical experts, equipment inspection personnel, and supervisors. Each expert uses the nine-level scale in Table 1 to compare the first-level evaluation indicators and the second-level evaluation indicators in pairs to obtain the pairwise judgment matrix of each level. The specific meaning of the nine-level scale is shown in Table 1, and the judgment matrix D is shown in formula (4):

[0091]

[0092] Among them, a pq Indicates the importance ratio of factor q to factor p; a qp Indicates the importance ratio of factor p to factor q; a p Indicates the importance of factor p: a q It indicates the importance of factor q; n indicates the number of secondary evaluation indicators.

[0093] Table 1 1 to 9 scales and their meanings

[0094]

[0095]

[0096] Step S52: Based on the preset expert weights, a weighted average is performed on multiple judgment matrices to obtain a comprehensive judgment matrix.

[0097] Specifically, the equipment supervision department directly assigns weights to experts from the perspective of equipment supervision, and obtains the preset expert weights: β l , l = 1, 2, 3, 4, 5, 6, and l represents the serial number of the preset expert weight; based on the preset expert weight, the pairwise judgment matrices of each expert are weighted averaged to obtain a comprehensive judgment matrix.

[0098] Step S53, using SPSSPRO software, based on the group hierarchical analysis method, weight calculation is performed on the comprehensive judgment matrix to obtain the weight of each first-level evaluation index and the weight of each second-level evaluation index.

[0099] Step S6, based on the weights of the first-level evaluation indicators and the weights of the second-level evaluation indicators, determine the operating state of the gas boiler; the operating state is safe operation or fault operation.

[0100] As an optional implementation, step S6 specifically includes:

[0101] Step S61, determining the occurrence frequency of the risk characteristic words corresponding to each first-level evaluation indicator in the current historical inspection report text set and the occurrence frequency of the risk characteristic words corresponding to each second-level evaluation indicator in the current historical inspection report text set.

[0102] Step S62, determines the operating status of the gas boiler based on the frequency of occurrence of the risk characteristic words corresponding to each first-level evaluation indicator in the current historical inspection report text set, the weight of each first-level evaluation indicator, the frequency of occurrence of the risk characteristic words corresponding to each second-level evaluation indicator in the current historical inspection report text set, and the weight of each second-level evaluation indicator.

[0103] Specifically, for a certain boiler, or several boilers of a certain unit, the frequency of occurrence of the risk characteristic words corresponding to each first-level evaluation indicator is counted. At this time, the total number of inspection reports is the inspection reports of the gas boilers to be evaluated. The frequency of occurrence of the risk characteristic words corresponding to each first-level evaluation indicator in the current historical inspection report text set is multiplied by the weight of each first-level evaluation indicator, and the frequency of occurrence of the risk characteristic words corresponding to each second-level evaluation indicator in the current historical inspection report text set is multiplied by the weight of each second-level evaluation indicator, and the sum is obtained. The sum value is the final boiler operation status assessment result.

[0104] Beneficial effects of this application:

[0105] This application enables personnel with professional knowledge, professional practitioners, technical experts, supervisors, etc. to participate in the evaluation of the operating status of gas boilers with various knowledge and experience. The use of text mining and group AHP ensures the efficiency and objectivity of the method, and expresses the operating status evaluation results in a quantitative way. Users can understand the operating status of the boiler more intuitively, so as to take corresponding measures accurately and timely.

[0106] Based on the same inventive concept, the embodiment of the present application also provides a gas boiler operating state determination system for implementing the above-mentioned gas boiler operating state determination method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more gas boiler operating state determination systems provided below can refer to the limitations of the gas boiler operating state determination method above, and will not be repeated here.

[0107] In an exemplary embodiment, Figure 3 As shown, a system for determining the operating state of a gas boiler is provided, comprising:

[0108] The candidate new word determination unit 1 is used to obtain a plurality of historical inspection report text sets of gas boilers, and perform word segmentation processing on the historical inspection report text sets to obtain candidate new words.

[0109] The risk characteristic word determination unit 2 is used to extract characteristics of candidate new words by using the TF-IDF weighting method to obtain multiple risk characteristic words.

[0110] The risk expression statement determination unit 3 is used to calculate the correlation coefficients between each risk characteristic word, and combine the risk characteristic words whose correlation coefficients exceed a first preset threshold to obtain a corresponding risk expression statement.

[0111] The evaluation index determination unit 4 is used to determine the primary evaluation index and the secondary evaluation index of each risk performance statement; the primary evaluation index includes personnel risk factors, equipment risk factors and management risk factors.

[0112] The weight determination unit 5 is used to determine the weight of each first-level evaluation indicator and the weight of each second-level evaluation indicator by using the group hierarchy analysis method based on the first-level evaluation indicator, the second-level evaluation indicator and the preset expert weight.

[0113] The operation state determination unit 6 is used to determine the operation state of the gas boiler based on the weights of each primary evaluation index and the weights of each secondary evaluation index; the operation state is safe operation or fault operation.

[0114] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for determining an operating state of a gas boiler.

[0115] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a method for determining an operating state of a gas boiler is implemented.

[0116] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements a method for determining an operating state of a gas boiler when the computer program is executed by a processor.

[0117] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the operating state of a gas boiler is implemented.

[0118] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0119] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0120] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0121] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0122] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for determining the operating state of a gas boiler, characterized in that: The method for determining the operating state of the gas boiler comprises: Obtain a text set of historical inspection reports of multiple gas boilers, and perform word segmentation on the text set of historical inspection reports to obtain candidate new words; Using the TF-IDF weighted method, feature extraction is performed on candidate new words to obtain multiple risk feature words; Calculate the correlation coefficients between each risk characteristic word, combine the risk characteristic words whose correlation coefficients exceed a first preset threshold, and obtain corresponding risk expression statements; Determine the primary and secondary evaluation indicators for each risk performance statement; the primary evaluation indicator is a personnel risk factor, an equipment risk factor, or a management risk factor; Based on the first-level evaluation indicators, second-level evaluation indicators and preset expert weights, the group hierarchical analysis method is used to determine the weights of each first-level evaluation indicator and each second-level evaluation indicator; Based on the weights of the first-level evaluation indicators and the weights of the second-level evaluation indicators, the operating state of the gas boiler is determined; the operating state is safe operation or fault operation.

2. The method for determining the operating state of a gas boiler according to claim 1, characterized in that: The historical inspection report text set is segmented to obtain candidate new words, including: Jieba word segmentation tool is used to perform preliminary word segmentation on the historical inspection report text set to obtain word sequences; Determine the frequency of occurrence of each word in the word sequence in the historical inspection report text set and generate a word frequency table; Filter words whose appearance frequency exceeds a second preset threshold in the word frequency table, and take all words whose appearance frequency exceeds the second preset threshold as the first words; Combining the first words to generate a plurality of word pairs, and determining the frequency of occurrence of each word pair in the historical inspection report text set; Based on the frequency of each first word in each word pair appearing alone and the frequency of each word pair appearing in the historical inspection report text set, the mutual information value between each first word in each word pair is calculated; The word pairs whose mutual information values ​​exceed the third preset threshold are screened out, and the word pairs whose mutual information values ​​exceed the third preset threshold are used as candidate new words.

3. The method for determining the operating state of a gas boiler according to claim 1, characterized in that: Using the TF-IDF weighted method, feature extraction is performed on candidate new words to obtain multiple risk feature words, including: For any historical inspection report text set, use any historical inspection report text set as the current historical inspection report text set: Determine the characteristic words of the candidate new words in all historical inspection report text sets, and calculate the occurrence frequency of each characteristic word in the current historical inspection report text set; Based on the occurrence frequency of each feature word in the current historical inspection report text set, the TF-IDF weighting method is used to calculate the TF-IDF weight of each feature word in the current historical inspection report text set; The TF-IDF weights of all feature words in all historical inspection report text sets are ranked, and the feature words ranked in the top 50% are taken as risk feature words.

4. The method for determining the operating state of a gas boiler according to claim 3, characterized in that: The calculation formula of TF-IDF weight of each feature word is: Among them, T j ( t ) is the TF-IDF weight of the jth feature word in the current historical inspection report text set; TF(t ij ) is the frequency of occurrence of the jth feature word in the i-th historical inspection report text set; N is the total number of historical inspection report text sets; n j is the number of reports in which the jth feature word appears in the N historical inspection report text sets.

5. The method for determining the operating state of a gas boiler according to claim 1, characterized in that: Based on the first-level evaluation indicators, second-level evaluation indicators and preset expert weights, the group hierarchical analysis method is used to determine the weights of each first-level evaluation indicator and each second-level evaluation indicator, including: Using the nine-level scaling method, multiple judgment matrices are determined based on the secondary evaluation indicators and the primary evaluation indicators; Based on the preset expert weights, multiple judgment matrices are weighted averaged to obtain a comprehensive judgment matrix; Using SPSSPRO software and based on the group hierarchical analysis method, the weight of the comprehensive judgment matrix was calculated to obtain the weight of each first-level evaluation indicator and the weight of each second-level evaluation indicator.

6. The method for determining the operating state of a gas boiler according to claim 3, characterized in that: Based on the weights of each first-level evaluation index and each second-level evaluation index, the operating status of the gas boiler is determined, including: Determine the frequency of occurrence of the risk characteristic words corresponding to each first-level evaluation indicator in the current historical inspection report text set and the frequency of occurrence of the risk characteristic words corresponding to each second-level evaluation indicator in the current historical inspection report text set; The operating status of the gas boiler is determined based on the frequency of occurrence of the risk characteristic words corresponding to each first-level evaluation indicator in the current historical inspection report text set, the weight of each first-level evaluation indicator, the frequency of occurrence of the risk characteristic words corresponding to each second-level evaluation indicator in the current historical inspection report text set, and the weight of each second-level evaluation indicator.

7. A system for determining the operating state of a gas boiler, characterized in that: The operating state determination system of the gas boiler is used to implement the operating state determination method of the gas boiler according to any one of claims 1 to 6, and the operating state determination system of the gas boiler comprises: A candidate new word determination unit is used to obtain a plurality of historical inspection report text sets of gas boilers, and perform word segmentation processing on the historical inspection report text sets to obtain candidate new words; A risk characteristic word determination unit is used to extract features of candidate new words by using a TF-IDF weighting method to obtain multiple risk characteristic words; A risk expression statement determination unit, used to calculate the correlation coefficients between each risk characteristic word, and combine the risk characteristic words whose correlation coefficients exceed a first preset threshold value to obtain a corresponding risk expression statement; An evaluation index determination unit is used to determine the primary evaluation index and the secondary evaluation index of each risk performance statement; the primary evaluation index is a personnel risk factor, an equipment risk factor or a management risk factor; A weight determination unit, for determining the weight of each first-level evaluation indicator and the weight of each second-level evaluation indicator by using a group hierarchical analysis method based on the first-level evaluation indicator, the second-level evaluation indicator and the preset expert weight; The operating state determining unit is used to determine the operating state of the gas boiler based on the weights of each primary evaluation index and the weights of each secondary evaluation index; the operating state is safe operation or fault operation.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for determining the operating state of the gas boiler according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining the operating state of a gas boiler according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for determining the operating state of a gas boiler according to any one of claims 1 to 6 is implemented.

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