Artificial Intelligence-Based Standard Data Classification Method and System for Coal Chemical Industry

Abstract

The present invention discloses a method and system for classifying standard data in the coal chemical industry based on artificial intelligence, including collecting standard data and feedback data in the coal chemical industry, and preprocessing the standard data and the feedback data in the coal chemical industry; extracting features from the standard data in the coal chemical industry to obtain standard feature data, and performing associated clustering on the standard feature data to obtain first data; classifying the standard feature data according to energy to obtain second data, and comparing the first data and the second data to obtain consistent data and fuzzy data; correcting the fuzzy data according to the feedback data to obtain third data, and outputting the consistent data and the third data as classification results. This method can not only improve the accuracy of classifying standard data in the coal chemical industry based on artificial intelligence, but also has good interpretability and can be directly applied to the standard data classification system in the coal chemical industry.

Description

Technical Field

[0001] The present invention relates to the field of data classification, and particularly to a method and system for classifying standard data in the coal chemical industry based on artificial intelligence. Background Art

[0002] As an important part of the energy and chemical industry, the coal chemical industry involves numerous complex technical standards and technological processes in its production process. These standards and processes are of great significance for ensuring product quality, improving production efficiency, and ensuring safe production. However, with the rapid development of the coal chemical industry and the continuous progress of technology, the standard data in the industry shows the characteristics of being massive, heterogeneous, and constantly updated, which brings great challenges to the management and application of data.

[0003] Traditional methods for classifying standard data in the coal chemical industry mainly rely on manual review and expert experience. This method is not only time-consuming and laborious, but also easily affected by personal subjective factors, resulting in inaccuracy and inconsistency of classification results. In addition, due to the complexity and diversity of the standard data in the coal chemical industry, traditional classification methods often have difficulty covering all standards and processes comprehensively, thus limiting their effectiveness in practical applications.

[0004] In order to overcome the deficiencies of traditional classification methods, in recent years, artificial intelligence technology has been widely applied in the coal chemical industry. Among them, classification methods based on machine learning and data mining have attracted much attention due to their powerful data processing capabilities and pattern recognition capabilities. However, most of the existing artificial intelligence-based classification methods are designed for specific fields or datasets, lacking universality and adaptability for the standard data in the coal chemical industry. In addition, when dealing with complex and heterogeneous standard data in the coal chemical industry, these methods often have problems such as inaccurate feature extraction and unstable classification results. Therefore, there is an urgent need to invent a new artificial intelligence-based method for classifying standard data in the coal chemical industry to improve accuracy and stability. Summary of the Invention

[0005] The object of the present invention is to provide a method for classifying standard data in the coal chemical industry based on artificial intelligence.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] The present invention includes the following steps:

[0008] Collect standard data and feedback data in the coal chemical industry, and preprocess the standard data and the feedback data in the coal chemical industry;

[0009] Extract features from the standard data in the coal chemical industry to obtain standard feature data, and perform associated clustering on the standard feature data to obtain first data;

[0010] Classify the standard feature data according to the energy to obtain second data, and compare the first data with the second data to obtain consistent data and fuzzy data;

[0011] Correct the fuzzy data according to the feedback data to obtain third data, and output the consistent data and the third data as the classification result.

[0012] Furthermore, the method for extracting features from the coal chemical industry standard data to obtain standard feature data includes:

[0013] Extract text data from the coal chemical industry standard data, and quantify the weights of words in the text data according to their parts of speech. When the part of speech is a noun or a verb, the initial weight of the word is 0.8; when the part of speech is an adjective or an adverb, the initial weight of the word is 0.6; when the part of speech is other parts of speech, the initial weight of the word is 0;

[0014] Calculate the semantic coupling similarity of word pairs:

[0015] β(b w ,b z )=(1 - ω)·CuR(b w ,b z )+ω·CvR(b w ,b z )

[0016] CuR(b w ,b z )=CS(P1(b w ),P1(b z ))

[0017] CvR(b w ,b z )=CS(P2(b w ),P2(b z ))

[0018] P1(b w )=(P1(b1|b w ),P1(b2|b w ),R,P1(b v |b w ),R,P1(b N1 |b w ))

[0019]

[0020] P2(b w )=(P2(b1|b w ),P2(b2|bw ), R, P2(b v |b w ), R, P2(b N1 |b w ))

[0021]

[0022] where the relationship strength function is CS(·, ·), the z-th word is b z , the w-th word is b w , the word pair is (b z , b w ), the internal coupling relationship of the word pair (b z , b w ) is CuR(b w , b z ), the external coupling relationship of the word pair (b z , b w ) is CvR(b w , b z ), the text set is U, the total number of texts is |U|, the probability of the word pair (b z , b w ) in this text is P1(b z |b w ), the shortest path between the word b z and the word b w is SM(b z , b w ), the decision parameter is ω, the frequency of the word pair (b z , b w ) appearing is DF(b z , b w ), the probability distribution of the word b w in the text set is P1(b w ), the similarity probability distribution of the word b w is P2(b w ), the similarity degree between the word b z and the word b w is P2(b w |b z ), the probability of the word pair (b z , b w ) in the text set is P1(b z |b w ), the single text in the text set is u, the first word is b1, the second word is b2, the v-th word is b v , the N1-th word is b N1 , the number of texts in the text set is N1, the word pair (b z , b w) The number of occurrences in the single text u is TPF((b z ,b w ), u);

[0023] Calculate the similarity between word attributes:

[0024]

[0025] Among them, the w-th word attribute is a w , the z-th word attribute is a z , and the similarity between the word attribute a w and the word attribute a z is The square of the Euclidean norm is The positive control parameter is η,

[0026] Construct a text graph according to the text set, use words as the feature terms of the text graph, and calculate the weights of the feature terms:

[0027] θ(t + 1) = (1 - γ)·Q·θ(t) + γ·β·θ(t)

[0028]

[0029] Among them, the damping coefficient is γ, the structural feature similarity between the feature term w and the feature term z in the t-th iteration is Q w,z , the similarity between word attributes is β, the weight vector of the feature terms in the t-th iteration is θ(t), the weight vector of the feature terms in the (t + 1)-th iteration is θ(t + 1), and the structural feature similarity is Q;

[0030] Continuously iterate until all feature terms are traversed, obtain the weight vector of the feature terms, and output the weight vector of the feature terms as standard feature data.

[0031] Further, a method for performing association clustering on the standard feature data to obtain the first data includes:

[0032] Construct a feature data set according to the standard feature data and calculate the kernel density gradient:

[0033]

[0034] Among them, the kernel function is G(·), and the kernel density gradient of the standard feature data g c is K(g c ), the c-th standard feature data is g c , the number of standard feature data is M, and the bandwidth parameter of dimension σ is The standard feature data is g;

[0035] Calculate the association degree of the standard feature data:

[0036]

[0037] where the fuzzy neighborhood radius of the standard feature data is The association decision is W, the feature data set is S, the fuzzy neighborhood radius is Z, and the standard feature data is g c The fuzzy neighborhood granule of the key data set is Z S (g c ), and the standard feature data is g c The fuzzy neighborhood granule of the standard feature data with respect to the association decision is Z W (g c ), the standard constant is δ, and the bandwidth parameter is The c-th standard feature data is g c , the kernel density gradient estimate is K(·), and the degree of association of the standard feature data g c is

[0038] Cluster the standard feature data according to the degree of association to obtain clusters, and take the standard feature data with the maximum kernel density gradient as the cluster center;

[0039] Sort the fuzzy standard feature data according to the degree of association, divide the fuzzy standard feature data into the cluster with the largest degree of association, and obtain fuzzy clusters; output the clustering result as the first clustering data.

[0040] Further, a method for classifying the standard feature data according to energy to obtain the second data includes:

[0041] Obtain the energy type of the standard feature data according to the coal chemical industry standard data, and classify the standard feature data containing only one energy into a single energy data set, and vice versa into a mixed energy data set;

[0042] Sort the mixed energy data set in descending order according to the importance of the mixed energy, and label the standard feature data of the mixed energy data set according to the sorting; output the single energy data set and the mixed energy data set as the second data.

[0043] Further, a method for comparing the first data and the second data to obtain consistent data and fuzzy data includes:

[0044] Calculate the similarity between the first data and the second data:

[0045]

[0046] where the r-th first data of the i-th category is h i,r , the j-th second data of the i-th category is d i,j , the first data h i,r and the second data d i,jThe similarity is The number of first data of the i-th category is M h , the number of second data of the i-th category is M d , the control factor is α, and the optimization coefficient is

[0047] When the similarity is greater than 0.681, the corresponding first data or second data is classified as consistent data; otherwise, the first data and the second data are classified as fuzzy data.

[0048] Furthermore, the method for correcting the fuzzy data according to the feedback data to obtain the third data includes:

[0049] Calculate the accuracy according to the feedback data:

[0050]

[0051] where the accuracy of the s-th test is χ s , the test data of the s-th test is y s , the x-th feedback data of the s-th test is f s,x , the correlation degree between the test data and the feedback data is

[0052] Calculate the satisfaction degree:

[0053]

[0054] where the satisfaction degree of the s-th test is φ s , the error factor is ε, and the matching degree between the test data and the feedback data is The feedback duration of the s-th test is T s , the slowest feedback duration acceptable to the customer is T min , the set response time of the s-th test is T o ;

[0055] When the satisfaction degree is lower than 0.539, perform ambiguity correction on the feedback data to obtain the corrected data, and the expression is:

[0056]

[0057] where the x-th corrected data is The number of topics of the feedback data is The p-th cluster center is D p , the topic and the matching degree between the cluster center is The decision function of the corrected data is C(x), the deviation factor is μ, and the m-th topic of the x-th corrected data is The objective weight of the m-th topic is ζ m, the x-th feedback data is f x , the theme The distance from the cluster center is

[0058] Calculate the matching degree between the corrected data and the cluster center, sort the cluster centers in descending order according to the matching degree, divide the corrected data into the first cluster center, and output the corrected division result as the third data.

[0059] In a second aspect, an artificial intelligence-based classification system for coal chemical industry standard data includes:

[0060] Data acquisition module: used to collect coal chemical industry standard data and feedback data, and preprocess the coal chemical industry standard data and the feedback data;

[0061] Feature extraction and classification module: used to extract features from the coal chemical industry standard data to obtain standard feature data, and perform associated clustering on the standard feature data to obtain the first data;

[0062] Classification and comparison module: used to classify the standard feature data according to energy to obtain the second data, and compare the first data and the second data to obtain consistent data and fuzzy data;

[0063] Correction and output module: used to correct the fuzzy data according to the feedback data to obtain the third data, and output the consistent data and the third data as the classification result.

[0064] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0065] A processor; and a memory arranged to store computer-executable instructions, the executable instructions when executed cause the processor to execute the method steps described in the first aspect.

[0066] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, the computer-readable storage medium stores one or more programs, when the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device is caused to execute the method steps described in the first aspect.

[0067] The beneficial effects of the present invention are:

[0068] The present invention is an artificial intelligence-based classification method and system for coal chemical industry standard data. Compared with the prior art, the present invention has the following technical effects:

[0069] Through the steps of preprocessing, feature extraction, correlation clustering, feature classification, data comparison, and classification correction, the present invention can improve the accuracy of classifying standard data in the coal chemical industry based on artificial intelligence, thereby enhancing the precision of classifying standard data in the coal chemical industry based on artificial intelligence. Optimizing the classification of standard data in the coal chemical industry based on artificial intelligence can greatly save resources, improve work efficiency, enable intelligent classification of standard data in the coal chemical industry based on artificial intelligence, and perform classification correction on the classification of standard data in the coal chemical industry based on artificial intelligence in real time, which is of great significance for the classification of standard data in the coal chemical industry based on artificial intelligence, and can adapt to different standards and requirements of standard data classification in the coal chemical industry, having a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 is a flowchart of the steps of the method for classifying standard data in the coal chemical industry based on artificial intelligence according to the present invention;

[0071] Figure 2 is a schematic structural diagram of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] The present invention will be further described below through specific embodiments. The illustrative embodiments and explanations of this invention are used to explain the present invention, but do not limit the present invention.

[0073] The method and system for classifying standard data in the coal chemical industry based on artificial intelligence according to the present invention include the following steps:

[0074] As Figure 1 shown, in this embodiment, the following steps are included:

[0075] Collect standard data and feedback data in the coal chemical industry, and perform preprocessing on the standard data and the feedback data in the coal chemical industry;

[0076] In actual evaluation, the feedback data includes test content, response time, and feedback content;

[0077] Take three pieces of standard data in the coal chemical industry, including the production process, energy consumption, and safety standards of coal chemical products, as the research objects;

[0078] Extract features from the standard data in the coal chemical industry to obtain standard feature data, and perform correlation clustering on the standard feature data to obtain the first data;

[0079] In actual evaluation, the standard feature data includes energy consumption, the number of safety accidents, production efficiency, and safety standards; The first data: 1 group of high correlation includes Standard 1 and Standard 3 in the coal chemical industry, and 2 groups of low correlation are Standard 2 in the coal chemical industry;

[0080] Classify the standard feature data according to the energy to obtain the second data, and compare the first data with the second data to obtain the consistent data and the fuzzy data;

[0081] In the actual evaluation, the second data: 1 group of single energy is the coal chemical industry standard 1, and 2 groups of mixed energy include the coal chemical industry standard 2 and the coal chemical industry standard 3; the consistent data are the coal chemical industry standard 1 and the coal chemical industry standard 2, and the fuzzy data is the coal chemical industry standard 3;

[0082] Correct the fuzzy data according to the feedback data to obtain the third data, and output the consistent data and the third data as the classification result;

[0083] In the actual evaluation, the third data: 1 group of coal chemical industry standard 1, and 2 groups include the coal chemical industry standard 2 and the coal chemical industry standard 3.

[0084] In this embodiment, the method for extracting the standard feature data from the coal chemical industry standard data includes:

[0085] Extract the text data from the coal chemical industry standard data, and quantify the weights of the words in the text data according to the part of speech. When the part of speech is a noun or a verb, the initial weight of the word is 0.8; when the part of speech is an adjective or an adverb, the initial weight of the word is 0.6; when the part of speech is other parts of speech, the initial weight of the word is 0;

[0086] Calculate the semantic coupling similarity of the word pairs:

[0087] β(b w ,b z )=(1 - ω)·CuR(b w ,b z )+ω·CvR(b w ,b z )

[0088] CuR(b w ,b z )=CS(P1(b w ),P1(b z ))

[0089] CvR(b w ,b z )=CS(P2(b w 0,P2(b z ))

[0090] P1(b w )=(P1(b1|b w ),P1(b2|b w ),R,P1(bv |b w ), R, P1(b N1 |b w ))

[0091]

[0092] P2(b w ) = (P2(b1|b w ), P2(b2|b w ), R, P2(b v |b w ), R, P2(b N1 |b w ))

[0093]

[0094] where the relationship strength function is CS(·, ·), the z-th word is b z , the w-th word is b w , the word pair is (b z , b w ), the internal coupling relationship of the word pair (b z , b w ) is CuR(b w , b z ), the external coupling relationship of the word pair (b z , b w ) is CvR(b w , b z ), the text set is U, the total number of texts is |U|, the probability of the word pair (b z , b w ) in this text is P1(b z |b w ), the shortest path between the word b z and the word b w is SM(b z , b w ), the decision parameter is ω, the frequency of the word pair (b z , b w ) is DF(b z , b w ), the probability distribution of the word b w in the text set is P1(b w ), the similarity probability distribution of the word b w is P2(b w ), the similarity degree between the word b z and the word b w is P2(b w |b z ), the word pair (b z , bw ) The probability in the text set is P1(b z |b w ), the single text in the text set is u, the first word is b1, the second word is b2, the v-th word is b v , the N1-th word is b N1 , the number of texts in the text set is N1, and the number of occurrences of the word pair (b z ,b w ) in the single text u is TPF((b z ,b w ),u);

[0095] Calculate the similarity between word attributes:

[0096]

[0097] Among them, the w-th word attribute is a w , the z-th word attribute is a z , and the similarity between the word attributes a w and a z is The square of the Euclidean norm is The positive control parameter is η,

[0098] Construct a text graph based on the text set, use words as the feature terms of the text graph, and calculate the weights of the feature terms:

[0099] θ(t + 1) = (1 - γ)·Q·θ(t) + γ·β·θ(t)

[0100]

[0101] Among them, the damping coefficient is γ, the structural feature similarity between the feature term w and the feature term z in the t-th iteration is Q w,z , the similarity between word attributes is β, the weight vector of the feature terms in the t-th iteration is θ(t), the weight vector of the feature terms in the (t + 1)-th iteration is θ(t + 1), and the structural feature similarity is Q;

[0102] Continuously iterate until all feature terms are traversed, obtain the weight vector of the feature terms, and output the weight vector of the feature terms as standard feature data.

[0103] In this embodiment, the method for performing associated clustering on the standard feature data to obtain the first data includes:

[0104] Construct a feature data set based on the standard feature data, and calculate the kernel density gradient:

[0105]

[0106] Among them, the kernel function is G(·), and the standard feature data is g c The kernel density gradient of c is K(g c ), the c-th standard feature data is g , the number of standard feature data is M, and the bandwidth parameter of dimension σ is

[0107] Calculate the correlation degree of the standard feature data:

[0108]

[0109] Among them, the fuzzy neighborhood radius of the standard feature data is The association decision is W, the feature data set is S, the fuzzy neighborhood radius is Z, and the standard feature data g c The fuzzy neighborhood granule of the standard feature data with respect to the key data set is Z S (g c ), the standard feature data g c The fuzzy neighborhood granule of the standard feature data with respect to the association decision is Z W (g c ), the standard constant is δ, and the bandwidth parameter is The c-th standard feature data is g c , the kernel density gradient estimation is K(·), and the correlation degree of the standard feature data g c is

[0110] Cluster the standard feature data according to the correlation degree to obtain clusters, and use the standard feature data with the largest kernel density gradient as the cluster center;

[0111] Sort the fuzzy standard feature data according to the correlation degree, divide the fuzzy standard feature data into the cluster with the largest correlation degree to obtain fuzzy clusters; output the clustering result as the first clustering data.

[0112] In this embodiment, the method for classifying the standard feature data according to energy to obtain the second data includes:

[0113] Obtain the energy type of the standard feature data according to the coal chemical industry standard data, and classify the standard feature data containing only one type of energy into the single energy data set, and vice versa into the mixed energy data set;

[0114] Sort the mixed energy according to its importance in descending order, and label the standard feature data of the mixed energy data set according to the sorting; output the single energy data set and the mixed energy data set as the second data.

[0115] In this embodiment, the method for comparing the first data and the second data to obtain consistent data and fuzzy data includes:

[0116] Calculate the similarity between the first data and the second data:

[0117]

[0118] where the r-th first data of the i-th category is h i,r , and the j-th second data of the i-th category is d i,j , the similarity between the first data h i,r and the second data d i,j is The number of first data of the i-th category is M h , and the number of second data of the i-th category is M d , the control factor is α, and the optimization coefficient is

[0119] When the similarity is greater than 0.681, the corresponding first data or second data is classified as consistent data; otherwise, the first data and the second data are classified as fuzzy data.

[0120] In this embodiment, the method for correcting the fuzzy data according to the feedback data to obtain the third data includes:

[0121] Calculate the accuracy according to the feedback data:

[0122]

[0123] where the accuracy of the s-th test is χ s , the test data of the s-th test is y s , the x-th feedback data of the s-th test is f s,x , and the correlation degree between the test data and the feedback data is

[0124] Calculate the satisfaction degree:

[0125]

[0126] where the satisfaction degree of the s-th test is φ s , the error factor is ε, and the matching degree between the test data and the feedback data is The feedback duration of the s-th test is T s , the slowest feedback duration acceptable to the customer is T min , and the set response time of the s-th test is T o ;

[0127] When the satisfaction degree is lower than 0.539, perform ambiguity correction on the feedback data to obtain the corrected data, and the expression is:

[0128]

[0129] where the x-th correction data is the number of topics of the feedback data is the p-th cluster center is D p , topic and the matching degree with the cluster center is The decision function of the correction data is C(x), the deviation factor is μ, and the m-th topic of the x-th correction data is the objective weight of the m-th topic is ζ m , the x-th feedback data is f x , topic and the distance from the cluster center is

[0130] Calculate the matching degree between the correction data and the cluster center, sort the cluster centers in descending order according to the matching degree, divide the correction data into the first cluster center, and output the correction division result as the third data.

[0131] In a second aspect, an artificial intelligence-based classification system for coal chemical industry standard data includes:

[0132] Data acquisition module: used to collect coal chemical industry standard data and feedback data, and preprocess the coal chemical industry standard data and the feedback data;

[0133] Feature extraction and classification module: used to extract features from the coal chemical industry standard data to obtain standard feature data, and perform association clustering on the standard feature data to obtain the first data;

[0134] Classification and comparison module: used to classify the standard feature data according to energy to obtain the second data, and compare the first data and the second data to obtain consistent data and fuzzy data;

[0135] Correction and output module: used to correct the fuzzy data according to the feedback data to obtain the third data, and output the consistent data and the third data as the classification result.

[0136] Figure 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Please refer to Figure 2 , at the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory, such as at least one disk memory, etc. Of course, the electronic device may also include other hardware required for other services.

[0137] The processor, network interface, and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 2 only a bidirectional arrow is used in

[0138] Memory, used to store programs. Specifically, the program can include program code, and the program code includes computer operation instructions. The memory can include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0139] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a standard data classification device for the coal chemical industry based on artificial intelligence at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the aforementioned standard data classification methods for the coal chemical industry based on artificial intelligence.

[0140] As described above in this application Figure 1The coal chemical industry standard data classification method based on artificial intelligence disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0141] The electronic device may also perform Figure 1 The coal chemical industry standard data classification method based on artificial intelligence is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0142] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions, which, when executed by an electronic device including multiple applications, executes any of the aforementioned artificial intelligence-based coal chemical industry standard data classification methods.

[0143] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0144] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0145] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0147] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0148] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0149] A computer-readable medium includes permanent and non-permanent, removable and non-removable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media do not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0150] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0151] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, system, or computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for classifying standard data in the coal chemical industry based on artificial intelligence, characterized in that, It includes the following steps: Collect standard data and feedback data in the coal chemical industry, and preprocess the standard data and the feedback data in the coal chemical industry; Extract features from the standard data in the coal chemical industry to obtain standard feature data, and perform associated clustering on the standard feature data to obtain the first data; Classify the standard feature data according to energy to obtain the second data, and compare the first data and the second data to obtain consistent data and fuzzy data; including: Obtain the energy type of the standard feature data according to the standard data in the coal chemical industry, and classify the standard feature data containing only one kind of energy into a single energy data set, and vice versa into a mixed energy data set; Sort the standard feature data in the mixed energy data set in descending order according to the importance of the mixed energy, and label the standard feature data in the mixed energy data set according to the sorting; output the single energy data set and the mixed energy data set as the second data; Calculate the similarity between the first data and the second data: ， where the r-th first data of the i-th category is , the j-th second data of the i-th category is , the similarity between the first data and the second data is , the number of first data of the i-th category is , the number of second data of the i-th category is , the control factor is , the optimization coefficient is ; When the similarity is greater than 0.681, classify the corresponding first data or second data as consistent data, otherwise classify the first data and the second data as fuzzy data; Correct the fuzzy data according to the feedback data to obtain the third data, and output the consistent data and the third data as the classification result; including: Calculate the accuracy according to the feedback data: ， where the accuracy of the s-th test is , the test data of the s-th test is , the x-th feedback data of the s-th test is , and the correlation degree between the test data and the feedback data is ; Calculate the satisfaction degree: ， where the satisfaction of the s-th test is , the error factor is , the matching degree of the test data and the feedback data is , the feedback duration of the s-th test is , the slowest feedback duration acceptable to the customer is , the set response time of the s-th test is ; When the satisfaction degree is lower than 0.539, perform ambiguity correction on the feedback data to obtain the corrected data, and the expression is: ， ， where the x-th corrected data is , the number of topics of the feedback data is , the p-th cluster center is , the matching degree between the topic and the cluster center is , the decision function of the corrected data is , the deviation factor is , the m-th topic of the x-th corrected data is , the objective weight of the m-th topic is , the x-th feedback data is , the distance between the topic and the cluster center is ;​ Calculate the matching degree between the corrected data and the cluster center, sort the cluster centers in descending order according to the matching degree, divide the corrected data into the first cluster center, and output the corrected division result as the third data.

2. The method for classifying standard data in the coal chemical industry based on artificial intelligence according to claim 1, wherein The method for extracting features from the standard data in the coal chemical industry to obtain standard feature data includes: Extract text data from the standard data in the coal chemical industry, quantify the weights of words in the text data according to the part of speech, when the part of speech is a noun and a verb, the initial weight of the word is 0.8; when the part of speech is an adjective and an adverb, the initial weight of the word is 0.6; when the part of speech is other parts of speech, the initial weight of the word is 0; Calculate the semantic coupling similarity of word pairs: ， ， ， ， ， ， ， where the relationship strength function is , the z-th word is , the w-th word is , the word pair is , the word pair 's internal coupling relationship is , the word pair 's external coupling relationship is , the text set is U, and the total number of texts is , the word pair 's probability in this text is , the word and the word 's shortest path between them is , the decision parameter is , the word pair 's appearance frequency is , in the text set, the probability distribution of the word is , the word 's similar probability distribution is , the word and the word 's similarity degree between them is , the word pair 's probability in the text set is , a single text in the text set is u, the first word is , the second word is , the v-th word is , the -th word is , the number of texts in the text set is , the word pair 's number of occurrences in the single text u is ; Calculate the similarity between word attributes: ， where the attribute of the w-th word is , the z-th word is , the word attribute and the word attribute have a similarity of , the square of the Euclidean norm is , the positive control parameter is , Construct a text graph according to the text set, use the word as the feature term of the text graph, and calculate the weight of the feature term: ， ， where the damping coefficient is , the structural feature similarity between the feature term w and the feature term z in the t-th iteration is , the similarity between word attributes is , the weight vector of the feature term in the t-th iteration is , the weight vector of the feature term in the (t + 1)-th iteration is , and the structural feature similarity is Q; Iterate continuously until all feature terms are traversed, obtain the feature term weight vector, and output the feature term weight vector as the standard feature data.

3. The method for classifying standard data in the coal chemical industry based on artificial intelligence according to claim 1, wherein, The method for performing associated clustering on the standard feature data to obtain the first data includes: Construct a feature data set according to the standard feature data, and calculate the kernel density gradient: ， where the kernel function is , the kernel density gradient of the standard feature data is , the c-th standard feature data is , the number of standard feature data is M, and the bandwidth parameter of the dimension is , the standard feature data is ; Calculate the association degree of the standard feature data: ， where the fuzzy neighborhood radius of the standard feature data is , the association decision is W, the feature data set is S, the fuzzy neighborhood radius is Z, and the standard feature data The fuzzy neighborhood granule of the standard feature data with respect to the key data set is , and the standard feature data The fuzzy neighborhood granule of the standard feature data with respect to the association decision is , the standard constant is , the bandwidth parameter is , the th standard feature data is , the kernel density gradient estimate is , and the association degree of the standard feature data is ;​ Cluster the standard feature data according to the association degree to obtain clusters, and use the standard feature data with the largest kernel density gradient as the cluster center; Sort the fuzzy standard feature data according to the association degree, and divide the fuzzy standard feature data into the cluster with the largest association degree to obtain fuzzy clusters; output the clustering result as the first clustering data.

4. An artificial intelligence-based standard data classification system for the coal chemical industry, which is used to execute the method described in any one of claims 1-3, characterized in that, Including: Data acquisition module: used to collect standard data and feedback data in the coal chemical industry, and preprocess the standard data and the feedback data in the coal chemical industry; Extraction and Classification Module: It is used to extract features from the standard data of the coal chemical industry to obtain standard feature data, and perform associated clustering on the standard feature data to obtain the first data; Classification and Comparison Module: It is used to classify the standard feature data according to energy to obtain the second data, and compare the first data and the second data to obtain consistent data and fuzzy data; Correction and Output Module: It is used to correct the fuzzy data according to the feedback data to obtain the third data, and output the consistent data and the third data as the classification result.

5. An electronic device, comprising: Processor; And A memory arranged to store computer-executable instructions, the executable instructions, when executed, cause the processor to execute the method according to any one of claims 1 to 3.

6. A computer-readable storage medium storing one or more programs, the one or more programs, when executed by an electronic device including a plurality of application programs, cause the electronic device to execute the method according to any one of claims 1 to 3.

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