Data processing method and device for data visualization processing

Abstract

The invention discloses a data processing method and device for data visualization processing. The method comprises the steps of obtaining to-be-processed user data information obtained based on user interaction; the to-be-processed user data information comprises a plurality of pieces of terminal user data information input by a user at the intelligent terminal; performing data optimization processing on the to-be-processed user data information to obtain target optimization data information; the target optimization data information comprises a plurality of target category optimization data information; the target category optimization data information comprises a plurality of pieces of target category data value information; performing classification processing on the target optimization data information to obtain target data analysis result information; the target data analysis result information comprises a plurality of pieces of target classification analysis result information; the target classification analysis result information comprises at least one piece of target category analysis result information.

Description

Technical Field

[0001] The present invention relates to the fields of visualization technology and industrial big data processing, and in particular to a data processing method and device for data visualization processing. Background Art

[0002] In today's digital age, industrial production data is generated and accumulated at a rapid pace. Companies and factories across various sectors need to efficiently process and analyze large amounts of data to unlock its value. Data visualization technology has emerged as a result, presenting data in an intuitive form, helping users quickly understand the data and make decisions. Current industrial data visualization and fault analysis processes have several shortcomings:

[0003] Data collection: Industrial data comes from diverse sources and has complex formats. When collecting data from multiple sources such as databases, files, and networks, we often face problems such as format incompatibility and confusing coding, resulting in low collection efficiency and prone to data errors. When collecting data from various sensors, the data collection time and industrial scenarios are not counted, making it difficult to achieve effective data registration.

[0004] Data preprocessing: Data quality varies widely, with issues such as missing data, duplications, and errors. Traditional preprocessing methods struggle to automatically adapt to varying data characteristics, making the process cumbersome and error-prone. This makes it difficult to balance efficiency and accuracy, especially when processing large amounts of data.

[0005] Data Analysis and Processing: As the volume and complexity of industrial data increase, traditional industrial fault detection and analysis methods and visualization approaches struggle to quickly extract valuable information. For example, in high-dimensional data visualization analysis, dimensionality reduction is ineffective, impacting the accuracy of subsequent industrial fault analysis. In industrial data processing, achieving effective fault prediction analysis in industrial production scenarios based on multi-dimensional industrial data is a critical issue that needs to be addressed.

[0006] Data visualization: User needs are becoming increasingly diverse, with higher demands for visualization. Existing technologies struggle to meet their personalized demands for display formats and interactive features. For example, the lack of flexible and convenient methods for adjusting visualization configuration parameters limits user experience and in-depth data exploration.

[0007] Therefore, a data processing method and device for data visualization processing are provided to improve the efficiency of data analysis and processing, enrich the diversity and interactivity of visualization display, and meet the complex needs of current data visualization processing and industrial production fault analysis and processing. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a data processing method and device for data visualization processing, which is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing and industrial production fault analysis and processing.

[0009] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a data processing method for data visualization processing, the method comprising:

[0010] Acquire user data information to be processed based on interaction with the user; the user data information to be processed includes a plurality of terminal user data information input by the user on the smart terminal;

[0011] Performing data optimization processing on the user data information to be processed to obtain target optimized data information; the target optimized data information includes a plurality of target category optimized data information; the target category optimized data information includes a plurality of target category data value information;

[0012] The target optimization data information is classified and analyzed to obtain target data analysis result information; the target data analysis result information is used to characterize the fault assessment value of the production equipment of the target category corresponding to the target vector data information.

[0013] The performing data optimization processing on the user data information to be processed to obtain target optimized data information includes:

[0014] Performing data conversion processing on the user data information to be processed to obtain first user data information;

[0015] Based on the first user data information, target optimization data information is determined.

[0016] The determining target optimization data information based on the first user data information includes:

[0017] performing data cleaning processing on the first user data information to obtain first processed data information;

[0018] Perform data category inspection on the first processed data information to obtain target optimized data information.

[0019] The generalizing the first processed data information to obtain target optimized data information includes:

[0020] performing generalized data processing on the first processed data information to obtain second processed data information;

[0021] Decomposing the second processed data information to obtain third processed data information;

[0022] performing smoothing processing on the third processed data information to obtain fourth processed data information;

[0023] Perform linear mapping processing on the fourth processed data information to obtain target optimized data information.

[0024] The target optimization data information is classified and analyzed to obtain target data analysis result information, including:

[0025] Performing vectorization processing on the target category data value information in the target optimization data information to obtain target vector data information; the target vector data information includes a plurality of target category vector data information; the target category vector data information includes a plurality of vectorized data value information;

[0026] Based on the target vector data information, target data analysis result information is determined.

[0027] The determining target data analysis result information based on the target vector data information includes:

[0028] The vectorized data value information is a sequence of collected working status information used to represent a target category; the sequence of collected working status information can be the working voltage, working temperature, power, working noise, etc. of various target categories of production equipment in the factory; one vectorized data value information corresponds to a sequence of collected information of a type of working parameters; working parameters include working voltage, working temperature, power, and working noise;

[0029] Obtaining a standard working status value set corresponding to each target category; the standard working status value set includes several standard working status values;

[0030] Subtract each vectorized data value information included in the target category vector data information from the corresponding standard working state value to obtain a difference sequence;

[0031] Using each difference sequence as a column vector, a difference matrix is constructed;

[0032] Performing eigenvalue decomposition on the difference matrix to obtain an eigenvalue sequence; each eigenvalue of the eigenvalue sequence corresponds to a row vector of the difference matrix;

[0033] Based on the eigenvalue sequence, performing an integration operation on each row vector of the difference matrix to obtain a cumulative eigenvalue corresponding to the row vector;

[0034] Perform RVMD transformation on each column vector of the difference matrix to obtain the corresponding transformation vector;

[0035] Using each transformation vector as a column vector, a transformation difference matrix is constructed;

[0036] Performing fusion matrix calculation on the transformation difference matrix and the difference matrix to obtain a fusion matrix;

[0037] Calculating the trace value and rank value of the fusion matrix;

[0038] Performing a fault assessment calculation on the cumulative eigenvalues, trace values, and rank values to obtain target data analysis result information; a larger fault assessment value indicates a greater probability of failure of the target category of production equipment corresponding to the target vector data information;

[0039] The expression of the integral operation is:

[0040]

[0041] Among them, z is the eigenvalue corresponding to the row vector, x is the integral variable, and y i is the i-th element of the row vector, and θ is the cumulative eigenvalue;

[0042] The expression for the fault assessment calculation is:

[0043]

[0044] Among them, θ i is the cumulative eigenvalue corresponding to the i-th row vector, D i () is the i-order Weibull function, M is the number of row vectors, Rg is the target data analysis result information, α and β are the trace value and rank value of the fusion matrix respectively;

[0045] The expression for calculating the fusion matrix is:

[0046]

[0047] Among them, YU is the fusion matrix, A is the difference matrix, and B is the transformation difference matrix;

[0048] The RVMD transformation is a reduced-order variational mode decomposition transformation.

[0049] The determining target data analysis result information based on the target vector data information includes:

[0050] Performing rough classification processing on the target vector data information to obtain first classified user data information; the first classified user data information includes a plurality of first classified data value information; the first classified data value information includes at least one first vectorized data information;

[0051] Filtering the first classified user data information to obtain second classified user data information;

[0052] The second classified user data information is finely classified to obtain target data analysis result information.

[0053] The performing rough classification processing on the target vector data information to obtain first classified user data information includes:

[0054] For any target category vector data information in the target vector data information, randomly selecting one of the vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0055] Determining whether the vectorized data information to be processed is the first vectorized data value information selected from the target category vector data information, and obtaining a sequential determination result;

[0056] When the result of the sequence judgment is yes, a basic classification data value information is generated, and the vectorized data information to be processed is used as the basic vectorized data information in the basic classification data information;

[0057] Eliminate the vectorized data value information corresponding to the vectorized data information to be processed from the target category vector data information;

[0058] Triggering the execution of randomly selecting one of the vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0059] When the result of the sequential judgment is no, obtaining at least one classification value by using a classification calculation model for the vectorized data information to be processed and the basic vectorized data information in all the basic classification data value information;

[0060] Wherein, the classification calculation model is:

[0061] GLZ = ||DYXL-DEXL||;

[0062] Wherein, GLZ represents the classification value; DYXL represents the vectorized data information to be processed; DEXL represents the basic vectorized data information;

[0063] Determine whether there is a classification value less than or equal to a first classification threshold among the classification values, which is regarded as a first comparison judgment result;

[0064] When the first comparison judgment result is yes, the to-be-processed vectorized data information is used as a second vectorized data information in the basic classification data value information corresponding to the classification value that is less than or equal to the first classification threshold;

[0065] When the first comparison judgment result is no, determining whether there is a classification value less than or equal to a second classification threshold among the classification values, which serves as a second comparison judgment result;

[0066] When the second comparison judgment result is no, triggering the execution of randomly selecting one of the vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0067] When the second comparison result is yes, generating a basic classification data value information, and using the vectorized data information to be processed as the basic vectorized data information in the basic classification data information;

[0068] Determine whether the vectorized data value information corresponding to the vectorized data information to be processed is the last vectorized data value information in the target category vector data information, and obtain a third comparison and judgment result;

[0069] When the third comparison result is negative, the vectorized data value information corresponding to the vectorized data information to be processed is removed from the target category vector data information;

[0070] Triggering the execution of randomly selecting one of the vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0071] When the third comparison judgment result is yes, the first classification data value information corresponding to the target category vector data information is determined based on all the basic classification data value information.

[0072] A second aspect of an embodiment of the present invention discloses a data processing device for data visualization processing, the device comprising:

[0073] An acquisition module is used to acquire user data information to be processed based on interaction with the user; the user data information to be processed includes a plurality of terminal user data information input by the user on the smart terminal;

[0074] a first processing module configured to perform data optimization processing on the user data information to be processed to obtain target optimized data information; the target optimized data information includes a plurality of target category optimized data information; the target category optimized data information includes a plurality of target category data value information;

[0075] The second processing module is used to classify and analyze the target optimization data information to obtain target data analysis result information; the target data analysis result information includes several target classification analysis result information; the target classification analysis result information includes at least one target category analysis result information; the target category analysis result information includes at least one target classification data value information; the target classification data value information corresponds to the target category data value information.

[0076] A third aspect of the present invention discloses another data processing device for data visualization processing, the device comprising:

[0077] a memory storing executable program code;

[0078] a processor coupled to a memory;

[0079] The processor calls the executable program code stored in the memory to execute part or all of the steps in the data processing method for data visualization processing disclosed in the first aspect of the embodiment of the present invention.

[0080] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the data processing method for data visualization processing disclosed in the first aspect of an embodiment of the present invention.

[0081] The beneficial effects of the present invention include:

[0082] Through the data processing method of the present invention, a series of optimization processes are performed on the user data information to be processed, and the data collected from multiple sources (such as databases, files, networks, and various sensors, etc.) can be regularized at the source, effectively solving problems such as format incompatibility and coding confusion, and avoiding data alignment difficulties caused by inconsistencies in data collection time and industrial scenarios, making the collected data more standardized and accurate, thereby significantly improving the efficiency of the data collection link and reducing the possibility of data errors.

[0083] In view of the uneven data quality, and the existence of problems such as missing, duplication, and errors, the present invention first performs data conversion processing during the data optimization process, and then sequentially undergoes multi-step fine processing such as data cleaning, data category checking, generalized data processing, disassembly, smoothing, and linear mapping. It can automatically adapt to different data characteristics and perform targeted optimization on data in different conditions, making the preprocessing process more scientific and efficient, and can also take into account efficiency and accuracy when processing large-scale data, effectively improving the effect of the data preprocessing link.

[0084] When classifying and analyzing the target optimization data information, the use of vectorization, comparison with standard working state values to construct a difference matrix, and subsequent eigenvalue decomposition, integral operation, RVMD transformation, fusion matrix calculation and other complex and scientific analysis methods can better extract key information from massive and complex industrial data. Especially for the processing of high-dimensional data, it can achieve better dimensionality reduction effects than traditional methods, thereby greatly improving the accuracy of industrial fault analysis and providing strong support for effective industrial production scenario fault prediction analysis based on multi-dimensional industrial data. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0086] Figure 1 This is a schematic diagram of a scenario of a data processing system for data visualization processing provided by an embodiment of the present invention;

[0087] Figure 2 This is a flow chart of a data processing method for data visualization disclosed in an embodiment of the present invention;

[0088] Figure 3 It is a structural diagram of a data processing device for data visualization processing disclosed in an embodiment of the present invention;

[0089] Figure 4 It is a structural schematic diagram of another data processing device for data visualization processing disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0090] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0091] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0092] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0093] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that one of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0094] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, the corresponding data exist for the computer device to process. The details will not be repeated here.

[0095] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that machines have the functions of perception, reasoning and decision-making.

[0096] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0097] Computer vision (CV) is the science of making machines "see." Specifically, it refers to machine vision, where cameras and computers replace the human eye in identifying and measuring objects, performing further image processing to create images more suitable for human observation or transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, optical character recognition (OCR), video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping, and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0098] Unimodal information is data consisting of only one type, such as text, images, audio, video, or electromagnetic signals. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require integrating multiple information sources, such as sentiment analysis, robot interaction, and autonomous driving. By integrating information from multiple modalities, higher performance and accuracy can often be achieved on the task.

[0099] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are generally used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In the embodiment of the present application, the large model can be a large-scale pre-trained model such as the ChatGPT series, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Tongwen Qianyi model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Skylark model, vivoLM model, deepseek, Tencent Yuanbao and Wenxin Yiyan, etc., which is not limited in the embodiment of the present application.

[0100] The embodiments of the present application provide a data processing method, apparatus, computer device, and computer-readable storage medium for data visualization processing, which are described in detail below.

[0101] See also Figure 1 , Figure 1 This is a schematic diagram of a data processing system for data visualization provided by an embodiment of the present application. The data processing system for data visualization may include a computer device 100, in which a data processing device for data visualization is integrated, such as Figure 1 Computer equipment in.

[0102] In the embodiment of the present application, the computer device 100 is mainly used to obtain user data information to be processed based on interaction with the user; the user data information to be processed includes a plurality of terminal user data information input by the user on the smart terminal;

[0103] Performing data optimization processing on the user data information to be processed to obtain target optimized data information; the target optimized data information includes a plurality of target category optimized data information; the target category optimized data information includes a plurality of target category data value information;

[0104] The target optimization data information is classified and analyzed to obtain target data analysis result information; the target data analysis result information includes several target classification analysis result information; the target classification analysis result information includes at least one target category analysis result information; the target category analysis result information includes at least one target classification data value information; the target classification data value information corresponds to the target category data value information.

[0105] It can improve the efficiency of data analysis and processing, enrich the diversity and interactivity of visual display, and meet the complex needs of current data visualization processing.

[0106] In the embodiments of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. A cloud server is composed of a large number of computers or network servers based on cloud computing.

[0107] It is understood that the computer device 100 used in the embodiments of the present application can be a device that includes both receiving and transmitting hardware, that is, a device that has receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device that has a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. The specific computer device 100 can be a desktop terminal or a mobile terminal. The computer device 100 can also be a mobile phone, a tablet computer, a laptop computer, etc.

[0108] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer computer devices as shown in Figure 1 Only one computer device is shown. It can be understood that the data processing system for data visualization processing can also include one or more other services, which are not specifically limited here.

[0109] In addition, if Figure 1 As shown, the data processing system for data visualization processing may further include a memory 200 for storing data, such as image data, location information, and the like.

[0110] It should be noted that Figure 1 The scenario diagram of the data processing system for data visualization processing shown is only an example. The data processing system and scenario for data visualization processing described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided by the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the data processing system for data visualization processing and the emergence of new business scenarios, the technical solution provided by the embodiment of the present application is also applicable to similar technical problems.

[0111] The present invention discloses a data processing method and device for data visualization, which are beneficial for improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization, and meeting the complex needs of current data visualization processing. Detailed descriptions are given below.

[0112] Example 1

[0113] See also Figure 2 , Figure 2 This is a flow chart of a data processing method for data visualization disclosed in an embodiment of the present invention. Figure 2 The data processing method for data visualization processing described above is applied to a management system, such as a local server or a cloud server for management, and is not limited in the embodiment of the present invention. Figure 2 As shown, the data processing method for data visualization processing may include the following operations:

[0114] 101. Obtain user data information to be processed based on interaction with the user.

[0115] In the embodiment of the present invention, the user data information to be processed includes a plurality of terminal user data information input by the user in the smart terminal.

[0116] 102. Perform data optimization processing on the user data information to be processed to obtain target optimized data information.

[0117] In an embodiment of the present invention, the target optimization data information includes a plurality of target category optimization data information; the target category optimization data information includes a plurality of target category data value information.

[0118] 103. Classify and process the target optimization data information to obtain target data analysis result information.

[0119] In an embodiment of the present invention, the target data analysis result information includes several target classification analysis result information; the target classification analysis result information includes at least one target category analysis result information; the target category analysis result information includes at least one target classification data value information; the target classification data value information corresponds to the target category data value information.

[0120] It should be noted that the above-mentioned smart terminal can be a PC or a mobile device, and this embodiment of the present invention does not limit this. Furthermore, the above-mentioned terminal user data information represents the information input by the user for data management, such as user unit, data size, user name, etc., and this embodiment of the present invention does not limit this.

[0121] It should be noted that the target data analysis results are used to map data and charts appropriately based on their characteristics. For example, if a line chart is time series data, time should be mapped to the x-axis and data to the y-axis. If a bar chart is categorical data, categories should be mapped to the x-axis and data to the y-axis. If a pie chart is percentage data, categories should be mapped to the x-axis and percentages to the y-axis.

[0122] It should be noted that after the target optimization data information is classified and processed to obtain the target data analysis result information, the present application can also display the target data analysis result information on the display screen and update the data regularly via Ajax. The embodiment of the present invention is not limited to this, and real-time monitoring and tracking of data change trends and patterns can be achieved. At the same time, the animation effects provided by Echarts can be used to enhance the data visualization effect and user experience.

[0123] The determining target optimization data information based on the first user data information includes:

[0124] performing data cleaning processing on the first user data information to obtain first processed data information;

[0125] Perform data category inspection on the first processed data information to obtain target optimized data information.

[0126] The data category check is to determine whether the data category of each data in the first processed data information is consistent with the preset data category, and to delete the inconsistent data from the first processed data information to obtain the target optimized data information.

[0127] It can be seen that implementing the data processing method for data visualization processing described in the embodiment of the present invention is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0128] In an optional embodiment, the above-mentioned data optimization processing is performed on the user data information to be processed to obtain target optimized data information, including:

[0129] Performing data conversion processing on the user data information to be processed to obtain first user data information;

[0130] Based on the first user data information, target optimization data information is determined.

[0131] In this optional embodiment, as an optional implementation manner, the above-mentioned data conversion processing is performed on the user data information to be processed to obtain the first user data information, including:

[0132] Performing format conversion on the terminal user data information in the user data information to be processed to obtain user data information in a plurality of formats;

[0133] All the formatted user data information are spliced and combined from front to back according to the sequence number of the smart terminal to obtain the first user data information.

[0134] It should be noted that the above-mentioned format conversion of the terminal user data information in the user data information to be processed is to convert the data into standard data in JSON format so as to unify the data representation of each smart terminal and facilitate subsequent data analysis and display, which is not limited in the embodiment of the present invention.

[0135] It should be noted that each of the above-mentioned smart terminals corresponds to a serial number. Furthermore, all formatted user data information is spliced and combined from front to back according to the serial number of the smart terminal, so that all data is spliced into a data matrix according to the serial number of the smart terminal to form a standard data structure representation form, which is conducive to improving the analysis efficiency of user data. The embodiment of the present invention does not limit this. The data matrix can be:

[0136] Terminal serial number User Unit Username Data A Data B 1 Unit 1 Name 1 A1 B1 2 Unit 2 Name 2 A2 B2 3 Unit 3 Name 3 A3 B3

[0137] It can be seen that implementing the data processing method for data visualization processing described in the embodiment of the present invention is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0138] In another optional embodiment, determining target optimization data information based on the first user data information includes:

[0139] Performing data cleaning processing on the first user data information to obtain first processed data information;

[0140] Perform data category inspection on the first processed data information to obtain target optimized data information.

[0141] It should be noted that the above-mentioned data cleaning processing of the first user data information is mainly to clear the problematic data in the user data, such as cleaning missing values (filling, deleting, etc.), correcting logical errors, etc., so as to ensure the consistency of the user data in various dimensions, thereby ensuring the quality of the user data, so as to improve the analysis quality and efficiency of the user data, and the embodiments of the present invention are not limited thereto.

[0142] It can be seen that implementing the data processing method for data visualization processing described in the embodiment of the present invention is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0143] In yet another optional embodiment, generalizing the first processed data information to obtain target optimized data information includes:

[0144] performing generalized data processing on the first processed data information to obtain second processed data information;

[0145] Decomposing the second processed data information to obtain third processed data information;

[0146] performing smoothing processing on the third processed data information to obtain fourth processed data information;

[0147] Perform linear mapping processing on the fourth processed data information to obtain target optimized data information.

[0148] It should be noted that the above-mentioned generalized data processing of the first processed data information is to replace the user unit with a number, such as unit A, which is generalized with the number DW001, and to fuzzy the user name, such as user Zhang AA, which is replaced with Zhang San, thereby ensuring the privacy of the user. This embodiment of the present invention is not limited to this.

[0149] It should be noted that the third processed data information includes a plurality of third sub-processed data information, which is not limited in the embodiment of the present invention. Further, the third sub-processed data information includes identity information and decomposed data information, which is not limited in the embodiment of the present invention. Further, the decomposed data information can be expressed in the form of Table 1:

[0150] Table 1

[0151] Identity information 1 Identity information 2 Decomposition data information 3 Decomposition data information 4 Decomposition data information 5 Decomposition data information 1 Decomposition data information 2 Decomposition data information 3 Decomposition data information 4 Decomposition data information 5

[0152] In this optional embodiment, as an optional implementation manner, the above-mentioned disassembling and processing of the second processed data information to obtain the third processed data information includes:

[0153] Extracting non-data information from the second processed data information to obtain a plurality of identity identification information;

[0154] The same type of data information is sequentially assigned with an identity identification information and arranged in order according to the sequence of the terminal numbers to obtain a plurality of third sub-processing data information.

[0155] It should be noted that the above-mentioned identity identification information represents non-data value information in the data, such as terminal serial number, user unit, user name, etc., which is not limited in the embodiment of the present invention.

[0156] It should be noted that the above-mentioned method of assigning an identity identification information to the same type of data information in sequence and arranging them in order according to the order of the terminal serial numbers is to assign corresponding identity identification information to the data in the same column of the data matrix in sequence, and then sort and distribute them according to the terminal serial numbers in the identity identification information into the expression form of Table 1, which is not limited in the embodiment of the present invention.

[0157] It should be noted that the above-mentioned smoothing of the third processed data information is to smooth the decomposed data information in the third processed data information using linear regression, so that the regression of the collected user data is close to reality, so as to remove noise in the data and improve the quality of user data. The embodiments of the present invention do not limit this.

[0158] It should be noted that the above-mentioned linear mapping processing of the fourth processed data information is to normalize the decomposed data information after smoothing, so as to further eliminate the excessive relative numerical differences between the data and improve the reliability and accuracy of data analysis, which is not limited in the embodiment of the present invention.

[0159] It can be seen that implementing the data processing method for data visualization processing described in the embodiment of the present invention is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0160] In yet another optional embodiment, the target optimization data information is classified and analyzed to obtain target data analysis result information, including:

[0161] Vectorizing the target category data value information in the target optimization data information to obtain target vector data information; the target vector data information includes a plurality of target category vector data information; the target category vector data information includes a plurality of vectorized data value information;

[0162] Based on the target vector data information, the target data analysis result information is determined.

[0163] The determining target data analysis result information based on the target vector data information includes:

[0164] The vectorized data value information is a sequence of collected working status information used to represent a target category; the sequence of collected working status information can be the working voltage, working temperature, power, working noise, etc. of various target categories of production equipment in the factory; one vectorized data value information corresponds to a sequence of collected information of a type of working parameters; working parameters include working voltage, working temperature, power, and working noise;

[0165] Obtaining a standard working status value set corresponding to each target category; the standard working status value set includes several standard working status values;

[0166] Subtract each vectorized data value information included in the target category vector data information from the corresponding standard working state value to obtain a difference sequence;

[0167] Using each difference sequence as a column vector, a difference matrix is constructed;

[0168] Performing eigenvalue decomposition on the difference matrix to obtain an eigenvalue sequence; each eigenvalue of the eigenvalue sequence corresponds to a row vector of the difference matrix;

[0169] Based on the eigenvalue sequence, performing an integration operation on each row vector of the difference matrix to obtain a cumulative eigenvalue corresponding to the row vector;

[0170] Perform RVMD transformation on each column vector of the difference matrix to obtain the corresponding transformation vector;

[0171] Using each transformation vector as a column vector, a transformation difference matrix is constructed;

[0172] Performing fusion matrix calculation on the transformation difference matrix and the difference matrix to obtain a fusion matrix;

[0173] Calculating the trace value and rank value of the fusion matrix;

[0174] Performing a fault assessment calculation on the cumulative eigenvalues, trace values, and rank values to obtain target data analysis result information; a larger fault assessment value indicates a greater probability of failure of the target category of production equipment corresponding to the target vector data information;

[0175] The expression of the integral operation is:

[0176]

[0177] Among them, z is the eigenvalue corresponding to the row vector, x is the integral variable, and y i is the i-th element of the row vector, and θ is the cumulative eigenvalue;

[0178] The integral operation expression incorporates multiple key elements, including cumulative eigenvalues, the trace and rank values of the fusion matrix, and the Weber function. Through specific mathematical relationships, it performs calculations to achieve scientific calculations of the target data analysis results (i.e., fault assessment values). It fully considers the equipment status characteristics reflected by the data at different processing stages, organically combining key data obtained from each link, avoiding the one-sidedness of single-indicator evaluation. The resulting fault assessment value more objectively, comprehensively, and accurately reflects the probability of failure of production equipment in the target category, providing a reliable quantitative basis for timely and accurate judgment of equipment failure risks in industrial production. This helps companies rationally arrange equipment maintenance plans, optimize production processes, and reduce production losses caused by equipment failures.

[0179] The expression for the fault assessment calculation is:

[0180]

[0181] Among them, θ i is the cumulative eigenvalue corresponding to the i-th row vector, D i () is the i-order Weibull function, M is the number of row vectors, Rg is the target data analysis result information, α and β are the trace value and rank value of the fusion matrix respectively;

[0182] This expression incorporates multiple key elements, including cumulative eigenvalues, the trace and rank values of the fusion matrix, and the Weber function. Through operations based on specific mathematical relationships, it achieves scientific calculation of the target data analysis results (i.e., the fault assessment value). It fully considers the characteristics of the equipment status reflected by the data at different processing stages, organically combining key data obtained from each link, avoiding the one-sidedness of single-metric evaluation. The resulting fault assessment value more objectively, comprehensively, and accurately reflects the probability of failure of the target category of production equipment. This provides a reliable quantitative basis for timely and accurate judgment of equipment failure risks in industrial production, helping companies to rationally arrange equipment maintenance plans, optimize production processes, and reduce production losses caused by equipment failures.

[0183] The expression for calculating the fusion matrix is:

[0184]

[0185] Among them, YU is the fusion matrix, A is the difference matrix, and B is the transformation difference matrix;

[0186] This fusion matrix calculation expression cleverly utilizes the difference matrix A and the transformed difference matrix B to effectively integrate the two important matrix information obtained through different processing steps to generate the fusion matrix YU. The fusion matrix can comprehensively reflect the relationship and difference between the original data and the data after complex transformations, providing a key data carrier for the subsequent calculation of its trace value and rank value, and then participating in the fault assessment calculation. This enables the entire data analysis process to more coherently and systematically explore the value of data, helping to improve the scientific nature and accuracy of the entire industrial production fault analysis, making data-based equipment fault judgment more in line with actual production conditions, helping enterprises better understand the operating status of equipment and ensure the stable and efficient development of production activities.

[0187] The RVMD transformation is a reduced-order variational mode decomposition transformation.

[0188] It should be noted that the above-mentioned vectorization processing of the target category data value information in the target optimization data information is to vectorize the target category data value information using a vectorization model, such as a BERT model, a large model, etc., which is not limited in the embodiments of the present invention.

[0189] It can be seen that implementing the data processing method for data visualization processing described in the embodiment of the present invention is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0190] In an optional embodiment, the target data analysis result information is determined based on the target vector data information, including:

[0191] Performing rough classification processing on the target vector data information to obtain first classified user data information; the first classified user data information includes a plurality of first classified data value information; the first classified data value information includes at least one first vectorized data information;

[0192] Filtering the first-classified user data information to obtain second-classified user data information;

[0193] The second classified user data information is finely classified to obtain target data analysis result information.

[0194] In this optional implementation, as an optional implementation manner, the filtering process of the first classified user data information to obtain the second classified user data information includes:

[0195] For any first classification data value information in the first classification user data information, determining whether the quantity of the first vectorized data information in the first classification data value information is greater than or equal to M, and obtaining a quantity determination result;

[0196] When the result of the quantity determination is yes, the first classification data value information is removed from the first classification user data information;

[0197] When the result of the quantity determination is negative, the analysis and determination process of the first classification data value information is terminated;

[0198] All first quantized data information in the analyzed and determined first classified user data information is randomly arranged and distributed to obtain second classified user data information.

[0199] It should be noted that the above M is a positive integer not less than 5, such as 6, which is not limited in the embodiment of the present invention.

[0200] It should be noted that the above-mentioned removal of the first classification data value information less than M from the first classification user data information is to ensure the adequacy of the user sample data, so as to characterize that the user data of this category is representative rather than abnormal or niche data, thereby improving the data analysis efficiency and accuracy of characterizing data characteristics, and the embodiments of the present invention are not limited thereto.

[0201] It should be noted that the above-mentioned random arrangement and distribution of all the first vectorized data information in the first classified user data information after analysis and judgment is to randomly arrange the vectorized data information after coarse classification and deletion of niche data, and return to the initial distribution state of the data, so as to not be affected by the classification of coarse classification during fine classification, thereby improving the independence of data analysis of fine classification. The embodiments of the present invention do not limit this.

[0202] It should be noted that the above-mentioned fine classification processing of the second classification user data information may be implemented based on a K-mean clustering algorithm, which is not limited in the embodiment of the present invention.

[0203] It can be seen that implementing the data processing method for data visualization processing described in the embodiment of the present invention is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0204] In another optional embodiment, performing rough classification processing on the target vector data information to obtain first classified user data information includes:

[0205] For any target category vector data information in the target vector data information, randomly select a vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0206] Determine whether the vectorized data information to be processed is the first vectorized data value information selected from the target category vector data information, and obtain a sequential determination result;

[0207] When the result of the sequential judgment is yes, a basic classification data value information is generated, and the vectorized data information to be processed is used as the basic vectorized data information in the basic classification data information;

[0208] Eliminate the vectorized data value information corresponding to the vectorized data information to be processed from the target category vector data information;

[0209] Triggering execution to randomly select a vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0210] When the result of the sequential judgment is no, obtaining at least one classification value by using the classification calculation model for the vectorized data information to be processed and the basic vectorized data information in all the basic classification data value information;

[0211] Among them, the classification calculation model is:

[0212] GLZ = ||DYXL-DEXL||;

[0213] In the formula, GLZ represents the classification value; DYXL represents the vectorized data information to be processed; DEXL represents the basic vectorized data information;

[0214] Determine whether there is a classification value less than or equal to the first classification threshold among the classification values, and obtain the first comparison judgment result;

[0215] When the first comparison result is yes, the vectorized data information to be processed is used as a second vectorized data information in the basic classification data value information corresponding to the classification value less than or equal to the first classification threshold;

[0216] When the first comparison judgment result is no, determining whether there is a classification value less than or equal to the second classification threshold value in the classification value, which is regarded as the second comparison judgment result;

[0217] When the second comparison result is negative, triggering execution to randomly select a vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0218] When the second comparison result is yes, a basic classification data value information is generated, and the vectorized data information to be processed is used as the basic vectorized data information in the basic classification data information;

[0219] Determine whether the vectorized data value information corresponding to the vectorized data information to be processed is the last vectorized data value information in the target category vector data information, and obtain a third comparison and judgment result;

[0220] When the third comparison result is negative, the vectorized data value information corresponding to the vectorized data information to be processed is removed from the target category vector data information;

[0221] Triggering execution to randomly select a vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0222] When the third comparison judgment result is yes, the first classification data value information corresponding to the target category vector data information is determined based on all the basic classification data value information.

[0223] It should be noted that the above-mentioned determination of the first classification data value information corresponding to the target category vector data information based on all the basic classification data value information is to use the basic vectorized data information and the second vectorized data information in the basic classification data value information as the first vectorized data information in the first classification data value information, and the embodiment of the present invention does not limit this.

[0224] It should be noted that the first and second classification thresholds described above can be user-defined or derived by a large model based on historical threshold data analysis, and are not limited in this embodiment of the present invention. Furthermore, the first classification threshold is lower than the second classification threshold. Furthermore, the first classification threshold is a value between [0.1, 0.3], such as 0.2, and the second classification threshold is a value between [0.5, 0.8], such as 0.5, and are not limited in this embodiment of the present invention. Furthermore, by analyzing and judging the size of the first classification threshold, classification values with the same collected features can be identified. When the classification value is between the two thresholds, it indicates that the classification values have a certain correlation but cannot be classified together. Therefore, it can be defined as a basic classification data value information separately. However, if it exceeds the second classification threshold, it indicates that they are not correlated with each other, but it cannot be determined that there is no data of the same category. Therefore, it is necessary to return to the target category vector data information to select the vectorized data value information. The vectorized data information to be processed that exceeds the second classification threshold will not be classified for the time being, and will be reclassified after the new basic classification data value information is determined later to improve the accuracy of the classification. This is not limited in the embodiments of the present invention.

[0225] It should be noted that the above-mentioned basic vectorized data information and other vectorized data value information with similar characteristics are used as the second vectorized data information. This is based on a single classification value for analysis and judgment, and its classification accuracy is relatively general. Therefore, it can only be used as a rough classification and a reference for the subsequent classification data of small samples, so as to filter out small categories of user data information, provide higher quality user data for subsequent fine classification, and improve the accuracy of data analysis. The embodiments of the present invention do not limit this.

[0226] It can be seen that implementing the data processing method for data visualization processing described in the embodiment of the present invention is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0227] By demonstrating the advantages of each of the above links, it can help enterprises and factories in various fields to more efficiently process and analyze large amounts of industrial production data, explore the value contained therein, and timely and accurately evaluate and predict the failure of production equipment, providing a reliable basis for decision-making, equipment maintenance, and production process optimization in industrial production, ultimately improving the efficiency and quality of the entire industrial production and enhancing the competitiveness of enterprises in the digital age.

[0228] Example 2

[0229] See also Figure 3 , Figure 3 This is a structural diagram of a data processing device for data visualization disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., and the embodiment of the present invention does not limit this. Figure 3 As shown, the device may include:

[0230] The acquisition module 201 is used to acquire user data information to be processed based on interaction with the user; the user data information to be processed includes a plurality of terminal user data information input by the user on the smart terminal;

[0231] The first processing module 202 is configured to perform data optimization processing on the user data information to be processed to obtain target optimized data information; the target optimized data information includes a plurality of target category optimized data information; the target category optimized data information includes a plurality of target category data value information;

[0232] The second processing module 203 is used to classify the target optimization data information to obtain target data analysis result information; the target data analysis result information includes several target classification analysis result information; the target classification analysis result information includes at least one target category analysis result information; the target category analysis result information includes at least one target classification data value information; the target classification data value information corresponds to the target category data value information.

[0233] It can be seen that implementation Figure 3 The described data processing device for data visualization processing is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0234] In another optional embodiment, as Figure 3As shown, the user data information to be processed is optimized to obtain target optimized data information, including:

[0235] Performing data conversion processing on the user data information to be processed to obtain first user data information;

[0236] Based on the first user data information, target optimization data information is determined.

[0237] The data conversion process may be data format conversion;

[0238] It can be seen that implementation Figure 3 The described data processing device for data visualization processing is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0239] In another optional embodiment, Figure 3 As shown, based on the first user data information, target optimization data information is determined, including:

[0240] Performing data cleaning processing on the first user data information to obtain first processed data information;

[0241] Perform data category inspection on the first processed data information to obtain target optimized data information.

[0242] It can be seen that implementation Figure 3 The described data processing device for data visualization processing is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0243] In another optional embodiment, Figure 3 As shown, the first processed data information is generalized to obtain target optimized data information, including:

[0244] performing generalized data processing on the first processed data information to obtain second processed data information;

[0245] Decomposing the second processed data information to obtain third processed data information;

[0246] performing smoothing processing on the third processed data information to obtain fourth processed data information;

[0247] Perform linear mapping processing on the fourth processed data information to obtain target optimized data information.

[0248] It can be seen that implementation Figure 3The described data processing device for data visualization processing is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0249] In another optional embodiment, Figure 3 As shown, the target optimization data information is classified and processed to obtain target data analysis result information, including:

[0250] Vectorizing the target category data value information in the target optimization data information to obtain target vector data information; the target vector data information includes a plurality of target category vector data information; the target category vector data information includes a plurality of vectorized data value information;

[0251] Based on the target vector data information, the target data analysis result information is determined.

[0252] It can be seen that implementation Figure 3 The described data processing device for data visualization processing is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0253] In another optional embodiment, Figure 3 As shown, based on the target vector data information, the target data analysis result information is determined, including:

[0254] Performing rough classification processing on the target vector data information to obtain first classified user data information; the first classified user data information includes a plurality of first classified data value information; the first classified data value information includes at least one first vectorized data information;

[0255] Filtering the first-classified user data information to obtain second-classified user data information;

[0256] The second classified user data information is finely classified to obtain target data analysis result information.

[0257] It can be seen that implementation Figure 3 The described data processing device for data visualization processing is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0258] In another optional embodiment, Figure 3 As shown, the target vector data information is roughly classified to obtain first classified user data information, including:

[0259] For any target category vector data information in the target vector data information, randomly select a vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0260] Determine whether the vectorized data information to be processed is the first vectorized data value information selected from the target category vector data information, and obtain a sequential determination result;

[0261] When the result of the sequential judgment is yes, a basic classification data value information is generated, and the vectorized data information to be processed is used as the basic vectorized data information in the basic classification data information;

[0262] Eliminate the vectorized data value information corresponding to the vectorized data information to be processed from the target category vector data information;

[0263] Triggering execution to randomly select a vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0264] When the result of the sequential judgment is no, obtaining at least one classification value by using the classification calculation model for the vectorized data information to be processed and the basic vectorized data information in all the basic classification data value information;

[0265] Among them, the classification calculation model is:

[0266] GLZ = ||DYXL-DEXL||;

[0267] In the formula, GLZ represents the classification value; DYXL represents the vectorized data information to be processed; DEXL represents the basic vectorized data information;

[0268] Determine whether there is a classification value less than or equal to the first classification threshold among the classification values, and obtain the first comparison judgment result;

[0269] When the first comparison result is yes, the vectorized data information to be processed is used as a second vectorized data information in the basic classification data value information corresponding to the classification value less than or equal to the first classification threshold;

[0270] When the first comparison judgment result is no, determining whether there is a classification value less than or equal to the second classification threshold value in the classification value, which is regarded as the second comparison judgment result;

[0271] When the second comparison result is negative, triggering execution to randomly select a vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0272] When the second comparison result is yes, a basic classification data value information is generated, and the vectorized data information to be processed is used as the basic vectorized data information in the basic classification data information;

[0273] Determine whether the vectorized data value information corresponding to the vectorized data information to be processed is the last vectorized data value information in the target category vector data information, and obtain a third comparison and judgment result;

[0274] When the third comparison result is negative, the vectorized data value information corresponding to the vectorized data information to be processed is removed from the target category vector data information;

[0275] Triggering execution to randomly select a vectorized data value information from the target category vector data information as the vectorized data information to be processed;

[0276] When the third comparison judgment result is yes, the first classification data value information corresponding to the target category vector data information is determined based on all the basic classification data value information.

[0277] It can be seen that implementation Figure 3 The described data processing device for data visualization processing is conducive to improving the efficiency of data analysis and processing, enriching the diversity and interactivity of visualization display, and meeting the complex needs of current data visualization processing.

[0278] Example 3

[0279] See also Figure 4 , Figure 4 This is a structural diagram of another data processing device for data visualization processing disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., and the embodiment of the present invention does not limit this. Figure 4 As shown, the device may include:

[0280] A memory 301 storing executable program code;

[0281] a processor 302 coupled to the memory 301;

[0282] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the data processing method for data visualization processing described in the first embodiment.

[0283] Example 4

[0284] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the data processing method for data visualization processing described in the first embodiment.

[0285] Example 5

[0286] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the data processing method for data visualization processing described in the first embodiment.

[0287] The device embodiments described above are merely illustrative. Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0288] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the above technical solution, in essence, or the portion that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0289] Finally, it should be noted that the data processing method and device for data visualization disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A data processing method for data visualization processing, characterized in that: The method comprises: Acquire user data information to be processed based on interaction with the user; the user data information to be processed includes a plurality of terminal user data information input by the user on the smart terminal; Performing data optimization processing on the user data information to be processed to obtain target optimized data information; the target optimized data information includes a plurality of target category optimized data information; the target category optimized data information includes a plurality of target category data value information; The target optimization data information is classified and analyzed to obtain target data analysis result information; the target data analysis result information is used to characterize the fault assessment value of the production equipment of the target category corresponding to the target vector data information.

2. The data processing method for data visualization according to claim 1, characterized in that: The performing data optimization processing on the user data information to be processed to obtain target optimized data information includes: Performing data conversion processing on the user data information to be processed to obtain first user data information; Based on the first user data information, target optimization data information is determined.

3. The data processing method for data visualization according to claim 2, characterized in that: The determining target optimization data information based on the first user data information includes: performing data cleaning processing on the first user data information to obtain first processed data information; Perform data category inspection on the first processed data information to obtain target optimized data information.

4. The data processing method for data visualization according to claim 3, characterized in that: The generalizing the first processed data information to obtain target optimized data information includes: performing generalized data processing on the first processed data information to obtain second processed data information; Decomposing the second processed data information to obtain third processed data information; performing smoothing processing on the third processed data information to obtain fourth processed data information; Perform linear mapping processing on the fourth processed data information to obtain target optimized data information.

5. The data processing method for data visualization according to claim 1, characterized in that: The target optimization data information is classified and analyzed to obtain target data analysis result information, including: Performing vectorization processing on the target category data value information in the target optimization data information to obtain target vector data information; the target vector data information includes a plurality of target category vector data information; the target category vector data information includes a plurality of vectorized data value information; Based on the target vector data information, target data analysis result information is determined.

6. The data processing method for data visualization according to claim 5, characterized in that: Determining target data analysis result information based on the target vector data information includes: The vectorized data value information is a sequence of work status collection information used to represent the target category; Obtaining a set of standard working status values corresponding to each target category; the set of standard working status values includes several standard working status values; Subtract each vectorized data value information included in the target category vector data information from the corresponding standard working state value to obtain a difference sequence; Using each difference sequence as a column vector, a difference matrix is constructed; Performing eigenvalue decomposition on the difference matrix to obtain an eigenvalue sequence, wherein each eigenvalue of the eigenvalue sequence corresponds to a row vector of the difference matrix; Based on the eigenvalue sequence, performing an integration operation on each row vector of the difference matrix to obtain a cumulative eigenvalue corresponding to the row vector; Perform RVMD transformation on each column vector of the difference matrix to obtain the corresponding transformation vector; Using each transformation vector as a column vector, a transformation difference matrix is constructed; Performing fusion matrix calculation on the transformation difference matrix and the difference matrix to obtain a fusion matrix; Calculating the trace value and rank value of the fusion matrix; Perform fault assessment calculation on the accumulated eigenvalues, trace values, and rank values to obtain target data analysis result information.

7. The data processing method for data visualization according to claim 5, characterized in that: Determining target data analysis result information based on the target vector data information includes: Performing rough classification processing on the target vector data information to obtain first classified user data information; the first classified user data information includes a plurality of first classified data value information; the first classified data value information includes at least one first vectorized data information; Filtering the first classified user data information to obtain second classified user data information; The second classified user data information is finely classified to obtain target data analysis result information.

8. The data processing method for data visualization according to claim 7, characterized in that: The performing rough classification processing on the target vector data information to obtain first classified user data information includes: For any target category vector data information in the target vector data information, randomly selecting one of the vectorized data value information from the target category vector data information as the vectorized data information to be processed; Determining whether the vectorized data information to be processed is the first vectorized data value information selected from the target category vector data information, and obtaining a sequential determination result; When the result of the sequence judgment is yes, a basic classification data value information is generated, and the vectorized data information to be processed is used as the basic vectorized data information in the basic classification data information; Eliminate the vectorized data value information corresponding to the vectorized data information to be processed from the target category vector data information; Triggering the execution of randomly selecting one of the vectorized data value information from the target category vector data information as the vectorized data information to be processed; When the result of the sequential judgment is no, obtaining at least one classification value by using a classification calculation model for the vectorized data information to be processed and the basic vectorized data information in all the basic classification data value information; Wherein, the classification calculation model is: GLZ = ||DYXL-DEXL||; Wherein, GLZ represents the classification value; DYXL represents the vectorized data information to be processed; DEXL represents the basic vectorized data information; Determine whether there is a classification value less than or equal to a first classification threshold among the classification values, which is regarded as a first comparison judgment result; When the first comparison judgment result is yes, the to-be-processed vectorized data information is used as a second vectorized data information in the basic classification data value information corresponding to the classification value that is less than or equal to the first classification threshold; When the first comparison judgment result is no, determining whether there is a classification value less than or equal to a second classification threshold among the classification values, which serves as a second comparison judgment result; When the second comparison judgment result is no, triggering the execution of randomly selecting one of the vectorized data value information from the target category vector data information as the vectorized data information to be processed; When the second comparison result is yes, generating a basic classification data value information, and using the vectorized data information to be processed as the basic vectorized data information in the basic classification data information; Determine whether the vectorized data value information corresponding to the vectorized data information to be processed is the last vectorized data value information in the target category vector data information, and obtain a third comparison and judgment result; When the third comparison judgment result is negative, the vectorized data value information corresponding to the vectorized data information to be processed is removed from the target category vector data information; Triggering the execution of randomly selecting one of the vectorized data value information from the target category vector data information as the vectorized data information to be processed; When the third comparison judgment result is yes, the first classification data value information corresponding to the target category vector data information is determined based on all the basic classification data value information.

9. A data processing device for data visualization processing, characterized in that: The device is configured to execute the data processing method for data visualization processing according to any one of claims 1 to 8, comprising: An acquisition module is used to acquire user data information to be processed based on interaction with the user; the user data information to be processed includes a plurality of terminal user data information input by the user on the smart terminal; a first processing module configured to perform data optimization processing on the user data information to be processed to obtain target optimized data information; the target optimized data information includes a plurality of target category optimized data information; the target category optimized data information includes a plurality of target category data value information; The second processing module is used to classify and analyze the target optimization data information to obtain target data analysis result information; the target data analysis result information includes several target classification analysis result information; the target classification analysis result information includes at least one target category analysis result information; the target category analysis result information includes at least one target classification data value information; the target classification data value information corresponds to the target category data value information.

10. A data processing device for data visualization processing, characterized in that: The device comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the data processing method for data visualization processing according to any one of claims 1 to 8.

Images