Python-based organization data prediction method, system and terminal
Through the Python-based organizational data prediction method, the problem of single data prediction system in the prior art is solved, and more efficient and accurate data analysis is achieved, which is suitable for complex and changeable market environments.
Patent Information
- Application Number
- CN202411715645.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-27
- Publication Date
- 2025-02-25
AI Technical Summary
The existing data prediction system has a single function, lacks flexibility and scalability, and is difficult to adapt to the complex and changing market environment, resulting in insufficient applicability and accuracy of data analysis.
Using Python-based organizational data prediction method, we use the initial data to obtain and clean the initial data, determine the data type and extract key features, perform pattern analysis to obtain data trend information and influencing factors, and finally input the characteristics and influencing factors into the trained prediction model to output the prediction results.
It improves the applicability and accuracy of the data analysis process, can adapt to complex market environments more flexibly and scalably, and meets the diversified forecasting needs of enterprises.
Smart Images
Figure CN119515001A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data analysis technology, and in particular to a Python-based organizational data prediction method, system, terminal and computer-readable storage medium. Background Art
[0002] As the scale of enterprises expands and market competition intensifies, accurate data forecasting is of great significance to the planning and resource allocation of enterprises.
[0003] However, traditional forecasting methods often rely on simple statistical analysis or expert experience, which makes it difficult to adapt to the complex and changing market environment. Existing forecasting systems often have single functions, lack flexibility and scalability, and are unable to meet the diverse forecasting needs of enterprises.
[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention
[0005] The main purpose of the present invention is to provide a Python-based organizational data prediction method, system, terminal and computer-readable storage medium, aiming to solve the problems in the prior art that the data prediction system is single, lacks flexibility and scalability, resulting in insufficient applicability of data analysis and inaccurate data analysis.
[0006] To achieve the above object, the present invention provides a Python-based tissue data prediction method, which comprises the following steps:
[0007] Acquire initial data of a target organization, clean the initial data, and obtain target data;
[0008] Determine the data type of the target data, extract key features according to the data type, and convert the key features to obtain target features;
[0009] According to the target characteristics, pattern analysis processing is performed on the target data to obtain data trend information, and the data trend information is analyzed to obtain target influencing factors;
[0010] The target features and the target influencing factors are input into a trained prediction model, and the prediction results of the target data are output.
[0011] Optionally, the Python-based tissue data prediction method, wherein the step of obtaining initial data of the target tissue and cleaning the initial data to obtain target data, specifically includes:
[0012] Obtaining a Python data crawling instruction, and obtaining initial data from a database of the target organization according to the data crawling instruction;
[0013] Obtain Python data cleaning instructions, and filter missing values and abnormal values of the initial data according to the data cleaning instructions;
[0014] Using a preset extension library of Python, constructing all the missing values and all the abnormal values into corresponding missing arrays respectively;
[0015] Sending all the missing arrays to the cloud, and receiving multiple filling arrays input by the user, wherein the multiple filling arrays are constructed by the user according to the multiple missing arrays;
[0016] According to all the filled arrays, all the missing values and all the abnormal values are updated to obtain the target data.
[0017] Optionally, the Python-based organizational data prediction method, wherein determining the data type of the target data, extracting key features according to the data type, and converting the key features to obtain target features, specifically includes:
[0018] Acquire data attributes of the target data, and determine the data type of the target data according to the data attributes;
[0019] Acquire multiple data features of the target data, and extract key features from the multiple data features according to the data type, wherein the key features are selected according to historical key features of historical target data;
[0020] The key features are transformed to obtain target features suitable for input into the prediction model.
[0021] Optionally, the Python-based organizational data prediction method, wherein the acquiring of multiple data features of the target data and extracting key features from the data features according to the data type, specifically includes:
[0022] Acquire multiple historical data features of the historical target data, and analyze the historical target data and the multiple historical key features to obtain multiple historical influencing factors affecting the historical target data;
[0023] According to all the historical influencing factors, all the historical data features are sorted by the degree of influence to obtain a historical data feature array, wherein the historical key feature represents the historical data feature with the greatest degree of influence;
[0024] A data analysis model is constructed using Python's data computing library, and the historical data feature array is input into the data analysis model for training;
[0025] All the data features and the data types are input into the trained data analysis model, a data feature array is output, and the key features are extracted from the data feature array.
[0026] Optionally, the Python-based organizational data prediction method, wherein the target data is subjected to pattern analysis processing according to the target characteristics to obtain data trend information, and the data trend information is analyzed to obtain target influencing factors, specifically includes:
[0027] Selecting an expression pattern of the target data according to the target feature, and constructing a data plot of the target data according to the expression pattern;
[0028] Analyze the data plot to obtain data trend information of the target data;
[0029] The data trend information is analyzed to obtain multiple target influencing factors of the target data.
[0030] Optionally, the Python-based organizational data prediction method, wherein the step of inputting the target features and the target influencing factors into a trained prediction model and outputting a plurality of prediction results of the target data, further comprises:
[0031] Using Python's machine learning library to build an initial prediction model, building a training data set based on the historical target data, and dividing the training data set into multiple subsets;
[0032] Each of the subsets is used as a validation set in turn, and the remaining subsets are used as validation sets to train and cross-validate the initial prediction model to obtain the prediction model.
[0033] Optionally, the Python-based organizational data prediction method, wherein the step of inputting the target features and the target influencing factors into a trained prediction model and outputting the prediction results of the target data, specifically includes:
[0034] Inputting the target characteristics and the target influencing factors into the prediction model to obtain the distribution and uncertainty factors of the target data;
[0035] Constructing a simulation scenario, adjusting the distribution and the uncertainty factor and adding them to the simulation scenario to obtain a collapse limit, wherein the collapse limit represents a critical value of the uncertainty factor;
[0036] The collapse limit is input into the prediction model, and the prediction result of the target data is output.
[0037] In addition, to achieve the above-mentioned purpose, the present invention also provides a Python-based organizational data prediction system, wherein the Python-based organizational data prediction system includes:
[0038] A data cleaning module is used to obtain the initial data of the target organization, clean the initial data, and obtain the target data;
[0039] A feature screening module is used to determine the data type of the target data, extract key features according to the data type, and convert the key features to obtain target features;
[0040] A trend analysis module, used to perform pattern analysis on the target data according to the target characteristics to obtain data trend information, and analyze the data trend information to obtain target influencing factors;
[0041] The result prediction module is used to input the target features and the target influencing factors into a trained prediction model and output the prediction results of the target data.
[0042] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a Python-based organizational data prediction program stored in the memory and executable on the processor, wherein the Python-based organizational data prediction program implements the steps of the Python-based organizational data prediction method as described above when executed by the processor.
[0043] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a Python-based organizational data prediction program, and when the Python-based organizational data prediction program is executed by a processor, it implements the steps of the Python-based organizational data prediction method as described above.
[0044] In the present invention, the initial data of the target organization is obtained, the initial data is cleaned to obtain the target data; the data type of the target data is determined, key features are extracted according to the data type, and the key features are converted to obtain the target features; according to the target features, the target data is subjected to pattern analysis to obtain data trend information, and the data trend information is analyzed to obtain target influencing factors; the target features and the target influencing factors are input into a trained prediction model, and the prediction results of the target data are output. The present invention pre-processes the collected data, extracts the key factors affecting the data and the key features of the data using statistical methods, and then uses the trained model to perform sensitivity analysis and scenario simulation, thereby improving the applicability and accuracy of the data analysis process. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] Figure 1 It is a flow chart of a preferred embodiment of the organizational data prediction method based on Python of the present invention;
[0046] Figure 2 It is a flow chart of a preferred embodiment of the Python-based organizational data prediction method of the present invention;
[0047] Figure 3 It is a structural diagram of a preferred embodiment of the Python-based organizational data prediction system of the present invention;
[0048] Figure 4 Schematic diagram of the operating environment of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION
[0049] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.
[0050] The Python-based organizational data prediction method described in the preferred embodiment of the present invention is as follows: Figure 1 As shown, the Python-based organizational data prediction method includes the following steps:
[0051] Step S10: Acquire initial data of the target tissue, clean the initial data, and obtain target data.
[0052] Among them, Figure 2 As shown, the initial data of the target organization can be collected from internal databases, external APIs or other data sources through Python's requests (an HTTP library that uses the Apache2 Licensed open source protocol, allowing users to send various HTTP requests, handle cookies, sessions, connection pools, redirections, and multiple authentication methods, etc., making it very convenient to process HTTP requests), pandas (an open source third-party Python library, mainly used for data analysis), and other libraries, mainly including the target organization's revenue data over a period of time.
[0053] Specifically, obtain Python's data crawling instructions, and obtain initial data from the database of the target organization according to the data crawling instructions; obtain Python's data cleaning instructions, and filter missing values and abnormal values of the initial data according to the data cleaning instructions; use Python's preset extension library to respectively construct all the missing values and all the abnormal values into corresponding missing arrays; send all the missing arrays to the cloud, and receive multiple filling arrays input by the user, wherein the multiple filling arrays are constructed by the user based on the multiple missing arrays; based on all the filling arrays, update all the missing values and all the abnormal values to obtain the target data.
[0054] Among them, the data cleaning process can be implemented through the pandas library, which mainly screens the missing values and outliers of the collected initial data. After these values are screened out, two missing arrays are formed (one array representing missing values and the other array representing outliers). These two missing arrays can be sent to the cloud and filled in by users, which helps to improve the accuracy and completeness of the data. The missing arrays can also be filled in by the numpy (an open source Python library, mainly used for numerical calculations and array processing) library to improve the simplicity and efficiency of data analysis when performing large-scale data processing and numerical calculations.
[0055] Furthermore, corresponding information will be generated for the elements in the missing array, so that when users fill in the missing array, it is convenient for users to understand the content of the missing data and improve the accuracy of filling the array.
[0056] Step S20: determine the data type of the target data, extract key features according to the data type, and convert the key features to obtain target features.
[0057] Among them, feature selection and transformation can be implemented using the pandas library or the scikit-learn library (an open source machine learning library based on Python that aims to provide users with simple and efficient tools for data mining and data analysis).
[0058] Specifically, data attributes of the target data are obtained, and the data type of the target data is determined according to the data attributes; multiple data features of the target data are obtained, and key features are extracted from the multiple data features according to the data type, wherein the key features are selected according to historical key features of historical target data; the key features are converted to obtain target features suitable for input into a prediction model.
[0059] Among them, the data attributes of the target data can be extracted based on the experience in historical data or expert evaluation. For example, in the air-conditioning industry, the demand is strong in summer and low in winter, so the industry is greatly affected by seasonality. Therefore, time and temperature are very critical attributes. According to these two attributes, the data types can be determined as integer and floating point. At the same time, the most important key feature can be extracted, that is, "temperature" with a floating point data type as the key feature, and then the selected key features are converted into target features that can be input into the prediction model.
[0060] Furthermore, a plurality of historical data features of the historical target data are obtained, and the historical target data and the plurality of historical key features are analyzed to obtain a plurality of historical influencing factors affecting the historical target data; based on all the historical influencing factors, all the historical data features are sorted by degree of influence to obtain a historical data feature array, wherein the historical key features represent the historical data features with the greatest degree of influence; a data analysis model is constructed using Python's data computing library, and the historical data feature array is input into the data analysis model for training; all the data features and the data types are input into the trained data analysis model, a data feature array is output, and the key features are extracted from the data feature array.
[0061] Among them, in the process of selecting data attributes based on experience in historical data, it is necessary to use multiple historical data features as training sets to train the data analysis model, so as to obtain a model that can rank the degree of influence of multiple data features, and then input the data features and data types of the target data into the trained model to obtain a sorted array of data features, and extract the most critical features from them, such as the floating-point "temperature" mentioned above. By comprehensively analyzing the target data, key features are extracted from multiple features to analyze the trend of the overall data, which effectively improves the accuracy of the data analysis process.
[0062] Step S30: performing pattern analysis on the target data according to the target characteristics to obtain data trend information, and analyzing the data trend information to obtain target influencing factors.
[0063] Specifically, according to the target characteristics, an expression pattern of the target data is selected, and according to the expression pattern, a data drawing of the target data is constructed; the data drawing is analyzed to obtain data trend information of the target data; and the data trend information is analyzed to obtain multiple target influencing factors of the target data.
[0064] Among them, after obtaining the characteristics of the target data, you can use the scipy library (built on the basis of NumPy, providing a large number of mathematical algorithms and practical functions to meet the needs of various scientific calculations) or the statsmodels library (based on NumPy and SciPy, which can be well integrated with Python's data science ecosystem, making it convenient for users to process and analyze data) to perform statistical tests, so as to obtain the data trend of the target data, improve the visualization of the data analysis process, and facilitate data tracking.
[0065] Furthermore, an initial prediction model is constructed using Python's machine learning library, a training data set is constructed based on the historical target data, and the training data set is divided into multiple subsets; each of the subsets is used as a validation set in turn, and the remaining subsets are used as validation sets to train and cross-validate the initial prediction model to obtain the prediction model.
[0066] Among them, before making result predictions, it is necessary to use historical target data to build a training set. The model selection can use linear regression, time series analysis, machine learning or deep learning models. The model construction and training can be performed using scikit-learn, tensorflow, pytorch and other libraries; cross-validation is mainly used to verify the performance of the prediction model. The main step is to divide the training set into multiple subsets, take each subset as a validation set in turn, and use the remaining subsets as training sets to reduce the variance of the model evaluation. This process can also be implemented using AIC (Akaike Information Criterion, a criterion for measuring the goodness of fit of a statistical model) or BIC (Bayesian Information Criterion).
[0067] Step S40: input the target features and the target influencing factors into the trained prediction model, and output the prediction results of the target data.
[0068] Specifically, the target characteristics and the target influencing factors are input into the prediction model to obtain the distribution and uncertainty factors of the target data; a simulation scenario is constructed, and the distribution and the uncertainty factors are adjusted and added to the simulation scenario to obtain a collapse limit, wherein the collapse limit represents the critical value of the uncertainty factor; the collapse limit is input into the prediction model to output the prediction result of the target data.
[0069] Among them, when making data predictions, uncertainty factors refer to unknown factors and potential losses that may affect the accuracy of the prediction results. Specifically, uncertainty refers to the situation where the distribution range and status of future data trends cannot be known for sure, which may come from statistical deviations of project data, technological development, and changes in market supply and demand structure. The impact of uncertainty factors on data evaluation indicators involves the uncertainty that the project may bear and its tolerance, which affects the reliability of the project for data development. In order to reduce the impact of uncertainty factors on the accuracy of data predictions, different simulation scenarios can be constructed, and the results of data in different scenarios can be analyzed to evaluate the performance of data under different conditions and improve the adaptability of data. Sensitivity analysis can also be performed on the data, that is, by studying the corresponding changes in data indicators when the uncertainty factors of the project change, the sensitive factors of the project are found out, and the sensitivity to these sensitive factors is determined. When the factors reach the critical value, the tolerance of the project is analyzed, which helps to identify factors that affect the target data, thereby improving the stability of the prediction model when the factors change.
[0070] The present invention pre-processes the collected data, uses statistical methods to extract key factors affecting the data and key features of the data, and then uses the trained model to perform sensitivity analysis and scenario simulation, thereby improving the applicability and accuracy of the data analysis process.
[0071] Furthermore, if Figure 3 As shown, based on the above-mentioned Python-based organizational data prediction method, the present invention also provides a Python-based organizational data prediction system, wherein the Python-based organizational data prediction system includes:
[0072] A data cleaning module 51 is used to obtain initial data of a target organization and clean the initial data to obtain target data;
[0073] A feature screening module 52 is used to determine the data type of the target data, extract key features according to the data type, and convert the key features to obtain target features;
[0074] The trend analysis module 53 is used to perform pattern analysis on the target data according to the target characteristics to obtain data trend information, and analyze the data trend information to obtain target influencing factors;
[0075] The result prediction module 54 is used to input the target features and the target influencing factors into the trained prediction model and output the prediction results of the target data.
[0076] Furthermore, if Figure 4As shown, based on the above-mentioned Python-based organizational data prediction method and system, the present invention also provides a terminal accordingly, and the terminal includes a processor 10, a memory 20 and a display 30. Figure 4 Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.
[0077] The memory 20 may be an internal storage unit of the terminal in some embodiments, such as a hard disk or memory of the terminal. The memory 20 may also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as the program code of the installation terminal, etc. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a Python-based organization data prediction program 40 is stored on the memory 20, and the Python-based organization data prediction program 40 can be executed by the processor 10, thereby realizing the Python-based organization data prediction method in the present application.
[0078] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the Python-based organizational data prediction method.
[0079] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.
[0080] In one embodiment, when the processor 10 executes the Python-based organizational data prediction program 40 in the memory 20, the following steps are implemented:
[0081] Acquire initial data of a target organization, clean the initial data, and obtain target data;
[0082] Determine the data type of the target data, extract key features according to the data type, and convert the key features to obtain target features;
[0083] According to the target characteristics, pattern analysis processing is performed on the target data to obtain data trend information, and the data trend information is analyzed to obtain target influencing factors;
[0084] The target features and the target influencing factors are input into a trained prediction model, and the prediction results of the target data are output.
[0085] The step of obtaining the initial data of the target organization and cleaning the initial data to obtain the target data specifically includes:
[0086] Obtaining a Python data crawling instruction, and obtaining initial data from a database of the target organization according to the data crawling instruction;
[0087] Obtain Python data cleaning instructions, and filter missing values and abnormal values of the initial data according to the data cleaning instructions;
[0088] Using a preset extension library of Python, constructing all the missing values and all the abnormal values into corresponding missing arrays respectively;
[0089] Sending all the missing arrays to the cloud, and receiving multiple filling arrays input by the user, wherein the multiple filling arrays are constructed by the user according to the multiple missing arrays;
[0090] According to all the filled arrays, all the missing values and all the abnormal values are updated to obtain the target data.
[0091] The step of determining the data type of the target data, extracting key features according to the data type, and converting the key features to obtain target features specifically includes:
[0092] Acquire data attributes of the target data, and determine the data type of the target data according to the data attributes;
[0093] Acquire multiple data features of the target data, and extract key features from the multiple data features according to the data type, wherein the key features are selected according to historical key features of historical target data;
[0094] The key features are transformed to obtain target features suitable for input into the prediction model.
[0095] The step of acquiring multiple data features of the target data and extracting key features from the data features according to the data type specifically includes:
[0096] Acquire multiple historical data features of the historical target data, and analyze the historical target data and the multiple historical key features to obtain multiple historical influencing factors affecting the historical target data;
[0097] According to all the historical influencing factors, all the historical data features are sorted by the degree of influence to obtain a historical data feature array, wherein the historical key feature represents the historical data feature with the greatest degree of influence;
[0098] A data analysis model is constructed using Python's data computing library, and the historical data feature array is input into the data analysis model for training;
[0099] All the data features and the data types are input into the trained data analysis model, a data feature array is output, and the key features are extracted from the data feature array.
[0100] The method of performing pattern analysis on the target data according to the target characteristics to obtain data trend information, and analyzing the data trend information to obtain target influencing factors specifically includes:
[0101] Selecting an expression pattern of the target data according to the target feature, and constructing a data plot of the target data according to the expression pattern;
[0102] Analyze the data plot to obtain data trend information of the target data;
[0103] The data trend information is analyzed to obtain multiple target influencing factors of the target data.
[0104] The step of inputting the target features and the target influencing factors into a trained prediction model and outputting a plurality of prediction results of the target data further includes:
[0105] Using Python's machine learning library to build an initial prediction model, building a training data set based on the historical target data, and dividing the training data set into multiple subsets;
[0106] Each of the subsets is used as a validation set in turn, and the remaining subsets are used as validation sets to train and cross-validate the initial prediction model to obtain the prediction model.
[0107] The step of inputting the target features and the target influencing factors into a trained prediction model and outputting the prediction results of the target data specifically includes:
[0108] Inputting the target characteristics and the target influencing factors into the prediction model to obtain the distribution and uncertainty factors of the target data;
[0109] Constructing a simulation scenario, adjusting the distribution and the uncertainty factor and adding them to the simulation scenario to obtain a collapse limit, wherein the collapse limit represents a critical value of the uncertainty factor;
[0110] The collapse limit is input into the prediction model, and the prediction result of the target data is output.
[0111] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a Python-based organizational data prediction program, and when the Python-based organizational data prediction program is executed by a processor, the steps of the Python-based organizational data prediction method as described above are implemented.
[0112] In summary, the present invention provides a Python-based organizational data prediction method and related equipment, the method comprising: obtaining the initial data of the target organization, cleaning the initial data to obtain the target data; determining the data type of the target data, extracting key features according to the data type, and converting the key features to obtain the target features; performing pattern analysis on the target data according to the target features to obtain data trend information, and analyzing the data trend information to obtain target influencing factors; inputting the target features and the target influencing factors into a trained prediction model, and outputting the prediction results of the target data. The present invention pre-processes the collected data, extracts the key factors affecting the data and the key features of the data using statistical methods, and then uses the trained model to perform sensitivity analysis and scenario simulation, thereby improving the applicability and accuracy of the data analysis process.
[0113] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or terminal including the element.
[0114] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium that can be read by a computer, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a disk, an optical disk, etc.
[0115] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.
Claims
1. A Python-based organizational data prediction method, characterized in that: The Python-based organizational data prediction method includes: Acquire initial data of a target organization, clean the initial data, and obtain target data; Determine the data type of the target data, extract key features according to the data type, and convert the key features to obtain target features; According to the target characteristics, pattern analysis processing is performed on the target data to obtain data trend information, and the data trend information is analyzed to obtain target influencing factors; The target features and the target influencing factors are input into a trained prediction model, and the prediction results of the target data are output.
2. The Python-based organizational data prediction method according to claim 1, characterized in that: The obtaining of initial data of the target organization and cleaning of the initial data to obtain target data specifically includes: Obtaining a Python data crawling instruction, and obtaining initial data from a database of the target organization according to the data crawling instruction; Obtain Python data cleaning instructions, and filter missing values and abnormal values of the initial data according to the data cleaning instructions; Using a preset extension library of Python, constructing all the missing values and all the abnormal values into corresponding missing arrays respectively; Sending all the missing arrays to the cloud, and receiving multiple filling arrays input by the user, wherein the multiple filling arrays are constructed by the user according to the multiple missing arrays; According to all the filled arrays, all the missing values and all the abnormal values are updated to obtain the target data.
3. The Python-based organizational data prediction method according to claim 1, characterized in that: The determining the data type of the target data, extracting key features according to the data type, and converting the key features to obtain target features specifically includes: Acquire data attributes of the target data, and determine the data type of the target data according to the data attributes; Acquire multiple data features of the target data, and extract key features from the multiple data features according to the data type, wherein the key features are selected according to historical key features of historical target data; The key features are transformed to obtain target features suitable for input into the prediction model.
4. The Python-based organizational data prediction method according to claim 3, characterized in that: The acquiring of multiple data features of the target data and extracting key features from the data features according to the data type specifically includes: Acquire multiple historical data features of the historical target data, and analyze the historical target data and the multiple historical key features to obtain multiple historical influencing factors affecting the historical target data; According to all the historical influencing factors, all the historical data features are sorted by the degree of influence to obtain a historical data feature array, wherein the historical key feature represents the historical data feature with the greatest degree of influence; A data analysis model is constructed using Python's data computing library, and the historical data feature array is input into the data analysis model for training; All the data features and the data types are input into the trained data analysis model, a data feature array is output, and the key features are extracted from the data feature array.
5. The Python-based organizational data prediction method according to claim 1, characterized in that: The method of performing pattern analysis on the target data according to the target characteristics to obtain data trend information, and analyzing the data trend information to obtain target influencing factors specifically includes: Selecting an expression pattern of the target data according to the target feature, and constructing a data plot of the target data according to the expression pattern; Analyze the data plot to obtain data trend information of the target data; The data trend information is analyzed to obtain multiple target influencing factors of the target data.
6. The Python-based organizational data prediction method according to claim 3, characterized in that: The step of inputting the target features and the target influencing factors into a trained prediction model and outputting a plurality of prediction results of the target data further includes: Using Python's machine learning library to build an initial prediction model, building a training data set based on the historical target data, and dividing the training data set into multiple subsets; Each of the subsets is used as a validation set in turn, and the remaining subsets are used as validation sets to train and cross-validate the initial prediction model to obtain the prediction model.
7. The Python-based organizational data prediction method according to claim 1, characterized in that: The step of inputting the target features and the target influencing factors into a trained prediction model and outputting the prediction result of the target data specifically includes: Inputting the target characteristics and the target influencing factors into the prediction model to obtain the distribution and uncertainty factors of the target data; Constructing a simulation scenario, adjusting the distribution and the uncertainty factor and adding them to the simulation scenario to obtain a collapse limit, wherein the collapse limit represents a critical value of the uncertainty factor; The collapse limit is input into the prediction model, and the prediction result of the target data is output.
8. A Python-based organizational data prediction system, characterized in that: The Python-based organizational data prediction system includes: A data cleaning module is used to obtain the initial data of the target organization, clean the initial data, and obtain the target data; A feature screening module is used to determine the data type of the target data, extract key features according to the data type, and convert the key features to obtain target features; A trend analysis module, used to perform pattern analysis on the target data according to the target characteristics to obtain data trend information, and analyze the data trend information to obtain target influencing factors; The result prediction module is used to input the target features and the target influencing factors into a trained prediction model and output the prediction results of the target data.
9. A terminal, characterized in that: The terminal includes: a memory, a processor, and a Python-based organizational data prediction program stored in the memory and executable on the processor. When the Python-based organizational data prediction program is executed by the processor, the steps of the Python-based organizational data prediction method as described in any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a Python-based tissue data prediction program, which, when executed by a processor, implements the steps of the Python-based tissue data prediction method as described in any one of claims 1 to 7.