Enterprise management decision analysis method based on artificial intelligence

Abstract

The invention provides an enterprise management decision analysis method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, collecting historical related data from a database of an enterprise, carrying out the data processing of the collected data, and building a data set; s2, performing normalization processing on the data set, and dividing the normalized data set into a training set and a test set; s3, substituting the training set into an XGBoost model for training to obtain a trained prediction analysis model; s4, substituting the test set into the trained prediction analysis model to perform decision analysis on the enterprise, and outputting a final analysis result; according to the method, the dynamic normalization algorithm is provided to adaptively process the data, the model parameter tuning strategy is used to improve the prediction and analysis accuracy of the algorithm model, accurate prediction of the enterprise management service demand is realized, the long-term trend of the enterprise can be analyzed, and data support is provided for enterprise decision making.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an enterprise management decision analysis method based on artificial intelligence. Background Art

[0002] With the rapid development of big data technology, enterprises are facing unprecedented amounts and types of data. These data cover various aspects such as market trends, consumer behavior, and supply chain status, providing enterprises with a wealth of decision-making basis. In this new form, traditional intelligent decision support systems can no longer adapt to this new requirement. There is an urgent need to seek new theories, methods, and technologies to guide the analysis, design, and implementation of a new generation of intelligent decision support systems. For this reason, artificial intelligence technology has emerged.

[0003] However, existing AI technologies still have many shortcomings in enterprise management decision-making: (1) The performance of AI technology depends largely on the quality and quantity of training data. If the data is biased or of poor quality, the performance of AI may be affected, resulting in inaccurate decision analysis; (2) Current AI models are usually trained for specific tasks and lack sufficient generalization capabilities. When the business environment changes, the model may not be able to adapt to new data sets or situations; (3) When using AI to process sensitive data, data security and privacy protection are particularly important. Technical defects or improper operations may lead to data leakage and cause serious consequences. Summary of the invention

[0004] The purpose of the present invention is to provide an enterprise management decision analysis method based on artificial intelligence, which can.

[0005] The technical solution of the present invention is:

[0006] In a first aspect, the present application provides an enterprise management decision analysis method based on artificial intelligence, which comprises the following steps:

[0007] S1. Collect historical relevant data from the enterprise's database and establish a data set after data processing the collected data;

[0008] S2, normalize the data set and divide the normalized data set into a training set and a test set;

[0009] S3, substituting the training set into the XGBoost model for training to obtain a trained prediction analysis model;

[0010] S4. Use the test set to substitute the trained predictive analysis model to conduct decision analysis on the enterprise and output the final analysis results.

[0011] Furthermore, in step S1, the above-mentioned historical related data includes the enterprise's historical sales data, customer feedback data and market trend data.

[0012] Furthermore, in step S1, the above data processing includes removing duplicate records, correcting errors and filling missing values.

[0013] Furthermore, in step S2, the process of normalizing the data set includes:

[0014] By dynamically calculating the quantiles of the data set, the range of the data set can be dynamically adjusted;

[0015] Dynamically select the optimal normalization algorithm based on the range of the data set.

[0016] Furthermore, the formula for dynamically calculating the quantile of the data set includes:

[0017]

[0018] In the formula, X' represents the normalized data, X represents the data to be processed in the data set, and X 5％ and X 95％ Represent the 5% and 95% quantiles of the data set, respectively.

[0019] Furthermore, when dynamically selecting the optimal normalization algorithm according to the range of the data set, if the data set is close to a normal distribution, the Z-score normalization algorithm is selected, and its calculation formula includes:

[0020]

[0021] In the formula, X' represents the normalized data, X represents the data to be processed in the data set, μ is the mean of the original data, k is an integer, σ is the standard deviation of the original data, c represents the deviation, t is the custom data deviation threshold, n is the number of samples, is the sample mean, s is the sample standard deviation, X i is the i-th sample value.

[0022] Furthermore, in step S3, the calculation formula of the above XGBoost model includes:

[0023]

[0024] In the formula, is the predicted value of the i-th sample, K is the number of trees, ω k is the weight of the kth tree, h k is the prediction function of the kth tree, and b represents the bias.

[0025] Further, step S4 includes:

[0026] S41, substituting the data of the test set into the trained prediction analysis model to obtain the prediction result of the model for the test set samples;

[0027] S42, comparing the predicted result with the actual result to obtain a comparison result;

[0028] S43. Calculate the evaluation indicators of the model's accuracy, precision, recall rate and F1 score by comparing the results to output the final analysis results.

[0029] In a second aspect, the present application provides an electronic device, including:

[0030] A memory for storing one or more programs;

[0031] processor;

[0032] When the one or more programs are executed by the processor, an enterprise management decision analysis method based on artificial intelligence as described in any one of the first aspects is implemented.

[0033] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an enterprise management decision analysis method based on artificial intelligence as described in any one of the first aspects above.

[0034] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0035] (1) The enterprise management decision analysis method based on artificial intelligence provided by the present invention can eliminate the dimensional differences between data by proposing a data dynamic normalization algorithm to adaptively process data, thereby realizing in-depth mining of enterprise historical data, improving the accuracy of algorithm model prediction and analysis, and being able to analyze the long-term trend of the enterprise, thus providing data support for enterprise decision-making;

[0036] (2) The present invention utilizes a model parameter tuning strategy to improve the prediction and analysis accuracy of the algorithm model, and further achieves accurate prediction of enterprise management service needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0038] Figure 1A step diagram of an enterprise management decision analysis method based on artificial intelligence according to the present invention;

[0039] Figure 2 The figure is a schematic structural block diagram of an electronic device according to an embodiment of the present invention.

[0040] Icon: 101, memory; 102, processor; 103, communication interface. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0042] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0043] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0044] It should be noted that, in this article, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprises..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0045] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0046] Example 1

[0047] See also Figure 1 , Figure 1 Shown is a step diagram of an enterprise management decision analysis method based on artificial intelligence provided in an embodiment of the present application.

[0048] In a first aspect, the present application provides an enterprise management decision analysis method based on artificial intelligence, which comprises the following steps:

[0049] S1. Collect historical relevant data from the enterprise's database and establish a data set after data processing the collected data;

[0050] S2, normalize the data set and divide the normalized data set into a training set and a test set;

[0051] S3, substituting the training set into the XGBoost model for training to obtain a trained prediction analysis model;

[0052] S4. Use the test set to substitute the trained predictive analysis model to conduct decision analysis on the enterprise and output the final analysis results.

[0053] As a preferred implementation, in step S1, the historical related data includes the enterprise's historical sales data, customer feedback data and market trend data.

[0054] As a preferred implementation, in step S1, data processing includes removing duplicate records, correcting errors and filling missing values.

[0055] Therefore, through data processing, the quality and accuracy of the data can be ensured so that a data set can be established after the processing is completed, thereby eliminating the dimensional differences between the data and improving the accuracy and stability of the model to ensure the data quality and the effectiveness of the algorithm.

[0056] As a preferred implementation, in step S2, the process of normalizing the data set includes:

[0057] By dynamically calculating the quantiles of the data set, the range of the data set can be dynamically adjusted;

[0058] Dynamically select the optimal normalization algorithm based on the range of the data set.

[0059] Therefore, by dynamically adjusting the range of the data set, the impact of outliers can be significantly reduced. At the same time, dynamically selecting the optimal normalization algorithm can further improve the data processing effect and model performance.

[0060] As a preferred implementation, the formula for dynamically calculating the quantile of a data set includes:

[0061]

[0062] In the formula, X' represents the normalized data, X represents the data to be processed in the data set, and X 5％ and X 95％ Represent the 5% and 95% quantiles of the data set, respectively.

[0063] Therefore, in min-max normalization, dynamically calculating the quantiles of the data set instead of using the global minimum and maximum values ​​can significantly reduce the impact of outliers.

[0064] As a preferred implementation, when dynamically selecting the optimal normalization algorithm according to the range of the data set, if the data set is close to a normal distribution, the Z-score normalization algorithm is selected, and its calculation formula includes:

[0065]

[0066] In the formula, X' represents the normalized data, X represents the data to be processed in the data set, μ is the mean of the original data, k is an integer, σ is the standard deviation of the original data, c represents the deviation, t is the custom data deviation threshold, n is the number of samples, is the sample mean, s is the sample standard deviation, X i is the i-th sample value.

[0067] As a preferred implementation, in step S3, the calculation formula of the XGBoost model includes:

[0068]

[0069] In the formula, is the predicted value of the i-th sample, K is the number of trees, ω k is the weight of the kth tree, h k is the prediction function of the kth tree, and b represents the bias.

[0070] Therefore, by using the XGBoost model as the prediction analysis model of the present invention, the accuracy of model prediction can be significantly improved by optimizing its parameter selection;

[0071] It should be noted that when the prediction model of the present invention optimizes parameters, a cross-validation method is adopted. By dividing the data set into K subsets, model evaluation and parameter adjustment are performed on the K subset data to ensure that the model has good generalization ability; then, hyperparameter tuning strategies such as grid search and Bayesian optimization are adopted to optimize the hyperparameters of the algorithm to find the optimal parameter combination, thereby improving the accuracy of model prediction, and realizing intelligent reminder and early warning services. When the prediction results show that service demand or trend has abnormal fluctuations, the system will automatically trigger the early warning mechanism, and promptly notify the decision makers of the enterprise through email, text message or in-application notification, so as to take corresponding countermeasures.

[0072] As a preferred implementation, step S4 includes:

[0073] S41, substituting the data of the test set into the trained prediction analysis model to obtain the prediction result of the model for the test set samples;

[0074] S42, comparing the predicted result with the actual result to obtain a comparison result;

[0075] S43. Calculate the evaluation indicators of the model's accuracy, precision, recall rate and F1 score by comparing the results to output the final analysis results.

[0076] Example 2

[0077] See also Figure 2 , Figure 2 A schematic structural block diagram of an electronic device provided in an embodiment of the present application.

[0078] An electronic device includes a memory 101, a processor 102 and a communication interface 103, wherein the memory 101, the processor 102 and the communication interface 103 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules, and the processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 101. The communication interface 103 can be used to communicate signaling or data with other node devices.

[0079] Among them, the memory 101 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc.

[0080] The processor 102 may be an integrated circuit chip with signal processing capability. The processor 102 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0081] It is understood that the structure shown in the figure is only for illustration, and an enterprise management decision analysis method based on artificial intelligence may also include more or fewer components than those shown in the figure, or have a different configuration than those shown in the figure. Each component shown in the figure may be implemented by hardware, software or a combination thereof.

[0082] In the embodiments provided in the present application, it should be understood that the disclosed method can also be implemented in other ways. The embodiments described above are merely schematic, for example, the flowchart or block diagram in the accompanying drawings shows the possible implementation architecture, functions and operations of the method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0083] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0084] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0085] To sum up, the embodiment of the present application provides an enterprise management decision analysis method based on artificial intelligence. It adaptively processes data through a dynamic normalization algorithm, utilizes a model parameter tuning strategy to improve the prediction and analysis accuracy of the algorithm model, and realizes accurate prediction of enterprise management service needs. It can analyze the long-term trends of the enterprise and provide data support for enterprise decision-making.

[0086] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0087] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An enterprise management decision analysis method based on artificial intelligence, characterized in that: The following steps are involved: S1. Collect historical relevant data from the enterprise's database and establish a data set after data processing the collected data; S2, normalize the data set and divide the normalized data set into a training set and a test set; S3, substituting the training set into the XGBoost model for training to obtain a trained prediction analysis model; S4. Use the test set to substitute the trained predictive analysis model to conduct decision analysis on the enterprise and output the final analysis results.

2. The enterprise management decision analysis method based on artificial intelligence according to claim 1, characterized in that: In step S1, the historical related data includes the enterprise's historical sales data, customer feedback data and market trend data.

3. The enterprise management decision analysis method based on artificial intelligence as claimed in claim 1, characterized in that: In step S1, the data processing includes removing duplicate records, correcting errors and filling missing values.

4. The enterprise management decision analysis method based on artificial intelligence as claimed in claim 1, characterized in that: In step S2, the process of normalizing the data set includes: By dynamically calculating the quantiles of the data set, the range of the data set can be dynamically adjusted; Dynamically select the optimal normalization algorithm based on the range of the data set.

5. The enterprise management decision analysis method based on artificial intelligence as claimed in claim 4, characterized in that: The formula for dynamically calculating the quantile of a data set includes: In the formula, X' represents the normalized data, X represents the data to be processed in the data set, and X 5％ and X 95％ Represent the 5% and 95% quantiles of the data set, respectively.

6. The enterprise management decision analysis method based on artificial intelligence as claimed in claim 4, characterized in that: When dynamically selecting the optimal normalization algorithm based on the range of the data set, if the data set is close to a normal distribution, the Z-score normalization algorithm is selected, and its calculation formula includes: In the formula, X' represents the normalized data, X represents the data to be processed in the data set, μ is the mean of the original data, k is an integer, σ is the standard deviation of the original data, c represents the deviation, t is the custom data deviation threshold, n is the number of samples, is the sample mean, s is the sample standard deviation, X i is the i-th sample value.

7. The enterprise management decision analysis method based on artificial intelligence as claimed in claim 1, characterized in that: In step S3, the calculation formula of the XGBoost model includes: In the formula, is the predicted value of the i-th sample, K is the number of trees, ω k is the weight of the kth tree, h k is the prediction function of the kth tree, and b represents the bias.

8. The enterprise management decision analysis method based on artificial intelligence as claimed in claim 1, characterized in that: Step S4 includes: S41, substituting the data of the test set into the trained prediction analysis model to obtain the prediction result of the model for the test set samples; S42, comparing the predicted result with the actual result to obtain a comparison result; S43. Calculate the evaluation indicators of the model's accuracy, precision, recall rate and F1 score by comparing the results to output the final analysis results.

9. An electronic device, characterized in that: include: A memory for storing one or more programs; processor; When the one or more programs are executed by the processor, an enterprise management decision analysis method based on artificial intelligence as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, an enterprise management decision analysis method based on artificial intelligence as described in any one of claims 1 to 8 is implemented.

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