Enterprise multidimensional profiling system and method

Abstract

The enterprise multi-dimensional profiling analysis system and method provided by this invention trains the enterprise profiling model with corrected dimensions by training on the dimensional data of historical enterprise profiling. Based on the corrected enterprise profiling model, the original enterprise data is processed to obtain and output the enterprise profiling results. The system can train the dimensions of the enterprise profiling model, making the enterprise profiling model more consistent with the enterprise data and the actual situation of the enterprise, thereby improving the comprehensiveness and accuracy of the enterprise profiling results.

Description

Technical Field

[0001] This invention relates to the field of enterprise profiling, and in particular to methods and systems for constructing enterprise profiling models, as well as storage media and electronic devices. Background Technology

[0002] As the most dynamic market players, enterprises play a vital role in the stable development of the economy and society. A scientific and reasonable evaluation of enterprise operations is crucial for both the enterprise's own development planning and investors' investment strategies. Currently, enterprise profiling is commonly used to evaluate an enterprise's performance.

[0003] Enterprise profiling refers to the process of comprehensively analyzing various data generated during the development and operation of an enterprise using big data analytics. The results of enterprise profiling can usually present the characteristics and operating status of the enterprise to relevant parties in a comprehensive and intuitive way, helping them to gain a deeper understanding of the enterprise. The appropriateness of the enterprise profiling model construction is related to the accuracy and comprehensiveness of the enterprise profiling results.

[0004] Existing enterprise profiling models typically divide various types of enterprise data into fixed dimensions. However, this fixed dimension division method cannot be applied to all types of enterprises. Therefore, the generated enterprise profiling results may deviate from the actual situation of the enterprise, making it impossible for relevant parties to accurately and comprehensively understand the characteristics and operating status of the enterprise. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-dimensional enterprise profiling analysis system and method to generate more personalized enterprise profiling results and improve the comprehensiveness and accuracy of the enterprise profiling results.

[0006] This invention provides a method for multi-dimensional enterprise profiling analysis, comprising the following steps:

[0007] Acquire enterprise data, which includes dimensional data of historical enterprise profiles and original enterprise data;

[0008] The historical corporate profile can be a profile of the company itself or a profile of other companies.

[0009] The model is trained using dimensional data of historical enterprise profiles to obtain an enterprise profile model with corrected dimensions.

[0010] The modification of enterprise profile dimensions includes adjusting the dimensional classification types and levels of the original enterprise data; training can be based on a portion of the original enterprise data and / or input training requirement data.

[0011] Based on the enterprise profile model with corrected dimensions, the original enterprise data is processed to obtain the target enterprise profile result;

[0012] The enterprise profile dimensions include positive evaluation dimensions, negative evaluation dimensions, and unquantifiable dimensions. Based on these dimensions, the original enterprise data is divided and calculated to obtain the target enterprise profile result.

[0013] Furthermore, the enterprise profiling target results conform to the evaluation target constraints, which are the evaluation targets input by the demand side of the enterprise profiling.

[0014] Output the target results of the enterprise profile.

[0015] Furthermore, the current enterprise profiling model and the original enterprise data will be linked and saved for use in the next enterprise multi-dimensional profiling process and profiling neural network training.

[0016] In accordance with the corresponding enterprise multi-dimensional profiling analysis method, this invention also provides an enterprise multi-dimensional profiling analysis system, including:

[0017] The data acquisition module is used to acquire enterprise data, which includes dimensional data of historical enterprise profiles and original enterprise data.

[0018] The dimension training module is used to train the enterprise profile model on the dimensional data of historical enterprise profiles to obtain the enterprise profile model after dimension correction.

[0019] The data processing and profiling module is used to process the original enterprise data based on the enterprise profiling model after the dimensions have been corrected, in order to obtain the target enterprise profiling results.

[0020] The output module is used to output the target results of the enterprise profile.

[0021] The present invention also provides an electronic device, the electronic device comprising: a memory and a processor, the memory and the processor being coupled; the memory storing program instructions, which, when executed by the processor, cause the electronic device to perform the enterprise multi-dimensional profiling analysis method of the present invention.

[0022] The present invention also provides a computer-readable storage medium including a computer program that, when run on an electronic device, causes the electronic device to execute the enterprise multi-dimensional profiling analysis method of the present invention.

[0023] The enterprise multi-dimensional profiling analysis system and method provided by this invention trains the enterprise profiling model with corrected dimensions by training on the dimensional data of historical enterprise profiling. Based on the corrected enterprise profiling model, the original enterprise data is processed to obtain and output the enterprise profiling results. The system can train the dimensions of the enterprise profiling model, making the enterprise profiling model more consistent with the enterprise data and the actual situation of the enterprise, thereby improving the comprehensiveness and accuracy of the enterprise profiling results. Attached Figure Description

[0024] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0025] Figure 1 This is a schematic diagram of the enterprise multi-dimensional profiling analysis method of the present invention.

[0026] Figure 2 This is a schematic diagram of the enterprise multi-dimensional profiling analysis device of the present invention. Detailed Implementation

[0027] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0028] Enterprise profiling refers to the process of comprehensively analyzing various data generated during an enterprise's development and operation using big data analytics. Enterprise profiling results typically present a comprehensive and intuitive picture of the enterprise's characteristics and operational status to relevant parties, helping them gain a deeper understanding of the enterprise. The suitability of the enterprise profiling model directly impacts the accuracy and comprehensiveness of the results. However, existing enterprise profiling models often have fixed data dimensions, making it impossible to adjust these dimensions based on the enterprise's actual situation and specific profiling needs. This can lead to discrepancies between the generated enterprise profiling results and the true state of the enterprise.

[0029] Based on this, the present invention provides a multi-dimensional enterprise profiling analysis system and method to generate more personalized enterprise profiling results and improve the comprehensiveness and accuracy of enterprise profiling results.

[0030] This invention provides a method for multi-dimensional enterprise profiling analysis, comprising the following steps:

[0031] Acquire enterprise data, which includes dimensional data of historical enterprise profiles and original enterprise data;

[0032] Enterprise raw data includes various types of data that reflect the actual situation of an enterprise, such as financial data, market data, operational data, employee data, and customer data. Enterprise raw data forms the basis for profiling and analyzing an enterprise. By dividing the raw data into different data dimensions, profiling and analysis can be performed on the enterprise's operating conditions and development, providing a clear and intuitive display of relevant information. Historical enterprise profiles can be from the current enterprise or from other enterprises; the dimensional data of these historical profiles form the foundation for subsequent profiling and analysis based on the raw data.

[0033] The model is trained using dimensional data of historical enterprise profiles to obtain an enterprise profile model with corrected dimensions.

[0034] Training is based on a portion of the enterprise's original data and / or input training requirement data. Correcting the enterprise profile dimensions includes adjusting the dimensional classification types and levels of the original enterprise data.

[0035] In the process of enterprise profiling, it is usually necessary to divide the original enterprise data into different data dimensions to conduct profiling analysis from different perspectives. Existing technologies typically use fixed data dimension division methods for enterprise profiling, meaning that selecting an enterprise profiling model is equivalent to selecting a fixed data dimension division method. However, this method may not match the actual situation of the enterprise, leading to a final enterprise profiling result that fails to reflect the true state of the enterprise. In this embodiment, suitable data for training the data dimensions is selected from the original enterprise data, or the expected goals of the enterprise profiling are input by the client as training requirement data. The original enterprise data and / or training requirement data are input as input parameters to the parameter generation module of the profiling neural network. The parameter generation module outputs model training constraints. The profiling neural network is a trained neural network model suitable for the enterprise profiling process. The model training constraints and historical enterprise profiling dimension data are input as input parameters to the model training module of the profiling neural network. The model training module corrects the division type and level of the enterprise profiling dimensions and outputs a revised enterprise profiling model. For example, historical enterprise profile dimensional data includes a certain number of primary and secondary profile dimensions. The original enterprise data corresponds to the historical primary or secondary dimensions. The model training module modifies the classification type and level of the enterprise profile dimensions based on the original enterprise data and / or the expected goals of the client. After modification, the enterprise profile dimensional data includes X dimension levels, and each dimension level includes Y dimensions. The original enterprise data corresponds to the modified dimensions. The correspondence between the original enterprise data and the modified dimension types and levels differs from the correspondence between the original enterprise data and the historical dimension types and levels. The dimension types and levels of the modified enterprise profile differ from those of the historical enterprise profile. By modifying the dimensions of the enterprise profile model, the original enterprise data corresponds to different profile dimension types and levels, making the modified enterprise profile model more suitable for the actual situation of the enterprise.

[0036] Based on the enterprise profile model with corrected dimensions, the original enterprise data is processed to obtain the target enterprise profile result;

[0037] The enterprise profile dimensions can be further divided into positive evaluation dimensions, negative evaluation dimensions, and non-quantifiable dimensions. Based on these dimensions, the original enterprise data is divided and calculated to obtain the initial enterprise profile results. The initial enterprise profile results include the initial enterprise profile evaluation matrix and the initial enterprise profile score G.

[0038] G = αA + βB + γ, where α is the positive dimension coefficient matrix, β is the negative dimension coefficient matrix, γ is the correction factor corresponding to the unquantifiable dimension, A is the positive dimension matrix, and A's elements are A'''''''''''''''''''''''''''''''"""","' ... i It includes N positive indicators, and B is a negative dimension matrix, where B is the element of the matrix. j It includes M negative indicators; the matrix elements and element indicators conform to the preset sorting rules;

[0039] Pos n For A i Positive indicators in Co n For Pos n The corresponding correction factor;

[0040] Among them, Neg m For B j Negative indicators in;

[0041] By further dividing the enterprise profile dimensions into positive evaluation dimensions, negative evaluation dimensions, and unquantifiable dimensions, and by pre-sorting the matrix elements and element indicators, the initial score of the enterprise profile is calculated. This allows for a more intuitive and accurate display of the enterprise profile results through the enterprise profile evaluation matrix and the initial enterprise score.

[0042] Furthermore, the enterprise profiling results conform to the evaluation target constraints, which are either the evaluation targets input by the demand side or automatically generated evaluation targets. The initial enterprise profiling results are correlated with the evaluation targets. When the initial enterprise profiling results match the evaluation targets, the initial enterprise profiling results are used as the actual enterprise evaluation target results, and subsequent steps are executed. When the initial enterprise profiling results do not match the evaluation targets, the initial enterprise profiling results and the evaluation targets are input as input parameters to the enterprise profiling module of the profiling neural network. The enterprise profiling module corrects the initial enterprise profiling results based on the evaluation target constraints and outputs the enterprise profiling target results, which include the enterprise profiling target evaluation matrix and the enterprise profiling target score. Furthermore, the profiling neural network can be further trained based on the enterprise profiling target results to further improve the accuracy of the enterprise profiling model.

[0043] Output the target results of the enterprise profile.

[0044] Furthermore, the current enterprise profiling model and the original enterprise data will be linked and saved for use in the next enterprise multi-dimensional profiling process and profiling neural network training.

[0045] In accordance with the corresponding enterprise multi-dimensional profiling analysis method, this invention also provides an enterprise multi-dimensional profiling analysis system, including:

[0046] The data acquisition module is used to acquire enterprise data, which includes dimensional data of historical enterprise profiles and original enterprise data.

[0047] The dimension training module is used to train the enterprise profile model on the dimensional data of the historical enterprise profile to obtain the enterprise profile model after dimension correction.

[0048] The data processing and profiling module is used to process the original enterprise data based on the enterprise profiling model after the dimensions are corrected, so as to obtain the target enterprise profiling result;

[0049] The output module is used to output the target results of the enterprise profile.

[0050] The present invention also provides an electronic device, the electronic device comprising: a memory and a processor, the memory and the processor being coupled; the memory storing program instructions, which, when executed by the processor, cause the electronic device to perform the enterprise multi-dimensional profiling analysis method of the present invention.

[0051] The present invention also provides a computer-readable storage medium including a computer program that, when run on an electronic device, causes the electronic device to execute the enterprise multi-dimensional profiling analysis method of the present invention.

[0052] In summary, the enterprise multi-dimensional profiling analysis system and method provided by this invention trains the system on the dimensional data of historical enterprise profiles to obtain a revised enterprise profile model. Based on the revised enterprise profile model, the original enterprise data is processed to obtain and output the enterprise profile results. This allows for the training of the enterprise profile model's dimensions, making the model more closely match the enterprise data and the enterprise's actual situation. This helps enterprises identify their strengths and weaknesses, formulate scientific development strategies, and provides decision support for investors and partners. It also enhances the enterprise's competitiveness and market position, improves the targeting of strengths and weaknesses identification, development strategy formulation, and investment decisions, and increases the comprehensiveness and accuracy of the enterprise profile results.

[0053] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the specific details described above.

Claims

1. A method for multi-dimensional enterprise profiling analysis, characterized in that, include: Acquire enterprise data, which includes dimensional data of historical enterprise profiles and original enterprise data; The model is trained using dimensional data of historical enterprise profiles to obtain an enterprise profile model with corrected dimensions. Based on the enterprise profile model with corrected dimensions, the original enterprise data is processed to obtain the target enterprise profile result; Output the target results of the enterprise profile; The enterprise profiling target results meet the evaluation target constraints, which are the evaluation targets input by the demand side of the enterprise profiling. Furthermore, the raw enterprise data is processed to obtain the target enterprise profile results, including: The enterprise profile dimensions include positive evaluation dimensions, negative evaluation dimensions, and non-quantifiable dimensions. Based on these dimensions, the original enterprise data is divided and calculated to obtain the initial enterprise profile result. The initial enterprise profile result includes an initial evaluation matrix and an initial enterprise profile score G, where G = αA + βB + γ, where α is the positive dimension coefficient matrix, β is the negative dimension coefficient matrix, γ is the correction factor corresponding to the non-quantifiable dimension, and A is the positive dimension matrix, with elements A1 and A2 in A1... i It includes N positive indicators, and B is a negative dimension matrix, where B is the element of the matrix. j Includes M negative indicators; Pos n For A i Positive indicators in Co n For Pos n The corresponding correction factor; Among them, Neg m For B j Negative indicators in; The initial enterprise profile results are correlated with the evaluation target. When the initial enterprise profile results match the evaluation target, the initial enterprise profile results are used as the actual enterprise evaluation target results. When the initial enterprise profile results do not match the evaluation target, the initial enterprise profile results and the evaluation target are used as input parameters into the enterprise profile module of the profile neural network. The enterprise profile module corrects the initial enterprise profile results based on the constraints of the evaluation target and outputs the enterprise profile target results.

2. The method according to claim 1, characterized in that: The historical corporate profile mentioned is either a historical profile of this company or a historical profile of other companies.

3. The method according to claim 1, characterized in that: Correcting the dimensions of the enterprise profile includes adjusting the dimension classification type and dimension classification level of the original enterprise data; the correspondence between the original enterprise data and the corrected dimension type and dimension level is different from the correspondence between the original enterprise data and the historical dimension type and dimension level, and the dimension type and dimension level of the corrected enterprise profile are different from the dimension type and dimension level of the historical enterprise profile. Training on the dimensional data of historical enterprise profiles is based on a portion of the enterprise's original data and / or the input training requirement data.

4. The method according to claim 3, characterized in that: The refinement of enterprise profile dimensions is implemented based on a profile neural network, including: The original enterprise data and / or training requirement data are input as input parameters into the parameter generation module of the portrait neural network. The parameter generation module outputs model training constraints. The portrait neural network is a neural network model that has been trained to be suitable for the enterprise portrait process. The model training constraints and the dimensional data of historical enterprise portraits are input as input parameters into the model training module of the portrait neural network. The model training module corrects the classification type and classification level of the enterprise portrait dimensions and outputs the enterprise portrait model after correction of dimensions.

5. The method according to claim 4, characterized in that, Further includes: The enterprise profile model and the original enterprise data will be linked and saved together; The neural network for characterization is then trained based on the target results of the enterprise characterization.

6. A multi-dimensional enterprise profiling analysis system, characterized in that, include: The data acquisition module is used to acquire enterprise data, which includes dimensional data of historical enterprise profiles and original enterprise data. The dimension training module is used to train the enterprise profile model on the dimensional data of the historical enterprise profile to obtain the enterprise profile model after dimension correction. The data processing and profiling module is used to process the original enterprise data based on the enterprise profiling model after the dimensions are corrected, so as to obtain the target enterprise profiling result; The output module is used to output the target results of the enterprise profile; The enterprise profiling target results meet the evaluation target constraints, which are the evaluation targets input by the demand side of the enterprise profiling. Furthermore, the raw enterprise data is processed to obtain the target enterprise profile results, including: The enterprise profile dimensions include positive evaluation dimensions, negative evaluation dimensions, and non-quantifiable dimensions. Based on these dimensions, the original enterprise data is divided and calculated to obtain the initial enterprise profile result. The initial enterprise profile result includes an initial evaluation matrix and an initial enterprise profile score G, where G = αA + βB + γ, where α is the positive dimension coefficient matrix, β is the negative dimension coefficient matrix, γ is the correction factor corresponding to the non-quantifiable dimension, and A is the positive dimension matrix, with elements A1 and A2 in A1... i It includes N positive indicators, and B is a negative dimension matrix, where B is the element of the matrix. j Includes M negative indicators; Pos n For A i Positive indicators in Co n For Pos n The corresponding correction factor; Among them, Neg m For B j Negative indicators in; The initial enterprise profile results are correlated with the evaluation target. When the initial enterprise profile results match the evaluation target, the initial enterprise profile results are used as the actual enterprise evaluation target results. When the initial enterprise profile results do not match the evaluation target, the initial enterprise profile results and the evaluation target are used as input parameters into the enterprise profile module of the profile neural network. The enterprise profile module corrects the initial enterprise profile results based on the constraints of the evaluation target and outputs the enterprise profile target results.

7. An electronic device, characterized in that, The electronic device includes: a memory and a processor, the memory and the processor being coupled; the memory stores program instructions, which, when executed by the processor, cause the electronic device to perform the enterprise multi-dimensional profiling analysis method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The method includes a computer program that, when run on an electronic device, causes the electronic device to perform the enterprise multi-dimensional profiling analysis method as described in any one of claims 1 to 5.

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