Enterprise management consulting management method and system based on big data technology

Abstract

The application discloses a kind of enterprise management consulting management method and system based on big data technology, which comprises the following steps: obtaining the operating condition data of enterprise;The preprocessed operating condition data of enterprise is analyzed using comprehensive analysis method, and the operating condition score of enterprise is obtained;The corresponding solution is recommended for enterprise using label-based recommendation algorithm combined with the operating condition score of enterprise;The operating condition score of enterprise and the corresponding solution corresponding thereto are fed back to the corresponding enterprise manager.The operating condition data of enterprise can be automatically and comprehensively analyzed, and the corresponding solution is quickly and efficiently recommended for enterprise, which not only effectively reduces the consulting cost of enterprise, but also eliminates the occurrence of enterprise management stagnation, improves the management defects of enterprise, and facilitates the efficient development of enterprise.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise management consultation, in particular to an enterprise management consultation management method and system based on big data technology. BACKGROUND

[0002] Enterprise management is a series of activities such as planning, organizing, commanding, coordinating and controlling of enterprise production and operation activities, and is an objective requirement of socialized production. Enterprise management is to utilize human, material, financial and information resources of the enterprise as much as possible to achieve the goal of more, faster, better and less, to achieve the maximum input-output efficiency, and the operation efficiency of the enterprise is greatly enhanced by enterprise management; the enterprise has a clear development direction; and each employee can fully develop their potential.

[0003] Enterprise management consultation is also called "enterprise diagnosis". Enterprise management experts use scientific methods to find out the problems in enterprise management on the basis of investigation and analysis, propose specific improvement plans, and guide implementation to help enterprises improve management. Its characteristics are: (1) service; (2) guidance; (3) scientific; (4) practical; (5) arduous. Management consultation is an investment behavior for enterprises. Through the services provided by management consultation companies, the management level of enterprises is improved, and the operation efficiency of enterprises is improved.

[0004] At present, the traditional enterprise management consultation is usually answered by relevant consulting experts, and due to the limited number of consulting experts, high compensation and long waiting time are often required to wait for consultation, which is easy to cause enterprise management stagnation. In addition, the diagnosis method of the traditional enterprise management consultation service in the diagnosis and analysis of enterprise problems is one-sided, it is difficult to clearly analyze the problems existing in the enterprise, and it is impossible to improve the management defects of the enterprise, which greatly hinders the efficient development of the enterprise.

[0005] In view of the problems in the related art, no effective solution has been proposed so far. SUMMARY

[0006] In view of the problems in the related art, the present application proposes an enterprise management consultation management method and system based on big data technology to overcome the above technical problems existing in the prior art.

[0007] Therefore, the specific technical solutions adopted by the present application are as follows:

[0008] According to one aspect of the present application, an enterprise management consultation management method based on big data technology is provided, which comprises the following steps:

[0009] S1, obtaining the operation data of the enterprise, and preprocessing the obtained operation data;

[0010] S2, analyze the pre-processed enterprise operation data by using a comprehensive analysis method to obtain an operation score of the enterprise;

[0011] S3, recommend a corresponding solution for the enterprise by using a label-based recommendation algorithm combined with the operation score of the enterprise;

[0012] S4, feed back the operation score of the enterprise and the corresponding solution to a corresponding enterprise manager.

[0013] Further, the operation data of the enterprise is obtained, and the obtained operation data is pre-processed, including the following steps:

[0014] S11, obtaining the operation data of the enterprise, and cleaning abnormal data in the operation data;

[0015] S12, classifying and storing the cleaned operation data of the enterprise to obtain production data, financial data and marketing data of the enterprise.

[0016] Further, the operation data of the enterprise is obtained, and the obtained operation data is pre-processed, including the following steps:

[0017] S21, obtaining the pre-processed operation data of the enterprise to obtain production data, financial data and marketing data of the enterprise;

[0018] S22, respectively analyzing and processing the production data, the financial data and the marketing data of the enterprise to obtain production state information, financial state information and marketing condition information of the enterprise;

[0019] S23, calculating the operation score of the enterprise based on the production state information, the financial state information and the marketing condition information of the enterprise.

[0020] Further, the production data, the financial data and the marketing data of the enterprise are respectively analyzed and processed to obtain the production state information, the financial state information and the marketing condition information of the enterprise, including:

[0021] the production data of the enterprise is analyzed and processed by using a production state analysis method to obtain corresponding production state information of the enterprise;

[0022] the financial data of the enterprise is analyzed and processed by using a financial condition analysis method to obtain corresponding financial state information of the enterprise;

[0023] the marketing data of the enterprise is analyzed and processed by using a marketing state analysis method to obtain marketing condition information of the enterprise.

[0024] Further, the production state analysis method is used to analyze and process the production data of the enterprise, and the corresponding production state information of the enterprise is obtained, including the following steps:

[0025] Obtain the number of equipment running, the number of staff on duty and the number of product finished products in the enterprise production data within a preset time;

[0026] Calculate the production coefficient of the enterprise within the preset time based on the number of equipment running, the number of staff on duty and the number of product finished products;

[0027] Compare the calculated production coefficient with the preset production coefficient standard interval value, if the production coefficient is less than the minimum value in the production coefficient standard interval value, a production excellent signal is obtained, if the production coefficient is within the production coefficient standard interval value, a production normal signal is obtained, and if the production coefficient is greater than the maximum value in the production coefficient standard interval value, a production abnormal signal is obtained.

[0028] Further, the financial state analysis method is used to analyze and process the financial data of the enterprise, and the corresponding financial state information of the enterprise is obtained, including the following steps:

[0029] Obtain the financial data of the enterprise within a preset time, and calculate the sales net profit margin, gross profit margin and asset net profit margin of the enterprise within the preset time by using the ratio method;

[0030] Compare the sales net profit margin, gross profit margin and asset net profit margin of the enterprise with the predicted sales net profit margin interval value, predicted gross profit margin interval value and predicted asset net profit margin interval value respectively, if two or more of the sales net profit margin, gross profit margin and asset net profit margin are greater than the maximum value in the predicted profit margin interval value, a financial excellent signal is obtained, if two or more of the sales net profit margin, gross profit margin and asset net profit margin are less than the minimum value in the predicted profit margin interval value, a financial abnormal signal is obtained, and the rest is a financial normal signal.

[0031] Further, the marketing state analysis method is used to analyze and process the marketing data of the enterprise, and the marketing state information of the enterprise is obtained, including the following steps:

[0032] Obtain the market share, influence duration and sales growth rate of the product in the enterprise marketing data within a preset time;

[0033] Compare the market share, influence duration and sales growth rate of the product of the enterprise with the predicted market share interval value, predicted influence duration interval value and predicted sales growth rate interval value of the product respectively, if two or more of the market share, influence duration and sales growth rate of the product are greater than the maximum value in the predicted interval value, a marketing excellent signal is obtained, if two or more of the market share, influence duration and sales growth rate of the product are less than the minimum value in the predicted interval value, a marketing abnormal signal is obtained, and the rest is a marketing normal signal.

[0034] Further, the calculation of the business operation state score value of the enterprise based on the enterprise production state information, financial state information and marketing state information comprises the following steps:

[0035] S231, respectively set the scores of the excellent signal, normal signal and abnormal signal in the state signal, and obtain the production state score, financial state score and marketing state score of the enterprise;

[0036] S232, calculate the business operation state score value of the enterprise based on the production state signal score, financial state signal score and marketing state signal score of the enterprise, wherein the calculation formula of the business operation state score value of the enterprise is:

[0037] S = α1·X + α2·Y + α3·Z

[0038] In the formula, S represents the business operation state score value of the enterprise, X represents the production state score of the enterprise, Y represents the financial state score of the enterprise, Z represents the marketing state score of the enterprise, α1, α2 and α3 respectively represent the weights of the production state, financial state and marketing state of the enterprise, and α1 + α2 + α3 = 1.

[0039] Further, the recommendation of the corresponding solution for the enterprise by using the label-based recommendation algorithm combined with the business operation state score value of the enterprise comprises the following steps:

[0040] S31, obtain the business operation state score value of the enterprise, and obtain a plurality of solutions corresponding to the score value from the historical database;

[0041] S32, obtain the label of the enterprise operation state data corresponding to each solution and the score of the solution by the user, and obtain a user score data set;

[0042] S33, judge whether the plurality of solutions obtained all have scores, if not, convert the label of the enterprise operation state data corresponding to the solution without score into a score, pre-fill the user score data set, and establish a solution score matrix at the same time;

[0043] S34, calculate the similarity between the operation state data of the target enterprise and the operation state data of the enterprise corresponding to the solution to be recommended, and select the top K neighbors to obtain a solution recommendation data set;

[0044] S35, recommend the corresponding solution for the enterprise from the solution recommendation data set combined with the solution score matrix.

[0045] According to another aspect of the present application, there is provided a big data technology-based enterprise management consulting management system, which comprises an operation data acquisition module, an operation condition score value calculation module, a solution recommendation module and an information feedback module.

[0046] The operation data acquisition module is configured to acquire operation condition data of an enterprise and pre-process the acquired operation condition data.

[0047] The operation condition score value calculation module is configured to analyze the pre-processed operation condition data of the enterprise by using a comprehensive analysis method to obtain an operation condition score value of the enterprise.

[0048] The solution recommendation module is configured to recommend a corresponding solution for the enterprise by using a tag-based recommendation algorithm in combination with the operation condition score value of the enterprise.

[0049] The information feedback module feeds back the operation condition score value of the enterprise and the corresponding solution to a corresponding enterprise manager.

[0050] The present application has the following advantages:

[0051] 1) By using a comprehensive analysis method to analyze the operation condition data of the enterprise from production data, financial data and marketing data of the enterprise, an operation condition score value of the enterprise is obtained, and a corresponding solution for the enterprise is recommended by using a tag-based recommendation algorithm in combination with the operation condition score value of the enterprise, so that a more accurate recommendation result can be obtained, and the operation condition score value of the enterprise and the corresponding solution are fed back to the corresponding enterprise manager, so that the enterprise manager can manage the enterprise according to the recommended solution. Compared with the traditional artificial expert solution mode, the present application can automatically and comprehensively analyze the operation condition data of the enterprise, so that the corresponding solution for the enterprise can be quickly and efficiently recommended, which not only effectively reduces the consulting cost of the enterprise, but also avoids the occurrence of enterprise management stagnation, thereby effectively improving the management defects of the enterprise and facilitating the efficient development of the enterprise.

[0052] 2) The corresponding solution is recommended for the enterprise by combining the business condition score value of the enterprise with the label-based recommendation algorithm, so that the corresponding solution is accurately recommended for the enterprise from the several solutions corresponding to the score value in the historical database according to the business condition score value of the enterprise, and the label of the un-scored solution is converted into the corresponding score by using the label-based recommendation algorithm, and the user score data set is pre-filled, thereby effectively reducing the influence of the sparse score data on the recommendation result, and further effectively improving the recommendation accuracy of the solution. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0054] Figure 1 is a flow chart of an enterprise management consulting management method based on big data technology according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] To further illustrate the embodiments, the present application provides drawings, which are part of the disclosure of the present application, mainly used to illustrate the embodiments, and can explain the operating principle of the embodiments in conjunction with the related description of the specification. Those skilled in the art should understand other possible embodiments and advantages of the present application by referring to these contents. The components in the drawings are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0056] According to an embodiment of the present application, an enterprise management consulting management method and system based on big data technology are provided.

[0057] The present application will be further described in conjunction with the drawings and specific embodiments, as shown in Figure 1 According to an embodiment of the present application, an enterprise management consulting management method based on big data technology is provided, which comprises the following steps:

[0058] S1, obtaining the business condition data of the enterprise, and preprocessing the obtained business condition data;

[0059] The obtaining of the business condition data of the enterprise and the preprocessing of the obtained business condition data comprises the following steps:

[0060] S11, acquire the business status data of an enterprise, and clean up abnormal data in the business status data;

[0061] S12, store the cleaned business status data of the enterprise in a classified manner to obtain production data, financial data and marketing data of the enterprise.

[0062] S2, analyze the pretreated business status data of the enterprise by using a comprehensive analysis method to obtain an operating status score of the enterprise;

[0063] The step of analyzing the pretreated business status data of the enterprise by using a comprehensive analysis method to obtain an operating status score of the enterprise includes the following steps:

[0064] S21, acquire the pretreated business status data of the enterprise to obtain production data, financial data and marketing data of the enterprise;

[0065] S22, analyze and process the production data, financial data and marketing data of the enterprise respectively to obtain production state information, financial state information and marketing condition information of the enterprise;

[0066] Specifically, the step of analyzing and processing the production data, financial data and marketing data of the enterprise respectively to obtain production state information, financial state information and marketing condition information of the enterprise includes:

[0067] 1) analyzing and processing the production data of the enterprise by using a production state analysis method to obtain corresponding production state information of the enterprise, including the following steps:

[0068] acquiring the number of devices in operation, the number of staff on duty and the number of product finished products of the enterprise within a preset time;

[0069] calculating the production coefficient of the enterprise within the preset time based on the number of devices in operation, the number of staff on duty and the number of product finished products, wherein the production coefficient = (the number of devices in operation / the number of product finished products) + (the number of staff on duty / the number of product finished products);

[0070] comparing the calculated production coefficient with a preset production coefficient standard interval value, if the production coefficient is less than the minimum value in the production coefficient standard interval value, a production excellent signal is obtained, if the production coefficient is within the production coefficient standard interval value, a production normal signal is obtained, and if the production coefficient is greater than the maximum value in the production coefficient standard interval value, a production abnormal signal is obtained.

[0071] 2) analyzing and processing the financial data of the enterprise by using a financial condition analysis method to obtain corresponding financial state information of the enterprise, including the following steps:

[0072] Obtaining the financial data of the enterprise in the preset time, and calculating the net profit margin, gross profit margin and asset net profit margin of the enterprise in the preset time by using the ratio method;

[0073] Comparing the net profit margin, gross profit margin and asset net profit margin of the enterprise with the predicted net profit margin interval value, predicted gross profit margin interval value and predicted asset net profit margin interval value respectively, if two or more of the net profit margin, gross profit margin and asset net profit margin are greater than the maximum value in the predicted profit margin interval value, a financial excellent signal is obtained, if two or more of the net profit margin, gross profit margin and asset net profit margin are less than the minimum value in the predicted profit margin interval value, a financial abnormal signal is obtained, and the rest is a financial normal signal.

[0074] Specifically, the net profit margin: the net profit margin refers to the percentage of net profit to sales revenue, and its calculation formula is: sales margin = (net profit / sales revenue) x 100%; this index reflects how much net profit each yuan of sales revenue brings, and is also a ratio of the income obtained by investors from sales revenue. Low net profit margin indicates that the management authorities of the enterprise have failed to create sufficient sales revenue or have failed to control costs and expenses.

[0075] Asset net profit margin: the asset net profit margin is the percentage of net profit to average total assets, and its calculation formula is: asset net profit margin = sales revenue / average total assets x annual net profit / sales revenue; this index indicates the utilization effect of the assets of the enterprise, and the higher the index, the better the utilization effect of the assets, indicating that the enterprise has achieved good results in increasing income and saving expenses and accelerating capital turnover.

[0076] Gross profit margin: the profit margin is the balance after deducting the product sales cost from the sales revenue, and its calculation formula is: gross profit margin = gross profit / sales revenue; this index reflects the level of production efficiency of the enterprise, which is the source of the profit of the enterprise. The change of gross profit margin is related to many factors, and is the comprehensive result of the change of sales revenue and product cost. When the economic situation changes and the product cost rises, the product selling price is often difficult to adjust in time, thereby showing the decrease of the gross profit margin.

[0077] 3) Using the marketing state analysis method to analyze and process the marketing data of the enterprise to obtain the marketing state information of the enterprise, including the following steps:

[0078] Obtaining the market share, influence duration and sales growth rate of the product in the marketing data of the enterprise in the preset time;

[0079] The market share, influence duration and sales growth rate of the product of the enterprise are compared with the predicted market share interval value, predicted influence duration interval value and predicted sales growth rate interval value respectively, if two or more of the market share, influence duration and sales growth rate of the product are greater than the maximum value in the predicted interval value, a marketing excellent signal is obtained, if two or more of the market share, influence duration and sales growth rate of the product are less than the minimum value in the predicted interval value, a marketing abnormal signal is obtained, and the rest is a marketing normal signal.

[0080] S23, calculating the business state score value of the enterprise based on the production state information, financial state information and marketing condition information of the enterprise.

[0081] Specifically, the step of calculating the business state score value of the enterprise based on the production state information, financial state information and marketing condition information of the enterprise comprises the following steps:

[0082] S231, setting the scores of the excellent signal, normal signal and abnormal signal in the state signal (for example, the score of the excellent signal is 3 points, the score of the normal signal is 2 points, and the score of the abnormal signal is 1 point), and obtaining the production state score value, financial state score value and marketing condition score value of the enterprise;

[0083] S232, calculating the business state score value of the enterprise based on the production state signal score value, financial state signal score value and marketing condition signal score value of the enterprise, wherein the calculation formula of the business state score value of the enterprise is:

[0084] S = α1·X + α2·Y + α3·Z

[0085] In the formula, S represents the business state score value of the enterprise, X represents the production state score value of the enterprise, Y represents the financial state score value of the enterprise, and Z represents the marketing condition score value of the enterprise, α1, α2 and α3 respectively represent the weights of the production state, financial state and marketing condition of the enterprise, and α1 + α2 + α3 = 1, specifically, in the embodiment, the weights of the production state, financial state and marketing condition of the enterprise can be set according to the importance degrees of the three in the business state score process of each enterprise, for example, the weight of the production state of the enterprise can be 0.25, the weight of the financial state can be 0.50, and the weight of the marketing condition can be 0.25.

[0086] S3, recommending a corresponding solution for the enterprise by using a label-based recommendation algorithm combined with the business condition score value of the enterprise;

[0087] Since there are multiple solutions corresponding to the business condition score value of the enterprise in the historical database, the label-based recommendation algorithm in the embodiment further screens the initially recommended solutions, so that a more accurate recommendation result can be obtained.

[0088] Specifically, the recommendation algorithm based on the label combines the business operation condition score value of the enterprise to recommend the corresponding solution for the enterprise, including the following steps:

[0089] S31, obtaining the business operation condition score value of the enterprise, and obtaining a plurality of solutions corresponding to the score value (i.e. the solutions to be recommended) from the historical database;

[0090] S32, obtaining the label of the enterprise operation state data corresponding to each solution and the score of the solution by the user, to obtain a user score data set;

[0091] S33, judging whether the plurality of solutions obtained exist scores, if not, the label of the enterprise operation state data corresponding to the solution without score is converted into score, and the user score data set is pre-filled, and a solution score matrix is established;

[0092] Because the user can label at will, the label has certain confusion, therefore, in the embodiment, the label is first preprocessed, in the process, the label is standardized and trimmed by Porter Stemming algorithm. For the user score data set and the user label data set, the user labels the enterprise operation state data but does not score the solution, in the embodiment, the label of the enterprise operation state data by the user (i.e. the manager of the enterprise in the historical database) is converted into the score of the solution by the user, the user score data set is pre-filled, which helps to alleviate the problem of low recommendation quality caused by the sparsity of the score data set.

[0093] Specifically, the label conversion into score is as follows:

[0094] 1) The user has used the label for two or more different enterprise operation state data, and there is at least one score record data of the label, then:

[0095]

[0096] 2) When the user has no score record of the label, it is judged whether there is the same label in the enterprise operation state data, if yes, then:

[0097]

[0098] If not, then:

[0099]

[0100] Wherein, R ui represents the pre-filled solution score, denotes the average score of the user on all the labeled tags t, denotes the average score of the user, denotes the average score of all the ratings of the solution labeled tag t, denotes the average score of the solution, μ denotes a weighting coefficient, and the range is (0, 1).

[0101] S34, calculate the similarity between the business state data of the target enterprise and the business state data of the corresponding enterprise of the solution to be recommended, select the top K neighbors, and obtain a recommendation data set of the solution;

[0102] S35, recommend the corresponding solution to the enterprise from the recommendation data set of the solution in combination with the solution rating matrix.

[0103] S4, feed the business state score value of the enterprise and the corresponding solution to the corresponding enterprise manager.

[0104] The manager of the enterprise can know the current business status of the company at any time through the business state score value, and can manage and rectify the problems existing in the company in a timely manner according to the recommended solution when the business status has a problem, so as to effectively solve the current problems of the company.

[0105] According to another embodiment of the application, an enterprise management consulting management system based on big data technology is provided, which comprises an operating data acquisition module, an operating state score value calculation module, a solution recommendation module and an information feedback module.

[0106] The operating data acquisition module is configured to acquire the operating state data of the enterprise and pre-process the acquired operating state data.

[0107] The operating state score value calculation module is configured to analyze the pre-processed operating state data of the enterprise by using a comprehensive analysis method to obtain the operating state score value of the enterprise.

[0108] The solution recommendation module is configured to recommend the corresponding solution to the enterprise by using a label-based recommendation algorithm in combination with the operating state score value of the enterprise.

[0109] The information feedback module feeds the operating state score value of the enterprise and the corresponding solution to the corresponding enterprise manager.

[0110] To sum up, by means of the technical scheme of the present application, the business operation condition data of an enterprise is analyzed from the production data, financial data and marketing data of the enterprise by using a comprehensive analysis method, a business operation condition score of the enterprise is obtained, and a corresponding solution is recommended for the enterprise by using a label-based recommendation algorithm combined with the business operation condition score of the enterprise, so that a more accurate recommendation result can be obtained, and the business operation condition score of the enterprise and the corresponding solution corresponding thereto can be fed back to the corresponding enterprise manager, so that the enterprise manager can manage the enterprise according to the recommended solution. Compared with the traditional artificial expert solution mode, the present application can automatically and comprehensively analyze the business operation condition data of the enterprise, so that the corresponding solution can be quickly and efficiently recommended for the enterprise, which not only effectively reduces the consulting cost of the enterprise, but also eliminates the need for the enterprise to wait for a long time, effectively avoiding the occurrence of enterprise management stagnation, thereby effectively improving the management defects of the enterprise and facilitating the efficient development of the enterprise.

[0111] In addition, by using a label-based recommendation algorithm combined with the business operation condition score of the enterprise to recommend a corresponding solution for the enterprise, a number of solutions corresponding to the score can be obtained from a historical database according to the business operation condition score of the enterprise, and a corresponding solution can be accurately recommended for the enterprise from the obtained number of solutions by using a label-based recommendation algorithm, and the labels of the business operation condition data of the un-scored solutions are converted into corresponding scores by using a label-based recommendation algorithm, and the user score data set is pre-filled, so that the influence of the sparse score data on the recommendation result is effectively reduced, and the recommendation accuracy of the solution is effectively improved.

[0112] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A big data technology-based enterprise management consulting management method, characterized in that, The method comprises the following steps: S1, obtaining the business operation data of an enterprise and preprocessing the obtained business operation data; S2, analyzing the preprocessed business operation data of the enterprise by using a comprehensive analysis method to obtain a business operation score value; the step of analyzing the preprocessed business operation data of the enterprise by using the comprehensive analysis method to obtain the business operation score value comprises the following steps: S21, obtaining the preprocessed business operation data of the enterprise to obtain production data, financial data and marketing data of the enterprise; S22, respectively analyzing and processing the production data, the financial data and the marketing data of the enterprise to obtain production state information, financial state information and marketing condition information of the enterprise; S23, calculating the business operation score value of the enterprise based on the production state information, the financial state information and the marketing condition information of the enterprise; the step of calculating the business operation score value of the enterprise based on the production state information, the financial state information and the marketing condition information of the enterprise comprises the following steps: S231, setting the scores of excellent signals, normal signals and abnormal signals in the state signals respectively, and obtaining production state scores, financial state scores and marketing condition scores of the enterprise; S232, calculating the business operation score value of the enterprise based on the production state signal scores, the financial state signal scores and the marketing condition signal scores, wherein the calculation formula of the business operation score value of the enterprise is: S=α1·X+α2·Y+α3·Z In the formula, S represents the business operation score value of the enterprise, X represents the production state score of the enterprise, Y represents the financial state score of the enterprise, Z represents the marketing condition score of the enterprise, α1, α2 and α3 respectively represent the weights of the production state, the financial state and the marketing condition of the enterprise, and α1+α2+α3=1; S3, recommending a corresponding solution for the enterprise by using a label-based recommendation algorithm combined with the business operation score value of the enterprise; wherein the step of recommending a corresponding solution for the enterprise by using the label-based recommendation algorithm combined with the business operation score value of the enterprise comprises the following steps: S31, obtaining the business operation score value of the enterprise, and obtaining a plurality of solutions corresponding to the score value from a historical database; S32, obtaining the labels of the enterprise business state data corresponding to each solution and the scores of the solution by users to obtain a user score data set; S33, judging whether the plurality of solutions obtained all have scores, if not, converting the labels of the enterprise business state data corresponding to the solutions without scores into scores, prefilling the user score data set, and establishing a solution score matrix; the step of converting the labels into scores is as follows: 1) the user has used the label for two or more different enterprise business state data, and there is at least one score record data for the label, then: 2) if the user has no score record for the label, then judge whether there is the same label in the enterprise business state data, if yes, then: if not, then: wherein R ui denotes the average score of the solution, denotes the average score of the solution by the user for all labeled tags t, denotes the average score of the user, denotes the average score of all scores of the solution labeled tag t, denotes the average score of the solution, μ denotes a weighting factor, ranging from (0, 1); S34, calculating the similarity between the business state data of the target enterprise and the business state data of the enterprise corresponding to the solution to be recommended, and selecting the first K neighbors to obtain a solution recommendation data set; S35, the solution recommendation scoring matrix recommends a corresponding solution for the enterprise from the solution recommendation data set; S4, the operating condition score of the enterprise and the corresponding solution are fed back to the corresponding enterprise manager.

2. The enterprise management consulting management method based on big data technology according to claim 1, characterized in that, The operating condition data of the enterprise is acquired, and the acquired operating condition data is preprocessed, including the following steps: S11, acquire the operating condition data of the enterprise, and clean the abnormal data in the operating condition data; S12, the cleaned enterprise operating condition data is classified and stored to obtain production data, financial data and marketing data of the enterprise. 3.The enterprise management consulting management method based on big data technology according to claim 1, characterized in that, The production data, financial data and marketing data of the enterprise are respectively analyzed and processed to obtain production state information, financial state information and marketing condition information of the enterprise, including: The production data of the enterprise is analyzed and processed by using the production state analysis method to obtain the corresponding production state information of the enterprise; The financial data of the enterprise is analyzed and processed by using the financial condition analysis method to obtain the corresponding financial state information of the enterprise; The marketing data of the enterprise is analyzed and processed by using the marketing state analysis method to obtain the marketing condition information of the enterprise.

4. The enterprise management consulting management method based on big data technology according to claim 3, characterized in that, The production data of the enterprise is analyzed and processed by using the production state analysis method to obtain the corresponding production state information of the enterprise, including the following steps: Acquire the number of equipment running, the number of staff on duty and the number of product finished products in the production data of the enterprise within a preset time; Calculate the production coefficient of the enterprise within a preset time based on the number of equipment running, the number of staff on duty and the number of product finished products; Compare the calculated production coefficient with the preset production coefficient standard interval value, if the production coefficient is less than the minimum value in the production coefficient standard interval value, a production excellent signal is obtained, if the production coefficient is within the production coefficient standard interval value, a production normal signal is obtained, and if the production coefficient is greater than the maximum value in the production coefficient standard interval value, a production abnormal signal is obtained.

5. The enterprise management consulting management method based on big data technology according to claim 4, characterized in that, The financial data of the enterprise is analyzed and processed by using the financial condition analysis method to obtain the corresponding financial state information of the enterprise, including the following steps: Acquire the financial data of the enterprise within a preset time, and calculate the sales net profit margin, the gross profit margin and the asset net profit margin of the enterprise within a preset time by using the ratio method; Compare the sales net profit margin, the gross profit margin and the asset net profit margin of the enterprise with the predicted sales net profit margin interval value, the predicted gross profit margin interval value and the predicted asset net profit margin interval value respectively, if two or more of the sales net profit margin, the gross profit margin and the asset net profit margin are greater than the maximum value in the predicted profit margin interval value, a financial excellent signal is obtained, if two or more of the sales net profit margin, the gross profit margin and the asset net profit margin are less than the minimum value in the predicted profit margin interval value, a financial abnormal signal is obtained, and the rest is a financial normal signal.

6. The enterprise management consulting management method based on big data technology according to claim 5, characterized in that, The marketing data of the enterprise is analyzed and processed by using the marketing state analysis method to obtain the marketing condition information of the enterprise, including the following steps: Acquire the market share, influence duration and sales growth rate of the product in the marketing data of the enterprise within a preset time; The market share, influence duration and sales growth rate of the product of the enterprise are compared with the predicted market share interval value, predicted influence duration interval value and predicted sales growth rate interval value respectively, if two or more of the market share, influence duration and sales growth rate of the product are greater than the maximum value in the predicted interval value, a marketing excellent signal is obtained, if two or more of the market share, influence duration and sales growth rate of the product are less than the minimum value in the predicted interval value, a marketing abnormal signal is obtained, and the rest is a marketing normal signal.

7. A big data technology-based enterprise management consulting management system for implementing the steps of the big data technology-based enterprise management consulting management method according to any one of claims 1-6, characterized in that, The system comprises a business data acquisition module, a business condition score value calculation module, a solution recommendation module and an information feedback module; The business data acquisition module is configured to acquire business condition data of an enterprise and pre-process the acquired business condition data; The business condition score value calculation module is configured to analyze the pre-processed business condition data of the enterprise by using a comprehensive analysis method to obtain a business condition score value of the enterprise; The solution recommendation module is configured to recommend a corresponding solution for the enterprise by using a label-based recommendation algorithm in combination with the business condition score value of the enterprise; The information feedback module feeds back the business condition score value of the enterprise and the corresponding solution to a corresponding enterprise manager.

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