Enterprise asset intelligent analysis method and system based on big data

Abstract

The invention relates to the technical field of big data analysis and mining, in particular to an enterprise asset intelligent analysis method and system based on big data, and the method comprises the steps: obtaining an asset distribution target direction set of an enterprise and the identity information of the enterprise; according to the identity information of the enterprise and the asset distribution target direction set, determining an enterprise-related industrial data set; substituting the asset distribution target direction set of the enterprise and the enterprise-related industrial data set into the intelligent asset analysis model to obtain at least one suggestion for asset distribution of the enterprise and reason analysis, and sending the suggestion and reason analysis to a management terminal of the enterprise; obtaining a feedback result of the management terminal of the enterprise; and the feedback result is fed back to the intelligent asset analysis model, suggestions for asset distribution of the enterprise are selectively adjusted, or an analysis result of each suggestion of which the feedback result is approved is obtained and sent to a management terminal of the enterprise. More accurate and more effective asset distribution suggestions are provided for enterprises, so that the operation efficiency and economic benefits of the enterprises are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis and mining, and in particular to the technical field of enterprise asset intelligent analysis, and in particular to a method and system for enterprise asset intelligent analysis based on big data. Background Art

[0002] In economic activities, the asset distribution decision-making plan of an enterprise has a direct impact on the sustainable development of the enterprise. However, the intelligent analysis method of enterprise assets in the existing technology generally requires experienced managers to make decisions, but this analysis method often lacks real-time, dynamic and targeted, which easily leads to the lag of resource distribution and even reduces the efficiency of the enterprise.

[0003] Therefore, there is an urgent need to study a method and system for intelligent analysis of enterprise assets based on big data that can quickly and accurately gain insight into the distribution trend of economic resources, thereby conducting timely intelligent analysis of enterprise assets and thus improving the operational efficiency and economic benefits of the enterprise. Summary of the invention

[0004] Based on this, the present invention proposes a method and system for intelligent analysis of enterprise assets based on big data, which solves the technical problems that the intelligent analysis methods of enterprise assets in the existing technology often lack real-time, dynamic and targeted performance, easily lead to lagging resource distribution, and even reduce enterprise benefits.

[0005] According to a first aspect of the present invention, there is provided a method for intelligent analysis of enterprise assets based on big data, the method comprising the following steps:

[0006] Acquire an asset distribution target direction set of an enterprise and identity information of the enterprise, wherein the asset distribution target direction set includes at least one target project and an asset target ratio of each target project;

[0007] Determine an enterprise-related industry data set according to the enterprise's asset distribution target direction set and the enterprise's identity information, wherein the enterprise-related industry data set at least includes identity information of multiple target enterprises matching the enterprise, identity information of multiple preset marked target enterprises, and a comprehensive portrait of each target enterprise or marked target enterprise;

[0008] Substitute the enterprise's asset distribution target direction set and the enterprise's related industry data set into the asset intelligent analysis model to obtain at least one suggestion on the enterprise's asset distribution and the reason analysis for each suggestion and send it to the enterprise's management terminal;

[0009] Obtaining feedback results from the management terminal of the enterprise, wherein the feedback results include approval or rejection of each suggestion, and reasons for approval or rejection of each feedback result;

[0010] The feedback results are fed back to the asset intelligent analysis model, so that according to each suggestion, the feedback results of each suggestion and the cause analysis of each suggestion, the suggestions for the enterprise's asset distribution are selectively adjusted, or a comprehensive prediction is made for the suggestions whose feedback results are approved, and the analysis results of each suggestion whose feedback results are approved are obtained and sent to the enterprise's management terminal, wherein the analysis results include the implementation plan and execution time of each suggestion.

[0011] According to an embodiment of the present invention, determining the enterprise-related industry data set according to the enterprise's asset distribution target direction set and the enterprise's identity information includes:

[0012] According to the asset distribution target direction set of the enterprise and the identity information of the enterprise, determine the identity information of multiple target enterprises matching the enterprise, the identity information of multiple preset marked target enterprises, the asset data set of each target enterprise and the asset data set of each marked target enterprise;

[0013] Determine a comprehensive profile of each target enterprise or the marked target enterprise based on the asset data set of each target enterprise and the asset data set of each marked target enterprise;

[0014] The enterprise-related industry data set is composed of the identity information of multiple target enterprises matching the enterprise, the identity information of multiple preset marked target enterprises, and a comprehensive portrait of each target enterprise or marked target enterprise.

[0015] According to an embodiment of the present invention, the step of determining the identity information of multiple target enterprises matching the enterprise, the identity information of multiple preset marked target enterprises, the asset data set of each target enterprise, and the asset data set of each marked target enterprise according to the asset distribution target direction set of the enterprise and the identity information of the enterprise includes:

[0016] According to the identity information of the enterprise, a current asset distribution direction set of the enterprise is obtained, wherein the current asset distribution direction set of the enterprise includes a plurality of current distribution projects and a current asset proportion of each current distribution project;

[0017] According to the current asset distribution direction set of the enterprise, determine multiple target enterprises matching the enterprise and an asset data set of each target enterprise, wherein the asset data set includes publicly published asset data and comprehensive analysis asset data;

[0018] Obtaining identity information of multiple preset marked target enterprises;

[0019] According to the preset identity information of multiple marked target enterprises, an asset data set of each marked target enterprise is obtained.

[0020] According to an embodiment of the present invention, determining a plurality of target enterprises matching the enterprise and an asset data set of each target enterprise according to the current asset distribution direction set of the enterprise includes:

[0021] Determining identity information of a plurality of pending target enterprises related to the enterprise according to a current asset distribution direction set of the enterprise;

[0022] According to the identity information of multiple pending target enterprises related to the enterprise, an asset data set of each pending target enterprise is obtained, wherein the asset data set includes publicly disclosed asset data and comprehensive analysis asset data;

[0023] Each of the pending target enterprises is screened according to the asset data set of each pending target enterprise to obtain a plurality of target enterprises matching the enterprise.

[0024] According to an embodiment of the present invention, determining a comprehensive profile of each target enterprise or a marked target enterprise based on the asset data set of each target enterprise and the asset data set of each marked target enterprise includes:

[0025] Generate a primary asset distribution profile corresponding to each target enterprise or each marked target enterprise according to the publicly disclosed asset data in the asset data set of each target enterprise or each marked target enterprise;

[0026] According to the comprehensive analysis of the asset data in the asset data set of each target enterprise or each marked target enterprise, the primary asset distribution portrait corresponding to each target enterprise or each marked target enterprise is corrected respectively to obtain a comprehensive portrait of each target enterprise or marked target enterprise.

[0027] According to an embodiment of the present invention, the step of substituting the enterprise's asset distribution target direction set and the enterprise's related industry data set into the asset intelligent analysis model, obtaining at least one suggestion on the enterprise's asset distribution and the reason analysis for each suggestion and sending them to the enterprise's management terminal includes:

[0028] According to the current asset distribution direction set of the enterprise and the asset distribution target direction set of the enterprise, an asset distribution target portrait of the enterprise is obtained, wherein the bottom layer of the asset distribution target portrait of the enterprise is the current asset distribution portrait of the enterprise drawn with solid lines, and the upper layer of the asset distribution target portrait of the enterprise is the asset distribution target direction portrait of the enterprise drawn with dotted lines;

[0029] According to the target direction set of the asset distribution of the enterprise, a collective portrait of the target enterprise and a collective portrait of the marked target enterprise are obtained;

[0030] According to the target portrait of the asset distribution of the enterprise, the collective portrait of the target enterprise and the collective portrait of the marked target enterprise, at least one suggestion on the asset distribution of the enterprise and the reason analysis of each suggestion are obtained;

[0031] At least one suggestion on the asset distribution of the enterprise and the reason analysis of each suggestion are sent to the management terminal of the enterprise.

[0032] According to an embodiment of the present invention, the feedback result is fed back to the asset intelligent analysis model, so that according to each suggestion, the feedback result of each suggestion and the reason analysis of each suggestion are analyzed, the suggestion on the asset distribution of the enterprise is selectively adjusted, or the suggestion with the approved feedback result is comprehensively predicted, and the analysis result of each suggestion with the approved feedback result is obtained and sent to the management terminal of the enterprise, which includes:

[0033] When at least one of the feedback results is a rejected suggestion:

[0034] Select each suggestion that was rejected as a result of feedback and the reason for rejection of each suggestion;

[0035] According to the rejection reason of each suggestion with a rejected feedback result, the target asset distribution portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, each suggestion with a rejected feedback result is selectively adjusted to obtain the adjusted suggestion:

[0036] If the suggestions are adjusted, all the adjusted suggestions are obtained, and all the adjusted suggestions are sent to the management terminal of the enterprise, and "obtaining feedback results from the management terminal of the enterprise" and subsequent steps are re-executed;

[0037] If the adjustment to the said recommendation is not implemented, the said recommendation is marked as a "mandatory approval" recommendation;

[0038] Furthermore, according to all suggestions with approved feedback results and all suggestions marked as "mandatory approval", the asset distribution target direction set of the enterprise is adjusted to obtain an adjusted asset distribution target direction set;

[0039] And comprehensively predict the adjusted asset distribution target direction set to obtain the recommended prediction data;

[0040] An analysis result of the suggestion is determined based on the predicted data of the suggestion.

[0041] According to an embodiment of the present invention, the feedback result is fed back to the asset intelligent analysis model, so that according to each suggestion, the feedback result of each suggestion and the reason analysis of each suggestion are analyzed, the suggestion on the asset distribution of the enterprise is selectively adjusted, or the suggestion with the approved feedback result is comprehensively predicted, and the analysis result of each suggestion with the approved feedback result is obtained and sent to the management terminal of the enterprise, and it also includes:

[0042] When there is no suggestion with a rejected feedback result in the feedback result:

[0043] Adjust the asset distribution target direction set of the enterprise according to all the suggestions that are approved by the feedback results, and obtain an adjusted asset distribution target direction set;

[0044] And comprehensively predict the adjusted asset distribution target direction set to obtain the recommended prediction data;

[0045] An analysis result of the suggestion is determined based on the predicted data of the suggestion.

[0046] According to an embodiment of the present invention, each suggestion with a rejected feedback result is selectively adjusted according to the rejection reason of each suggestion with a rejected feedback result, the asset distribution target portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, and the adjusted suggestion includes:

[0047] Obtain the correlation rate between the rejection reason of each suggestion with the rejection result and the asset distribution target portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise;

[0048] Whether the reasons for rejection of the proposal are reasonable is determined based on the correlation rates between the reasons for rejection of the proposal and the target asset distribution profile of the enterprise, the collective profile of the target enterprise, and the collective profile of the marked target enterprise:

[0049] If the judgment is yes, then based on the rejection reason of the suggestion, the asset distribution target portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, a suggestion for the asset distribution of the enterprise is regenerated to adjust the suggestion;

[0050] If the judgment is negative, the adjustment of the suggestion is not performed.

[0051] According to a second aspect of the present invention, there is provided an enterprise asset intelligent analysis system, the system comprising a data acquisition device, an asset intelligent analysis model and a control device, the data acquisition device interacts with the enterprise's management terminal and the Internet to realize respective data acquisition of the enterprise's management terminal and the Internet, the asset intelligent analysis model is used to generate multiple suggestions for the enterprise's asset distribution and the reason for each suggestion and send them to the enterprise's management terminal, and analyze the feedback results of each suggestion, selectively make a comprehensive prediction for the enterprise's asset distribution suggestions, or for the suggestions with approved feedback results, obtain the analysis results of each suggestion with approved feedback results and send them to the enterprise's management terminal, the control device comprises a processor and a memory, the memory is suitable for storing multiple program codes, the program codes are suitable for being loaded and run by the processor to execute the enterprise asset intelligent analysis method based on big data described in any one of the technical solutions of the above-mentioned enterprise asset intelligent analysis method based on big data.

[0052] It can be seen from the above technical solutions that the enterprise asset intelligent analysis method and system based on big data provided by the present invention have the following beneficial effects:

[0053] The enterprise asset intelligent analysis method and system based on big data provided by the present invention determines the enterprise-related industry data set by acquiring the enterprise's asset distribution target direction set and the enterprise's identity information, thereby realizing the acquisition and analysis of the enterprise and Internet data, so as to obtain a comprehensive portrait of the target enterprise related to the enterprise, so as to generate targeted suggestions for the enterprise's asset distribution based on the comprehensive portrait in the future, and substitute the enterprise's asset distribution target direction set and the enterprise-related industry data set into the asset intelligent analysis model, obtain multiple suggestions for the enterprise's asset distribution and the reason analysis for each suggestion and send them to the enterprise's management terminal, and then feed back the feedback results of the enterprise's management terminal to the asset intelligent analysis model, selectively adjust the suggestions for the enterprise's asset distribution, or obtain The analysis results of each suggestion with approved feedback are sent to the management terminal of the enterprise, which realizes the active optimization and execution prediction of the suggestions for the asset distribution of the enterprise, and automatically optimizes the execution plan of the suggestions for the asset distribution of the enterprise, and ensures that the execution plan of the suggestions for the asset distribution of the enterprise matches the enterprise, making the execution plan of each suggestion more intelligent and closer to actual needs, so as to provide more accurate services to the enterprise, and thus realizes the provision of more accurate and effective asset distribution suggestions to the enterprise, thereby improving the operational efficiency and economic benefits of the enterprise, and avoiding the technical problems that the intelligent analysis methods of enterprise assets in the existing technology often lack real-time, dynamic and targetedness, which easily leads to the lag of resource distribution and even leads to the reduction of enterprise benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In addition, similar numbers in the figures are used to represent similar components, among which:

[0055] Figure 1 This is a schematic diagram of the main steps of a method for intelligent analysis of enterprise assets based on big data according to an embodiment of the present invention;

[0056] Figure 2 It is a flowchart of some steps of data processing of an asset intelligent analysis model of an enterprise asset intelligent analysis method based on big data according to an embodiment of the present invention;

[0057] Figure 3 It is a schematic diagram of the main structural block diagram of an enterprise asset intelligent analysis system according to an embodiment of the present invention.

[0058] List of reference numerals:

[0059] 300: enterprise asset intelligent analysis system; 301: control device; 3011: processor; 3012: memory; 3013: program code; 302: asset intelligent analysis model; 303: data acquisition device. DETAILED DESCRIPTION

[0060] Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0061] In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program codes, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and the like. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and may include only A, only B, or A and B. The singular terms "one" and "the" may also include plural forms.

[0062] See attached Figure 1 , Figure 1 1 is a flow chart of the main steps of a method for intelligent analysis of enterprise assets based on big data according to an embodiment of the present invention. Figure 1 As shown, a method for intelligent analysis of enterprise assets based on big data in an embodiment of the present invention mainly includes the following steps S101 to S105.

[0063] Step S101: Acquire an asset distribution target direction set of an enterprise and identity information of the enterprise, wherein the asset distribution target direction set includes at least one target project and an asset target ratio of each target project;

[0064] Step S102: determining an enterprise-related industry data set according to the enterprise's asset distribution target direction set and the enterprise's identity information, wherein the enterprise-related industry data set at least includes identity information of multiple target enterprises matching the enterprise, identity information of multiple preset marked target enterprises, and a comprehensive portrait of each target enterprise or marked target enterprise;

[0065] Specifically, determining the enterprise-related industry data set according to the enterprise's asset distribution target direction set and the enterprise's identity information includes:

[0066] According to the asset distribution target direction set of the enterprise and the identity information of the enterprise, determine the identity information of multiple target enterprises matching the enterprise, the identity information of multiple preset marked target enterprises, the asset data set of each target enterprise and the asset data set of each marked target enterprise;

[0067] Determine a comprehensive profile of each target enterprise or the marked target enterprise based on the asset data set of each target enterprise and the asset data set of each marked target enterprise;

[0068] The enterprise-related industry data set is composed of the identity information of multiple target enterprises matching the enterprise, the identity information of multiple preset marked target enterprises, and a comprehensive portrait of each target enterprise or marked target enterprise.

[0069] Specifically, the determining, based on the asset distribution target direction set of the enterprise and the identity information of the enterprise, the identity information of multiple target enterprises matching the enterprise, the identity information of multiple preset marked target enterprises, the asset data set of each target enterprise, and the asset data set of each marked target enterprise includes:

[0070] According to the identity information of the enterprise, a current asset distribution direction set of the enterprise is obtained, wherein the current asset distribution direction set of the enterprise includes a plurality of current distribution projects and a current asset proportion of each current distribution project;

[0071] According to the current asset distribution direction set of the enterprise, determine multiple target enterprises matching the enterprise and an asset data set of each target enterprise, wherein the asset data set includes publicly published asset data and comprehensive analysis asset data;

[0072] Obtaining identity information of multiple preset marked target enterprises;

[0073] According to the preset identity information of multiple marked target enterprises, an asset data set of each marked target enterprise is obtained.

[0074] Specifically, determining a plurality of target enterprises matching the enterprise and an asset data set of each target enterprise according to the current asset distribution direction set of the enterprise includes:

[0075] Determining identity information of a plurality of pending target enterprises related to the enterprise according to a current asset distribution direction set of the enterprise;

[0076] According to the identity information of multiple pending target enterprises related to the enterprise, an asset data set of each pending target enterprise is obtained, wherein the asset data set includes publicly disclosed asset data and comprehensive analysis asset data;

[0077] Each of the pending target enterprises is screened according to the asset data set of each pending target enterprise to obtain a plurality of target enterprises matching the enterprise.

[0078] Specifically, according to the current asset distribution direction set of the enterprise, determining the identity information of multiple pending target enterprises related to the enterprise includes:

[0079] According to the current asset distribution direction set of the enterprise, enterprises whose overlap with multiple projects in the current asset distribution direction set of the enterprise reaches a preset overlap threshold are selected as pending target enterprises related to the enterprise;

[0080] According to all pending target enterprises related to the enterprise, identity information of multiple pending target enterprises related to the enterprise is obtained.

[0081] Specifically, the preset overlap threshold may be 70% or 80%. The setting of the preset overlap threshold here is only an example. In actual testing, those skilled in the art may set it according to actual needs, which will not be elaborated here.

[0082] Specifically, determining the comprehensive profile of each target enterprise or the marked target enterprise according to the asset data set of each target enterprise and the asset data set of each marked target enterprise includes:

[0083] Generate a primary asset distribution profile corresponding to each target enterprise or each marked target enterprise according to the publicly disclosed asset data in the asset data set of each target enterprise or each marked target enterprise;

[0084] According to the comprehensive analysis of the asset data in the asset data set of each target enterprise or each marked target enterprise, the primary asset distribution portrait corresponding to each target enterprise or each marked target enterprise is corrected respectively to obtain a comprehensive portrait of each target enterprise or marked target enterprise.

[0085] Step S103: Substitute the enterprise's asset distribution target direction set and the enterprise's related industry data set into the asset intelligent analysis model to obtain at least one suggestion for the enterprise's asset distribution and the reason analysis for each suggestion and send them to the enterprise's management terminal;

[0086] Specifically, the step of substituting the enterprise's asset distribution target direction set and the enterprise's related industry data set into the asset intelligent analysis model, obtaining at least one suggestion on the enterprise's asset distribution and the reason analysis for each suggestion and sending them to the enterprise's management terminal includes:

[0087] According to the current asset distribution direction set of the enterprise and the asset distribution target direction set of the enterprise, an asset distribution target portrait of the enterprise is obtained, wherein the bottom layer of the asset distribution target portrait of the enterprise is the current asset distribution portrait of the enterprise drawn with solid lines, and the upper layer of the asset distribution target portrait of the enterprise is the asset distribution target direction portrait of the enterprise drawn with dotted lines;

[0088] According to the target direction set of asset distribution of the enterprise, a collective portrait of the target enterprise and a collective portrait of the marked target enterprise are obtained;

[0089] According to the target portrait of the asset distribution of the enterprise, the collective portrait of the target enterprise and the collective portrait of the marked target enterprise, at least one suggestion on the asset distribution of the enterprise and the reason analysis of each suggestion are obtained;

[0090] At least one suggestion on the asset distribution of the enterprise and the reason analysis of each suggestion are sent to the management terminal of the enterprise.

[0091] Specifically, based on the target asset distribution profile of the enterprise, the collective profile of the target enterprise, and the collective profile of the marked target enterprise, at least one suggestion on the asset distribution of the enterprise and the reason analysis for each suggestion are obtained, including:

[0092] Determine the value of the asset ratio of each project based on the collective portrait of the target enterprise and the collective portrait of the marked target enterprise, as well as the weights corresponding to the preset target enterprises and the weights of the preset marked target enterprises;

[0093] Based on the company's asset distribution target profile, selectively make at least one suggestion for the company's asset distribution and analyze the reasons for each suggestion.

[0094] Specifically, according to the target portrait of the asset distribution of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, at least one suggestion on the asset distribution of the enterprise and the reason analysis of each suggestion can be obtained in the following scenarios:

[0095] If the collective portrait of the target enterprise and the collective portrait of the marked target enterprise both indicate that the assets of one of the projects account for a larger proportion, and the asset distribution target direction portrait in the asset distribution target portrait of the enterprise and the current asset distribution portrait of the enterprise both account for a smaller proportion of the assets of the project, then the asset distribution suggestion for the enterprise is to increase the asset proportion of the project, and the reason analysis for the suggestion is that the collective portrait of the target enterprise and the collective portrait of the marked target enterprise both indicate that the assets of the project account for a larger proportion;

[0096] If the collective portrait of the target enterprise indicates that one of the projects has a larger asset proportion, and the collective portrait of the marked target enterprise indicates that the project has a smaller asset proportion, and the asset distribution target direction portrait in the enterprise's asset distribution target portrait and the enterprise's current asset distribution portrait both have a smaller asset proportion for the project, then the weight corresponding to the preset target enterprise is compared with the weight of the preset marked target enterprise, and based on the comparison result, it is determined whether to give a recommendation to increase the asset proportion of the project.

[0097] Specifically, the processing method for at least one suggestion on the asset distribution of an enterprise and the analysis of the reasons for each suggestion can be a trained neural network model or a trained machine learning model. The choice of processing method here is only an exemplary explanation. In actual testing, technical personnel in this field can make a choice according to actual needs. As long as it is possible to obtain at least one suggestion on the asset distribution of an enterprise and an analysis of the reasons for each suggestion through the target portrait of the asset distribution of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, it will be sufficient.

[0098] Specifically, according to the current asset distribution direction set and the asset distribution target direction set of the enterprise, the asset distribution target portrait of the enterprise is obtained, including:

[0099] Generate a current asset distribution profile of the enterprise based on the current asset distribution direction set of the enterprise;

[0100] Generate an asset distribution target direction profile of the enterprise based on the asset distribution target direction set of the enterprise;

[0101] Re-draw the target direction portrait of the enterprise's asset distribution through dotted lines;

[0102] Redraw the current asset distribution portrait of the enterprise through solid lines;

[0103] The target direction portrait of the asset distribution of the enterprise composed of dotted lines is visually overlaid on the current asset distribution portrait of the enterprise composed of solid lines to obtain the target asset distribution portrait of the enterprise.

[0104] Among them, the solid line part in the enterprise's asset distribution target portrait is used to show the enterprise's current asset distribution direction, and the dotted line part in the enterprise's asset distribution target portrait is used to show the enterprise's asset distribution target direction.

[0105] Specifically, the method for generating the primary asset distribution portrait and the current asset distribution portrait of the enterprise can adopt the distribution portrait construction method in the prior art. The selection of the generation method here is only an exemplary explanation. In actual testing, technical personnel in this field can choose according to actual needs. As long as the primary asset distribution portrait and the current asset distribution portrait of the enterprise can be used to visualize the publicly disclosed asset data in the asset data set of each target enterprise or each marked target enterprise and the current asset distribution direction set of the enterprise, it will not be repeated here.

[0106] Specifically, obtaining a set portrait of a target enterprise and marking the set portrait of a target enterprise according to the target direction set of asset distribution of the enterprise include:

[0107] Perform cluster analysis on the comprehensive portrait of each target enterprise in the enterprise-related industry data set to obtain a collective portrait of the target enterprise;

[0108] Perform cluster analysis on the comprehensive portrait of each marked target enterprise in the enterprise-related industry data set to obtain a collective portrait of the marked target enterprise;

[0109] In order to obtain the collective portrait of the target enterprise and mark the collective portrait of the target enterprise.

[0110] In the above embodiment, in the model processing, the enterprise's asset distribution target image is generated through the enterprise's current asset distribution direction set and the enterprise's asset distribution target direction set, wherein the solid line in the enterprise's asset distribution target image draws the enterprise's current asset distribution portrait, and the dotted line draws the enterprise's asset distribution target direction portrait, which realizes the visualization of the current distribution of the enterprise's assets and the target distribution of the assets, so as to facilitate the subsequent accurate analysis based on the asset distribution target image, thereby improving the analysis efficiency of the enterprise's asset distribution situation, and then according to the enterprise's asset distribution target direction set, a collection portrait of the target enterprise and a collection portrait of the marked target enterprise are obtained, and combined with all the obtained portraits, at least one suggestion on the enterprise's asset distribution and the reason analysis of each suggestion are obtained, thereby realizing an intuitive analysis of the asset distribution situation, thereby ensuring that each suggestion matches the enterprise's asset distribution situation, and improving the practicality of each suggestion, by sending the obtained at least one suggestion on the enterprise's asset distribution and the reason analysis of each suggestion to the enterprise's management terminal, the enterprise's management terminal can obtain the asset distribution suggestions in a timely manner, thereby realizing providing the enterprise with more accurate and effective asset distribution suggestions, thereby improving the enterprise's operating efficiency and economic benefits.

[0111] Step S104: Obtaining feedback results from the management terminal of the enterprise, wherein the feedback results include approval or rejection of each suggestion, and reasons for approval or rejection of each feedback result;

[0112] Step S105: Feedback the feedback results to the asset intelligent analysis model, so that according to each suggestion, the feedback results of each suggestion and the cause analysis of each suggestion, the suggestions for the enterprise's asset distribution are selectively adjusted, or a comprehensive prediction is made for the suggestions whose feedback results are approved, and the analysis results of each suggestion whose feedback results are approved are obtained and sent to the enterprise's management terminal, wherein the analysis results include the implementation plan and execution time of each suggestion.

[0113] like Figure 2 As shown, specifically, the feedback result is fed back to the asset intelligent analysis model, so that according to each suggestion, the feedback result of each suggestion and the reason analysis of each suggestion are analyzed, the suggestion on the asset distribution of the enterprise is selectively adjusted, or the suggestion with the approved feedback result is comprehensively predicted, and the analysis result of each suggestion with the approved feedback result is obtained and sent to the management terminal of the enterprise, including:

[0114] When at least one of the feedback results is a rejected suggestion:

[0115] Select each suggestion that was rejected as a result of feedback and the reason for rejection of each suggestion;

[0116] According to the rejection reason of each suggestion with a rejected feedback result, the target asset distribution portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, each suggestion with a rejected feedback result is selectively adjusted to obtain the adjusted suggestion:

[0117] If the suggestions are adjusted, all the adjusted suggestions are obtained, and all the adjusted suggestions are sent to the management terminal of the enterprise, and "obtaining feedback results from the management terminal of the enterprise" and subsequent steps are re-executed;

[0118] If the adjustment to the said recommendation is not implemented, the said recommendation is marked as a "mandatory approval" recommendation;

[0119] Furthermore, according to all suggestions with approved feedback results and all suggestions marked as "mandatory approval", the asset distribution target direction set of the enterprise is adjusted to obtain an adjusted asset distribution target direction set;

[0120] And comprehensively predict the adjusted asset distribution target direction set to obtain the recommended prediction data;

[0121] An analysis result of the suggestion is determined based on the predicted data of the suggestion.

[0122] In the above embodiment, in the model processing, by performing different operations based on whether there are rejected suggestions in the feedback results, active verification of the feedback opinions of the enterprise's management terminal is achieved, the matching degree between the suggestions on the enterprise's asset distribution and the enterprise is improved, and by making a comprehensive prediction of the adjusted asset distribution target direction set, the analysis results of each suggestion are determined, and the automatic generation and prediction of the execution plan of each suggestion is achieved, thereby improving the accuracy and reliability of the execution plan and execution time output by the asset intelligent analysis model.

[0123] Specifically, the feedback result is fed back to the asset intelligent analysis model, so that according to each suggestion, the feedback result of each suggestion and the reason analysis of each suggestion are analyzed, the suggestion on the asset distribution of the enterprise is selectively adjusted, or the suggestion with the approved feedback result is comprehensively predicted, and the analysis result of each suggestion with the approved feedback result is obtained and sent to the management terminal of the enterprise, which also includes:

[0124] When there is no suggestion with a rejected feedback result in the feedback result:

[0125] Adjust the asset distribution target direction set of the enterprise according to all the suggestions that are approved by the feedback results, and obtain an adjusted asset distribution target direction set;

[0126] And comprehensively predict the adjusted asset distribution target direction set to obtain the recommended prediction data;

[0127] An analysis result of the suggestion is determined based on the predicted data of the suggestion.

[0128] Specifically, according to the rejection reason of each suggestion with a rejected feedback result, the target asset distribution portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, each suggestion with a rejected feedback result is selectively adjusted, and the adjusted suggestions include:

[0129] Obtain the correlation rate between the rejection reason of each suggestion with the rejection result and the asset distribution target portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise;

[0130] Whether the reasons for rejection of the proposal are reasonable is determined based on the correlation rates between the reasons for rejection of the proposal and the target asset distribution profile of the enterprise, the collective profile of the target enterprise, and the collective profile of the marked target enterprise:

[0131] If the judgment is yes, then based on the rejection reason of the suggestion, the asset distribution target portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, a suggestion for the asset distribution of the enterprise is regenerated to adjust the suggestion;

[0132] If the judgment is negative, the adjustment of the suggestion is not performed.

[0133] Specifically, judging whether the reason for rejection of the suggestion is reasonable according to the correlation rate between the reason for rejection of the suggestion and the target asset distribution profile of the enterprise, the collective profile of the target enterprise, and the collective profile of the marked target enterprise includes:

[0134] If the relevance rates of the rejection reasons of the proposals with the target asset distribution profile of the enterprise, the collective profile of the target enterprises, and the collective profile of the marked target enterprises are all lower than the respective preset relevance rate thresholds, the rejection reasons of the proposals are determined to be unreasonable;

[0135] Otherwise, it is determined that the reasons for rejecting the suggestion are reasonable.

[0136] Specifically, the preset correlation rate threshold corresponding to the enterprise's asset distribution target portrait can be 50%, the preset correlation rate threshold corresponding to the target enterprise's collection portrait can be 60%, and the preset correlation rate threshold corresponding to the marked target enterprise's collection portrait can be 65%. The settings of the respective preset correlation rate thresholds here are only exemplary. In actual testing, those skilled in the art can set them according to actual needs, which will not be repeated here.

[0137] Specifically, the prediction method for comprehensive prediction of the adjusted asset distribution target direction set can adopt the simulation technology in the prior art, or it can be the prediction method in the prior art. The selection of the prediction method here is only an exemplary explanation. In actual testing, technical personnel in this field can choose according to actual needs. As long as it can be achieved through the comprehensive prediction of the adjusted asset distribution target direction set, and the analysis results of each suggestion with approved feedback results can be obtained, it will not be repeated here.

[0138] Specifically, based on the predicted data of the suggestion, the method for generating the execution plan in the analysis result of the suggestion can adopt a trained neural network model or a trained machine learning model. The selection of the method for generating the execution plan here is only an exemplary description. In actual testing, technical personnel in this field can make a selection according to actual needs, as long as it can be achieved by determining the analysis result of the suggestion based on the predicted data of the suggestion. It will not be repeated here.

[0139] Based on the above steps S101 to S105, the enterprise-related industry data set is determined by obtaining the enterprise's asset distribution target direction set and the enterprise's identity information, thereby achieving the acquisition and analysis of the enterprise and Internet data, so as to obtain a comprehensive portrait of the target enterprise related to the enterprise, so as to generate targeted suggestions for the asset distribution of the enterprise based on the comprehensive portrait, and substitute the enterprise's asset distribution target direction set and the enterprise-related industry data set into the asset intelligent analysis model to obtain multiple suggestions for the enterprise's asset distribution and the reason analysis for each suggestion and send them to the enterprise's management terminal, and then feed back the feedback results obtained from the enterprise's management terminal to the asset intelligent analysis model, selectively adjust the suggestions for the enterprise's asset distribution, or obtain feedback. The result is the analysis result of each approved suggestion and it is sent to the management terminal of the enterprise, which realizes the active optimization and execution prediction of the suggestions for the asset distribution of the enterprise, and automatically optimizes the execution plan of the suggestions for the asset distribution of the enterprise, and ensures that the execution plan of the suggestions for the asset distribution of the enterprise matches the enterprise, making the execution plan of each suggestion more intelligent and closer to actual needs, so as to provide more accurate services to the enterprise, and thus realizes the provision of more accurate and effective asset distribution suggestions to the enterprise, thereby improving the operational efficiency and economic benefits of the enterprise, and avoiding the technical problems that the intelligent analysis methods of enterprise assets in the existing technology often lack real-time, dynamic and targetedness, which easily leads to the lag of resource distribution and even leads to the reduction of enterprise benefits.

[0140] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0141] Furthermore, the present invention also provides an enterprise asset intelligent analysis system.

[0142] See attached Figure 3 , Figure 3 FIG. 1 is a main structural block diagram of an enterprise asset intelligent analysis system according to an embodiment of the present invention. Figure 3 As shown, an enterprise asset intelligent analysis system 300 in an embodiment of the present invention mainly includes a data acquisition device 303, an asset intelligent analysis model 302 and a control device 301. The data acquisition device 303 interacts with the enterprise's management terminal and the Internet to realize data collection for the enterprise's management terminal and the Internet. The asset intelligent analysis model 302 is used to generate multiple suggestions for the enterprise's asset distribution and the reason for each suggestion and send them to the enterprise's management terminal, and analyze the feedback results of each suggestion, selectively make a comprehensive prediction for the suggestions for the enterprise's asset distribution, or for the suggestions with approved feedback results, obtain the analysis results of each suggestion with approved feedback results and send them to the enterprise's management terminal. The control device 301 includes a processor 3011 and a memory 3012. The memory 3012 can be configured to store a program code 3013 for executing the enterprise asset intelligent analysis method based on big data of the above method embodiment. The processor 3011 can be configured to execute the program code 3013 in the memory 3012, and the program code 3013 includes but is not limited to the program code 3013 for executing the enterprise asset intelligent analysis method based on big data of the above method embodiment. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown. For the specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device 301 can be a control device device formed by various electronic devices.

[0143] Specifically, the data acquisition device 303 can be various communication devices or data acquisition devices of different communication types. The selection of the data acquisition device 303 here is only an exemplary description. In actual testing, technical personnel in this field can make a selection according to actual needs, as long as information interaction can be achieved between the data acquisition device 303 and the enterprise's management terminal and the Internet. No further details will be given here.

[0144] In one implementation, the description of the specific implementation functions may refer to steps S101 to S105.

[0145] The enterprise asset intelligent analysis system 300 is used to perform Figure 1 The embodiment of the enterprise asset intelligent analysis method based on big data shown in the figure has similar technical principles, technical problems solved and technical effects produced. Technical personnel in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the enterprise asset intelligent analysis system 300 can refer to the contents described in the embodiment of the enterprise asset intelligent analysis method based on big data, and will not be repeated here.

[0146] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor 3011, the steps of each of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electric carrier signal and telecommunication signal.

[0147] Furthermore, the enterprise asset intelligent analysis system 300 of the present invention also includes a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program code 3013 for executing the enterprise asset intelligent analysis method based on big data of the above-mentioned method embodiment, and the program code 3013 can be loaded and run by the processor 3011 to implement the above-mentioned enterprise asset intelligent analysis method based on big data. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a memory device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0148] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor 3011 itself, or a part of the software, a part of the hardware, or a part of the combination of software and hardware in the processor 3011. Therefore, the number of each module in the figure is only schematic.

[0149] Those skilled in the art will appreciate that the modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0150] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for intelligent analysis of enterprise assets based on big data, characterized in that: The method comprises the following steps: Acquire an asset distribution target direction set of an enterprise and identity information of the enterprise, wherein the asset distribution target direction set includes at least one target project and an asset target ratio of each target project; Determine an enterprise-related industry data set according to the enterprise's asset distribution target direction set and the enterprise's identity information, wherein the enterprise-related industry data set at least includes identity information of multiple target enterprises matching the enterprise, identity information of multiple preset marked target enterprises, and a comprehensive portrait of each target enterprise or marked target enterprise; Substitute the enterprise's asset distribution target direction set and the enterprise's related industry data set into the asset intelligent analysis model to obtain at least one suggestion on the enterprise's asset distribution and the reason analysis for each suggestion and send it to the enterprise's management terminal; Obtaining feedback results from the management terminal of the enterprise, wherein the feedback results include approval or rejection of each suggestion, and reasons for approval or rejection of each feedback result; The feedback results are fed back to the asset intelligent analysis model, so that according to each suggestion, the feedback results of each suggestion and the cause analysis of each suggestion, the suggestions for the enterprise's asset distribution are selectively adjusted, or a comprehensive prediction is made for the suggestions whose feedback results are approved, and the analysis results of each suggestion whose feedback results are approved are obtained and sent to the enterprise's management terminal, wherein the analysis results include the implementation plan and execution time of each suggestion.

2. According to the method of enterprise asset intelligent analysis based on big data in claim 1, it is characterized in that: Determining the enterprise-related industry data set according to the enterprise's asset distribution target direction set and the enterprise's identity information includes: According to the asset distribution target direction set of the enterprise and the identity information of the enterprise, determine the identity information of multiple target enterprises matching the enterprise, the identity information of multiple preset marked target enterprises, the asset data set of each target enterprise and the asset data set of each marked target enterprise; Determine a comprehensive profile of each target enterprise or the marked target enterprise based on the asset data set of each target enterprise and the asset data set of each marked target enterprise; The enterprise-related industry data set is composed of the identity information of multiple target enterprises matching the enterprise, the identity information of multiple preset marked target enterprises, and a comprehensive portrait of each target enterprise or marked target enterprise.

3. The enterprise asset intelligent analysis method based on big data according to claim 2 is characterized in that: The step of determining the identity information of multiple target enterprises matching the enterprise, the identity information of multiple preset marked target enterprises, the asset data set of each target enterprise, and the asset data set of each marked target enterprise according to the asset distribution target direction set of the enterprise and the identity information of the enterprise includes: According to the identity information of the enterprise, a current asset distribution direction set of the enterprise is obtained, wherein the current asset distribution direction set of the enterprise includes a plurality of current distribution projects and a current asset proportion of each current distribution project; According to the current asset distribution direction set of the enterprise, determine multiple target enterprises matching the enterprise and an asset data set of each target enterprise, wherein the asset data set includes publicly published asset data and comprehensive analysis asset data; Obtaining identity information of multiple preset marked target enterprises; According to the preset identity information of multiple marked target enterprises, an asset data set of each marked target enterprise is obtained.

4. The enterprise asset intelligent analysis method based on big data according to claim 3 is characterized in that: The step of determining a plurality of target enterprises matching the enterprise and an asset data set of each target enterprise according to the current asset distribution direction set of the enterprise includes: Determining identity information of a plurality of pending target enterprises related to the enterprise according to a current asset distribution direction set of the enterprise; According to the identity information of multiple pending target enterprises related to the enterprise, an asset data set of each pending target enterprise is obtained, wherein the asset data set includes publicly disclosed asset data and comprehensive analysis asset data; Each of the pending target enterprises is screened according to the asset data set of each pending target enterprise to obtain a plurality of target enterprises matching the enterprise.

5. The enterprise asset intelligent analysis method based on big data according to claim 4 is characterized in that: Determining the comprehensive profile of each target enterprise or the marked target enterprise according to the asset data set of each target enterprise and the asset data set of each marked target enterprise includes: Generate a primary asset distribution profile corresponding to each target enterprise or each marked target enterprise according to the publicly disclosed asset data in the asset data set of each target enterprise or each marked target enterprise; According to the comprehensive analysis of the asset data in the asset data set of each target enterprise or each marked target enterprise, the primary asset distribution portrait corresponding to each target enterprise or each marked target enterprise is corrected respectively to obtain a comprehensive portrait of each target enterprise or marked target enterprise.

6. The enterprise asset intelligent analysis method based on big data according to claim 5 is characterized in that: Substituting the enterprise's asset distribution target direction set and the enterprise's related industry data set into the asset intelligent analysis model, obtaining at least one suggestion for the enterprise's asset distribution and the reason analysis for each suggestion and sending them to the enterprise's management terminal includes: According to the current asset distribution direction set of the enterprise and the asset distribution target direction set of the enterprise, an asset distribution target portrait of the enterprise is obtained, wherein the bottom layer of the asset distribution target portrait of the enterprise is the current asset distribution portrait of the enterprise drawn with solid lines, and the upper layer of the asset distribution target portrait of the enterprise is the asset distribution target direction portrait of the enterprise drawn with dotted lines; According to the target direction set of the asset distribution of the enterprise, a collective portrait of the target enterprise and a collective portrait of the marked target enterprise are obtained; According to the target portrait of the asset distribution of the enterprise, the collective portrait of the target enterprise and the collective portrait of the marked target enterprise, at least one suggestion on the asset distribution of the enterprise and the reason analysis of each suggestion are obtained; At least one suggestion on the asset distribution of the enterprise and the reason analysis of each suggestion are sent to the management terminal of the enterprise.

7. The enterprise asset intelligent analysis method based on big data according to claim 6 is characterized in that: Feeding back the feedback results to the asset intelligent analysis model, so as to analyze each suggestion, the feedback results of each suggestion and the reason analysis of each suggestion, selectively adjust the suggestions for the asset distribution of the enterprise, or make a comprehensive prediction for the suggestions with approved feedback results, obtain the analysis results of each suggestion with approved feedback results and send them to the management terminal of the enterprise, including: When at least one of the feedback results is a rejected suggestion: Select each suggestion that was rejected as a result of feedback and the reason for rejection of each suggestion; According to the rejection reason of each suggestion with a rejected feedback result, the target asset distribution portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, each suggestion with a rejected feedback result is selectively adjusted to obtain the adjusted suggestion: If the suggestions are adjusted, all the adjusted suggestions are obtained, and all the adjusted suggestions are sent to the management terminal of the enterprise, and "obtaining feedback results from the management terminal of the enterprise" and subsequent steps are re-executed; If the adjustment to the suggestion is not performed, the suggestion is marked as a "mandatory approval" suggestion; Furthermore, the asset distribution target direction set of the enterprise is adjusted according to all suggestions with approved feedback results and all suggestions marked as "mandatory approval" to obtain an adjusted asset distribution target direction set; And comprehensively predict the adjusted asset distribution target direction set to obtain the recommended prediction data; An analysis result of the suggestion is determined based on the predicted data of the suggestion.

8. The enterprise asset intelligent analysis method based on big data according to claim 7 is characterized in that: Feeding back the feedback results to the asset intelligent analysis model, so that according to each suggestion, the feedback results of each suggestion and the reason analysis of each suggestion are analyzed, the suggestions for the asset distribution of the enterprise are selectively adjusted, or the suggestions with approved feedback results are comprehensively predicted, and the analysis results of each suggestion with approved feedback results are obtained and sent to the management terminal of the enterprise, further comprising: When there is no suggestion with a rejected feedback result in the feedback result: Adjust the asset distribution target direction set of the enterprise according to all the suggestions that are approved by the feedback results, and obtain an adjusted asset distribution target direction set; And comprehensively predict the adjusted asset distribution target direction set to obtain the recommended prediction data; An analysis result of the suggestion is determined based on the predicted data of the suggestion.

9. The enterprise asset intelligent analysis method based on big data according to claim 8 is characterized in that: According to the rejection reason of each suggestion with a rejected feedback result, the target asset distribution portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, each suggestion with a rejected feedback result is selectively adjusted, and the adjusted suggestions include: Obtain the correlation rate between the rejection reason of each suggestion with the rejection result and the asset distribution target portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise; Whether the reason for rejection of the suggestion is reasonable is determined based on the correlation rate between the reason for rejection of the suggestion and the target asset distribution profile of the enterprise, the collective profile of the target enterprise, and the collective profile of the marked target enterprise: If the judgment is yes, then based on the rejection reason of the suggestion, the asset distribution target portrait of the enterprise, the collective portrait of the target enterprise, and the collective portrait of the marked target enterprise, a suggestion for the asset distribution of the enterprise is regenerated to adjust the suggestion; If the judgment is negative, the adjustment of the suggestion is not performed.

10. An enterprise asset intelligent analysis system, characterized in that: The system includes a data collection device, an asset intelligent analysis model and a control device. The data collection device interacts with the enterprise's management terminal and the Internet to realize data collection on the enterprise's management terminal and the Internet. The asset intelligent analysis model is used to generate multiple suggestions for the enterprise's asset distribution and the reason for each suggestion and send them to the enterprise's management terminal, and analyze the feedback results of each suggestion, selectively make a comprehensive prediction on the suggestions for the enterprise's asset distribution, or make a comprehensive prediction on the suggestions for the enterprise's asset distribution with approved feedback results, obtain the analysis results of each suggestion with approved feedback results and send them to the enterprise's management terminal. The control device includes a processor and a memory. The memory is suitable for storing multiple program codes. The program code is suitable for being loaded and run by the processor to execute an enterprise asset intelligent analysis method based on big data as described in any one of claims 1 to 9.

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