A blockchain-based enterprise management method and system

Abstract

The embodiment of the present invention relates to the field of enterprise management technology, and specifically discloses a blockchain-based enterprise management method and system. The embodiment of the present invention determines multiple enterprise R&D products; based on blockchain technology, classifies and stores multiple R&D process models; determines the model requester and performs association analysis; when there is a direct association for use, directly transmits the use to the model requester; when there is no direct association for use, after the indirect feedback is passed, indirectly transmits the use to the model requester. Based on blockchain technology, multiple R&D process models can be classified and stored, and when there is a model R&D request, association analysis can be performed. In different situations, direct use transmission or indirect use transmission can be performed to the model requester, and R&D product models can be classified and stored and effectively managed for use transmission, which can avoid the difficulty in obtaining R&D product models and the risk of leakage of R&D information.

Description

Technical Field

[0001] The present invention belongs to the field of enterprise management technology, and in particular, relates to an enterprise management method and system based on blockchain. Background Art

[0002] Enterprise management is the general term for a series of activities such as planning, organizing, directing, coordinating and controlling the production and operation activities of an enterprise. It is an objective requirement of large-scale socialized production and covers all aspects and links of the production and operation activities of an enterprise. Through effective management activities, enterprises can achieve optimal allocation and efficient utilization of resources and improve their competitiveness and market position.

[0003] Due to the diversity of enterprise R&D products, there is no effective technology in the existing technology to effectively manage the classified storage and use and transmission of R&D product models, resulting in chaotic storage of R&D product models and no effective standards for the use of R&D product models. It is difficult to obtain the desired R&D product models during the R&D process, and it is easy to cause the leakage of R&D information. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a blockchain-based enterprise management method and system, aiming to solve the problems raised in the background technology.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A blockchain-based enterprise management method, the method specifically comprising the following steps:

[0007] Conduct enterprise product R&D monitoring, identify multiple enterprise R&D products, and generate R&D process information for multiple enterprise R&D products;

[0008] According to the multiple R&D process information, multiple R&D process models are obtained, and based on the blockchain technology, the multiple R&D process models are classified and stored according to the multiple enterprise R&D products;

[0009] Receive a model development request, determine the model requester, and perform a correlation analysis on the model requester to determine whether there is a direct correlation with the use;

[0010] When there is a direct usage association, matching the target process model and requesting the personnel to perform direct usage transfer to the model;

[0011] When there is no direct association of use, the target process model and the indirectly associated personnel are matched, and after the indirect feedback is passed, the indirect use transmission is performed to the model requesting personnel through the indirectly associated personnel.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the monitoring of enterprise product development, determining multiple enterprise R&D products, and generating R&D process information of multiple enterprise R&D products specifically include the following steps:

[0013] Conduct enterprise product production monitoring and identify products produced by multiple enterprises;

[0014] Obtaining internal product information of multiple products produced by the enterprise;

[0015] Perform R&D identification on the internal information of the plurality of products, and select a plurality of enterprise R&D products from the products produced by the plurality of enterprises;

[0016] Extracting R&D product attributes of the multiple R&D products of the enterprise from the internal product information corresponding to the multiple R&D products of the enterprise;

[0017] According to the multiple R&D product attributes, basic R&D process analysis is performed to generate multiple R&D process information.

[0018] As a further limitation of the technical solution of the embodiment of the present invention, the obtaining of multiple R&D process models according to the multiple R&D process information, and classifying and storing the multiple R&D process models according to the multiple enterprise R&D products based on blockchain technology specifically include the following steps:

[0019] According to the multiple R&D process information, the processes of the multiple enterprise R&D products are tracked to obtain multiple R&D process models;

[0020] Based on blockchain technology, build multiple blockchain storage spaces;

[0021] Based on the multiple R&D products of the enterprise, classify and plan the multiple R&D process models, and record the classification planning information;

[0022] According to the classification planning information, the multiple R&D process models are classified and stored in the multiple blockchain storage spaces.

[0023] As a further limitation of the technical solution of the embodiment of the present invention, the receiving of the model development request, determining the model requester, performing association analysis on the model requester, and determining whether there is a direct association specifically comprises the following steps:

[0024] Receive model development requests and obtain the address where the requests are sent;

[0025] Analyze the address where the request is sent to determine the person who requested the model;

[0026] Obtaining requester information of a person requesting the model;

[0027] According to the requesting person information, an association analysis is performed on the model requesting person to determine whether there is a direct association with use.

[0028] As a further limitation of the technical solution of the embodiment of the present invention, when there is a direct association of use, matching the target process model and directly transmitting the use to the model requesting personnel specifically includes the following steps:

[0029] When there is a direct association for use, a direct transfer instruction is generated;

[0030] Matching target process model;

[0031] According to the direct transfer instruction, the target process model is directly transferred to the model requester for use.

[0032] As a further limitation of the technical solution of the embodiment of the present invention, when there is no direct association of use, matching the target process model and the indirectly associated personnel, after the indirect feedback is passed, performing indirect use transmission to the model requesting personnel through the indirectly associated personnel specifically includes the following steps:

[0033] generating an indirect transmission instruction and an indirect feedback request when there is no direct association for use;

[0034] Matching target process models and indirectly related personnel;

[0035] sending the indirect feedback request to the indirectly associated person;

[0036] Receiving indirect feedback information from the indirectly associated person, and determining whether the indirect feedback is approved;

[0037] After the indirect feedback is passed, the indirect use transmission is performed to the model requesting person through the indirect associated person.

[0038] A blockchain-based enterprise management system, the system comprising a product development monitoring unit, a model classification storage unit, an association analysis and judgment unit, a direct use transmission unit and an indirect use transmission unit, wherein:

[0039] A product R&D monitoring unit, used to monitor the R&D of enterprise products, determine multiple enterprise R&D products, and generate R&D process information of multiple enterprise R&D products;

[0040] A model classification storage unit, used to obtain multiple R&D process models according to the multiple R&D process information, and classify and store the multiple R&D process models according to the multiple enterprise R&D products based on blockchain technology;

[0041] A correlation analysis and judgment unit, used to receive a model development request, determine a model requester, perform correlation analysis on the model requester, and determine whether there is a direct correlation with use;

[0042] A direct usage transmission unit, for matching a target process model when there is a direct usage association, and performing a direct usage transmission to a person requesting the model;

[0043] The indirect use transmission unit is used to match the target process model and the indirectly associated personnel when there is no direct use association, and after the indirect feedback is passed, perform indirect use transmission to the model requesting personnel through the indirectly associated personnel.

[0044] As a further limitation of the technical solution of the embodiment of the present invention, the product development monitoring unit specifically includes:

[0045] The production monitoring module is used to monitor the production of enterprise products and determine the products produced by multiple enterprises;

[0046] An information acquisition module, used to acquire internal product information of multiple products produced by the enterprise;

[0047] A product selection module is used to identify the internal information of the plurality of products and select a plurality of enterprise R&D products from the plurality of enterprise production products;

[0048] An attribute extraction module, used to extract the R&D product attributes of the multiple R&D products of the enterprise from the internal product information corresponding to the multiple R&D products of the enterprise;

[0049] The process analysis module is used to perform basic R&D process analysis according to the multiple R&D product attributes and generate multiple R&D process information.

[0050] As a further limitation of the technical solution of the embodiment of the present invention, the model classification storage unit specifically includes:

[0051] A model acquisition module, used to track the processes of the multiple enterprise R&D products according to the multiple R&D process information, and acquire multiple R&D process models;

[0052] Space construction module, used to build multiple blockchain storage spaces based on blockchain technology;

[0053] A classification planning module, used to classify and plan the multiple R&D process models based on the multiple enterprise R&D products, and record classification planning information;

[0054] A classification storage module is used to classify and store the multiple R&D process models in the multiple blockchain storage spaces according to the classification planning information.

[0055] As a further limitation of the technical solution of the embodiment of the present invention, the indirect use transmission unit specifically includes:

[0056] An instruction generation module, for generating an indirect transmission instruction and an indirect feedback request when there is no direct association for use;

[0057] Personnel matching module, used to match target process models and indirectly associated personnel;

[0058] A request sending module, used for sending the indirect feedback request to the indirectly associated person;

[0059] A feedback analysis module, used to receive indirect feedback information from the indirectly associated personnel and determine whether the indirect feedback is passed;

[0060] The indirect transmission module is used to perform indirect use transmission to the model requesting person through the indirect associated person after the indirect feedback is passed.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The embodiment of the present invention determines multiple enterprise R&D products; based on blockchain technology, classifies and stores multiple R&D process models; determines the model requester and performs association analysis; when there is a direct association, directly transmits the use to the model requester; when there is no direct association, after the indirect feedback is passed, indirectly transmits the use to the model requester. Based on blockchain technology, multiple R&D process models can be classified and stored, and when there is a model R&D request, association analysis can be performed. In different situations, direct or indirect use transmission can be performed to the model requester, and R&D product models can be classified and stored and effectively managed for use transmission, which can avoid the difficulty of obtaining R&D product models and the risk of leakage of R&D information. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0064] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0065] Figure 2 A flow chart of generating R&D process information in the method provided in an embodiment of the present invention is shown.

[0066] Figure 3 A flow chart of classified storage of R&D process models in the method provided by an embodiment of the present invention is shown.

[0067] Figure 4 A flow chart showing the use of direct association judgment in the method provided by an embodiment of the present invention.

[0068] Figure 5 A flow chart showing direct use of transmission in the method provided by an embodiment of the present invention is shown.

[0069] Figure 6 A flow chart of indirect use transmission in the method provided by an embodiment of the present invention is shown.

[0070] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0071] Figure 8 A structural block diagram of a product development monitoring unit in a system provided by an embodiment of the present invention is shown.

[0072] Fig. 9 A structural block diagram of a model classification storage unit in a system provided by an embodiment of the present invention is shown.

[0073] Fig.10 A structural block diagram showing indirect use of a transmission unit in a system provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] It is understandable that due to the diversity of enterprise R&D products, there is no effective technology in the existing technology to effectively manage the classified storage and use and transmission of R&D product models, resulting in chaotic storage of R&D product models and no effective standards for the use of R&D product models. It is difficult to obtain the desired R&D product models during the R&D process, and it is easy to cause the leakage of R&D information.

[0076] To solve the above problems, the embodiment of the present invention determines multiple enterprise R&D products and generates R&D process information of multiple enterprise R&D products by monitoring enterprise product R&D; according to multiple R&D process information, multiple R&D process models are obtained, and multiple R&D process models are classified and stored based on blockchain technology according to multiple enterprise R&D products; model R&D requests are received, model request personnel are determined, and correlation analysis is performed on model request personnel to determine whether there is a direct association for use; when there is a direct association for use, the target process model is matched, and direct use transmission is performed to the model request personnel; when there is no direct association for use, the target process model and the indirectly associated personnel are matched, and after the indirect feedback is passed, the indirect use transmission is performed to the model request personnel through the indirectly associated personnel. Based on blockchain technology, multiple R&D process models can be classified and stored, and when there is a model R&D request, correlation analysis is performed, and direct use transmission or indirect use transmission is performed to the model request personnel in different situations, and effective management of classified storage and use transmission of R&D product models can be performed, which can avoid the difficulty of obtaining R&D product models and the risk of leakage of R&D information.

[0077] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0078] Specifically, a blockchain-based enterprise management method comprises the following steps:

[0079] Step S101 , monitor the R&D of enterprise products, determine multiple R&D products of the enterprise, and generate R&D process information of multiple R&D products of the enterprise.

[0080] In an embodiment of the present invention, during the formal production and trial production process of an enterprise, product production monitoring is performed to determine multiple enterprise products. By obtaining internal product information of the multiple enterprise products, R&D identification is performed on the multiple product internal information. From the multiple enterprise products, multiple enterprise R&D products that are in the R&D and production process are selected, and from the multiple product internal information, R&D internal information corresponding to the multiple enterprise R&D products is selected. Then, from the multiple R&D internal information, R&D product attributes of the multiple enterprise R&D products are extracted. According to the multiple R&D product attributes, basic R&D process analysis is performed on the multiple enterprise R&D products to generate multiple R&D process information.

[0081] Specifically, Figure 2 A flow chart of generating R&D process information in the method provided in an embodiment of the present invention is shown.

[0082] Among them, in the preferred implementation mode provided by the present invention, the monitoring of enterprise product R&D, determining multiple enterprise R&D products, and generating R&D process information of multiple enterprise R&D products specifically include the following steps:

[0083] Step S1011, monitor the production of enterprise products and determine the products produced by multiple enterprises;

[0084] Step S1012, obtaining internal product information of a plurality of products produced by the enterprise;

[0085] Step S1013, performing R&D identification on the internal information of the plurality of products, and selecting a plurality of enterprise R&D products from the plurality of enterprise-produced products;

[0086] Step S1014, extracting R&D product attributes of the plurality of R&D products of the enterprise from the internal product information corresponding to the plurality of R&D products of the enterprise;

[0087] Step S1015, performing basic R&D process analysis according to the multiple R&D product attributes to generate multiple R&D process information.

[0088] Furthermore, the enterprise management method based on blockchain also includes the following steps:

[0089] Step S102: Acquire multiple R&D process models based on the multiple R&D process information, and classify and store the multiple R&D process models according to the multiple enterprise R&D products based on blockchain technology.

[0090] In an embodiment of the present invention, based on multiple R&D process information, process tracking is performed on multiple enterprise R&D products, and multiple R&D process models related to the R&D production process, such as the products themselves and related equipment, fixtures, and auxiliary tools of the multiple enterprise R&D products, are obtained. Then, based on blockchain technology, multiple blockchain storage spaces are constructed, and based on the multiple enterprise R&D products, multiple R&D process models are classified and planned, and the classification planning information is recorded. Then, according to the classification planning information, multiple R&D process models are classified and stored in multiple blockchain storage spaces.

[0091] Specifically, Figure 3 A flow chart of classified storage of R&D process models in the method provided by an embodiment of the present invention is shown.

[0092] Among them, in the preferred implementation mode provided by the present invention, the acquisition of multiple R&D process models based on the multiple R&D process information, and the classification and storage of the multiple R&D process models according to the multiple enterprise R&D products based on blockchain technology specifically include the following steps:

[0093] Step S1021, tracking the processes of the multiple enterprise R&D products according to the multiple R&D process information, and obtaining multiple R&D process models;

[0094] Step S1022, constructing multiple blockchain storage spaces based on blockchain technology;

[0095] Step S1023, based on the multiple R&D products of the enterprise, classify and plan the multiple R&D process models, and record the classification planning information;

[0096] Step S1024: Classify and store the multiple R&D process models in the multiple blockchain storage spaces according to the classification planning information.

[0097] Further, according to the multiple R&D process information, the process of tracking the multiple enterprise R&D products is performed, and obtaining multiple R&D process models specifically includes the following steps:

[0098] Multiple R&D process information are combined into an R&D process information matrix, where each row in the R&D process information matrix represents an R&D project, and each column represents numerical information of an R&D step;

[0099] A high-dimensional feature matrix of the R&D process information matrix is ​​extracted using a first deep neural network;

[0100] The high-dimensional feature matrix is ​​standardized in turn, and then the standardized high-dimensional features are subjected to polynomial feature generation to obtain a polynomial feature matrix;

[0101] The polynomial feature matrix is ​​used to group the data using an adaptively optimized clustering algorithm to obtain a clustering result, in which each element represents the cluster to which the corresponding data point belongs;

[0102] Calculate the statistical characteristics of each cluster, including the number of samples, average distance, maximum / minimum distance, variance of the covariance matrix, and the correlation matrix;

[0103] Calculate the eigenvalues ​​of the covariance matrix to analyze the distribution characteristics of the data; and calculate the entropy of the distance entropy between the cluster center and the data point center to measure the complexity and uncertainty of the data;

[0104] The polynomial feature matrix is ​​transformed through nonlinear transformation and deep feature extraction, combined with adaptive weights to generate additional high-level features;

[0105] The number of samples, cluster center, average distance, maximum / minimum distance, variance, correlation matrix, eigenvalues, entropy, and high-level features are used as models for each cluster.

[0106] Furthermore, the polynomial feature matrix is ​​grouped using an adaptively optimized clustering algorithm to obtain clustering results, which specifically includes the following steps:

[0107] Randomly select a data point from the polynomial features as the initial cluster center;

[0108] For each data point, calculate the distance between the single data point and all cluster centers and assign it to the cluster with the closest distance;

[0109] A nonlinear function is used to convert the importance of data points into weight values, and the weighted sum of the distances from the data points in the cluster to the cluster center is used as the loss function;

[0110] Calculate the gradient of the loss function with respect to the cluster center, use the momentum update strategy for adaptive iterative optimization, and adaptively adjust the learning rate according to the distribution of data points within the cluster; the learning rate determines the step when the cluster center is updated each time, and the learning rate decreases as the variance of the distribution of data points within the cluster increases, and increases when the variance is small;

[0111] Determine whether the change of cluster centers converges. If the change of all cluster centers is less than the preset threshold, the algorithm terminates and obtains the clustering result; otherwise, return to the data point allocation step to continue iteration.

[0112] Furthermore, the polynomial feature matrix is ​​subjected to nonlinear transformation and deep feature extraction, combined with adaptive weights, to generate additional advanced features, specifically including the following steps:

[0113] Perform a sine transform on the polynomial feature matrix to capture the periodic information in the data and obtain a sinusoidal feature matrix;

[0114] The polynomial feature matrix is ​​exponentially transformed to amplify the eigenvalues ​​and capture the exponential growth information in the data to obtain the exponential feature matrix;

[0115] The polynomial characteristic matrix is ​​logarithmically transformed to compress the eigenvalue range and prevent uneven distribution of eigenvalues, thereby obtaining a logarithmic characteristic matrix;

[0116] Merge the sinusoidal feature matrix, exponential feature matrix, logarithmic feature matrix and polynomial feature matrix to capture the nonlinear relationship in the data and obtain the merged features;

[0117] Using a second deep neural network to extract features from the merged features, and obtaining an output feature matrix of an intermediate layer of the deep neural network;

[0118] Two adaptive weights are introduced, and the merged features and the feature matrix of the intermediate layer output of the deep neural network are adjusted and combined through the two adaptive weights to obtain adaptive features.

[0119] Further, based on the multiple R&D products of the enterprise, the multiple R&D process models are classified and planned, and recording the classification planning information specifically includes the following steps:

[0120] Create an empty classification planning information collection;

[0121] Perform a nested traversal for each product and each model. If the model belongs to the current product, check the product priority and the model's R&D stage.

[0122] Concatenate the product category, model R&D stage, and product priority into a string, and hash the string to generate a unique classification key;

[0123] Recursively process the model's dependencies and generate a dependency dictionary;

[0124] Find the corresponding blockchain storage space according to the product category;

[0125] Record the model, dependency dictionary, and blockchain storage space into the empty classification planning information set, corresponding to the generated classification key.

[0126] Furthermore, the enterprise management method based on blockchain also includes the following steps:

[0127] Step S103, receiving a model development request, determining a model requester, performing a correlation analysis on the model requester, and determining whether there is a direct correlation.

[0128] In an embodiment of the present invention, by receiving a model development request, obtaining the request sending address of the model development request, and then performing personnel matching analysis on the request sending address to determine the model request person who sends the model development request, and then obtaining the request person information of the model request person, performing association analysis on the model request person according to the request person information to determine whether there is a direct association for use.

[0129] It can be understood that when using direct association, the relevant model requester has direct permission to use the model.

[0130] Specifically, Figure 4 A flow chart showing the use of direct association judgment in the method provided by an embodiment of the present invention.

[0131] Among them, in the preferred implementation manner provided by the present invention, the receiving of the model development request, determining the model requester, performing association analysis on the model requester, and judging whether there is a direct association specifically comprises the following steps:

[0132] Step S1031, receiving a model development request and obtaining a request sending address;

[0133] Step S1032, analyzing the request sending address to determine the model requester;

[0134] Step S1033, obtaining the requesting person information of the model requesting person;

[0135] Step S1034: performing association analysis on the model requester according to the requester information to determine whether there is a direct association.

[0136] Furthermore, the enterprise management method based on blockchain also includes the following steps:

[0137] Step S104, when there is a direct association of use, matching the target process model, and requesting personnel to perform direct use transmission to the model.

[0138] In an embodiment of the present invention, in the case of direct association of use, a direct transmission instruction is generated, and by matching the target process model, the target process model is directly transmitted to the model requester according to the direct transmission instruction, so that the model requester can directly use the target process model.

[0139] Specifically, Figure 5 A flow chart showing direct use of transmission in the method provided by an embodiment of the present invention is shown.

[0140] Among them, in the preferred implementation mode provided by the present invention, when there is a direct association of use, matching the target process model and performing direct use transmission to the model requesting personnel specifically includes the following steps:

[0141] Step S1041, when there is a direct association, a direct transmission instruction is generated;

[0142] Step S1042, matching the target process model;

[0143] Step S1043: According to the direct transmission instruction, the target process model is directly transmitted to the model requesting person for use.

[0144] Furthermore, the enterprise management method based on blockchain also includes the following steps:

[0145] Step S105, when there is no direct association of use, match the target process model and the indirectly associated personnel, and after the indirect feedback is passed, perform indirect use transmission to the model requesting personnel through the indirectly associated personnel.

[0146] In an embodiment of the present invention, in the absence of direct association, an indirect transmission instruction and an indirect feedback request are generated, and the target process model and the indirectly associated personnel are matched, and then the indirect feedback request is sent to the indirectly associated personnel, and then the indirect feedback information of the indirectly associated personnel is received, and the indirect feedback information is analyzed to determine whether the indirect feedback is passed. In the case of the indirect feedback being passed, the target process model is indirectly transmitted to the model requesting personnel through the indirectly associated personnel, so that the model requesting personnel can indirectly use the target process model.

[0147] Specifically, Figure 6 A flow chart of indirect use transmission in the method provided by an embodiment of the present invention is shown.

[0148] Among them, in the preferred implementation mode provided by the present invention, when there is no direct association for use, matching the target process model and the indirectly associated personnel, after the indirect feedback is passed, performing indirect use transmission to the model requesting personnel through the indirectly associated personnel specifically includes the following steps:

[0149] Step S1051, when there is no direct association, generating an indirect transmission instruction and an indirect feedback request;

[0150] Step S1052, matching the target process model and the indirectly associated personnel;

[0151] Step S1053, sending the indirect feedback request to the indirectly associated person;

[0152] Step S1054, receiving indirect feedback information from the indirectly associated person, and determining whether the indirect feedback is approved;

[0153] Step S1055, after the indirect feedback is passed, the indirect use transmission is performed to the model requesting person through the indirect associated person.

[0154] Furthermore, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0155] Among them, in another preferred embodiment provided by the present invention, a blockchain-based enterprise management system includes:

[0156] The product R&D monitoring unit 101 is used to monitor the enterprise product R&D, determine multiple enterprise R&D products, and generate R&D process information of multiple enterprise R&D products.

[0157] In an embodiment of the present invention, during the formal production and trial production process of an enterprise, the product R&D monitoring unit 101 performs product production monitoring, determines multiple enterprise products, obtains internal product information of the multiple enterprise products, and then performs R&D identification on the multiple product internal information. From the multiple enterprise products, multiple enterprise R&D products that are in the R&D and production process are selected, and from the multiple product internal information, R&D internal information corresponding to the multiple enterprise R&D products is selected, and then R&D product attributes of the multiple enterprise R&D products are extracted from the multiple R&D internal information. According to the multiple R&D product attributes, a basic R&D process analysis is performed on the multiple enterprise R&D products to generate multiple R&D process information.

[0158] Specifically, Figure 8It shows a structural block diagram of the product development monitoring unit 101 in the system provided by the embodiment of the present invention.

[0159] Among them, in the preferred embodiment provided by the present invention, the product development monitoring unit 101 specifically includes:

[0160] The production monitoring module 1011 is used to monitor the production of enterprise products and determine the products produced by multiple enterprises;

[0161] The information acquisition module 1012 is used to acquire the internal product information of the products produced by the enterprise;

[0162] The product selection module 1013 is used to identify the internal information of the multiple products and select multiple enterprise R&D products from the multiple enterprise production products;

[0163] The attribute extraction module 1014 is used to extract the R&D product attributes of the multiple R&D products of the enterprise from the internal product information corresponding to the multiple R&D products of the enterprise;

[0164] The process analysis module 1015 is used to perform basic R&D process analysis according to the multiple R&D product attributes to generate multiple R&D process information.

[0165] Furthermore, the blockchain-based enterprise management system also includes:

[0166] The model classification storage unit 102 is used to obtain multiple R&D process models according to the multiple R&D process information, and classify and store the multiple R&D process models according to the multiple enterprise R&D products based on blockchain technology.

[0167] In an embodiment of the present invention, the model classification storage unit 102 tracks the processes of multiple enterprise R&D products based on multiple R&D process information, obtains multiple R&D process models related to the R&D production process, such as the products themselves and related equipment, fixtures, and auxiliary tools of the multiple enterprise R&D products, and then constructs multiple blockchain storage spaces based on blockchain technology. Based on the R&D products of multiple enterprises, multiple R&D process models are classified and planned, and the classification planning information is recorded. Then, according to the classification planning information, multiple R&D process models are classified and stored in multiple blockchain storage spaces.

[0168] Specifically, Fig. 9 It shows a structural block diagram of the model classification storage unit 102 in the system provided by an embodiment of the present invention.

[0169] Among them, in the preferred embodiment provided by the present invention, the model classification storage unit 102 specifically includes:

[0170] The model acquisition module 1021 is used to track the processes of the multiple enterprise R&D products according to the multiple R&D process information and acquire multiple R&D process models;

[0171] A space construction module 1022 is used to construct multiple blockchain storage spaces based on blockchain technology;

[0172] A classification planning module 1023 is used to classify and plan the multiple R&D process models based on the multiple enterprise R&D products, and record classification planning information;

[0173] The classification storage module 1024 is used to classify and store the multiple R&D process models in the multiple blockchain storage spaces according to the classification planning information.

[0174] Furthermore, the blockchain-based enterprise management system also includes:

[0175] The association analysis and judgment unit 103 is used to receive a model development request, determine a model requester, perform association analysis on the model requester, and determine whether there is a direct association.

[0176] In an embodiment of the present invention, the association analysis and judgment unit 103 receives a model development request, obtains the request sending address of the model development request, then performs a personnel matching analysis on the request sending address, determines the model request person who sends the model development request, then obtains the request person information of the model request person, and performs an association analysis on the model request person according to the request person information to determine whether there is a direct association.

[0177] The direct usage transmission unit 104 is used to match the target process model when there is a direct usage association, and to perform direct usage transmission to the model requesting personnel.

[0178] In an embodiment of the present invention, in the case of direct association, the direct use transmission unit 104 generates a direct transmission instruction, matches the target process model, and then transmits the target process model to the model requester for direct use according to the direct transmission instruction, so that the model requester can directly use the target process model.

[0179] The indirect use transmission unit 105 is used to match the target process model and the indirectly associated personnel when there is no direct use association, and after the indirect feedback is passed, perform indirect use transmission to the model requesting personnel through the indirectly associated personnel.

[0180] In an embodiment of the present invention, in the absence of direct association, the indirect use transmission unit 105 generates an indirect transmission instruction and an indirect feedback request, matches the target process model and the indirectly associated personnel, and then sends the indirect feedback request to the indirectly associated personnel, and then receives the indirect feedback information of the indirectly associated personnel, and determines whether the indirect feedback is passed by analyzing the indirect feedback information. If the indirect feedback is passed, the target process model is indirectly used and transmitted to the model requesting personnel through the indirectly associated personnel, so that the model requesting personnel can indirectly use the target process model.

[0181] Specifically, Fig.10 It shows a structural block diagram of the indirect use transmission unit 105 in the system provided by the embodiment of the present invention.

[0182] Among them, in the preferred implementation mode provided by the present invention, the indirect use transmission unit 105 specifically includes:

[0183] The instruction generation module 1051 is used to generate an indirect transmission instruction and an indirect feedback request when there is no direct association;

[0184] A personnel matching module 1052 is used to match the target process model with indirectly associated personnel;

[0185] A request sending module 1053, configured to send the indirect feedback request to the indirectly associated person;

[0186] Feedback analysis module 1054, used to receive indirect feedback information from the indirectly associated personnel and determine whether the indirect feedback is passed;

[0187] The indirect transmission module 1055 is used to perform indirect use transmission to the model requesting person through the indirect associated person after the indirect feedback is passed.

[0188] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0189] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0190] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0191] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0192] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A blockchain-based enterprise management method, characterized in that: The method specifically comprises the following steps: Conduct enterprise product R&D monitoring, identify multiple enterprise R&D products, and generate R&D process information for multiple enterprise R&D products; According to the multiple R&D process information, multiple R&D process models are obtained, and based on the blockchain technology, the multiple R&D process models are classified and stored according to the multiple enterprise R&D products; Receive a model development request, determine the model requester, and perform a correlation analysis on the model requester to determine whether there is a direct correlation with the use; When there is a direct usage association, matching the target process model and requesting the personnel to perform direct usage transfer to the model; When there is no direct association of use, matching the target process model and the indirectly associated personnel, after the indirect feedback is passed, performing indirect use transmission to the model requesting personnel through the indirectly associated personnel; The method of obtaining multiple R&D process models based on the multiple R&D process information and classifying and storing the multiple R&D process models according to the multiple enterprise R&D products based on blockchain technology specifically includes the following steps: According to the multiple R&D process information, the processes of the multiple enterprise R&D products are tracked to obtain multiple R&D process models; Based on blockchain technology, build multiple blockchain storage spaces; Based on the multiple R&D products of the enterprise, classify and plan the multiple R&D process models, and record the classification planning information; According to the classification planning information, the multiple R&D process models are classified and stored in the multiple blockchain storage spaces; Wherein, based on the multiple R&D products of the enterprise, the multiple R&D process models are classified and planned, and the classification planning information is recorded, which specifically includes the following steps: Create an empty classification planning information collection; Perform a nested traversal for each product and each model. If the model belongs to the current product, check the product priority and the model's R&D stage. Concatenate the product category, model R&D stage, and product priority into a string, and hash the string to generate a unique classification key; Recursively process the model's dependencies and generate a dependency dictionary; Find the corresponding blockchain storage space according to the product category; Record the model, dependency dictionary and blockchain storage space into the empty classification planning information set, corresponding to the generated classification key; According to the R&D process information, the process of tracking the R&D products of the enterprises is carried out to obtain the R&D process models, which specifically includes the following steps: Multiple R&D process information are combined into an R&D process information matrix, where each row in the R&D process information matrix represents an R&D project, and each column represents numerical information of an R&D step; A high-dimensional feature matrix of the R&D process information matrix is ​​extracted using a first deep neural network; The high-dimensional feature matrix is ​​standardized in turn, and then the standardized high-dimensional features are subjected to polynomial feature generation to obtain a polynomial feature matrix; The polynomial feature matrix is ​​used to group the data using an adaptively optimized clustering algorithm to obtain a clustering result, in which each element represents the cluster to which the corresponding data point belongs; Calculate the statistical characteristics of each cluster, including the number of samples, average distance, maximum / minimum distance, variance of the covariance matrix, and the correlation matrix; Calculate the eigenvalues ​​of the covariance matrix to analyze the distribution characteristics of the data; and calculate the entropy of the distance between the cluster center and the data point center to measure the complexity and uncertainty of the data; The polynomial feature matrix is ​​transformed through nonlinear transformation and deep feature extraction, combined with adaptive weights to generate additional high-level features; The number of samples, cluster center, average distance, maximum / minimum distance, variance, correlation matrix, eigenvalues, entropy, and high-level features are used as models for each cluster.

2. According to claim 1, a blockchain-based enterprise management method is characterized in that: The monitoring of enterprise product R&D, determining multiple enterprise R&D products, and generating R&D process information of multiple enterprise R&D products specifically include the following steps: Conduct enterprise product production monitoring and identify products produced by multiple enterprises; Obtaining internal product information of multiple products produced by the enterprise; Perform R&D identification on the internal information of the plurality of products, and select a plurality of enterprise R&D products from the products produced by the plurality of enterprises; Extracting R&D product attributes of the multiple R&D products of the enterprise from the internal product information corresponding to the multiple R&D products of the enterprise; According to the multiple R&D product attributes, basic R&D process analysis is performed to generate multiple R&D process information.

3. According to the enterprise management method based on blockchain in claim 1, it is characterized in that: The polynomial feature matrix is ​​grouped using an adaptively optimized clustering algorithm to obtain clustering results, which specifically includes the following steps: Randomly select a data point from the polynomial features as the initial cluster center; For each data point, calculate the distance between the single data point and all cluster centers and assign it to the cluster with the closest distance; A nonlinear function is used to convert the importance of data points into weight values, and the weighted sum of the distances from the data points in the cluster to the cluster center is used as the loss function; Calculate the gradient of the loss function with respect to the cluster center, use the momentum update strategy for adaptive iterative optimization, and adaptively adjust the learning rate according to the distribution of data points within the cluster; the learning rate determines the step when the cluster center is updated each time, and the learning rate decreases as the variance of the distribution of data points within the cluster increases, and increases when the variance is small; Determine whether the change of cluster centers converges. If the change of all cluster centers is less than the preset threshold, the algorithm terminates and obtains the clustering result; otherwise, return to the data point allocation step to continue iteration.

4. According to claim 3, a blockchain-based enterprise management method is characterized in that: The polynomial feature matrix is ​​transformed through nonlinear transformation and deep feature extraction, combined with adaptive weights to generate additional high-level features. The specific steps include: Perform a sine transform on the polynomial feature matrix to capture the periodic information in the data and obtain a sinusoidal feature matrix; The polynomial feature matrix is ​​exponentially transformed to amplify the eigenvalues ​​and capture the exponential growth information in the data to obtain the exponential feature matrix; The polynomial characteristic matrix is ​​logarithmically transformed to compress the eigenvalue range and prevent uneven distribution of eigenvalues, thereby obtaining a logarithmic characteristic matrix; Merge the sinusoidal feature matrix, exponential feature matrix, logarithmic feature matrix and polynomial feature matrix to capture the nonlinear relationship in the data and obtain the merged features; Using a second deep neural network to extract features from the merged features, and obtaining an output feature matrix of an intermediate layer of the deep neural network; Two adaptive weights are introduced, and the merged features and the feature matrix of the intermediate layer output of the deep neural network are adjusted and combined through the two adaptive weights to obtain adaptive features.

5. The enterprise management method based on blockchain according to claim 4 is characterized in that: The receiving of the model development request, determining the model requester, performing association analysis on the model requester, and judging whether there is a direct association specifically comprises the following steps: Receive model development requests and obtain the address where the requests are sent; Analyze the address where the request is sent to determine the person who requested the model; Obtaining requester information of a person requesting the model; According to the requesting person information, an association analysis is performed on the model requesting person to determine whether there is a direct association with use.

6. The enterprise management method based on blockchain according to claim 5 is characterized in that: When there is a direct association, the target process model is matched, and the model is requested to directly use the transmission specific The following steps are involved: When there is a direct association for use, a direct transfer instruction is generated; Matching target process model; According to the direct transfer instruction, the target process model is directly transferred to the model requester for use.

7. The enterprise management method based on blockchain according to claim 6 is characterized in that: When there is no direct association, matching the target process model and the indirectly associated personnel, after the indirect feedback is passed, performing indirect use transmission to the model requesting personnel through the indirectly associated personnel specifically includes the following steps: generating an indirect transmission instruction and an indirect feedback request when there is no direct association for use; Matching target process models and indirectly related personnel; sending the indirect feedback request to the indirectly associated person; Receiving indirect feedback information from the indirectly associated person, and determining whether the indirect feedback is approved; After the indirect feedback is passed, the indirect use transmission is performed to the model requesting person through the indirect associated person.

8. An enterprise management system based on blockchain, characterized in that: The enterprise management method based on blockchain as described in any one of claims 1 to 7 above is applied, wherein the system includes a product development monitoring unit, a model classification storage unit, an association analysis and judgment unit, a direct use transmission unit and an indirect use transmission unit, wherein: A product R&D monitoring unit, used to monitor the R&D of enterprise products, determine multiple enterprise R&D products, and generate R&D process information of multiple enterprise R&D products; A model classification storage unit, used to obtain multiple R&D process models according to the multiple R&D process information, and classify and store the multiple R&D process models according to the multiple enterprise R&D products based on blockchain technology; A correlation analysis and judgment unit, used to receive a model development request, determine a model requester, perform correlation analysis on the model requester, and determine whether there is a direct correlation with use; A direct usage transmission unit, for matching a target process model when there is a direct usage association, and performing a direct usage transmission to a person requesting the model; The indirect use transmission unit is used to match the target process model and the indirectly associated personnel when there is no direct use association, and after the indirect feedback is passed, perform indirect use transmission to the model requesting personnel through the indirectly associated personnel.

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