Knowledge flow analysis method, electronic equipment, computer storage medium and program product

Abstract

The embodiment of the invention provides a knowledge flow analysis method, electronic equipment, a computer storage medium and a program product. The method comprises the steps that a knowledge network diagram of a target mechanism is acquired, in the knowledge network diagram, knowledge management units of the target mechanism serve as nodes, knowledge flow relations between the knowledge management units serve as edges, and the nodes comprise attribute information used for indicating knowledge quantity; performing spatial autocorrelation analysis on adjacent nodes in the knowledge network diagram according to attribute information, used for indicating knowledge quantity, of the nodes to obtain spatial autocorrelation between the adjacent nodes; and performing knowledge flow analysis on the target mechanism according to the spatial autocorrelation. According to the scheme, the flow condition of knowledge in the target mechanism can be effectively analyzed.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technologies, and in particular, to a method for knowledge flow analysis, an electronic device, a computer storage medium, and a computer program product. Background Art

[0002] In the era of knowledge economy, knowledge is the origin of an enterprise's core competitiveness. However, in the context of traditional organizations, due to the lack of data support, the flow of knowledge is difficult to be accurately observed. Enterprise managers cannot clearly understand how knowledge spreads, accumulates, and is utilized within the organization, resulting in problems such as information silos, knowledge retention, and uneven knowledge diffusion, which greatly limit the innovation ability and operational efficiency of enterprises. How to address these challenges, timely understand the flow of knowledge in enterprises to improve the knowledge transfer efficiency, and further improve the overall operational efficiency of enterprises has become an urgent problem to be solved. Summary of the Invention

[0003] In view of this, the embodiments of the present application provide a knowledge flow analysis solution to at least partially solve the above problems.

[0004] According to a first aspect of the embodiments of the present application, a method for knowledge flow analysis is provided, including:

[0005] Obtain a knowledge network graph of a target institution, where in the knowledge network graph, knowledge management units of the target institution are used as nodes, and knowledge flow relationships between knowledge management units are used as edges, and the nodes include attribute information for indicating the amount of knowledge;

[0006] According to the attribute information for indicating the amount of knowledge of the nodes, perform spatial autocorrelation analysis on adjacent nodes in the knowledge network graph to obtain the spatial autocorrelation between adjacent nodes;

[0007] According to the spatial autocorrelation, perform knowledge flow analysis on the target institution.

[0008] According to a second aspect of the embodiments of the present application, an electronic device is provided, including: a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus; the memory is used for storing a computer program; the processor is used for executing the knowledge flow analysis method described in the first aspect above by running the computer program stored on the memory.

[0009] According to a third aspect of the embodiments of the present application, a computer storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the knowledge flow analysis method described in the first aspect is implemented.

[0010] According to a fourth aspect of the embodiments of the present application, there is provided a computer program product, including a computer program which, when executed by a processor, implements the knowledge flow analysis method as described in the first aspect.

[0011] According to the knowledge flow analysis solution provided by the embodiments of the present application, a knowledge network graph of a target organization can be obtained. Among them, in the knowledge network graph, the knowledge management units of the target organization can be used as nodes, and the knowledge flow relationships between the knowledge management units can be used as edges. The nodes include attribute information for indicating the amount of knowledge. Then, according to the attribute information of the nodes for indicating the amount of knowledge, spatial autocorrelation analysis is performed on adjacent nodes in the knowledge network graph to obtain the spatial autocorrelation between adjacent nodes. After that, according to the spatial autocorrelation between adjacent nodes, knowledge flow analysis of the target organization can be realized. Through such a solution, the flow of knowledge in the target organization (such as including but not limited to enterprises, etc.) can be effectively analyzed, and information such as the spread, accumulation, and utilization of knowledge within the organization of the target organization can be clearly presented to the managers of the target organization, thereby reducing the risks of problems such as information islands, knowledge retention, and uneven knowledge diffusion, facilitating the effective improvement of the knowledge transfer efficiency within the target organization, and further being conducive to improving the overall operation efficiency of the target organization. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0013] Figure 1 It is a schematic diagram of a knowledge flow analysis system according to an embodiment of the present application.

[0014] Figure 2 It is a flowchart of the steps of a knowledge flow analysis method according to an embodiment of the present application.

[0015] Figure 3 It is an alternative flowchart of the steps for generating the knowledge network graph of the embodiments of the present application.

[0016] Figure 4 It is an alternative flowchart of the method for spatial autocorrelation analysis of the embodiments of the present application.

[0017] Figure 5 It is a schematic diagram of a scenario of an example of the knowledge flow analysis solution of the embodiments of the present application.

[0018] Figure 6 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Detailed Implementation Manner

[0019] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art shall fall within the protection scope of the embodiments of the present application.

[0020] In the era of knowledge economy, knowledge is the origin of the core competitiveness of enterprises. How to understand the existing knowledge volume and knowledge flow situation of enterprises, effectively promote the accumulation, sharing and utilization of knowledge, and then improve the knowledge value has become a key challenge faced by knowledge-intensive enterprises. However, in the traditional organizational context, due to the lack of data support, the flow of knowledge is often difficult to observe, and enterprise managers cannot effectively master how knowledge spreads, accumulates and is utilized. As a result, problems such as information islands, knowledge stagnation and uneven knowledge diffusion have occurred, which have greatly restricted the innovation ability and operation efficiency of enterprises.

[0021] The following further illustrates the specific implementation of the embodiments of the present application with reference to the accompanying drawings of the embodiments of the present application.

[0022] Figure 1 An exemplary system applicable to the solution of the embodiments of the present application is shown. As Figure 1 shown, the system 100 may include a cloud server 102, a communication network 104, and / or one or more user devices 106. Figure 1 For example, there are multiple user devices 106, and an application for displaying web pages is provided on the user device 106.

[0023] The cloud server 102 can be any suitable device for storing information, data, programs, and / or any other appropriate type of content, including but not limited to distributed storage system devices, server clusters, computing cloud server clusters, etc. In some embodiments, the cloud server 102 can perform any suitable function. For example, when implementing the solution of the embodiments of the present application by the cloud server 102, in some embodiments, the cloud server 102 can be used to execute a knowledge flow analysis method. As an alternative example, in some embodiments, the cloud server 102 can first obtain a knowledge network diagram of a target organization, where in the knowledge network diagram, the knowledge management units of the target organization are used as nodes, and the knowledge flow relationships between the knowledge management units are used as edges, and the nodes include attribute information for indicating the amount of knowledge; furthermore, the spatial autocorrelation analysis can be performed on adjacent nodes in the knowledge network diagram according to the attribute information of the nodes for indicating the amount of knowledge to obtain the spatial autocorrelation between adjacent nodes; then, the knowledge flow analysis of the target organization can be performed according to the spatial autocorrelation. In some embodiments, the cloud server 102 can send the result obtained by the knowledge flow analysis to the user device 106 so that the user device 106 can display the result.

[0024] In some embodiments, the communication network 104 can be any suitable combination of one or more wired and / or wireless networks. For example, the communication network 104 can include any one or more of the following: the Internet, an intranet, a wide area network (WAN), a local area network (LAN), a wireless network, a digital subscriber line (DSL) network, a frame relay network, an asynchronous transfer mode (ATM) network, a virtual private network (VPN), and / or any other suitable communication network. The user device 106 can be connected to the communication network 104 through one or more communication links (for example, the communication link 112), and the communication network 104 can be linked to the cloud server 102 through one or more communication links (for example, the communication link 114). The communication link can be any communication link suitable for transmitting data between the user device 106 and the cloud server 102, such as a network link, a dial-up link, a wireless link, a hardwired link, any other suitable communication link, or any suitable combination of such links.

[0025] Optionally, the user device 106 may include any one or more user devices suitable for presenting information, interacting with the user, and so on. In some embodiments, the user device 106 may include any suitable type of device. For example, in some embodiments, the user device 106 may include a mobile device, a tablet computer, a laptop computer, a desktop computer, and / or any other suitable type of user device.

[0026] Based on the above system, an embodiment of the present application provides a knowledge flow analysis solution, which will be described below through multiple embodiments.

[0027] Figure 2 FIG. is a flowchart of steps of a knowledge flow analysis method according to an embodiment of the present application. According to a first aspect in the embodiments of the present application, a knowledge flow analysis method is provided. Referring to Figure 2 as shown, the method includes steps S202, S204, and S206. Specifically:

[0028] S202: Obtain a knowledge network graph of a target organization.

[0029] In the embodiments of the present application, in the knowledge network graph, the knowledge management units of the target organization can be used as nodes, and the knowledge flow relationships between the knowledge management units can be used as edges. The nodes include attribute information for indicating the amount of knowledge.

[0030] In the embodiments of the present application, the target organization can be any organization. For example, it can include but is not limited to enterprises, schools, societies, etc., or it can also be a certain department thereof. The knowledge management unit can be an entity that manages knowledge in the target organization. Here, the entity can be a department, a team, or an individual in the target organization. For example, taking the target organization as an enterprise, there can be multiple departments (such as including but not limited to the personnel department, the finance department, the legal department, etc.) as multiple knowledge management units; or, multiple employees of the enterprise can also be used as multiple knowledge management units. Another example is taking the target organization as a certain department of an enterprise, then multiple employees of this department can also be used as multiple knowledge management units. Just do it according to actual needs. In the following, the target organization can be an enterprise and the knowledge management unit can be an employee for illustration, and the rest of the cases can be analogized.

[0031] The knowledge network diagram in the embodiments of the present application is a network model constructed for a target organization based on the social network analysis method. A node is the basic unit in the knowledge network diagram. In the embodiments of the present application, a node transmits and receives knowledge through connections with other nodes, and the connection degree of a node (i.e., the number of connections of a node with other nodes) determines its importance in the knowledge diffusion process. In the embodiments of the present application, the knowledge management units of the target organization are used as the nodes of the knowledge network diagram, the knowledge flow relationship between the knowledge management units is used as the edges of the knowledge network diagram, and attribute information for indicating the amount of knowledge is set for the nodes, which can facilitate the analysis of knowledge flow in the target organization.

[0032] The knowledge network diagram in the embodiments of the present application can be implemented in any network diagram structure form. In some alternative embodiments, a process of generating the knowledge network diagram is further included before step S202. For example, as Figure 3 shown in the flowchart, the process of generating the knowledge diagram may include the following steps S200A to S200D:

[0033] S200A: Obtain the organizational structure information of the target organization and the information of the shared files shared among the knowledge management units of the target organization.

[0034] The organizational structure information of the target organization may include relevant information of each organization (where the organization may include departments, employees, etc.) that make up the target organization. Through the organizational structure information of the target organization, each knowledge management unit included in the target organization can be determined.

[0035] The shared files may be files shared and used among the knowledge management units. The shared files can record knowledge, and the knowledge in different shared files may be different or the same. By accessing the shared files (for example, the access here may include but is not limited to reading, downloading, forwarding, etc.), the knowledge management unit can obtain and use the knowledge recorded in the shared files and accumulate the amount of knowledge. Optionally, the shared files may include various suitable types of readable documents.

[0036] The above-mentioned organizational structure information and the information of the shared files can be pre-stored (for example, stored in a database, a memory, etc. including but not limited to these), and can be directly obtained when needed.

[0037] S200B: Determine the knowledge quantity weight of the shared files according to the accessed information of the shared files.

[0038] In the embodiments of the present application, the amount of knowledge can be used to measure the total amount of knowledge that a node has at a certain moment. The amount of knowledge increases through an individual's innovative activities (such as self-study) and by absorbing knowledge from other nodes. In the embodiments of the present application, the amount of knowledge can be used as a quantitative indicator to measure the total amount of knowledge accumulated, acquired, and used by a knowledge management unit. Optionally, the amount of knowledge can be calculated based on the files accessed by the knowledge management unit and the amount of knowledge they contain, reflecting the contribution degree of the knowledge management unit in the knowledge sharing and innovation activities of the target organization.

[0039] Optionally, the access information of the shared file can include, for example, but not limited to: information related to which knowledge management unit the role accessing the shared file belongs to, information related to the content accessed by the knowledge management unit in the shared file, the number of times the shared file has been accessed, the number of people accessing the shared file, the access time of the shared file, etc., at least one of them.

[0040] Optionally, the access behavior of the knowledge management unit to the shared file can be recorded, so that the access information of the shared file can be pre-stored and directly obtained when needed.

[0041] In the embodiments of the present application, the knowledge amount weight of the shared file can be used to measure the importance of the knowledge in the shared file. Generally speaking, the greater the knowledge amount weight, the higher the importance of the indicated knowledge in the shared file; the smaller the knowledge amount weight, the lower the importance of the indicated knowledge in the shared file.

[0042] The determination method of the knowledge amount weight of the shared file can be selected in any way. For example, in one embodiment, different knowledge amount weights can be assigned to the shared file according to the number of access times. The more the access times, the greater the assigned knowledge amount; the fewer the access times, the smaller the assigned knowledge amount. In another embodiment, different knowledge amount weights can be assigned to the shared file according to the number of people accessing the shared file. The more the number of people accessing, the greater the assigned knowledge amount; the fewer the number of people accessing, the smaller the assigned knowledge amount.

[0043] In some alternative embodiments, the knowledge amount weight of the shared file can be determined in the following way: determine the knowledge amount weight of the shared file according to the number of people accessing the shared file and the number of access times.

[0044] It should be understood that since the content of the shared file is generally sensitive and not suitable for direct viewing, in the embodiments of the present application, the knowledge amount weight is not directly determined by the content of the shared file, but is indirectly determined by behavioral data such as the number of people accessing the shared file and the number of access times, so as to indirectly evaluate the importance of the shared file, innovatively avoiding the privacy problem of directly viewing the content of the shared file, and at the same time providing a scientific quantitative analysis basis for the knowledge content.

[0045] In addition, since the knowledge quantity weight of the shared file is determined in the above optional solution by referring to both the number of accesses and the access times of the shared file, the determined knowledge quantity weight is more accurate and reliable.

[0046] It should be understood that the knowledge quantity weight of the shared file can be determined according to the number of accesses and the access times of the shared file in any feasible manner, and can be set as needed. For example, optionally, the target quantile value of the shared file can be calculated using the number of accesses and the access times (for example, the target quantile value can be obtained by performing a weighted sum processing on the number of accesses and the access times according to a preset weight; or other methods can also be used for calculation as long as the requirements can be met), so as to score the shared file. In addition, multiple quantile value ranges can be divided, and it can be determined which range the target quantile value is in. The higher the range, the higher the knowledge quantity level of the shared file, the greater the knowledge contribution and importance of the shared file, and thus the higher the corresponding knowledge quantity weight (that is, the knowledge quantity weight of the shared file in the high range should be greater than that of the shared file in the low range). This method of dividing multiple quantile value ranges can effectively distinguish the usage frequency and relative importance of the shared file. Different knowledge quantity weights can be assigned to each quantile value range. Therefore, once the quantile value range of the shared file is obtained, the corresponding knowledge quantity weight can be determined.

[0047] For example, assume that it is divided into 10 quantile value ranges, namely quantile value ranges 1 to 10. The quantile value range corresponding to quantile value range 1 is (0, a1] and the assigned knowledge quantity weight is w1, the quantile value range corresponding to quantile value range 2 is (a1, a2] and the assigned knowledge quantity weight is w2, and so on. The quantile value range corresponding to quantile value range 10 is (a9, a10] and the assigned knowledge quantity weight is w10. Then, for example, if the target quantile value of a certain shared file calculated according to the number of accesses and the access times is within (a1, a2], it can be determined that the shared file is in quantile value range 2, and the knowledge quantity weight of the shared file can be correspondingly determined to be w2. The rest of the cases can be deduced by analogy and will not be elaborated here. In addition, the above example is a simple example and does not impose any limitation on the embodiments of the present application.

[0048] It should be understood that the calculation of the knowledge quantity in the embodiments of the present application dynamically adjusts the knowledge quantity weight according to the quantile value range of the shared file, and can reflect the change of the importance of knowledge over time and usage. And this dynamicization of the knowledge quantity weight is difficult to achieve in traditional systems, endowing the system with more flexible knowledge management capabilities.

[0049] S200C: Determine the knowledge quantity for each knowledge management unit according to the accessed information and the knowledge quantity weight.

[0050] Optionally, the amount of knowledge of a certain knowledge management unit can be calculated according to the following formula:

[0051] Amount of knowledge = ∑(shared files accessed by the knowledge management unit × knowledge amount weight of the shared file)

[0052] That is, the amount of knowledge of the knowledge management unit is the total weight of all accessed files, reflecting the total amount of knowledge it has obtained, and can reflect the contribution degree of the knowledge management unit in the knowledge sharing and innovation activities of the target organization. For example, assume that the knowledge management unit (i.e., the node) has accessed a total of 3 shared files, and the knowledge amount weights of the 3 shared files are w1, w2, and w3 respectively. Then the amount of knowledge of this knowledge management unit can be calculated as: w1 + w2 + w3, and the rest can be deduced by analogy.

[0053] Thus, according to the above formula, the amount of knowledge of each knowledge management unit can be conveniently calculated for use in subsequent steps.

[0054] S200D: Generate a knowledge network diagram based on the organizational structure information, the access information of each knowledge management unit to the shared files, and the amount of knowledge of each knowledge management unit.

[0055] It can be understood that through the organizational structure information of the target organization, each knowledge management unit of the target organization can be determined to be used as each node of the knowledge network diagram; through the access information of each knowledge management unit to the shared files, the knowledge flow relationship between the knowledge management units can be determined to be used as the edges of the knowledge network diagram; through the amount of knowledge of each knowledge management unit, it is convenient to assign attribute information for indicating the amount of knowledge to each node. Thus, it is convenient to effectively generate a knowledge network diagram for subsequent knowledge flow analysis.

[0056] Based on this, the optional embodiments of steps S200A to S200D in the embodiments of the present application can effectively generate a knowledge network diagram of the target organization by using the relevant information of the target organization, so that the subsequent solutions can effectively use the knowledge network diagram for analysis to analyze the flow of knowledge in the target organization (such as including but not limited to enterprises, etc.), and can clearly present information such as the dissemination, accumulation, and utilization of knowledge within the organization of the target organization to the managers of the target organization, thereby reducing the risks of information islands, knowledge retention, and uneven knowledge diffusion, facilitating the effective improvement of the knowledge transfer efficiency within the target organization, and further being beneficial to improving the overall operation efficiency of the target organization.

[0057] The knowledge network diagram in the embodiments of the present application can be implemented in any network diagram structure form and can be selected according to needs. In some alternative embodiments, step S200D can be implemented as: generating a scale-free network diagram as the knowledge network diagram according to the organization information, the access information of each knowledge management unit to the shared files, and the knowledge amount of each knowledge management unit.

[0058] A scale-free network is a network structure whose node degree distribution follows a power-law distribution, that is, most nodes in the network have a small number of connections, but a few nodes (referred to as "hub nodes") have a large number of connections. In this network structure, hub nodes play an important role in the knowledge transfer process and can quickly spread innovation and knowledge.

[0059] It should be understood that in the embodiments of the present application, by generating a knowledge network diagram in the form of a scale-free network diagram for the target organization and adopting the unique topological structure of its scale-free network diagram, compared with other types of network structures, it can have significant advantages in promoting knowledge transfer and innovation diffusion. The hub nodes in the scale-free network can play a key role in promoting knowledge flow and can greatly accelerate the information and knowledge dissemination process.

[0060] In the above alternative solution, the embodiments of the present application develop an alternative solution for knowledge flow analysis for measuring and optimizing the internal knowledge flow of a digital organization dedicated to the target organization by combining social network analysis methods, especially the knowledge flow mechanism of scale-free networks. Specifically, this alternative solution can use the digital trace data of employees to construct a knowledge network diagram of a scale-free network, and real-time track and analyze the flow path, diffusion speed, and knowledge transfer efficiency between nodes of knowledge within the target organization (such as including but not limited to enterprises, etc.). By accurately identifying key knowledge hub nodes, this alternative solution can reveal the bottlenecks in the internal knowledge flow of the organization and help enterprises formulate more scientific knowledge sharing and innovation strategies. With the help of this alternative solution, the target organization can build a comprehensive and dynamic knowledge flow monitoring platform, significantly improve the rate and quality of knowledge sharing, and promote collaboration and innovation within the organization.

[0061] It should also be understood that traditional knowledge management solutions are difficult to quantify and track the knowledge dissemination path. Compared with traditional solutions, in the embodiments of the present application, through alternative solutions including but not limited to the above, using a knowledge network diagram (such as a scale-free network diagram) is convenient for real-time capturing of how knowledge flows in the target organization, especially for quantitative analysis of the diffusion speed of knowledge, the interaction frequency between nodes, etc., filling the deficiencies of traditional solutions in dynamic monitoring.

[0062] In some alternative embodiments, when collecting data such as information on shared files and accessed information of shared files, it can be implemented based on the application program of the shared document, and the threshold for collecting relevant data can be reduced through the application program of the shared document. For example, it can be implemented by means of online data collection. In some examples, the application program of the shared file can cover data collection across the entire link from the production to the use of the shared file, and its functions can include: ① File creation record: Record file-related attributes such as the author and creation time of each shared file. ② Reading behavior tracking: Record information such as the number of readings and readers of each shared file. Through the collection of these data, a complete knowledge asset database can be constructed, providing a solid foundation for subsequent analysis.

[0063] Optionally, after calculating the knowledge amount of the knowledge management unit (node) at a certain moment, the knowledge growth rate of the knowledge management unit during this time period can also be determined based on the calculated knowledge amount and the knowledge amount at a previous moment. The knowledge growth rate can represent the proportion of the increase in the knowledge amount of the node within a specific time period, and can be used as an important indicator to measure the knowledge diffusion speed in the network, and thus can be effectively used for knowledge management analysis.

[0064] S204: Perform a spatial autocorrelation analysis on adjacent nodes in the knowledge network diagram according to the attribute information of the node for indicating the knowledge amount, and obtain the spatial autocorrelation between adjacent nodes.

[0065] In the embodiments of the present application, any method can be adopted to implement the spatial autocorrelation analysis. For example, in some alternative embodiments, the Moran coefficient can be used to perform a spatial autocorrelation analysis on adjacent nodes in the knowledge network diagram, so as to obtain the spatial autocorrelation between adjacent nodes.

[0066] It should be understood that in the embodiments of the present application, a quantitative means for detecting the knowledge island phenomenon is provided by using the Moran coefficient. The Moran coefficient is a statistical indicator for measuring spatial autocorrelation. The Moran coefficient can be used to measure the spatial autocorrelation of knowledge in an organization, and evaluate whether knowledge is concentrated at a specific knowledge management unit or is more evenly distributed throughout the knowledge network.

[0067] It should also be understood that, in general, spatial autocorrelation describes whether there is similarity or correlation between the values ​​of a variable at adjacent positions in geographic space. The Moran's coefficient is used to judge the correlation between point or regional data in geographic space by evaluating them, and is commonly used in fields such as geography, ecology, and economics. In an embodiment of the present application, the Moran's coefficient is creatively transferred to knowledge management analysis, and the spatial autocorrelation analysis of adjacent nodes in the knowledge network diagram can be effectively performed to obtain the spatial autocorrelation between adjacent nodes, which can reveal the concentration and diffusion pattern of knowledge within the organization, and help enterprises identify the phenomenon of insufficient knowledge concentration or diffusion. Such spatial autocorrelation analysis is lacking in traditional knowledge management solutions, and can intuitively reflect the flow and distribution pattern of knowledge.

[0068] In addition, the above optional scheme is used in the embodiment of the present application to facilitate the identification of knowledge islands. A positive Moran's coefficient indicates that nodes with similar knowledge levels are concentrated together, while a negative Moran's coefficient indicates that nodes with different knowledge levels are adjacent. When the Moran's coefficient is high, it indicates that knowledge is too concentrated in certain nodes, indicating that there may be knowledge islands. Therefore, this scheme can detect these problems in a timely manner through dynamic monitoring of the Moran's coefficient, and provide decision-making basis for managers to take measures to promote balanced knowledge dissemination.

[0069] In some optional embodiments, referring to Figure 4 As shown in the flowchart, the Moran coefficient can be used in the optional manner of the following steps S2042 to S2044 to perform spatial autocorrelation analysis on adjacent nodes in the knowledge network graph:

[0070] S2042: Determine a calculation formula for the Moran's coefficient based on the amount of knowledge of adjacent nodes in the knowledge network graph at a certain moment, the average amount of knowledge of all nodes at that moment, the connection weights between adjacent nodes, and the variance of the amount of knowledge at that moment.

[0071] Optionally, the Moran coefficient calculation formula can be determined as follows:

[0072]

[0073] Where: S(t) is the Moran coefficient at time t; S is the set of all nodes in the knowledge network graph;

[0074] σ 2 (t) is the knowledge variance of all nodes at time t, which can be calculated by the following formula: Here, υ(t) is the average knowledge of all nodes at time t, N is the total number of nodes, and υ i (t) is the amount of knowledge of the i-th node at time t;

[0075] υ i (t) and vj K(i, t) and K(j, t) are the knowledge amounts of the i-th node and the j-th node at time t respectively, and υ(t) is the average knowledge amount of all nodes at time t;

[0076] ω i,j represents the connection weight between the i-th node and the j-th node (which can characterize the intensity of knowledge flow), and its definition is: where X(i, j) represents whether there is a direct connection between the i-th node and the j-th node (if there is a direct connection, it can be considered that the two nodes are adjacent), and if there is a direct connection, the value is 1, otherwise it is 0.

[0077] The value range of the Moran coefficient is usually between -1 and 1. When the Moran coefficient is a relatively high positive value, it indicates that knowledge shows positive correlation, that is, knowledge is concentrated in certain specific nodes (i.e., knowledge islands). The Moran coefficient S(t) at time t can be determined by the above formula.

[0078] S2044: According to the Moran coefficient calculation formula, perform spatial autocorrelation analysis on adjacent nodes in the knowledge network graph.

[0079] After obtaining the Moran coefficient calculation formula, this formula can be used to implement spatial autocorrelation analysis to obtain the spatial autocorrelation between adjacent nodes.

[0080] Based on this, in the embodiments of the present application, through the optional solutions of the above steps S2042 to S2044, the Moran coefficient can be effectively used to perform spatial autocorrelation analysis on adjacent nodes in the knowledge network graph, which is convenient to obtain the spatial autocorrelation between adjacent nodes for subsequent knowledge flow analysis of the target organization.

[0081] S206: Perform knowledge flow analysis on the target organization according to the spatial autocorrelation.

[0082] After obtaining the spatial autocorrelation between adjacent nodes in step S204, knowledge flow analysis can be performed on the target organization to obtain the corresponding analysis results.

[0083] Based on this, through the knowledge flow analysis solution provided in the above steps S202 to S206, a knowledge network diagram of the target organization can be obtained. Among them, in the knowledge network diagram, the knowledge management units of the target organization can be used as nodes, and the knowledge flow relationship between the knowledge management units can be used as edges. The nodes include attribute information for indicating the amount of knowledge. Then, according to the attribute information of the nodes for indicating the amount of knowledge, spatial autocorrelation analysis is performed on adjacent nodes in the knowledge network diagram to obtain the spatial autocorrelation between adjacent nodes. After that, according to the spatial autocorrelation between adjacent nodes, knowledge flow analysis of the target organization can be realized. Through such a solution, the flow of knowledge in the target organization (such as including but not limited to enterprises, etc.) can be effectively analyzed, and the information such as the spread, accumulation, and utilization of knowledge within the organization of the target organization can be clearly presented to the managers of the target organization, thereby reducing the risks of information silos, knowledge retention, and uneven knowledge diffusion, facilitating the effective improvement of the knowledge transfer efficiency within the target organization, and further contributing to the improvement of the overall operation efficiency of the target organization.

[0084] In some alternative embodiments, before step S206, the knowledge flow analysis method in the embodiments of the present application further includes: calculating the coefficient of variation of the nodes in the knowledge network diagram; determining the knowledge distribution uniformity of the nodes in the knowledge network diagram according to the coefficient of variation. Optionally, in step S206, knowledge flow analysis of the target organization can be performed according to the spatial autocorrelation and the knowledge distribution uniformity.

[0085] In the embodiments of the present application, the coefficient of variation of the nodes is an index for measuring the distribution uniformity of knowledge among different nodes (i.e., knowledge management units). By calculating the coefficient of variation of the knowledge amount of each node, it is evaluated whether there is a significant imbalance phenomenon of knowledge. The smaller the coefficient of variation, the more uniform the distribution of knowledge among the nodes, and the smaller the difference; the larger the coefficient of variation, the greater the difference in the knowledge amount between different nodes, and there may be a phenomenon that knowledge is overly concentrated in some nodes while the knowledge amount of other nodes is less.

[0086] Based on this, on the one hand, in the embodiments of the present application, by calculating the coefficient of variation of the nodes in the knowledge network graph and then determining the knowledge distribution uniformity of the nodes in the knowledge network graph according to the coefficient of variation, it is convenient to accurately and conveniently analyze the knowledge flow of the target organization according to the spatial autocorrelation and the knowledge distribution uniformity. On the other hand, in the embodiments of the present application, such an alternative solution can quantify knowledge imbalance, that is, through the coefficient of variation, the degree of imbalance in knowledge distribution among different nodes can be quantified, thus providing a means to directly reveal the knowledge distribution difference, which is difficult to measure in the traditional solution, while this solution can dynamically reflect the balance of knowledge distribution through the calculation of the coefficient of variation. On the further hand, in the embodiments of the present application, such an alternative solution can realize the early identification of knowledge islands, that is, the coefficient of variation adopted in this solution can be used as an early signal for identifying knowledge islands. When the knowledge volume of a certain node (i.e., the knowledge management unit) significantly exceeds that of other nodes, the coefficient of variation will increase, so as to prompt the manager of the target organization of the possible knowledge centralization phenomenon and help take measures early to optimize knowledge sharing.

[0087] In some alternative embodiments, the coefficient of variation of the nodes in the knowledge network graph can be calculated according to the standard deviation of the knowledge volume of all nodes in the knowledge network graph at a certain moment and the average knowledge volume of all nodes at this moment.

[0088] Optionally, the coefficient of variation can be calculated according to the following formula:

[0089]

[0090] Where: c(t) is the coefficient of variation of the nodes in the knowledge network graph at time t; σ(t) is the standard deviation of the knowledge volume of all nodes at time t, which can measure the degree of dispersion of the knowledge volume; υ(t) is the average knowledge volume of all nodes at time t.

[0091] It should be understood that through the above alternative methods, the coefficient of variation of the nodes in the knowledge network graph can be effectively calculated, so as to determine the knowledge distribution uniformity of the nodes in the knowledge network graph according to the coefficient of variation, and further facilitate the accurate and convenient analysis of the knowledge flow of the target organization.

[0092] In some alternative embodiments, the standard deviation of the knowledge volume can be determined in the following way: according to the average knowledge volume of all nodes in the knowledge network graph at a certain moment, the knowledge volume of each node at this moment, and the total number of nodes, the variance of the knowledge volume at this moment is obtained; according to the variance of the knowledge volume, the standard deviation of the knowledge volume is obtained.

[0093] Optionally, as described above, the variance of the knowledge volume of all nodes at time t can be determined by the following formula: Among them, υ(t) is the average knowledge amount of all nodes at time t, N is the total number of nodes, and υ i (t) is the knowledge amount of the i-th node at time t;

[0094] For example, the standard deviation of knowledge amount σ(t) can be calculated by taking the square root of the variance of knowledge amount σ 2 (t) in the above text, that is:

[0095]

[0096] It should be understood that through the above optional method, the standard deviation of the knowledge amount of all nodes in the knowledge network diagram at a certain moment can be effectively determined, so as to facilitate the calculation of the variance coefficient of the nodes in the knowledge network diagram, so as to facilitate the determination of the knowledge distribution uniformity of the nodes in the knowledge network diagram according to the variance coefficient, and further facilitate the accurate and convenient analysis of the knowledge flow of the target organization.

[0097] In the embodiments of the present application, any method can be adopted to display the analysis results obtained after the knowledge flow analysis. For example, visual methods including but not limited to text, charts, or multimodal forms can be adopted to display the analysis results, so as to clearly show information such as the dissemination, accumulation, and utilization of knowledge within the organization of the target organization to the managers of the target organization, thereby reducing the risks of information islands, knowledge retention, and uneven knowledge diffusion, facilitating the effective improvement of the knowledge transfer efficiency within the target organization, and further being beneficial to improving the overall operation efficiency of the target organization.

[0098] Optionally, in the analysis results, a treemap can also be used to visualize data including but not limited to the knowledge amount and average knowledge amount of each node (knowledge management unit). Thus, the management can effectively identify the distribution of knowledge and help formulate corresponding knowledge sharing strategies.

[0099] Next, with reference to Figure 5 the following scenario schematic diagram, an exemplary description of the implementation process of the knowledge flow analysis solution of the embodiments of the present application is given. As Figure 5As shown, knowledge flow analysis can be performed on a target organization (such as a certain enterprise), and its knowledge network diagram can be generated (for example, it can be a scale-free network diagram, and the knowledge network diagram can be generated in the manner described above, which will not be elaborated here). In the knowledge network diagram, the knowledge management units of the target organization (such as the employees of the enterprise) are used as nodes, and the knowledge flow relationships between the knowledge management units are used as edges. The nodes include attribute information for indicating the amount of knowledge. Subsequently, based on the attribute information of the nodes for indicating the amount of knowledge, spatial autocorrelation analysis can be performed on adjacent nodes in the knowledge network diagram to obtain the spatial autocorrelation between adjacent nodes (for example, the Moran coefficient can be used to perform spatial autocorrelation analysis on adjacent nodes in the knowledge network diagram, which can be generated in the manner described above, and will not be elaborated here). Then, based on the spatial autocorrelation, knowledge flow analysis is performed on the target organization to obtain a knowledge flow analysis result presented in the form of a combination of text and a bar chart ( Figure 5 The text and bar chart in the figure are only examples, and other forms can also be used to present the analysis result). It should be understood that Figure 5 More details of the implementation process shown can also be understood in combination with the previous embodiments, and Figure 5 The implementation process shown is only some examples for easy understanding of the embodiments of the present application and does not impose any limitation on the embodiments of the present application.

[0100] In summary, the knowledge flow analysis solution provided in the embodiments of the present application can achieve knowledge flow analysis of a target organization, effectively analyze the flow of knowledge in the target organization (such as including but not limited to enterprises, etc.), clearly present information such as the dissemination, accumulation, and utilization of knowledge within the organization of the target organization to the managers of the target organization, thereby reducing the risks of problems such as information islands, knowledge retention, and uneven knowledge diffusion, facilitating the effective improvement of the knowledge transfer efficiency within the target organization, and further being beneficial to improving the overall operation efficiency of the target organization.

[0101] As can be seen from the above, in the embodiments of the present application, a method for measuring the internal knowledge flow of a target organization based on social network analysis is proposed. This method constructs a network model (such as a related model of a knowledge network diagram) based on social network analysis, and captures and analyzes the transfer trajectory, diffusion speed, and knowledge transfer efficiency between nodes of knowledge in the organization in real time. By accurately identifying the core hub nodes in the knowledge flow, this solution can reveal potential knowledge bottlenecks, and provide customized optimization strategies based on data analysis to promote organizational knowledge sharing and innovation. The technical solution of the embodiments of the present application effectively solves the common problems of information islands and uneven knowledge diffusion in traditional knowledge management practices, and significantly improves the transparency and collaboration efficiency of the internal knowledge flow of the target organization. With the help of this solution, the target organization (such as including but not limited to enterprises, etc.) can realize the dynamic tracking and management of knowledge resources, thereby improving the overall operation efficiency and cultivating core competitiveness.

[0102] It should be noted that the related technologies mainly include knowledge management solutions such as Knowledge Management Systems (KMS), knowledge management modules of Enterprise Resource Planning (ERP) systems, and knowledge map tools. Compared with the technical solutions of the embodiments of the present application, they all have obvious disadvantages.

[0103] For example:

[0104] 1) The traditional KMS system is a tool for enterprises to store, share, and utilize knowledge. By constructing a knowledge base and a sharing platform, it enables employees to create, upload, and share knowledge resources such as documents, reports, and experiences, aiming to promote the accumulation and dissemination of knowledge within the organization. The traditional KMS system has the following disadvantages: (1) Passivity: The traditional KMS system is usually a storage tool based on documents or databases. Knowledge transfer depends on employees to actively upload and share, and the system itself cannot actively capture or analyze knowledge flow, lacking real-time monitoring of the dynamics of knowledge diffusion. (2) Difficulty in quantification: The traditional KMS system lacks quantitative analysis of knowledge flow and cannot measure how knowledge spreads within the organization and the efficiency of knowledge diffusion. It is difficult for managers to make decisions based on specific data. (3) Dependence on knowledge explicitization: The traditional KMS system mainly deals with explicit knowledge (such as documents, reports, etc.), and has weak support for capturing and sharing implicit knowledge (such as employees' experiences, skills, etc.), resulting in ineffective management of a large amount of implicit knowledge.

[0105] Some optional technical solutions of the embodiments of the present application can overcome problems such as the passivity and difficulty in quantification of traditional KMS systems. In some optional technical solutions of the embodiments of the present application, by combining social network analysis and intelligent algorithms, the knowledge flow within the organization of a target institution (such as an enterprise) is tracked in real time. In particular, by using the digital trajectory data of knowledge management units (such as employees) in a digital organization, the spread and flow of knowledge between different nodes are actively captured, significantly improving the initiative of knowledge management. Different from the passive information uploading method of traditional KMS, the technical solutions of the embodiments of the present application can dynamically identify and analyze knowledge dissemination paths, helping managers to grasp knowledge flow in real time. At the same time, the technical solutions of the embodiments of the present application use network models (such as relevant models of knowledge network diagrams) and data analysis to provide multiple quantitative indicators of knowledge flow (such as Moran coefficient, variance coefficient, knowledge growth rate, etc.), which can quantify the diffusion speed of knowledge, the transfer efficiency between nodes, etc., overcoming the deficiency that traditional KMS cannot quantify knowledge flow.

[0106] 2) Traditional ERP systems may include a knowledge management module, which aims to help enterprises manage knowledge and information in service processes through an integrated approach. The knowledge management module of traditional ERP systems is usually embedded in the processes of work scenarios such as finance, human resources, and procurement to help capture the knowledge generated in the processes of work scenarios. Traditional ERP systems have the following disadvantages: (1) Excessive process orientation: The knowledge management module in traditional ERP systems is more embedded in the processes of specific scenarios, tending to provide support for scenarios rather than paying attention to the overall knowledge flow and innovation process of the enterprise, and it is difficult to effectively capture cross-departmental or cross-functional knowledge flow. (2) Lack of flexibility: Traditional ERP systems are relatively closed and modularly designed, making it difficult to be flexibly adjusted according to the specific needs of enterprises, and unable to quickly adapt to the changing needs in enterprise knowledge management or handle unstructured knowledge. (3) High implementation cost: The implementation cost of traditional ERP systems is very high. In particular, small and medium-sized enterprises may find it difficult to afford it, and the system complexity is relatively high, and the employee training and maintenance costs are also very large.

[0107] Some alternative technical solutions of the embodiments of the present application can overcome problems such as the process orientation and high implementation cost of the knowledge management module of traditional ERP systems. In some alternative technical solutions of the embodiments of the present application, not only the knowledge management in the processes of specific work scenarios is concerned, but also the cross-knowledge management unit (such as department or employee) and cross-functional knowledge flow can be analyzed through a network model (such as the related model of the knowledge network diagram). By constructing a global map of knowledge flow, the technical solutions of the embodiments of the present application can cope with the organizational structure of a more complex target organization (such as an enterprise), provide an optimization plan for the knowledge flow of the entire organization, and break through the limitations of the knowledge management module of traditional ERP systems. At the same time, compared with traditional ERP systems, the technical solutions of the embodiments of the present application focus on the monitoring and optimization of knowledge flow, can be seamlessly docked with existing knowledge management systems, and avoid the high cost of comprehensively replacing enterprise systems. Therefore, the technical solutions of this solution are also easier to deploy, can be integrated with existing digital tools, and can effectively reduce the implementation and maintenance costs.

[0108] 3) Some traditional knowledge map tools display the distribution of knowledge in the organization in a graphical way, helping managers identify the key nodes and bottlenecks of knowledge and understand in which departments or personnel the knowledge is stored. It usually shows the flow of knowledge within the organization in a visual way. The traditional knowledge map tools have the following disadvantages: (1) Lack of dynamics: The knowledge map is mainly a static chart showing the knowledge distribution, making it difficult to reflect the real-time and dynamic nature of knowledge flow and unable to quickly respond to changes in the knowledge flow process. (2) Poor scalability: The display of the knowledge map usually depends on a pre-set structure and cannot cope with large-scale enterprises or complex organizational structures. The knowledge map will become difficult to manage as the enterprise scale expands. (3) Difficulty in capturing tacit knowledge: The knowledge map system focuses on the location and display of explicit knowledge and is relatively difficult to capture and display the transfer process of tacit knowledge, unable to help managers comprehensively understand the dissemination of knowledge within the organization.

[0109] Some alternative technical solutions of the embodiments of the present application can overcome problems such as the static display and insufficient capture of tacit knowledge of traditional knowledge map tools. In some alternative technical solutions of the embodiments of the present application, not only a static knowledge map is provided, but also the knowledge network diagram of the target organization (such as an enterprise) can be updated in real time dynamically, realizing the update of the knowledge flow map, and combining multiple quantitative indicators to conduct a comprehensive knowledge flow analysis. This enables the state of knowledge flow to be monitored and evaluated in real time, exceeding the static display function of traditional knowledge maps. At the same time, through the analysis of the digital trace data of knowledge management units (such as employees) by intelligent algorithms, this solution can capture and quantify the flow and sharing of tacit knowledge, which makes up for the deficiency of traditional knowledge map tools in capturing tacit knowledge.

[0110] It is understandable that in the technical solution of the embodiment of the present application, the comprehensive knowledge flow visualization and quantitative analysis functions can help the target organization (such as including but not limited to enterprises, etc.) to understand the efficiency of knowledge dissemination and the interaction frequency between nodes (i.e., knowledge management units) in real time, and provide a basis for scientific decision-making. The dynamic optimization ability of the system can timely discover and solve the bottlenecks in knowledge dissemination, ensure the uniform and efficient dissemination of knowledge within the organization, and improve the efficiency of knowledge sharing and innovation. In addition, the technical solution of the embodiment of the present application can also promote the knowledge flow and collaborative innovation across nodes (i.e., knowledge management units, such as departments or employees, etc.) of the target organization, break the information silos, and promote the improvement of the overall innovation ability of the organization. At the same time, the flexible implementation characteristics and low-cost advantages of the technical solution of the embodiment of the present application make it applicable to various types of target organizations, such as enterprises, especially small and medium-sized enterprises, which can also reduce the complexity of knowledge management and improve their knowledge management level through this.

[0111] Generally speaking, on the one hand, the technical solution of the embodiment of the present application can innovatively combine the social network analysis method with knowledge management. Some technical solutions of the embodiment of the present application introduce the social network analysis method in the field of knowledge management, construct a knowledge network diagram based on the scale-free network, and thus effectively establish a knowledge flow model. Traditional knowledge management solutions are difficult to quantify and track the knowledge dissemination path, while through the social network model, the technical solution of the embodiment of the present application can capture in real time how knowledge flows in the organization of the target organization, especially conduct quantitative analysis on the diffusion speed of knowledge, the interaction frequency between nodes, etc., filling the deficiencies of traditional methods in dynamic monitoring.

[0112] On the other hand, the technical solution of the embodiment of the present application can use intelligent algorithms to identify and optimize the key nodes in knowledge flow. Some technical solutions of the embodiment of the present application use intelligent algorithms to automatically identify the hub nodes in knowledge flow (i.e., the nodes with high connectivity and key roles in knowledge dissemination in the knowledge network diagram). Different from the traditional static evaluation method, this solution can not only identify the bottlenecks in knowledge dissemination, but also provide accurate optimization suggestions for the target organization (such as including but not limited to enterprises) according to the influence of these nodes, promote the efficient flow of knowledge, and help the target organization break through the information silos and knowledge retention problems.

[0113] On the other hand, the technical solution of the embodiment of the present application can solve the quantification problem in knowledge management through interdisciplinary integration. Some technical solutions of the embodiment of the present application combine social network analysis with data analysis to propose a new quantitative knowledge management method. It can systematically generate a global map of enterprise knowledge flow based on the massive knowledge data in the digital organization, and provide a way to specifically measure the knowledge flow efficiency through quantitative indicators (such as Moran coefficient, variance coefficient, knowledge growth rate, etc.). Compared with the traditional qualitative-based knowledge management method, this quantitative method improves the accuracy and pertinence of analysis.

[0114] On another hand, the technical solution of the embodiment of the present application can optimize the balance and fairness of knowledge flow by using digital means. Some technical solutions of the embodiment of the present application make full use of the knowledge digital trace data brought by digital transformation to real-time evaluate the distribution balance of knowledge in the organization, ensuring that knowledge can be fairly spread among different levels and departments. Digital means make the knowledge management process more transparent and efficient, thus helping the organization to make breakthroughs in knowledge sharing and innovation capabilities.

[0115] It can be seen that the knowledge flow analysis solution of the embodiment of the present application can conveniently and efficiently implement the knowledge management of the target organization.

[0116] It can be understood that the foregoing description of the knowledge flow analysis method is only some exemplary descriptions of the embodiment of the present application, and does not impose any limitation on the embodiment of the present application.

[0117] According to the second aspect of the embodiment of the present application, an electronic device is provided, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; the memory is used to store a computer program; the processor is used to execute the knowledge flow analysis method described in the foregoing first aspect by running the computer program stored on the memory.

[0118] Figure 6 The block diagram of an optional electronic device in the embodiment of the present application is shown. The embodiment of the present application does not limit the specific implementation of the electronic device 1000. As an example, referring to Figure 6 , the electronic device 1000 provided by the embodiment of the present application includes: a processor 1002, a communication interface 1004, a memory 1006, and a communication bus 1008. Among them:

[0119] The processor 1002, the communication interface 1004, and the memory 1006 complete communication with each other through the communication bus 1008.

[0120] A communication interface 1004 for communicating with other electronic devices or servers.

[0121] A processor 1002 for executing a computer program 1010, which can specifically execute the relevant steps in any of the foregoing embodiments of the knowledge flow analysis method.

[0122] Specifically, the computer program 1010 may include program code, and the program code includes computer operation instructions.

[0123] The processor 1002 may be a CPU, or a GPU (Graphic Processing Unit), or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application. One or more processors included in the intelligent device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0124] A memory 1006 for storing the computer program 1010. The memory 1006 may include a high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0125] The computer program 1010 can specifically be used to cause the processor 1002 to execute the knowledge flow analysis method in any of the foregoing embodiments.

[0126] For the specific implementation of each step in the computer program 1010, reference may be made to the corresponding steps and units in any of the foregoing embodiments of the knowledge flow analysis method, which will not be elaborated herein. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described devices and modules can refer to the corresponding process descriptions in the foregoing method embodiments, which will not be elaborated herein.

[0127] The electronic device 1000 in the embodiments of the present application has been described in detail in the foregoing embodiments of the knowledge flow analysis method. Therefore, the relevant content and beneficial effects thereof can be understood with reference to the foregoing method embodiments, which will not be elaborated herein.

[0128] According to the third aspect of the embodiments of the present application, the embodiments of the present application further provide a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the knowledge flow analysis method described in any one of the foregoing multiple method embodiments. The computer storage medium includes, but is not limited to: Compact Disc Read-Only Memory (CD-ROM), Random Access Memory (RAM), floppy disk, hard disk, magneto-optical disk, etc.

[0129] According to the fourth aspect of the embodiments of the present application, the embodiments of the present application further provide a computer program product, including a computer program, and the computer program implements the knowledge flow analysis method described in any one of the foregoing multiple method embodiments when executed by a processor.

[0130] The embodiments of the electronic device 1000 / computer storage medium / computer program product in the embodiments of the present application have been described in detail in the foregoing embodiments of the knowledge flow analysis method. Therefore, the relevant content and beneficial effects thereof can be understood with reference to the foregoing method embodiments, and will not be elaborated herein.

[0131] In addition, it should be noted that the information related to users (including but not limited to user device information, user personal information, etc.) and data (including but not limited to sample data for training the model, data for analysis, stored data, displayed data, etc.) involved in the embodiments of the present application are all information and data authorized by the users or fully authorized by all parties. And the collection, use, and processing of the relevant data need to comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0132] It should be pointed out that according to the needs of implementation, each component / step described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of the components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0133] The method according to the embodiments of the present application can be implemented in hardware, firmware, or be implemented as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or be implemented as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored on such a software process on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an Application Specific Integrated Circuit (ASIC) or a Field Programmable Gate Array (FPGA)). It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component (such as a Random Access Memory (RAM), a Read-Only Memory (ROM), a flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0134] Those of ordinary skill in the art can realize that the units and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for a specific application, but such an implementation should not be considered to exceed the scope of the embodiments of the present application.

[0135] As used herein, the term "including" and its variants are open-ended, that is, "including but not limited to". The term "based on" is "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". It should be noted that the concepts such as "first" and "second" mentioned in the embodiments of the present application are only used to distinguish different devices, modules, or units, and are not used to limit the order or interdependence relationship of the functions executed by these devices, modules, or units. It should be noted that the modifications of "one" and "multiple" mentioned in the embodiments of the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless clearly stated otherwise in the context, it should be understood as "one or more".

[0136] The above embodiments are only used to illustrate the embodiments of the present application, rather than to limit the embodiments of the present application. Those of ordinary skill in the relevant technical field can also make various changes and modifications without departing from the spirit and scope of the embodiments of the present application. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present application. The patent protection scope of the embodiments of the present application shall be defined by the claims.

Claims

1. A method for knowledge flow analysis, comprising: Obtaining a knowledge network graph of a target institution, wherein in the knowledge network graph, knowledge management units of the target institution are used as nodes, and knowledge flow relationships between the knowledge management units are used as edges, and the nodes include attribute information for indicating the amount of knowledge; Performing spatial autocorrelation analysis on adjacent nodes in the knowledge network graph according to the attribute information of the nodes for indicating the amount of knowledge, to obtain the spatial autocorrelation between adjacent nodes; Performing knowledge flow analysis on the target institution according to the spatial autocorrelation.

2. The method according to claim 1, wherein, The performing spatial autocorrelation analysis on adjacent nodes in the knowledge network graph includes: Using Moran's coefficient to perform spatial autocorrelation analysis on adjacent nodes in the knowledge network graph.

3. The method according to claim 2, wherein The using Moran's coefficient to perform spatial autocorrelation analysis on adjacent nodes in the knowledge network graph includes: Determining a calculation formula for Moran's coefficient according to the knowledge amount of adjacent nodes in the knowledge network graph at a certain moment, the average knowledge amount of all nodes at this moment, the connection weight between the adjacent nodes, and the variance of the knowledge amount at this moment; Performing spatial autocorrelation analysis on adjacent nodes in the knowledge network graph according to the calculation formula for Moran's coefficient.

4. The method according to claim 3, wherein, Before performing knowledge flow analysis on the target institution according to the spatial autocorrelation, the method further includes: calculating the coefficient of variation of the nodes in the knowledge network graph; determining the knowledge distribution uniformity of the nodes in the knowledge network graph according to the coefficient of variation; The performing knowledge flow analysis on the target institution according to the spatial autocorrelation includes: performing knowledge flow analysis on the target institution according to the spatial autocorrelation and the knowledge distribution uniformity.

5. The method according to claim 4, wherein The calculating the coefficient of variation of the nodes in the knowledge network graph includes: Calculating the coefficient of variation of the nodes in the knowledge network graph according to the standard deviation of the knowledge amount of all nodes in the knowledge network graph at a certain moment and the average knowledge amount of all nodes at this moment.

6. The method according to claim 5, wherein The standard deviation of the knowledge amount is obtained by the following method: Obtaining the variance of the knowledge amount at this moment according to the average knowledge amount of all nodes in the knowledge network graph at a certain moment, the knowledge amount of each node at this moment, and the total number of nodes; Obtaining the standard deviation of the knowledge amount according to the variance of the knowledge amount.

7. The method according to any one of claims 1-6, wherein, Before obtaining the knowledge network graph of the target institution, the method further includes: Obtaining the organizational structure information of the target institution and the information of shared files shared among the knowledge management units of the target institution; Determining the knowledge amount weight of the shared file according to the access information of the shared file; Determining the knowledge amount for each knowledge management unit according to the access information and the knowledge amount weight; Generating the knowledge network graph according to the organizational structure information, the access information of each knowledge management unit to the shared file, and the knowledge amount of each knowledge management unit.

8. The method according to claim 7, wherein, Generating the knowledge network graph according to the organizational structure information, the access information of each knowledge management unit to the shared file, and the knowledge amount of each knowledge management unit, includes: Generating a scale-free network graph as the knowledge network graph according to the organizational structure information, the access information of each knowledge management unit to the shared file, and the knowledge amount of each knowledge management unit.

9. The method according to claim 7, wherein Determining the knowledge amount weight of the shared file according to the accessed information of the shared file, includes: Determining the knowledge amount weight of the shared file according to the number of access people and the number of access times of the shared file.

10. An electronic device, comprising: A processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; The memory is used for storing a computer program; The processor is used for executing the method according to any one of claims 1-9 by running the computer program stored on the memory.

11. A computer storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method according to any one of claims 1-9 is implemented.

12. A computer program product, including a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1-9 is implemented.

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