Enterprise digital capability operation detection and evaluation method

Abstract

The invention relates to the technical field of enterprise evaluation, in particular to an enterprise digital capability operation detection and evaluation method, which comprises the following steps of: collecting data from digital systems of a plurality of departments in an enterprise, and generating data flow information of each department; based on the data flow information of all the departments, a cross-department digital cooperation graph is constructed by adopting a graph model cooperation graph, nodes in the cooperation graph represent all the departments of the enterprise, and edges in the cooperation graph represent cooperation relations and data flow among all the departments; analyzing the collaboration map by using data flow network analysis, and identifying a data island of cross-department data flow; and constructing a cross-department cooperation efficiency scoring model, and calculating cooperation efficiency scores of all departments and the whole enterprise. According to the invention, an enterprise can accurately know the cooperation efficiency and the overall digitization capability level of each department, so that a reliable data basis is provided for a management layer, low-efficiency departments and cooperation bottlenecks can be found, and an accurate promotion plan can be made.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise evaluation, and in particular to a method for detecting and evaluating the operation of enterprise digital capability. Background Art

[0002] With the acceleration of enterprise digital transformation, cross-departmental collaboration has become an important indicator for measuring an enterprise's digital capabilities. Effective cross-departmental collaboration can not only improve the operational efficiency of an enterprise, but also promote the full utilization of data, thereby supporting more scientific decision-making. Currently, most enterprises face multiple challenges in evaluating the efficiency of digital collaboration.

[0003] Traditional collaboration efficiency evaluation methods are mostly based on static data and a single dimension, usually focusing on the overall business process or financial performance of the enterprise. They lack quantitative means for the details of cross-departmental collaboration and cannot accurately reflect the real-time data flow and collaboration between departments. At the same time, with the increase in business complexity, information bottlenecks and data silos within the enterprise are becoming more and more obvious. Data exchange and task response between many departments are not timely, affecting the overall completion rate of collaborative tasks. These bottlenecks and silos are not only difficult to quickly identify in the existing evaluation framework, but also usually lack a dynamic adjustment mechanism, making it difficult to solve practical problems in the collaboration process in a timely manner.

[0004] In addition, most current evaluation systems are difficult to generate targeted optimization suggestions and cannot effectively help companies improve specific weak links in cross-departmental collaboration. In a rapidly changing market environment, companies need a solution that can monitor, dynamically analyze and optimize cross-departmental collaboration in real time to comprehensively improve their digital capabilities and overall operational efficiency. Summary of the invention

[0005] The present invention provides a method for detecting and evaluating the operation of enterprise digital capability.

[0006] A method for detecting and evaluating the operation of digital capabilities of an enterprise comprises the following steps:

[0007] S1, collects data from the digital systems of multiple departments within the enterprise, including the frequency of data exchange across departments, the response time of collaborative tasks, and the completion rate of collaborative tasks, and generates data flow information for each department;

[0008] S2, digital collaboration graph construction: Based on the data flow information of each department, a cross-departmental digital collaboration graph is constructed using a collaboration graph of a graph model. The nodes in the collaboration graph represent the departments of the enterprise, and the node attributes include the digital capability level. The edges in the collaboration graph represent the collaboration relationship and data flow between departments. The edge weights in the collaboration graph are updated in real time to reflect the frequency and timeliness of data flow, forming a dynamic collaboration relationship network of the enterprise.

[0009] S3, Identification of data islands and information bottlenecks: Based on the edge weights in the collaboration graph and the frequency of data flow between nodes, the collaboration graph is analyzed using data flow network analysis to identify data islands in cross-departmental data flow, helping enterprises optimize cross-departmental collaboration mechanisms;

[0010] S4, cross-departmental collaboration efficiency scoring: Construct a cross-departmental collaboration efficiency scoring model. Based on the relationship between nodes and edges in the collaboration graph, combined with data exchange frequency indicators, collaboration task response time indicators, and task completion rate indicators, calculate the collaboration efficiency scores of each department and the entire enterprise. The collaboration efficiency scores of each department are obtained by directly converting the node attributes of each department's nodes, that is, the level of digital capabilities. The enterprise's collaboration efficiency score provides a quantitative assessment of the enterprise's overall data mobility.

[0011] Optionally, the S1 specifically includes:

[0012] S11, data exchange frequency collection: through the digital system log records of various departments of the enterprise, monitor the real-time data request and transmission frequency between departments, record the number of times the data request is sent and received, and record the occurrence time and duration of data exchange, and generate a data exchange frequency matrix across departments;

[0013] S12, Collection of collaborative task response time: Based on the enterprise's internal task management system, monitor the response of each department in cross-departmental tasks in real time, record the task assignment time, task acceptance time and task completion time, calculate the time difference between task initiation and initial response, so as to generate a task response time record table and quantify the response time of the department;

[0014] S13, Collection of collaborative task completion rate: Through the enterprise's project management and workflow system, track the completion status of each department in the collaborative tasks, count the ratio of the total number of completed tasks to the total number of assigned tasks, generate a collaborative task completion rate data table, and reflect the execution ability and stability of each department in cross-departmental collaborative tasks.

[0015] Optionally, the collaboration graph is represented by an undirected weighted graph G=(V,E), where V is a node set representing various departments in the enterprise, and each node v i ∈V corresponds to a department, E is an edge set, representing the collaborative relationship between departments, and each edge e i,j ∈E represents the collaborative relationship between department i and department j;

[0016] Node attribute N i :Each node v i The property N i Indicates the level of digital capabilities of the department;

[0017] Edge weight Wi,j :Each edge e i,j The weight W i,j Indicates the degree of collaboration between departments i and j.

[0018] Optionally, the node attribute N i It is expressed as:

[0019] N i =α·TCR i +β·TRR i +γ·DEF i , where TCR i is the task completion rate of department i, TRR i is the average response speed of department i to all collaboration requests, DEF i is the data exchange frequency of department i, which means the sum of the frequencies of data exchange between department i and all other departments. α, β, and γ are the weight coefficients of task completion rate, response speed, and data exchange frequency, respectively. They are determined by empirical data or actual business needs and satisfy α+β+γ=1.

[0020] Optionally, the setting of edge weights in S2 includes:

[0021] Data exchange frequency: The edge weight is based on the frequency of data exchange between departments. The higher the frequency, the closer the collaborative relationship.

[0022] Collaboration task response speed: Based on the frequency, the edge weight is adjusted according to the response time of the collaborative tasks between departments. The shorter the response time, the higher the collaboration efficiency.

[0023] Collaboration task completion rate: The edge weight is adjusted based on the collaboration task completion rate between departments. The collaboration relationship with a higher collaboration task completion rate has a higher weight.

[0024] Optionally, the edge weight setting is expressed as:

[0025] Among them, f i,j The frequency of data exchange between department i and department j is defined as the total number of data exchanges between departments i and j within a specified time. Avg Response Time i,j It represents the average response time of department i to all tasks of department j, indicating the timeliness of collaborative response, TCR i,j It represents the completion rate of the collaborative tasks between departments i and j, and the completion status of the tasks assigned between the two departments. λ, μ, and v represent the weight coefficients of data exchange frequency, response speed, and task completion rate, respectively, reflecting the importance of each indicator in the collaborative relationship, satisfying λ+μ+v=1, which is set according to enterprise needs or experience.

[0026] Optionally, the S3 specifically includes:

[0027] S31, Data Island Identification: Based on the edge weights in the collaboration graph and the frequency of data flow between nodes, the collaboration graph is analyzed using the data flow network analysis algorithm to define the connectivity threshold W of data flow. t h res h old , when the connectivity threshold W t h res h old Or the data exchange frequency DEF of the node i When it is significantly lower than other nodes, the node is marked as a potential data island, indicating that there are obstacles to data flow in cross-departmental collaboration in this department.

[0028] S32, improvement suggestion generation: Generate improvement suggestions for each data island, including increasing data exchange frequency, shortening task response time, and improving task completion rate.

[0029] Optionally, the connectivity threshold W t h res h old It is set to 0.7 times the mean of all edge weights in the graph, calculated as:

[0030] When the edge weight of any node is lower than W t h res h old ,It is believed that the node collaboration is weak and there are obstacles to data flow, forming a data island;

[0031] The data exchange frequency DEF i <0.6×DEF avg , then we consider that node v i Data exchange frequency DEF i Obviously lower than other nodes, among which, DEF avg is the mean of the data exchange frequency of all nodes, and 0.6 is the influence coefficient.

[0032] Optionally, the collaboration efficiency score of each department C i By node attribute N i The table is directly converted, that is: C i =α·TCR i +β·TRR i +γ·DEF i .

[0033] Optionally, the collaboration efficiency score of the entire enterprise is C total Based on the collaboration efficiency score of all departments, C i, to quantify the overall digital collaboration level and data liquidity of the enterprise, the calculation formula is: Among them, |V| is the total number of nodes in the graph, that is, the total number of departments, C i is the collaboration efficiency score of department i.

[0034] Beneficial effects of the present invention:

[0035] The present invention, by constructing a cross-departmental collaboration map, quantifies the digital collaboration efficiency of various departments within the enterprise, including key indicators such as data exchange frequency, task response time and task completion rate, and uses node attributes as department collaboration efficiency scores, which can intuitively reflect the performance of various departments in digital collaboration and form a unified scoring standard, so that enterprises can accurately understand the collaboration efficiency and overall digital capability level of each department, thereby providing management with reliable data basis for discovering inefficient departments and collaboration bottlenecks and formulating accurate improvement plans.

[0036] The present invention adopts a dynamically updated collaboration graph model, and applies the data exchange frequency, response time and task completion rate data in cross-departmental collaboration to the weight adjustment of graph nodes and edges in real time, forming a dynamic collaboration relationship network of the enterprise. Not only can the collaboration graph reflect the real-time status of the enterprise's digital collaboration efficiency, but it can also timely capture data flow obstacles, information bottlenecks and data islands through the dynamic changes of edge weights and node attributes, and provide real-time collaboration visualization and data flow status warning. Compared with traditional static evaluation methods, the dynamic update mechanism of the present invention greatly improves the timeliness and accuracy of collaboration evaluation, and provides strong support for enterprises to timely optimize collaboration architecture and enhance digital capabilities.

[0037] The present invention, based on the analysis results of the collaboration map, can generate targeted optimization suggestions for each department and the enterprise as a whole, including specific measures such as increasing the frequency of data exchange, shortening task response time, improving task completion rate, and optimizing collaboration processes. Through the identification and analysis of data islands and information bottlenecks, the present invention helps enterprises to deeply understand the reasons for low collaboration efficiency and provide special improvement strategies. Compared with the single scoring method in the prior art that lacks detailed improvement directions, the customized optimization suggestions of the present invention can effectively solve the weak links in collaboration, improve the overall data mobility and collaboration efficiency of the enterprise, thereby promoting the digital transformation process and achieving continuous business optimization and innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or the prior art, 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 for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of the evaluation method flow in an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of the collaboration efficiency scoring of each department and the entire enterprise according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0042] like Figure 1-Figure 2 As shown, a method for detecting and evaluating the operation of digital capabilities of an enterprise includes the following steps:

[0043] S1, collects data from the digital systems of multiple departments within the enterprise, including the frequency of data exchange across departments, the response time of collaborative tasks, and the completion rate of collaborative tasks, and generates data flow information for each department;

[0044] S2, digital collaboration graph construction: Based on the data flow information of each department, a cross-departmental digital collaboration graph is constructed using a collaboration graph of a graph model. The nodes in the collaboration graph represent the departments of the enterprise, and the node attributes include the digital capability level. The edges in the collaboration graph represent the collaboration relationship and data flow between departments. By updating the edge weights in the collaboration graph in real time to reflect the frequency and timeliness of data flow, a dynamic collaboration relationship network of the enterprise is formed. The collaboration graph dynamically adjusts the node attributes and edge weights according to the real-time collected data, forming a dynamic network that reflects the cross-departmental collaboration efficiency and data flow of the enterprise.

[0045] S3, Identification of data islands and information bottlenecks: Based on the edge weights in the collaboration graph and the frequency of data flow between nodes, the collaboration graph is analyzed using data flow network analysis to identify data islands in cross-departmental data flow, helping enterprises optimize cross-departmental collaboration mechanisms;

[0046] S4, cross-departmental collaboration efficiency scoring: Construct a cross-departmental collaboration efficiency scoring model. Based on the relationship between nodes and edges in the collaboration graph, combined with data exchange frequency indicators, collaboration task response time indicators, and task completion rate indicators, calculate the collaboration efficiency scores of each department and the entire enterprise. The collaboration efficiency scores of each department are obtained by directly converting the node attributes of each department's nodes, that is, the level of digital capabilities. The enterprise's collaboration efficiency score provides a quantitative assessment of the enterprise's overall data mobility.

[0047] S1 specifically includes:

[0048] S11, data exchange frequency collection: through the digital system log records of various departments of the enterprise, monitor the real-time data request and transmission frequency between departments, record the number of times the data request is sent and received, and record the occurrence time and duration of data exchange, and generate a data exchange frequency matrix across departments;

[0049] S12, Collection of collaborative task response time: Based on the enterprise's internal task management system, monitor the response of each department in cross-departmental tasks in real time, record the task assignment time, task acceptance time and task completion time, calculate the time difference between task initiation and initial response, so as to generate a task response time record table and quantify the response time of the department;

[0050] S13, Collection of collaborative task completion rate: Through the enterprise's project management and workflow system, track the completion status of each department in the collaborative tasks, count the ratio of the total number of completed tasks to the total number of assigned tasks, generate a collaborative task completion rate data table, and reflect the execution ability and stability of each department in cross-departmental collaborative tasks.

[0051] The collaboration graph is represented by an undirected weighted graph G = (V, E), where V is a set of nodes representing the various departments in the enterprise, and each node v i ∈V corresponds to a department, E is an edge set, representing the collaborative relationship between departments, and each edge e i,j ∈E represents the collaborative relationship between department i and department j;

[0052] Node attribute N i :Each node v i The property N i Indicates the level of digital capabilities of the department;

[0053] Edge weight W i,j :Each edge e i,j The weight W i,j Indicates the degree of collaboration between departments i and j.

[0054] According to the above definition, the graph model of the collaboration graph can be represented as a weighted adjacency matrix A, where each element a i,j Represents node v i and node v j The relationship weight W between i,j The adjacency matrix A is defined as follows:

[0055]

[0056] Among them, the diagonal element a i,i =0: indicates no self-connection relationship, non-diagonal element ai,j =W i,j : indicates node v i and v j The weight of the collaborative relationship between them can be used to intuitively represent the collaborative relationship and data flow between departments within the enterprise through the adjacency matrix A.

[0057] Node attribute N i It is expressed as:

[0058] N i =α·TCR i +β·TRR i +γ·DEF i , where TCR i is the task completion rate of department i, TRR i is the average response speed of department i to all collaboration requests, DEF i is the data exchange frequency of department i, which means the sum of the frequencies of data exchange between department i and all other departments. α, β, and γ are the weight coefficients of task completion rate, response speed, and data exchange frequency, respectively. They are determined by empirical data or actual business needs and satisfy α+β+γ=1. They are used to adjust the influence of each indicator on the digital capability level of the node according to the needs of different enterprises.

[0059] The setting of edge weights in S2 includes:

[0060] Data exchange frequency: The edge weight is based on the frequency of data exchange between departments. The higher the frequency, the closer the collaborative relationship.

[0061] Collaboration task response speed: Based on the frequency, the edge weight is adjusted according to the response time of the collaborative tasks between departments. The shorter the response time, the higher the collaboration efficiency.

[0062] Collaboration task completion rate: The edge weight is adjusted based on the collaboration task completion rate between departments. The collaboration relationship with a higher collaboration task completion rate has a higher weight.

[0063] The edge weight setting is expressed as:

[0064] Among them, f i,j The frequency of data exchange between department i and department j is defined as the total number of data exchanges between departments i and j within a specified time. Avg Response Time i,j It represents the average response time of department i to all tasks of department j, indicating the timeliness of collaborative response. The formula is: Response Time i,j,k represents the response time of department i to the kth collaborative task of department j, n is the number of tasks, TCR i,jIt represents the completion rate of the collaborative tasks between departments i and j, and the completion status of the tasks assigned between the two departments. λ, v, and ν represent the weight coefficients of data exchange frequency, response speed, and task completion rate, respectively, reflecting the importance of each indicator in the collaborative relationship, satisfying λ+μ+ν=1. It is set according to enterprise needs or experience.

[0065] Data exchange frequency f i,j With DEF i Different: f i,j The frequency of data exchange between department i and department j directly reflects the closeness of the collaboration between the two designated departments. i is the total data exchange frequency between department i and all other departments, reflecting the data mobility of the department in the overall collaborative network. Therefore, f is used in the edge weight. i,j More accurately measure the specific collaboration closeness between the two departments.

[0066] Avg Response Time i,j With TRR i Difference: Avg Response Time i,j is the average response time of department i to department j’s collaboration request, which directly measures the timeliness of the collaboration between the two departments. i It indicates the overall response speed of department i to all collaboration requests, reflecting the overall response efficiency of the department. i,j The response efficiency between two departments is evaluated more specifically, so that the edge weight expression can more accurately reflect the quality of cross-departmental collaboration;

[0067] TCR in edge weight calculation expressions i,j (Task Completion Rate) itself is a direct indicator of the quality of collaboration between departments i and j. It reflects the actual completion of the collaborative tasks between the two departments and can accurately measure the success rate and reliability of collaboration. Therefore, TCR i,j Retained in edge weight calculations.

[0068] S3 specifically includes:

[0069] S31, Data Island Identification: Based on the edge weights in the collaboration graph and the frequency of data flow between nodes, the collaboration graph is analyzed using the data flow network analysis algorithm to define the connectivity threshold W of data flow. t h res h old , when the connectivity threshold W t h res h old Or the data exchange frequency DEF of the node iWhen it is significantly lower than other nodes, the node is marked as a potential data island, indicating that there are obstacles to data flow in cross-departmental collaboration in this department.

[0070] S32, Generation of improvement suggestions: Generate improvement suggestions for each data island, including increasing the frequency of data exchange, shortening task response time, and improving task completion rate; feedback the improvement suggestions to relevant departments in order to optimize the cross-departmental collaboration mechanism, gradually eliminate data flow bottlenecks, and improve the overall digital collaboration efficiency of the enterprise.

[0071] Generate improvement suggestions based on the results of the cause analysis, including:

[0072] If the formation of data islands is due to insufficient frequency of data exchange, it is recommended to increase the frequency of data exchange and set up a cross-departmental data sharing plan.

[0073] If the reason is a high delay in task response, it is recommended to optimize the department’s digital tools or processes to improve response speed.

[0074] If the reason is the low task completion rate, it is recommended to conduct special training or increase task resources to improve the task execution effect.

[0075] Connectivity threshold W t h res h old It is set to 0.7 times the mean of all edge weights in the graph to ensure that nodes with significantly weaker collaborations are identified, and is calculated as:

[0076] When the edge weight of any node is lower than W t h res h old ,It is believed that the node collaboration is weak and there are obstacles to data flow, forming a data island;

[0077] Data exchange frequency DEF i <0.6×DEF avg , then we consider that node v i Data exchange frequency DEF i Obviously lower than other nodes, among which, DEF avg It is the mean of the data exchange frequency of all nodes, and 0.6 is the influence coefficient. The influence coefficient can be adjusted between 0.5 and 0.8. If it is set to 0.6, it means that the exchange frequency of the node is lower than 60% of the average value, and thus it is regarded as a data island.

[0078] Collaboration efficiency score of each department is C i By node attribute N i The table is directly converted, that is: C i =α·TCR i +β·TRRi +γ·DEF i .

[0079] The overall enterprise collaboration efficiency score is C total Based on the collaboration efficiency score of all departments, C i , to quantify the overall digital collaboration level and data liquidity of the enterprise, the calculation formula is: Among them, |V| is the total number of nodes in the graph, that is, the total number of departments, C i is the collaboration efficiency score of department i.

[0080] According to the collaboration efficiency scores of each department and the entire enterprise, targeted optimization suggestions can be generated to improve the overall collaboration efficiency and digital capabilities of the enterprise. The optimization suggestions should be developed around the key indicators in the collaboration efficiency score (data exchange frequency, task response time, task completion rate) to help enterprises identify weak links and formulate improvement strategies. The following are specific suggestion generation methods and optimization directions:

[0081] 1. Optimization suggestions for low data exchange frequency:

[0082] For departments with low data exchange frequency, the following recommendations are generated:

[0083] Improve cross-departmental data sharing: It is recommended to increase the frequency of data sharing between this department and other collaborative departments, such as regularly updating key data and establishing a data synchronization mechanism.

[0084] Tools or systems to strengthen data circulation: It is recommended to deploy a data sharing platform or improve existing tools to ensure that data can be transmitted between departments efficiently and conveniently.

[0085] Establish an incentive mechanism for data flow: encourage departments to actively share data, and motivate each department to maintain the enthusiasm for data circulation through performance appraisal and other means.

[0086] 2. Optimization suggestions for long task response time:

[0087] For departments with long task response times, the following recommendations are generated:

[0088] Optimize processes and task allocation: It is recommended to analyze and optimize the department’s task processing process, reduce waiting and approval links, and improve task response speed.

[0089] Introduce automated tools: If the response delay is mainly due to manual operations, it is recommended to introduce automated tools (such as automatic reminders, task dispatching systems, etc.) to speed up the response.

[0090] Strengthen personnel training and resource allocation: It is recommended to improve the efficiency of personnel in handling cross-departmental tasks through training, or increase resource support to ensure that tasks can be responded to in a timely manner within the specified time.

[0091] 3. Optimization suggestions for low task completion rate:

[0092] For departments with low task completion rates, the following recommendations are generated:

[0093] Analyze task bottlenecks and provide resource support: It is recommended to identify the bottlenecks in the department's task completion (lack of skills, lack of resources) and provide corresponding resource support (increase personnel, provide technical support).

[0094] Improve project management and progress tracking: It is recommended to introduce or improve project management tools so that departments can track task progress in real time to ensure that tasks can be completed on time.

[0095] Improve employees' awareness of collaboration: It is recommended to carry out cross-departmental collaboration training or team building activities to increase employees' attention to collaborative tasks and strengthen their sense of responsibility.

[0096] 4. Optimization suggestions for low overall collaboration efficiency score:

[0097] For situations where the overall enterprise collaboration efficiency score is low, the following recommendations are generated:

[0098] Optimize the cross-departmental collaboration structure: It is recommended to review and optimize the cross-departmental collaboration structure and clarify the roles and responsibilities of each department in the collaborative tasks to improve the fluency of collaboration.

[0099] Establish a regular collaboration review mechanism: It is recommended that companies hold regular cross-departmental collaboration review meetings to analyze the causes of inefficiency and propose improvement plans to ensure continuous optimization of the collaboration process.

[0100] Introduce a collaborative incentive mechanism: It is recommended to establish a collaborative performance incentive mechanism to reward efficient collaborative departments and encourage each department to actively improve collaborative efficiency.

[0101] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0102] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for detecting and evaluating the operation of enterprise digital capabilities, characterized in that: The following steps are involved: S1, collects data from the digital systems of multiple departments within the enterprise, including the frequency of data exchange across departments, the response time of collaborative tasks, and the completion rate of collaborative tasks, and generates data flow information for each department; S2, digital collaboration graph construction: Based on the data flow information of each department, a cross-departmental digital collaboration graph is constructed using a collaboration graph of a graph model. The nodes in the collaboration graph represent the departments of the enterprise, and the node attributes include the digital capability level. The edges in the collaboration graph represent the collaboration relationship and data flow between departments. The edge weights in the collaboration graph are updated in real time to reflect the frequency and timeliness of data flow, forming a dynamic collaboration relationship network of the enterprise. S3, Identification of data islands and information bottlenecks: Based on the edge weights in the collaboration graph and the frequency of data flow between nodes, the collaboration graph is analyzed using data flow network analysis to identify data islands of cross-departmental data flow; S4, cross-departmental collaboration efficiency scoring: Construct a cross-departmental collaboration efficiency scoring model. Based on the relationship between nodes and edges in the collaboration graph, combined with data exchange frequency indicators, collaboration task response time indicators, and task completion rate indicators, calculate the collaboration efficiency scores of each department and the entire enterprise. The collaboration efficiency scores of each department are obtained by directly converting the node attributes of each department's nodes. The enterprise's collaboration efficiency score provides a quantitative assessment of the enterprise's overall data liquidity.

2. According to claim 1, a method for detecting and evaluating the operation of digital capabilities of an enterprise is characterized in that: The S1 specifically includes: S11, data exchange frequency collection: through the digital system log records of various departments of the enterprise, monitor the real-time data request and transmission frequency between departments, record the number of times the data request is sent and received, and record the occurrence time and duration of data exchange, and generate a data exchange frequency matrix across departments; S12, Collection of collaborative task response time: Based on the enterprise's internal task management system, monitor the response of each department in cross-departmental tasks in real time, record the task assignment time, task acceptance time and task completion time, calculate the time difference between task initiation and initial response, so as to generate a task response time record table and quantify the response time of the department; S13, Collection of collaborative task completion rate: Through the enterprise's project management and workflow system, track the completion status of each department in the collaborative tasks, count the ratio of the total number of completed tasks to the total number of assigned tasks, and generate a collaborative task completion rate data table.

3. According to claim 1, a method for detecting and evaluating the operation of digital capabilities of an enterprise is characterized in that: The collaboration graph is represented by an undirected weighted graph G = (V, E), where V is a node set representing various departments in the enterprise, and each node v i ∈V corresponds to a department, E is an edge set, representing the collaborative relationship between departments, and each edge e i,j ∈E represents the collaborative relationship between department i and department j; Node attribute N i :Each node v i The property N i Indicates the level of digital capabilities of the department; Edge weight W i,j :Each edge e i,j The weight W i,j Indicates the degree of collaboration between departments i and j.

4. The enterprise digital capability operation detection and evaluation method according to claim 3 is characterized in that: The node attribute N i It is expressed as: N i =α·TCR i +β·TRR i +γ·DEF i , where TCR i is the task completion rate of department i, TRR i is the average response speed of department i to all collaboration requests, DEF i is the data exchange frequency of department i, which means the sum of the frequencies of data exchange between department i and all other departments. α, β, and γ are the weight coefficients of task completion rate, response speed, and data exchange frequency, respectively, and α+β+γ=1.

5. The method for detecting and evaluating the operation of digital capabilities of an enterprise according to claim 4, characterized in that: The setting of edge weights in S2 includes: Data exchange frequency: The edge weight is based on the frequency of data exchange between departments. The higher the frequency, the closer the collaborative relationship. Collaboration task response speed: Based on the frequency, the edge weight is adjusted according to the response time of the collaborative tasks between departments. The shorter the response time, the higher the collaboration efficiency. Collaboration task completion rate: The edge weight is adjusted based on the collaboration task completion rate between departments. The collaboration relationship with a higher collaboration task completion rate has a higher weight.

6. The method for detecting and evaluating the operation of digital capabilities of an enterprise according to claim 5, characterized in that: The edge weight setting is expressed as: Among them, f i,j The frequency of data exchange between department i and department j is defined as the total number of data exchanges between departments i and j within a specified time. Avg Response Time i,j It represents the average response time of department i to all tasks of department j, indicating the timeliness of collaborative response, TCR i,j represents the completion rate of the collaborative tasks between departments i and j, and represents the completion status of the tasks assigned between the two departments. λ, μ, and v represent the weight coefficients of data exchange frequency, response speed, and task completion rate, respectively, satisfying λ+μ+ν=1.

7. The method for detecting and evaluating the operation of digital capabilities of an enterprise according to claim 4, characterized in that: The S3 specifically includes: S31, Data Island Identification: Based on the edge weights in the collaboration graph and the frequency of data flow between nodes, the collaboration graph is analyzed using the data flow network analysis algorithm to define the connectivity threshold W of data flow. t h res h old , when the connectivity threshold W t h res h old Or the data exchange frequency DEF of the node i When it is lower than other nodes, the node is marked as a potential data island, indicating that there are obstacles to data flow in cross-departmental collaboration in this department; S32, improvement suggestion generation: Generate improvement suggestions for each data island, including increasing data exchange frequency, shortening task response time, and improving task completion rate.

8. The method for detecting and evaluating the operation of digital capabilities of an enterprise according to claim 7, characterized in that: The connectivity threshold W t h res h old It is set to 0.7 times the mean of all edge weights in the graph, calculated as: When the edge weight of any node is lower than W t h res h old ,It is believed that the node collaboration is weak and there are obstacles to data flow, forming a data island; The data exchange frequency DEF i <0.6×DEF avg , then we consider that node v i Data exchange frequency DEF i Lower than other nodes, among which, DEF avg is the mean of the data exchange frequency of all nodes, and 0.6 is the influence coefficient.

9. The method for detecting and evaluating the operation of digital capabilities of an enterprise according to claim 4, characterized in that: The collaboration efficiency score of each department is C i By node attribute N i The table is directly converted, that is: C i =α·TCR i +β·TRR i +γ·DEF i .

10. The enterprise digital capability operation detection and evaluation method according to claim 9 is characterized in that: The overall enterprise collaboration efficiency score is C total Based on the collaboration efficiency score of all departments, C i , to quantify the overall digital collaboration level and data liquidity of the enterprise, the calculation formula is: Among them, |V| is the total number of nodes in the graph, that is, the total number of departments, C i is the collaboration efficiency score of department i.

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