Solution recommendation method and system for digital transformation of small and medium-sized enterprises

Abstract

The present application relates to the field of enterprise management, and discloses a method and system for recommending solutions for the digital transformation of small and medium-sized enterprises, the method comprising: obtaining business data between multiple business nodes and multiple business nodes in a historical time period; determining the time variation characteristics of the business data between multiple business nodes based on the business data between multiple business nodes in a historical time period, and determining the digital transformation solution between multiple business nodes based on the business data between multiple business nodes in a historical time period and the time variation characteristics of the business data between multiple business nodes. Determine multiple first business nodes whose fluctuation values ​​of the data volume of multiple data types are less than a preset fluctuation value, and determine multiple second business nodes whose fluctuation values ​​of the data volume of multiple data types are greater than or equal to the preset fluctuation value. The internal data of the enterprise in the present application can provide precise guidance on the transformation direction of the enterprise.

Description

Technical Field

[0001] The present application relates to the field of enterprise management technology, and more specifically, to a solution recommendation method and system for digital transformation of small and medium-sized enterprises. Background Art

[0002] With the development of informatization, small and medium-sized enterprises are facing unprecedented competitive pressure. The traditional business model can no longer meet the rapid changes in the modern market and the diversification of customer needs. Digital transformation has become a key path for small and medium-sized enterprises to enhance their competitiveness and achieve sustainable development. Digitalization can not only improve the operational efficiency of enterprises, but also optimize customer experience and enhance market response speed, thereby occupying a favorable position in the fierce market competition. Digital transformation is inseparable from advanced technical support. Emerging technologies such as cloud computing, big data, the Internet of Things, and artificial intelligence provide small and medium-sized enterprises with a variety of tools and platforms to help them build intelligent production, management, and service systems.

[0003] Small and medium-sized enterprises also face many challenges in the process of digital transformation, especially the choice of the direction of digital transformation. Therefore, how to effectively determine the direction of digital transformation has become the key to the success of digital transformation of small and medium-sized enterprises. Patent application CN118446588A (application number: CN202410617681.X) provides an intelligent evaluation method for the digital transformation of small and medium-sized enterprises, which includes: collecting transformation data of target enterprises in different fields; analyzing the transformation data to obtain potential transformation problems; determining the evaluation results based on the potential transformation problems; when the evaluation results are greater than the preset results, generating personalized suggestions for potential transformation problems, and presenting the evaluation results and personalized suggestions through visualization. The method in patent application CN118446588A can provide guidance for digital transformation through transformation data, but the internal data of the enterprise cannot provide accurate guidance on the transformation direction of the enterprise. Summary of the invention

[0004] The purpose of this application is to provide a solution recommendation method and system for the digital transformation of small and medium-sized enterprises, which solves the technical problem that the internal data of the enterprise cannot provide accurate guidance on the transformation direction of the enterprise, and achieves the technical effect that the internal data of the enterprise can provide accurate guidance on the transformation direction of the enterprise.

[0005] An embodiment of the present application provides a method for recommending a solution for digital transformation of small and medium-sized enterprises, the method comprising: obtaining business data of multiple business nodes and between multiple business nodes within a historical time period; determining time variation characteristics of the business data between multiple business nodes based on the business data between multiple business nodes within the historical time period, and determining a digital transformation solution between multiple business nodes based on the business data between multiple business nodes within the historical time period and the time variation characteristics of the business data between multiple business nodes.

[0006] In one possible implementation, based on business data between multiple business nodes in a historical time period and time-varying characteristics of business data between multiple business nodes, and based on the time-varying characteristics of business data between multiple business nodes, a recommended plan for digital transformation is determined, including: determining fluctuation values ​​of data volumes of multiple data types based on business data between multiple business nodes in a historical time period, and determining multiple first business nodes whose fluctuation values ​​of data volumes of multiple data types are less than a preset fluctuation value, and determining multiple second business nodes whose fluctuation values ​​of data volumes of multiple data types are greater than or equal to the preset fluctuation value; determining a digital transformation plan between multiple first business nodes based on business data between multiple first business nodes in a historical time period; determining an observation time period between multiple second business nodes based on business data between multiple second business nodes in a historical time period, and determining a digital transformation plan between multiple second business nodes within the observation time period.

[0007] In another possible implementation, an observation time period between multiple second business nodes is determined based on business data between multiple second business nodes within a historical time period, including: determining a fluctuation period of the business data between the multiple second business nodes within the historical time period based on a fluctuation value of the business data between the multiple second business nodes within the historical time period; and determining a target number based on the type of business data between the multiple second business nodes within the historical time period; and using the fluctuation period of the target number as the observation time period.

[0008] In another possible implementation, within an observation period, a digital transformation plan between multiple second business nodes is determined, including: within each fluctuation period, determining multiple data similarities corresponding to the business data between the multiple second business nodes; determining the maximum similarity among the multiple data similarities, and determining the digital transformation plan between the multiple second business nodes based on the most similar business data between the multiple second business nodes corresponding to the maximum similarity within the fluctuation period.

[0009] In another possible implementation, a digital transformation plan between multiple second business nodes is determined based on the business data between multiple second business nodes within the fluctuation period corresponding to the maximum similarity, including: determining multiple data increment amplitudes corresponding to the most similar business data in different time periods, and determining multiple most similar business data with data increment amplitudes greater than a preset data increment amplitude as mutation business data between multiple second business nodes; determining a digital transformation plan between multiple second business nodes based on the mutation business data between multiple second business nodes.

[0010] In another possible implementation, a digital transformation plan between multiple second business nodes is determined based on the mutant business data between multiple second business nodes, including: obtaining business blocking times corresponding to multiple mutant business data between multiple second business nodes, and obtaining business weights corresponding to multiple mutant business data between multiple second business nodes; determining the digital transformation plan between multiple second business nodes based on the business blocking times and business weights corresponding to multiple mutant business data between multiple second business nodes.

[0011] In another possible implementation, the method also includes: obtaining business congestion times corresponding to multiple business data between multiple first business nodes, and obtaining business weights corresponding to multiple mutation business data between multiple first business nodes; when the business congestion times corresponding to multiple business data between multiple first business nodes and the business congestion times corresponding to multiple mutation business data between multiple second business nodes coincide with each other, determining digital transformation plans corresponding to multiple first business nodes and multiple second business nodes based on the business congestion times and business weights corresponding to multiple mutation business data between multiple first business nodes, and the business congestion times and business weights corresponding to multiple mutation business data between multiple second business nodes.

[0012] An embodiment of the present application also provides a solution recommendation system for digital transformation of small and medium-sized enterprises, including a unit for executing any of the methods described above.

[0013] An embodiment of the present application also provides a solution recommendation system for digital transformation of small and medium-sized enterprises, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements any of the methods described above.

[0014] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above items is implemented.

[0015] An embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of any of the methods described above when executed by a processor.

[0016] In another possible implementation,

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0018] The embodiment of the present application provides a method for recommending a solution for digital transformation of small and medium-sized enterprises, and the method includes: obtaining multiple business nodes and business data between multiple business nodes in a historical time period; determining the time variation characteristics of the business data between multiple business nodes according to the business data between multiple business nodes in a historical time period, and determining the digital transformation solution between multiple business nodes according to the business data between multiple business nodes in a historical time period and the time variation characteristics of the business data between multiple business nodes. In the embodiment of the present application, the characteristics of the business data between business nodes changing over time can be determined, and the digital transformation solution between multiple business nodes can be determined according to the characteristics of the business data changing over time, thereby improving the accuracy of determining the digital transformation solution of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 A flowchart of a first method for recommending solutions for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application;

[0021] Figure 2 A flowchart of a second method for recommending a solution for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application;

[0022] Figure 3 A flowchart of a third method for recommending a solution for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application;

[0023] Figure 4 A flowchart of a fourth method for recommending a solution for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of the logical structure of a solution recommendation system for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application;

[0025] Figure 6 A schematic diagram of the physical structure of a solution recommendation system for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0027] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0028] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0029] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0030] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0031] Existing enterprise transformation analysis methods can provide guidance for digital transformation through transformation data, but internal enterprise data cannot provide accurate guidance on the direction of enterprise transformation.

[0032] Based on the above reasons, the embodiment of the present application provides a solution recommendation method for the digital transformation of small and medium-sized enterprises, and the method includes: obtaining multiple business nodes and business data between multiple business nodes in a historical time period; determining the time change characteristics of the business data between multiple business nodes according to the business data between multiple business nodes in a historical time period, and determining the digital transformation solution between multiple business nodes according to the business data between multiple business nodes in a historical time period and the time change characteristics of the business data between multiple business nodes. In the embodiment of the present application, it is possible to determine the characteristics of the business data between business nodes that change over time, and determine the digital transformation solution between multiple business nodes according to the characteristics of the business data that change over time, thereby improving the accuracy of determining the digital transformation solution of the enterprise.

[0033] In some scenarios, a solution recommendation method for digital transformation of small and medium-sized enterprises in an embodiment of the present application can be applied to enterprise consulting management, and can provide accurate enterprise digital transformation recommendations for the digital transformation of enterprises, thereby improving the scientificity and accuracy of the direction of enterprise digital transformation.

[0034] The following is a detailed description of a method for recommending solutions for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application with reference to specific examples.

[0035] Figure 1 A flowchart of a first method for recommending a solution for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes S110 to S120, and S110 to S120 are described in detail below.

[0036] S110: Acquire business data of multiple business nodes and between multiple business nodes in a historical time period.

[0037] In the process of enterprise digital transformation, business processes need to be digitally transformed. In the business process, there are often multiple data flows between different business nodes. The embodiment of the present application processes the data flow characteristics between business nodes to accurately locate the direction of the enterprise's digital transformation.

[0038] While working, you can obtain business data from multiple business nodes and between multiple business nodes in a historical time period, and you can analyze the business data between multiple business nodes in a historical time period to improve the accuracy of the company's digital transformation.

[0039] Exemplarily, the historical time period may be 10 days, 15 days or 30 days.

[0040] Exemplarily, when obtaining business data between multiple business nodes and multiple business nodes within a historical time period, the data type and data volume of the data between the multiple business nodes can be manually counted, and the data can be archived according to the data type and data volume to facilitate analysis of the business data between the multiple business nodes within the historical time period.

[0041] Exemplarily, for multiple service chains, each service chain includes multiple service nodes. Service intersections may exist between service nodes of multiple service chains, and thus service data between the multiple service nodes may be obtained and analyzed.

[0042] Exemplarily, the business data between multiple business nodes may be files, communication information, etc. sent via a chat tool.

[0043] S120. Determine the time variation characteristics of the business data between the multiple business nodes based on the business data between the multiple business nodes in the historical time period, and determine the digital transformation plan between the multiple business nodes based on the business data between the multiple business nodes in the historical time period and the time variation characteristics of the business data between the multiple business nodes.

[0044] During operation, after obtaining the business data between multiple business nodes, the time change characteristics of the business data between multiple business nodes can be determined based on the business data between the multiple business nodes in a historical time period. The time change characteristics of the business data between multiple business nodes characterize the characteristics of the business data between different business nodes changing over time, thereby improving the accuracy and scientificity of the business data between multiple business nodes.

[0045] After obtaining the time-varying characteristics of business data between multiple business nodes, the digital transformation plan between the multiple business nodes can be determined based on the business data between the multiple business nodes in the historical time period and the time-varying characteristics of the business data between the multiple business nodes. The digital transformation plan can be determined in combination with the time-varying characteristics of the business data between the multiple business nodes, thereby improving the accuracy and scientificity of the business data between the multiple business nodes.

[0046] Exemplarily, a digital transformation plan between multiple business nodes can be determined based on experience, time-varying characteristics of business data between multiple business nodes, business data between multiple business nodes within a historical time period, and time-varying characteristics of business data between multiple business nodes.

[0047] For example, a neural network model can be trained using enterprise data cases that have completed digital transformation, and the trained neural network model can be used to determine the digital transformation plan between multiple business nodes based on business data between multiple business nodes in a historical time period and the time change characteristics of business data between multiple business nodes.

[0048] Exemplarily, when determining a digital transformation plan based on business data between multiple business nodes in a historical time period and time-varying characteristics of business data between multiple business nodes, a corresponding digital transformation plan can be determined through a classification model.

[0049] Exemplarily, when determining a corresponding digital transformation plan based on business data between multiple business nodes in a historical time period through a deep learning model, the business data between multiple business nodes in the historical time period can be first standardized and serialized to obtain structural data corresponding to the business data between multiple business nodes in the historical time period, and the digital transformation plan can be determined based on the structural data corresponding to the business data between multiple business nodes in the historical time period.

[0050] The beneficial effect brought about by the above-mentioned implementation method is that, based on the business data between multiple business nodes in the historical time period and the time change characteristics of the business data between multiple business nodes, the digital transformation plan between multiple business nodes is determined, which can improve the accuracy and scientificity of the digital transformation and improve the effect of the enterprise's digital transformation.

[0051] Figure 2 A flowchart of a second method for recommending a solution for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, based on the business data between multiple business nodes in a historical time period, the time change characteristics of the business data between multiple business nodes are determined, and based on the business data between multiple business nodes in a historical time period and the time change characteristics of the business data between multiple business nodes, a digital transformation plan between multiple business nodes is determined, including S121 to S122, and S121 to S122 are specifically described below.

[0052] S121. Determine the fluctuation values ​​of the data volumes of multiple data types based on the business data between multiple business nodes in a historical time period, and determine multiple first business nodes whose fluctuation values ​​of the data volumes of the multiple data types are less than a preset fluctuation value, and determine multiple second business nodes whose fluctuation values ​​of the data volumes of the multiple data types are greater than or equal to the preset fluctuation value.

[0053] During operation, the fluctuation values ​​of the data volumes of multiple data types can be determined based on the business data between multiple business nodes in a historical time period. The fluctuation values ​​of the data volumes of multiple data types represent the extent of changes in the business data between multiple business nodes over time.

[0054] Exemplarily, when determining the fluctuation values ​​of the data volumes of multiple data types, the variance value of the data volume of each data type within a historical time period may be used as the fluctuation value of the data volumes of the multiple data types.

[0055] After obtaining the fluctuation values ​​of the data volumes of different data types, multiple first business nodes whose fluctuation values ​​of the data volumes of different data types are less than the preset fluctuation values ​​can be determined, and multiple second business nodes whose fluctuation values ​​of the data volumes of different data types are greater than or equal to the preset fluctuation values ​​can be determined, and then a digital transformation plan can be determined based on the multiple first business nodes and the multiple second business nodes.

[0056] Exemplarily, when determining the first service node, when the fluctuation value of the data volume of multiple data types in the service data between service node A and service node B in the historical time period is greater than a preset fluctuation value, service node A and service node B can be used as the first service node.

[0057] S122: Determine a digital transformation plan between the plurality of first service nodes based on service data between the plurality of first service nodes in a historical time period. Determine an observation time period between the plurality of second service nodes based on service data between the plurality of second service nodes in a historical time period, and determine a digital transformation plan between the plurality of second service nodes within the observation time period.

[0058] After obtaining multiple first business nodes, the business data fluctuation value between the multiple first business nodes is small and the business data volume is relatively stable. The digital transformation plan between the multiple first business nodes can be determined based on the business data between the multiple first business nodes in the historical time period.

[0059] After obtaining multiple second business nodes, the business data fluctuation value between the multiple second business nodes is relatively large. The observation time period between the multiple second business nodes can be determined based on the business data between the multiple second business nodes in the historical time period, and then the business data between the multiple second business nodes can be further statistically analyzed within the observation time period, and the digital transformation plan between the multiple second business nodes can be determined within the observation time period to ensure the accuracy of the digital transformation.

[0060] The beneficial effect brought about by the above-mentioned implementation method is that the business data fluctuation value between multiple first business nodes is small and the business data volume is relatively stable. Based on the business data between multiple first business nodes in a historical time period, the digital transformation plan between multiple first business nodes is determined, thereby ensuring the accuracy of the digital transformation of multiple first business nodes.

[0061] The beneficial effect brought about by the above-mentioned implementation method is that the business data fluctuation value between multiple second business nodes is relatively large. During the observation period, the business data between multiple second business nodes is further statistically analyzed to determine the digital transformation plan between multiple second business nodes and ensure the accuracy of the digital transformation of the second business nodes.

[0062] In some implementations, in the above S122, the observation time period between the multiple second service nodes is determined based on the service data between the multiple second service nodes in the historical time period, including S122a to S122b, and S122a to S122b are specifically described below.

[0063] S122a, determining a fluctuation period of the business data between the plurality of second business nodes in the historical time period according to the fluctuation value of the business data between the plurality of second business nodes in the historical time period, and determining a target quantity according to the type of business data between the plurality of second business nodes in the historical time period.

[0064] During operation, the fluctuation period of the business data between multiple second business nodes in the historical time period can be determined based on the fluctuation value of the business data between multiple second business nodes in the historical time period through statistical analysis or image fluctuation analysis of the data volume, and then the business data between the multiple second business nodes in the fluctuation period can be analyzed to ensure the accuracy of subsequent digital transformation.

[0065] After obtaining the fluctuation cycle of the business data between multiple second business nodes in the historical time period, the target number can be determined according to the type of business data between multiple second business nodes in the historical time period. The target number is the number of fluctuation cycles for statistical analysis of the business data between the second business nodes.

[0066] Exemplarily, when determining the target number based on the types of service data between the plurality of second service nodes in a historical time period, the target number may be determined based on empirical values.

[0067] Exemplarily, when the business data type is daily communication data, the target number may be three.

[0068] Exemplarily, when the business data type is business process data, the target quantity may be ten.

[0069] S122b. Use the fluctuation period of the target quantity as the observation period.

[0070] When working, you can use the fluctuation period of the target quantity as the observation period, and then you can perform accurate business data analysis within the observation period.

[0071] The beneficial effect brought about by the above-mentioned implementation method is that the fluctuation cycle of business data between multiple second business nodes in a historical time period is determined, and the target number of fluctuation cycles is determined according to the type of business data, and then the business data within the target number of fluctuation cycles is analyzed to provide guidance for digital transformation, thereby improving the accuracy and scientificity of digital transformation.

[0072] In some implementations, in the above S122, within the observation period, a digital transformation plan between multiple second service nodes is determined, including S122c and S122d. S122c and S122d are described in detail below.

[0073] S122c. Determine, within each fluctuation period, a plurality of data similarities respectively corresponding to the business data between the plurality of second business nodes.

[0074] When planning a digital transformation plan, when there are multiple types of business data between multiple second business nodes, multiple data similarities corresponding to the business data between the multiple second business nodes can be determined within each fluctuation cycle, and the accuracy of the digital transformation plan can be improved based on the similarities between the business data between the multiple second business nodes.

[0075] Exemplarily, when determining the multiple data similarities respectively corresponding to the business data between the multiple second business nodes, the similarities of the multiple business data may be determined according to the data volume of the business data of different data types.

[0076] Exemplarily, when determining multiple data similarities respectively corresponding to the business data between multiple second business nodes, it is also possible to construct a data content matrix for the data content of the business data between the multiple second business nodes, perform principal component analysis on the data content matrix and determine the similarities as the multiple data similarities respectively corresponding to the business data between the multiple second business nodes.

[0077] S122d. Determine the maximum similarity among the multiple data similarities, and determine the digital transformation plan between the multiple second business nodes based on the most similar business data between the multiple second business nodes corresponding to the maximum similarity within the fluctuation period.

[0078] When conducting digital transformation planning, the maximum similarity among multiple data similarities can be determined, and the digital transformation plan between multiple second business nodes can be determined based on the most similar business data among multiple second business nodes corresponding to the maximum similarity within the fluctuation period. This realizes the determination of the digital transformation plan based on the most similar business data among the business data between the second business nodes within the fluctuation period, thereby improving the accuracy and scientific nature of the digital transformation plan.

[0079] It should be noted that the business data between multiple second business nodes can also be screened according to the similarity threshold of the business data, and the digital transformation plan between the multiple second business nodes can be determined based on the business data between the multiple second business nodes corresponding to the similarity threshold of the business data within the fluctuation period.

[0080] The beneficial effect brought about by the above-mentioned implementation method is that by determining multiple data similarities corresponding to the business data between multiple second business nodes, and then screening the business data according to the similarities between the business data between multiple second business nodes, the accuracy of determining the digital transformation plan based on the business data between multiple second business nodes is improved.

[0081] Figure 3 A flowchart of a third method for recommending a solution for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, in the above S122d, a digital transformation plan between multiple second business nodes is determined based on the business data between multiple second business nodes corresponding to the maximum similarity within the fluctuation period, including S210 to S220. S210 to S220 are specifically described below.

[0082] S210. Determine multiple data increment amplitudes corresponding to the most similar business data in different time periods, and determine multiple most similar business data with data increment amplitudes greater than a preset data increment amplitude as mutation business data between multiple second business nodes.

[0083] After obtaining the most similar business data, the data increment amplitude of the most similar business data within the fluctuation period can be further determined in time, that is, based on the multiple data increment amplitudes corresponding to the most similar business data within the fluctuation period in different time periods, the accuracy of the digital transformation plan can be improved according to the data increment amplitude.

[0084] After obtaining multiple data increment amplitudes corresponding to the most similar business data in different time periods, multiple most similar business data with data increment amplitudes greater than the preset data increment amplitude can be determined as mutation business data between multiple second business nodes, and then the traffic adaptability of the digital transformation plan can be improved based on the mutation business data, thereby improving the stability of the digital transformation plan.

[0085] Exemplarily, the preset data increment range may be 30%, 80% or 120%.

[0086] S220. Determine a digital transformation plan among the multiple second business nodes according to the mutation business data among the multiple second business nodes.

[0087] After obtaining the mutated business data, the digital transformation plan between the multiple second business nodes can be determined based on the mutated business data between the multiple second business nodes to adapt to the volatility of the data traffic between the multiple second business nodes, improve the robustness of the digital transformation plan, and improve the accuracy of the recommended digital transformation plan.

[0088] The beneficial effect brought about by the above implementation method is that the digital transformation plan between multiple second service nodes is determined according to the sudden change service data with a large amplitude of data flow change, thereby improving the robustness and accuracy of the digital transformation plan.

[0089] Figure 4 A flowchart of a fourth method for recommending a solution for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, in the above S220, a digital transformation plan between multiple second business nodes is determined according to the mutation business data between multiple second business nodes, including S221 to S222, and S221 to S222 are specifically described below.

[0090] S221. Obtain service blocking times respectively corresponding to multiple pieces of sudden change service data between multiple second service nodes, and obtain service weights respectively corresponding to multiple pieces of sudden change service data between multiple second service nodes.

[0091] Before digital transformation, business data may cause business processes to be blocked due to poor data circulation. Therefore, the business blocking times corresponding to multiple mutation business data between multiple second business nodes can be obtained, and the scientific nature of the digital transformation plan can be improved based on the business blocking time.

[0092] Exemplarily, the service congestion time corresponding to multiple mutation service data between multiple second service nodes can be obtained by counting the service congestion time between multiple second service nodes within an observation period. The service congestion time can be the service delay time caused by the impact of data flow.

[0093] At the same time, when improving the scientific nature of the digital transformation plan based on the business blocking time, it is also possible to obtain the business weights corresponding to multiple mutation business data between multiple second business nodes, and improve the scientific nature of the digital transformation based on the business weights.

[0094] Exemplarily, the service weights respectively corresponding to the multiple mutation service data between the multiple second service nodes may be manually allocated.

[0095] S222. Determine a digital transformation plan between the multiple second service nodes according to the service blocking time and service weights respectively corresponding to the multiple mutation service data between the multiple second service nodes.

[0096] When planning a digital transformation plan, the digital transformation plan between multiple second business nodes can be determined based on the business blocking time and business weight corresponding to multiple mutation business data between the multiple second business nodes to further improve the accuracy of the digital transformation plan.

[0097] Exemplarily, a deep learning model can be used to determine a digital transformation plan between the corresponding multiple second business nodes according to different business blocking times and business weights corresponding to multiple mutation business data between the multiple second business nodes.

[0098] The beneficial effect brought about by the above implementation method is that, according to the multiple mutation business data between the multiple second business nodes corresponding to different business blocking times and business weights, the digital transformation plan between the corresponding multiple second business nodes is determined, thereby improving the scientific nature of the digital transformation plan.

[0099] In some implementations, the above method further includes S310 to S320, and S310 to S320 are described in detail below.

[0100] S310: Obtain service blocking times respectively corresponding to multiple service data between multiple first service nodes, and obtain service weights respectively corresponding to multiple mutation service data between multiple first service nodes.

[0101] When analyzing business data, business blocking times corresponding to multiple business data between multiple first business nodes may also be obtained. This business blocking time represents the business blocking time corresponding to multiple business data between first business nodes with smaller data volume fluctuations.

[0102] At the same time, the business weights respectively corresponding to the multiple mutation business data between the multiple first business nodes can be obtained, and the accuracy of the digital transformation plan can be improved according to the business weights respectively corresponding to the multiple mutation business data between the multiple first business nodes.

[0103] S320. When the business blocking times corresponding to multiple business data between multiple first business nodes and the business blocking times corresponding to multiple mutation business data between multiple second business nodes coincide with each other, determine the digital transformation plans corresponding to the multiple first business nodes and the multiple second business nodes according to the business blocking times and business weights corresponding to the multiple mutation business data between the multiple first business nodes and the business blocking times and business weights corresponding to the multiple mutation business data between the multiple second business nodes.

[0104] When planning a digital transformation plan, when the business blocking times corresponding to multiple business data between multiple first business nodes and the business blocking times corresponding to multiple mutation business data between multiple second business nodes overlap, it means that the multiple mutation business data between multiple second business nodes with larger data volume fluctuation values ​​and the multiple business data between multiple first business nodes with smaller data volume fluctuation values ​​have similarities in business blocking times, and thus the multiple mutation business data between the second business nodes with overlapping business blocking times and the multiple business data between multiple first business nodes are optimized through the same digital transformation plan.

[0105] Exemplarily, when determining the overlap of business congestion time, the business congestion time corresponding to multiple business data between multiple first business nodes and the business congestion time corresponding to multiple mutation business data between multiple second business nodes can be determined on the time axis respectively, and the overlap time of the business congestion time corresponding to multiple business data between multiple first business nodes and the business congestion time corresponding to multiple mutation business data between multiple second business nodes can be determined on the time axis. When the overlap time is greater than the preset time length, it can be determined that the business congestion time corresponding to multiple business data between multiple first business nodes and the business congestion time corresponding to multiple mutation business data between multiple second business nodes overlap.

[0106] When planning a digital transformation plan, the digital transformation plans corresponding to multiple first business nodes and multiple second business nodes can be determined based on the business congestion time and business weight corresponding to multiple mutation business data between multiple first business nodes, and the business congestion time and business weight corresponding to multiple mutation business data between multiple second business nodes. This allows the digital transformation plan to be simultaneously applied to business data between multiple first business nodes and multiple second business nodes, thereby minimizing the business congestion time.

[0107] The beneficial effect brought about by the above-mentioned implementation method is that, by judging whether the service blocking time overlaps, it is judged that multiple mutation service data between multiple second service nodes with larger data volume fluctuation values ​​and multiple service data between multiple first service nodes with smaller data volume fluctuation values ​​have similarity in service blocking time, and then the digital transformation plan is determined according to the multiple mutation service data between multiple second service nodes with overlapping blocking time and the multiple service data between multiple first service nodes with smaller data volume fluctuation values, thereby improving the versatility of the digital transformation plan and being able to minimize the service blocking time through the digital transformation plan.

[0108] An embodiment of the present application also provides a solution recommendation system for digital transformation of small and medium-sized enterprises, including a unit for executing any of the methods described above.

[0109] Figure 5 A logical structure diagram of a solution recommendation system for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the system 1 of this embodiment includes a processing unit 11, a storage unit 12 and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12 and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects brought by the embodiment of the present application have been described in the above method and will not be repeated here.

[0110] An embodiment of the present application also provides a solution recommendation system for digital transformation of small and medium-sized enterprises, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements any of the methods described above.

[0111] Figure 6 A schematic diagram of the physical structure of a solution recommendation system for digital transformation of small and medium-sized enterprises provided in an embodiment of the present application, such as Figure 6 As shown, the system 2 of this embodiment includes: at least one processor 20 ( Figure 6Only one processor 20 is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20. When the processor 20 executes the computer program 22, the steps in any of the above-mentioned method embodiments are implemented. The beneficial effects brought about by the embodiments of the present application have been described in the above-mentioned methods and will not be repeated here.

[0112] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0113] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0114] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0115] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0117] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0119] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A solution recommendation method for digital transformation of small and medium-sized enterprises, characterized in that: The method comprises: Obtain business data of multiple business nodes and between multiple business nodes in a historical time period; Determine the time variation characteristics of the business data between the multiple business nodes based on the business data between the multiple business nodes in the historical time period, and determine the digital transformation plan between the multiple business nodes based on the business data between the multiple business nodes in the historical time period and the time variation characteristics of the business data between the multiple business nodes; Among them, according to the business data between multiple business nodes in the historical time period and the time variation characteristics of the business data between multiple business nodes, and according to the time variation characteristics of the business data between multiple business nodes, the recommended solution for digital transformation is determined, including: Determine fluctuation values ​​of data volumes of multiple data types according to business data between multiple business nodes in a historical time period, determine multiple first business nodes whose fluctuation values ​​of data volumes of the multiple data types are less than a preset fluctuation value, and determine multiple second business nodes whose fluctuation values ​​of data volumes of the multiple data types are greater than or equal to the preset fluctuation value; Determine a digital transformation plan between the multiple first business nodes based on business data between the multiple first business nodes in a historical time period; determine an observation time period between the multiple second business nodes based on business data between the multiple second business nodes in a historical time period, and determine a digital transformation plan between the multiple second business nodes within the observation time period; The step of determining the observation time period between the plurality of second service nodes according to the service data between the plurality of second service nodes in the historical time period includes: Determine the fluctuation period of the business data between the multiple second business nodes in the historical time period according to the fluctuation value of the business data between the multiple second business nodes in the historical time period; and determine the target quantity according to the type of business data between the multiple second business nodes in the historical time period; The fluctuation period of the target quantity is used as the observation period; During the observation period, a digital transformation plan between multiple second service nodes is determined, including: In each fluctuation period, determining multiple data similarities respectively corresponding to the business data between multiple second business nodes; The maximum similarity among the multiple data similarities is determined, and the digital transformation plan between the multiple second business nodes is determined according to the most similar business data between the multiple second business nodes corresponding to the maximum similarity within the fluctuation period.

2. The method according to claim 1, characterized in that Determining a digital transformation plan between the plurality of second business nodes according to business data between the plurality of second business nodes within a fluctuation period corresponding to the maximum similarity includes: Determine multiple data increment amplitudes corresponding to the most similar business data in different time periods, and determine multiple most similar business data with data increment amplitudes greater than a preset data increment amplitude as mutation business data between multiple second business nodes; According to the mutation business data among the multiple second business nodes, a digital transformation plan among the multiple second business nodes is determined.

3. The method according to claim 2, characterized in that According to the mutation service data between the multiple second service nodes, a digital transformation plan between the multiple second service nodes is determined, including: Obtaining service blocking times respectively corresponding to multiple mutation service data between multiple second service nodes, and obtaining service weights respectively corresponding to multiple mutation service data between multiple second service nodes; A digital transformation plan between the multiple second service nodes is determined according to the service blocking times and service weights respectively corresponding to the multiple mutation service data between the multiple second service nodes.

4. A solution recommendation system for digital transformation of small and medium-sized enterprises, characterized in that: The method comprises means for performing the method according to any one of claims 1 to 3.

5. A solution recommendation system for digital transformation of small and medium-sized enterprises, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

Images