Digital enterprise recommendation method, device, electronic device and storage medium

Abstract

The present application relates to the field of data processing, and provides a method, device, electronic device, and storage medium for recommending digital enterprises. The method includes: obtaining the scores of the evaluation indicators corresponding to each candidate enterprise while determining the indicator model of each candidate enterprise; determining the digital score of each candidate enterprise based on the indicator model and the score of the corresponding evaluation indicator; determining the target recommended enterprise based on the digital score and the target digital score of each candidate enterprise. The method determines the digital score of each candidate enterprise based on the indicator model of each candidate enterprise, unifies the quantitative standards, quantifies the digital capabilities of the candidate enterprises, and characterizes the digital capabilities of the candidate enterprises through digital scores; recommends based on the digital scores of each candidate enterprise and the target digital scores, thereby determining the enterprises that meet the digital needs of the user and making recommendations.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a digital enterprise recommendation method, device, electronic device and storage medium. Background Art

[0002] Digitalization is the conversion of traditional physical forms, processes or services into digital forms to achieve more efficient information processing, storage and transmission. A digital enterprise refers to an enterprise that has integrated digital technology into its business activities or new business models. The core business processes of a digital enterprise are connected and communicated through digital information systems. At the same time, core enterprise assets are also managed and operated in the form of digital information systems. Therefore, digital enterprises can respond to the external environment faster than traditional enterprises and are more competitive.

[0003] At present, enterprise recommendations usually adopt the method of popular recommendations, that is, popular push is carried out according to the number of times the enterprise is searched on the entire network, but popular push does not mean that the pushed enterprise is a high-quality enterprise. In particular, the current enterprise recommendation methods usually do not take into account the digital capabilities of the enterprise and cannot make enterprise recommendations based on the digital needs of users.

[0004] Therefore, how to recommend companies based on users' digital needs has become an urgent problem to be solved. Summary of the invention

[0005] The present invention provides a digital enterprise recommendation method, device, electronic device and storage medium, which are used to solve the technical problem that the digitalization capability of an enterprise cannot be accurately evaluated in the prior art.

[0006] The present invention provides a digital enterprise recommendation method, comprising:

[0007] After determining the indicator model of each candidate enterprise, obtaining the score of the evaluation indicator corresponding to each candidate enterprise;

[0008] Determine the digital score of each candidate enterprise based on the score of each indicator model and the corresponding evaluation indicator;

[0009] Based on the digital scores of the candidate enterprises and the target digital scores, target recommended enterprises are determined.

[0010] According to a digital enterprise recommendation method provided by the present invention, before determining the target recommended enterprise based on the digital scores of the candidate enterprises and the target digital scores, the method further includes:

[0011] Acquire an assessment target tag, where the assessment target tag represents the expected digital maturity of the enterprise;

[0012] Based on the evaluation target label, the target digital score is determined.

[0013] According to a digital enterprise recommendation method provided by the present invention, the step of determining a target recommended enterprise based on the digital score of each candidate enterprise and the target digital score comprises:

[0014] Based on the evaluation target label, determining a target digital scoring range;

[0015] Based on the digital score of each candidate enterprise and the target digital score range, determine a number of first-level preferred enterprises, wherein the first-level preferred enterprises are candidate enterprises whose digital scores are within the target digital score range;

[0016] Determine a number of second-level preferred enterprises based on the enterprise information data of each of the first-level preferred enterprises, wherein the enterprise information data represents the operating conditions of the first-level preferred enterprises;

[0017] Based on the digital scores of each of the secondary preferred enterprises and the target digital score, a target recommended enterprise is determined.

[0018] According to a digital enterprise recommendation method provided by the present invention, each of the indicator models includes multiple indicator levels, the first indicator level includes an evaluation indicator, and the other indicator levels except the first indicator level each include several evaluation indicators, and the evaluation indicator of the i-th level is composed of several evaluation indicators of the i+1-th level, where i is a positive integer;

[0019] The digital score is the score of the evaluation indicator of level 1, and the score of the j-th evaluation indicator of level ith is determined based on the scores of several subordinate evaluation indicators of the j-th evaluation indicator, and the several subordinate evaluation indicators are several i+1-th level evaluation indicators corresponding to the j-th evaluation indicator.

[0020] According to a digital enterprise recommendation method provided by the present invention, each of the indicator models is determined based on the following steps:

[0021] Based on the evaluation scope label of the candidate enterprise, select several evaluation indicators from the preset capability indicator library to obtain the underlying evaluation indicators of the candidate enterprise;

[0022] Based on each of the underlying evaluation indicators, the evaluation indicators of each indicator level are determined layer by layer from bottom to top to obtain the indicator model of the candidate enterprise.

[0023] According to a digital enterprise recommendation method provided by the present invention, the preset capability index library includes a first index library, and the first index library stores digital capability evaluation indexes commonly used by enterprises.

[0024] According to a digital enterprise recommendation method provided by the present invention, the preset capability indicator library also includes at least one of a second indicator library and a third indicator library, wherein the second indicator library stores digital capability evaluation indicators unique to the enterprise under various business models, and the third indicator library stores digital capability evaluation indicators unique to the enterprise in various industries.

[0025] According to a digital enterprise recommendation method provided by the present invention, when the preset capability indicator library includes the first indicator library, the second indicator library and the third indicator library, the evaluation range label includes a first range label, a second range label and a third range label, the evaluation range label based on the candidate enterprise is selected from the preset capability indicator library to obtain the underlying evaluation indicators of the candidate enterprise, including:

[0026] Based on the first range tag, the second range tag and the third range tag, several evaluation indicators are selected from the first indicator library, the second indicator library and the third indicator library respectively to obtain the underlying evaluation indicators of the candidate enterprise.

[0027] According to a digital enterprise recommendation method provided by the present invention, the digital score of each candidate enterprise is determined based on the score of each indicator model and the corresponding evaluation indicator, including:

[0028] Determine the weight of the corresponding evaluation index in each of the index models;

[0029] Based on the weight of each evaluation indicator, the score of each evaluation indicator is determined step by step from bottom to top.

[0030] According to a digital enterprise recommendation method provided by the present invention, when the i+1th level is the bottom indicator level, the weights corresponding to the subordinate evaluation indicators of the jth evaluation indicator are determined based on the following steps:

[0031] Establishing a judgment matrix based on a preset scaling method, wherein the judgment matrix is ​​used to characterize the relative importance of each subordinate evaluation index;

[0032] Dividing each element in the judgment matrix by the sum of the elements in the column where the element is located, to obtain a standardized judgment matrix;

[0033] The average of each row of the standardized judgment matrix is ​​taken to obtain the weight corresponding to each of the subordinate evaluation indicators.

[0034] According to a digital enterprise recommendation method provided by the present invention, when the i+1th level is not the bottom indicator level, the weights corresponding to the subordinate evaluation indicators of the jth evaluation indicator are determined based on the following steps:

[0035] Based on the structural equation model, determining the correlation coefficients between the subordinate evaluation indicators;

[0036] Based on the correlation coefficients, determining the index conflict between the subordinate evaluation indicators, the greater the correlation coefficient between the subordinate evaluation indicators, the smaller the corresponding index conflict;

[0037] Based on the variability of each indicator and the conflict of each indicator, the CRITIC weighting method is used to determine the weight corresponding to each subordinate evaluation indicator, wherein the variability of each indicator represents the difference fluctuation of each subordinate evaluation indicator.

[0038] According to a digital enterprise recommendation method provided by the present invention, the score of the j-th evaluation indicator is determined based on the following steps:

[0039] The scores of the subordinate evaluation indicators and the weights corresponding to the subordinate evaluation indicators are weighted and aggregated to obtain the score of the j-th evaluation indicator.

[0040] The present invention also provides a device for evaluating enterprise digitalization capability, comprising:

[0041] The indicator acquisition module is used to: obtain the score of the evaluation indicator corresponding to each candidate enterprise when the indicator model of each candidate enterprise is determined;

[0042] A scoring determination module, used to: determine the digital score of each candidate enterprise based on the score of each indicator model and the corresponding evaluation indicator;

[0043] The target determination module is used to determine the target recommended enterprise based on the digital score of each candidate enterprise and the target digital score.

[0044] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any one of the digital enterprise recommendation methods described above is implemented.

[0045] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the digital enterprise recommendation method as described in any one of the above is implemented.

[0046] The digital enterprise recommendation method, device, electronic device and storage medium provided in the embodiments of the present application, when determining the indicator model of each candidate enterprise, obtains the score of the evaluation index corresponding to each candidate enterprise; determines the digital score of each candidate enterprise based on the indicator model and the score of the corresponding evaluation index; determines the target recommended enterprise based on the digital score and the target digital score of each candidate enterprise. The method determines the digital score of each candidate enterprise based on the indicator model of each candidate enterprise, unifies the quantitative standards, quantifies the digital capabilities of the candidate enterprises, and characterizes the digital capabilities of the candidate enterprises through digital scores; makes recommendations based on the digital scores of each candidate enterprise and the target digital scores, thereby determining enterprises that meet the digital needs of users and making recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 It is a flowchart of a digital enterprise recommendation method provided in an embodiment of the present application;

[0049] Figure 2 It is a schematic diagram of the process of determining the indicator model in the digital enterprise recommendation method provided in the embodiment of the present application;

[0050] Figure 3 It is a structural schematic diagram of an enterprise digital capability assessment device provided in an embodiment of the present application;

[0051] Figure 4 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] Figure 1 Schematic diagram of the process of the digital enterprise recommendation method provided in the embodiment of the present application. Figure 1 , the present application embodiment provides a digital enterprise recommendation method, which may include:

[0054] S110. After determining the indicator model of each candidate enterprise, obtain the score of the evaluation indicator corresponding to each candidate enterprise.

[0055] In the embodiment of the present invention, the execution subject of the digital enterprise recommendation method may be an enterprise digital capability assessment device, which may include but is not limited to: a server, a desktop computer, a laptop computer, a tablet computer, a mobile phone, etc. The execution subject of the digital enterprise recommendation method may also be an enterprise digital capability assessment system, which belongs to the enterprise digital capability assessment device.

[0056] Specifically, the indicator models of the candidate enterprises may be the same or different, and different indicator models have different evaluation indicators. After the indicator model of each candidate enterprise is determined, the score of each evaluation indicator in the indicator model is obtained.

[0057] S120: Determine a digital score for each candidate enterprise based on the score of each indicator model and the corresponding evaluation indicator.

[0058] Specifically, the digital score of a candidate enterprise is determined based on its indicator model and the scores of the corresponding evaluation indicators. For example, if the indicator model of a candidate enterprise is a pre-trained neural network model, and the scores of each evaluation indicator are the input of the neural network model, then after the scores of each evaluation indicator are input into the neural network model, the digital score output by the neural network model can be obtained.

[0059] S130: Determine a target recommended enterprise based on the digital score of each candidate enterprise and the target digital score.

[0060] In one embodiment, the candidate enterprise with the smallest difference between the digital score and the target digital score is determined as the target recommended enterprise.

[0061] In one embodiment, based on the absolute value of the difference between the digital score of each candidate enterprise and the target digital score, each candidate enterprise is ranked in ascending order of the difference, and based on the number of target enterprises, the top candidate enterprises are determined as target recommended enterprises. For example, if the number of target enterprises is 5, the top 5 candidate enterprises are determined as target recommended enterprises.

[0062] In another embodiment, the target digital score is the lowest value, that is, the digital score of the target recommended enterprise should be greater than or equal to the target digital score. After determining the candidate enterprises whose digital scores are greater than or equal to the target digital score from the candidate enterprises, the selected candidate enterprises are ranked in descending order of digital scores, and the candidate enterprises ranked first are determined as target recommended enterprises based on the number of target enterprises. For example, the digital scores of the five candidate enterprises are 78, 79, 80, 81 and 82 respectively, and the target digital score is 80. Then the three candidate enterprises with digital scores of 80, 81 and 82 meet the conditions. If the number of target enterprises is 2, the target recommended enterprises are determined to be enterprises with digital scores of 82 and 81.

[0063] The digital enterprise recommendation method provided in the embodiment of the present application, when determining the indicator model of each candidate enterprise, obtains the score of the evaluation index corresponding to each candidate enterprise; determines the digital score of each candidate enterprise based on the indicator model and the score of the corresponding evaluation index; determines the target recommended enterprise based on the digital score and the target digital score of each candidate enterprise. The method determines the digital score of each candidate enterprise based on the indicator model of each candidate enterprise, unifies the quantitative standards, quantifies the digital capabilities of the candidate enterprises, and characterizes the digital capabilities of the candidate enterprises through digital scores; makes recommendations based on the digital scores of each candidate enterprise and the target digital scores, thereby determining enterprises that meet the digital needs of users and making recommendations.

[0064] In an optional embodiment, before determining the target recommended enterprise based on the digital score of each candidate enterprise and the target digital score, the method further includes:

[0065] Acquire an assessment target tag, where the assessment target tag represents the expected digital maturity of the enterprise;

[0066] Based on the evaluation target label, the target digital score is determined.

[0067] Specifically, the expected enterprise digital maturity recommends the maturity of the enterprise's digital technology for the user's desired goal.

[0068] In an optional embodiment, please refer to Table 1, which divides the maturity of enterprise digital technology into four stages, namely S1 traditional operator, S2 digital explorer, S3 digital operator, and S4 ecosystem creator.

[0069] Table 1 Enterprise digitalization level

[0070]

[0071] In an optional embodiment, the digitalization score range corresponding to S1 traditional operators is 0-30 points, the digitalization score range corresponding to S2 digital explorers is 30-50 points, the digitalization score range corresponding to S3 digital operators is 50-80 points, and the digitalization score range corresponding to S4 ecosystem creators is 80-100 points.

[0072] Optionally, the evaluation target label is obtained by semantic recognition. For example, if the evaluation target label is S2, the characterization target digital score range is 30-50 points.

[0073] Optionally, taking 50 points as the target digital score, if the digital score of a candidate enterprise is 42.2 points, the relative digital score of the candidate enterprise is 42.2 / 50*100%=84%, indicating that the candidate enterprise is still 16% away from the target digital score.

[0074] It should be noted that, in the specific implementation process, the maturity of enterprise digital technology is not limited to the four stages shown in Table 1, and the digital score range of each stage can also be adjusted accordingly according to actual usage needs; the target digital score is not limited to the highest score in the digital score range, and the corresponding target digital score can be determined by refining the evaluation target label. For example, S21 means that the target digital score is the lowest score in the S2 stage, that is, 30 points; S22 means that the target digital score is the middle value of the S2 stage, that is, 40 points; S23 means that the target digital score is the highest score in the S2 stage, that is, 50 points.

[0075] The digital enterprise recommendation method provided in the embodiment of the present application quantifies the expected digital maturity of an enterprise and determines a target digital score, which facilitates the subsequent determination of the gap between each candidate enterprise and the expected target, thereby recommending enterprises that meet user needs.

[0076] In an optional embodiment, determining a target recommended enterprise based on the digital score of each candidate enterprise and the target digital score includes:

[0077] Based on the evaluation target label, determining a target digital scoring range;

[0078] Based on the digital score of each candidate enterprise and the target digital score range, determine a number of first-level preferred enterprises, wherein the first-level preferred enterprises are candidate enterprises whose digital scores are within the target digital score range;

[0079] Determine a number of second-level preferred enterprises based on the enterprise information data of each of the first-level preferred enterprises, wherein the enterprise information data represents the operating conditions of the first-level preferred enterprises;

[0080] Based on the digital scores of each of the secondary preferred enterprises and the target digital score, a target recommended enterprise is determined.

[0081] Here, the enterprise information data includes but is not limited to one or more of abnormal business information, judicial litigation information, and quality supervision and inspection information. From the first-level preferred enterprises, enterprises with abnormal business information, negative judicial disputes, and abnormal quality supervision and inspection are screened out to obtain one or more second-level preferred enterprises.

[0082] The step of determining the target recommended enterprise based on the digital score of each of the secondary preferred enterprises and the target digital score is the same as the above S130 and will not be repeated here.

[0083] The digital enterprise recommendation method provided in the embodiment of the present application first screens out enterprises that match the evaluation target tags, on the one hand to determine the first-level preferred enterprises that meet the user's digital needs, and on the other hand to first screen out some candidate enterprises to reduce the number of candidate enterprises, thereby reducing the amount of calculation for subsequent enterprise screening and recommendation; based on the enterprise information data of each first-level candidate enterprise, screen out second-level preferred enterprises, and remove enterprises with abnormal operations, thereby ensuring that the enterprises recommended to users are high-quality enterprises.

[0084] In an optional embodiment, each of the indicator models includes multiple indicator levels, the first indicator level includes one evaluation indicator, and the other indicator levels except the first indicator level each include several evaluation indicators, and the evaluation indicator of the i-th level is composed of several evaluation indicators of the i+1-th level, where i is a positive integer. For ease of understanding, the digital enterprise recommendation method provided by this application is described below by taking the indicator model including 5 indicator levels as an example.

[0085] In the embodiment of the present invention, the first level of the indicator model has and only has one evaluation indicator, which is the digitalization capability of the candidate enterprise. The score of the evaluation indicator is the digitalization score of the candidate enterprise.

[0086] It should be noted that the candidate enterprise can be an enterprise, which can be the entire group or a subsidiary of the group. The candidate enterprise can also be other entity organizations, such as an association organization.

[0087] Please refer to Table 2, which is an exemplary schematic table of the indicator model provided by an embodiment of the present invention. The indicator model includes 5 indicator levels. Level 1 evaluation indicators are evaluation indicators of level 1, level 2 evaluation indicators are evaluation indicators of level 2, and so on. Level 5 evaluation indicators are evaluation indicators of level 5. Exemplarily, product digital upgrade (level 4 evaluation indicators) is composed of the digital transformation and upgrade of existing products or services (level 5 evaluation indicators) and the effect of the digital transformation and upgrade of products or services (level 5 evaluation indicators).

[0088] Table 2 Exemplary indicator model

[0089]

[0090]

[0091] It should be noted that this table is only used to understand the composition structure of the indicator model. In the specific implementation process, the evaluation indicators at all levels are not limited to the evaluation indicators shown in the table. It may only include some of the evaluation indicators in the table, or may also include evaluation indicators not shown in other tables.

[0092] Optionally, the digital score is the score of the evaluation indicator of level 1, the score of the j-th evaluation indicator of level ith is determined based on the scores of several subordinate evaluation indicators of the j-th evaluation indicator, the j-th evaluation indicator is an evaluation indicator of level ith, and the several subordinate evaluation indicators are several evaluation indicators of level i+1 corresponding to the j-th evaluation indicator.

[0093] In an optional embodiment, the scores of the underlying evaluation indicators of the indicator model are calculated based on the calculation models of each underlying evaluation indicator. For example, in one embodiment, the underlying evaluation indicators include agile delivery efficiency, and the scores of the agile delivery efficiency of the candidate enterprises are determined based on a preset agile delivery model, and the specific agile delivery model used is not limited; wherein, the underlying evaluation indicators are level 5 evaluation indicators.

[0094] In another optional embodiment, the score of the underlying evaluation indicator of the indicator model is determined based on a preset feature domain system. Please refer to Table 3, which is an exemplary table of the feature domain system provided in the embodiment of the present application, and is used to determine the score of the flexible product portfolio. The flexible product portfolio is a bottom-level evaluation indicator. L1, L2, L3, L4, L5, and L6 are the scoring levels of the evaluation indicators. The flexible product portfolio score of the candidate enterprise is determined based on the feature description of each scoring level. For example, if the candidate enterprise does not provide personalized customized products, then the candidate enterprise meets the feature description 1.1 of L1, and its flexible product portfolio score is within the scoring range corresponding to L1. If the candidate enterprise has plans to launch customized products, and / or recognizes that providing personalized products and services for thousands of people is a future trend in the digital age, then the candidate enterprise meets the feature description 2.1 of L2, and / or 2.2, and the flexible product portfolio score of the candidate enterprise is within the scoring range corresponding to L2.

[0095] Table 3 Exemplary feature domain system

[0096]

[0097]

[0098] Specifically, L1 represents that the candidate enterprise's flexible product portfolio score level is at level one, and L2 represents that the candidate enterprise's flexible product portfolio score level is at level two. The degree of digitization of level two representation is higher than that of level one representation. The same applies to L3, L4, L5, and L6, which will not be elaborated here.

[0099] For example, the flexible product portfolio score range for L1 is 0-10 points, which indicates that the main processes are mechanized, with little automation or digitization involved. The identification and operation are mainly carried out manually and the response is coordinated. The system technology is mostly a closed silo system.

[0100] The flexible product portfolio score range for L2 is 10-30 points, which indicates that the digital transformation pilot projects are the main focus. The efficiency of some functional points is improved through single technology drive. Digital-related planning has not been systematically considered, and customer experience is not paid attention to.

[0101] The flexible product portfolio score range for L3 is 30-50 points, indicating that the pilot project has highlighted its value and has been promoted within the department, but the execution is still limited to the project level and is not replicable. Digital planning has begun to be considered, but it is out of touch with the corporate strategy;

[0102] The flexible product portfolio score range for L4 is 50-70 points, which represents the promotion of digitalization to core systems. Focusing on digital products and experiences, the digitalization of core systems is promoted. With the goal of business and technology collaboration, a connection mechanism between systems is built to improve system and operational efficiency through data governance scale.

[0103] The flexible product portfolio score range for L5 is 70-80 points, which indicates that the internal system of the organization has been digitized, the data of the entire organization has been acquired and intelligently applied, the efficiency and agility of the organization have been improved, the internal operation of the organization is efficient, and the digital optimized product / service experience can be provided, but the disruptive potential of digitalization has not been paid attention to;

[0104] The flexible product portfolio score range for L6 is 80-100 points, which represents the establishment of a digital ecosystem, the possession of disruptive digital technologies and business models, the active response to external changes based on data intelligence, the control of the ecological market, and the improvement of ecosystem-oriented system efficiency and resilience through feedback and improvement of the ecosystem.

[0105] In the specific implementation process, the level of evaluation indicators is not limited to 6 levels, and can be adjusted according to actual conditions, and the scores corresponding to each level can also be adjusted according to actual conditions.

[0106] In another optional embodiment, the scores of some underlying evaluation indicators in the indicator model are calculated based on the calculation model of each underlying evaluation indicator, and the scores of another part of the underlying evaluation indicators are determined based on the feature domain system of each evaluation indicator.

[0107] In an embodiment of the present invention, the scores of the evaluation indicators at the previous level can be determined based on the scores of the underlying evaluation indicators, and the scores of the evaluation indicators at each level can be determined step by step. That is, the scores of the evaluation indicators at the third level are determined based on the scores of the evaluation indicators at the fourth level, and the scores of the evaluation indicators at the second level are determined based on the scores of the evaluation indicators at the third level, and finally the scores of the evaluation indicators at the first level, that is, the digital scores of the candidate enterprises, are determined.

[0108] The digital enterprise recommendation method provided in the embodiment of the present application divides the indicator model used to evaluate the digital capabilities of an enterprise into several indicator levels, and determines the scores of the upper-level evaluation indicators step by step based on the scores of the underlying evaluation indicators, so as to obtain the final enterprise digitalization score. On the one hand, the upper-level evaluation indicators are determined based on the lower-level evaluation indicators, taking into account the correlation between the evaluation indicators. On the other hand, the indicator model is used to evaluate the digital capabilities of the enterprise, so that the evaluation of the digital capabilities of the enterprise is not affected by the experience of the staff, and the digital capabilities of the enterprise are accurately quantified, which improves the reliability of the evaluation results.

[0109] Figure 2 Schematic diagram of the process of determining the indicator model in the digital enterprise recommendation method provided in the embodiment of the present application. Figure 2 , the embodiment of the present application provides a digital enterprise recommendation method, wherein each of the indicator models is determined based on the following steps:

[0110] S201. Based on the evaluation scope label of the candidate enterprise, select several evaluation indicators from a preset capability indicator library to obtain the underlying evaluation indicators of the candidate enterprise.

[0111] In an embodiment of the present invention, the preset capability indicator library includes several predefined evaluation indicators. Similar to the indicator model, the preset capability indicator library also includes several indicator levels, each indicator level includes several evaluation indicators. In addition, the number of indicator levels in the preset capability indicator library is the same as the number of indicator levels in the indicator model, and the level 1 evaluation indicator in the preset capability indicator library is also the digital capability of the candidate enterprise.

[0112] Please refer to Table 4, which is an exemplary table of product capability categories in the preset capability indicator library provided in an embodiment of the present application. The product capability category is a level 2 evaluation indicator.

[0113] Table 4 Exemplary product capability evaluation indicators

[0114]

[0115]

[0116] It should be noted that this table is only used to understand the composition structure of the preset capability indicator library. In the specific implementation process, in the preset capability indicator library, the evaluation indicators of the second level include several capability categories; the subordinate evaluation indicators of the product capability category include but are not limited to product innovation and R&D design; the subordinate evaluation indicators of product innovation include but are not limited to product digitization and customization support; the subordinate evaluation indicators of R&D design include but are not limited to R&D design digitization and R&D management digitization; the subordinate evaluation indicators of product digital upgrade include but are not limited to the digital transformation and upgrade of existing products or services, and the effects of digital transformation and upgrade of products or services; the subordinate evaluation indicators of customized support include but are not limited to flexible product portfolio, data value empowerment and user-centered creation of new value; the subordinate evaluation indicators of R&D design digitization include but are not limited to the use of R&D design tools, digital simulation and module integration collaboration; the subordinate evaluation indicators of R&D management digitization include but are not limited to the digitization of R&D life cycle management and the effects of digital R&D management.

[0117] In the embodiment of the present invention, when selecting an evaluation indicator from the preset capability indicator library, the bottom-level evaluation indicator, that is, the fifth-level evaluation indicator, is selected, corresponding to the evaluation dimension in Table 4.

[0118] In the embodiment of the present invention, the evaluation scope label is a set of labels that reflect the evaluation scope of the candidate enterprise, and each label corresponds to one or more underlying evaluation indicators.

[0119] In an optional embodiment, the evaluation scope label is obtained through semantic recognition.

[0120] In one embodiment, a guided question-and-answer method is used to collect customer evaluation needs, that is, firstly, the first-level questions and different answer ranges are preset, and then corresponding sub-questions are further preset for the answers to different questions, and the required customer demand information is obtained by analogy. Specifically, the customer's responses to the preset questions are collected, and the question and answer tags corresponding to the responses are determined using semantic recognition technology, and then questions related to the question and answer tags are further pushed, so as to guide the questions step by step and determine the evaluation scope of the candidate enterprises.

[0121] In another embodiment, the user describes the current status of the candidate enterprise in an entire paragraph, and the evaluation scope label is determined by semantic recognition technology.

[0122] In an optional embodiment, the preset capability indicator library includes a first indicator library, which stores digital capability evaluation indicators commonly used by enterprises.

[0123] Optionally, the first indicator library includes eight capability categories (the first indicator library includes eight level 2 evaluation indicators):

[0124] Product capability category measures the level of an organization's product lifecycle management and operation capabilities, such as product digital iteration capabilities and product R&D and design capabilities. This refers to the use of digital collaborative design systems and digital tools such as parametric object modeling to carry out product R&D and design activities starting from the conceptual design stage;

[0125] Operational capabilities: With the continuous and agile iteration of products and services, operational work will also change at any time, and traditional decentralized operations will be integrated into an inseparable operational system in the value chain. The transparency, collaboration, data sharing mechanism, timely response feedback and execution process of the operational system are all the results of the value chain reconstruction driven by data and technology;

[0126] Operational capability measures the comprehensive strength of an organization to continuously create customer value by relying on its digital capability advantages. It covers all customer touchpoints, from marketing, sales to decision-making, purchase, and after-sales, helping to achieve customer success;

[0127] Ecological capabilities: Digital technology and data transcend time and distance, making digital products or services immaterial, transcending marginal benefit limits and achieving exponential growth. All of these technologies are the construction of ubiquitous ecological capabilities, which maximize the benefits of organizational value based on ecology.

[0128] Strategic capability category, which is used to evaluate how an organization defines and implements an effective digital strategy based on a clear digital strategic vision and a series of goals, and configures corresponding guarantee strategies to guide organizational members to connect with digital concepts;

[0129] Management capability measures the digital capabilities of functions, processes and controls, human resources, finance, taxation, legal affairs and comprehensive administrative services in the organization, and comprehensively evaluates the tools, functions, skills and processes required for organizational management. It aims to establish a new type of organization that is adaptive to changes, fast-interactive and data-driven;

[0130] Data capability measures the organization’s ability to manage and apply data, including the combination of data and organizational strategy, the construction of overall data governance and organizational data architecture, the application and value realization of data in the organization, and the organization’s assurance and control of data.

[0131] In terms of technical capabilities, technology plays a vital role as a support and driving force for transformation. Improving technical capabilities can improve business efficiency, promote performance growth, and further drive innovation and transformation of the organization. Through upgraded IT architecture, organizations can maintain technical acumen and maximize the value of the technology system.

[0132] The digital enterprise recommendation method provided in the embodiment of the present application has a preset capability indicator library including digital capability evaluation indicators commonly used by enterprises, which improves the versatility of the capability indicator library and is suitable for digital capability evaluation of enterprises in various industries and business models.

[0133] In an optional embodiment, the preset capability indicator library also includes at least one of a second indicator library and a third indicator library.

[0134] In this embodiment, the second indicator library stores the digital capability evaluation indicators specific to the enterprise in each business model, and the third indicator library stores the digital capability evaluation indicators specific to the enterprise in each industry.

[0135] In an embodiment of the present invention, the second indicator library includes enterprise digital capability evaluation indicators applicable to various business models. For example, omni-channel service providers have their own unique evaluation indicators, and product manufacturers also have their own unique evaluation indicators, such as digital management of production equipment for example; the third indicator library includes enterprise digital evaluation indicators applicable to various industries. For example, the education industry has its own unique evaluation indicators, and the medical industry also has its own unique evaluation indicators, such as digital management of medical equipment for example.

[0136] In an optional embodiment, the capability classes (level 2 evaluation indicators) in the second indicator library are the same as those in the first indicator library, but the capability sets (level 3 evaluation indicators) contained in each capability class are different, and the capability sets in the second indicator library include capability sets applicable to each business model. Similarly, for the third indicator library, for example, the technical capability class (level 2 evaluation indicator) of the medical industry in the third indicator library includes professional facilities (digital evaluation indicators of medical device resource management, which is a level 3 evaluation indicator).

[0137] In an optional embodiment, the capability classes (level 2 evaluation indicators) in the second indicator library only include some capability classes in the first indicator library, and / or, the capability classes (level 2 evaluation indicators) in the third indicator library only include some capability classes in the first indicator library.

[0138] For example, the first indicator library includes the above-mentioned eight capability classes, the second indicator library only includes data capability classes and technical capability classes, the capability set in the data capability class of the second indicator library is different from the capability set in the first indicator library, the capability set in the technical capability class of the second indicator library is different from the capability set in the first indicator library, and the third indicator library only includes management capability classes and data capability classes, and the capability sets included therein are different from the management capability set and data capability set in the first indicator library respectively.

[0139] In an optional embodiment, the second indicator library includes but is not limited to evaluation indicators of product / service manufacturers, evaluation indicators of common module service providers, evaluation indicators of omni-channel service providers, and evaluation indicators of ecosystem-leading service providers.

[0140] In an optional embodiment, the third indicator library includes but is not limited to evaluation indicators of the education industry, evaluation indicators of the medical industry, evaluation indicators of the transportation industry, evaluation indicators of the financial industry, evaluation indicators of the energy industry, and evaluation indicators of the industrial industry.

[0141] The digital enterprise recommendation method provided in the embodiment of the present application selects evaluation indicators from the first indicator library, the second indicator library, and the third indicator library. The generated indicator model fully considers the characteristics of the business model of the candidate enterprise and the characteristics of the industry to which it belongs, improves the digital evaluation system, and the generated indicator model can objectively and comprehensively characterize the digital capabilities of the candidate enterprise.

[0142] In an optional embodiment, when the preset capability indicator library includes the first indicator library, the second indicator library and the third indicator library, the evaluation range tag includes a first range tag, a second range tag and a third range tag, and the selecting of a plurality of evaluation indicators from the preset capability indicator library based on the evaluation range tag of the candidate enterprise to obtain the bottom-level evaluation indicators of the candidate enterprise includes:

[0143] Based on the first range tag, the second range tag and the third range tag, several evaluation indicators are selected from the first indicator library, the second indicator library and the third indicator library respectively to obtain the underlying evaluation indicators of the candidate enterprise.

[0144] In an embodiment of the present invention, the first range tag includes several first tags, each first tag corresponds to one or more underlying evaluation indicators in the first indicator library; the second range tag includes several second tags, each second tag corresponds to one or more underlying evaluation indicators in the second indicator library; the third range tag includes several third tags, each third tag corresponds to one or more underlying evaluation indicators in the third indicator library.

[0145] In an embodiment of the present invention, several evaluation indicators are selected from a first indicator library based on a first range label, several evaluation indicators are selected from a second indicator library based on a second range label, and several evaluation indicators are selected from a third indicator library based on a third range label to obtain the underlying evaluation indicators of the candidate enterprise.

[0146] Optionally, when the preset capability index library includes a first index library and a second index library, the evaluation range tag includes a first range tag and a second range tag, and the step of selecting a plurality of evaluation indicators from the preset capability index library based on the evaluation range tag of the candidate enterprise to obtain the underlying evaluation indicators of the candidate enterprise includes:

[0147] Based on the first range label and the second range label, several evaluation indicators are selected from the first indicator library and the second indicator library respectively to obtain the underlying evaluation indicators of the candidate enterprise.

[0148] Optionally, when the preset capability index library includes a first index library and a third index library, the evaluation range tag includes a first range tag and a third range tag, and the step of selecting a plurality of evaluation indicators from the preset capability index library based on the evaluation range tag of the candidate enterprise to obtain the underlying evaluation indicators of the candidate enterprise includes:

[0149] Based on the first range tag and the third range tag, several evaluation indicators are selected from the first indicator library and the third indicator library respectively to obtain the underlying evaluation indicators of the candidate enterprise.

[0150] In an optional embodiment, the second range tag of the candidate enterprise consists of a second tag, and the second tag is NULL, that is, the second range tag of the candidate enterprise has no corresponding evaluation index, and there is no need to select an evaluation index from the second index library.

[0151] In an optional embodiment, the third range tag of the candidate enterprise consists of a third tag, and the third tag is NULL, that is, the third range tag of the candidate enterprise has no corresponding evaluation index, and there is no need to select an evaluation index from the third index library.

[0152] The digital enterprise recommendation method provided in the embodiment of the present application selects evaluation indicators from the corresponding indicator library according to the first range label, the second range label and the third range label, respectively, and determines the corresponding evaluation indicators in each indicator library respectively, so as to meet the personalized evaluation needs of different enterprises. The generated indicator model can objectively and comprehensively characterize the digital capabilities of candidate enterprises.

[0153] S202: Based on the underlying evaluation indicators, determine the evaluation indicators of each indicator level layer by layer from bottom to top, and determine the indicator model of the candidate enterprise.

[0154] Continuing to refer to Table 4, in the embodiment of the present invention, each evaluation dimension has a corresponding level 4 evaluation indicator (capability indicator), level 3 evaluation indicator (capability set) and level 2 evaluation indicator (capability class). Therefore, after determining the underlying evaluation indicator, the corresponding upper-level evaluation indicator can be determined, thereby determining the indicator model of the candidate enterprise.

[0155] It should be noted that, as mentioned above, in the preset capability indicator library, the second-level evaluation indicators include several capability classes. In the specific implementation process, the indicator model generated after determining the underlying evaluation indicators based on the evaluation scope label may only contain some capability classes, and each capability class may only contain some capability sets, each capability set may only contain some capability indicators, and each capability indicator may only contain some evaluation dimensions.

[0156] The digital enterprise recommendation method provided in the embodiment of the present application determines the underlying evaluation indicators based on the evaluation scope labels of the candidate enterprises, thereby generating an indicator model for the candidate enterprises. The evaluation indicators of the candidate enterprises can be determined based on actual usage needs, meet the usage needs of different scenarios and different candidate enterprises, and quickly build a personalized indicator model.

[0157] Based on any of the above embodiments, in the method, determining the digital score of each candidate enterprise based on the score of each indicator model and the corresponding evaluation indicator includes:

[0158] Determine the weight of the corresponding evaluation index in each of the index models;

[0159] Based on the weight of each evaluation indicator, the score of each evaluation indicator is determined step by step from bottom to top.

[0160] In the embodiment of the present invention, the score of the 4-level evaluation index in the index model is determined by the score of the corresponding underlying evaluation index and the corresponding weight, that is, it is determined based on the score of the corresponding 5-level evaluation index and the corresponding weight of each 5-level evaluation index, the score of the 3-level evaluation index is determined by the score of the corresponding 4-level evaluation index and the corresponding weight, the score of the 2-level evaluation index is determined by the score of the corresponding 3-level evaluation index and the corresponding weight, and finally the digital score of the first level is determined by the score of each evaluation index of the second level and the corresponding weight. The method for determining the corresponding weight of each evaluation index is not limited.

[0161] The digital enterprise recommendation method provided in the embodiment of the present application determines the score of each evaluation indicator step by step from bottom to top, fully evaluates the digital capability based on the weight of each evaluation indicator of the candidate enterprise, and accurately characterizes the digital capability of the candidate enterprise.

[0162] Based on any of the above embodiments, in this method, when the i+1th level is the bottom indicator level, the weights corresponding to the subordinate evaluation indicators of the jth evaluation indicator are determined based on the following steps:

[0163] Establishing a judgment matrix based on a preset scaling method, wherein the judgment matrix is ​​used to characterize the relative importance of each subordinate evaluation index;

[0164] Dividing each element in the judgment matrix by the sum of the elements in the column where the element is located, to obtain a standardized judgment matrix;

[0165] The average of each row of the standardized judgment matrix is ​​taken to obtain the weight corresponding to each of the subordinate evaluation indicators.

[0166] In this embodiment, the preset scaling method has a total of 9 scaling levels: level 1 means that the two factors are equally important; level 2 means that the former is slightly more important than the latter; level 5 means that the former is obviously more important than the latter; level 7 means that the former is strongly more important than the latter; level 9 means that the former is extremely more important than the latter; level 2 represents the middle value between levels 1 and 3, and levels 4, 6, and 8 can be obtained similarly, which will not be repeated here.

[0167] The following takes the example that the j-th evaluation indicator has three subordinate evaluation indicators to illustrate the steps of determining the weights corresponding to the subordinate evaluation indicators of the j-th evaluation indicator. The three subordinate evaluation indicators are the first subordinate evaluation indicator, the second subordinate evaluation indicator and the third subordinate evaluation indicator. The first subordinate evaluation indicator is slightly more important than the second subordinate evaluation indicator, and the first subordinate evaluation indicator is obviously more important than the third subordinate evaluation indicator:

[0168] (1) Construct the following judgment matrix:

[0169]

[0170] With a mn The element in the mth row and nth column of the judgment matrix represents the importance of the mth subordinate evaluation indicator compared to the nth subordinate evaluation indicator. For example, a 12 =3 means that the first subordinate evaluation indicator is slightly more important than the second subordinate evaluation indicator. 31 =1 / 5 means that the importance of the third subordinate evaluation indicator is 1 / 5 of the importance of the first subordinate evaluation indicator.

[0171] (2) Constructing a standardized judgment matrix:

[0172] Divide each item in the judgment matrix by the sum of each item in the column where the item is located:

[0173]

[0174] Among them, p represents the number of elements in a column of the judgment matrix, that is, the number of rows in the judgment matrix. In this example, p = 3, b mn Represents the element in the mth row and nth column of the standardized judgment matrix.

[0175] (3) Take the mean of each row of the standardized judgment matrix to obtain the weight of each subordinate evaluation indicator:

[0176]

[0177] Where q represents the number of elements in a row of the standardized judgment matrix, that is, the number of columns in the standardized judgment matrix. In this example, q = 3, w m Represents the weight corresponding to the mth subordinate evaluation indicator.

[0178] The digital enterprise recommendation method provided in the embodiment of the present application determines the weights of the underlying evaluation indicators through the hierarchical analysis method, compares the importance of evaluation indicators of the same level in pairs, and makes quantitative descriptions, thereby simply and effectively describing the relative weights between the evaluation indicators.

[0179] Based on any of the above embodiments, in this method, when the i+1th level is not the bottom indicator level, the weights corresponding to the subordinate evaluation indicators of the jth evaluation indicator are determined based on the following steps:

[0180] Based on the structural equation model, determining the correlation coefficients between the subordinate evaluation indicators;

[0181] Based on the correlation coefficients, determining the index conflict between the subordinate evaluation indicators, the greater the correlation coefficient between the subordinate evaluation indicators, the smaller the corresponding index conflict;

[0182] Based on the variability of each indicator and the conflict of each indicator, the CRITIC weighting method is used to determine the weight corresponding to each subordinate evaluation indicator, wherein the variability of each indicator represents the difference fluctuation of each subordinate evaluation indicator.

[0183] In the embodiment of the present invention, the structural equation model describes the relationship between variables through measurement equations and structural equations, wherein the measurement equations describe the relationship between latent variables and observed variables, and the structural equations describe the relationship between latent variables. The measurement equations and structural equations are trained using training data to obtain the correlation coefficients between the latent variables.

[0184] In this embodiment, the underlying evaluation indicators (evaluation dimensions) in the indicator model are observed variables, and the level 1 evaluation indicators (digital capabilities of candidate enterprises), level 2 evaluation indicators (capability categories), level 3 evaluation indicators (capability sets), and level 4 evaluation indicators (capability indicators) are latent variables.

[0185] In the embodiment of the present invention, the CRITIC weighting method (Criteria Importance Through IntercrieriaCorrelation) is a weighting method for determining the importance of indicators through inter-crieria correlation.

[0186] For ease of understanding, the following takes the example of the j-th evaluation indicator having three subordinate evaluation indicators to explain the method for determining the weights corresponding to each subordinate evaluation indicator of the j-th evaluation indicator, where the three subordinate evaluation indicators are the first subordinate evaluation indicator, the second subordinate evaluation indicator and the third subordinate evaluation indicator:

[0187] (1) In the trained structural equation model, the factor loading matrix represents the correlation coefficients between the latent variables in the indicator model, from which the correlation coefficients between the first subordinate evaluation indicator, the second subordinate evaluation indicator, and the third subordinate evaluation indicator are read.

[0188] (2) The indicator conflict of the nth subordinate evaluation indicator is calculated based on the following formula:

[0189]

[0190] Among them, R n Indicates the index conflict of the nth subordinate evaluation index, r mn represents the correlation coefficient between the mth subordinate evaluation indicator and the nth subordinate evaluation indicator, and p represents the number of subordinate evaluation indicators. In this example, p=3.

[0191] The stronger the correlation between an evaluation indicator and other evaluation indicators, the smaller the conflict between the evaluation indicator and other evaluation indicators, the more the same information is reflected, the more repeated evaluation contents can be reflected, the weaker the evaluation strength of the evaluation indicator, and the smaller the weight should be assigned to the evaluation indicator.

[0192] (3) Based on the training data, the standard deviation of each evaluation indicator is calculated. In this example, the indicator variability of each subordinate evaluation indicator is the standard deviation of each subordinate evaluation indicator. The indicator variability of each subordinate evaluation indicator is read. The indicator variability represents the difference fluctuation of the evaluation indicator. The greater the indicator variability, the greater the numerical difference of the corresponding evaluation indicator in the training data, the more information the evaluation indicator can reflect, the stronger the evaluation strength of the evaluation indicator, and the greater the weight should be assigned to the evaluation indicator;

[0193] The information content of the nth subordinate evaluation indicator is calculated based on the following formula:

[0194] C n =S n R n ;

[0195] Among them, C n represents the information content of the nth subordinate evaluation index, S n represents the indicator variability of the nth subordinate evaluation indicator, R n Indicates the indicator conflict of the nth subordinate evaluation indicator;

[0196] The weight calculation formula of the nth subordinate evaluation index is as follows:

[0197]

[0198] Among them, W n represents the weight of the nth subordinate evaluation index, C n represents the amount of information of the nth subordinate evaluation indicator, and p represents the number of subordinate evaluation indicators. In this example, p=3.

[0199] The digital enterprise recommendation method provided in the embodiment of the present application can perform causal inference by calculating the correlation coefficient of latent variables through the structural equation model, accurately describe the complex relationship between multiple variables, thereby improving the accuracy of the correlation between subordinate evaluation indicators, and further improving the accuracy of weight calculation.

[0200] Based on any of the above embodiments, in this method, the score of the j-th evaluation indicator is determined based on the following steps:

[0201] The scores of the subordinate evaluation indicators and the weights corresponding to the subordinate evaluation indicators are weighted and aggregated to obtain the score of the j-th evaluation indicator.

[0202] For example, the digitalization score of a candidate company is shown in Table 5 below.

[0203] Table 5 Digitalization scores of candidate companies

[0204]

[0205]

[0206] The candidate enterprise's cloud native score = cloud native architecture application score * cloud native architecture application weight + cloud native technology application score * cloud native technology application weight + cloud computing cost concentration score * cloud computing cost concentration weight = 35*25% + 40*45% + 40*30% = 38.75;

[0207] The candidate enterprise's digital intelligence technology score = AI score * AI weight + data lake score * data lake weight + digital intelligence technology cost-intensive score * digital intelligence technology cost-intensive weight = 35*35% + 45*35% + 38*30% = 39.4;

[0208] The candidate enterprise's digital technology score = cloud native score * cloud native weight + digital intelligence technology score * digital intelligence technology weight = 38.75*55% + 39.4*45% = 39.0425.

[0209] The calculation method of other evaluation indicators is similar. The scores of each level of evaluation indicators are calculated step by step from bottom to top, and the final score of digital capability is 41.5, that is, the digital score of the candidate enterprise is 41.5;

[0210] If the target digitalization score of the candidate enterprise is 50, then the relative digitalization score of the candidate enterprise is 41.5 / 50*100%=83%, indicating that the candidate enterprise is still 17% away from the assessment target.

[0211] The digital enterprise recommendation method provided in the embodiment of the present application calculates the score of the j-th evaluation indicator by weighted calculation, fully considering the influence of each subordinate evaluation indicator on the j-th evaluation indicator, and is suitable for calculating the evaluation indicator scores in various scenarios.

[0212] To sum up, the digital enterprise recommendation method provided in the embodiment of the present application determines the weights of the underlying evaluation indicators through the hierarchical analysis method, which is suitable for the quantitative calculation of the weights of evaluation indicators in various scenarios; the weights of other evaluation indicators are determined through the structural equation model and the CRITIC weighting method, and the complex relationship between multiple evaluation indicators is accurately described through causal inference, thereby improving the accuracy of weight calculation; the present application combines the hierarchical analysis method, the structural equation model and the CRITIC weighting method to determine the weights of each evaluation indicator in the indicator model, and uses the corresponding method to determine the weights according to the characteristics of each evaluation indicator, so as to achieve objective weighting of the evaluation indicator weights, thereby improving the accuracy of enterprise digital score calculation.

[0213] The enterprise digital capability assessment device provided in an embodiment of the present application is described below. The enterprise digital capability assessment device described below and the digital enterprise recommendation method described above can be referenced to each other.

[0214] Figure 3 is a schematic diagram of the structure of the enterprise digital capability assessment device provided in the embodiment of the present application, such as Figure 3 As shown, the enterprise digital capability assessment device includes:

[0215] The indicator acquisition module 310 is used to: obtain the score of the evaluation indicator corresponding to each candidate enterprise when the indicator model of each candidate enterprise is determined;

[0216] The scoring determination module 320 is used to determine the digital score of each candidate enterprise based on the score of each indicator model and the corresponding evaluation indicator;

[0217] The target determination module 330 is used to determine the target recommended enterprise based on the digital score of each candidate enterprise and the target digital score.

[0218] The enterprise digital capability assessment device provided in the embodiment of the present application obtains the score of the evaluation index corresponding to each candidate enterprise when the index model of each candidate enterprise is determined; the digital score of each candidate enterprise is determined based on the index model and the score of the corresponding evaluation index; the target recommended enterprise is determined based on the digital score and the target digital score of each candidate enterprise. The method determines the digital score of each candidate enterprise based on the index model of each candidate enterprise, unifies the quantitative standards, quantifies the digital capability of the candidate enterprise, and characterizes the digital capability of the candidate enterprise through the digital score; makes recommendations based on the digital score and the target digital score of each candidate enterprise, thereby determining the enterprise that meets the digital needs of the user and making recommendations.

[0219] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the computer program in the memory 430 to execute the steps of the digital enterprise recommendation method, for example, including: obtaining the score of the evaluation index corresponding to each candidate enterprise when the index model of each candidate enterprise is determined; determining the digital score of each candidate enterprise based on the index model and the score of the corresponding evaluation index; determining the target recommended enterprise based on the digital score of each candidate enterprise and the target digital score.

[0220] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0221] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the digital enterprise recommendation method provided by the above-mentioned methods, which includes: obtaining the score of the evaluation indicator corresponding to each candidate enterprise when determining the indicator model of each candidate enterprise; determining the digital score of each candidate enterprise based on each indicator model and the corresponding evaluation indicator score; determining the target recommended enterprise based on the digital score of each candidate enterprise and the target digital score.

[0222] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the digital enterprise recommendation method provided by the above-mentioned methods, the method comprising: obtaining the score of the evaluation indicator corresponding to each candidate enterprise when determining the indicator model of each candidate enterprise; determining the digital score of each candidate enterprise based on each indicator model and the corresponding evaluation indicator score; determining the target recommended enterprise based on the digital score of each candidate enterprise and the target digital score.

[0223] The non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0224] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0225] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0226] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these 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.

Claims

1. A digital enterprise recommendation method, characterized in that: include: When the indicator model of each candidate enterprise is determined, the score of the evaluation indicator corresponding to each candidate enterprise is obtained, and the indicator model corresponds to the candidate enterprise one by one; Determine the digital score of each candidate enterprise based on the score of each indicator model and the corresponding evaluation indicator; Determine the target recommended enterprise based on the digital score of each candidate enterprise and the target digital score; Wherein, the indicator model includes multiple indicator levels, the score of the jth evaluation indicator of the i-th level in the indicator model is determined based on a number of corresponding lower-level evaluation indicators, and the lower-level evaluation indicators are evaluation indicators of the i+1-th level, and the digital score of each candidate enterprise is determined based on the scores of each indicator model and the corresponding evaluation indicators, including: determining the weight of the corresponding evaluation indicator in each indicator model; and determining the score of each evaluation indicator step by step from bottom to top based on the weight of each evaluation indicator; When the i+1th level is not the bottom indicator level, the weights corresponding to the subordinate evaluation indicators of the jth evaluation indicator are determined based on the following steps: based on the factor loading matrix in the structural equation model, the correlation coefficients between the subordinate evaluation indicators are determined; based on the correlation coefficients, the indicator conflicts between the subordinate evaluation indicators are determined, and the larger the correlation coefficients between the subordinate evaluation indicators, the smaller the corresponding indicator conflicts; based on the variability of each indicator and the conflict of each indicator, the weights corresponding to the subordinate evaluation indicators are determined using the CRITIC weighting method, wherein the variability of each indicator represents the difference fluctuation of each subordinate evaluation indicator; Before determining the target recommended enterprise based on the digitalization score and the target digitalization score of each candidate enterprise, the method further includes: obtaining an evaluation target tag, wherein the evaluation target tag represents the expected digital maturity of the enterprise; and determining the target digitalization score based on the evaluation target tag; Determining the target recommended enterprise based on the digital score and the target digital score of each candidate enterprise includes: determining the target digital score range based on the evaluation target label; determining a number of first-level preferred enterprises based on the digital score of each candidate enterprise and the target digital score range, wherein the first-level preferred enterprises are candidate enterprises whose digital scores are within the target digital score range; determining a number of second-level preferred enterprises based on the enterprise information data of each first-level preferred enterprise, wherein the enterprise information data represents the operating conditions of the first-level preferred enterprise; and determining the target recommended enterprise based on the digital score of each second-level preferred enterprise and the target digital score; The score of the underlying evaluation indicator in the indicator model is determined based on the feature domain system corresponding to the underlying evaluation indicator, and the feature domain system includes multiple scoring levels, each of which corresponds to a scoring range; Each of the indicator models is determined based on the following steps: Based on the evaluation scope label of the candidate enterprise, several evaluation indicators are selected from the preset capability indicator library to obtain the bottom-level evaluation indicators of the candidate enterprise; based on each of the bottom-level evaluation indicators, the evaluation indicators of each indicator level are determined layer by layer from bottom to top to obtain the indicator model of the candidate enterprise; The preset capability index library includes a first index library, which stores digital capability evaluation indicators commonly used by enterprises; The preset capability index library also includes at least one of a second index library and a third index library, wherein the second index library stores the digital capability evaluation indexes specific to the enterprise under each business model, and the third index library stores the digital capability evaluation indexes specific to the enterprise in each industry; When the preset capability index library includes the first index library, the second index library and the third index library, the evaluation range tag includes a first range tag, a second range tag and a third range tag, and the step of selecting a plurality of evaluation indicators from the preset capability index library based on the evaluation range tag of the candidate enterprise to obtain the bottom-level evaluation indicators of the candidate enterprise includes: selecting a plurality of evaluation indicators from each of the first index library, the second index library and the third index library based on the first range tag, the second range tag and the third range tag to obtain the bottom-level evaluation indicators of the candidate enterprise; The evaluation scope label is a set of labels that characterize the evaluation scope of the candidate enterprise, and each label corresponds to one or more underlying evaluation indicators; The evaluation range label is obtained based on the following steps: presetting the first-level questions and the answer ranges corresponding to the first-level questions; repeatedly executing the step of "for any of the answer ranges, presetting the corresponding next-level questions and the multiple answer ranges corresponding to the next-level questions" until the preset number of levels is reached, and obtaining a question-answering model; based on the question-answering model, using semantic recognition technology to identify the user's answers to the questions of each level, and obtaining the corresponding evaluation range label; The digital enterprise recommendation method further includes: determining a relative digital score of each candidate enterprise based on a ratio of the digital score of each candidate enterprise to a target digital score.

2. The digital enterprise recommendation method according to claim 1, characterized in that: When the i+1th level is the bottom level indicator level, the weights corresponding to the lower level evaluation indicators of the jth evaluation indicator are determined based on the following steps: Establishing a judgment matrix based on a preset scaling method, wherein the judgment matrix is ​​used to characterize the relative importance of each subordinate evaluation index; Dividing each element in the judgment matrix by the sum of the elements in the column where the element is located, to obtain a standardized judgment matrix; The average of each row of the standardized judgment matrix is ​​taken to obtain the weight corresponding to each of the subordinate evaluation indicators.

3. The digital enterprise recommendation method according to claim 1 or 2, characterized in that: The score of the j-th evaluation indicator is determined based on the following steps: The scores of the subordinate evaluation indicators and the weights corresponding to the subordinate evaluation indicators are weighted and aggregated to obtain the score of the j-th evaluation indicator.

4. A digital enterprise recommendation device, characterized in that: The digital enterprise recommendation method according to claim 1 is applied, comprising: The indicator acquisition module is used to: obtain the score of the evaluation indicator corresponding to each candidate enterprise when the indicator model of each candidate enterprise is determined; A scoring determination module, used to: determine the digital score of each candidate enterprise based on the score of each indicator model and the corresponding evaluation indicator; A target determination module is used to: determine a target recommended enterprise based on the digital score of each candidate enterprise and the target digital score; Wherein, the indicator model includes multiple indicator levels, the score of the jth evaluation indicator of the i-th level in the indicator model is determined based on a number of corresponding lower-level evaluation indicators, and the lower-level evaluation indicators are evaluation indicators of the i+1-th level, and the digital score of each candidate enterprise is determined based on the scores of each indicator model and the corresponding evaluation indicators, including: determining the weight of the corresponding evaluation indicator in each indicator model; and determining the score of each evaluation indicator step by step from bottom to top based on the weight of each evaluation indicator; When the i+1th level is not the bottom-level indicator level, the weights corresponding to the subordinate evaluation indicators of the jth evaluation indicator are determined based on the following steps: based on the structural equation model, the correlation coefficients between the subordinate evaluation indicators are determined; based on the correlation coefficients, the indicator conflicts between the subordinate evaluation indicators are determined, and the larger the correlation coefficients between the subordinate evaluation indicators, the smaller the corresponding indicator conflicts; based on the variability of each indicator and the conflict of each indicator, the weights corresponding to the subordinate evaluation indicators are determined using the CRITIC weighting method, wherein the variability of each indicator characterizes the differential fluctuations of each subordinate evaluation indicator.

5. An electronic device 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 program, the digital enterprise recommendation method according to any one of claims 1 to 3 is implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the digital enterprise recommendation method according to any one of claims 1 to 3 is implemented.

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