Enterprise multi-dimensional index integrated management method and system for multi-object unified modeling, terminal equipment and medium

Abstract

The invention discloses an enterprise multi-dimensional index integrated management method and system for multi-object unified modeling, terminal equipment and a medium, and relates to the technical field of enterprise informatization management. The method comprises the steps of abstracting features of various management objects based on enterprise requirements, determining object types, basic attribute sets and extension attribute rules, and forming universal object classes; constructing a general index template containing index types, calculation rules and potential data source meta-information to form index classes; and based on the object class or a specific object demand thereof, instantiating the index class into an index, and associating the index through a mapping rule to obtain an object index system set. According to the method, the problems of index confusion, model splitting and cross-system incomparability caused by various management objects in the prior art are solved, unified modeling and flexible configuration of multi-object indexes are realized, the management efficiency is improved, and transverse comparison and strategic decision of the multi-object indexes are supported.

Description

Technical Field

[0001] This invention relates to the field of enterprise information management technology, and in particular to a method, system, terminal equipment and medium for integrated management of multi-dimensional indicators of enterprises with unified multi-object modeling. Background Technology

[0002] As the complexity of business operations continues to increase, the number of management objects, such as products, customers, users, and suppliers, continues to expand, and the need for cross-departmental and cross-business unit management is becoming increasingly prominent.

[0003] Existing performance systems are mostly designed for single business objects, lack flexible modeling capabilities, and cannot adapt to the unified indicator definition requirements of multiple types of management objects. This leads to common problems in enterprise management, such as data silos, inconsistent performance standards, and difficulty in unified analysis across systems, which seriously restricts management efficiency and strategic decision support capabilities.

[0004] Therefore, there is an urgent need for a method that supports integrated management of multiple objects and multiple indicators to fill the gaps in existing technologies. Summary of the Invention

[0005] The technical problem this invention aims to solve is that, in the field of enterprise information management, existing technologies suffer from inconsistent indicator definitions, fragmented models, and incomparability across systems due to the diversity of management objects, making it impossible to achieve unified performance analysis for multiple objects. Therefore, an effective solution is urgently needed to address these technical problems.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for integrated management of multi-dimensional indicators of an enterprise through unified modeling of multiple objects, the method comprising: Based on enterprise needs, the characteristics of various management objects are abstracted, the object types, basic attribute sets and extended attribute rules are determined, and a general object class is formed. Construct a general indicator template containing metadata to form an indicator class. The metadata includes indicator type, calculation rules, and potential data sources. Based on the requirements of the object class or the object instantiated from the object class, the indicator class is instantiated into an indicator, and the indicator is associated with the corresponding object class or object through mapping rules to obtain a set of object indicator systems.

[0007] In one implementation, based on enterprise needs, the characteristics of various management objects are abstracted, object types, basic attribute sets, and extended attribute rules are determined to form a general object class, including: Based on enterprise needs, analyze the object types of enterprise management objects, including at least one of customers, users, products, suppliers, services, technology, assets, and organizations; Based on the object type, a set of basic attributes is set to identify and describe the object class. The set of basic attributes is used for the identification and management of the object class. Based on the basic attribute set, configure the extended attribute rules for the object class, wherein the extended attribute rules are used to extend the basic attribute set of the object class; The object type, the basic attribute set, and the extended attribute rules are integrated to form a general object class.

[0008] In one implementation, the construction of a general indicator template containing metadata to form an indicator class, wherein the metadata includes indicator type, calculation rules, and potential data sources, including: The indicator types are categorized into categories, and the indicator types include at least one of scale, quality, efficiency, and competitiveness. Each indicator category belongs to only one of the indicator types. Based on the evaluation requirements of indicator data, parameterized calculation rules are defined, which are used to realize the quantitative calculation of indicator data. Define potential data sources for indicator categories, wherein the potential data sources are used to obtain the raw data for quantitative calculation; The indicator type, the parameterized calculation rules, and the potential data sources are integrated into metadata. Based on the metadata, a general indicator template is constructed to obtain the indicator class.

[0009] In one implementation, instantiating the indicator class into an indicator based on the requirements of the object class or an object instantiated from the object class includes: Based on the object class, the object class is instantiated into a specific object with a unique identifier through attribute assignment operation; Based on the aforementioned indicator class, and in conjunction with the relevant requirements of the object class or specific object, the indicator class is instantiated into an indicator adapted to the corresponding object class or specific object.

[0010] In one implementation, the step of establishing an association between the indicators and corresponding object classes or objects through mapping rules to obtain an object indicator system set includes: Determine the mapping matching factors, assign weights to each mapping matching factor, and calculate the matching degree between the index and the object class or object based on the weighted summation algorithm; Set a matching degree threshold. If the matching degree is greater than or equal to the matching degree threshold, an association relationship between the indicator and the object class or object will be automatically established. By integrating the aforementioned relationships, as well as the indicators and their corresponding object classes or objects, a set of object indicator systems is obtained.

[0011] In one implementation, the method further includes: Based on the potential data sources of the aforementioned indicator categories, the raw data for quantitative calculation is obtained through automatic collection or manual input. The original data is standardized to obtain normalized data; The standardized data is then filled into the corresponding indicators to form the indicator data.

[0012] In one implementation, the method further includes: Based on the parameterized calculation rules of the aforementioned indicator class, the filled indicator data is subjected to quantitative calculation to obtain indicator evaluation results. The indicator evaluation results include at least one of the following: actual indicator value, deviation value, ranking result, year-on-year analysis result, and month-on-month analysis result. The evaluation results of the aforementioned indicators are output in a visual form using visualization tools.

[0013] Secondly, embodiments of the present invention also provide an enterprise multi-dimensional indicator integration management system for unified modeling of multiple objects, the system comprising: The object class definition module is used to abstract the characteristics of various management objects based on enterprise needs, determine the object type, basic attribute set and extended attribute rules, and form a general object class; The indicator class definition module is used to construct a general indicator template containing metadata to form an indicator class. The metadata includes indicator type, calculation rules, and potential data sources. The object indicator system set acquisition module is used to instantiate the indicator class into indicators based on the requirements of the object class or the objects instantiated from the object class, and establish an association between the indicators and the corresponding object class or objects through mapping rules to obtain the object indicator system set.

[0014] Thirdly, embodiments of the present invention also provide a terminal device, the terminal device including a memory, a processor, and a multi-object unified modeling enterprise multi-dimensional indicator integration management program stored in the memory and executable on the processor. When the processor executes the multi-object unified modeling enterprise multi-dimensional indicator integration management program, it implements the steps of the multi-object unified modeling enterprise multi-dimensional indicator integration management method described in any of the above schemes.

[0015] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a multi-object unified modeling enterprise multi-dimensional indicator integration management program. When the multi-object unified modeling enterprise multi-dimensional indicator integration management program is executed by a processor, it implements the steps of the multi-object unified modeling enterprise multi-dimensional indicator integration management method described in any of the above schemes.

[0016] Beneficial Effects: This invention discloses a method, system, terminal device, and medium for integrated management of multi-dimensional indicators for enterprises using unified multi-object modeling, relating to the field of enterprise information management technology. The method first abstracts the characteristics of various management objects based on enterprise needs, determining object types, basic attribute sets, and extended attribute rules to form a general object class. Then, a general indicator template containing meta-information is constructed to form an indicator class, whereby the meta-information includes indicator type, calculation rules, and potential data sources. Finally, based on the needs of the object class or objects instantiated from the object class, the indicator class is instantiated into indicators, and the indicators are associated with the corresponding object class or objects through mapping rules to obtain a set of object indicator systems. This invention effectively solves the problems of chaotic indicator definitions, model fragmentation, and cross-system incomparability caused by the diversity of management objects in existing technologies, achieving unified modeling and flexible configuration of multi-object indicators. It avoids system redundancy, improves management efficiency, supports horizontal comparison and comprehensive analysis of multi-object indicators, and provides data support for cross-departmental and cross-business unit management of enterprises. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a specific implementation method for the enterprise multi-dimensional indicator integration management method using unified multi-object modeling provided in this invention.

[0018] Figure 2 This is a schematic diagram of the object indicator system set of the enterprise multi-dimensional indicator integration management method for multi-object unified modeling provided in the embodiments of the present invention.

[0019] Figure 3 This is a schematic diagram of the principle of the enterprise multi-dimensional indicator integration management device with unified modeling of multiple objects provided in the embodiments of the present invention.

[0020] Figure 4 This is a block diagram illustrating the internal structure of the terminal device provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content, operations, or steps, nor does it require execution in the described order. For example, some operations or steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0023] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. For example, "first control information" and "second control information" are only used to distinguish different control information and do not limit their order.

[0025] Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or the order of execution, and that the words "first" and "second" do not necessarily imply that they are different.

[0026] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] In the field of enterprise information management technology, with the expansion of enterprise business scope and the enrichment of operating models, the types and number of objects managed internally by enterprises have increased significantly. These management objects include, but are not limited to, products, projects, customers, suppliers, users, services, technologies, assets, and organizations. These management objects are distributed across different departments and business units within the enterprise, and the corresponding performance management and data analysis needs are diverse, necessitating the construction of a unified management platform across departments and business units to achieve integrated analysis.

[0028] In existing technologies, traditional performance evaluation and indicator management systems are mostly designed and developed for single business objects or specific business scenarios, lacking a unified modeling framework for multiple types of management objects. On the one hand, due to the differences in attribute characteristics and evaluation dimensions of different management objects, existing systems cannot provide a universal object type definition mechanism, resulting in independent indicator systems for various management objects and the formation of data silos. On the other hand, the indicator models of existing systems are highly fixed, lacking flexible modeling capabilities and expansion space. They cannot adapt to the indicator definition needs of newly added management objects in enterprises, nor can they meet the needs of adjusting indicator models when business needs change. This leads to problems such as inconsistent performance standards within enterprises, inability to compare data between different systems, and difficulty in conducting comprehensive analysis of multiple objects. These shortcomings make it difficult for enterprises to obtain comprehensive and unified performance data support when conducting cross-business unit management and strategic decision-making, restricting the improvement of management efficiency and failing to meet the actual needs of rapid enterprise development.

[0029] This embodiment provides a method for integrated management of multi-dimensional enterprise indicators through unified multi-object modeling, such as... Figure 1 As shown, the specific steps include the following: Step S100: Based on enterprise needs, abstract the characteristics of various management objects, determine the object type, basic attribute set and extended attribute rules, and form a general object class.

[0030] In this embodiment, the managed objects are various entities or abstract concepts used for performance evaluation, data statistics, and business management during enterprise operations. Specifically, they can be objects directly related to the enterprise's business, such as products, customers, users, suppliers, services, technologies, assets, and organizations. Object types are classifications of different managed objects based on core attributes and business scenarios, used to establish unified standards for classifying managed objects. Specifically, these can be categories such as capital types, customer types, user types, product types, supplier types, service types, technology types, asset types, and organizational types. Clearly defining object types avoids discrepancies in the definition of the same type of managed object by different personnel or departments.

[0031] A generic object class is a reusable template formed by integrating object types, basic attribute sets, and extended attribute rules. Essentially, it's a standardized definition of a class of managed objects. The basic attribute set is a collection of attributes used to uniquely identify and corely describe an object class, enabling unified identification and basic management. Specifically, it can include common attributes such as unique object identifier, creation time, business domain, and responsible person. These attributes are essential and universally present information for all types of object classes. Extended attribute rules are constraints and configuration specifications used to add attributes on demand beyond the basic attribute set. They adapt to the personalized description needs of object classes in different business scenarios. Specifically, they can include rules regarding attribute data type restrictions, value range constraints, and whether attributes are mandatory. Extended attribute rules allow object classes to flexibly adapt to changes and expansions in enterprise business.

[0032] By abstracting the characteristics of managed objects, clarifying object types, standardizing basic attribute sets, and configuring extended attribute rules, a unified object class definition mechanism was established, breaking the traditional modeling pattern of independent modeling for various managed objects. Because different managed objects are incorporated into a unified classification standard and attribute definition framework, the indicator systems of various objects are no longer independent, effectively preventing the formation of data silos. Simultaneously, extended attribute rules endow object classes with flexible adaptability. When enterprises add new managed objects or existing business needs change, there is no need to reconstruct the entire modeling framework; only attributes need to be added or adjusted based on the extended attribute rules. This improves system adaptability, reduces the cost of enterprise information management, and achieves a unified modeling foundation for multiple types of managed objects.

[0033] In one implementation, the process of abstracting the characteristics of various management objects based on enterprise needs, determining object types, basic attribute sets, and extended attribute rules to form a general object class specifically includes the following steps: Step S110: Based on enterprise needs, analyze the object types of enterprise management objects. The object types include at least one of customers, users, products, suppliers, services, technology, assets, and organizations. Step S120: Based on the object type, set a basic attribute set for identifying and describing the object class. The basic attribute set is used for the identification and management of the object class. Step S130: Based on the basic attribute set, configure the extended attribute rules of the object class, wherein the extended attribute rules are used to extend the basic attribute set of the object class; Step S140: Integrate the object type, the basic attribute set, and the extended attribute rules to form a general object class.

[0034] In this embodiment, the scope of objects that the enterprise needs to manage is first clearly defined and standardized. The enterprise's business needs determine the range of types of managed objects. For example, manufacturing enterprises may focus on product, supplier, and asset types, while service enterprises may focus more on customer, service, and user types. Specifically, customer types can include individual customers, corporate customers, and channel customers, classified according to customer attributes and cooperation models; user types can include registered users, paying users, and active users, defined based on user behavior and status; product types can include physical products, virtual products, and service products, classified according to product form and function; supplier types can include raw material suppliers, component suppliers, and service suppliers, classified according to the content supplied; service types can include pre-sales consulting services, after-sales services, and technical support services, classified according to service scenarios; technology types can include core technologies, supporting technologies, and technologies under development, defined based on the importance and status of the technology; asset types can include fixed assets, current assets, and intangible assets, classified according to asset nature; and organizational types can include departments, branches, and project teams, classified according to the enterprise's organizational structure. By clearly defining these object types, all managed objects within an enterprise can be included in a unified classification framework, avoiding differences in classification of the same object by different departments.

[0035] Subsequently, basic attribute sets are defined based on object types. While these sets differ across object types, they all fulfill the requirements for unique identification and basic management. For example, the basic attribute set for a product type might include product code, product name, launch date, product line, and responsible person. The product code enables unique product identification, while launch date and product line attributes facilitate basic management. Similarly, the basic attribute set for a customer type might include customer ID, customer name, registration date, industry, and contact person. The customer ID uniquely identifies the customer, while industry and contact person attributes support basic customer maintenance. The basic attribute set for an asset type might include asset number, asset name, purchase date, original value, and department. The asset number ensures unique asset identification, while purchase date and original value are used for basic asset management. These basic attribute sets are indispensable core information for object classes, ensuring unified identification and management within the enterprise and preventing management chaos caused by missing attributes.

[0036] Subsequently, extended attribute rules are configured based on the basic attribute set. The purpose of extended attribute rules is to provide flexible attribute expansion space beyond the basic attributes. For example, in addition to the basic attribute set for product types, extended attribute rules can be configured according to business needs. For example, extended attributes such as "warranty period" and "operating system" can be configured for electronic products, and extended attributes such as "shelf life" and "ingredient list" can be configured for food products. The extended attribute rules need to clearly define the data type, value range, and whether these attributes are mandatory. Extended attribute rules for customer types can be configured with extended attributes such as "membership level," "credit limit," and "cooperation period," clearly defining the value range of membership level, the numerical range of credit limit, and the calculation method of cooperation period. Extended attribute rules are not fixed and can be adjusted according to the development and changes of the enterprise's business. For example, when the enterprise adds product customization services, it can add the "customization requirements" extended attribute and corresponding rules.

[0037] Finally, object types, basic attribute sets, and extended attribute rules are integrated to form general object classes. This systematic integration of classification criteria, core attributes, and extended rules creates reusable templates. For example, integrating product types, corresponding basic attribute sets, and extended attribute rules forms a product object class; integrating customer types, corresponding basic attribute sets, and extended attribute rules forms a customer object class. Each general object class possesses a clear type affiliation, complete basic attributes, and flexible extension capabilities.

[0038] By refining the definition of object classes, the construction process of general object classes becomes more operational and standardized. Clearly defining the scope of object types ensures that all managed objects are covered; standardizing the set of basic attributes guarantees unified object identification and management; configuring extended attribute rules gives object classes flexible adaptability; and integrating the process to form a standardized template enables object class reuse. These steps collectively solve the problems of non-standard object definitions, chaotic attributes, and lack of extensibility in traditional systems. This allows different departments to manage based on unified object classes, improving internal collaboration efficiency. At the same time, flexible extended attribute rules allow object classes to adapt to the rapid development of enterprise business, avoiding the cost of frequent reconstruction of the modeling framework.

[0039] Step S200: Construct a general indicator template containing metadata to form an indicator class. The metadata includes indicator type, calculation rules, and potential data sources.

[0040] In this embodiment, the general indicator template is a reusable indicator framework built based on metadata, representing a standardized definition of a class of indicators. Indicator classes are the specific presentation of the general indicator template and the foundation for instantiating specific indicators. Meta-information is the core set of elements for constructing the general indicator template, providing a standardized definition basis for indicator classes. Specifically, it can include at least indicator type, calculation rules, and potential data sources, which collectively determine the function and application scenarios of the indicator class. Indicator type is the classification of indicators according to evaluation dimensions, used to establish a unified indicator evaluation framework. Specifically, it can be scale type, quality type, efficiency type, or competitiveness type, allowing for horizontal comparison of indicators from different objects. Calculation rules are the logical specifications for quantifying indicator data, used to achieve standardized calculation of indicator data. Specifically, they can be parameterized rules such as arithmetic operation rules, statistical analysis rules, and logical judgment rules. Clearly defined calculation rules ensure consistent calculation methods for the same type of indicator. Potential data sources are the channels for obtaining the original data required for indicator calculation, providing data support for indicator calculation. Specifically, they can be existing business systems, manually entered ledgers, external data interfaces, etc. Clearly defined potential data sources ensure the accessibility of indicator data.

[0041] By clearly defining indicator types, calculation rules, and potential data sources, a standardized indicator class definition mechanism was constructed, forming reusable and universal indicator templates. Traditional systems suffer from highly standardized indicator models and inconsistent calculation methods for different objects, leading to chaotic performance standards and incomparable data across systems. Indicator classes, through unified indicator type classification, allow indicators from different objects to be categorized along the same dimension; through parameterized calculation rules, they ensure consistent calculation logic for the same type of indicator; and by clearly defining potential data sources, they guarantee consistent acquisition of indicator data. These designs collectively solve the problems of chaotic indicator definitions and inconsistent calculation methods in traditional systems, providing a foundation for horizontal comparisons of indicators across multiple objects. Furthermore, the reusable indicator class templates reduce repetitive workload in indicator modeling and improve the efficiency of enterprise indicator management.

[0042] In one implementation, the construction of a general indicator template containing metadata to form an indicator class, wherein the metadata includes indicator type, calculation rules, and potential data sources, specifically includes the following steps: Step S210: Divide the indicator types into categories. The indicator types include at least one of scale, quality, efficiency, and competitiveness. Each indicator category belongs to only one of the indicator types. Step S220: Based on the evaluation requirements of the indicator data, define parameterized calculation rules, which are used to realize the quantitative calculation of indicator data; Step S230: Set potential data sources for indicator categories, wherein the potential data sources are used to obtain the raw data for quantitative calculation; Step S240: Integrate the indicator type, the parameterized calculation rule, and the potential data source into metadata; Step S250: Construct a general indicator template based on the metadata to obtain the indicator class.

[0043] In this embodiment, indicator types are categorized into categories, with each indicator category belonging to only one indicator type, ensuring the uniqueness and standardization of indicator classification. The classification of indicator types is based on the core dimensions of enterprise performance evaluation and aligns with the logic of enterprise management. Scale-type indicators are primarily used to measure the size or quantity of managed objects, specifically including total assets, net assets, sales volume, revenue, number of customers, number of suppliers, number of activated users, and organizational revenue. For example, scale-type indicators for asset objects include total assets and net assets; for product objects, they include sales volume and revenue; and for customer objects, they include the number of customers and total customer transaction amount. Quality-type indicators are primarily used to assess the quality, compliance, or experience level of managed objects, specifically including customer satisfaction, software failure rate, defect rate, renewal rate, user satisfaction, employee satisfaction, quality incident rate, and SLA service availability. For example, quality-type indicators for technology objects include software failure rate and bug rate per thousand lines of code; for product objects, they include defect rate and return rate; and for service objects, they include customer satisfaction and SLA service availability. Efficiency-type indicators... The main indicators are used to measure the operational efficiency or resource utilization efficiency of the managed object. Specifically, they can include total asset turnover, R&D efficiency, after-sales response time, customer acquisition cost, revenue per employee, cash cycle, customer complaint resolution rate, and R&D expense ratio. For example, efficiency indicators for asset objects include total asset turnover, efficiency indicators for organizational objects include revenue per employee and cash cycle, and efficiency indicators for customer objects include customer complaint resolution rate. The competitive indicators are mainly used to assess the market competitiveness or core advantages of the managed object. Specifically, they can include the number of patents, gross profit margin, repurchase rate, customer retention rate, user retention rate, monthly active user ARPU, return on equity, and salary competitiveness index. For example, competitive indicators for product objects include the number of patents and gross profit margin, competitive indicators for customer objects include repurchase rate and customer retention rate, and competitive indicators for organizational objects include return on equity and salary competitiveness index.

[0044] Subsequently, parametric design enables the calculation rules to be flexibly configured and reused. Calculation rules can be flexibly set according to the type of indicator and evaluation needs. For example, the calculation rules for scale-type indicators can be summation rules (e.g., total assets = sum of original values ​​of all types of assets), cumulative rules (e.g., cumulative sales = sum of sales in each time period), and statistical rules (e.g., number of customers = total number of valid customers at the end of the period). The calculation rules for quality-type indicators can be ratio rules (e.g., customer satisfaction = number of positive reviews / total number of reviews × 100%), threshold judgment rules (e.g., software failure rate = number of failures / runtime × 100%, with a set threshold for the number of failures), and mean rules (e.g., average SLA service availability = sum of sales in each time period). The calculation rules for efficiency-type indicators can be ratio rules (e.g., total asset turnover = operating revenue / average total assets × 100%), difference rules (e.g., customer acquisition cost = total marketing expenses / number of new customers), and time rules (e.g., after-sales response time = average time from customer submission of request to response). The calculation rules for competitiveness-type indicators can be ratio rules (e.g., gross profit margin = (revenue - cost) / revenue × 100%), growth rate rules (e.g., customer retention rate = number of retained customers at the end of the period / number of customers at the beginning of the period × 100%), and statistical rules (e.g., number of patents = total number of valid patents). All of the above calculation rules adopt parameterized configuration. For example, in the summation rule, parameters such as the data source field for summation and the statistical period can be configured. In the ratio rule, parameters such as the data source fields of the numerator and denominator and the calculation precision can be configured. By adjusting the parameters, the calculation needs of the same type of indicator for different objects can be adapted.

[0045] Subsequently, potential data sources for the indicators are defined to ensure the availability of the raw data required for indicator calculation. Potential data sources should be integrated with the company's existing information systems and data management models. Specifically, these could include the company's information management system, customer management system, after-sales service system, technical management system, financial system, business ledgers, and manually entered data. For example, the potential data source for the scale-type indicator "Total Assets" could be the asset ledger module of the financial system, obtaining the original value data of various assets; the potential data source for the quality-type indicator "Customer Satisfaction" could be the customer evaluation module of the customer management system, obtaining customer evaluation data; the potential data source for the efficiency-type indicator "After-sales Response Time" could be the work order module of the after-sales service system, obtaining work order submission and response time data; and the potential data source for the competitiveness-type indicator "Number of Patents" could be the patent module of the technical management system, obtaining valid patent data. For some data that currently lacks system support, manually entered ledgers can be used as a potential data source to ensure comprehensive acquisition of indicator data.

[0046] The metadata integrates indicator types, parameterized calculation rules, and potential data sources, systematically organizing the core definition elements of indicator classes to form a complete basis for indicator definitions. The elements within the metadata are interconnected; the indicator type determines the design direction of the calculation rules, and the potential data sources provide data support for the calculation rules. Together, these three elements constitute the definition of an indicator class. For example, the metadata for the scale-type indicator "sales volume" shows the indicator type as scale-type, the calculation rule as "cumulative rule (sum of sales volume across all sales channels within the statistical period)," and the potential data source as the sales data module of the sales management system.

[0047] Finally, general indicator templates are constructed based on metadata to obtain indicator classes. General indicator templates are structured representations of metadata, possessing standardized and reusable characteristics. For example, based on metadata such as "scale type, cumulative sales calculation rules, and sales management system data source," a "scale-based sales indicator template" is constructed, i.e., the sales indicator class; based on metadata such as "quality type, customer satisfaction ratio calculation rules, and customer management system evaluation data source," a "quality-based customer satisfaction indicator template" is constructed, i.e., the customer satisfaction indicator class.

[0048] By refining the construction steps of indicator classes, standardization, reusability, and precision of indicator classes are achieved. Indicator type classification ensures that indicators for different objects have a unified evaluation dimension, laying the foundation for horizontal comparisons; parameterized calculation rules ensure consistent calculation methods for the same type of indicator, solving the problem of chaotic indicator calculations in traditional systems; the setting of potential data sources guarantees the accessibility of indicator data, avoiding the inability to use indicators due to missing data; metadata integration and template construction form reusable indicator classes, reducing the repetitive workload of indicator modeling. These steps together construct a standardized indicator class system, allowing indicators for different objects to be defined and calculated based on unified standards, effectively solving the problems of chaotic indicator definitions and inconsistent performance standards in existing technologies, and improving the efficiency and accuracy of enterprise indicator management.

[0049] Step S300: Based on the requirements of the object class or the object instantiated from the object class, the indicator class is instantiated into an indicator, and the indicator is associated with the corresponding object class or object through mapping rules to obtain a set of object indicator systems.

[0050] In this embodiment, instantiation is the process of transforming a general template into a specific application entity. It is used to combine the standardized definitions of object classes and indicator classes with the actual business scenarios of the enterprise to form specific objects and indicators that can be directly used for management.

[0051] A concrete object is the product of instantiating an object class. It is an actual management entity with a unique identifier and complete attribute values. It can be product A, customer A, supplier B, etc. Each concrete object inherits the type attributes and attribute rules of its parent object class. A metric is the product of instantiating a metric class. It is a specific evaluation tool adapted to the evaluation needs of a particular object class or concrete object. It can be sales metrics, satisfaction metrics, etc. Each metric inherits the type attributes, calculation rules, and data source of its parent metric class.

[0052] Mapping rules are logical specifications for establishing the association between metrics and object classes or specific objects. They are used to match metrics with managed objects and can be matching rules based on factors such as business scenarios, object attributes, and evaluation requirements.

[0053] An object indicator system set is a complete system formed by integrating the relationships between indicators and object classes or specific objects. It is a set of relationships between multiple objects and multiple indicators, and serves as the foundation for data collection, evaluation, and analysis. Specifically, based on the above example, the object indicator system set after mapping and matching could include sales indicators for product A, customer satisfaction indicators for customer A, etc.

[0054] The construction of the object indicator system set realizes the association and unified system construction between indicators and managed objects. In traditional systems, the binding of indicators and objects is mostly a fixed configuration, lacking a flexible mapping mechanism. When objects or indicators change, the association relationship is difficult to adjust synchronously, resulting in mismatch between indicators and objects. Through the instantiation process, a general template is transformed into specific objects and indicators that fit the actual business, and then the association relationship between the two is established through mapping rules, forming a complete object indicator system set. This design allows each specific object or abstract object class to be matched with suitable indicators, and indicators can also be accurately mapped to the objects or object classes that need to be evaluated, realizing unified management of multi-object indicators. Since indicators and objects are associated based on unified mapping rules, indicators of the same type in different objects have the conditions for horizontal comparison, solving the problem of incomparability of cross-object indicators in traditional systems, and providing support for comprehensive analysis across departments and business units within enterprises.

[0055] In one implementation, the instantiation of the indicator class into an indicator based on the requirements of the object class or an object instantiated from the object class specifically includes the following steps: Step S310: Based on the object class, instantiate the object class into a specific object with a unique identifier through attribute assignment operation; Step S320: Based on the indicator class and in combination with the relevant requirements of the object class or specific object, instantiate the indicator class into an indicator adapted to the corresponding object class or specific object.

[0056] In this embodiment, the object class is first instantiated into a specific object with a unique identifier through attribute assignment operations. Attribute assignment is the process of converting the basic attribute set and extended attribute rules of the object class into specific attribute values. The unique identifier is the basis for distinguishing different specific objects. The unique identifier of a specific object can be a system-generated code, number, etc., or it can be a unique identifier generated in combination with object attributes. For example, the unique identifier of a product object can be "product type code, serial number" (such as P001-0001), and the unique identifier of a customer object can be "customer type code, registration year, serial number" (such as C002-2024-0005). During attribute assignment, the entry of basic attribute values ​​must follow the basic attribute set specification of the object class. For example, when the product object class is instantiated as "Product A", basic attributes such as product code, product name, launch time, product line, and person in charge must be assigned values. Assume the product code is P001-0001, the product name is "Smart Bracelet X1", the launch time is January 15, 2024, the product line is "Smart Wearable Devices", and the person in charge is Zhang San. When the asset object class is instantiated as "Device B", the basic attribute is assigned as asset number Z003-0012, asset name is "Production Line Equipment", purchase time is May 20, 2023, original value is 1.5 million yuan, and the department is the Production Department. When the customer object class is instantiated as "Customer A", the basic attribute is assigned as customer ID C002-2024-0005, customer name is "a certain technology company", registration time is March 10, 2024, industry is electronic manufacturing, and the contact person is Li Si. For extended attributes, values ​​are assigned according to business needs. For example, the extended attribute "warranty period" of "smart bracelet X1" is assigned to 2 years, the extended attribute "depreciation period" of "device B" is assigned to 10 years, and the extended attribute "membership level" of "customer A" is assigned to gold level.

[0057] Subsequently, based on the relevant requirements of the indicator class and the object class or specific object, the indicator class is instantiated into an adapted indicator. According to specific evaluation needs, the meta-information parameters of the indicator class are adjusted to form a specific indicator that fits the object class or specific object. For example, when instantiating a scale-type indicator class, for the "sales volume" indicator of the product object class, the calculation rule parameters are adjusted according to the product sales evaluation needs, the statistical period is set to monthly, and the data source is the sales data field of the product in the sales management system, forming the "product monthly sales volume indicator"; for the "total assets" indicator of the asset object class, the calculation rule parameters are adjusted according to the asset evaluation needs, the statistical scope is set to all fixed assets and current assets under the asset object class, and the data source is the asset ledger field of the financial system, forming the "asset class total assets indicator". When instantiating metrics for specific objects, further refinement and adaptation are required. For example, the "monthly sales volume metric" can be instantiated as the "monthly sales volume metric for smart bracelet X1," and the calculation rule parameters can be adjusted to statistically analyze the monthly sales data of "smart bracelet X1," with the data source located in the sales records corresponding to "product code P001-0001" in the sales management system. Similarly, the "total asset metric" can be instantiated as the "total asset metric for device B," and the calculation rule parameters can be adjusted to statistically analyze the original value of "device B" and the value of related ancillary assets, with the data source located in the asset records corresponding to "asset number Z003-0012" in the financial system.

[0058] Through explicit instantiation steps, the system seamlessly integrates general templates with specific business scenarios. The object class instantiation process transforms standardized object classes into concrete objects managed by the enterprise. Each concrete object possesses a unique identifier and complete attribute values, ensuring the uniqueness and identifiability of the managed objects. The indicator class instantiation process adjusts indicator class parameters to form specific indicators based on the evaluation needs of the object class or concrete objects, allowing indicators to accurately adapt to the evaluation scenarios of different objects. This instantiation design solves the problem of templates being disconnected from actual business in traditional systems. The reusability of general object classes and indicator classes is fully utilized, while the instantiated concrete objects and indicators align with the actual business needs of the enterprise. Since specific indicators are formed based on indicator class instantiation, their calculation rules, data sources, and other core elements remain consistent with the indicator class, ensuring a unified calculation caliber for the same type of indicator and providing a guarantee for subsequent horizontal comparisons. Furthermore, the instantiated indicators for specific objects accurately reflect the indicator status of that object, improving the targeting and accuracy of indicator evaluation.

[0059] In one implementation, the step of establishing an association between the indicators and corresponding object classes or objects through mapping rules to obtain an object indicator system set specifically includes the following steps: Step S330: Determine the mapping matching factors, assign weights to each mapping matching factor, and calculate the matching degree between the index and the object class or object based on the weighted summation algorithm; Step S340: Set a matching degree threshold. If the matching degree is greater than or equal to the matching degree threshold, an association relationship between the indicator and the object class or object is automatically established. Step S350: Integrate the relationships and the indicators with the corresponding object classes or objects to obtain a set of object indicator systems.

[0060] In this embodiment, mapping matching factors are first determined and weighted. The matching degree is calculated based on a weighted summation algorithm, achieving precise association between indicators and object classes or specific objects. Mapping matching factors are selected based on the adaptability of indicators and objects, and may specifically include factors such as business scenario relevance, object attribute fit, and assessment requirement fit. Business scenario relevance refers to the degree of fit between the application scenario of the indicator and the business scenario of the object class or specific object. For example, indicators in the sales scenario have a high relevance to the business scenario of the product object class, and indicators in the financial scenario have a high relevance to the business scenario of the asset object class. The weight of this factor can be assigned to 0.4, because the matching of business scenarios is the foundation for the association between indicators and objects. Object attribute fit refers to the degree of fit between the evaluation dimension of the indicator and the core attributes of the object class or specific object. For example, scale-type indicators have a high fit with the core attributes of the product object class, such as "output" and "sales revenue," and quality-type indicators have a high fit with the core attributes of the service object class, such as "service response speed" and "customer evaluation." The weight of this factor can be assigned to 0.3, because the core attributes of the object determine whether the indicator can effectively evaluate the object's performance. The assessment of demand fit refers to the degree of match between the assessment purpose of an indicator and the company's assessment needs for a particular object or category. For example, if a company needs to assess the market performance of a product, the "sales growth rate" indicator has a high assessment demand fit; if it needs to assess customer loyalty, the "repurchase rate" indicator has a high assessment demand fit. The weight of this factor can be assigned as 0.3, because the assessment demand directly determines the direction of indicator selection. The matching degree can be calculated using a weighted summation algorithm. Based on the weights assigned in the above manner, a weighted summation is performed to obtain the matching degree.

[0061] Subsequently, a matching threshold is set, and associations are established based on the matching results. The matching threshold is designed to balance accuracy and flexibility, and can be adjusted according to business needs. When the matching degree between an indicator and an object class or specific object is greater than or equal to the preset threshold, it indicates a high degree of compatibility, and an association is automatically established. For example, the matching degree between the "sales indicator" and the "product object class" is greater than the threshold, so an association is automatically established. When the matching degree is less than the preset threshold, it indicates that the compatibility needs verification, triggering a manual review process. Relevant personnel determine whether to establish an association based on the actual business situation. For example, the matching degree between the "customer satisfaction indicator" and the "product object class" is less than the preset threshold, triggering a manual review. If the company believes customer satisfaction is crucial to product evaluation, an association can be manually established; otherwise, if the compatibility is deemed insufficient, no association is established.

[0062] Finally, by integrating the relationships, indicators, and corresponding object classes or specific objects, a set of object indicator systems is obtained. All objects and indicators with established relationships are systematically integrated to form a complete management system. For example, the relationships between "product object classes" and "sales volume indicators," "revenue indicators," and "defect rate indicators" are integrated; the relationships between "customer object classes" and "customer satisfaction indicators," "repurchase rate indicators," and "customer retention rate indicators" are integrated; and the relationships between "asset object classes" and "total asset indicators," "total asset turnover rate indicators," and "debt-to-equity ratio indicators" are integrated, ultimately forming a complete system set covering the company's main management objects and corresponding indicators.

[0063] The quantitative mapping rules enable precise and efficient association between indicators and objects. The weighted summation algorithm provides a quantitative basis for matching degree calculation, avoiding the subjectivity and arbitrariness of traditional manual matching and improving the accuracy of the association. The setting of matching degree thresholds combines automatic association with manual review, ensuring both the efficiency of highly compatible associations and the flexibility of associations in special scenarios through manual review. The resulting object indicator system integrates scattered objects and indicators into a unified management system, enabling enterprises to conduct unified management and analysis of multiple objects and indicators based on this set. This solves the problems of chaotic object-indicator associations and fragmented management in traditional systems, providing a clear foundational framework for subsequent data collection, evaluation, and analysis.

[0064] Figure 2This demonstrates the correspondence between the above-mentioned indicator types and the object indicator system within the object indicator system set. Specifically, for asset objects, scale indicators include total assets and net assets; quality indicators include debt-to-equity ratio and current ratio; efficiency indicators include total asset turnover and net cash flow from operating activities; and competitiveness indicators include return on equity and net asset value per share. For product objects, scale indicators include sales volume and revenue; quality indicators include defect rate and return rate; efficiency indicators include customer acquisition cost and R&D efficiency; and competitiveness indicators include gross profit margin and repurchase rate. For customer objects, scale indicators include number of customers and average transaction value per customer; quality indicators include customer satisfaction and paid conversion rate; efficiency indicators include after-sales response time and customer complaint resolution rate; and competitiveness indicators include customer retention rate and Net Promoter Score (NPS). Indicators under each object type belong to one of the indicator types, ensuring the uniformity of indicator classification.

[0065] In one implementation, the enterprise multi-dimensional indicator integration management method for unified modeling of multiple objects further includes the following steps: Step S410: Based on the potential data sources of the aforementioned indicator class, obtain the raw data for quantitative calculation through automatic collection or manual input. Step S420: Standardize the raw data to obtain standardized data; Step S430: Fill the standardized data into the corresponding indicators as indicator data.

[0066] In this embodiment, raw data is first obtained based on the potential data sources of the indicator category. The data acquisition method combines the type of potential data source to select automatic collection or manual entry. Automatic collection is suitable for potential data sources already incorporated into the enterprise information system. Data is automatically retrieved through system interface linkage. For example, when the potential data source for the indicator category is the information management system, raw data is periodically retrieved from specified modules and fields of the information management system by configuring system interface parameters; when the potential data source is the customer management system, customer-related raw data is obtained through interface linkage; when the potential data source is the financial system, relevant data from the financial ledger is automatically collected. The automatic collection cycle can be set according to the update requirements of the indicator data, such as daily, weekly, or monthly updates. For indicators with high real-time requirements, real-time collection can be set. Manual data entry is suitable for potential data sources that currently lack system support or are scattered. Specifically, it can be achieved through pre-defined data entry forms. The fields in the entry form correspond one-to-one with the fields in the original data required for indicator calculation. For example, when the potential data source for the indicator "number of patents" is a manual ledger, the entry form should include fields such as patent name, application date, and authorization status, with relevant personnel entering the data regularly. When the potential data source for the indicator "labor costs" is manual statistical data, the entry form should include fields such as department, number of personnel, and total salary. Manually entered data supports batch import, which can be done by uploading spreadsheet files in batches, improving entry efficiency.

[0067] Subsequently, the raw data undergoes standardization to eliminate data format differences, supplement missing data, and remove outliers, ensuring the data meets the requirements for indicator calculation. Format standardization includes unifying data types and format specifications, uniformly retaining two decimal places for numerical data of different precisions, and standardizing the encoding rules for character data. Missing value completion employs methods adapted to data characteristics; for numerical data, mean imputation, median imputation, or interpolation can be used; for character data, mode imputation or default value imputation can be used. Outlier removal uses statistical methods to identify and remove outliers, using the principle that data values ​​exceeding the mean ± 3 standard deviations are considered outliers and removed. Data that does not conform to business logic is identified and removed using logical verification methods.

[0068] Finally, the standardized data is populated into the corresponding indicators as indicator data. This links the standardized, valid data with specific indicators, providing data support for indicator calculation. Data population requires precise matching based on the unique identifier of the indicator and the time and object dimensions of the data. For example, standardized sales data for "Smart Bracelet X1" in May 2024 is populated into the "Smart Bracelet X1 Monthly Sales Indicator," and standardized asset data for "Device B" in the first quarter of 2024 is populated into the "Device B Total Asset Indicator." After data population, the system automatically records the time, source, and processing method of the data population, forming a data traceability record for easy subsequent data verification and tracing. Simultaneously, combined with the data update mechanism, when the original data is updated or added, the above process is automatically repeated to dynamically update the indicator data, ensuring that the indicator data can reflect the latest status of the object in a timely manner.

[0069] A systematic data collection and processing workflow ensures the accuracy, consistency, and timeliness of indicator data. Automated data collection improves efficiency and reduces human error; manual data entry supplements system data deficiencies, ensuring comprehensiveness. Standardized processing eliminates format differences and quality issues in raw data, enabling data from different sources and formats to uniformly adapt to indicator calculation needs, resolving calculation errors caused by inconsistent data formats and quality in traditional systems. Data population and dynamic update mechanisms ensure timely and accurate association of indicator data with corresponding indicators, allowing indicators to truly reflect the actual state of managed objects and providing a reliable data foundation for subsequent indicator evaluation. Simultaneously, data traceability records enhance the standardization and traceability of data management.

[0070] In one implementation, the enterprise multi-dimensional indicator integration management method for unified modeling of multiple objects further includes the following steps: Step S510: Based on the parameterized calculation rules of the indicator class, perform quantitative calculation on the filled indicator data to obtain the indicator evaluation result. The indicator evaluation result includes at least one of the following: actual indicator value, deviation value, ranking result, year-on-year analysis result, and month-on-month analysis result. Step S520: Output the evaluation results of the indicators in a visual form using a visualization tool.

[0071] In this embodiment, the indicator data is first quantified based on the parameterized calculation rules of the indicator class to obtain the indicator evaluation result. The quantification calculation is based on the preset calculation rules of the indicator class, combined with the filled standardized data, to generate an evaluation result that reflects the performance of the object. The actual value of the indicator is the basic result of the indicator quantification calculation and directly reflects the actual performance of the object. For example, the actual value of the "monthly sales volume of smart bracelet X1" is 1000 units, the actual value of the "customer A repurchase rate" is 75%, and the actual value of the "total asset turnover rate of device B" is 1.2 times / year. The deviation value is the difference between the actual value and the preset target value, used to reflect the gap between actual performance and the target. The calculation formula is Deviation value = Actual value - Target value. A positive deviation value indicates that the target has been exceeded, and a negative value indicates that the target has not been reached. For example, the target value for the "Monthly Sales Indicator of Smart Bracelet X1" is 1050 units, and the deviation value = 1000 - 1050 = -50 units, indicating that the monthly sales target has not been reached. The target value for the "Customer A Repurchase Rate Indicator" is 70%, and the deviation value = 75% - 70% = 5%, indicating that the repurchase rate target has been exceeded. The ranking result is obtained by sorting the actual values ​​of different objects under the same indicator type, used to reflect the performance level of the object in the same category. The sorting method can be ascending or descending. For example, if the actual monthly sales values ​​of all products are sorted in descending order, and "Smart Bracelet X1" ranks 3rd, it means that its sales are among the top of all products. If the actual repurchase rate values ​​of all customers are sorted in descending order, and "Customer A" ranks 5th, it means that its loyalty is in the middle to upper range among all customers. Year-on-year analysis results are the growth rate obtained by comparing the actual value of the current period with the actual value of the same period last year. The calculation formula is: Year-on-year growth rate = (Actual value of the current period - Actual value of the same period last year) / Actual value of the same period last year × 100%. It is used to reflect the long-term growth trend of the indicator. For example, the year-on-year growth rate of sales of "Smart Bracelet X1" in May 2024 = (1000-800) / 800 × 100% = 25%, indicating that its sales have increased significantly compared with the same period last year. Month-on-month analysis results are the growth rate obtained by comparing the actual value of the current period with the actual value of the previous period. The calculation formula is: Month-on-month growth rate = (Actual value of the current period - Actual value of the previous period) / Actual value of the previous period × 100%. It is used to reflect the short-term trend of the indicator. For example, the month-on-month growth rate of sales of "Smart Bracelet X1" in May 2024 = (1000-900) / 900 × 100% ≈ 11.11%, indicating that its sales have increased compared with the previous month.

[0072] Subsequently, visualization tools are used to output the indicator evaluation results in a visual format. The visualization format is selected based on the presentation requirements of the evaluation results to ensure that the results are intuitive and easy to understand, facilitating corporate decision-making. Bar charts are suitable for displaying the comparison of actual values ​​of indicators for different objects or different periods; line charts are suitable for displaying the changing trends of indicator evaluation results; radar charts are suitable for displaying the evaluation results of the same object under multiple indicator types; and dashboards are suitable for displaying the achievement status of the actual value and target value of a single indicator. The visualization output supports interactive operations. Users can select to view the evaluation results of specific objects, specific periods, and specific indicator types through filtering conditions, and can also use drill-down operations to view the detailed data sources and calculation processes of the evaluation results.

[0073] By employing quantitative calculations and visual output, the evaluation results of indicators are made more practical and readable. The actual values, deviation values, ranking results, and year-on-year or month-on-month analysis results generated by quantitative calculations reflect the performance of the objects from different dimensions. They not only demonstrate actual performance levels but also allow for comparisons with target gaps, similar levels, and changing trends, providing comprehensive data support for corporate decision-making. Visual output transforms abstract data into intuitive charts, reducing the difficulty of data interpretation and enabling corporate managers to quickly grasp the performance of objects, improving decision-making efficiency. Simultaneously, the interactive visualization design allows users to view evaluation results on demand, meeting the decision-making needs of different levels and departments. This design solves the problem of traditional systems presenting indicator evaluation results in a single, difficult-to-interpret form, enabling horizontal comparison and comprehensive analysis of the performance of multiple objects, ultimately supporting the company's strategic execution and organizational optimization.

[0074] In summary, the method proposed in this embodiment constructs a complete technical system encompassing object class definition, indicator class construction, instantiation association, data collection, and evaluation visualization. First, based on enterprise needs, it abstracts the characteristics of management objects to form a general object class with flexible extensibility. Then, it constructs standardized and reusable indicator classes around indicator types. Through quantitative mapping rules, it achieves precise association between indicators and objects, forming a unified set of object indicator systems. Finally, through standardized data processing and visualization output, it realizes comprehensive analysis of multi-object performance. This entire technical solution fundamentally solves the problems of indicator confusion, model fragmentation, and cross-system incomparability caused by the diversity of management objects in existing technologies. It ensures the uniformity and standardization of multi-object indicator modeling while endowing the system with flexible adaptability through extended attribute rules and parameterized calculation rules, effectively avoiding system redundancy, improving the efficiency of enterprise indicator management and the accuracy of decision-making, and providing solid technical support for integrated management across departments and business units. It perfectly aligns with the core management logic of enterprises: increasing asset scale, maintaining quality standards, improving work efficiency, and building core competitiveness.

[0075] like Figure 3As shown in the figure, this embodiment of the invention provides an enterprise multi-dimensional indicator integration management system for unified modeling of multiple objects. The system includes: an object class definition module 10, an indicator class definition module 20, and an object indicator system set acquisition module 30.

[0076] Specifically, the object class definition module 10 is used to abstract the characteristics of various management objects based on enterprise needs, determine object types, basic attribute sets, and extended attribute rules, and form a general object class; the indicator class definition module 20 is used to construct a general indicator template containing meta-information to form an indicator class, wherein the meta-information includes indicator type, calculation rules, and potential data sources; the object indicator system set acquisition module 30 is used to instantiate the indicator class into indicators based on the needs of the object class or objects instantiated from the object class, and establish an association between the indicators and the corresponding object class or objects through mapping rules to obtain an object indicator system set.

[0077] Based on the above embodiments, the present invention also provides a terminal device, the principle block diagram of which can be as follows: Figure 4 As shown, the terminal device includes a processor, memory, network interface, display screen, and temperature sensor connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-object unified modeling method for integrated management of enterprise multi-dimensional indicators. The display screen can be an LCD screen or an e-ink screen. The temperature sensor is pre-installed inside the terminal device to detect the operating temperature of internal components.

[0078] Those skilled in the art will understand that Figure 4 The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal device to which the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0079] In one embodiment, a terminal device is provided, including a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs including instructions for performing operations as described in the embodiments of the methods above.

[0080] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0081] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for integrated management of multi-dimensional enterprise indicators through unified multi-object modeling, characterized in that, The method includes: Based on enterprise needs, the characteristics of various management objects are abstracted, the object types, basic attribute sets and extended attribute rules are determined, and a general object class is formed. Construct a general indicator template containing metadata to form an indicator class. The metadata includes indicator type, calculation rules, and potential data sources. Based on the requirements of the object class or the object instantiated from the object class, the indicator class is instantiated into an indicator, and the indicator is associated with the corresponding object class or object through mapping rules to obtain a set of object indicator systems.

2. The enterprise multi-dimensional indicator integration management method for unified multi-object modeling according to claim 1, characterized in that, Based on enterprise needs, the characteristics of various management objects are abstracted, object types, basic attribute sets, and extended attribute rules are determined, forming a general object class, including: Based on enterprise needs, analyze the object types of enterprise management objects, including at least one of customers, users, products, suppliers, services, technology, assets, and organizations; Based on the object type, a set of basic attributes is set to identify and describe the object class. The set of basic attributes is used for the identification and management of the object class. Based on the basic attribute set, configure the extended attribute rules for the object class, wherein the extended attribute rules are used to extend the basic attribute set of the object class; The object type, the basic attribute set, and the extended attribute rules are integrated to form a general object class.

3. The enterprise multi-dimensional indicator integration management method for unified multi-object modeling according to claim 1, characterized in that, The construction of a general indicator template containing metadata forms an indicator class. This metadata includes indicator type, calculation rules, and potential data sources, including: The indicator types are categorized into categories, and the indicator types include at least one of scale, quality, efficiency, and competitiveness. Each indicator category belongs to only one of the indicator types. Based on the evaluation requirements of indicator data, parameterized calculation rules are defined, which are used to realize the quantitative calculation of indicator data. Define potential data sources for indicator categories, wherein the potential data sources are used to obtain the raw data for quantitative calculation; The indicator type, the parameterized calculation rules, and the potential data sources are integrated into metadata. Based on the metadata, a general indicator template is constructed to obtain the indicator class.

4. The enterprise multi-dimensional indicator integration management method for unified multi-object modeling according to claim 1, characterized in that, The instantiation of the indicator class into an indicator based on the requirements of the object class or an object instantiated from the object class includes: Based on the object class, the object class is instantiated into a specific object with a unique identifier through attribute assignment operation; Based on the aforementioned indicator class, and in conjunction with the relevant requirements of the object class or specific object, the indicator class is instantiated into an indicator adapted to the corresponding object class or specific object.

5. The enterprise multi-dimensional indicator integration management method for unified multi-object modeling according to claim 1, characterized in that, The process of establishing associations between the indicators and corresponding object classes or objects through mapping rules to obtain a set of object indicator systems includes: Determine the mapping matching factors, assign weights to each mapping matching factor, and calculate the matching degree between the index and the object class or object based on the weighted summation algorithm; Set a matching degree threshold. If the matching degree is greater than or equal to the matching degree threshold, an association relationship between the indicator and the object class or object will be automatically established. By integrating the aforementioned relationships, as well as the indicators and their corresponding object classes or objects, a set of object indicator systems is obtained.

6. The enterprise multi-dimensional indicator integration management method for unified multi-object modeling according to claim 3, characterized in that, The method further includes: Based on the potential data sources of the aforementioned indicator categories, the raw data for quantitative calculation is obtained through automatic collection or manual input. The original data is standardized to obtain normalized data; The standardized data is then filled into the corresponding indicators to form the indicator data.

7. The enterprise multi-dimensional indicator integration management method for unified multi-object modeling according to claim 6, characterized in that, The method further includes: Based on the parameterized calculation rules of the aforementioned indicator class, the filled indicator data is subjected to quantitative calculation to obtain indicator evaluation results. The indicator evaluation results include at least one of the following: actual indicator value, deviation value, ranking result, year-on-year analysis result, and month-on-month analysis result. The evaluation results of the aforementioned indicators are output in a visual form using visualization tools.

8. A multi-object unified modeling enterprise multi-dimensional indicator integrated management system, characterized in that, The system includes: The object class definition module is used to abstract the characteristics of various management objects based on enterprise needs, determine the object type, basic attribute set and extended attribute rules, and form a general object class; The indicator class definition module is used to construct a general indicator template containing metadata to form an indicator class. The metadata includes indicator type, calculation rules, and potential data sources. The object indicator system set acquisition module is used to instantiate the indicator class into indicators based on the requirements of the object class or the objects instantiated from the object class, and establish an association between the indicators and the corresponding object class or objects through mapping rules to obtain the object indicator system set.

9. A terminal device, characterized in that, The terminal device includes a memory, a processor, and a multi-object unified modeling enterprise multi-dimensional indicator integration management program stored in the memory and capable of running on the processor. When the processor executes the multi-object unified modeling enterprise multi-dimensional indicator integration management program, it implements the steps of the multi-object unified modeling enterprise multi-dimensional indicator integration management method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a multi-object unified modeling enterprise multi-dimensional indicator integration management program. When the multi-object unified modeling enterprise multi-dimensional indicator integration management program is executed by a processor, it implements the steps of the multi-object unified modeling enterprise multi-dimensional indicator integration management method as described in any one of claims 1-7.

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