Big customer visiting portrait visualization method, device, equipment, medium and product

Abstract

The invention provides a big customer visit portrait visualization method, device and equipment, a medium and a product, and relates to the technical field of computers, and the method comprises the steps: obtaining the power grid business domain data of a big customer; constructing a data logic model based on the power grid service domain data; and based on the data logic model, determining big customer portrait data of the big customer, and displaying the big customer portrait data on the target terminal. Through the above mode, the data logic model is constructed based on the power grid business domain data of the major customers, then the major customer portrait data is determined based on the data logic model, and the major customer portrait data is displayed at the target terminal, so that visualization of major customer portraits is realized, diversified service means are provided for major customer visiting service work, and the experience of the major customers is improved. The system deepens the understanding of power grid workers on major customers, improves the marketing capability and the service level of the major customers, further improves the business work efficiency and service quality of the power grid workers, and improves the customer satisfaction.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, equipment, medium and product for visualizing a profile of a key customer visit. Background Art

[0002] With the development of the times, the requirements of power grid customers for the quality of power grid services are gradually increasing. In order to facilitate the visits of power grid staff to power grid customers, it is necessary to determine and update customer portraits in a timely manner and push them to the terminals of power grid staff for reference by power grid staff, provide diversified service means for key customer visits, deepen the understanding of power grid staff to key customers, improve their marketing capabilities and key customer service level, thereby further improving the business work efficiency and service quality of power grid staff and improving customer satisfaction.

[0003] Currently, there is still a lack of a customer visit portrait visualization method suitable for the power grid service field. Summary of the invention

[0004] The present invention provides a method, device, equipment, medium and product for visualizing a portrait of a visit to a major customer, so as to solve the defect that there is still a lack of a method for visualizing a portrait of a visit to a major customer applicable to the field of power grid services in the prior art.

[0005] The present invention provides a method for visualizing a key customer visit portrait, comprising: obtaining power grid business domain data of the key customer; constructing a data logic model based on the power grid business domain data; determining the key customer portrait data of the key customer based on the data logic model, and displaying the key customer portrait data on a target terminal.

[0006] According to a method for visualizing a visit portrait of a major customer provided by the present invention, power grid business domain data of the major customer is obtained, including: obtaining the original power grid business domain data of the major customer; performing data validity analysis and data quality analysis on the original power grid business domain data to obtain data analysis results; based on the data analysis results, performing data cleaning processing on the original power grid business domain data to obtain power grid business domain data.

[0007] According to a method for visualizing a visit portrait of a major customer provided by the present invention, the power grid business domain data includes one or more of the major customer's marketing archive data, electricity consumption data, electricity fee data, customer information, meter reading data, power grid operation status data, power grid energy consumption data, and customer tag data; based on the power grid business domain data, a data logic model is constructed, including: determining data encoding rules and data classification rules for the power grid business domain data; modeling the power grid business domain data based on the data encoding rules and data classification rules to obtain a data logic model for the power grid business domain data; storing the data logic model in a relational database and / or a big data platform; wherein the data logic model includes a hierarchical model, a network model, a relational model, and an object-oriented model.

[0008] According to a method for visualizing key customer visit portraits provided by the present invention, key customer portrait data of key customers is determined based on a data logic model, and the key customer portrait data is displayed on a target terminal, including: in response to a display instruction of the target terminal, based on the display instruction, obtaining a data logic model from a relational database and / or a big data platform; based on the data logic model, performing user feature characterization on the key customer and determining the key customer portrait data of the key customer; generating a visualization result based on the key customer portrait data; and displaying the visualization result on a visualization interface of the target terminal.

[0009] According to a method for visualizing a key customer visit portrait provided by the present invention, the key customer portrait data includes at least two of chart type data, graphic type data, map type data, and text type data; based on the key customer portrait data, a visualization result is generated, including: based on the data type of the key customer portrait data, a visualization result of the key customer portrait data is generated; wherein the visualization result includes one or more of an interactive report of the key customer portrait data, data description information, an infographic, a heat map, a network diagram, and a time series visualization data.

[0010] According to a method for visualizing a key customer visit portrait provided by the present invention, before displaying the visualization result on the visualization interface of the target terminal, the method further includes: obtaining customer demand information; based on the customer demand information, performing prototype design of the visualization interface to obtain a prototype design result of the visualization interface; the prototype design result includes a low-fidelity prototype design result, a medium-fidelity prototype design result, and a high-fidelity prototype design result of the visualization interface; based on the prototype design result, generating a visualization interface through a prototype design tool.

[0011] The present invention also provides a device for visualizing the visit portrait of a major customer, including: an acquisition module for acquiring the power grid business domain data of the major customer; a construction module for constructing a data logic model based on the power grid business domain data; a visualization module for determining the major customer portrait data of the major customer based on the data logic model, and displaying the major customer portrait data on a target terminal.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the above-mentioned methods for visualizing a portrait of a key customer visit is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for visualizing a portrait of a key customer visit.

[0014] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for visualizing a portrait of a key customer visit.

[0015] The method, device, equipment, medium and product for visualizing the portrait of a key customer visit provided by the present invention obtain the power grid business domain data of the key customer; construct a data logic model based on the power grid business domain data; determine the key customer portrait data of the key customer based on the data logic model, and display the key customer portrait data on the target terminal. In the above manner, a data logic model is constructed based on the power grid business domain data of the key customer, and then the key customer portrait data is determined based on the data logic model, and the key customer portrait data is displayed on the target terminal, thereby realizing the visualization of the key customer portrait, which is conducive to providing diversified service means for key customer visit service work, deepening the power grid staff's understanding of key customers, improving their marketing work ability and key customer service level, thereby further improving the business work efficiency and service quality of the power grid staff, and improving customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of the key customer visit portrait visualization method provided by the present invention.

[0018] Figure 2 It is a structural schematic diagram of the VIP customer visit portrait visualization device provided by the present invention.

[0019] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be noted that in the description of the present invention, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. The orientation or position relationship indicated by the terms "upper", "lower" and the like is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0022] The terms "first", "second", etc. in the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" means at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0023] It should be noted that all actions of acquiring signals, information or data in the present invention are performed in compliance with the corresponding data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0024] See also Figure 1 , Figure 1 : is a flow chart of a method for visualizing a visit portrait of a key customer provided by the present invention. In this embodiment, the method for visualizing a visit portrait of a key customer includes steps S110 to S130, and each step is specifically as follows: S110: Obtaining power grid business domain data of major customers.

[0025] Specifically, for each major customer, the original power grid business domain data of the major customer can be obtained through the business system, and data analysis and data preprocessing are performed on the original power grid business domain data to obtain the power grid business domain data of the major customer.

[0026] S120: Build a data logic model based on the power grid business domain data.

[0027] After obtaining the power grid business domain data of major customers, the power grid business domain data can be modeled according to the data encoding rules and data classification rules of the power grid business domain data to obtain a data logic model of the power grid business domain data.

[0028] Optionally, the data logic model is a mathematical model.

[0029] S130: Determine key customer portrait data of key customers based on the data logic model, and display the key customer portrait data on the target terminal.

[0030] The method for visualizing the portrait of a key customer visit provided in this embodiment obtains the power grid business domain data of the key customer; constructs a data logic model based on the power grid business domain data; determines the key customer portrait data of the key customer based on the data logic model, and displays the key customer portrait data on the target terminal. In the above manner, a data logic model is constructed based on the power grid business domain data of the key customer, and then the key customer portrait data is determined based on the data logic model, and the key customer portrait data is displayed on the target terminal, thereby realizing the visualization of the key customer portrait, which is conducive to providing diversified service means for key customer visit service work, deepening the power grid staff's understanding of key customers, improving their marketing work ability and key customer service level, thereby further improving the business work efficiency and service quality of the power grid staff, and improving customer satisfaction.

[0031] In some embodiments, obtaining the power grid business domain data of a large customer includes: obtaining the original power grid business domain data of the large customer; performing data validity analysis and data quality analysis on the original power grid business domain data to obtain data analysis results; and based on the data analysis results, performing data cleaning processing on the original power grid business domain data to obtain the power grid business domain data.

[0032] Specifically, for each major customer, the original power grid business domain data of the major customer can be obtained through the business system, and data validity analysis and data quality analysis can be performed on the original power grid business domain data to obtain data analysis results.

[0033] Data validity analysis and data quality analysis are the basis of model construction and the key to ensuring stable performance and accurate prediction of data logic models. They mainly include two aspects: data quality and data feature analysis.

[0034] Understandably, there may be a large amount of dirty data or data that cannot be used directly in the original power grid business domain data. Therefore, it is necessary to check whether there is dirty data in the original power grid business domain data through data validity analysis and data quality analysis.

[0035] Optionally, dirty data refers to data that does not meet requirements and cannot be directly analyzed accordingly, such as missing values, abnormal values, inconsistent values, duplicate data, and data containing special symbols.

[0036] Optionally, data feature analysis can be achieved by drawing charts, calculating feature quantities of certain data, etc.; data feature analysis includes but is not limited to quantitative or qualitative data distribution analysis, statistical analysis (central tendency statistics, dispersion statistics, and distribution shape statistics), periodic analysis (annual periodic trends and seasonal periodic trends, etc.), data contribution analysis, and data correlation analysis.

[0037] Furthermore, based on the data analysis results, the original power grid business domain data is cleansed to obtain the power grid business domain data.

[0038] Specifically, after obtaining the data analysis results, the missing values ​​in the dirty data can be filled, the abnormal values ​​can be replaced or deleted, and the inconsistent values, duplicate data, and data containing special symbols can be corrected; after completing the above processing, the original power grid business domain data can be reduced in dimensionality, and statistical hypothesis, test, and verification methods can be used to ensure the accuracy of the model.

[0039] In some embodiments, the power grid business domain data includes one or more of marketing archive data of major customers, electricity consumption data, electricity bill data, customer information, meter reading data, power grid operation status data, power grid energy consumption data, and customer tag data; based on the power grid business domain data, a data logical model is constructed, including: determining data encoding rules and data classification rules for the power grid business domain data; based on the data encoding rules and data classification rules, modeling the power grid business domain data to obtain a data logical model for the power grid business domain data; storing the data logical model in a relational database and / or a big data platform; wherein the data logical model includes a hierarchical model, a network model, a relational model, and an object-oriented model.

[0040] The power grid business domain data includes one or more of the following: marketing archive data of major customers, electricity consumption data, electricity fee data, customer information, meter reading data, power grid operation status data, power grid energy consumption data, and customer tag data.

[0041] Specifically, after the power grid service domain data is obtained, the data encoding rules and data classification rules of the power grid service domain data may be determined.

[0042] Optionally, the data encoding rules include uniqueness rules, completeness rules, accuracy rules, adaptability rules, inheritance rules, extensibility rules, stability rules and ease of use rules.

[0043] The uniqueness rule means that one code in the same system can only represent one object, avoiding the phenomenon of one number having multiple codes or one code having multiple objects.

[0044] The integrity rule states that all data objects should be encoded to maintain the integrity of the encoding.

[0045] The accuracy rule means that the definition and description of the coding must be concise and accurate, and cannot be ambiguous; attributes that are prone to change should be described through independent data items.

[0046] Adaptive rules mean that when designing data encoding rules, the needs of various departments and business domain users of the power grid should be fully considered.

[0047] The inheritance rule refers to the use of commonly used codes as much as possible, referring to internal corporate or industry coding standards.

[0048] The scalability rule means that when designing the coding structure and bit capacity, future changes in business, operations, and management should be taken into consideration, and space for coding expansion should be reserved.

[0049] The stability rule means that the coding must be unified and consistent. Once the coding and coding rules are determined, they cannot be easily changed to avoid data confusion and loss.

[0050] The usability rule means that the code should be as short as possible, the rules should be as simple as possible, easy to associate and enter, and convenient for data maintenance and use.

[0051] Optionally, the data classification rules are as follows: (1) Dimensional consistency: In the same category structure, the division dimensions and standards must be completely unified, and classification methods with different standards are not allowed.

[0052] (2) Unified data granularity: In a complete category structure, we should try to avoid large granularity differences and overlaps in category divisions at the same level, because the different levels of the category structure will increase the difficulty of subsequent management.

[0053] (3) Avoid the emergence of “other categories”: The classification dimensions and completeness of categories should be fully considered to avoid coding confusion caused by the emergence of “other categories”.

[0054] (4) Allowing multiple category structures to exist: While establishing a category structure, allowing the old category structure to continue to be used can not only ensure the scientific nature and integrity of the category structure, but also follow the existing business specifications and enhance the controllability of project implementation.

[0055] Furthermore, based on the data encoding rules and data classification rules, the power grid business domain data is modeled to obtain a data logic model of the power grid business domain data.

[0056] Optionally, the modeling methods include but are not limited to entity-relationship modeling, dimensional modeling, and hybrid modeling.

[0057] Entity-relationship modeling is the main method of data modeling. It describes data by converting entities in the real world (such as electricity users, electricity bills, meter reading records, etc.) and the relationships between entities into data structures. The relational model (ERM) obtained by entity-relationship modeling can clearly show the relationship between entities, thus helping power grid workers better understand the data.

[0058] Dimensional modeling is a fact-based modeling method that divides data into two parts: facts and dimensions. Facts are data records of business events, such as electricity bill information, power consumption information, etc., while dimensions describe the environment or characteristics of these events, such as time, location, etc. The main advantage of dimensional modeling is that it is highly flexible, allowing users to analyze data in different dimensions.

[0059] In actual data model construction, entity-relationship modeling and dimensional modeling methods are usually combined to fully utilize the advantages of both methods. This hybrid modeling method can provide richer data analysis and query capabilities.

[0060] In this embodiment, the data logic model includes a hierarchical model, a network model, a relational model, and an object-oriented model.

[0061] The Hierarchical Model is the earliest data model that uses a hierarchical data structure to organize data. The Hierarchical Model uses record types to describe entities, fields to describe attributes, and lines between nodes to represent the relationships between entities. It can simply and intuitively represent entities, entity attributes, and one-to-many relationships between entities in the information world.

[0062] A data model that meets the following two conditions is called a hierarchical model: (1) There is only one node without a parent node (the parent node is also called the father node), in which case the node is called the root node; (2) All nodes other than the root node have one and only one parent node.

[0063] Hierarchical models can naturally represent family structures, administrative organizational structures, etc.

[0064] The advantages of the hierarchical model include but are not limited to: simple, clear and easy to understand structure; simple connections between nodes and high query efficiency.

[0065] The disadvantages of the hierarchical model include but are not limited to: it cannot represent the situation where a node has multiple parents; it cannot directly represent many-to-many relationships, and the many-to-many relationships need to be decomposed into multiple one-to-many relationships. The commonly used decomposition methods are the redundant node method and the virtual node method; there are many restrictions on insertion and deletion. For example, if the parent node is deleted, the corresponding child node will also be deleted at the same time.

[0066] The network model uses a network structure, which can directly describe the situation where a node has multiple parent nodes and there are many-to-many connections between nodes.

[0067] A data model that satisfies the following two conditions is called a network model: (1) it is allowed to have more than one node without a parent node; (2) a node can have more than one parent node.

[0068] The advantages of the network model include but are not limited to: good performance and high access efficiency; the connections between nodes are more flexible, good at expressing complex connections between objects, and more suitable for describing the objective world.

[0069] The disadvantages of the network model include but are not limited to: complex structure, difficult to understand and expand quickly; the connection between entities is essentially expressed through access paths, which is cumbersome when accessing data.

[0070] In the relational model, entities and the connections between entities are represented by relationships, which are used to describe one-to-one, one-to-many, and many-to-many connections.

[0071] The advantages of the relational model include but are not limited to: (1) Easy to understand and manage: The relational model uses an intuitive table structure to represent data, which makes data management and understanding relatively simple; (2) Data consistency: By defining foreign key relationships between different tables, the relational model can maintain data consistency; (3) Flexibility: The relational model can flexibly adapt to changes in data requirements by adding, deleting, and modifying tables and columns; (4) Strict mathematical foundation: The relational model is built on strict mathematical concepts and has a high level of abstraction, making the model simple and clear, easy to understand and use.

[0072] The disadvantages of the relational model include but are not limited to: (1) Performance issues: When processing large amounts of data, the relational model may face performance issues, especially when executing complex queries; (2) Data redundancy: There is a problem of data redundancy in the relational model, which may lead to waste of storage space and abnormal data updates; (3) Complexity: The design and optimization of the relational model requires certain skills and experience, and has high requirements for developers; (4) Semantic overload: When expressing data and relationships between data, the relational model lacks sufficient mechanisms to distinguish between entities and relationships, and to distinguish different types of relationships between different entities, which leads to semantic overload.

[0073] The object-oriented model supports a large number of analytical applications with its relatively abstract, logically unified and robust structure, and is an important foundation for realizing business intelligence.

[0074] The advantages of the object-oriented model include, but are not limited to: (1) Suitable for processing a variety of data types: Unlike traditional databases (such as hierarchical, network or relational), object-oriented databases are suitable for storing different types of data, such as pictures, sounds, videos, including text, numbers, etc.; (2) Combination of object-oriented programming and database technology: The object-oriented data model combines object-oriented programming with database technology, thus providing an integrated application development system; (3) Improving development efficiency: The object-oriented data model provides powerful features, such as inheritance, polymorphism and dynamic binding, which allow users to construct objects and provide solutions without writing specific object code. These features can effectively improve the development efficiency of database application developers; (4) Improving data access: The object-oriented data model explicitly represents relationships and supports both navigational and associative information access, which can improve data access performance more than relationships based on relationship values.

[0075] The disadvantages of the object-oriented data model include but are not limited to: (1) No precise definition: This is because different products and prototypes may consider different aspects; (2) Difficulty in maintenance: As the information needs of the organization change, the definition of the object also needs to be changed and the existing database needs to be migrated to complete the definition of the new object. When changing the definition of the object and migrating the database, it may face real challenges; (3) Not suitable for all applications: Object-oriented data models are used for applications that need to manage complex relationships between data objects. They are particularly suitable for specific applications, such as engineering, e-commerce, medical, etc., but not for all applications. When used in general applications, their performance will be reduced and require high processing power.

[0076] Furthermore, the data logic model is encapsulated into a relational database and / or a big data platform.

[0077] Optionally, the data logic model is encapsulated into a relational database and / or a big data platform from multiple encapsulation perspectives, such as user type, channel content, business scenario, etc., and the encapsulation interface is configured.

[0078] In some embodiments, based on the data logic model, the key customer portrait data of the key customer is determined, and the key customer portrait data is displayed on the target terminal, including: in response to the display instruction of the target terminal, based on the display instruction, obtaining the data logic model from the relational database and / or the big data platform; based on the data logic model, performing user feature characterization on the key customer and determining the key customer portrait data of the key customer; based on the key customer portrait data, generating visualization results; and displaying the visualization results on the visualization interface of the target terminal.

[0079] The data logic model can be encapsulated into a relational database and / or a big data platform. The data logic model contains basic tags and analytical knowledge tags, which can fully describe user characteristics and push data in real time based on interface configuration, so that user portrait data can be updated in real time to the visualization interface of the target terminal for display, and operational and marketing information issues can be fed back in real time to ensure the timeliness of data application. The encapsulation of the visualization interface relies on user portraits, and different customer portraits can be visualized, centralized activity operations, and a thousand-faced operational effect can be achieved.

[0080] Optionally, the visualization interface of the target terminal involves basic information of major customers, electricity fee information, electricity consumption information, and operation plan visualization function modules, so that power grid staff can quickly understand major customer information and improve customer service capabilities.

[0081] Specifically, in response to a display instruction of the target terminal, a data logic model is obtained from a relational database and / or a big data platform based on the display instruction.

[0082] Furthermore, based on the basic tags and analytical knowledge tags of the data logic model, user characteristics of key customers are characterized and key customer portrait data of key customers is determined.

[0083] Furthermore, based on the key customer portrait data, visualization results are generated; and the visualization results are displayed on the visualization interface of the target terminal.

[0084] In some embodiments, the key customer portrait data includes at least two of chart type data, graphic type data, map type data, and text type data; based on the key customer portrait data, a visualization result is generated, including: based on the data type of the key customer portrait data, a visualization result of the key customer portrait data is generated; wherein the visualization result includes one or more of interactive reports of the key customer portrait data, data description information, infographics, heat maps, network diagrams, and time series visualization data.

[0085] Common visualization methods include chart visualization, graph visualization, map visualization, and text visualization. Different visualization methods have their own specific application scenarios and characteristics. The specific method to be selected can be selected based on the actual data type.

[0086] Chart visualization is a common way of presenting data. By displaying data in the form of graphics, it helps us quickly understand and compare different data. Common chart visualizations include bar charts, line charts, pie charts, scatter plots, etc. For example, a bar chart can be used to compare the values ​​of different categories, a line chart can be used to show the trend of time series data, a pie chart can be used to show the proportional structure of data, and a scatter plot can be used to show the relationship between two variables.

[0087] Graph visualization is to display data in a geometric form. Unlike chart visualization, graph visualization usually focuses more on expressing the spatial distribution and topological relationship of data. Common graph visualizations include heat maps, tree maps, Sankey diagrams, etc. For example, heat maps can be used to display the density distribution of data, tree maps can be used to display hierarchical structures or classified data, and Sankey diagrams can be used to display the process and flow of time series data.

[0088] Map visualization is a technology that presents geographic data on a map in a graphical way. It can help users better understand the distribution characteristics and trends of geographic data. Common map visualizations include heat maps, point maps, line maps, etc. For example, heat maps can be used to show the value size and density distribution of different regions, point maps can be used to show the discrete distribution of geographic data, and line maps can be used to show the continuous distribution and flow of geographic data.

[0089] Specifically, after obtaining the key customer portrait data, a visualization result of the key customer portrait data is generated based on the data type of the key customer portrait data.

[0090] Among them, the visualization results include one or more of interactive reports of key customer portrait data, data description information, infographics, heat maps, network diagrams, and time series visualization data.

[0091] Different from the static presentation of traditional charts, interactive dashboards do not just display information, but increase the interactivity through charts to enhance the expression of key information or the overall picture. Users can interact with the charts to obtain deeper analysis and information.

[0092] Interactive charts can give users a sense of control and provide sufficiently convenient filtering components. Together with mouse hover, click, box selection and other operations, it is convenient for users to view more data information, quickly locate content of interest, sort, highlight, and reduce the dimension of data according to specific variables.

[0093] Data description information can be a story, that is, storytelling with data. Combining data visualization with fascinating storytelling techniques to obtain data description information in the form of a story can guide users to understand data-driven stories and make complex information easier to understand.

[0094] Information graphics (Infographics) usually refers to a higher standard of graphics, both for summarizing and for expressing details, and there are variations in dimensionality and quantity. Infographics combine visuals, text, and data to present complex information in a concise and visually appealing way.

[0095] Heatmaps are widely used in various big data analysis scenarios due to their rich color variations and vivid information expression. At the same time, the R language, which is dedicated to big data statistical analysis, drawing, and visualization, also provides a series of powerful and comprehensive function libraries and toolkits in visualization. Heatmaps use color gradients to represent the intensity of data values, providing a quick and intuitive way to identify hot spots, clusters, and outliers.

[0096] Network diagrams can be used to represent the relationships and connections between entities. Network diagrams can intuitively display multidimensional data sets, see which variables have similar values, whether there are outliers between variables, and are suitable for seeing which variables have higher or lower scores in the data set. They can well demonstrate performance and advantages, especially suitable for showing multiple key features of a data set, or showing the comparison of multiple key features of a data set and standard values. They are generally suitable for comparing the values ​​of multiple data in multiple dimensions. They can easily visualize social networks, organizational structures, or complex systems. By visualizing nodes and edges, network diagrams help to discover hidden patterns and dependencies.

[0097] Time Series Visualization is essential for analyzing constantly changing data. A time series (or dynamic series) is a series of values ​​of the same statistical indicator arranged in the order of their occurrence. The main purpose of time series analysis is to make predictions about the future based on existing historical data. Most of the power grid data is given in the form of a time series. Depending on the observation time, the time in the time series can be years, quarters, months, or any other time form. Line charts, area charts, and stacked charts are common techniques for showing trends, seasonal patterns, and data fluctuations over a specific period of time.

[0098] In some embodiments, before the visualization results are displayed on the visualization interface of the target terminal, it also includes: obtaining customer demand information; based on the customer demand information, performing prototype design of the visualization interface to obtain prototype design results of the visualization interface; the prototype design results include low-fidelity prototype design results, medium-fidelity prototype design results and high-fidelity prototype design results of the visualization interface; based on the prototype design results, generating a visualization interface through a prototype design tool.

[0099] Understandably, before visual presentation, prototyping is required to generate a visual interface.

[0100] Prototype design plays a vital role in the field of interface development and user experience. By designing interface prototypes, customer needs can be visualized, and the feasibility of strategies can be quickly tested and verified.

[0101] In this embodiment, it is necessary to design a visual interface prototype for modules such as key customer basic information, electricity fee information, power information, operating status, and energy consumption level to provide information and strategy reference for key customer visit services.

[0102] Specifically, customer demand information is obtained; based on the customer demand information, a prototype design of a visualization interface is performed to obtain a prototype design result of the visualization interface.

[0103] Among them, the prototype design results include low-fidelity prototype design results, medium-fidelity prototype design results and high-fidelity prototype design results of the visual interface.

[0104] Low-fidelity prototypes present preliminary concepts and ideas. They can use different media and have prototype interfaces of different sizes. Low-fidelity prototypes are the simplest and lowest cost.

[0105] Optionally, low-fidelity prototypes include sketches, paper prototypes, wireframes, etc.

[0106] Sketches are the initial ideas or assumptions. Based on the business process and information architecture, the core user interface is drawn. Sketches can be whiteboard sketches, paper sketches, etc. By drawing a large number of different versions, different ways to solve interface interactions can be considered.

[0107] Information architecture and sketches can quickly evolve into wireframes. Wireframes are static page layouts that better visualize information architecture by placing different elements on the screen. Low-fidelity wireframes have no visual design, and the interactions are simple jump events. Most use grayscale and placeholders to indicate content.

[0108] Mid-fidelity prototypes look like the final product in some ways, balancing cost and value, combining visual, interactive, and presentation media.

[0109] Optionally, the mid-fidelity prototype includes a clickable prototype, a coded prototype, etc. The user can complete a closed loop of a task based on the mid-fidelity prototype, that is, complete the work task by interacting with the prototype interface.

[0110] Clickable prototypes generally use prototyping software to build pages and add interactive elements to each page so that the pages can interact with each other.

[0111] Coding prototypes can be created in the browser and built using technologies such as HTML and CSS.

[0112] Furthermore, based on the prototype design results, a visual interface is generated through a prototype design tool.

[0113] The present invention also provides a device for visualizing the visit portrait of a major customer. Figure 2 , Figure 2 2 is a schematic diagram of the structure of the VIP customer visit portrait visualization device provided by the present invention. In this embodiment, the VIP customer visit portrait visualization device includes an acquisition module 210 , a construction module 220 and a visualization module 230 .

[0114] The acquisition module 210 is used to acquire the power grid business domain data of the major customers.

[0115] The construction module 220 is used to construct a data logic model based on the power grid business domain data.

[0116] The visualization module 230 is used to determine the key customer portrait data of key customers based on the data logic model, and display the key customer portrait data on the target terminal.

[0117] In some embodiments, the acquisition module 210 is used to obtain the original power grid business domain data of large customers; perform data validity analysis and data quality analysis on the original power grid business domain data to obtain data analysis results; based on the data analysis results, perform data cleaning processing on the original power grid business domain data to obtain power grid business domain data.

[0118] In some embodiments, the power grid business domain data includes one or more of marketing profile data of major customers, electricity consumption data, electricity bill data, customer information, meter reading data, power grid operation status data, power grid energy consumption data, and customer tag data.

[0119] Construction module 220 is used to determine the data encoding rules and data classification rules of the power grid business domain data; based on the data encoding rules and data classification rules, the power grid business domain data is modeled to obtain the data logic model of the power grid business domain data; the data logic model is stored in a relational database and / or a big data platform; wherein the data logic model includes a hierarchical model, a network model, a relational model and an object-oriented model.

[0120] In some embodiments, the visualization module 230 is used to respond to the display instructions of the target terminal, and based on the display instructions, obtain a data logic model from a relational database and / or a big data platform; based on the data logic model, perform user feature characterization on key customers and determine key customer portrait data of the key customers; based on the key customer portrait data, generate visualization results; and display the visualization results on the visualization interface of the target terminal.

[0121] In some embodiments, the key customer portrait data includes at least two of chart type data, graphic type data, map type data, and text type data.

[0122] The visualization module 230 is used to generate visualization results of the key customer portrait data based on the data type of the key customer portrait data; wherein the visualization results include one or more of interactive reports of the key customer portrait data, data description information, information charts, heat maps, network diagrams, and time series visualization data.

[0123] In some embodiments, the visualization module 230 is used to obtain customer demand information; based on the customer demand information, perform prototype design of the visualization interface to obtain prototype design results of the visualization interface; the prototype design results include low-fidelity prototype design results, medium-fidelity prototype design results and high-fidelity prototype design results of the visualization interface; based on the prototype design results, generate a visualization interface through a prototype design tool.

[0124] The invention also provides an electronic device. Figure 3 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 3As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the visualization method of the key customer visit portrait.

[0125] In addition, the logic instructions in the above-mentioned memory 330 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 such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for 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 invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0126] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for visualizing the portrait of a key customer visit provided by the above-mentioned methods is implemented.

[0127] 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 key customer visit portrait visualization method provided by the above methods.

[0128] 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.

[0129] 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, 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.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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 invention.

Claims

1. A method for visualizing a visit profile of a major customer, characterized in that: include: Obtain power grid business domain data from major customers; Based on the power grid business domain data, construct a data logic model; Based on the data logic model, the key customer portrait data of the key customer is determined, and the key customer portrait data is displayed on the target terminal.

2. The method for visualizing the visit portrait of key customers according to claim 1 is characterized in that: The obtaining of the power grid business domain data of the major customers includes: Obtaining original power grid business domain data of the major customer; Performing data validity analysis and data quality analysis on the original power grid business domain data to obtain data analysis results; Based on the data analysis result, data cleaning is performed on the original power grid business domain data to obtain the power grid business domain data.

3. The method for visualizing the visit portrait of key customers according to claim 1 is characterized in that: The power grid business domain data includes one or more of the marketing archive data, power consumption data, electricity fee data, customer information, meter reading data, power grid operation status data, power grid energy consumption data, and customer tag data of the major customer; The step of constructing a data logic model based on the power grid business domain data includes: Determine data encoding rules and data classification rules for the power grid business domain data; Modeling the power grid business domain data based on the data encoding rule and the data classification rule to obtain a data logic model of the power grid business domain data; Storing the data logic model in a relational database and / or a big data platform; The data logic model includes a hierarchical model, a network model, a relational model and an object-oriented model.

4. The method for visualizing the visit portrait of key customers according to claim 3 is characterized in that: Determining the key customer portrait data of the key customer based on the data logic model, and displaying the key customer portrait data on the target terminal, includes: In response to a display instruction of the target terminal, based on the display instruction, acquiring the data logic model from the relational database and / or the big data platform; Based on the data logic model, the user characteristics of the major customer are characterized to determine the major customer portrait data of the major customer; Generate visualization results based on the key customer portrait data; The visualization result is displayed on the visualization interface of the target terminal.

5. The method for visualizing the visit portrait of key customers according to claim 4 is characterized in that: The key customer portrait data includes at least two of chart type data, graphic type data, map type data, and text type data; The generating of visualization results based on the key customer portrait data includes: Based on the data type of the key customer portrait data, generate a visualization result of the key customer portrait data; Among them, the visualization results include one or more of the interactive reports of the key customer portrait data, data description information, information charts, heat maps, network diagrams, and time series visualization data.

6. The method for visualizing the visit portrait of key customers according to claim 4 is characterized in that: Before displaying the visualization result on the visualization interface of the target terminal, the method further includes: Obtain information about customer needs; Based on the customer demand information, a prototype design of the visualization interface is performed to obtain a prototype design result of the visualization interface; the prototype design result includes a low-fidelity prototype design result, a medium-fidelity prototype design result, and a high-fidelity prototype design result of the visualization interface; Based on the prototype design result, the visualization interface is generated by a prototype design tool.

7. A visualization device for visiting portraits of major customers, characterized in that: include: The acquisition module is used to obtain the power grid business domain data of major customers; A construction module, used to construct a data logic model based on the power grid business domain data; A visualization module is used to determine the key customer portrait data of the key customer based on the data logic model, and display the key customer portrait data on the target terminal.

8. 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 computer program, the method for visualizing the portrait of a key customer visit as described in any one of claims 1 to 6 is implemented.

9. 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 method for visualizing a key customer visit portrait as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for visualizing a key customer visit portrait as described in any one of claims 1 to 6 is implemented.

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