Accurate information pushing method and system based on user portrait

Abstract

The invention provides an accurate information pushing method and system based on a user portrait, and relates to the technical field of data processing, and the method comprises the steps: obtaining enterprise behavior data and platform service data of an economic comprehensive service platform; according to the enterprise behavior data, carrying out industrial ecological cluster classification on the enterprise, according to the platform service data, extracting dynamic demand characteristics of an industrial ecological cluster, and according to the dynamic demand characteristics, constructing a cluster user portrait of the industrial ecological cluster; extracting service features of different services according to the platform service data; matching the service features with the cluster user portraits based on technical standards corresponding to the services; and pushing the service information to the industrial ecological cluster of which the matching degree reaches a preset condition threshold. Service information accurate pushing based on industrial ecological group dynamic demand perception can be realized, services are recommended to specific industrial groups in a targeted manner, and the accuracy and coverage range of service recommendation are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for accurately pushing information based on user portraits. Background Art

[0002] Intelligent recommendation technology is in a stage of rapid development. With the advancement of big data, cloud computing and artificial intelligence (AI), especially the development of machine learning and deep learning, intelligent recommendation systems have been able to process more complex data sets and provide more accurate personalized services.

[0003] Intelligent recommendation technology based on industrial ecological clusters and service sets is a complex but effective strategy for improving the accuracy of personalized recommendation of services and products. This technology combines the interaction patterns, business processes, and user preferences of various participants in the industrial ecosystem to optimize the recommendation effect through data analysis and machine learning algorithms. Services based on industrial ecological clusters often have strong context dependence. For example, the recommended content should be different according to seasonal changes, holiday promotions, or the user's geographical location. The recommendation system must have strong context awareness and quickly adjust the recommendation strategy.

[0004] Therefore, how to provide a method for accurately pushing information based on full perception of the needs of industrial ecological clusters is an urgent problem that needs to be solved. Summary of the invention

[0005] In order to improve the above problems, the present invention provides a method and system for accurately pushing information based on user portraits.

[0006] A first aspect of an embodiment of the present invention provides a method for accurately pushing information based on a user portrait, the method comprising: Obtain enterprise behavior data and platform service data from the comprehensive economic service platform; Classify enterprises into industrial ecological clusters according to the enterprise behavior data, extract dynamic demand characteristics of industrial ecological clusters according to platform service data of various industrial ecological clusters within a certain time range, and construct cluster user portraits of industrial ecological clusters according to the dynamic demand characteristics; Extract service features of different services based on platform service data; Matching the service characteristics with the cluster user profile based on the technical standards corresponding to the service; Push service information to industrial ecological clusters whose matching degree reaches the preset condition threshold.

[0007] Optionally, the step of classifying enterprises into industrial ecological clusters according to the enterprise behavior data specifically includes: Extracting basic information, business information, product and service information, and activity information of the enterprise from the enterprise behavior data; Input the extracted information into the pre-trained industrial ecological cluster classification model; Obtain the results of industrial ecological cluster classification.

[0008] Optionally, the step of extracting dynamic demand characteristics of industrial ecological clusters based on platform service data of various industrial ecological clusters within a certain time range specifically includes: For each industrial ecological cluster, extract service demand information, service demand time and service demand target within a certain time range from the platform service data; Obtain the industry event information and industry event time corresponding to each industrial ecological cluster within the same time range from the comprehensive economic service platform; According to the time sequence of service demand time and industry event time, the service demand information and the industry event information are associated; The dynamic demand characteristics of each industrial ecological cluster are extracted based on the association results.

[0009] Optionally, the step of constructing a cluster user portrait of the industrial ecological cluster according to the dynamic demand characteristics specifically includes: Obtaining cluster characteristics of each industrial ecological cluster classification based on the industrial ecological cluster classification model; Extract the common characteristics of enterprises belonging to the same industrial ecological cluster classification based on the classification results of the enterprises; A cluster user profile of each industrial ecological cluster is constructed by combining the cluster characteristics, the common characteristics of the enterprises and the dynamic demand characteristics.

[0010] Optionally, the step of extracting service features of different services according to the platform service data specifically includes: Obtain the service source, service type, service content and service timeliness of different services; Feature extraction is performed on the acquired information to obtain service features.

[0011] Optionally, the step of matching the service feature with the cluster user profile based on the technical standard corresponding to the service specifically includes: Determining whether there are technical standards associated with the service based on the service content; If it exists, extract the standard parameters of the technical standard associated with the service; Matching the standard parameters with the service demand targets, and screening matching industrial ecological clusters; The service characteristics of the successfully matched services are further matched with the cluster user portraits of the screened industrial ecological clusters.

[0012] Optionally, the step of matching the service feature with the cluster user profile based on the technical standard corresponding to the service specifically further includes: Establish binding relationships between services based on the associations between the extracted technical standards; The service characteristics of other services bound to the successfully matched service will also be further matched with the cluster user portraits of the screened industrial ecological clusters.

[0013] Optionally, the step of further matching the service characteristics with the cluster user portrait of the screened industrial ecological cluster specifically includes: Matching the service demand information with the service type and service content; The service demand time is matched with the service effectiveness time.

[0014] Optionally, the step of pushing the service information to the industrial ecological cluster whose matching degree reaches a preset condition threshold specifically includes: For industrial ecological clusters whose matching degree reaches the preset condition threshold but does not reach the preset preferred threshold, the service information of multiple bound services will be pushed; For industrial ecological clusters whose matching degree reaches the preset optimal threshold, only the service information of the services involved in the matching will be pushed.

[0015] A second aspect of an embodiment of the present invention provides a user portrait-based accurate information push system, including: A data acquisition unit, used to acquire enterprise behavior data and platform service data of the economic comprehensive service platform; A user portrait unit is used to classify enterprises into industrial ecological clusters according to the enterprise behavior data, extract dynamic demand characteristics of industrial ecological clusters according to platform service data of various industrial ecological clusters within a certain time range, and construct cluster user portraits of industrial ecological clusters according to the dynamic demand characteristics; A feature extraction unit, used to extract service features of different services based on platform service data; A feature matching unit, used to match the service feature with the cluster user profile based on the technical standard corresponding to the service; The information push unit is used to push service information to industrial ecological clusters whose matching degree reaches a preset condition threshold.

[0016] In summary, the present invention provides a method and system for accurate information push based on user portraits, which can construct industrial ecological clusters, service sets and service feature libraries according to basic data such as enterprise data and platform service data, establish a mapping relationship between industrial ecological clusters and service sets according to service-related technical standards, and recommend the service content used in the service set to all enterprises in the industrial ecological group, thereby realizing accurate push of service information based on dynamic demand perception of industrial ecological groups, recommending services to specific industrial groups in a targeted manner, and improving the accuracy and coverage of service recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A method flow chart of a method for accurately pushing information based on user portraits according to an embodiment of the present invention; Figure 2 This is a functional module block diagram of a user portrait-based accurate information push system according to an embodiment of the present invention.

[0019] Reference numerals: Data acquisition unit 110; user profiling unit 120; feature extraction unit 130; feature matching unit 140; information pushing unit 150. DETAILED DESCRIPTION

[0020] Intelligent recommendation technology based on industrial ecological clusters and service sets is a complex but effective strategy for improving the accuracy of personalized recommendation of services and products. This technology combines the interaction patterns, business processes, and user preferences of various participants in the industrial ecosystem to optimize the recommendation effect through data analysis and machine learning algorithms. Services based on industrial ecological clusters often have strong context dependence. For example, the recommended content should be different according to seasonal changes, holiday promotions, or the user's geographical location. The recommendation system must have strong context awareness and quickly adjust the recommendation strategy.

[0021] Therefore, how to provide a method for accurately pushing information based on full perception of the needs of industrial ecological clusters is an urgent problem that needs to be solved.

[0022] In view of this, the designers of the present invention have designed a method and system for accurately pushing information based on user portraits.

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0025] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0026] In the description of the present invention, it should be noted that the terms "top", "bottom", "inside", "outside", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the invented product is usually placed when in use, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply 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 limiting the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0027] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" 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 indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0028] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0029] The following is a detailed description of a method for accurately pushing information based on user portraits provided in this embodiment.

[0030] See also Figure 1 This embodiment provides a method for accurately pushing information based on user portraits, the method comprising: Step S101, obtaining the enterprise behavior data and platform service data of the comprehensive economic service platform.

[0031] The economic comprehensive service platform is a digital platform that integrates multiple functions and services, aiming to provide comprehensive economic information and decision-making support for the government, enterprises and the public. Such platforms usually integrate macroeconomic data, industry trends, enterprise information, policies and regulations, and provide a series of tools and services such as data analysis, market forecasts, and consulting services.

[0032] The enterprise behavior data on the comprehensive economic service platform reflects the various interaction records generated by enterprises on the platform and in their business activities. These data can provide an important basis for enterprise profiling, demand analysis, and personalized service push. Enterprise behavior data includes basic information, transaction activities, financial performance, cooperation network, innovation and development, marketing promotion and other aspects of the information generated by enterprises in the production and operation process.

[0033] Platform service data covers a range of services provided by the platform itself to enterprises and other users and their usage. This helps to evaluate the service quality and efficiency of the platform, and also provides a reference for improving existing services and developing new services. Platform service data includes information services, transaction support, training and education, technical support, community interaction and other information.

[0034] It can be seen that the comprehensive economic service platform not only provides rich resources and services for enterprises, but also accumulates a large amount of valuable data. By obtaining the enterprise behavior data and platform service data of the comprehensive economic service platform and effectively utilizing these data, it can play a huge role in accurately pushing information.

[0035] As a preferred implementation method, external information such as macroeconomic data and changes in policies and regulations can also be obtained on the comprehensive economic service platform, providing more dimensional support for the subsequent dynamic perception of industrial ecological cluster service needs.

[0036] It should be noted that although the comprehensive economic service platform also provides data analysis tools and services, its functions are usually more general and not customized for specific application scenarios, so the analysis results that can be achieved are limited.

[0037] Step S102, classify the enterprises into industrial ecological clusters according to the enterprise behavior data, extract the dynamic demand characteristics of the industrial ecological clusters according to the platform service data of various industrial ecological clusters within a certain time range, and construct a cluster user portrait of the industrial ecological cluster according to the dynamic demand characteristics.

[0038] Industrial ecological cluster refers to a network of interconnected enterprises, institutions and individuals in a specific region or virtual environment, which work together to promote innovation and improve competitiveness. Industrial ecological cluster emphasizes the cooperative and competitive relationship between multiple enterprises and service providers. By establishing a network consisting of manufacturers, suppliers, distributors, retailers and technical service providers, resource sharing and innovation capabilities can be promoted. In this scenario, the recommendation logic can be optimized by analyzing the interaction patterns between these entities to provide them with services that better meet their needs.

[0039] To achieve accurate information push for industrial ecological clusters, the premise is to classify the industrial ecological clusters. Specifically, as a preferred method of the embodiment of the present invention, the method for classifying industrial ecological clusters in step S102 specifically includes: Extracting basic information, business information, product and service information, and activity information of the enterprise from the enterprise behavior data; Input the extracted information into the pre-trained industrial ecological cluster classification model; Obtain the results of industrial ecological cluster classification.

[0040] For specific classification, it can be completed with the help of existing industrial ecological cluster classification models, such as K-means clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), spectral clustering, hierarchical clustering, etc. The basic information, business information, product and service information, and activity behavior information of the enterprise will be extracted from the enterprise behavior data and input into the existing industrial ecological cluster classification model to realize the industrial ecological cluster classification of enterprises on the current economic comprehensive service platform.

[0041] In addition, you can also build a classification model based on customized classification requirements. The specific implementation method is as follows: 1. Data collection and preprocessing: Multi-source data integration: Internal operation data: Obtain detailed operating data from the company's ERP system, CRM system, financial statements, etc.

[0042] Transaction records: including purchase orders, sales invoices, logistics information and other data reflecting interactions between enterprises.

[0043] External data: Integrate macro-environmental factors such as industry reports, market research results, and changes in policies and regulations.

[0044] Data cleaning: Remove duplicates, fill in missing values, and correct erroneous data to ensure data quality.

[0045] Standardize data formats from different sources to facilitate subsequent analysis.

[0046] 2. Feature Engineering: Define the base features: Enterprise attributes: such as size, years of establishment, location, industry, etc.

[0047] Behavioral patterns: such as purchasing frequency, number of partners, investment in technological innovation, etc.

[0048] Performance indicators: such as sales growth rate, profit margin, market share change, etc.

[0049] Advanced Feature Extraction: Use NLP technology to parse text content (such as announcements, news reports) and extract semantic features from it.

[0050] Apply time series data analysis methods to discover patterns in time series, such as seasonal fluctuations or long-term trends.

[0051] 3. Choose a suitable clustering algorithm: Unsupervised Learning Methods: K-means clustering: Applicable to situations with a clear number of clusters, assigning data points to the nearest centroid through iterative optimization.

[0052] DBSCAN (Density-Based Spatial Clustering): Suitable for processing noisy data sets and can find clusters of arbitrary shapes.

[0053] HDBSCAN (Hierarchical Density Scaling): extends the functionality of DBSCAN and can automatically determine the optimal number of clusters.

[0054] Spectral Clustering: It uses graph theory principles to segment data, which is particularly suitable for data with non-convex distribution.

[0055] Semi-supervised learning methods: If there are some enterprise samples with known labels, a semi-supervised learning algorithm with label propagation can be used to improve the classification accuracy.

[0056] 4. Constructing industrial ecological cluster classification model: Train the model: The preprocessed data is input into the selected clustering algorithm for training to generate preliminary industrial ecological clusters.

[0057] Model Evaluation: Model performance was evaluated using internal metrics such as silhouette coefficient, Calinski-Harabasz index and external metrics such as mutual information metric, F1 score.

[0058] Compare the results of different algorithms and select the model that best suits the current data set and application scenario.

[0059] Whether using a pre-existing classification model or temporarily constructing a classification model, after completing the classification of the enterprise's industrial ecological clusters through the classification model, the dynamic demand perception of the industrial ecological clusters can be carried out.

[0060] It should be noted that although the industrial ecological cluster classification model also defines the characteristics of different industrial ecological clusters, for enterprise groups on different platforms, even if they belong to the same industrial ecological cluster according to the classification results, the cluster characteristics they embody are different, and accordingly, the service demands of industrial ecological clusters are also different. At the same time, due to the influence of external environmental factors such as time and industry events, the service demand of industrial ecological clusters is also dynamically changing. To achieve dynamic demand perception of industrial ecological clusters, the influence of time factors and industry events must be considered. Specifically, as a preferred method of an embodiment of the present invention, the method of extracting dynamic demand characteristics of each industrial ecological cluster in step S102 specifically includes: For each industrial ecological cluster, extract service demand information, service demand time and service demand target within a certain time range from the platform service data; Obtain the industry event information and industry event time corresponding to each industrial ecological cluster within the same time range from the comprehensive economic service platform; According to the time sequence of service demand time and industry event time, the service demand information and the industry event information are associated; The dynamic demand characteristics of each industrial ecological cluster are extracted based on the association results.

[0061] It should be noted that the selection of the time range needs to be determined based on the accuracy and timeliness of the service information push. The larger the time range, the more accurate the results will be when extracting features. The closer the time range is to the current time point, the more timely the results will be when extracting features. In corporate activities, many needs are highly time-sensitive and have a strong correlation with holidays and dates.

[0062] Therefore, after obtaining the service demand information and industry event information of each industrial ecological cluster, the two are associated according to the chronological order. Among them, the service demand information corresponds to the demand information of the service content required by the enterprise. The service demand time refers to the time point or time period when the enterprise has this demand. The service demand target refers to the goal or result that the enterprise expects to achieve based on this demand. For example, a company hopes to complete the preparation of 5 million pieces of its best-selling products in early November to cope with the shopping street activities on November 11. The service demand information it proposed is XX product production, the service demand time is before November 11, and the service demand target is to complete the generation of 5 million pieces. The corresponding industry event information is the shopping festival, and the industry event time is November 11.

[0063] Extracting demand characteristics based on time association can more accurately reflect the actual demand of the industrial ecological cluster within a time range.

[0064] After the dynamic demand feature extraction is completed, combined with the cluster characteristics of the industrial ecological cluster classification and the common characteristics of enterprises on the current economic comprehensive service platform, the cluster user portrait for each industrial ecological cluster classification can be obtained. Specifically, as a preferred method of the embodiment of the present invention, the method of constructing the cluster user portrait in step S102 specifically includes: Obtaining cluster characteristics of each industrial ecological cluster classification based on the industrial ecological cluster classification model; Extract the common characteristics of enterprises belonging to the same industrial ecological cluster classification based on the classification results of the enterprises; A cluster user profile of each industrial ecological cluster is constructed by combining the cluster characteristics, the common characteristics of the enterprises and the dynamic demand characteristics.

[0065] Cluster characteristics are universal characteristics that reflect the classification of the industrial ecological clusters constructed in the industrial ecological cluster classification model. Enterprise common characteristics reflect the common characteristics of enterprises that belong to the industrial ecological cluster classification on the current economic comprehensive service platform. They have the same parts as cluster characteristics, and also include some characteristics related to the current economic comprehensive service platform that are not reflected in cluster characteristics. When constructing cluster user portraits, the same parts can be merged for processing.

[0066] By combining the cluster characteristics, the enterprise common characteristics and the dynamic demand characteristics, it is possible to obtain a cluster user portrait for a certain industrial ecological cluster on the current economic comprehensive service platform.

[0067] On this basis, as a preferred method of an embodiment of the present invention, in order to achieve more refined service information push, an enterprise user portrait can be further constructed for a specific enterprise, and the enterprise common characteristics can be replaced by the enterprise characteristics extracted based on the enterprise information, and then the enterprise user portrait is jointly constructed with the cluster characteristics and the dynamic demand characteristics. The reason why cluster characteristics and dynamic demand characteristics are used here is that, on the one hand, it is hoped that the constructed enterprise user portrait can reflect the characteristics of the industrial ecological cluster where the enterprise is located, and on the other hand, it is considered that many enterprises cannot clearly understand their current real needs. By using dynamic demand characteristics and the common needs of the industrial ecological cluster where they are located, the hidden needs of the enterprise can be excavated and then the service information can be matched.

[0068] Step S103: extracting service features of different services based on the platform service data.

[0069] Service characteristics reflect the basic attributes of the services provided. By matching service characteristics with cluster user portraits, service information push to specific industrial ecological clusters can be achieved.

[0070] Specifically, as a preferred method of the embodiment of the present invention, step S103 specifically includes: Obtain the service source, service type, service content and service timeliness of different services; Feature extraction is performed on the acquired information to obtain service features.

[0071] As mentioned in the above description, the needs of enterprises or industrial ecological clusters are strongly correlated with time. Therefore, when extracting service features, in addition to considering the matching of service content and demand content, the matching in time must also be considered.

[0072] On the basis of extracting service features, as a preferred method of the embodiment of the present invention, a service set can also be constructed based on the extracted service features. In order to improve the efficiency and accuracy of service information push, the service is characterized and aggregated from aspects such as service source, service type, service content and service timeliness to form a multi-granularity and multi-level service set, and the service set is automatically labeled. Due to the characteristics of many types, large number and overlapping content of services, it is difficult to accurately reveal the various cluster structures presented in the service using a traditional single clustering method. In this embodiment, an integrated clustering method is used to solve this problem. First, feature elements are extracted from the original data such as the content, type, and service objects of multi-source services, and feature screening is performed to construct a feature database of all services; then, a variety of traditional classical clustering algorithms or subspace clustering algorithms are used, using random parameters, random sampling and other methods to quickly generate base clusters, and perform cluster difference analysis; finally, a weighted method is used to integrate the base clusters.

[0073] When pushing information later, you can push individual service information or push service sets to corresponding industrial ecological clusters or enterprises.

[0074] Step S104, matching the service characteristics with the cluster user portrait based on the technical standards corresponding to the service.

[0075] The enterprise behavior data and platform service data obtained on the economic comprehensive service platform, due to the complexity of their sources and content and the heterogeneity of the data, will inevitably lead to many inaccuracies or mismatches if the demand and service are directly matched after feature extraction. The technical standards are selected as the intermediate bridge for matching because the descriptions of standardized terms, technical requirements, performance indicators, etc. in the technical standards are fixed and unified. The non-standard related content in the demand or service can be mapped to the description in the technical standards, thereby achieving alignment of the content of the two. Matching on this basis can make the matching results more consistent.

[0076] Specifically, as a preferred method of the embodiment of the present invention, step S104 specifically includes: Determining whether there are technical standards associated with the service based on the service content; If it exists, extract the standard parameters of the technical standard associated with the service; Matching the standard parameters with the service demand targets, and screening matching industrial ecological clusters; The service characteristics of the successfully matched services are further matched with the cluster user portraits of the screened industrial ecological clusters.

[0077] In the above steps, the matching of standard parameters and service demand targets specifically refers to whether the standardized descriptions of specific parameters are consistent, that is, whether the two can be represented by the same standardized description. If they can match, it means that the two can be benchmarked to the same technical standard. After matching at this stage, further matching is performed on specific content.

[0078] The further matching process specifically includes: Matching the service demand information with the service type and service content; Match the service demand time with the service effectiveness: The matching content in this stage is aimed at matching specific content and time. Through the matching of two stages, the mapping relationship between the industrial ecological cluster and the service or service set can be effectively determined, and the service information can be accurately pushed based on the mapping relationship.

[0079] It should be noted that in many industries, technical standards are ubiquitous and have a lot of data, so service content can be associated with multiple technical standards at the same time, and these technical standards naturally have a relationship. At the same time, multiple different service contents may also correspond to the same or related technical standards. At this time, in order to improve the effectiveness of information push, as a preferred method of the embodiment of the present invention, step S104 specifically includes: Establish binding relationships between services based on the associations between the extracted technical standards; The service characteristics of other services bound to the successfully matched service will also be further matched with the cluster user portraits of the screened industrial ecological clusters.

[0080] That is, in addition to the services and industrial ecological clusters that are successfully matched directly through technical standards, the service characteristics of other services that will be bound to the successfully matched services are also further matched with the cluster user portraits of the screened industrial ecological clusters, thereby effectively expanding the scope of industrial ecological clusters that have demand for services.

[0081] The specific content of the further matching performed at this time is the same as described above.

[0082] Step S105: Push the service information to the industrial ecological cluster whose matching degree reaches the preset condition threshold.

[0083] If the matching degree reaches the preset condition threshold, it is considered that a mapping relationship between the industrial ecological cluster and the service or service set is established through the above matching process. At this time, the service information can be pushed to the industrial ecological cluster that has established a mapping relationship with the service or service set.

[0084] On this basis, as a preferred implementation, the scope of the push service information can be adjusted more flexibly based on the specific matching degree during further matching. Specifically, the above step S105 specifically includes: For industrial ecological clusters whose matching degree reaches the preset condition threshold but does not reach the preset preferred threshold, the service information of multiple bound services will be pushed; For industrial ecological clusters whose matching degree reaches the preset optimal threshold, only the service information of the services involved in the matching will be pushed.

[0085] The value set for the optimization threshold is greater than the condition threshold. For industrial ecological clusters whose matching degree reaches the optimization threshold, it is considered that their demand and service are highly matched. At this time, further pushing other service information in the service set is also more likely to achieve good feedback effects.

[0086] Through the above process, accurate service information push to the industrial ecological cluster is completed.

[0087] It should be noted that during the execution of the method, the enterprises on the economic comprehensive service platform are changing, and the services are also changing. For newly added enterprises, the industrial ecological cluster classification model is used to classify them, determine the industrial ecological group to which they belong, and use the group recommendation method to recommend services or service sets to the enterprise. For newly added services, natural language processing, machine learning and other methods are used to determine the service set to which they belong, and the group recommendation method is used to recommend the service to the industrial ecological cluster or enterprise with the highest matching degree.

[0088] To sum up, the method for precise information push based on user portraits provided by the implementation of the present invention can construct industrial ecological clusters, service sets and service feature libraries based on basic data such as enterprise data and platform service data, establish a mapping relationship between industrial ecological clusters and service sets based on service-related technical standards, and recommend the service content used in the service set to all enterprises in the industrial ecological group, thereby realizing precise push of service information based on dynamic demand perception of the industrial ecological group, recommending services to specific industrial groups in a targeted manner, and improving the accuracy and coverage of service recommendations.

[0089] like Figure 2 As shown, the present invention implements a user portrait-based accurate information push system, the system comprising: The data acquisition unit 110 is used to acquire the enterprise behavior data and platform service data of the economic comprehensive service platform; The user portrait unit 120 is used to classify the enterprise into industrial ecological clusters according to the enterprise behavior data, extract the dynamic demand characteristics of the industrial ecological clusters according to the platform service data of various industrial ecological clusters within a certain time range, and construct a cluster user portrait of the industrial ecological cluster according to the dynamic demand characteristics; A feature extraction unit 130, configured to extract service features of different services based on the platform service data; A feature matching unit 140, configured to match the service feature with the cluster user profile based on a technical standard corresponding to the service; The information push unit 150 is used to push the service information to the industrial ecological cluster whose matching degree reaches a preset condition threshold.

[0090] The user portrait-based information accurate push system provided in the embodiment of the present invention is used to implement the above-mentioned user portrait-based information accurate push method, so the specific implementation method is the same as the above-mentioned method and will not be repeated here.

[0091] In summary, the present invention provides a method and system for accurate information push based on user portraits, which can construct industrial ecological clusters, service sets and service feature libraries according to basic data such as enterprise data and platform service data, establish a mapping relationship between industrial ecological clusters and service sets according to service-related technical standards, and recommend the service content used in the service set to all enterprises in the industrial ecological group, thereby realizing accurate push of service information based on dynamic demand perception of industrial ecological groups, recommending services to specific industrial groups in a targeted manner, and improving the accuracy and coverage of service recommendations.

[0092] In several embodiments disclosed in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a module, a program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0093] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0094] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Claims

1. A method for accurately pushing information based on user portraits, characterized in that: The method comprises: Obtain enterprise behavior data and platform service data from the comprehensive economic service platform; Classify enterprises into industrial ecological clusters according to the enterprise behavior data, extract dynamic demand characteristics of industrial ecological clusters according to platform service data of various industrial ecological clusters within a certain time range, and construct cluster user portraits of industrial ecological clusters according to the dynamic demand characteristics; Extract service features of different services based on platform service data; Matching the service characteristics with the cluster user profile based on the technical standards corresponding to the service; Push service information to industrial ecological clusters whose matching degree reaches the preset condition threshold.

2. The method for accurate information push based on user portrait according to claim 1, characterized in that: The step of classifying the enterprises into industrial ecological clusters according to the enterprise behavior data specifically includes: Extracting basic information, business information, product and service information, and activity information of the enterprise from the enterprise behavior data; Input the extracted information into the pre-trained industrial ecological cluster classification model; Obtain the results of industrial ecological cluster classification.

3. The method for accurate information push based on user portrait according to claim 2, characterized in that: The step of extracting the dynamic demand characteristics of industrial ecological clusters based on the platform service data of various industrial ecological clusters within a certain time range specifically includes: For each industrial ecological cluster, extract service demand information, service demand time and service demand target within a certain time range from the platform service data; Obtain the industry event information and industry event time corresponding to each industrial ecological cluster within the same time range from the comprehensive economic service platform; According to the time sequence of service demand time and industry event time, the service demand information and the industry event information are associated; The dynamic demand characteristics of each industrial ecological cluster are extracted based on the association results.

4. The method for accurately pushing information based on user portrait according to claim 3 is characterized in that: The step of constructing a cluster user portrait of the industrial ecological cluster according to the dynamic demand characteristics specifically includes: Obtaining cluster characteristics of each industrial ecological cluster classification based on the industrial ecological cluster classification model; Extract the common characteristics of enterprises belonging to the same industrial ecological cluster classification based on the classification results of the enterprises; A cluster user profile of each industrial ecological cluster is constructed by combining the cluster characteristics, the common characteristics of the enterprises and the dynamic demand characteristics.

5. The method for accurate information push based on user portrait according to claim 4 is characterized in that: The step of extracting service features of different services based on platform service data specifically includes: Obtain the service source, service type, service content and service timeliness of different services; Feature extraction is performed on the acquired information to obtain service features.

6. The method for accurate information push based on user portrait according to claim 5, characterized in that: The step of matching the service characteristics with the cluster user profile based on the technical standard corresponding to the service specifically includes: Determining whether there are technical standards associated with the service based on the service content; If it exists, extract the standard parameters of the technical standard associated with the service; Matching the standard parameters with the service demand targets, and screening matching industrial ecological clusters; The service characteristics of the successfully matched services are further matched with the cluster user portraits of the screened industrial ecological clusters.

7. The method for accurate information push based on user portrait according to claim 6, characterized in that: The step of matching the service characteristics with the cluster user profile based on the technical standard corresponding to the service specifically includes: Establish binding relationships between services based on the associations between the extracted technical standards; The service characteristics of other services bound to the successfully matched service will also be further matched with the cluster user portraits of the screened industrial ecological clusters.

8. The method for accurate information push based on user portrait according to claim 7, characterized in that: The step of further matching the service characteristics with the cluster user portrait of the screened industrial ecological cluster specifically includes: Matching the service demand information with the service type and service content; The service demand time is matched with the service effectiveness time.

9. The method for accurate information push based on user portrait according to claim 8, characterized in that: The step of pushing the service information to the industrial ecological cluster whose matching degree reaches the preset condition threshold specifically includes: For industrial ecological clusters whose matching degree reaches the preset condition threshold but does not reach the preset preferred threshold, the service information of multiple bound services will be pushed; For industrial ecological clusters whose matching degree reaches the preset optimal threshold, only the service information of the services involved in the matching will be pushed.

10. A precise information push system based on user portrait, characterized in that: include: A data acquisition unit, used to acquire enterprise behavior data and platform service data of the economic comprehensive service platform; A user portrait unit is used to classify enterprises into industrial ecological clusters according to the enterprise behavior data, extract dynamic demand characteristics of industrial ecological clusters according to platform service data of various industrial ecological clusters within a certain time range, and construct cluster user portraits of industrial ecological clusters according to the dynamic demand characteristics; A feature extraction unit, used to extract service features of different services based on platform service data; A feature matching unit, used to match the service feature with the cluster user profile based on the technical standard corresponding to the service; The information push unit is used to push service information to industrial ecological clusters whose matching degree reaches a preset condition threshold.

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