Branch marketing method based on intelligent matching geographic data and new enterprise information

Abstract

The invention relates to a website marketing method based on intelligent matching geographic data and newly-opened enterprise information, which comprises the following steps: acquiring detailed information of a business website, creating a basic snapshot information base, and acquiring and preprocessing the newly-opened enterprise information at regular time; converting the website position and the enterprise registration address into latitude and longitude coordinates, and based on a preset matching distance range, screening out a matching website set meeting conditions by using a Haversine formula; performing clustering on stock enterprises corresponding to the matching network point set through a K-Means clustering algorithm, and matching a clustering center in combination with the characteristics of a new enterprise to obtain an optimal network point list; presetting website scoring indexes, calculating website comprehensive scores according to the website scoring indexes, and selecting the website with the highest score as an optimal matching website; and pushing the information of the new enterprise to a customer manager corresponding to the optimal matching network to carry out marketing activities, and if the information is returned, carrying out matching again. According to the invention, the accuracy and efficiency of website marketing are improved, and the service experience of newly opened enterprises is optimized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and mainly to a network marketing method based on intelligent matching of geographic data and new enterprise information. Background Art

[0002] In modern banking, marketing planning and customer relationship management are key to improving customer satisfaction and promoting business growth. With the development of financial technology, banks' marketing strategies are gradually turning to intelligence and data-driven.

[0003] For example, the Chinese patent with publication number CN115169859A discloses “a method for intelligent dispatching control based on marketing tasks, comprising: S1, obtaining real-time marketing tasks and account manager information, classifying the marketing tasks according to the account manager by the intelligent marketing model, and calculating the classification confidence of the marketing tasks; S2, judging whether the classification confidence of the marketing tasks exceeds the confidence threshold, if so, directly dispatching them to the personal operation pool of the corresponding account manager, otherwise, going to step S3; S3, judging whether there is a management-account relationship between the marketing tasks, if so, dispatching them to the personal operation pool of the corresponding account manager, otherwise, dispatching them to the personal operation pool of the corresponding account manager. Sent to the public operation pool of the branch; S4. The public operation pool of the branch distributes the marketing tasks therein to the personal operation pool of the corresponding account manager or receives the marketing tasks from the personal operation pool of the account manager according to the received fine-tuning operation information. Compared with the prior art, the present invention has the advantages of reducing the distribution cost, improving the distribution efficiency, and enhancing the intelligence of task distribution. However, the above invention lacks consideration of geographical location and distance information in the process of business opportunity allocation, which leads to insufficient refinement of business opportunity allocation, increases the risk of wasting business opportunities, and also increases the difficulty and cost of account managers and marketing personnel to perform tasks. In banking business, geographical location and corporate information are crucial to the successful execution of marketing tasks. Account managers need to accurately allocate marketing tasks according to the customer's geographical location, corporate size and business needs to ensure the effectiveness and pertinence of marketing activities. Therefore, there is an urgent need for a branch marketing method that takes geographical location and corporate information into consideration. Summary of the invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the present application provides a network marketing method based on intelligent matching of geographic data and newly opened business information.

[0005] The technical solution of this application is as follows:

[0006] A network marketing method based on intelligent matching of geographic data and new enterprise information, the method comprising:

[0007] Obtaining relevant information about business outlets and creating a basic snapshot information database for business outlets; regularly obtaining new enterprise information from an external system, performing data preprocessing on the new enterprise information, and obtaining a new enterprise information data set;

[0008] Based on the basic snapshot information database of business outlets and the information data set of newly opened enterprises, the intelligent matching algorithm is used to obtain the best matching outlets for newly opened enterprises;

[0009] The newly opened enterprise information data set is pushed to the account manager of the corresponding best matching outlet, and the account manager conducts corresponding marketing activities.

[0010] As a preferred embodiment of the present invention, the business outlet related information includes the outlet number, outlet name, outlet location, outlet type, outlet specialty business types, number of outlet employees, average waiting time for public business calls at the queuing machine, average daily number of public business transactions, number of public counters and number of foreign exchange counters; the newly opened enterprise information includes the legal representative, establishment time, registered name, registered address, registered amount, industry type and business scope.

[0011] As a preferred embodiment of the present invention, based on the basic snapshot information base of business outlets and the new enterprise information data set, the best matching outlets for new enterprises are obtained by using an intelligent matching algorithm:

[0012] Convert the outlet locations in the basic snapshot information database of business outlets and the registered addresses in the new enterprise information dataset into structured longitude and latitude coordinate data;

[0013] Preset the matching distance range, and obtain the matching network point set according to the latitude and longitude coordinate data corresponding to the registered address, the latitude and longitude coordinate data corresponding to the network point location, and the matching distance range;

[0014] The existing enterprises corresponding to the matching network point set are clustered by the K-Means clustering algorithm, and the cluster centers of the newly opened and existing enterprises are matched with characteristics to obtain matching clustering results, and the optimal network point list is obtained according to the matching clustering results; wherein the K-Means clustering algorithm uses the silhouette coefficient method to determine the optimal cluster number K;

[0015] Calculate the outlet scoring indicators corresponding to the outlets in the best outlet list according to the basic snapshot information database of business outlets and the new enterprise information data set, wherein the outlet scoring indicators include the busyness of corporate business, distance priority and existing industrial and commercial matching priority;

[0016] Determine the weights of the network point scoring indicators based on user needs, calculate the comprehensive score of the network point based on the weights of the network point scoring indicators and the network point scoring indicators, and select the network point with the highest comprehensive score as the best matching network point.

[0017] As a preferred embodiment of the present invention, the branch locations in the basic snapshot information library of business branches and the registered addresses in the newly-opened enterprise information data set are converted into structured longitude and latitude coordinate data by using a geocoding service to convert the branch locations of business branches and the registered addresses of newly-opened enterprises into longitude and latitude coordinate data; the converted longitude and latitude coordinate data are stored in a structured data table, wherein the longitude and latitude coordinate data include ID data, longitude and latitude.

[0018] As a preferred embodiment of the present invention, a matching distance range is preset, and a matching network point set is obtained according to the latitude and longitude coordinate data corresponding to the registered address, the latitude and longitude coordinate data corresponding to the network point location and the matching distance range:

[0019] Preset the matching distance range according to business needs and network coverage, including the initial matching distance, matching distance step and maximum matching distance; use the Haversine formula to calculate the geographical distance between the registered address of the newly-opened enterprise and the location of each business outlet, and match the list of outlets of the newly-opened enterprise within the current matching distance range. If the matching result is empty, increase the matching distance step until the maximum matching distance is reached to obtain the matching outlet set.

[0020] As a preferred implementation of the present invention, the existing enterprises corresponding to the matching network point set are clustered by the K-Means clustering algorithm, and the cluster centers of the newly opened enterprises and existing enterprises are matched with characteristics to obtain matching clustering results, which are specifically:

[0021] Extracting the business information of corresponding existing enterprises from the outlets in the matching outlet set, the business information including enterprise scale, industry classification and registered capital; standardizing the extracted business information using z-score standardization or min-max standardization to obtain standardized business information;

[0022] A range of K values ​​is set, and the optimal K value is found by performing multiple rounds of clustering. Specifically, for each K value, the standardized business information is clustered using the K-Means algorithm; the silhouette coefficient corresponding to each K value is calculated and recorded; the silhouette coefficients under different K values ​​are compared, and the K value with the largest silhouette coefficient is selected as the optimal clustering number K of the K-Means algorithm;

[0023] The optimal cluster number K is used as the K value of the K-Means clustering algorithm to perform cluster analysis on the standardized business information and obtain multiple cluster centers.

[0024] Calculate the Euclidean distance between the standardized business information of each newly-established enterprise and the cluster center of each existing enterprise to obtain the matching clustering results, and sort the matching clustering results from small to large; assign the newly-established enterprise to the cluster corresponding to the cluster center with the highest ranking in the matching clustering results, extract the corresponding outlet information of all existing enterprises in the cluster, and obtain the best outlet list.

[0025] As a preferred embodiment of the present invention, the outlet scoring indicators corresponding to the outlets in the best outlet list are calculated based on the business outlet basic snapshot information database and the newly opened enterprise information data set. The outlet scoring indicators include the busyness of corporate business, distance priority and existing industrial and commercial matching priority, specifically:

[0026] According to the average waiting time for calling public business numbers in the queuing machine, the average daily number of public business transactions and the number of public counters in the basic snapshot information database of the business outlets, the expressions of the time factor, the business quantity factor and the counter quantity factor are obtained, and the public business busyness is calculated based on the time factor, the business quantity factor and the counter quantity factor, which is expressed as follows:

[0027] Busyness of public business = (average waiting time for public business number calling at the queuing machine / maximum acceptable waiting time)*ω1+(average number of public business transactions per day / maximum average number of transactions per day)*ω2+(1 / number of public counters)*ω3;

[0028] In the formula, ω1, ω2 and ω3 are the weights corresponding to the time factor, the business quantity factor and the counter quantity factor respectively. The time factor is expressed as the average waiting time for the public business call of the queuing machine / the maximum acceptable waiting time, the business quantity factor is expressed as the average daily public business handling number / the maximum daily average handling number, and the counter quantity factor is expressed as 1 / the number of public counters;

[0029] Calculate the actual geographical distance between the longitude and latitude coordinates corresponding to the registered address of the newly opened enterprise and the business outlets in the best outlets list, and standardize the calculated actual geographical distance to express it in the formula:

[0030] Normalized distance = 1-(actual geographical distance / maximum acceptable distance);

[0031] Using the normalized distance as the distance priority;

[0032] The matching clustering results are converted into matching priority, which can be expressed as follows:

[0033] Matching priority = 1 / (1+Euclidean distance);

[0034] The comprehensive score of the outlets is expressed as follows:

[0035] Comprehensive score of outlets = busyness of corporate business*ω'1+distance priority*ω'2+matching priority*ω'3;

[0036] Where ω'1, ω'2 and ω'3 are the weights corresponding to the busyness of public business, distance priority and matching priority respectively.

[0037] As a preferred implementation of the present invention, the newly opened enterprise information data set is pushed to the account manager of the corresponding best matching outlet. The account manager of the best matching outlet chooses whether to manually return the newly opened enterprise information data set based on the actual situation of the outlet. If the newly opened enterprise information data set is returned, the newly opened enterprise is reallocated to the list to be allocated, and the best matching outlet other than the current best matching outlet is re-matched.

[0038] The present invention also provides a network marketing system based on intelligent matching of geographic data and new enterprise information, the system comprising a data collection module, a geographic location encoding module, a distance calculation and network matching module and a result output module, wherein:

[0039] The data acquisition module is used to obtain business outlets related information from the API interface of the external system and create a basic snapshot information library of business outlets; regularly obtain new enterprise information from the external system, perform data preprocessing on the new enterprise information, and obtain a new enterprise information data set;

[0040] The geographic location encoding module is used to convert the location of the outlets in the basic snapshot information library of the business outlets and the registered address in the new enterprise information data set into structured longitude and latitude coordinate data by using the API interface of the public map service, and transmit it to the distance calculation and outlet matching module;

[0041] The distance calculation and network point matching module is internally provided with an intelligent matching algorithm for obtaining the best matching network point for a newly opened enterprise, specifically:

[0042] Preset the matching distance range, and obtain the matching network point set according to the latitude and longitude coordinate data corresponding to the registered address, the latitude and longitude coordinate data corresponding to the network point location, and the matching distance range;

[0043] The existing enterprises corresponding to the matching network point set are clustered by the K-Means clustering algorithm, and the cluster centers of the newly opened and existing enterprises are matched with characteristics to obtain matching clustering results, and the optimal network point list is obtained according to the matching clustering results; wherein the K-Means clustering algorithm uses the silhouette coefficient method to determine the optimal cluster number K;

[0044] Calculate the outlet scoring indicators corresponding to the outlets in the best outlet list according to the basic snapshot information database of business outlets and the new enterprise information data set, wherein the outlet scoring indicators include the busyness of corporate business, distance priority and existing industrial and commercial matching priority;

[0045] Determine the weight of the network point scoring index according to user needs, calculate the comprehensive score of the network point according to the weight of the network point scoring index and the network point scoring index, and select the network point with the highest comprehensive score as the best matching network point;

[0046] The result output module is used to push the newly opened enterprise information data set to the account manager of the corresponding best matching outlet, and the account manager conducts corresponding marketing activities.

[0047] As a preferred embodiment of the present invention, the system is obtained by modular design based on the JAVA microservice architecture, and is internally provided with a Quartz timing tool. By combining the Quartz timing tool and the big data cloud platform intelligent scheduling component, the automatic operation of the data acquisition module, the geographic location encoding module, the distance calculation and network point matching module and the result output module is realized, and a data flow link corresponding to the automatic operation is constructed, and a tracking mechanism and job lineage monitoring are realized based on the data flow link.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1) The present invention provides a network marketing method based on intelligent matching of geographic data and newly opened enterprise information. By converting the location information of business outlets and newly opened enterprises into longitude and latitude coordinates, using the Haversine formula to calculate the distance, and combining the K-Means clustering algorithm for feature matching, the matching accuracy of newly opened enterprises and business outlets is greatly improved, the matching errors that may occur in traditional methods are avoided, and the effectiveness of marketing activities is improved;

[0050] 2) The present invention provides a network marketing method based on intelligent matching of geographic data and new enterprise information, calculates a comprehensive score based on the busyness of corporate business, distance priority and existing industrial and commercial matching priority, selects the network with the highest comprehensive score as the best matching network, and dynamically adjusts it based on the feedback of the account manager, optimizes the configuration of network resources, enables new enterprises to quickly and accurately obtain the most appropriate financial services, and reduces ineffective waste of resources;

[0051] 3) The present invention provides a network marketing method based on intelligent matching of geographic data and newly-opened business information, which directly pushes the newly-opened business information data set to the account manager of the best matching network. The account manager conducts marketing activities based on actual conditions. If the information is returned, it is reallocated and matched, which greatly improves the efficiency of marketing activities and reduces the time delay of information transmission and processing, so that newly-opened businesses can obtain the required services and support more quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0054] The present invention provides the following technical solution: a network marketing method based on intelligent matching of geographic data and new enterprise information.

[0055] Example 1

[0056] This embodiment provides a network marketing method based on intelligent matching of geographic data and new business information, the method comprising:

[0057] S1. Obtaining relevant information about business outlets and creating a basic snapshot information database of business outlets;

[0058] S11. The relevant information of the business outlets includes the outlet number, outlet name, outlet location, outlet type, types of outlet specialty services, number of outlet employees, average waiting time for public business calls at the queuing machine, average daily number of public business transactions, number of public counters, and number of foreign exchange counters;

[0059] S2. Regularly obtain new enterprise information from an external system, perform data preprocessing on the new enterprise information, and obtain a new enterprise information data set;

[0060] Obtain new enterprise information from the external system regularly. The new enterprise information is usually uploaded in batches in the form of files (such as CSV, JSON, etc.) and parsed into internal system database tables. The new enterprise information includes legal representative, establishment time, registered name, registered address, registered amount, industry type and business scope;

[0061] In order to ensure the quality and availability of the newly-established enterprise information, the acquired newly-established enterprise information is subjected to data preprocessing, which includes data standardization and data cleaning. Specifically:

[0062] S21. Data standardization is to ensure that the data format is unified to facilitate subsequent processing and analysis, including field unification, data format conversion and data segmentation, among which:

[0063] The field unification includes field naming unification and data type unification. Field naming unification specifically refers to modifying the names of various fields to be consistent, such as "company name", "registered address", "date of establishment", etc. Data type unification specifically refers to standardizing the data types of all fields, such as converting the "date of establishment" field into a unified date format (YYYY-MM-DD);

[0064] The data format conversion includes address standardization, telephone number standardization and code conversion. Address standardization specifically refers to standardizing address information and removing unnecessary symbols and redundant information, such as "No.123, ABC Street, City, Province, ZIP Code"; telephone number standardization specifically refers to unifying the telephone number format and removing spaces and specific characters; code conversion specifically refers to converting different formats of codes (such as UTF-8 code) into a unified code format.

[0065] The data segmentation includes company name segmentation and address segmentation. The company name segmentation specifically includes segmenting the company name and extracting keywords to facilitate subsequent matching and analysis; the address segmentation specifically includes segmenting the address information and extracting information such as province, city, district, street, etc. to facilitate the processing of geographic information;

[0066] S22. Data cleaning is to remove invalid and incomplete data to ensure data quality, including missing value processing, outlier processing, deduplication processing and invalid data elimination, among which:

[0067] The missing value processing includes missing value detection and missing value filling, specifically detecting whether there are missing values ​​in the data, such as if some fields are empty or have invalid data, and filling the missing values ​​using appropriate strategies, such as using the mean, median, or using a specific default value.

[0068] The outlier processing includes outlier detection and outlier processing, specifically detecting whether there are outliers in the data, such as obviously unreasonable establishment date, address, etc., and processing the outliers, such as marking them as invalid data or correcting them;

[0069] The deduplication process includes duplicate data detection and duplicate data processing, specifically detecting whether there are duplicate records in the data and deleting the duplicate records to ensure the uniqueness of the data;

[0070] The invalid data elimination includes invalid data detection and invalid data processing, specifically, detecting whether there is obviously invalid data in the data, such as a company with a blank address or a company whose registration has been revoked, and eliminating the invalid data to ensure that the data is clean and usable;

[0071] S23, storing the pre-processed new enterprise information in a suitable database or data warehouse to obtain a new enterprise information data set for subsequent analysis and use, wherein the suitable database is a relational database, such as ORACLE, MySQL or PostgreSQL, and indexes are created for commonly used fields during storage, including company name and registered address, etc., to improve query efficiency;

[0072] S3, based on the basic snapshot information database of business outlets and the information data set of newly opened enterprises, the best matching outlets for newly opened enterprises are obtained by using intelligent matching algorithms;

[0073] S31, converting the outlet locations in the business outlet basic snapshot information database and the registered addresses in the newly opened enterprise information data set into structured latitude and longitude coordinate data;

[0074] Use geocoding services to convert the location of business outlets and the registered address of newly-established enterprises into longitude and latitude coordinate data, where the geocoding services include but are not limited to Amap API, Google Maps API, and BaiduMap API; store the converted longitude and latitude coordinate data in a structured data table, ensuring that each record contains the business outlet ID / enterprise ID, longitude, and latitude;

[0075] Preferably, the converted coordinates are verified to ensure that the parsing results of the geocoding service are accurate; if the conversion fails, the address is marked as an invalid address and an error log is recorded for subsequent processing;

[0076] S32, preset a matching distance range, and obtain a matching network point set according to the longitude and latitude coordinate data corresponding to the registered address, the longitude and latitude coordinate data corresponding to the network point location, and the matching distance range;

[0077] A reasonable matching distance range is preset according to business needs and network coverage. In this embodiment, the initial matching distance is set to 1 km, the matching distance step is 500 meters, and the maximum matching distance is 5 km. The Haversine formula or other geographic distance calculation methods are used to calculate the geographic distance between the registered address of the newly-opened enterprise and the location of each business network, and the network list of the newly-opened enterprise within the current matching distance range is matched. If the matching result is empty, the matching distance step is increased until the maximum matching distance is reached to obtain a matching network set.

[0078] S33, clustering the existing enterprises corresponding to the matching network point set by using the K-Means clustering algorithm, matching the characteristics of the cluster centers of the newly opened enterprises and the existing enterprises, obtaining matching clustering results, and obtaining the best network point list according to the matching clustering results; wherein the K-Means clustering algorithm uses the silhouette coefficient method to determine the best cluster number K;

[0079] S331. Extracting the business information of the corresponding existing enterprises (existing corporate customers) from the outlets in the matching outlet set, including enterprise size, industry classification, registered capital, etc. Preferably, ensuring that the extracted data also includes the outlet ID and the corresponding corporate customer information;

[0080] Use z-score standardization or min-max standardization to standardize the extracted business information to eliminate the dimensional differences between different features;

[0081] S332, using the silhouette coefficient method to determine the optimal cluster number K is specifically:

[0082] Set a range of K values, for example, from 2 to 10, and prepare to perform multiple rounds of clustering to find the best K value; for each K value, use the K-Means algorithm to cluster the standardized business information; calculate and record the silhouette coefficient corresponding to each K value;

[0083] The silhouette coefficient measures the compactness and separation of the clustering results. The larger the value, the better the clustering effect. Therefore, the silhouette coefficients under different K values ​​are compared, and the K value with the largest silhouette coefficient is selected as the optimal clustering number K of the K-Means algorithm.

[0084] S333, clustering the matching network point set by using the K-Means clustering algorithm is specifically as follows:

[0085] Use the optimal cluster number K to run the K-Means clustering algorithm, perform cluster analysis on the standardized business information, and obtain multiple cluster centers;

[0086] S334. Standardize the business information (such as registered capital, industry classification, etc.) of newly-established enterprises, i.e., the characteristics of newly-established enterprises, based on the basic snapshot information database of business outlets, so that the information is comparable with the standardized data of existing enterprises;

[0087] Calculate the Euclidean distance between the standardized characteristics of each newly-opened enterprise and the cluster center of each existing enterprise to obtain the matching clustering results, and sort the matching clustering results from small to large; assign the newly-opened enterprise to the cluster corresponding to the cluster center with the highest ranking in the matching clustering results, extract the network point information corresponding to all the existing enterprises in the cluster, and obtain the best network point list;

[0088] S34. Calculate and obtain outlet scoring indicators corresponding to outlets in the best outlet list based on the basic snapshot information database of business outlets and the newly opened enterprise information data set, wherein the outlet scoring indicators include the busyness of corporate business, distance priority, and existing industrial and commercial matching priority;

[0089] S341, busyness of public business;

[0090] The busyness of public business is an important indicator for measuring the ability of business outlets to handle public business. It takes into account the efficiency and load of the outlets in handling public business. In this embodiment, the busyness of public business is calculated by the average waiting time for the queuing machine to call the public business number, the average daily number of public business transactions and the number of public counters. The average waiting time for the queuing machine to call the public business number reflects the busyness of the outlet's public business. The longer the average waiting time, the busier the outlet's public business is, which affects the efficiency of new enterprises in handling business at the outlet; the average daily number of public business transactions shows the outlet's processing capacity for public business. The more the average daily number of transactions, the stronger the outlet's processing capacity for public business, but it also means a longer waiting time; the number of public counters: the number of public counters directly affects the outlet's capacity to handle public business. The more counters there are, the more public business can be processed simultaneously, thereby reducing waiting time;

[0091] In summary, this embodiment considers the time factor, the business quantity factor and the counter quantity factor to calculate the public business busyness. The time factor is expressed by the formula of the average waiting time for the public business call of the queuing machine / the maximum acceptable waiting time, the business quantity factor is expressed by the formula of the average daily public business processing number / the maximum daily average processing number, and the counter quantity factor is expressed by the formula of 1 / the number of public counters. Therefore, the public business busyness is expressed by the formula:

[0092] Busyness of public business = (average waiting time for public business number calling at the queuing machine / maximum acceptable waiting time)*ω1+(average number of public business transactions per day / maximum average number of transactions per day)*ω2+(1 / number of public counters)*ω3;

[0093] In the formula, ω1, ω2 and ω3 are weights corresponding to the time factor, the business quantity factor and the counter quantity factor, respectively, and the weights are set according to actual needs;

[0094] S342, distance priority;

[0095] The distance priority is obtained based on the physical distance between the newly-opened enterprise and the business outlets, and the impact of the physical distance on the convenience of the enterprise's daily operations. Preferably, the closer the distance, the more convenient it is for the enterprise to handle business;

[0096] Therefore, the Haversine formula or other geographic distance calculation methods are used to calculate the actual geographic distance between the registered address of the newly-opened enterprise and the location of the business outlets in the best outlet list based on the latitude and longitude coordinate data of the two. In order to convert the actual geographic distance into a unified scoring range (such as between 0 and 1), in this embodiment, the calculated actual geographic distance is standardized and expressed as follows:

[0097] Normalized distance = 1-(actual geographical distance / maximum acceptable distance);

[0098] Preferably, in order to more comprehensively evaluate the convenience of distance, the traffic conditions around the business outlets, such as the accessibility of public transportation facilities, road congestion, etc., may be considered when calculating the distance priority. By taking the above traffic conditions as influencing factors, a traffic condition score is assigned to each business outlet according to the accessibility of public transportation, traffic congestion, etc., and the traffic condition score ranges from 0 to 1, where 1 represents the best traffic condition; the standardized distance is combined with the traffic condition score to form the final distance priority, which is expressed as follows:

[0099] Comprehensive distance priority = standardized distance × traffic status score;

[0100] S343, existing industrial and commercial matching priority;

[0101] The existing industrial and commercial matching priority is an indicator to measure the similarity between the newly opened enterprise and the existing enterprise customer base of the business outlets. The higher the similarity, the more experience and professional services the outlets have in serving similar enterprises. In this embodiment, the matching clustering result in step S334 is converted into a priority score, specifically, the calculated Euclidean distance, i.e., the similarity value, is converted into a priority score. The higher the score, the higher the matching priority, which can be expressed as:

[0102] Matching priority = 1 / (1+Euclidean distance);

[0103] S35, determining the weight of the network point scoring index according to the user's needs, calculating the network point comprehensive score according to the network point scoring index weight and the network point scoring index, and taking the network point with the highest network point comprehensive score as the best matching network point;

[0104] The comprehensive score of the outlets is expressed as follows:

[0105] Comprehensive score of outlets = busyness of corporate business*ω'1+distance priority*ω'2+matching priority*ω'3;

[0106] In the formula, ω'1, ω'2 and ω'3 are the weights corresponding to the busyness of public business, distance priority and matching priority respectively;

[0107] The outlet with the highest comprehensive score in the best outlet list is selected as the best matching outlet;

[0108] S4. Use the Quartz timing tool to automatically push the newly opened enterprise information data set to the account manager of the corresponding best matching outlet in the specified time period, and the account manager will carry out corresponding marketing activities;

[0109] Preferably, the account manager of the best matching outlet can choose to manually return the newly opened enterprise information data set, reallocate the newly opened enterprise to the list to be allocated, and re-match the remaining best matching outlets;

[0110] S5. Construct the data flow link corresponding to steps S1-S4 to ensure the update frequency and transmission timeliness of new business opportunity data; implement the number-tracking mechanism and job lineage monitoring based on the data flow link to track and monitor the method of pushing new customer data to outlets, wherein the number-tracking mechanism specifically relies on the big data cloud platform operation and maintenance center module to manually re-run the offline calculation job flow to recalculate the data for a certain time period in the past, and the system re-executes the data processing task of a previous specific business date or batch; the job lineage monitoring specifically monitors the operation status of upstream and downstream jobs, and quickly locates the external dependencies of jobs.

[0111] Example 2

[0112] This embodiment provides an application system for configuring dynamic forms based on agreements in a workflow engine, the system comprising a data collection module, a geographic location encoding module, a distance calculation and network point matching module and a result output module, wherein:

[0113] The data acquisition module is used to obtain business outlets related information from the API interface of the external system and create a basic snapshot information library of business outlets; regularly obtain new enterprise information from the external system, perform data preprocessing on the new enterprise information, and obtain a new enterprise information data set;

[0114] The geographic location encoding module is used to convert the location of the outlets in the basic snapshot information library of the business outlets and the registered address in the new enterprise information data set into structured longitude and latitude coordinate data by using the API interface of the public map service, and transmit it to the distance calculation and outlet matching module;

[0115] The distance calculation and network point matching module is internally provided with an intelligent matching algorithm for obtaining the best matching network point for a newly opened enterprise, specifically:

[0116] Preset the matching distance range, and obtain the matching network point set according to the latitude and longitude coordinate data corresponding to the registered address, the latitude and longitude coordinate data corresponding to the network point location, and the matching distance range;

[0117] The existing enterprises corresponding to the matching network point set are clustered by the K-Means clustering algorithm, and the cluster centers of the newly opened and existing enterprises are matched with characteristics to obtain matching clustering results, and the optimal network point list is obtained according to the matching clustering results; wherein the K-Means clustering algorithm uses the silhouette coefficient method to determine the optimal cluster number K;

[0118] Calculate the outlet scoring indicators corresponding to the outlets in the best outlet list according to the basic snapshot information database of business outlets and the new enterprise information data set, wherein the outlet scoring indicators include the busyness of corporate business, distance priority and existing industrial and commercial matching priority;

[0119] Determine the weight of the network point scoring index according to user needs, calculate the comprehensive score of the network point according to the weight of the network point scoring index and the network point scoring index, and select the network point with the highest comprehensive score as the best matching network point;

[0120] The result output module is used to push the newly opened enterprise information data set to the account manager of the corresponding best matching outlet, and the account manager conducts corresponding marketing activities;

[0121] Preferably, the system described in this embodiment is obtained by modular design based on the JAVA microservice architecture, and is internally provided with a Quartz timing tool. By combining the Quartz timing tool and the big data cloud platform intelligent scheduling component, the automatic operation of the data acquisition module, the geographic location encoding module, the distance calculation and network point matching module and the result output module is realized, and a data flow link corresponding to the automatic work is constructed, and a tracking mechanism and job lineage monitoring are realized based on the data flow link.

[0122] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A network marketing method based on intelligent matching of geographic data and new enterprise information, characterized in that: The method comprises: Obtaining relevant information about business outlets and creating a basic snapshot information database for business outlets; regularly obtaining new enterprise information from an external system, performing data preprocessing on the new enterprise information, and obtaining a new enterprise information data set; Based on the basic snapshot information database of business outlets and the information data set of newly opened enterprises, the intelligent matching algorithm is used to obtain the best matching outlets for newly opened enterprises; The newly opened enterprise information data set is pushed to the account manager of the corresponding best matching outlet, and the account manager conducts corresponding marketing activities.

2. The network marketing method based on intelligent matching of geographic data and new enterprise information according to claim 1 is characterized in that: The relevant information of the business outlets includes the outlet number, outlet name, outlet location, outlet type, types of outlet featured business, number of outlet employees, average waiting time for corporate business calls at the queuing machine, average daily number of corporate business transactions, number of corporate counters and number of foreign exchange counters; the newly opened enterprise information includes the legal representative, establishment time, registered name, registered address, registered amount, industry type and business scope.

3. The network marketing method based on intelligent matching of geographic data and new enterprise information according to claim 2 is characterized in that: Based on the basic snapshot information database of business outlets and the new enterprise information data set, the best matching outlets for new enterprises are obtained by using the intelligent matching algorithm: Convert the outlet locations in the basic snapshot information database of business outlets and the registered addresses in the new enterprise information dataset into structured longitude and latitude coordinate data; Preset the matching distance range, and obtain the matching network point set according to the latitude and longitude coordinate data corresponding to the registered address, the latitude and longitude coordinate data corresponding to the network point location, and the matching distance range; The existing enterprises corresponding to the matching network point set are clustered by the K-Means clustering algorithm, and the cluster centers of the newly opened and existing enterprises are matched with characteristics to obtain matching clustering results, and the optimal network point list is obtained according to the matching clustering results; wherein the K-Means clustering algorithm uses the silhouette coefficient method to determine the optimal cluster number K; Calculate the outlet scoring indicators corresponding to the outlets in the best outlet list according to the basic snapshot information database of business outlets and the new enterprise information data set, wherein the outlet scoring indicators include the busyness of corporate business, distance priority and existing industrial and commercial matching priority; Determine the weights of the network point scoring indicators based on user needs, calculate the comprehensive score of the network point based on the weights of the network point scoring indicators and the network point scoring indicators, and select the network point with the highest comprehensive score as the best matching network point.

4. The network marketing method based on intelligent matching of geographic data and new enterprise information according to claim 3 is characterized in that: The branch locations in the basic snapshot information library of business branches and the registered addresses in the newly-opened enterprise information data set are converted into structured longitude and latitude coordinate data by using geocoding services to convert the branch locations of business branches and the registered addresses of newly-opened enterprises into longitude and latitude coordinate data; the converted longitude and latitude coordinate data are stored in a structured data table, wherein the longitude and latitude coordinate data includes ID data, longitude and latitude.

5. The network marketing method based on intelligent matching of geographic data and new enterprise information according to claim 4 is characterized in that: The preset matching distance range is used to obtain the matching network point set according to the latitude and longitude coordinate data corresponding to the registered address, the latitude and longitude coordinate data corresponding to the network point location, and the matching distance range: Preset the matching distance range according to business needs and network coverage, including the initial matching distance, matching distance step and maximum matching distance; use the Haversine formula to calculate the geographical distance between the registered address of the newly-opened enterprise and the location of each business outlet, and match the list of outlets of the newly-opened enterprise within the current matching distance range. If the matching result is empty, increase the matching distance step until the maximum matching distance is reached to obtain the matching outlet set.

6. The network marketing method based on intelligent matching of geographic data and new enterprise information according to claim 5 is characterized in that: The K-Means clustering algorithm is used to cluster the existing enterprises corresponding to the matching network point set, and the characteristics of the cluster centers of the newly opened enterprises and existing enterprises are matched to obtain the matching clustering results, which are as follows: Extracting the business information of corresponding existing enterprises from the outlets in the matching outlet set, the business information including enterprise scale, industry classification and registered capital; standardizing the extracted business information using z-score standardization or min-max standardization to obtain standardized business information; A range of K values ​​is set, and the optimal K value is found by performing multiple rounds of clustering. Specifically, for each K value, the standardized business information is clustered using the K-Means algorithm; the silhouette coefficient corresponding to each K value is calculated and recorded; the silhouette coefficients under different K values ​​are compared, and the K value with the largest silhouette coefficient is selected as the optimal clustering number K of the K-Means algorithm; The optimal cluster number K is used as the K value of the K-Means clustering algorithm to perform cluster analysis on the standardized business information and obtain multiple cluster centers. Calculate the Euclidean distance between the standardized business information of each newly-established enterprise and the cluster center of each existing enterprise to obtain matching clustering results, and sort the matching clustering results from small to large; Assign the newly opened enterprise to the cluster corresponding to the cluster center with the highest ranking in the matching clustering results, extract the outlet information corresponding to all existing enterprises in the cluster, and obtain the best outlet list.

7. The network marketing method based on intelligent matching of geographic data and new enterprise information according to claim 5 is characterized in that: The outlet scoring indicators corresponding to the outlets in the best outlet list are calculated based on the basic snapshot information database of business outlets and the newly opened enterprise information data set. The outlet scoring indicators include the busyness of corporate business, distance priority and existing industrial and commercial matching priority, specifically: According to the average waiting time for calling public business numbers in the queuing machine, the average daily number of public business transactions and the number of public counters in the basic snapshot information database of the business outlets, the expressions of the time factor, the business quantity factor and the counter quantity factor are obtained, and the public business busyness is calculated based on the time factor, the business quantity factor and the counter quantity factor, which is expressed as follows: Busyness of public business = (average waiting time for public business number calling at the queuing machine / maximum acceptable waiting time)*ω1+(average number of public business transactions per day / maximum average number of transactions per day)*ω2+(1 / number of public counters)*ω3; In the formula, ω1, ω2 and ω3 are the weights corresponding to the time factor, the business quantity factor and the counter quantity factor respectively. The time factor is expressed as the average waiting time for the public business call of the queuing machine / the maximum acceptable waiting time, the business quantity factor is expressed as the average daily public business handling number / the maximum daily average handling number, and the counter quantity factor is expressed as 1 / the number of public counters; Calculate the actual geographical distance between the longitude and latitude coordinates corresponding to the registered address of the newly opened enterprise and the business outlets in the best outlets list, and standardize the calculated actual geographical distance to express it in the formula: Normalized distance = 1-(actual geographical distance / maximum acceptable distance); Using the normalized distance as the distance priority; The matching clustering results are converted into matching priority, which can be expressed as follows: Matching priority = 1 / (1+Euclidean distance); The comprehensive score of the outlets is expressed as follows: Comprehensive score of outlets = busyness of corporate business*ω'1+distance priority*ω'2+matching priority*ω'3; Where ω'1, ω'2 and ω'3 are the weights corresponding to the busyness of public business, distance priority and matching priority respectively.

8. The network marketing method based on intelligent matching of geographic data and new enterprise information according to claim 7 is characterized in that: The newly opened enterprise information data set is pushed to the account manager of the corresponding best matching outlet. The account manager of the best matching outlet chooses whether to manually return the newly opened enterprise information data set based on the actual situation of the outlet. If the newly opened enterprise information data set is returned, the newly opened enterprise is reallocated to the list to be allocated, and the best matching outlet other than the current best matching outlet is re-matched.

9. A network marketing system based on intelligent matching of geographic data and new business information, characterized by: The system includes a data collection module, a geographic location encoding module, a distance calculation and network point matching module and a result output module, wherein: The data acquisition module is used to obtain business outlets related information from the API interface of the external system and create a basic snapshot information library of business outlets; regularly obtain new enterprise information from the external system, perform data preprocessing on the new enterprise information, and obtain a new enterprise information data set; The geographic location encoding module is used to convert the location of the outlets in the basic snapshot information library of the business outlets and the registered address in the new enterprise information data set into structured longitude and latitude coordinate data by using the API interface of the public map service, and transmit it to the distance calculation and outlet matching module; The distance calculation and network point matching module is internally provided with an intelligent matching algorithm for obtaining the best matching network point for a newly opened enterprise, specifically: Preset the matching distance range, and obtain the matching network point set according to the latitude and longitude coordinate data corresponding to the registered address, the latitude and longitude coordinate data corresponding to the network point location, and the matching distance range; The existing enterprises corresponding to the matching network point set are clustered by the K-Means clustering algorithm, and the cluster centers of the newly opened and existing enterprises are matched with characteristics to obtain matching clustering results, and the optimal network point list is obtained according to the matching clustering results; wherein the K-Means clustering algorithm uses the silhouette coefficient method to determine the optimal cluster number K; Calculate the outlet scoring indicators corresponding to the outlets in the best outlet list according to the basic snapshot information database of business outlets and the new enterprise information data set, wherein the outlet scoring indicators include the busyness of corporate business, distance priority and existing industrial and commercial matching priority; Determine the weight of the network point scoring index according to user needs, calculate the comprehensive score of the network point according to the weight of the network point scoring index and the network point scoring index, and select the network point with the highest comprehensive score as the best matching network point; The result output module is used to push the newly opened enterprise information data set to the account manager of the corresponding best matching outlet, and the account manager conducts corresponding marketing activities.

10. The network marketing system based on intelligent matching of geographic data and new enterprise information according to claim 9, characterized in that: The system is modularly designed based on the JAVA microservice architecture, and is equipped with a Quartz timing tool internally. By combining the Quartz timing tool and the intelligent scheduling component of the big data cloud platform, the automatic operation of the data acquisition module, the geographic location encoding module, the distance calculation and network point matching module and the result output module is realized, and a data flow link corresponding to the automatic operation is constructed. Based on the data flow link, a number tracking mechanism and job lineage monitoring are realized.

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