Potential service mining method and system, computer equipment and storage medium

Abstract

The invention relates to the technical field of finance and data processing, and discloses a potential business mining method and system, computer equipment and a storage medium, and the method comprises the steps: obtaining a leasing ledger, and determining first industry region data according to the leasing ledger; public information is acquired, and second industry region data is determined according to the public information; determining potential industry region data according to the first industry region data and the second industry region data; and forming a service adjustment strategy according to the potential industry region data. Comprehensive analysis is carried out based on the financial leasing company leasing data and public data to assist the financial leasing company in leasing. By analyzing the current rented contract data of the financial leasing company and combining the public data for comparison, the industry region data of the financial leasing company with the potential business opportunities are determined, so that the financial leasing company dynamically adjusts resource investment according to the industry region data with the potential business opportunities, and the leasing amount of the financial leasing company is increased.

Description

Technical Field

[0001] The present invention relates to the field of finance and data processing technology, and in particular to a potential business mining method, system, computer equipment and storage medium. Background Art

[0002] Small and micro enterprises are widely distributed in various industries. Affected by factors such as the level of social and economic development and industrial layout of various regions, the development level and industrial characteristics of small and micro enterprises in different regions are very different. With the continuous increase of market participants and the increasingly fierce competition in the small and micro financial industry, financial institutions need to implement differentiated development strategies in order to stand out from the competition.

[0003] Currently, financial leasing companies develop small and micro enterprise customers mainly through channel recommendations, introductions from old customers, etc. to explore new customers. After the customers start leasing, they need to give rebates to the channels, which requires a lot of manpower and costs. In addition, the current method cannot make timely strategic adjustments based on market changes and industry and regional characteristics, and there is a lag in information, which affects the efficiency of leasing. Summary of the invention

[0004] The present invention provides a potential business mining method, system, computer equipment and storage medium, which assists financial leasing companies in starting leases by conducting comprehensive analysis based on the lease start data and public data of financial leasing companies. By analyzing the contract data of the financial leasing companies that have currently started leases, and comparing them with the public data, the industry and regional data where the financial leasing companies have potential business opportunities are determined, so that the financial leasing companies can dynamically adjust resource investment according to the industry and regional data where potential business opportunities exist, and increase the starting lease amount of the financial leasing companies.

[0005] In a first aspect, a potential business mining method is provided, comprising:

[0006] Obtain the rent start record, and determine the first industry region data based on the rent start record;

[0007] Obtain public information and determine the secondary industry regional data based on the public information;

[0008] Determine potential industry region data based on the first industry region data and the second industry region data;

[0009] Form business adjustment strategies based on potential industry and regional data.

[0010] In a second aspect, a potential business mining system is provided, comprising:

[0011] The rent account unit is used to store the rent account;

[0012] The acquisition unit is used to obtain the rental ledger and public information;

[0013] An analysis unit, configured to determine first industry-region data according to the lease-start ledger, determine second industry-region data according to public information, and determine potential industry-region data according to the first industry-region data and the second industry-region data;

[0014] A decision-making unit, configured to form a business adjustment strategy according to the potential industry-region data.

[0015] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned potential business mining method are implemented.

[0016] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned potential business mining method are implemented.

[0017] In the solution implemented by the above-mentioned potential business mining method, system, computer device, and storage medium, obtain the lease-start ledger, and determine the first industry-region data according to the lease-start ledger; obtain public information, and determine the second industry-region data according to the public information; determine the potential industry-region data according to the first industry-region data and the second industry-region data; form a business adjustment strategy according to the potential industry-region data.

[0018] In the present invention, classify the lease-start ledger of a financial leasing company, analyze the industry data and region data of the leased assets, then obtain authoritative industry data and region data through publicly released information, compare and analyze the industry-region data of the leased assets of the financial leasing company with the authoritative industry-region data, obtain the industry-region data where there are potential business opportunities for the financial leasing company, and then timely adjust the allocation of customer managers and the proportion of capital investment according to the industry-region data where there are potential business opportunities, and conduct business expansion in the industry-region where there are potential business opportunities in a targeted manner, improve the lease-start efficiency, and increase the lease amount of the financial leasing company. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic flowchart of the potential business mining method in an embodiment of the present invention;

[0021] Figure 2It is a schematic flowchart of a potential business mining method in another embodiment of the present invention;

[0022] Figure 3 It is a schematic structural diagram of a potential business mining system in an embodiment of the present invention;

[0023] Figure 4 It is a schematic structural diagram of a potential business mining system in another embodiment of the present invention;

[0024] Figure 5 It is a schematic structural diagram of a computer device in an embodiment of the present invention. Detailed implementation manners

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] The present invention will be described in detail through specific embodiments below.

[0027] Please refer to Figure 1 as shown in Figure 1 which is a schematic flowchart of a potential business mining method provided by an embodiment of the present invention, including the following steps:

[0028] Step S110: Obtain the lease start ledger and determine the first industry area data according to the lease start ledger.

[0029] When a financial leasing company conducts potential business mining on the server side, it can be completed based on the potential business mining system. The potential business mining system may include a lease start ledger unit, an acquisition unit, an analysis unit, a decision-making unit, etc.

[0030] After the financial leasing company signs a lease start contract with a customer, the lease start contract can be stored in the lease start ledger unit in real time. The lease start ledger unit can maintain the information of the financial leasing company and all lease start customers in the form of data records, that is, the lease start ledger unit stores the information of all lease start contracts. In the lease start ledger, many information of the lease start contract can be recorded, including but not limited to: contract content, contract number, lease start time, lessee, enterprise area, lease start amount, product use, features, etc.

[0031] When potential business mining is required, the acquisition unit can download the required lease start ledger from the lease start ledger unit. Potential business mining is usually carried out at a certain cycle. Therefore, the acquisition unit can download the lease start ledger within the required cycle period from the lease start ledger unit. For example: The acquisition unit downloads the lease start ledger from the lease start ledger unit whose lease start time is within the first half of the current time.

[0032] The acquisition unit transmits the obtained lease start ledger to the analysis unit so that the analysis unit can determine the first industry area data based on the lease start ledger.

[0033] The first industry area data can be used to indicate the ranking data after classifying the lease start contracts recorded in the lease start ledger according to enterprise regions and industries. The lease start contracts can be classified by industry, and the lease start contracts in the same industry can be grouped into one category; for the lease start contracts in the same industry, the lease start contracts can also be classified according to enterprise regions, and the lease start contracts in the same region can be grouped into one category. In this way, the number of lease start contracts in each region under each industry can be counted.

[0034] The region can be a province, a city, or other regions divided according to actual needs. The industry can be the plastics industry, the automotive industry, the home appliance industry, the daily necessities industry, the new energy industry, or other industries divided according to actual needs.

[0035] Step S120: Obtain public information and determine the second industry area data based on the public information.

[0036] When potential business mining is required, the acquisition unit can obtain the required public information from the external environment. Potential business mining is usually carried out at a certain cycle. Therefore, the acquisition unit can obtain the public information within the required cycle period from the external environment. For example: The acquisition unit obtains the public information from the external environment whose release time is within the first half of the current time.

[0037] The public information obtained by the acquisition unit from the external environment can include, but is not limited to, industry reports published by the country, authoritative enterprise data, etc. The acquisition unit can download public information based on an interface from the network environment, and the acquisition unit can also obtain public information based on manual upload.

[0038] The acquisition unit transmits the obtained public information to the analysis unit so that the analysis unit can determine the second industry area data based on the public information.

[0039] The second industry-region data can be used to indicate the ranking data after classifying enterprises across the country by industry and enterprise region. Enterprises can be classified by industry, and enterprises in the same industry can be grouped into one category; for enterprises in the same industry, they can also be classified by enterprise region, and enterprises in the same region can be grouped into one category. In this way, the number of enterprises in each region under each industry can be counted.

[0040] To ensure the accuracy of potential business mining results, the classification of regions and industries in the second industry-region data is usually consistent with that in the first industry-region data.

[0041] Step S130: Determine the potential industry-region data based on the first industry-region data and the second industry-region data.

[0042] The analysis unit counts the number of lease contracts in each region under each industry, and the analysis unit also counts the number of enterprises in each region under each industry. Therefore, the analysis unit can determine the potential industry-region data based on the first industry-region data and the second industry-region data.

[0043] The potential industry-region data can be used to indicate in which industries and / or regions financial leasing companies can develop potential business.

[0044] For example: For the same industry, if the number of lease contracts in a region in the first industry-region data is small, while the number of enterprises in this region in the second industry-region data is large, then potential business needs to be developed in this region under this industry. For the same region, if the number of lease contracts in an industry in the first industry-region data is small, while the number of enterprises in this industry in the second industry-region data is large, then potential business needs to be developed in this industry under this region.

[0045] Step S140: Form a business adjustment strategy based on the potential industry-region data.

[0046] After the analysis unit determines the potential industry-region data, it can transmit the potential industry-region data to the decision-making unit. The decision-making unit forms a business adjustment strategy based on the potential industry-region data.

[0047] The business adjustment strategy can be used to indicate the decision-making information for financial leasing companies to make targeted business adjustments for potential industry regions.

[0048] The decision-making unit can form a business adjustment decision aiming to increase the lease amount of financial leasing companies based on the existing resources in potential industries and potential regions.

[0049] For example: Based on the existing customer managers and financial budgets in potential industries and regions, the decision-making unit aims to increase the lease commencement amount of the financial leasing company and forms a business adjustment decision to adjust the allocation of customer managers and the proportion of capital investment.

[0050] Through Figure 1 the method shown, the present invention obtains a lease commencement ledger, determines the first industry-region data according to the lease commencement ledger; obtains public information, determines the second industry-region data according to the public information; determines the potential industry-region data according to the first industry-region data and the second industry-region data; and forms a business adjustment strategy according to the potential industry-region data.

[0051] In the present invention, the lease commencement ledgers of the financial leasing company are classified to analyze the leased industry data and region data. Then, authoritative industry data and region data are obtained through publicly released information. The leased industry-region data of the financial leasing company is compared and analyzed with the authoritative industry-region data to obtain the industry-region data where the financial leasing company has potential business opportunities. Furthermore, the allocation of customer managers and the proportion of capital investment can be adjusted in a timely manner according to the industry-region data with potential business opportunities, and business expansion can be carried out specifically in the industry-regions with potential business opportunities to improve the lease commencement efficiency and increase the lease commencement amount of the financial leasing company.

[0052] In some alternative embodiments, S110 obtains a lease commencement ledger and determines the first industry-region data according to the lease commencement ledger, including: periodically obtaining the lease commencement ledger; according to the lease commencement ledger, determining the ranking of the proportion of lease commencement contracts in each region under each industry to form the first industry-region data.

[0053] The lease commencement ledger unit maintains the information of the financial leasing company and all lease commencement customers in the form of data records. For example, the information of the lease commencement contracts stored in the lease commencement ledger unit is exemplarily shown in Table 1. Only one data content is shown in Table 1. In actual situations, the information of the lease commencement contracts stored in the lease commencement ledger unit includes multiple data contents.

[0054] Table 1:

[0055]

[0056] The obtaining unit may include an internal obtaining module, and the internal obtaining module downloads the lease commencement ledger within the required cycle period from the lease commencement ledger unit, that is, the internal obtaining module downloads the corresponding lease commencement ledger from the lease commencement ledger unit at a certain cycle.

[0057] The internal obtaining module transmits the downloaded lease commencement ledger to the analysis unit. The analysis unit may include an internal data analysis module, and the internal data analysis module cleans, integrates, and classifies the industry and region of the lease commencement ledger data according to the downloaded lease commencement ledger, so as to analyze the ranking of the leased industry-regions.

[0058] Specifically, according to the lease account, the ranking of the proportion of lease contracts in each region under each industry is determined to form the first industry region data, including: semantic recognition of each lease contract in the lease account to determine the industry label, region label and lease of each lease contract; according to the industry label and lease amount, the number of lease contracts and the total lease amount of each industry are determined; according to the region label, the number of lease contracts in each region under each industry is determined; according to the number of lease contracts in each region under each industry and the number of lease contracts corresponding to each industry, the proportion of lease contracts in each region under each industry is determined; according to the total lease amount of each industry, each industry is ranked from most to least, and for each industry, each region is ranked from most to least according to the proportion of lease contracts to form the first industry region data.

[0059] The analysis unit may include an industry parameter configuration module. The industry parameter configuration module pre-configures the correspondence between the industry and the key information. For example, Table 2 exemplarily shows the correspondence between the industry and the key information. Table 2 only shows three correspondences, and in actual situations, the correspondences include more.

[0060] Table 2:

[0061] Industry Product Use Automobile Industry Automobile Inner Shell, Automobile Wheel Hub, Automobile Chassis, Automobile Fasteners Household Appliance Industry TV Set Shell Plastic Industry Plastic Mould, Plastic Products

[0062] The analysis unit may include a region parameter configuration module in which the corresponding relationship between regions and enterprise regions is preconfigured.

[0063] The internal data analysis module performs semantic recognition on each lease contract in the lease account, and extracts at least the product purpose, characteristics, enterprise region and lease amount of each lease contract. For example, from the information of the lease contract shown in Table 1, "Product purpose: automotive inner shell, motor shell", "Characteristics: plastic mold", "Enterprise region: Suzhou City, Jiangsu Province", and "Lease amount: 5 million yuan" are identified and extracted.

[0064] The extracted product uses and features are integrated into fields to determine the key information of each lease contract. For example, the key information of the lease contract shown in Table 1 is the car inner shell, motor shell, and plastic mold.

[0065] The extracted key information is matched by industry in the correspondence between the industry and key information in Table 2, so as to label each lease contract in the lease account with the industry. The extracted enterprise region is matched by region in the correspondence between the region and the enterprise region, so as to label each lease contract in the lease account with the region. For example: the lease contract in Table 1 has the industry labels of automobile industry and plastic industry, and the region label of Suzhou City.

[0066] After the internal data analysis module performs industry tagging and regional tagging on each lease contract in the lease start ledger, it determines the number of lease contracts and the total lease amount for each industry based on the industry tags and the lease start amount.

[0067] Taking the industries shown in Table 2 as an example, the internal data analysis module determines that the number of lease contracts in the automotive industry is A, the number of lease contracts in the home appliance industry is B, and the number of lease contracts in the plastics industry is C. Taking the lease contract shown in Table 1 as an example, since the industry tags include both the automotive industry and the plastics industry, this lease contract is counted both in the number of lease contracts A in the automotive industry and in the number of lease contracts C in the plastics industry.

[0068] Taking the industries shown in Table 2 as an example, the internal data analysis module determines that the total lease amount for the automotive industry is m, the total lease amount for the home appliance industry is n, and the total lease amount for the plastics industry is p.

[0069] The internal data analysis module then determines the number of lease contracts for each region of each industry based on the regional tags.

[0070] The internal data analysis module further determines that the number of lease contracts in Dongguan City under the automotive industry is a1, the number of lease contracts in Ningbo City is a2, the number of lease contracts in Jinan City is a3, etc. The calculation method for other industries is the same.

[0071] The internal data analysis module then determines the proportion of lease contracts for each region of each industry based on the number of lease contracts for each region of each industry and the number of lease contracts for each industry.

[0072] a1 / A is the proportion of lease contracts in Dongguan City under the automotive industry, a2 / A is the proportion of lease contracts in Ningbo City under the automotive industry, and a3 / A is the proportion of lease contracts in Jinan City under the automotive industry. The calculation method for other industries is the same.

[0073] Finally, the internal data analysis module ranks each industry according to the total lease amount of each industry from most to least, and ranks each region according to the proportion of lease contracts from most to least for each industry, forming the first industry-region data.

[0074] The internal data analysis module ranks each industry according to the total lease amount of each industry from most to least. For example: Table 3 exemplarily shows the ranking of the total lease amounts of different industries in the lease start ledger. Only five rankings are shown in Table 3, and in actual situations, there are more rankings.

[0075] Table 3:

[0076] Ranking Industry Total Leasing Amount 1 Plastic Industry 3 Billion 2 Automobile Industry 2 Billion 3 Household Appliance Industry 1.5 Billion 4 Daily Necessities Industry 1.3 Billion 5 New Energy Industry 1.1 Billion

[0077] The internal data analysis module ranks each region for each industry according to the proportion of lease contracts from the most to the least. For example, Table 4 exemplarily shows the ranking of the proportion of lease contracts in different regions of the plastics industry. Only ten rankings are shown in Table 4. In actual situations, the rankings include more. And in actual situations, it also includes the ranking of the proportion of lease contracts in different regions of other industries.

[0078] Table 4:

[0079] Ranking Industry Region Proportion of Leasing Contracts 1 Plastic Industry Dongguan City 18％ 2 Plastic Industry Shenzhen City 7.5％ 3 Plastic Industry Suzhou City 6.11％ 4 Plastic Industry Ningbo City 6.82％ 5 Plastic Industry Huizhou City 5.4％ 6 Plastic Industry Foshan City 4％ 7 Plastic Industry Guangzhou City 3.5％ 8 Plastic Industry Jinan City 3.11％ 9 Plastic Industry Wenzhou City 2.82％ 10 Plastic Industry Jinhua City 2.4％

[0080] That is to say, the first industry-region data formed by the internal data analysis module is first ranked according to the total lease amount of different industries from the most to the least, and then for each industry, it is ranked according to the proportion of lease contracts in different regions from the most to the least.

[0081] In some optional embodiments, S120 obtains public information and determines the second industry-region data according to the public information, including: periodically obtaining public information; according to the public information, determining the ranking of the number of enterprises in each region under each industry to form the second industry-region data.

[0082] The obtaining unit further includes an external obtaining module. The external obtaining module periodically obtains public information from industry reports published by the state, enterprise information published by the state, public data publicly available in the network environment, etc., and transmits the public information to the analysis unit. The analysis unit may include an external data analysis module. The external data analysis module performs cleaning, integration, industry and region division of enterprises according to the incoming public information, so as to analyze the national industry-region ranking.

[0083] Specifically, according to the public information, determining the ranking of the number of enterprises in each region under each industry to form the second industry-region data includes: performing semantic recognition on the public information to determine the total number of enterprise values in each industry; for each industry, determining the number of enterprise values in each region; ranking each industry according to the total number of enterprise values in each industry from the most to the least, and ranking each region according to the number of enterprise values from the most to the least for each industry to form the second industry-region data.

[0084] The external data analysis module performs semantic recognition on the public information, and then classifies the public information according to the industries involved. After that, it performs data integration on the public information of each industry, and then analyzes the total number of enterprise values in each industry across the country. For example, the external data analysis module determines that the total number of enterprise values in the automotive industry is Z. The calculation method for other industries is the same.

[0085] The external data analysis module then refines and analyzes the number of enterprises in each industry and region across the country. For example, the external data analysis module determines that in the automobile industry, the number of enterprises in Dongguan is z1, the number of enterprises in Ningbo is z2, the number of enterprises in Jinan is z3, etc. The calculation method for other industries is the same.

[0086] The external data analysis module finally ranks each industry from most to least based on the total number of enterprises in each industry, and ranks each region from most to least based on the number of enterprises in each industry, to form the second industry region data.

[0087] For example, Table 5 shows the ranking of the number of enterprises in different regions in the plastics industry. Table 5 shows only ten rankings, but in actual situations, the rankings include more. In actual situations, the ranking of the number of enterprises in different regions in other industries is also included.

[0088] Table 5:

[0089]

[0090]

[0091] That is to say, the second industry region data formed by the external data analysis module is first ranked from most to least according to the total number of enterprises in different industries, and then ranked from most to least according to the number of enterprises in different regions for each industry.

[0092] In some optional embodiments, S130 determines potential industry region data based on the first industry region data and the second industry region data, including: determining a first preset number of first industry sets that are ranked high in the first industry region data, determining a first preset number of second industry sets that are ranked high in the second industry region data, and determining industries that belong to the second industry set and do not belong to the first industry set as potential industries; for each industry, determining a second preset number of first region sets that are ranked high in the first industry region data, determining a second preset number of second region sets that are ranked high in the second industry region data, and determining regions that belong to the second region set and do not belong to the first region set as potential regions; forming potential industry region data based on potential industries and potential regions.

[0093] The analysis unit may include a comparative analysis module, which performs comparative analysis on industries with high industry rankings in the first industry region data and industries with high industry rankings in the second industry region data, and determines industries that belong to the second industry set and do not belong to the first industry set as potential industries.

[0094] For example, in the case of the total starting rent amount ranking of different industries in the starting rent ledger shown in Table 3, the top 3 industries, namely the plastics industry, the automotive industry, and the home appliance industry, are selected to form the first industry set. In the case of the total enterprise quantity value ranking of different industries across the country, the top 3 industries, namely the new energy industry, the plastics industry, and the daily necessities industry, are selected to form the second industry set. The new energy industry and the daily necessities industry belong to the second industry set and do not belong to the first industry set. Therefore, the new energy industry and the daily necessities industry are added to the potential industry regional data.

[0095] For one industry, the comparison and analysis module compares and analyzes the regions with higher regional rankings in the first industry regional data and the regions with higher regional rankings in the second industry regional data, and determines the regions that belong to the second region set and do not belong to the first region set as potential regions.

[0096] For example, in the case of the ranking of the proportion of starting rent contracts in different regions of the plastics industry shown in Table 4, the top 5 regions, namely Dongguan City, Shenzhen City, Suzhou City, Ningbo City, and Huizhou City, are selected to form the first region set. In the case of the ranking of the enterprise quantity value in different regions of the plastics industry shown in Table 5, the top 5 regions, namely Ningbo City, Jinhua City, Guangzhou City, Suzhou City, and Chengdu City, are selected to form the second region set. Jinhua City, Guangzhou City, and Chengdu City belong to the second region set and do not belong to the first region set. Therefore, Jinhua City, Guangzhou City, and Chengdu City in the plastics industry are added to the potential industry regional data. The calculation method for other industries is the same.

[0097] The potential industry regional data formed by the comparison and analysis module includes potential industries and also includes the potential regions of each industry. The potential industries are several analyzed industries, and each industry includes all industries.

[0098] In some alternative embodiments, S140 forms a business adjustment strategy based on the potential industry regional data, including: obtaining the existing business resources of each region; forming a business resource adjustment plan for each region of each industry according to the potential industry regional data and the existing business resources.

[0099] The analysis unit transmits the potential industry regional data to the decision-making unit. The decision-making unit then generates a targeted business resource adjustment plan based on the existing business resources and the potential industry regional data. The decision-making unit may include a query module, an adjustment module, a display module, etc.

[0100] The existing business resources may include but are not limited to: the capital investment ratio of an industry in a region, the allocation of customer managers, etc. The query module queries and obtains the existing business resources of each industry in each region and transmits the existing business resources to the adjustment module.

[0101] The adjustment module adjusts the resources in a targeted manner according to the existing business resources and the potential industry regional data, and forms a business resource adjustment plan.

[0102] Continuing with the previous example: For instance, Jinhua City, Guangzhou City, and Chengdu City in the plastics industry are potential regions, but the plastics industry is not a potential industry. Based on the existing business resources in Jinhua City, Guangzhou City, and Chengdu City of the plastics industry, slightly increase the number of customer managers and the amount of capital investment to effectively develop the business in Jinhua City, Guangzhou City, and Chengdu City of the plastics industry. For example, if the new energy industry is a potential industry, then for the potential regions under the new energy industry, based on the existing business resources, significantly increase the number of customer managers and the amount of capital investment to develop the business in the potential regions under the new energy industry with greater intensity.

[0103] The adjustment module can transmit potential industry region data, business adjustment strategies, etc. to the display module so that the personnel of the financial leasing company can implement potential business mining based on the potential industry region data, business adjustment strategies, etc.

[0104] Please refer to Figure 2 as shown Figure 2 Another process schematic diagram of the potential business mining method provided by the embodiment of the present invention, including the following steps:

[0105] Step S201, periodically obtain the lease start ledger; go to step S202.

[0106] Step S202, perform semantic recognition on each lease start contract in the lease start ledger to determine the industry label, regional label, and lease start amount of each lease start contract; go to step S203;

[0107] Step S203, according to the industry label and the lease start amount, determine the number of lease start contracts and the total lease start amount of each industry; go to step S204;

[0108] Step S204, according to the regional label, determine the number of lease start contracts in each region under each industry; go to step S205;

[0109] Step S205, according to the number of lease start contracts in each region under each industry and the number of lease start contracts corresponding to each industry, determine the proportion of lease start contracts in each region under each industry; go to step S206;

[0110] Step S206, rank each industry according to the total lease start amount of each industry from more to less, and rank each region according to the proportion of lease start contracts from more to less for each industry to form the first industry-region data; go to step S211;

[0111] Step S207, periodically obtain public information; go to step S208;

[0112] Step S208, perform semantic recognition on the public information to determine the total number of enterprise values of each industry; go to step S209;

[0113] Step S209: For each industry, determine the number of enterprises in each region; go to step S210;

[0114] Step S210: Rank each industry according to the total number of enterprises in each industry from most to least, and rank each region according to the number of enterprises in each industry from most to least to form the second industry-region data; go to step S211;

[0115] Step S211: Determine the first preset number of first industry sets ranked at the top in the first industry-region data, determine the first preset number of second industry sets ranked at the top in the second industry-region data, and determine the industries that belong to the second industry set and do not belong to the first industry set as potential industries; go to step S212;

[0116] Step S212: For each industry, determine the second preset number of first region sets ranked at the top in the first industry-region data, determine the second preset number of second region sets ranked at the top in the second industry-region data, and determine the regions that belong to the second region set and do not belong to the first region set as potential regions; go to step S213.

[0117] Step S213: Form potential industry-region data based on potential industries and potential regions; go to step S214.

[0118] Step S214: Obtain the existing business resources of each region; go to step S215;

[0119] Step S215: According to the potential industry-region data and the existing business resources, form a business resource adjustment plan for each industry in each region.

[0120] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0121] In one embodiment, a potential business mining system is provided, and the potential business mining system corresponds one-to-one with the potential business mining method in the above embodiment. As Figure 3 shown, the potential business mining system 300 includes:

[0122] A lease start ledger unit 310 for storing the lease start ledger;

[0123] An acquisition unit 320 for acquiring the lease start ledger and public information;

[0124] An analysis unit 330 is configured to determine first industry-region data based on a lease start ledger, determine second industry-region data based on public information, and determine potential industry-region data based on the first industry-region data and the second industry-region data;

[0125] A decision-making unit 340 is configured to form a business adjustment strategy based on the potential industry-region data.

[0126] In some alternative embodiments, the acquisition unit 320 is specifically configured to: periodically acquire the lease start ledger; the analysis unit 330 is specifically configured to: based on the lease start ledger, determine the ranking of the proportion of lease start contracts in each region under each industry, and form the first industry-region data.

[0127] In some alternative embodiments, the analysis unit 330 is specifically configured to: perform semantic recognition on each lease start contract in the lease start ledger to determine the industry label, region label, and lease start amount of each lease start contract; based on the industry label and the lease start amount, determine the number of lease start contracts and the total lease start amount for each industry; based on the region label, determine the number of lease start contracts in each region under each industry; based on the number of lease start contracts in each region under each industry and the number of lease start contracts corresponding to each industry, determine the proportion of lease start contracts in each region under each industry; rank each industry according to the total lease start amount from more to less, and rank each region for each industry according to the proportion of lease start contracts from more to less, to form the first industry-region data.

[0128] In some alternative embodiments, the acquisition unit 320 is specifically configured to: periodically acquire public information; the analysis unit 330 is specifically configured to: based on the public information, determine the ranking of the number of enterprises in each region under each industry, and form the second industry-region data.

[0129] In some alternative embodiments, the analysis unit 330 is specifically configured to: perform semantic recognition on the public information to determine the total number of enterprises value for each industry; for each industry, determine the number of enterprises value for each region; rank each industry according to the total number of enterprises value from more to less, and rank each region for each industry according to the number of enterprises value from more to less, to form the second industry-region data.

[0130] In some alternative embodiments, the analysis unit 330 is specifically configured to: determine a first preset number of top-ranked first industry sets in the first industry-region data, determine a first preset number of top-ranked second industry sets in the second industry-region data, and identify industries that belong to the second industry set but not the first industry set as potential industries; for each industry, determine a second preset number of top-ranked first region sets in the first industry-region data, determine a second preset number of top-ranked second region sets in the second industry-region data, and identify regions that belong to the second region set but not the first region set as potential regions; and form potential industry-region data based on the potential industries and potential regions.

[0131] In some alternative embodiments, the decision-making unit 340 is specifically configured to: obtain the existing business resources of each region; and form business resource adjustment plans for each region of each industry according to the potential industry-region data and the existing business resources.

[0132] Please refer to Figure 4 shown in Figure 4 FIG. 4, which is another schematic structural diagram of the potential business mining system provided by the embodiment of the present invention. The potential business mining system 400 includes:

[0133] The lease start ledger unit 410 is used to store the lease start ledger.

[0134] The acquisition unit 420 is used to acquire the lease start ledger and public information.

[0135] Among them, the acquisition unit 420 includes:

[0136] The internal acquisition module 4201 is used to acquire the lease start ledger.

[0137] The external acquisition module 4202 is used to acquire public information.

[0138] The analysis unit 430 is used to determine the first industry-region data according to the lease start ledger, determine the second industry-region data according to the public information, and determine the potential industry-region data according to the first industry-region data and the second industry-region data.

[0139] Among them, the analysis unit 430 includes:

[0140] The industry parameter configuration module 4301 is used to pre-configure the correspondence between industries and key information.

[0141] The region parameter configuration module 4302 is used to pre-configure the correspondence between regions and enterprise regions.

[0142] The internal data analysis module 4303 is used to perform semantic recognition on each lease contract in the lease ledger, determine the industry label, regional label, and lease amount of each lease contract; determine the number of lease contracts and the total lease amount of each industry according to the industry label and lease amount; determine the number of lease contracts in each region under each industry according to the regional label; determine the proportion of lease contracts in each region under each industry according to the number of lease contracts in each region under each industry and the number of lease contracts in each corresponding industry; rank each industry according to the total lease amount of each industry from more to less, and rank each region in each industry according to the proportion of lease contracts from more to less to form the first industry-region data.

[0143] The external data analysis module 4304 is used to perform semantic recognition on public information, determine the total number of enterprises in each industry; for each industry, determine the number of enterprises in each region; rank each industry according to the total number of enterprises in each industry from more to less, and rank each region in each industry according to the number of enterprises from more to less to form the second industry-region data.

[0144] The comparative analysis module 4305 is used to determine the first preset number of first industry sets ranked at the top in the first industry-region data, determine the first preset number of second industry sets ranked at the top in the second industry-region data, and determine the industries that belong to the second industry set and do not belong to the first industry set as potential industries; for each industry, determine the second preset number of first region sets ranked at the top in the first industry-region data, determine the second preset number of second region sets ranked at the top in the second industry-region data, and determine the regions that belong to the second region set and do not belong to the first region set as potential regions; form potential industry-region data based on potential industries and potential regions.

[0145] The decision-making unit 440 is used to form a business adjustment strategy according to the potential industry-region data.

[0146] Among them, the decision-making unit 440 includes:

[0147] The query module 4401 is used to obtain the existing business resources in each region.

[0148] The adjustment module 4402 is used to form a business resource adjustment plan for each region in each industry according to the potential industry-region data and the existing business resources.

[0149] The display module 4403 is used to display the potential industry-region data and the business adjustment strategy.

[0150] For the specific limitations of the potential business mining system, reference can be made to the limitations of the potential business mining method in the foregoing text, which will not be elaborated here. Each module in the above potential business mining system can be implemented in whole or in part by software, hardware, and their combinations. The above modules can be embedded in the processor of the computer device in the form of hardware or independent of it, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0151] In one embodiment, a computer device is provided, and the internal structure diagram of the computer device can be as Figure 5 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps of a potential business mining method.

[0152] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are realized:

[0153] Obtain the lease start ledger and determine the first industry area data according to the lease start ledger;

[0154] Obtain public information and determine the second industry area data according to the public information;

[0155] Determine the potential industry area data according to the first industry area data and the second industry area data;

[0156] Form a business adjustment strategy according to the potential industry area data.

[0157] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the following steps are realized:

[0158] Obtain the lease start ledger and determine the first industry area data according to the lease start ledger;

[0159] Obtain public information and determine the second industry area data according to the public information;

[0160] Determine the potential industry area data according to the first industry area data and the second industry area data;

[0161] Form a business adjustment strategy based on potential industry regional data.

[0162] It should be noted that for the functions or steps that can be achieved by the above-mentioned computer-readable storage medium or computer device, reference may be made to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described in detail here.

[0163] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0164] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0165] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A potential business mining method, characterized in that: include: Obtain a rent account, and determine the first industry region data according to the rent account; Obtaining public information, and determining the second industry regional data based on the public information; Determine potential industry region data according to the first industry region data and the second industry region data; Form business adjustment strategies based on the potential industry and regional data.

2. The potential business mining method according to claim 1, characterized in that: The obtaining of the rent start record and determining the first industry region data according to the rent start record include: Periodically obtain the rental ledger; Based on the lease record, the ranking of the proportion of lease contracts in each region under each industry is determined to form the first industry region data.

3. The potential business mining method according to claim 2, characterized in that: The ranking of the proportion of the initial lease contracts in each region under each industry is determined according to the initial lease account to form the first industry and region data, including: Perform semantic recognition on each rental contract in the rental ledger to determine the industry label, regional label and rental amount of each rental contract; Determine the number of starting lease contracts and the total starting lease amount for each industry based on industry labels and starting lease amounts; Based on the regional labels, determine the starting number of lease contracts in each region under each industry; Determine the proportion of the initial lease contracts in each region under each industry based on the number of initial lease contracts in each region under each industry and the number of initial lease contracts corresponding to each industry; Rank each industry by its total starting lease amount from most to least, and rank each region by its proportion of starting lease contracts for each industry from most to least, to form the first industry region data.

4. The potential business mining method according to claim 1, characterized in that: The obtaining of public information and determining the second industry region data according to the public information includes: Periodically obtaining the public information; Based on the public information, the ranking of the number of enterprises in each region under each industry is determined to form the second industry region data.

5. The potential business mining method according to claim 4, characterized in that: Determining the ranking of the number of enterprises in each region under each industry based on the public information to form the second industry region data includes: Perform semantic recognition on the public information to determine the total number of enterprises in each industry; For each industry, determine the value of the number of enterprises in each region; The industries are ranked from most to least according to the total number of enterprises in each industry, and the regions are ranked from most to least according to the number of enterprises in each industry to form the second industry region data.

6. The potential business mining method according to claim 1, characterized in that: The determining of potential industry region data according to the first industry region data and the second industry region data includes: Determine a first preset number of first industry sets that are ranked high in the first industry region data, determine a first preset number of second industry sets that are ranked high in the second industry region data, and determine industries that belong to the second industry set and do not belong to the first industry set as potential industries; For each industry, determine a second preset number of first region sets that rank high in the first industry region data, determine a second preset number of second region sets that rank high in the second industry region data, and determine regions that belong to the second region set and do not belong to the first region set as potential regions; The potential industry and region data are formed based on potential industries and potential regions.

7. The potential business mining method according to claim 1, characterized in that: The forming of a business adjustment strategy based on the potential industry and region data includes: Obtain existing business resources in various regions; Based on the potential industry region data and existing business resources, a business resource adjustment plan for each region in each industry is formed.

8. A potential business mining system, characterized in that: include: The rent account unit is used to store the rent account; The acquisition unit is used to obtain the rental ledger and public information; An analysis unit, configured to determine first industry region data according to the rental account, determine second industry region data according to the public information, and determine potential industry region data according to the first industry region data and the second industry region data; A decision-making unit is used to form a business adjustment strategy according to the potential industry and region data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the potential business mining method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the potential business mining method according to any one of claims 1 to 7 are implemented.

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