CN117455549A - A consumption power assessment method based on urban physical indicators - Google Patents

Abstract

The invention discloses a consumption capability assessment method based on urban sign indexes, which relates to the technical field of smart cities, and comprises the following steps of: the data source at least comprises Unionpay consumption data and operator user data; and (3) data source fusion: searching an intersection between data sources, and establishing an intersection data set; feature engineering processing, extracting relevant features of modeling targets; and adopting a federal learning method to evaluate the consumption capability and the consumption portraits. The evaluation method provided by the invention creates a residential consumption capacity model and a consumption portrait model by integrating the Unionpay consumption data and the operator user data and applying a privacy calculation technology and a Federal learning method, thereby providing decision support for urban management and business development; the method can better understand the consumption behaviors and demands of residents, and provides powerful support for the fields of business decision making, policy making, city planning and the like, so that sustainable development and prosperity of cities are promoted.

Description

A consumption power assessment method based on urban physical indicators

Technical field

The invention relates to the technical field of smart cities, and specifically relates to a consumption capacity assessment method based on urban physical indicators.

Background technique

As urbanization continues to accelerate, urban planning and management have become increasingly complex and critical. Urban physical indicators are key factors in assessing urban development and quality, including but not limited to population structure, air quality, traffic congestion, residents' consumption and other aspects. The city is regarded as an organic life form and is also faced with various "urban disease" problems. Just like the human body requires regular physical examinations, cities also need regular physical examinations to discover problems, diagnose the causes and take effective measures. Urban vital indicator data has become an indispensable tool in this process.

Taking resident consumption as an example, under the influence of external environment and other factors, weak consumption has become a significant problem. It is particularly important to provide effective consumption stimulation strategies to drive urban economic development. However, the traditional analysis of residents’ consumption behavior and consumption power relies on a single financial system consumption data, which lacks comprehensive data support, cannot fully reflect the multi-dimensional characteristics of residents’ consumption, and is difficult to form a comprehensive portrait of residents’ consumption. In addition, traditional methods often fail to fully consider the integration relationship between various urban physical indicators. For example, the Chinese patent with publication number CN109829763A discloses a consumption power assessment method and device, electronic equipment, and storage media. The method includes: obtaining the historical consumption data of a target user; responding to transactions in the target user's historical consumption data If there are missing categories, perform multiple interpolation on the missing data associated with the missing transaction categories to obtain multiple complete data sets; perform data analysis on the multiple complete data sets to obtain the characteristic parameters corresponding to each complete data set. ; Obtain a target value representing the consumption ability through the characteristic parameters, and evaluate the consumption ability of the target user according to the target value.

Therefore, a new data model construction method is urgently needed to solve the problem of multi-dimensional and deep consumption data mining and analysis, so as to better support decision-making for urban economic growth and sustainable development.

Contents of the invention

In view of the problems existing in the existing technology, the present invention provides a consumption power assessment method based on urban physical signs indicators.

In order to achieve the above objects, the technical solutions adopted by the present invention are as follows:

A consumption capacity assessment method based on urban physical indicators, including the following steps:

Step S1: Data source preparation: The data source at least includes UnionPay consumption data and operator user data;

Step S2: Data source fusion: find the intersection between data sources;

Step S3: Perform feature engineering processing on the fused data source;

Step S4: Use federated learning methods to predict and evaluate consumption power and consumption portraits.

Based on the above technical solution, further, in step S1, the basic information and consumption information of residents are obtained through the UnionPay consumption data package interface, where the UnionPay consumption data includes the user's basic information and consumption information, and the user's consumption information is stored in in the database. The user's basic information such as name, ID number, bank card number, etc., and the user's consumption information such as consumption amount, consumption date, consumption behavior (consumption type), consumption location, etc.

Based on the above technical solution, further, in step S1, the user's privacy data involved in the operator's user data is jointly modeled in the operator's database through privacy calculation methods. Among them, operator user data includes user portrait data such as mobile phone number, gender, age, occupation, education level, marital status, number of children, etc.

Based on the above technical solution, further, in step S2, the hidden set intersection method is used to fuse the two data sources, and encrypted matching is performed based on the common key attributes existing between the two data sources to determine the existence of the two data sources. common information, and store the common information into the established intersection data set. Among them, this process requires the use of encryption technology to protect data privacy and ensure that no clear text sensitive information is leaked during the data fusion process.

Based on the above technical solution, further, in step S2, the data source fusion process includes the following steps: Step S21: Use a hash function to perform hash processing on the two data sources; Step S22: Hash the two processed data sources respectively. The corresponding hash value is sent to the other party, and the two parties exchange hash values; Step S23: Both parties locally compare the other party's hash value with their own hash value, find a common hash value, and search for storage through the common hash value to a common intersection data set in their respective databases.

Based on the above technical solution, further, in step S3, the feature engineering process includes the process of feature construction, where feature construction is to select useful features from the original data source and combine the useful features into a new subset. . For example: original feature data set: age, income, marital status, consumption behavior, number of children, etc.; some new features are constructed from these original features.

Based on the above technical solution, further, in step S3, feature engineering processing includes feature derivation processing, and the processing process is: constructing new features based on business knowledge or relationships between features. For example: based on consumption behavior, consumption location, consumption time and other data, calculate the business district economy, form the business district consumption characteristics, business district night economy characteristics, and analyze and rank the city's business districts.

Based on the above technical solution, further, in step S3, feature engineering processing includes the processing of feature selection. The processing process is: first use machine learning method recursive features to eliminate features that have the ability to predict the target variable; then build a model and gradually Features are selected by eliminating features that contribute little to the model's predictive ability, where the target variable refers to the output or label in the model being built; finally, the features are sorted according to their importance scores, and the features with the lowest importance scores are eliminated until Until the specified number of features is reached or the model performance no longer improves.

Based on the above technical solution, further, in step S3, feature engineering processing includes feature combination processing; the processing process is: combining different features to form new features.

Based on the above technical solution, further, in step S4, the evaluation process using the federated learning method includes the following steps: Step S41: Select a federated learning model; Step S42: Construct a consumption power model and evaluate it; Step S43: Based on the selected federation The learning model performs training operations on the consumption portrait model; step S44: perform prediction and evaluation processing on the trained consumption portrait model. Specifically, the consumption portrait is obtained through joint modeling through UnionPay and operator data, and then taking out the parts of the participant data that have the same users but different characteristics for federated learning modeling training.

Based on the above technical solution, further, in step S42, the consumption amount of the user cardholder within the set time period is calculated based on the local UnionPay consumption data, and the consumption capacity is divided according to the percentile calculation of the transaction amount of each cardholder. The gears are set as the gear with significantly low consumption power, the gear with low consumption power, the gear with considerable consumption power, the gear with high consumption power, the gear with significantly high consumption power, and the gear with high consumption power.

Based on the above technical solutions, furthermore, the comprehensive application scenarios of the constructed consumption power model and the trained consumption portrait model include at least: urban business district economic analysis, real estate policy regulation, resident consumption voucher issuance, urban night economy analysis and transportation planning. and urban development.

Compared with the existing technology, the present invention has the following beneficial effects:

(1) The evaluation method provided by the present invention integrates UnionPay consumption data and operator user data, and uses privacy technology and federated learning methods to create residents' consumption power models and consumption portrait models. Its main goals include providing accurate consumption capacity assessments and decision-making support for urban management and business development. It can better understand the consumption behavior and needs of residents, provide strong support for business decision-making, policy formulation, urban planning and other fields, thereby promoting the sustainable development and prosperity of the city.

(2) Data fusion and comprehensiveness: This invention realizes the fusion of UnionPay consumption data and operator user data, enabling city managers to obtain more comprehensive and comprehensive information, and helping to more accurately assess and analyze the consumption of urban residents. abilities and behaviors.

(3) Privacy protection: By concealing the set intersection PSI method, the present invention protects the user's privacy, ensures that the user's sensitive information will not be leaked, and at the same time achieves safe cross-matching of data.

(4) Joint model training: Federated learning technology is used to allow multiple parties to conduct model training on local data, avoiding the privacy risks of data sharing and transmission. At the same time, by sharing model parameters, a more powerful global model is built, improving the model's accuracy. Multidimensionality and precision.

(5) Decision support and urban management: By constructing data models such as consumption capacity and consumption portraits, the present invention provides a powerful tool for city managers, which can be used for urban planning and management decision-making, helping cities better meet the needs of residents, Improve residents' quality of life and promote urban economic development.

(6) Customized analysis: This invention can flexibly customize different models according to the specific needs of city managers, such as urban night economy analysis, city business district rankings, resident consumption voucher issuance, real estate policy regulation, etc., supporting multiple levels and In-depth analysis of the field provides more targeted data and insights for decision-making in different aspects.

Description of the drawings

Figure 1 is a flow chart of the evaluation method of the present invention.

Detailed ways

The present invention will be further elaborated and described below in conjunction with the accompanying drawings and specific embodiments. The technical features of various embodiments of the present invention can be combined accordingly as long as they do not conflict with each other.

In order to make the above objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, the present invention can be implemented in many other ways different from those described here. Those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in various embodiments of the present invention can be combined accordingly as long as they do not conflict with each other.

Example 1

This method provides a consumption power assessment method based on urban physical indicators, which includes data fusion, privacy protection, joint model training and algorithm selection. Specifically, through the integration of UnionPay consumption data and operator user data, different data sources are merged and the model data dimension is improved. It also pays attention to the protection of user privacy and uses the method of hidden set and intersection PSI to encrypt and match user sensitive information to ensure that data privacy is not leaked. Through federated learning technology, multiple parties can train models on local data, avoiding the transmission of sensitive data, while sharing model parameters to build a more powerful global model. Algorithms such as logistic regression and linear regression were selected to construct data models such as consumption capacity and consumption portraits, providing city managers with decision support and in-depth analysis tools. This method combines the integration and application of technology to form an innovative method that can mine residents' consumption capabilities and consumption profiles to more accurately and comprehensively assess the city's situation, identify problems and formulate countermeasures. Through this method, it can play a key role in urban planning and management, provide comprehensive data support for urban development, while protecting residents' privacy, and providing a more scientific basis for decision-making and resource allocation.

As shown in Figure 1, this method includes the following steps: Step S1: Data source preparation: the data source at least includes UnionPay consumption data and operator user data; specifically, obtain basic resident information and consumption through the UnionPay consumption data package interface Information, in which UnionPay consumption data includes users' basic information and consumption information, and the user's consumption information is stored in the UnionPay consumption database. The user's basic information such as name, ID number, bank card number, etc., and the user's consumption information such as consumption amount, consumption date, consumption behavior (consumption type), consumption location, etc.

Among them, the specific data structure of UnionPay consumption data is as follows: Table 1:

Table 1

As for the preparation process of operator user data, due to data security requirements, operator user data involving user privacy data cannot be exported from the database, and will be jointly modeled through privacy calculations in the operator database. Among them, operator user data includes user portrait data such as mobile phone number, gender, age, occupation, education level, marital status, number of children, etc. The specific data structure provided by the operator is as follows in Table 2:

Table 2

Step S2: Data source fusion: Find the intersection between data sources; specifically, use the hidden set intersection PSI method to fuse the two data sources. It is necessary to find the two data sources without leaking any information. intersection between. Encryption matching is performed based on the common key attributes existing between the two data sources, the common information existing in the two data sources is determined, and the common information is stored in the established intersection data set. That is to say, for UnionPay consumption data and operator user data, encrypted matching can be performed based on common key attributes (such as mobile phone numbers) to determine common users existing in the two data sources. Create an intersection data set, which contains the intersection of data held by both parties. This process requires the use of encryption technology to protect data privacy and ensure that no plaintext sensitive information is leaked during the data fusion process. Among them, encryption technology is the main security and confidentiality measure taken by e-commerce and is the most commonly used security and confidentiality method. Technical means are used to protect important information. The data becomes garbled (encrypted) and transmitted, and then restored (decrypted) using the same or different means after reaching the destination.

Further, the data source fusion process includes the following steps:

Step S21: Use the hash function to perform hash processing on the two data sources; specifically, the two data sources of UnionPay consumption data (hereinafter referred to as participant A) and operator user data (hereinafter referred to as participant B) are data respectively. Source X and data source Y. The hope is to find the intersection of these two data sources while protecting data privacy. Mobile phone number is a common key attribute in each data source. Select the MD5 hash function. Use the MD5 hash function for each participant to hash their mobile phone number, as shown in Table 3 below:

table 3

Step S22: Send the hash values corresponding to the two processed data sources to the other party, and the two parties exchange hash values; specifically, after participant A and participant B perform hash processing on their own data sources respectively, For each data element, a hash value is generated. Party A calculates the hash value of data source X, stores these hash values and sends them to Party B. Participant B calculates the hash value of data source Y, stores these hash values and sends them to participant A.

Step S23: Both parties locally compare the other party's hash value with their own hash value, find a common hash value, and use the common hash value to find a common intersection data set stored in their respective databases. Specifically, taking the secure exchange of mobile phone numbers as an example, the data of both parties uses the user's 11-digit mobile phone number to be md5-encrypted with a 32-digit lowercase value as the matching field for secure exchange of mobile phone numbers. Among them, after the hidden set intersection (PSI) is completed, the data of both parties are still maintained in their respective databases. Hidden set intersection is a privacy-preserving technique that aims to find the intersection between two data sets without revealing the details of the data, meaning the data remains private between both parties.

Step S3: Perform feature engineering processing on the fused data source to extract features related to the modeling target; specifically, feature engineering processing includes feature construction processing, where feature construction is to select useful features from the original data source. , and combine useful features into new subsets. This method can obtain features from UnionPay consumption data and operator user data, transform and combine features, etc. For example: original feature data sources: age, income, marital status, consumption behavior, number of children, etc.; some new features are constructed from these original features. As a further example, first create a data source containing original features; then, demonstrate multiple different feature constructions, for example: 1. Create a new feature "household income" by multiplying the "income" feature and the "quantity" feature. Represents the total household income. 2. Create a new feature "high-income occupation" and process the "occupation" feature to determine whether it belongs to a high-income occupation. 3. Create a new feature "middle-aged married" and use conditions to determine whether a person is a middle-aged married person.

Feature engineering processing includes feature derivation processing. The processing process is: constructing new features based on business knowledge or the relationship between features. For example: based on consumption behavior, consumption location, consumption time and other data, calculate the business district economy, form the business district consumption characteristics, business district night economy characteristics, and analyze and rank the city's business districts. Process example: First define three data tables: behavior_data represents consumption behavior data, location_data represents consumption location data, and time_data represents consumption time data. Then, use the merge function to merge the three tables according to the user_id field to obtain combined_data. Then, use the groupby function to classify consumption according to location, and calculate the total consumption at each location to obtain economic_feature. Finally, the rank function is used to rank the economic features to obtain economic_ranking.

Feature engineering processing includes feature selection processing. The processing process is: first use machine learning method recursive features to eliminate features with predictive ability for the target variable, and select features with strong predictive ability. Among them, "stronger" judgment conditions usually refer to The feature has significant predictive ability for the target variable, that is, there is an obvious correlation between the feature and the target variable, which can be measured through some statistical indicators (such as correlation coefficient, mutual information, variance analysis, etc.). The correlation between the feature and the target variable The higher the correlation, the more likely the feature is considered "stronger"; while the target variable refers to the output or label in the model being built; at each iteration, the model is trained using the remaining features and Calculate the importance score of each feature; then select features by building a model and gradually eliminating features that contribute little to the model's predictive ability. Specifically, build a business-related target model and perform it through the machine learning method Recursive Feature Elimination (RFE). Model construction; among them, features with small contribution are preferred. "Small contribution" means that the features do not significantly improve the prediction performance of the model. That is, in model training, even if these features are removed, the performance of the model will not change much. This is It can be determined through cross-validation or other performance evaluation methods. The small contribution of features means that their performance improvement to the model is not obvious; finally, the features are sorted according to the importance score, and the features with the lowest importance score are eliminated until the specified until the number of features or model performance no longer improves. Example of recursive feature elimination: Automatically adjust the number of selected features through cross-validation. It is best to draw the cross-certification score of the classification set for each number of features. You can see that RFECV can automatically select the number of effective features suitable for classification. The processing idea is to use the machine learning method Recursive Feature Elimination (RFE) to start from all features, first use all features for model training, and then perform each feature based on the performance of the model (usually based on the importance or weight of the feature) Sort. In each iteration, RFE removes the lowest-scoring feature or features and retrains the model. This process is repeated until a stopping condition (such as a specified number of features) is reached. Calculate the score of the feature through accuracy, F1 score or R-squared value. In each iteration, RFE trains the model and evaluates its performance, then ranks each feature based on the feature importance or weight of the model output. Features with lower scores are considered to contribute less to model performance and are gradually eliminated to improve model performance. This process is repeated until a stopping condition (such as a specified number of features or a target performance level) is reached.

Feature engineering processing includes feature combination processing; the processing process is: combining different features to form new features. For example, marital status and number of children can be combined into a family status feature; people who are accustomed to consuming fitness products and gyms can be combined into a sports expert feature; occupations such as doctors, lawyers, engineers, and education levels can be combined into a high education feature Career etc. Example: Combination of gender and consumption behavior: Binary logistic regression can be used to predict consumption behavior, for example, gender is divided into male and female, consumption behavior is divided into high and low, and then binary logistic regression is used to predict consumption behavior. In addition to using the binary logistic regression method, polynomial features or feature intersection methods can also be used to combine features to form new features to predict consumer behavior. Among them, polynomial features can generate high-order feature combinations by polynomial expansion of the original features. For example, two features a and b are combined into ab, b squared, a squared, etc. This can be done using machine learning such as Scikit-Learn. The PolynomialFeatures of the library are implemented. Feature crossover refers to combining the values of different features with each other, for example, multiplying or dividing the values of feature a and feature b. This crossover can capture the mutual relationship between features.

Step S4: Use federated learning method to evaluate consumption capacity. The evaluation process using federated learning methods includes the following steps:

Step S41: Select a federated learning model; specifically, federated learning is divided into three categories: horizontal federated learning, vertical federated learning, and federated transfer learning. Take two institutions in a certain city as an example. Institution A has user consumption records from UnionPay, and institution B has user data from operators. The two institutions have many overlapping users, but the recorded data characteristics are different. The two institutions want to By encrypting and aggregating different characteristics of users to jointly train a more powerful data model, vertical federated learning is the most appropriate choice. Vertical federated learning requires sample alignment first, that is, finding common samples owned by participants, which is also called "database stuffing". Vertical federation increases the feature dimensions of training samples. In this example, consumption power is divided by the absolute value of consumption. There is no profile information such as the user's occupation, marriage, children, education level, etc. If we want to obtain, for example, what are the profile characteristics of people with low consumption? What is the academic level? Are there more singles or marrieds? Another example is portrait information such as what are the characteristics of the occupations of high-income consumers or what fields they are concentrated in. Combined with the operator's user portrait data, after matching the encrypted primary keys (mobile phone numbers) on both sides, similar corresponding indicator classifications will be explained more clearly. Be more specific so that you can conduct more dimensional and deeper analysis and excavation.

Step S42: Construct a consumption power model; specifically, calculate the consumption amount of resident cardholders in the city in the past three months based on local UnionPay consumption data, calculate the percentile of each cardholder's transaction amount, and divide consumption capacity brackets The levels are set as the level with significantly low consumption power, the level with low consumption power, the level with considerable consumption power, the level with high consumption power, the level with significantly high consumption power, and the level with high consumption power. The consumption amount threshold is based on the monthly transactions of cardholders in this city. After running out the distribution using the logistic regression model, statistics are made according to the boundary conditions of the distribution. As an example in Table 4 below:

Table 4

Step S43: Perform training operations on the consumption portrait model based on the selected federated learning model; specifically, select a vertical federated learning model for training. For example, company A has consumption data of residents in a certain city, and company B has resident portrait data of the same city. Now, matching is performed based on the user mobile phone numbers of both parties, and the parts of the participants' data that have the same users but different characteristics are taken out for joint training. Due to user privacy and data security reasons, parties A and B cannot directly exchange data. In order to ensure data confidentiality during the training process, a third-party coordinator C needs to be added at this time. The specific training process is as follows: and/> Represents the local data of Company A and Company B respectively,/> Represent the local models trained by companies A and B respectively.

Then the objective function is:

can be further simplified, let

Merge b into , the loss function becomes:

make

Then the loss function after encryption is:

make ;

but

make

Then the loss function is The gradients of are respectively:/>

Because it involves decryption, the private key is stored in scheduler C. Neither A nor B has a private key for decryption, so the privacy will not be leaked during the exchange. The encryption gradient sent to C can be masked with a random number. This random number is known only to A and B themselves, so the gradient is not directly exposed to C.

Step S44: Predict and evaluate the trained consumption profile model. The consumption profile is jointly modeled through UnionPay and operator data, and the parts of the participant data that have the same users but different features are taken out for federated learning. Derived from modeling training. Specifically, based on the case, the prediction process of the consumption portrait model is: Step 1: Party A: None; Party B: None; Party C: Send i (i refers to the user’s mobile phone number) to Party A and Party B. Step 2: Side A: Calculation And send it to Party C; Party B: Calculate/> And send it to Party C; Party C: Calculate/> The result; C square: the calculated result.

The effect evaluation of this consumer portrait model is as shown in Table 5:

table 5

Among them, the KS statistic is used to measure the maximum gap between the true positive rate and the false positive rate of the classification model under different thresholds; the larger the KS value, the better the model performance; usually, the KS value ranges from 0 to 1 Between them, the closer the KS value is to 1, the better the model performance is. F1Score is an indicator that comprehensively considers the precision (Precision) and recall rate (Recall) of the classification model; it is the harmonic average of the two, used to measure the classification accuracy of the model in the positive category and the negative category; F1Score is The value ranges from 0 to 1, with the closer to 1 indicating better model performance. AUC refers to a performance indicator that measures the quality of a learner. It is the area under the ROC curve, which is used to measure the performance of a two-classification model; the ROC curve uses different classification thresholds as the abscissa, and the true positive rate (True Positive Rate) and False Positive Rate (False Positive Rate) are curves drawn on the ordinate; the value range of AUC is between 0 and 1, and the closer to 1, the better the performance of the model. AUC (Area Under the Curve), KS (Kolmogorov-Smirnov) and F1Score are common indicators for evaluating model performance. The value range is between 0 and 1. The closer the value is to 1, the better the model performance.

From the model recorded in Table 5, it can be seen that the auc can reach 0.8 and the ks can reach 0.57, and the model effect is stable.

Furthermore, the comprehensive application scenarios of the constructed consumption power model and the trained consumption portrait model include at least: urban business district economic analysis, real estate policy regulation, resident consumption coupon issuance, urban night economy analysis, and transportation planning and urban development. Specifically, the specific situation of the application scenario is comprehensively analyzed from the two dimensions of consumption capacity and consumption portrait, for example,

1) Economic analysis of urban business districts: Based on these models, city managers can have an in-depth understanding of the consumption power and behavioral habits of residents in different business districts. This helps in the planning and operation of commercial districts and determines appropriate commercial development strategies, pricing strategies and store positioning to best meet the needs of residents and improve the economic vitality of the commercial district.

2) Real estate policy regulation: Consumption power and cardholder value models can be used to evaluate the demand and purchasing power of home purchases in different regions. The government can adjust real estate policies, such as purchase restriction policies and loan policies, based on these models to maintain the stability and sustainable development of the real estate market.

3) Issuance of consumption coupons to residents: By understanding residents’ spending power and freedom, city managers can more accurately formulate consumption stimulus policies, such as the issuance of consumption coupons. This encourages consumption and promotes economic growth while minimizing waste.

4) Analysis of urban night economy: Through consumption portraits and consumption freedom models, city managers can understand residents’ nighttime consumption preferences and behaviors. This is very important for the development of the city's night economy and night service industry, helping to improve the safety and attractiveness of the city.

5) Transportation planning and urban development: These models can also be used for urban transportation planning and infrastructure construction. Understanding the travel habits and spending power of different residents can help optimize transportation networks, public transportation systems, and ensure the sustainable development of cities.

Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and does not limit the protection scope of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by those of ordinary skill in the art do not deviate from the scope of the present invention. The essence and scope of the technical solution of the invention.

Claims (10)

1. The consumption capability assessment method based on the urban sign index is characterized by comprising the following steps of:

step S1: data source preparation: the data source at least comprises Unionpay consumption data and operator user data;

step S2: and (3) data source fusion: searching for intersections between the data sources;

step S3: carrying out characteristic engineering treatment on the fused data sources;

step S4: and evaluating the consumption capacity and the consumption portraits by adopting a federal learning method.

2. The method for evaluating the consumption capability based on the urban sign index according to claim 1, wherein in the step S1, the resident basic information and the consumption information are obtained through a silver-linked consumption data packet interface, wherein the silver-linked consumption data comprises the basic information and the consumption information of the user, and the consumption information of the user is stored in a silver-linked consumption database.

3. The method for evaluating the consumption ability based on the urban sign index according to claim 1, wherein in step S1, privacy data of users involved in the operator user data are jointly modeled in an operator database by a privacy calculation mode.

4. The method for evaluating the consumption ability based on the urban sign index according to claim 1, wherein in the step S2, the data source fusion process comprises the following steps:

step S21: carrying out hash processing on the two data sources by adopting a hash function;

step S22: the hash values corresponding to the two processed data sources are sent to the other party, and the two parties exchange the hash values;

step S23: the two parties locally compare the hash value of the other party with the hash value of the other party, find a common hash value, and find a common intersection data set stored in the respective databases through the common hash value.

5. The method of claim 1, wherein in step S3, the feature engineering process includes a feature construction process in which feature constructions are selected from the original data sources to be useful features, and the useful features are combined into a new subset.

6. The method for evaluating the consumption ability based on the urban sign index according to claim 1, wherein in the step S3, the feature engineering process comprises a feature deriving process, and the processing procedure is as follows: and constructing new features according to the business knowledge or the relation between the features.

7. The method for evaluating the consumption ability based on the urban sign index according to claim 1, wherein in step S3, the feature engineering process comprises a feature selection process, and the processing procedure is as follows: firstly, adopting a machine learning method to recursively remove the characteristics with the prediction capability on the target variable; then selecting features by constructing a model and gradually removing features with small contribution to the model predictive ability, wherein the target variable refers to an output or a label in the model being constructed; and finally, sorting the features according to the importance scores, and eliminating the features with the lowest importance scores until the specified feature quantity or the model performance is not improved any more.

8. The method for evaluating the consumption ability based on the urban sign index according to claim 1, wherein in step S3, the feature engineering process comprises a feature combination process; the treatment process comprises the following steps: the different features are combined to form new features.

9. The method for evaluating the consumption ability based on the urban sign index according to claim 1, wherein in the step S4, the evaluation process comprises the steps of:

step S41: selecting a federal learning model;

step S42: constructing a consumption capability model and evaluating;

step S43: training the consumption portrait model according to the selected federal learning model;

step S44: and predicting and evaluating the trained consumer portrait model.

10. The method for evaluating the consumption capability based on the urban sign indexes according to claim 9, wherein in step S42, the consumption amount of the user cardholder in the set period of time is counted according to the local silver-linked consumption data, and the consumption capability gears are divided according to the calculation of the percentage of the transaction amount of each cardholder, and are set as a significantly low consumption capability gear, a relatively high consumption capability gear, a significantly high consumption capability gear and a high consumption capability gear.