Data processing method and device based on big data analysis, equipment and storage medium

Abstract

The invention belongs to the field of data processing and the field of financial science and technology, and relates to a data processing method based on big data analysis, comprising the following steps: acquiring initial business opportunity data based on a data capture strategy; performing data screening processing on the initial business opportunity data based on an input standard strategy to obtain first business opportunity data; performing data preprocessing on the first business opportunity data to obtain second business opportunity data; acquiring a specified key; screening out a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on the algorithm screening rule; performing encryption processing on the second business opportunity data based on a specified key and a specified encryption algorithm to obtain target business opportunity data; and uploading the target business opportunity data to a preset specified storage medium. The invention further provides a data processing device based on big data analysis, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the target business opportunity data can be stored in a block chain. According to the invention, the leakage risk of the business opportunity data is solved, and the transmission security of the business opportunity data is ensured.

Description

Technical Field

[0001] The present application relates to the fields of data processing technology and financial technology, and in particular to data processing methods, devices, computer equipment and storage media based on big data analysis. Background Art

[0002] In the rapidly developing financial industry, the efficiency and security of financial business systems, as the core platform supporting the daily operations and business expansion of financial institutions, have increasingly become the focus of industry attention. Among them, business opportunity management and tracking, as an indispensable part of the financial business system, plays a vital role in helping financial institutions identify potential market opportunities, conduct precision marketing, improve customer satisfaction and achieve business growth.

[0003] Traditionally, business opportunity management and tracking rely on manual recording, analysis, and follow-up, which is not only inefficient, but also difficult to fully capture market dynamics and changes in customer demand. With the rapid development of information technology, especially the widespread application of technologies such as big data, cloud computing, and artificial intelligence, financial business systems have begun to introduce advanced business opportunity management solutions, which achieve accurate identification, efficient allocation, dynamic tracking, and effect evaluation of business opportunities through automated and intelligent means, greatly improving the work efficiency and business output of business teams.

[0004] However, business opportunity management solutions involve a large amount of sensitive customer data during the processing process. In the existing technology, the processing of business opportunity data generally adopts the method of directly uploading it to the server or cloud storage medium, which makes the business opportunity data extremely vulnerable to unauthorized access, tampering or leakage during the transmission and storage process, and the transmission security of business opportunity data cannot be guaranteed. Summary of the invention

[0005] The purpose of the embodiments of the present application is to propose a data processing method, device, computer equipment and storage medium based on big data analysis, so as to solve the technical problem that the existing processing of business opportunity data generally adopts the method of directly uploading it to a server or cloud storage medium, which makes the business opportunity data extremely vulnerable to unauthorized access, tampering or leakage during the transmission and storage process, and the transmission security of the business opportunity data cannot be guaranteed.

[0006] In order to solve the above technical problems, the present application embodiment provides a data processing method based on big data analysis, which adopts the following technical solutions:

[0007] Acquire initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data at least includes customer data, sales data, and market data;

[0008] Performing data screening on the initial business opportunity data based on a preset input standard strategy to obtain corresponding first business opportunity data;

[0009] Performing data preprocessing on the first business opportunity data to obtain corresponding second business opportunity data;

[0010] Obtaining a preset designated key; wherein the length of the designated key is greater than a preset length threshold;

[0011] Filter out a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule;

[0012] Encrypting the second business opportunity data based on the specified key and the specified encryption algorithm to obtain encrypted target business opportunity data;

[0013] The target business opportunity data is uploaded to a preset designated storage medium.

[0014] Furthermore, the step of selecting a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule specifically includes:

[0015] Call multiple preset encryption algorithms;

[0016] Filter out the first encryption algorithm whose publication year is greater than a preset year threshold from all the encryption algorithms;

[0017] Obtaining security effect values ​​of all the first encryption algorithms;

[0018] Filter out a second encryption algorithm whose security effect value is greater than a preset effect threshold from all the first encryption algorithms;

[0019] Determining whether there is more than one second encryption algorithm;

[0020] If yes, test all the second encryption algorithms based on preset algorithm test data to obtain the processing efficiency of each of the second encryption algorithms;

[0021] Filter out a third encryption algorithm with the highest processing efficiency from all the second encryption algorithms;

[0022] The third encryption algorithm is used as the designated encryption algorithm.

[0023] Furthermore, the step of performing data preprocessing on the first business opportunity data to obtain corresponding second business opportunity data specifically includes:

[0024] Performing a deduplication process on the first business opportunity data to obtain corresponding first data;

[0025] Performing missing value processing on the first data to obtain corresponding second data;

[0026] Performing outlier processing on the second data to obtain corresponding third data;

[0027] Formatting the third data to obtain corresponding fourth data;

[0028] The fourth data is used as the second business opportunity data.

[0029] Furthermore, after the step of encrypting the second business opportunity data based on the designated key and the designated encryption algorithm to obtain the encrypted target business opportunity data, the method further includes:

[0030] Call the pre-trained opportunity assessment model;

[0031] Converting the target business opportunity data into a target data format that matches the business opportunity evaluation model to obtain corresponding designated business opportunity data;

[0032] Based on the business opportunity evaluation model, the designated business opportunity data is evaluated and processed to obtain a corresponding business opportunity evaluation result;

[0033] The business opportunity evaluation result is sent to the corresponding business personnel.

[0034] Furthermore, after the step of encrypting the second business opportunity data based on the designated key and the designated encryption algorithm to obtain the encrypted target business opportunity data, the method further includes:

[0035] Acquire user characteristic data related to the user from the target business opportunity data;

[0036] Generate a user portrait label under a preset feature dimension based on the user feature data;

[0037] Integrate all the portrait tags into a preset data structure to obtain a user portrait corresponding to the user;

[0038] The user portrait is stored.

[0039] Furthermore, the step of storing the user portrait specifically includes:

[0040] Determine a target storage strategy corresponding to the user profile;

[0041] Call the preset blockchain network;

[0042] Determining a target storage block corresponding to the portrait type in the blockchain network;

[0043] Based on the target storage strategy, the user portrait is stored in the target storage block.

[0044] Furthermore, after the step of uploading the target business opportunity data to a preset designated storage medium, the method further includes:

[0045] Determine whether a data access request triggered by a specified user is received; wherein the data access request carries user information and query information of the specified user;

[0046] If so, extracting the user information and the query information from the data access request;

[0047] Acquire a specified permission level corresponding to the user information based on a preset permission management model;

[0048] Query the specified permission level range corresponding to the business opportunity access operation from the preset business operation permission table;

[0049] Determining whether the specified permission level is within the specified permission level range;

[0050] If so, obtaining third business opportunity data corresponding to the query information from the designated storage medium;

[0051] The third business opportunity data is returned to the designated user.

[0052] In order to solve the above technical problems, the embodiment of the present application further provides a data processing device based on big data analysis, which adopts the following technical solution:

[0053] A first acquisition module is used to acquire initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data at least includes customer data, sales data and market data;

[0054] A first screening module, used to perform data screening processing on the initial business opportunity data based on a preset input standard strategy to obtain corresponding first business opportunity data;

[0055] A preprocessing module, used for performing data preprocessing on the first business opportunity data to obtain corresponding second business opportunity data;

[0056] A second acquisition module is used to acquire a preset designated key; wherein the length of the designated key is greater than a preset length threshold;

[0057] A second screening module, used for screening out a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule;

[0058] An encryption module, used for encrypting the second business opportunity data based on the specified key and the specified encryption algorithm to obtain encrypted target business opportunity data;

[0059] The uploading module is used to upload the target business opportunity data to a preset designated storage medium.

[0060] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0061] Acquire initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data at least includes customer data, sales data, and market data;

[0062] Performing data screening on the initial business opportunity data based on a preset input standard strategy to obtain corresponding first business opportunity data;

[0063] Performing data preprocessing on the first business opportunity data to obtain corresponding second business opportunity data;

[0064] Obtaining a preset designated key; wherein the length of the designated key is greater than a preset length threshold;

[0065] Filter out a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule;

[0066] Encrypting the second business opportunity data based on the specified key and the specified encryption algorithm to obtain encrypted target business opportunity data;

[0067] The target business opportunity data is uploaded to a preset designated storage medium.

[0068] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0069] Acquire initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data at least includes customer data, sales data, and market data;

[0070] Performing data screening on the initial business opportunity data based on a preset input standard strategy to obtain corresponding first business opportunity data;

[0071] Performing data preprocessing on the first business opportunity data to obtain corresponding second business opportunity data;

[0072] Obtaining a preset designated key; wherein the length of the designated key is greater than a preset length threshold;

[0073] Filter out a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule;

[0074] Encrypting the second business opportunity data based on the specified key and the specified encryption algorithm to obtain encrypted target business opportunity data;

[0075] The target business opportunity data is uploaded to a preset designated storage medium.

[0076] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0077] The present application first obtains initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data includes at least customer data, sales data and market data; then the initial business opportunity data is screened and processed based on a preset input standard strategy to obtain corresponding first business opportunity data; and the first business opportunity data is preprocessed to obtain corresponding second business opportunity data; then a preset designated key is obtained; wherein the length of the designated key is greater than a preset length threshold; subsequently, a designated encryption algorithm is screened from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule; the second business opportunity data is further encrypted based on the designated key and the designated encryption algorithm to obtain encrypted target business opportunity data; finally, the target business opportunity data is uploaded to a preset designated storage medium. After acquiring the initial business opportunity data based on the preset data capture strategy, the present application will intelligently perform data screening and data preprocessing on the initial business opportunity data to obtain the corresponding second business opportunity data. Subsequently, the second business opportunity data will be encrypted using the specified encryption algorithm and the specified key selected based on the preset algorithm screening rules to obtain the encrypted target business opportunity data. Finally, the target business opportunity data will be uploaded to the preset designated storage medium. This can effectively ensure the security of the target business opportunity data, solve the risk of unauthorized access, tampering or leakage of business opportunity data during transmission and storage, and ensure the transmission security of business opportunity data. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0079] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0080] Figure 2 A flowchart of an embodiment of a data processing method based on big data analysis according to the present application;

[0081] Figure 3 is a structural schematic diagram of an embodiment of a data processing device based on big data analysis according to the present application;

[0082] Figure 4It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0083] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0084] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0085] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0086] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0087] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0088] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, the tablet computer 1012 or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer, a desktop computer, etc.

[0089] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0090] It should be noted that the data processing method based on big data analysis provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the data processing device based on big data analysis is generally arranged in the server / terminal device.

[0091] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0092] Continue to refer Figure 2 , showing a flow chart of an embodiment of a data processing method based on big data analysis according to the present application. According to different requirements, the order of the steps in the flow chart can be changed, and some steps can be omitted. The data processing method based on big data analysis provided in the embodiment of the present application can be applied to any scenario that requires data processing, and the data processing method based on big data analysis can be applied to products in these scenarios, for example, business opportunity data processing in the financial insurance field. The data processing method based on big data analysis includes the following steps:

[0093] Step S201, acquiring initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data at least includes customer data, sales data and market data.

[0094] In this embodiment, the data processing method based on big data analysis is executed on the electronic device (e.g. Figure 1The server / terminal device shown in the figure) can obtain the initial business opportunity data through a wired connection or a wireless connection. It should be noted that the above-mentioned wireless connection method may include but is not limited to 3G / 4G / 5G connection, Wi-Fi connection, Bluetooth connection, Wi MAX connection, Zigbee connection, UWB (Ultra Wi DeBand) connection, and other wireless connection methods currently known or to be developed in the future. The execution subject of this application may specifically be a business opportunity management system. The above-mentioned data capture strategy may at least include strategies such as data capture based on crawler technology, data capture based on API interface, and data capture based on database synchronization. Among them, crawler technology: for unstructured or semi-structured data on the Internet (such as websites, social media, news articles, etc.), develop or use ready-made crawler tools to capture the required data. API interface: for data sources that have provided API (such as social media platforms, market research companies, etc.), batch data is obtained by writing interface call code. Database synchronization: for internal systems or partner databases, automatic data capture and update can be achieved through database synchronization tools or scheduled tasks. In addition, the above customer data may at least include: basic information of customers, purchase history, behavioral preferences, etc. The above sales data may at least include sales records, sales channel data, sales performance reports, etc. The above market data may at least include relevant market trend data, competitor dynamic information, etc. provided by market research institutions, industry reports or public data sources.

[0095] Step S202: Perform data screening on the initial business opportunity data based on a preset input standard strategy to obtain corresponding first business opportunity data.

[0096] In this embodiment, a corresponding input standard strategy can be pre-built according to the actual business opportunity input needs. Then, the initial business opportunity data is screened and processed according to the input standard strategy to obtain the first business opportunity data that complies with the input standard strategy to ensure the compliance of the obtained first business opportunity data. Among them, the policy content of the input standard strategy may include: clarifying the necessary fields of the business opportunity data, such as potential customer information (company name, contact person, contact information, etc.), sales opportunity description, expected transaction amount, expected transaction time, etc. And establish the division standards of business opportunity stages, such as initial contact, demand confirmation, solution provision, negotiation, transaction, etc., and define the roles and activities of each stage.

[0097] Step S203: preprocess the first business opportunity data to obtain corresponding second business opportunity data.

[0098] In this embodiment, the specific implementation process of performing data preprocessing on the first business opportunity data to obtain the corresponding second business opportunity data will be further described in detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0099] Step S204, obtaining a preset designated key; wherein the length of the designated key is greater than a preset length threshold.

[0100] In this embodiment, the length threshold may be specifically set to 128 bits. By selecting a strong key with a key length greater than 128 bits as the designated key, such as a 192-bit or 256-bit key, the security of data encryption can be effectively ensured.

[0101] Step S205: Filter out a designated encryption algorithm from the pre-stored multiple encryption algorithms based on a preset algorithm screening rule.

[0102] In this embodiment, the specific implementation process of selecting a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on preset algorithm screening rules will be described in further detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0103] Step S206: encrypt the second business opportunity data based on the designated key and the designated encryption algorithm to obtain encrypted target business opportunity data.

[0104] In this embodiment, the second business opportunity data may be encrypted using a specified key and the above-mentioned specified encryption algorithm to generate corresponding encrypted target business opportunity data, and the encrypted target business opportunity data can only be decrypted by an entity with a correct key.

[0105] Step S207: uploading the target business opportunity data to a preset designated storage medium.

[0106] In this embodiment, there is no specific limitation on the selection of the above-mentioned designated storage medium, which can be determined according to the actual storage requirements of business opportunity data, for example, it may include a local database, a cloud server, a blockchain network, etc.

[0107] The present application first obtains initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data includes at least customer data, sales data and market data; then the initial business opportunity data is screened and processed based on a preset input standard strategy to obtain corresponding first business opportunity data; and the first business opportunity data is preprocessed to obtain corresponding second business opportunity data; then a preset designated key is obtained; wherein the length of the designated key is greater than a preset length threshold; subsequently, a designated encryption algorithm is screened from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule; the second business opportunity data is further encrypted based on the designated key and the designated encryption algorithm to obtain encrypted target business opportunity data; finally, the target business opportunity data is uploaded to a preset designated storage medium. After acquiring the initial business opportunity data based on the preset data capture strategy, the present application will intelligently perform data screening and data preprocessing on the initial business opportunity data to obtain the corresponding second business opportunity data. Subsequently, the second business opportunity data will be encrypted using the specified encryption algorithm and the specified key selected based on the preset algorithm screening rules to obtain the encrypted target business opportunity data. Finally, the target business opportunity data will be uploaded to the preset designated storage medium. This can effectively ensure the security of the target business opportunity data, solve the risk of unauthorized access, tampering or leakage of business opportunity data during transmission and storage, and ensure the transmission security of business opportunity data.

[0108] In some optional implementations of this embodiment, step S205 includes the following steps:

[0109] Call multiple preset encryption algorithms.

[0110] In this embodiment, the above encryption algorithms may at least include AES, DES, RSA, ECC, Blowfile and other algorithms.

[0111] The first encryption algorithm whose publication year is greater than a preset year threshold is selected from all the encryption algorithms.

[0112] In this embodiment, the publication year of various encryption algorithms can be queried by querying the algorithm public data of various encryption algorithms. There is no specific limitation on the value of the above-mentioned preset year threshold, which can be set according to actual business usage requirements, for example, it can be set to 2016. The larger the value of the publication year of the encryption algorithm, the more excellent the function of the encryption algorithm can be preliminarily represented.

[0113] Obtain the security effect values ​​of all the first encryption algorithms.

[0114] In this embodiment, the open-source algorithm usage data about all first encryption algorithms can be consulted, and the number of cracking methods of all first encryption algorithms can be queried from the algorithm usage data, and the security effect value of the first encryption algorithm can be calculated by using 1 / the number of cracking methods. Among them, if the value of the number of cracking methods of the first encryption algorithm is higher, the corresponding security effect value of the first encryption algorithm is lower, and thus the cracking efficiency of the first encryption algorithm is higher. If the value of the number of cracking methods of the first encryption algorithm is lower, the corresponding security effect value of the first encryption algorithm is higher, and thus the cracking efficiency of the first encryption algorithm is lower.

[0115] A second encryption algorithm having a security effect value greater than a preset effect threshold is selected from all the first encryption algorithms.

[0116] In this embodiment, there is no specific limitation on the value of the effect threshold, which can be set according to actual business processing requirements.

[0117] Determine whether there is more than one second encryption algorithm.

[0118] In this embodiment, the number of the second encryption algorithms may be 1 or more. If the number of the second encryption algorithms is 1, the second encryption algorithm is directly used as the designated encryption algorithm.

[0119] If so, all the second encryption algorithms are tested based on preset algorithm test data to obtain the processing efficiency of each of the second encryption algorithms.

[0120] In this embodiment, the processing efficiency refers to the amount of data that can be processed by the encryption algorithm per unit time during the encryption process, such as the amount of data encrypted per second. The algorithm test data is pre-collected data for processing efficiency of the second encryption algorithm.

[0121] A third encryption algorithm with the highest processing efficiency is selected from all the second encryption algorithms.

[0122] In this embodiment, the processing efficiencies of all the second encryption algorithms may be numerically compared to select a third encryption algorithm with the highest processing efficiency from among all the second encryption algorithms.

[0123] The third encryption algorithm is used as the designated encryption algorithm.

[0124] This application calls a plurality of preset encryption algorithms; then selects the first encryption algorithm whose publication year is greater than the preset year threshold from all the encryption algorithms; then obtains the security effect value of all the first encryption algorithms; subsequently selects the second encryption algorithm whose security effect value is greater than the preset effect threshold from all the first encryption algorithms, and determines whether the number of the second encryption algorithms is multiple; if so, tests and processes all the second encryption algorithms based on the preset algorithm test data to obtain the processing efficiency of each second encryption algorithm; finally selects the third encryption algorithm with the highest processing efficiency from all the second encryption algorithms, and uses the third encryption algorithm as the designated encryption algorithm, thereby realizing the selection of the third encryption algorithm with the best comprehensive ability from a plurality of encryption algorithms based on the publication year, security effect value and processing efficiency and using it as the final designated encryption algorithm, thereby improving the determination accuracy and selection intelligence of the designated encryption algorithm. And subsequently, by using the selected designated encryption algorithm to encrypt the second business opportunity data to obtain the corresponding target business opportunity data, the encryption processing efficiency of the business opportunity data can be effectively improved, and the data accuracy of the obtained target business opportunity data can be guaranteed.

[0125] In some optional implementations, step S203 includes the following steps:

[0126] Deduplication processing is performed on the first business opportunity data to obtain corresponding first data.

[0127] In this embodiment, the above-mentioned deduplication processing may include using SQL query, data deduplication tool or deduplication function in programming language to identify and delete duplicate records.

[0128] Perform missing value processing on the first data to obtain corresponding second data.

[0129] In this embodiment, the missing value processing may include filling or marking the missing values ​​as missing according to data characteristics and business requirements, wherein the data filling method may be mean filling, median filling, mode filling, etc.

[0130] Perform outlier processing on the second data to obtain corresponding third data.

[0131] In this embodiment, the above-mentioned outlier processing may include identifying and processing outliers in the data, such as deleting or correcting obvious erroneous data.

[0132] The third data is formatted to obtain corresponding fourth data.

[0133] In this embodiment, the formatting process may include unifying the data formats from different sources, such as standardizing the formats of date format, currency unit, and digital precision.

[0134] The fourth data is used as the second business opportunity data.

[0135] The present application can quickly and accurately complete the preprocessing of the first business opportunity data by performing duplicate data removal, missing value processing, outlier processing and formatting processing on the first business opportunity data, thereby effectively ensuring the data accuracy of the preprocessed second business opportunity data.

[0136] In some optional implementations, after step S206, the electronic device may further perform the following steps:

[0137] Call the pre-trained opportunity assessment model.

[0138] In this embodiment, the above-mentioned business opportunity evaluation model is a business opportunity evaluation model based on random forest (Random Forest) generated by training based on historical business opportunity data. The business opportunity evaluation model can process multi-dimensional input features and output the corresponding business opportunity conversion rate. Specifically, the construction process of the business opportunity evaluation model includes: 1. Collecting historical business opportunity data, including customer data, sales data and market data contained in success cases, and customer data, sales data and market data contained in failure cases. And cleaning and sorting the data to ensure the accuracy and consistency of the data. 2. Feature selection and processing. Extract features useful for the business opportunity evaluation model from historical business opportunity data. Then preprocess the features, such as missing value processing, outlier processing, data standardization, etc. 3. Model selection and construction. Select a suitable evaluation model, such as logistic regression, decision tree, random forest, gradient boosting tree (GBDT) or neural network, etc. In this embodiment, decision tree is preferentially used as the initial evaluation model. Then the initial evaluation model is trained using the preprocessed feature training, the model performance is optimized by adjusting the model parameters and feature weights, and the stability and generalization ability of the model are ensured by using methods such as cross-validation. 4. Model verification and optimization. Use an independent verification set to verify the model to ensure that the model performs well on unknown data. And tune the model based on the verification results, such as adjusting feature selection, improving model structure, etc., until a business opportunity evaluation model that meets the expected requirements is obtained.

[0139] The target business opportunity data is converted into a target data format that matches the business opportunity evaluation model to obtain corresponding designated business opportunity data.

[0140] In this embodiment, the target data format is a format that meets the input requirements of the business opportunity evaluation model. The target business opportunity data is converted according to the target data format to meet the input requirements of the business opportunity evaluation model.

[0141] The designated business opportunity data is evaluated based on the business opportunity evaluation model to obtain a corresponding business opportunity evaluation result.

[0142] In this embodiment, the designated business opportunity data can be input into the business opportunity evaluation model. After receiving the designated business opportunity data, the business opportunity evaluation model will perform reasoning based on its internally trained decision tree set. Each decision tree will evaluate the new business opportunity based on its own rules and weights, and output a local prediction result. The prediction results of all decision trees are then aggregated, and the final quantitative evaluation result, i.e., the above-mentioned business opportunity evaluation result, is obtained by voting or weighted averaging. The business opportunity evaluation result is a business opportunity conversion rate between 0 and 1, indicating the possibility of the business opportunity being successfully converted into actual sales or cooperation.

[0143] The business opportunity evaluation result is sent to the corresponding business personnel.

[0144] In this embodiment, the business personnel may be sales personnel. By sending the business opportunity evaluation results to the sales personnel, the sales personnel can determine the priority and follow-up strength of the business opportunity based on the business opportunity evaluation results, and then formulate targeted sales strategies and action plans, and can also allocate resources, adjust budgets or formulate long-term strategic plans.

[0145] This application calls a pre-trained business opportunity evaluation model; then converts the target business opportunity data into a target data format that matches the business opportunity evaluation model to obtain the corresponding designated business opportunity data; then evaluates and processes the designated business opportunity data based on the business opportunity evaluation model to obtain the corresponding business opportunity evaluation result; and subsequently sends the business opportunity evaluation result to the corresponding business personnel. After encrypting the second business opportunity data based on a designated key and a designated encryption algorithm to obtain the target business opportunity data, this application will also intelligently convert the target business opportunity data into a target data format that matches the business opportunity evaluation model to obtain the designated business opportunity data, and then use the pre-trained business opportunity evaluation model to evaluate and process the designated business opportunity data to obtain the business opportunity evaluation result, and send the business opportunity evaluation result to the corresponding business personnel, thereby intelligently providing the relevant business personnel with objective and comprehensive business opportunity evaluation results, which is conducive to helping business personnel make better decisions and optimize business operations.

[0146] In some optional implementations, after step S206, the electronic device may further perform the following steps:

[0147] User characteristic data related to the user is obtained from the target business opportunity data.

[0148] In this embodiment, the user may be any one of all customers corresponding to the target business opportunity data. The user characteristic data may be characteristic data corresponding to a preset information type. Specifically, the user characteristic data may include at least basic information (such as age, gender, region), purchase habits (such as purchase frequency, purchase preferences), behavior preferences (such as browsing habits, search keywords), and other data.

[0149] Generate a user portrait label under a preset feature dimension based on the user feature data.

[0150] In this embodiment, the preset feature dimensions may include at least a basic information dimension, a purchasing habit dimension, and a behavior preference dimension. The index value of the user under the preset feature dimension may be calculated based on the user feature data, and then the index value of the user under the preset feature dimension may be converted into a portrait label of the user under the preset feature dimension. Among them, the index value of the user under the preset feature dimension may be converted into a portrait label of the user under the preset feature dimension according to the preset label quantification strategy. Specifically, specific labels and quantification standards may be designed in advance for each feature dimension according to the actual portrait construction requirements to obtain the above-mentioned label quantification rules. Furthermore, after obtaining the index value of the user under the preset feature dimension, the corresponding label may be assigned to the user according to the label and quantification standard defined in the above-mentioned label quantification rule, thereby obtaining the portrait label of the user under the preset feature dimension. Exemplarily, for the purchasing habit dimension, the label definition is: "high-frequency purchasing user" (purchase frequency ≥ 10 times / month), and the quantification standard is calculated based on data such as the number of purchase orders.

[0151] Integrate all the portrait tags into a preset data structure to obtain a user portrait corresponding to the user.

[0152] In this embodiment, the above data structure is a pre-constructed structure for forming a portrait. All the portrait tags can be integrated into a unified data structure to form the user's portrait data, that is, the above user portrait.

[0153] The user portrait is stored.

[0154] In this embodiment, the specific implementation process of storing and processing the user portrait will be further described in detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0155] This application obtains user feature data related to the user from the target business opportunity data; then generates a portrait tag of the user under a preset feature dimension based on the user feature data; then integrates all the portrait tags into a preset data structure to obtain a user portrait corresponding to the user; and subsequently stores and processes the user portrait. After encrypting the second business opportunity data based on a specified key and a specified encryption algorithm to obtain the target business opportunity data, this application will also intelligently obtain user feature data related to the user from the target business opportunity data, and generate a portrait tag of the user under a preset feature dimension based on the user feature data, and then integrate all the portrait tags into a preset data structure to obtain a user portrait corresponding to the user, so as to automatically and accurately generate a user portrait, improve the generation efficiency and intelligence of the user portrait, and help the business team understand business opportunities based on user portraits, and formulate effective business strategies, thereby tracking and managing business opportunities. In addition, the user portrait will also be stored to ensure the data security of the user portrait.

[0156] In some optional implementations of this embodiment, the storing process of the user portrait includes the following steps:

[0157] Determine a target storage strategy corresponding to the user profile.

[0158] In this embodiment, the target storage strategy may specifically adopt strategies such as timed backup or cyclic overwriting to ensure that the target storage strategy will not be lost due to insufficient storage space.

[0159] Call the preset blockchain network.

[0160] In this embodiment, by using the blockchain network for data storage, during the on-chain process, the user portrait will be verified by consensus of multiple nodes to ensure the authenticity and security of the user portrait. At the same time, each data record will be associated with a unique hash fingerprint to provide a basis for subsequent tracing.

[0161] A target storage block corresponding to the portrait type is determined in the blockchain network.

[0162] In this embodiment, a storage block for storing portrait data of the portrait type can be pre-built in the blockchain network, thereby improving the storage standardization of the portrait data.

[0163] Based on the target storage strategy, the user portrait is stored in the target storage block.

[0164] In this embodiment, the user portrait can be stored in the target storage block in the blockchain network in a storage manner according to the target storage strategy.

[0165] This application determines the target storage strategy corresponding to the user portrait; then calls the preset blockchain network; then determines the target storage block corresponding to the portrait type in the blockchain network; and then stores the user portrait in the target storage block based on the target storage strategy. This application determines the target storage strategy corresponding to the user portrait, and determines the target storage block corresponding to the portrait type in the called blockchain network, and then stores the user portrait in the target storage block based on the target storage strategy, so as to realize the intelligent storage and standardized storage of the user portrait, effectively ensure the data security of the user portrait, and provide a basis for subsequent traceability.

[0166] In some optional implementations of this embodiment, after step S207, the electronic device may further perform the following steps:

[0167] Determine whether a data access request triggered by a specified user is received, wherein the data access request carries user information and query information of the specified user.

[0168] In this embodiment, the data access request is a request for accessing business opportunity data triggered by a specified user. The user information includes the user's name information, and the query information may refer to the data index of the business opportunity data to be queried.

[0169] If so, extract the user information and the query information from the data access request.

[0170] In this embodiment, the user information and query information carried in the data access request may be extracted by performing information parsing on the data access request.

[0171] A specified permission level corresponding to the user information is obtained based on a preset permission management model.

[0172] In this embodiment, the authority management model is a pre-built big data model that stores a one-to-one mapping relationship between the name information of each enterprise employee and the authority level. The authority management model can be queried using the user information to obtain the specified authority level corresponding to the user information.

[0173] Query the specified permission level range corresponding to the business opportunity access operation from the preset business operation permission table.

[0174] In this embodiment, the business operation permission table is a pre-constructed data table storing a variety of business operation identifiers and permission level intervals corresponding to the various business operation identifiers. Specifically, the target business operation identifier corresponding to the business opportunity access operation can be obtained, and then the target business operation identifier can be used to query the business operation permission table to obtain the specified permission level interval corresponding to the business opportunity access operation.

[0175] Determine whether the specified permission level is within the specified permission level range.

[0176] In this embodiment, the specified permission level can be compared with the two endpoint values ​​included in the above-mentioned specified permission level range to obtain a corresponding numerical comparison result, and based on the numerical comparison result, it can be identified whether the specified permission level is within the specified permission level range.

[0177] If so, third business opportunity data corresponding to the query information is obtained from the designated storage medium.

[0178] In this embodiment, if the specified authority level is within the specified authority level range, it indicates that the specified user has the operation authority to perform the business opportunity access operation, thereby determining that the user has passed the authority verification, and subsequently obtaining the third business opportunity data corresponding to the query information from the specified storage medium.

[0179] The third business opportunity data is returned to the designated user.

[0180] In this embodiment, the third business opportunity data may be returned to the designated user by using SMS notification, email notification, or the like.

[0181] The present application determines whether a data access request triggered by a designated user is received; if so, extracts the user information and the query information from the data access request; then obtains the designated permission level corresponding to the user information based on a preset permission management model; then queries the designated permission level interval corresponding to the business opportunity access operation from the preset business operation permission table; subsequently determines whether the designated permission level is within the designated permission level interval; if so, obtains the third business opportunity data corresponding to the query information from the designated storage medium; and finally returns the third business opportunity data to the designated user. After receiving a data access request triggered by a designated user, the present application will intelligently use the permission management model and the business operation permission table to verify the user's permissions. Only when it is detected that the designated user has passed the permission verification, will the data access request be responded to later to obtain the third business opportunity data corresponding to the query information from the designated storage medium and return it to the designated user, effectively realizing intelligent access control for business opportunity data access, improving the standardized processing and security processing of data access requests, and ensuring the security of responses to data access requests.

[0182] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0183] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned target business opportunity data, the above-mentioned target business opportunity data can also be stored in a node of a blockchain.

[0184] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0185] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0186] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0187] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0188] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0189] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a data processing device based on big data analysis, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0190] like Figure 3 As shown, the data processing device 300 based on big data analysis described in this embodiment includes: a first acquisition module 301, a first screening module 302, a preprocessing module 303, a second acquisition module 304, a second screening module 305, an encryption module 306 and an upload module 307. Among them:

[0191] The first acquisition module 301 is used to acquire initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data at least includes customer data, sales data and market data;

[0192] A first screening module 302 is used to screen the initial business opportunity data based on a preset input standard strategy to obtain corresponding first business opportunity data;

[0193] A preprocessing module 303 is used to perform data preprocessing on the first business opportunity data to obtain corresponding second business opportunity data;

[0194] The second acquisition module 304 is used to acquire a preset designated key; wherein the length of the designated key is greater than a preset length threshold;

[0195] The second screening module 305 is used to screen out a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule;

[0196] An encryption module 306, configured to encrypt the second business opportunity data based on the specified key and the specified encryption algorithm to obtain encrypted target business opportunity data;

[0197] The uploading module 307 is used to upload the target business opportunity data to a preset designated storage medium.

[0198] In some optional implementations of this embodiment, the second screening module 305 includes:

[0199] The first calling submodule is used to call a plurality of preset encryption algorithms;

[0200] A screening submodule, used for screening out a first encryption algorithm whose publication year is greater than a preset year threshold from all the encryption algorithms;

[0201] An acquisition submodule, used to obtain the security effect values ​​of all the first encryption algorithms;

[0202] A second screening submodule, used to screen out a second encryption algorithm whose security effect value is greater than a preset effect threshold from all the first encryption algorithms;

[0203] A determination submodule, used to determine whether the number of the second encryption algorithms is multiple;

[0204] A testing submodule, for testing all the second encryption algorithms based on preset algorithm test data to obtain the processing efficiency of each of the second encryption algorithms;

[0205] A third screening submodule, used for screening out a third encryption algorithm with the highest processing efficiency from all the second encryption algorithms;

[0206] The first determination submodule is used to use the third encryption algorithm as the designated encryption algorithm.

[0207] In some optional implementations of this embodiment, the preprocessing module 303 includes:

[0208] A first processing submodule, configured to perform a deduplication process on the first business opportunity data to obtain corresponding first data;

[0209] A second processing submodule, used to perform missing value processing on the first data to obtain corresponding second data;

[0210] A third processing submodule, used for performing outlier processing on the second data to obtain corresponding third data;

[0211] A fourth processing submodule, used for formatting the third data to obtain corresponding fourth data;

[0212] The second determining submodule is configured to use the fourth data as the second business opportunity data.

[0213] In some optional implementations of this embodiment, the data processing device based on big data analysis further includes:

[0214] A calling module is used to call a pre-trained business opportunity assessment model;

[0215] A conversion module, used to convert the target business opportunity data into a target data format that matches the business opportunity evaluation model to obtain corresponding designated business opportunity data;

[0216] An evaluation module, used to evaluate the specified business opportunity data based on the business opportunity evaluation model to obtain a corresponding business opportunity evaluation result;

[0217] The sending module is used to send the business opportunity evaluation result to the corresponding business personnel.

[0218] In some optional implementations of this embodiment, the data processing device based on big data analysis further includes:

[0219] A third acquisition module, used to acquire user characteristic data related to the user from the target business opportunity data;

[0220] A generating module, used to generate a user portrait label under a preset feature dimension based on the user feature data;

[0221] An integration module, used to integrate all the portrait tags into a preset data structure to obtain a user portrait corresponding to the user;

[0222] A storage module is used to store and process the user portrait.

[0223] In some optional implementations of this embodiment, the storage module includes:

[0224] A third determination submodule is used to determine a target storage strategy corresponding to the user portrait;

[0225] The second calling submodule is used to call the preset blockchain network;

[0226] A fourth determination submodule is used to determine a target storage block corresponding to the portrait type in the blockchain network;

[0227] The storage submodule is used to store the user portrait in the target storage block based on the target storage strategy.

[0228] In some optional implementations of this embodiment, the data processing device based on big data analysis further includes:

[0229] A first judgment module is used to judge whether a data access request triggered by a specified user is received; wherein the data access request carries user information and query information of the specified user;

[0230] An extraction module, configured to extract the user information and the query information from the data access request;

[0231] A third acquisition module, used to acquire a specified permission level corresponding to the user information based on a preset permission management model;

[0232] A query module is used to query the specified permission level range corresponding to the business opportunity access operation from the preset business operation permission table;

[0233] A second judgment module, used to judge whether the specified permission level is within the specified permission level range;

[0234] A fourth acquisition module, configured to, if yes, acquire third business opportunity data corresponding to the query information from the designated storage medium;

[0235] A returning module is used to return the third business opportunity data to the designated user.

[0236] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0237] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital processors (DSPs), embedded devices, etc.

[0238] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0239] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of a data processing method based on big data analysis, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0240] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the data processing method based on big data analysis.

[0241] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0242] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the data processing method based on big data analysis as described above.

[0243] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0244] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A data processing method based on big data analysis, characterized in that: The steps include: Acquire initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data at least includes customer data, sales data, and market data; Performing data screening on the initial business opportunity data based on a preset input standard strategy to obtain corresponding first business opportunity data; Performing data preprocessing on the first business opportunity data to obtain corresponding second business opportunity data; Obtaining a preset designated key; wherein the length of the designated key is greater than a preset length threshold; Filter out a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule; Encrypting the second business opportunity data based on the specified key and the specified encryption algorithm to obtain encrypted target business opportunity data; The target business opportunity data is uploaded to a preset designated storage medium.

2. The data processing method based on big data analysis according to claim 1 is characterized in that: The step of selecting a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule specifically includes: Call multiple preset encryption algorithms; Filter out the first encryption algorithm whose publication year is greater than a preset year threshold from all the encryption algorithms; Obtaining security effect values ​​of all the first encryption algorithms; Filter out a second encryption algorithm whose security effect value is greater than a preset effect threshold from all the first encryption algorithms; Determining whether there is more than one second encryption algorithm; If yes, test all the second encryption algorithms based on preset algorithm test data to obtain the processing efficiency of each of the second encryption algorithms; Filter out a third encryption algorithm with the highest processing efficiency from all the second encryption algorithms; The third encryption algorithm is used as the designated encryption algorithm.

3. The data processing method based on big data analysis according to claim 1 is characterized in that: The step of performing data preprocessing on the first business opportunity data to obtain corresponding second business opportunity data specifically includes: Performing a deduplication process on the first business opportunity data to obtain corresponding first data; Performing missing value processing on the first data to obtain corresponding second data; Performing outlier processing on the second data to obtain corresponding third data; Formatting the third data to obtain corresponding fourth data; The fourth data is used as the second business opportunity data.

4. The data processing method based on big data analysis according to claim 1 is characterized in that: After the step of encrypting the second business opportunity data based on the designated key and the designated encryption algorithm to obtain the encrypted target business opportunity data, the method further includes: Call the pre-trained opportunity assessment model; Converting the target business opportunity data into a target data format that matches the business opportunity evaluation model to obtain corresponding designated business opportunity data; Based on the business opportunity evaluation model, the designated business opportunity data is evaluated and processed to obtain a corresponding business opportunity evaluation result; The business opportunity evaluation result is sent to the corresponding business personnel.

5. The data processing method based on big data analysis according to claim 1 is characterized in that: After the step of encrypting the second business opportunity data based on the designated key and the designated encryption algorithm to obtain the encrypted target business opportunity data, the method further includes: Acquire user characteristic data related to the user from the target business opportunity data; Generate a user portrait label under a preset feature dimension based on the user feature data; Integrate all the portrait tags into a preset data structure to obtain a user portrait corresponding to the user; The user portrait is stored.

6. The data processing method based on big data analysis according to claim 5 is characterized in that: The step of storing and processing the user portrait specifically includes: Determine a target storage strategy corresponding to the user profile; Call the preset blockchain network; Determining a target storage block corresponding to the portrait type in the blockchain network; Based on the target storage strategy, the user portrait is stored in the target storage block.

7. The data processing method based on big data analysis according to claim 1 is characterized in that: After the step of uploading the target business opportunity data to a preset designated storage medium, the method further includes: Determine whether a data access request triggered by a specified user is received; wherein the data access request carries user information and query information of the specified user; If so, extracting the user information and the query information from the data access request; Acquire a specified permission level corresponding to the user information based on a preset permission management model; Query the specified permission level range corresponding to the business opportunity access operation from the preset business operation permission table; Determining whether the specified permission level is within the specified permission level range; If so, obtaining third business opportunity data corresponding to the query information from the designated storage medium; The third business opportunity data is returned to the designated user.

8. A data processing device based on big data analysis, characterized in that: include: A first acquisition module is used to acquire initial business opportunity data based on a preset data capture strategy; wherein the initial business opportunity data at least includes customer data, sales data and market data; A first screening module, used to perform data screening processing on the initial business opportunity data based on a preset input standard strategy to obtain corresponding first business opportunity data; A preprocessing module, used for performing data preprocessing on the first business opportunity data to obtain corresponding second business opportunity data; A second acquisition module is used to acquire a preset designated key; wherein the length of the designated key is greater than a preset length threshold; A second screening module, used to screen out a specified encryption algorithm from a plurality of pre-stored encryption algorithms based on a preset algorithm screening rule; An encryption module, used for encrypting the second business opportunity data based on the specified key and the specified encryption algorithm to obtain encrypted target business opportunity data; The uploading module is used to upload the target business opportunity data to a preset designated storage medium.

9. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the data processing method based on big data analysis as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data processing method based on big data analysis as described in any one of claims 1 to 7.

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