Automatic traffic simulation method, electronic equipment and program product

Abstract

The invention provides an automatic flow simulation method and system, and the method comprises the steps: receiving a configuration instruction of a user to generate a flow simulation scheme, and storing a generated corresponding to-be-executed SQL statement in a first database; reading a to-be-executed SQL statement in the first database through a first timed task, loading corresponding broker operation data in a second database for calculation to obtain a broker weight value and a store weight value, and updating a scheme state of a traffic simulation scheme in the first database; scanning the first database through a second timed task, if it is monitored that the scheme state is updated, generating an executable simulation task according to the traffic simulation scheme, determining a target broker based on the broker weight value and the store weight value, obtaining interaction data between the target broker and the customer, and executing the simulation task to obtain simulated business opportunity detail data; and then summarizing in combination with the data of each dimension to obtain final business opportunity conversion data.

Description

Technical Field

[0001] The present invention relates to the field of computer processing technology, and in particular to an automatic flow simulation method, electronic equipment and program product. Background Art

[0002] In the digital age, data-driven decision-making has become the core of corporate competitiveness. Especially in the field of real estate services, how to effectively match user needs with the resources of service providers (such as brokers) is the key to improving user experience and business efficiency. Policy adjustment and traffic simulation technology play a vital role in this process. The policy effect simulator provides a framework for simulating and predicting the effects of policy adjustments. Traffic simulation uses these policies to verify the predictions of the policy effect simulator by simulating actual traffic and user behavior. The two can be used together to provide a complete solution. The application of this technology enables enterprises to safely test and evaluate new strategies without interfering with actual business processes, thereby reducing risks, improving the accuracy of decision-making, and ultimately achieving business goals.

[0003] The main function of the traffic distribution engine is to achieve accurate matching between users and brokers to meet the needs of users to the greatest extent possible. With the increase in business complexity, a forward-looking tool is needed - a strategy effect simulator, which can not only provide solid data support for product decisions, but also effectively estimate the potential risks brought about by strategy adjustments, thereby avoiding unnecessary online challenges at the source. At present, the average daily exposure of the traffic distribution engine is over 1 billion, so the demand for strategy effect prediction is also increasing.

[0004] Take the new housing business as an example. Before the opening of each new city, the complex and huge strategy map behind it needs to be adjusted. However, each adjustment of the strategy map will have online risks. In the existing traffic simulation business system, although the traffic simulation capability is realized, this kind of traffic simulation is all manual operation from parameter adjustment, material construction, traffic playback, and effect analysis, which is extremely labor-intensive and time-consuming and inefficient. Summary of the invention

[0005] The present invention provides an automatic flow simulation method electronic device and a program product, which are used to solve the defects of the prior art that the flow simulation based on material data consumes manpower and time and has low efficiency.

[0006] The present invention provides a method for automatic flow simulation, comprising: Receive the configuration instructions of the user, generate a traffic simulation plan, and generate corresponding SQL statements to be executed after the configuration is completed and store them in the first database; wherein the traffic simulation plan includes parameters and rules for defining simulation tasks, and the broker operation data corresponding to the traffic simulation plan and the interaction data between the broker and the customer are stored in the second database; The SQL statement to be executed in the first database is read through the first scheduled task, and the corresponding broker job data is loaded into the second database for calculation to obtain the broker weight value and the store weight value, and the solution status of the traffic simulation solution in the first database is updated; Scan the first database through the second scheduled task, if it is monitored that the scheme status has been updated, generate an executable simulation task according to the traffic simulation scheme, determine the target broker based on the broker weight value and the store weight value, obtain the interaction data between the target broker and the customer, execute the simulation task through the predefined business opportunity allocation strategy, and obtain the simulated business opportunity detail data; Based on the detailed business opportunity data after simulation, combined with the data of each dimension, the final business opportunity conversion data is obtained.

[0007] According to the method for automatic traffic simulation provided by the present invention, the traffic simulation scheme includes: simulation scheme parameters, the state of the simulation scheme, and configuration information required to trigger the execution of the simulation task; Among them, the status of the newly created traffic simulation plan is waiting; after the broker weight value and the store weight value are generated, the status of the traffic simulation plan is updated to simulating; after the traffic simulation is completed, the status of the traffic simulation plan is updated to simulated.

[0008] According to the method for automatic traffic simulation provided by the present invention, after obtaining the broker weight value and the store weight value, the method also includes: synchronizing the broker weight value, the store weight value and the store's historical business opportunity details data as generated material data to the search engine.

[0009] According to the method for automatic traffic simulation provided by the present invention, an executable simulation task is generated according to the traffic simulation scheme, and a target broker is determined based on the broker weight value and the store weight value, which specifically includes: Generate an executable target simulation task according to the traffic simulation scheme through a second scheduled task, and store it in the first database; Read the target simulation task in the first database through the first scheduled task, obtain the historical business opportunity detail data corresponding to the target simulation task from the second database, and write it into the stream processing platform; Monitoring the historical business opportunity detail data in the stream processing platform through the supplier platform, and when the historical business opportunity detail data is monitored, retrieving the broker data related to the target simulation task, as well as the broker weight value and the store weight value corresponding to the broker data in the search engine through the supplier platform; After the broker data is determined, the broker weight value and store weight value are screened according to predefined screening rules to determine the target broker.

[0010] According to the method for automatic traffic simulation provided by the present invention, the traffic simulation scheme includes: an actual scheme and a simulation scheme; the simulation task includes: an actual scheme simulation task and a simulation scheme simulation task; the predefined business opportunity allocation strategy is used to simulate the actual business opportunity allocation logic; According to the interaction data between the target broker and the customer, the simulation task is executed through the predefined business opportunity allocation strategy to obtain the simulated business opportunity detail data, specifically including: The supplier platform uses multiple threads to execute in parallel the steps of executing the simulation task according to the interaction data between the target broker and the customer through a predefined business opportunity allocation strategy to obtain the simulated business opportunity detail data, wherein each thread performs simulation in the order of the actual scenario simulation task and the simulated scenario simulation task.

[0011] According to the method for automatic traffic simulation provided by the present invention, the predefined business opportunity allocation strategy includes: formula gain, theoretical gain, business gain and actual gain; For each thread, the supplier platform uses multiple threads to execute the steps of performing the simulation task according to the broker weight value, the store weight value and the broker operation data through the predefined business opportunity allocation strategy in parallel, and obtains the simulated business opportunity detail data, specifically including: For each thread, according to the broker weight value, store weight value and broker operation data, the actual scheme simulation task is first performed based on the allocation strategies of formula gain, theoretical gain, business gain and actual gain to obtain the detailed data of business opportunities after actual simulation; then the simulation scheme simulation task is performed based on the allocation strategies of formula gain, theoretical gain, business gain and actual gain to obtain the detailed data of business opportunities after simulation.

[0012] According to the method for automatic traffic simulation provided by the present invention, the detailed business opportunity data after simulation is summarized in combination with the data of each dimension to obtain the final business opportunity conversion data, which specifically includes: performing comparative analysis based on the detailed business opportunity data after actual simulation and the detailed business opportunity data after simulation; According to the business opportunity detail data after actual simulation, the business opportunity detail data after simulation and the comparative analysis results, the data of each dimension are summarized to obtain the final business opportunity conversion data, wherein the data of each dimension includes at least one of the following: broker dimension data, store dimension data, and brand dimension data; The business opportunity conversion data corresponding to the brand dimension includes at least one of the following: business opportunity volume, transaction volume, fluctuation in business opportunity volume ratio, business opportunity conversion rate, and business opportunity conversion rate fluctuation; The business opportunity conversion data corresponding to the store dimension includes at least one of the following: actual business opportunity volume, simulated business opportunity volume, actual number of customers, simulated number of customers, actual per capita business opportunities, simulated per capita business opportunities, per capita business opportunity fluctuations, actual transaction volume, simulated transaction volume; The business opportunity conversion data corresponding to the broker dimension includes at least one of the following: actual transaction volume and simulated transaction volume.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for automatic flow simulation as described in any one of the above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for automatic flow simulation as described in any one of the above is implemented.

[0015] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for automatic flow simulation as described above is implemented.

[0016] The method and system for automatic traffic simulation provided by the present invention automatically receive user configuration instructions and generate a traffic simulation plan, then generate corresponding SQL statements to be executed and store them in a first database, automatically read the SQL statements to be executed from the first database through a first scheduled task, and load the corresponding broker job data in the second database for calculation to obtain broker weight values ​​and store weight values, which not only improves the accuracy and efficiency of data calculation, but also ensures dynamic monitoring and timely response of the simulation process by updating the traffic simulation plan status in the first database in real time; then scan the first database through the second scheduled task, once the plan status update is monitored, an executable simulation task is generated according to the traffic simulation plan, and the target broker is determined based on the broker weight value and the store weight value, and the interaction data between the target broker and the customer is obtained to execute the simulation task, and the detailed business opportunity data after simulation is obtained, thereby effectively solving the problems of cumbersome manual operation and low efficiency in the traditional traffic simulation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is one of the flowcharts of the method for automatic traffic simulation provided by an embodiment of the present invention.

[0019] Figure 2 It is a schematic diagram of a simulation scheme list provided by an embodiment of the present invention.

[0020] Figure 3 This is the second flowchart of the method for automatic traffic simulation provided by an embodiment of the present invention.

[0021] Figure 4 It is a schematic diagram of the processing process of the traffic simulation system provided by an embodiment of the present invention.

[0022] Figure 5 This is the third flow chart of the method for automatic traffic simulation provided by an embodiment of the present invention.

[0023] Figure 6 It is a module diagram of the system for automatic traffic simulation provided by an embodiment of the present invention.

[0024] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] First, the terms involved in the embodiments of the present invention are schematically explained.

[0027] Traffic: refers to the total amount of user visits and interactions, especially in the traffic distribution engine, it specifically refers to the interaction traffic between users and brokers. This term is usually used to describe user visits, requests or transactions, etc., and is a key indicator for measuring platform activity and business scale.

[0028] Opportunity data: refers to data related to potential sales opportunities, including user needs, broker information, transaction conditions, etc. This data is crucial for matching users and brokers because it can help determine which users are most likely to benefit from the broker's services and which brokers can best meet the user's needs.

[0029] Materials: refers to the data set used for traffic simulation, including user behavior data, broker information, transaction records, etc. These data are used to build a simulation environment and simulate real business processes to test and evaluate the effects of policy adjustments.

[0030] Simulation: refers to the use of computer simulation technology to replicate real-world business processes and user behaviors to predict the effects of strategy adjustments. In this application, simulation technology is used to simulate user traffic and business opportunity allocation to evaluate the impact of different strategies on business indicators, such as user satisfaction, transaction rate, etc.

[0031] Distribution: refers to the process of allocating constructed materials (i.e., data sets) to different users or brokers according to specific rules and logic during the simulation process. This process simulates the allocation of business opportunities in actual business and is a key step in the simulation process. It determines which users see which brokers' information and how brokers respond to user needs.

[0032] The embodiment of the present invention discloses a method for automatic flow simulation. Figure 1 ,include: 101. Receive configuration instructions from the user, generate a traffic simulation plan, and generate corresponding SQL statements to be executed and store them in the first database after the configuration is completed; wherein the traffic simulation plan includes parameters and rules for defining simulation tasks, and the broker job data corresponding to the traffic simulation plan and the interaction data between the broker and the customer are stored in the second database.

[0033] The execution subject of this embodiment may be a traffic simulation system. A user logs in to the simulation system through a graphical user interface (GUI). The user chooses to create a new traffic simulation plan, and the system provides a form in which the user can enter the parameters and rules of the simulation plan.

[0034] For example, the customer segmentation rule parameters set by the user for the simulation scenario will affect how opportunities are allocated to brokers. The parameters include: Online weighted coefficient: the weight that affects the allocation of online business opportunities.

[0035] Pairing ratio by region: may refer to the proportion of business opportunities allocated by region.

[0036] Daily upper limit of customer allocation: the upper limit of the number of business opportunities that can be allocated to each broker or store each day.

[0037] Discontinuous customer distribution time: the time interval during which the broker cannot obtain business opportunities continuously within a certain period of time.

[0038] Special post weighting coefficient: the weight of a specific post or role in the allocation of business opportunities.

[0039] Transaction limit policy: A policy that controls the number of transactions to prevent excessive concentration or unfair distribution.

[0040] In this embodiment, the first database may be a Mongo database, which is a database based on distributed file storage.

[0041] The user inputs simulation scenario parameters, such as simulation time range, list of brokers and stores participating in the simulation, and business rule selection (e.g., formula deservingness, theoretical deservingness, etc.).

[0042] Users can also define specific business logic, such as priority settings, weight adjustment factors, etc.

[0043] The system automatically generates corresponding SQL statements based on the parameters and rules entered by the user. These SQL statements are used to query and process broker job data from the second database. For example, if the user selects a specific city and time period, the SQL statement will contain the corresponding filter conditions.

[0044] The generated SQL statements and related simulation scheme parameters are stored in a first database (e.g., MongoDB) together with the metadata of the simulation scheme. Each simulation scheme has a unique identifier and a status tag, with the initial status being "waiting", e.g. Figure 2 shown.

[0045] After the user submits the simulation plan, the system performs a preliminary format and logic check. The system administrator or business analyst can review the submitted simulation plan to ensure that it meets business requirements and rules. After the review is passed, the user will receive a notification that the simulation plan is ready and the SQL statements to be executed have been stored. If problems are found during the review process, the system will provide feedback, and the user can adjust the simulation plan parameters based on the feedback.

[0046] The traffic simulation scheme includes: simulation scheme parameters, simulation scheme status, and configuration information required to trigger the simulation task execution. The simulation scheme status includes: waiting, simulating, and simulated.

[0047] Specifically, simulation scheme parameters are used to define the specific settings and rules of the simulation process, which usually include the following: Simulation Time Range: Specify the time period covered by the simulation, such as the start date and end date.

[0048] List of participating brokers and stores: List which brokers and stores will participate in this simulation.

[0049] Opportunity allocation strategy: Select the opportunity allocation strategy to use, such as formula deserved, theoretical deserved, business deserved and actual deserved, etc.

[0050] Weight Adjustment Factors: Define how broker and store weights are calculated, including possible weighting coefficients.

[0051] Filter conditions: Set filter conditions for opportunity data, such as geographic location, customer type, etc.

[0052] Simulation goal: Clarify the simulation goal, such as improving transaction rate, optimizing resource allocation, etc.

[0053] In addition, the status of the simulation program is used to track the execution progress and current status of the program, which usually includes the following states: Waiting: Indicates that the plan has been created but has not yet started execution.

[0054] Simulating: Indicates that the solution is executing simulation tasks, calculating weight values ​​and allocating business opportunities.

[0055] Simulated: Indicates that the simulation task has been completed and all calculations and allocations have ended.

[0056] Error: Indicates that an error occurred during execution and troubleshooting is required.

[0057] The configuration information required to trigger the execution of the simulation task refers to the specific information required when executing the simulation task, which usually includes: Database connection information: configuration information used to connect the first database and the second database, such as database address, user name, password, etc.

[0058] SQL statement: The SQL statement to be executed, used to extract broker job data from the database.

[0059] Scheduled task configuration: define the execution frequency and time of scheduled tasks, such as how many minutes to check the solution status.

[0060] Logging settings: Specify the logging level and storage location for subsequent monitoring and troubleshooting.

[0061] Notification settings: Configure how to notify relevant personnel during or after the simulation task is executed (such as through email or system message).

[0062] By setting these parameters, status and configuration information, the traffic simulation solution can effectively manage and execute simulation tasks, thereby optimizing business opportunity allocation and evaluating strategy effectiveness.

[0063] Step 101 not only involves automated SQL generation and storage at the technical level, but also includes user interaction, review process, and feedback mechanism to ensure the accuracy and effectiveness of the traffic simulation solution.

[0064] 102. Read the SQL statements to be executed in the first database through the first scheduled task, load the corresponding broker job data in the second database for calculation, obtain the broker weight value and the store weight value, and update the solution status of the traffic simulation solution in the first database.

[0065] In addition, the broker weight value, store weight value and broker operation data are used as the generated material data, and after the material data is generated, the solution status of the traffic simulation solution in the first database is updated to "simulating", such as Figure 2 shown.

[0066] The system sets a first scheduled task, which is automatically triggered at a predetermined time interval (for example, every 10 minutes). The purpose of the scheduled task is to check whether there are new or updated SQL statements to be executed in the first database (for example, MongoDB).

[0067] The scheduled task reads the SQL statements to be executed in the first database, which are previously configured and stored by the user. For example, the SQL statements may be used to extract business opportunity data for a specific time period and region from the Hive database.

[0068] Based on the read SQL statements, the system executes these statements in the second database (such as Hive) and loads the corresponding broker job data. This data may include user query records, broker performance data, store business opportunity conversion data, etc.

[0069] The system calculates broker weight values ​​and store weight values ​​based on the loaded broker job data and predefined algorithms.

[0070] Among them, the broker weight value can be calculated based on the broker's performance, customer satisfaction, transaction volume, viewing volume and other indicators, reflecting the broker's competitiveness in the allocation of business opportunities. In the simulation task, the broker weight value is used to determine which brokers are more likely to obtain business opportunities. Brokers with high weight values ​​are likely to receive more business opportunities.

[0071] The store weight value can be calculated based on the store's performance, geographical location, service scope, customer base and other indicators, reflecting the store's competitiveness in business opportunity allocation. In the simulation task, the store weight value is used to determine which stores are more likely to obtain business opportunities. Stores with high weight values ​​are likely to receive more business opportunity allocations.

[0072] Once the calculation is completed, the system will update the status of the corresponding traffic simulation scheme in the first database from "waiting" to "simulating", indicating that the simulation scheme is being processed. The update operation may involve modifying the metadata of the simulation scheme, adding the calculated weight value, and updating the timestamp.

[0073] The system records the weight value calculation process and results for subsequent review and monitoring. If an error or anomaly occurs during the calculation process, the system will generate an alarm and notify the system administrator.

[0074] Step 102 shows how to automatically process the execution of SQL statements, data loading, weight value calculation, and updating of simulation scheme status, thereby improving the efficiency and accuracy of traffic simulation.

[0075] 103. Scan the first database through the second scheduled task. If it is monitored that the solution status has been updated, generate an executable simulation task according to the traffic simulation solution, determine the target broker based on the broker weight value and the store weight value, obtain the interaction data between the target broker and the customer, execute the simulation task through the predefined business opportunity allocation strategy, and obtain the simulated business opportunity detail data.

[0076] Interaction data: This usually refers to the direct interaction information between the broker and the client. This data may include, but is not limited to: client consultation records, broker response and follow-up records, client feedback and evaluation, broker-client communication frequency and timing, client behavior data, such as browsing records, search history, etc.

[0077] The business opportunity allocation strategy is used to distribute business opportunity data to corresponding brokers, and how to allocate business opportunity data according to Q value and strategy rules.

[0078] The system sets a second scheduled task, which periodically (for example, every minute) scans the status of the traffic simulation scheme in the first database. The purpose of this task is to detect whether there is a new simulation task that needs to be generated and executed.

[0079] The scheduled task checks whether the status of the traffic simulation plan has been updated from "Simulating" to "Executable". If the status has been updated, it means that the relevant broker weight values ​​and store weight values ​​have been calculated and the broker job data is ready.

[0080] The system generates specific simulation tasks based on the updated traffic simulation scheme. These tasks include actual scheme simulation tasks and simulated scheme simulation tasks, each of which contains all the parameters and rules required to perform the simulation.

[0081] The system obtains the latest opportunity detail data from a second database (such as Hive or a business database). This may involve executing SQL queries or calling APIs to obtain the latest opportunity records.

[0082] In this embodiment, the system can execute the actual scenario simulation task and the simulated scenario simulation task in parallel by using the supply side platform (SSP). For each task, the system uses multiple threads to process in parallel, and each thread performs simulation according to the interaction data between the target broker and the customer through a predefined business opportunity allocation strategy.

[0083] The system stores the simulated opportunity detail data in an appropriate database for further analysis and summary. This data includes the allocation results under the actual scenario and the simulated scenario.

[0084] The system records the execution details of each simulation task, including the start time, end time, parameters used, and results. Any exceptions or errors will be captured and recorded for subsequent troubleshooting and performance optimization.

[0085] Step 103 shows how to automatically generate and execute simulation tasks, and how to process and store simulation results, so as to provide support for subsequent data analysis and decision-making.

[0086] 104. Based on the detailed business opportunity data after simulation, the data of each dimension are summarized to obtain the final business opportunity conversion data.

[0087] The system of this embodiment extracts the simulated business opportunity details from the database, which includes the allocation results under the actual scheme and the simulated scheme, and ensures that the collected data includes all relevant business indicators, such as the number of business opportunities, the possibility of closing a deal, customer feedback, etc.

[0088] Also, determine the dimensions that need to be summarized. Common dimensions include brokers, stores, and brands. Define specific summary indicators for each dimension, such as total transaction volume, average transaction time, customer satisfaction, etc. Then use data processing tools or programming languages ​​to aggregate the collected detailed data according to the defined dimensions. For example, you can use SQL queries or data analysis software (such as Python's Pandas library) to organize and summarize data.

[0089] Calculate the final business opportunity conversion data based on the aggregation results, including key indicators such as total transaction volume, transaction amount, and transaction rate. You can use formulas or scripts to calculate these indicators, such as transaction rate = transaction volume / total number of business opportunities.

[0090] Combine data from different dimensions for comprehensive analysis, such as comparing performance differences between different brokers and stores. Analyze the performance differences between the simulation plan and the actual plan in various dimensions to evaluate the effect of strategy adjustments.

[0091] Verify the calculated business opportunity conversion data to ensure the accuracy and completeness of the data. Check for outliers or data inconsistencies, and perform necessary data cleaning and corrections.

[0092] In addition, the results of the summary and analysis can be organized into reports, including charts, trend analysis and summary of key indicators. The report can clearly show the effect of the simulation and provide suggestions for strategy optimization.

[0093] The method for automatic traffic simulation provided by an embodiment of the present invention automatically receives user configuration instructions and generates a traffic simulation plan, then generates corresponding SQL statements to be executed and stores them in a first database, automatically reads the SQL statements to be executed from the first database through a first scheduled task, and loads the corresponding broker job data in the second database for calculation to obtain broker weight values ​​and store weight values, which not only improves the accuracy and efficiency of data calculation, but also ensures dynamic monitoring and timely response of the simulation process by updating the traffic simulation plan status in the first database in real time; then scans the first database through a second scheduled task, and once the plan status update is monitored, an executable simulation task is generated according to the traffic simulation plan, and a target broker is determined based on the broker weight value and the store weight value, and the interaction data between the target broker and the customer is obtained to execute the simulation task, and the business opportunity detail data after simulation is obtained, thereby effectively solving the problems of cumbersome manual operation and low efficiency in the traditional traffic simulation process.

[0094] Furthermore, after obtaining the broker weight value and the store weight value, the broker weight value, the store weight value and the store's historical business opportunity detail data are used as generated material data and synchronized to the search engine.

[0095] The search engine may be Elasticsearch to utilize its efficient search and analysis capabilities.

[0096] The calculated material data is formatted into a data structure acceptable to the search engine, such as JSON format. For example, for each broker and store, create a JSON object containing its weight value and related properties.

[0097] Use Kafka Connect, Logstash, or other data synchronization tools to import material data into the search engine in batches to ensure data consistency and integrity during the data synchronization process.

[0098] In addition, a mechanism can be implemented to automatically synchronize the latest data to the search engine when the material data is updated. Database triggers or scheduled tasks can be set to monitor database changes. For example, when the broker weight value or store weight value in the database changes, the trigger can automatically start the data synchronization process. Scheduled tasks can regularly check for data changes in the database and perform synchronization operations to ensure that the data in the search engine is always up to date.

[0099] Further, see Figure 3 , step 103 comprises: 301. Generate an executable target simulation task according to the traffic simulation solution through a second scheduled task, and store it in a first database.

[0100] According to the traffic simulation scheme configured by the user, the system automatically generates specific simulation tasks, including actual scheme simulation tasks and simulated scheme simulation tasks. Each simulation task contains all the parameters and rules required to perform the simulation.

[0101] The generated simulation tasks are stored in a first database (eg, MongoDB) and marked as pending execution. A task scheduling system (eg, Quartz Scheduler) is used to manage the execution order and time of the simulation tasks.

[0102] 302. Read the target simulation task in the first database through the first scheduled task, obtain the historical business opportunity detail data corresponding to the target simulation task from the second database, and write it into the stream processing platform.

[0103] Use Spark tasks or other data extraction tools to obtain the latest opportunity detail data from the business database. Ensure that the acquired data is up-to-date and matches the time range and conditions of the simulation task.

[0104] Write the latest business opportunity detail data to a stream processing platform such as Apache Kafka. Configure Kafka topics and partitions to support high throughput data writing and reading.

[0105] 303. The historical business opportunity detail data in the stream processing platform is monitored by the supplier platform. When the historical business opportunity detail data is monitored, the broker data related to the target simulation task and the broker weight value and store weight value corresponding to the broker data are retrieved from the search engine by the supplier platform.

[0106] Specifically, relevant broker data can be retrieved in the search engine based on natural customer segmentation, CPS customer segmentation or CPT customer segmentation.

[0107] Natural distribution: refers to business opportunities that have not been processed by any commercial model. Such business opportunities are provided free of charge, which means that the platform will not charge brokers for providing these business opportunities. Natural distribution is usually allocated according to rules such as geographical regions.

[0108] CPS customer segmentation: CPS (Cost Per Sales) refers to a business model that charges according to the transaction results. In this way, the platform will only charge brokers after the business opportunity is completed. For brokers, the more transactions are completed in this way, the more fees they need to pay, which is likely to cause brokers to resist placing orders and move orders.

[0109] CPT customer sharing: CPT (Cost Per Time) is a business model that charges according to time. Brokers who purchase CPT membership will continue to receive new housing opportunities within a certain period of time (such as one month). In this way, the more transactions a broker completes, the lower the cost of a single opportunity. This greatly reduces the broker's resistance to transactions and also avoids the problem of cheating by moving orders. The CPT model can help the platform screen out brokers who are more willing to accept orders and operate business opportunities in the form of upfront fees, thereby promoting the healthy development of the business opportunity ecosystem.

[0110] In addition, Apache Kafka is configured as a stream processing platform, and specific topics are created to store historical business opportunity details. Ensure that historical business opportunity details can be correctly written into the Kafka topic, and each message contains complete business opportunity information, such as business opportunity ID, broker ID, store ID, etc.

[0111] Listeners are configured in the supply-side platform (SSP). These listeners subscribe to relevant topics in Kafka and monitor the newly incoming historical business opportunity details in real time. After receiving the Kafka message, the supply-side platform SSP parses the message content and extracts the business opportunity details, such as broker ID and store ID.

[0112] Create an index in Elasticsearch to store the weight values ​​and related data of brokers and stores. Format the broker and store data into JSON format for easy storage and retrieval in Elasticsearch.

[0113] Once SSP monitors new opportunity detail data, it will retrieve the corresponding weight value in Elasticsearch based on the broker ID and store ID. SSP associates the retrieved weight value with the opportunity detail data to form a complete simulation task input data set. SSP generates specific simulation tasks based on the retrieved data, each of which contains specific opportunity details and related weight values.

[0114] 304. After the broker is determined, the broker weight value and the store weight value are screened according to a predefined screening rule to determine a target broker.

[0115] Define screening rules: First, analyze business requirements to define predefined screening rules. These rules may be based on factors such as brokers' historical performance, customer satisfaction, geographic location, etc. Then formulate specific screening rules, such as: broker weight value is higher than a certain threshold (such as 0.8); store weight value is within a specific range; broker's recent performance growth rate (such as the last 30 days) exceeds the average.

[0116] Retrieve weight value data for all brokers and stores from Elasticsearch. Integrate other relevant information about brokers and stores, such as performance data, customer feedback, etc. Filter the collected data according to predefined filtering rules, such as returning brokers with a weight value greater than 0.8 and a recent performance growth rate of more than 5% as target brokers.

[0117] Through steps 301-304, by automatically generating and executing simulation tasks, manual intervention is reduced, operation errors are reduced, and the efficiency of the simulation process is improved. By using real-time or near real-time broker weight values ​​and store weight values, the simulation results are closer to the actual business situation, providing more accurate data support for decision-making.

[0118] Furthermore, the traffic simulation scheme includes: an actual scheme and a simulation scheme; the simulation task includes: an actual scheme simulation task and a simulation scheme simulation task; the predefined business opportunity allocation strategy is used to simulate the actual business opportunity allocation logic.

[0119] In step 103, according to the interaction data between the target broker and the customer, the simulation task is executed through a predefined business opportunity allocation strategy to obtain detailed business opportunity data after simulation, which specifically includes: using multiple threads in parallel to execute the steps of executing the simulation task according to the interaction data between the target broker and the customer through a predefined business opportunity allocation strategy to obtain detailed business opportunity data after simulation, wherein each thread performs simulation in the order of the actual scenario simulation task and the simulated scenario simulation task.

[0120] During the simulation process, historical opportunity detail data is used as a baseline or reference to ensure that the simulation task setup and execution matches historical performance. In addition, by comparing the simulated opportunity detail data with the actual historical opportunity detail data, it can be used to verify the accuracy of the simulation results.

[0121] Interaction data is the key input for executing simulation tasks, while historical opportunity detail data is used to set up the simulation environment and verify simulation results. Together, they provide the necessary information for simulation, making the simulation process closer to actual business operations, thereby improving the reliability and practicality of simulation results.

[0122] Specifically, the actual solution is to use the current business rules to allocate business opportunities; the simulation solution is to use the modified business rules or new strategies to allocate business opportunities.

[0123] Actual scenario simulation task: According to the current business rules, use broker weight values ​​and store weight values ​​to allocate business opportunities.

[0124] Simulation task of the simulation plan: According to the modified business rules, use the broker weight value and store weight value to allocate business opportunities.

[0125] Execute simulation tasks in parallel: Using the Supply Side Platform (SSP), multiple threads are created to execute simulation tasks for both real and simulated scenarios in parallel.

[0126] Assume that there are 5 brokers and 5 stores, each with a pre-calculated weight value. The supply-side platform creates 10 threads, each responsible for allocating business opportunities to a broker and store. Threads 1 to 5 perform actual scenario simulation tasks, and threads 6 to 10 perform simulated scenario simulation tasks.

[0127] Assume that there are 100 business opportunity details, including business opportunity ID, broker ID, store ID and other information. Broker A's personal Q value is 0.9, and store X's store Q value is 0.85. According to the actual solution, broker A obtains the number of business opportunities that match his weight according to his weight value. The supply-side platform processes these business opportunities in parallel, and each thread performs simulation tasks according to the predefined business opportunity allocation strategy.

[0128] The predefined opportunity allocation strategies include: 1) Formula result: Pure competitive optimization scenario, without considering the filter key.

[0129] 2) Theoretical result: In a pure competitive optimization scenario, only the broker booth status filtering key is considered.

[0130] 3) Business deserved: CPT scenario: first guarantee the minimum -> then compete for the best, non-CPT: pure competition for the best. Consider the broker booth status, discontinuous customer division, and customer division upper limit. Filter out stores with a store score of 0.

[0131] 4) Actual gain: CPT: guaranteed first -> competitive later, non-CPT: pure competitive. Consider the broker booth status, discontinuous customer distribution, customer distribution limit. There are long-distance filtering and feedback filtering.

[0132] Among them, there are three types of Filter: Broker booth status: Get the booth status of the broker closest to the time when the currently played business opportunity occurred. Redis ZSet is used here to implement it. Score is the time and member is the broker booth status.

[0133] Discontinuous customer division: The duration of discontinuous customer division configured by the broker in the background is generally 600s to 1800s.

[0134] Upper limit of distribution: the maximum number of business opportunities that can be distributed by the broker configured in the background in one day, usually 6 to 10.

[0135] For each thread, the supplier platform first performs actual scheme simulation tasks based on the broker weight value, store weight value and broker operation data using the allocation strategies of formula gain, theoretical gain, business gain and actual gain to obtain detailed business opportunity data after actual simulation; then performs simulation scheme simulation tasks based on the allocation strategies of formula gain, theoretical gain, business gain and actual gain to obtain detailed business opportunity data after simulation.

[0136] After each simulation, the actual performance is recorded. If it is actually achieved, the performance details are recorded additionally, and the discontinuous customer division and customer division upper limit are updated. After all simulations are completed, the performance records of all brokers in Redis are obtained, and the results are aggregated and output. The results include the performance summary of each person in each district and the summary of the current city. The final output performance details and performance records are used for data analysis.

[0137] Assume we have the following broker job data and weight values: Broker weight values: Broker A: Person Q = 0.9; Broker B: Person Q = 0.7; Store weight values: Store X: Store Q = 0.85; Store Y: Store Q = 0.6; Broker job data: Business Opportunity 1: The customer wants to buy a house in Area A; Business Opportunity 2: The customer wants to buy a house in Area B; 1. Formula result (pure competitive optimization scenario): Business opportunities are directly allocated based on the weight values ​​of brokers and stores without considering any filtering conditions. Business opportunity 1 is allocated to broker A (because he has the highest Q value), and business opportunity 2 is allocated to store X (because the store has the highest Q value).

[0138] 2. Theoretical deserving (considering the broker booth status): Assuming that the current booth status of broker A is unavailable, opportunity 1 is allocated to broker B with the next highest weight value.

[0139] 3. Business earned (CPT scenario: guaranteed first -> competitive later): Each broker and store first gets a guaranteed number of business opportunities, for example, 1 business opportunity, and then the remaining business opportunities are allocated according to the competitive logic. Brokers A and B each get a guaranteed 1 business opportunity, and stores X and Y each get a guaranteed 1 business opportunity. The remaining business opportunities are allocated according to the weight value.

[0140] 4. Actual gain (considering long-distance filtering and feedback filtering): Assuming that areas A and B are far from the service range of brokers and stores, these opportunities will not be considered. Assuming that customer feedback affects the allocation of opportunities, the allocation results will be adjusted based on the feedback.

[0141] Examples of simulation tasks for simulation scenarios: Assume that the weight value and opportunity data are modified to test the new business rule: Modified broker weight values: Broker A: Person Q = 0.95 (improves competitiveness); Broker B: Person Q = 0.75; The modified store weight values ​​are: Store X: Store Q = 0.9 (improved competitiveness); Store Y: Store Q = 0.65; Modified broker job data: Business opportunity 3: The customer requires to buy a house in area C; Business opportunity 4: The customer requires to buy a house in area D; Repeat the steps of the above actual scenario simulation task, but use the modified weight values ​​and opportunity data. This will generate a new set of opportunity detail data for comparing the performance of the actual scenario and the simulated scenario.

[0142] Result comparison: Detailed data of business opportunities after actual simulation: Broker A obtains business opportunity 1 and the business opportunity of region A; Broker B obtains business opportunity 2 and the business opportunity of region B; Store X obtains business opportunity 1; Store Y obtains business opportunity 2.

[0143] Detailed business opportunity data after simulation: Broker A obtains more business opportunities due to the increased weight value; Store X obtains more business opportunities due to the increased weight value.

[0144] By comparing the two sets of data, the method of this embodiment can evaluate the impact of modifying the weight value on the allocation of business opportunities and the effectiveness of the new business rules. This comparative analysis helps to optimize the business opportunity allocation strategy and improve business efficiency and transaction rate.

[0145] Furthermore, step 104 includes: performing comparative analysis based on the business opportunity detail data after actual simulation and the business opportunity detail data after simulated simulation; summarizing the business opportunity detail data after actual simulation, the business opportunity detail data after simulated simulation and the comparative analysis results in combination with the data of each dimension to obtain the final business opportunity conversion data, wherein the data of each dimension includes at least one of the following: broker dimension data, store dimension data, and brand dimension data.

[0146] Further, in order to understand the technical solution of the embodiment of the present invention, the architecture diagram of the traffic simulation system of the present embodiment is shown in FIG. Figure 4 .Depend on Figure 4 As can be seen from the figure, this system is mainly completed by the collaboration of Mongo database, Hive database, Kafka stream processing platform, ES search engine and SSP platform. Figure 5 As shown, including: 501. Receive the user's configuration instructions, generate a traffic simulation plan, and generate corresponding SQL statements to be executed and store them in the Mongo database after the configuration is completed.

[0147] The traffic simulation scheme includes parameters and rules for defining simulation tasks, and the broker job data corresponding to the traffic simulation scheme is stored in a Hive database.

[0148] 502. Read the SQL statements to be executed in the Mongo database through the Spark scheduled task, load the corresponding broker job data in the Hive database for calculation, obtain the broker weight value and the store weight value, and update the solution status of the traffic simulation solution in the Mongo database.

[0149] 503. The broker weight value, store weight value and store historical business opportunity detail data are used as generated material data and synchronized to the ES search engine.

[0150] 504. Scan the Mongo database through the keSchedule scheduled task. If it is detected that the solution status has been updated, generate an executable simulation task according to the traffic simulation solution through the keSchedule scheduled task and store it in the Mongo database.

[0151] 505. Read the target simulation task in the Mongo database through the Spark scheduled task, obtain the historical business opportunity detail data corresponding to the target simulation task from the Hive database, and write it into the Kafka stream processing platform.

[0152] 506. The historical business opportunity detail data in the stream processing platform is monitored through the SSP platform. When the historical business opportunity detail data is monitored, the broker data related to the target simulation task and the broker weight value and store weight value corresponding to the broker data are retrieved from the search engine through the SSP platform. After the broker data is determined, the broker weight value and store weight value are screened according to predefined screening rules to determine the target broker.

[0153] 507. Use multiple threads to execute in parallel the steps of executing the simulation task according to the interaction data between the target broker and the customer through the predefined business opportunity allocation strategy through the SSP platform to obtain the simulated business opportunity detail data, wherein each thread performs simulation in the order of the actual scenario simulation task and the simulated scenario simulation task.

[0154] For each thread, based on the interaction data between the target broker and the customer, the actual scheme simulation task is first performed based on the allocation strategies of formula deserving, theoretical deserving, business deserving and actual deserving to obtain the detailed data of business opportunities after actual simulation; then the simulation task of simulation scheme is performed based on the allocation strategies of formula deserving, theoretical deserving, business deserving and actual deserving to obtain the detailed data of business opportunities after simulation.

[0155] Referring to the above-mentioned embodiment, filtering operations are required in the theoretical gain, business gain and actual gain processes, wherein the filtering operations include: broker booth status, discontinuous customer division, and customer division upper limit.

[0156] During the simulation process of the new house simulator, the traffic simulation system of this embodiment interacts a lot with the NAF simulator and the Filter filter, and the Redis library performs a lot of read and write operations. Therefore, the following isolation measures are required: 1) Set up a new Redis library for the simulator to use to avoid affecting online business.

[0157] 2) The filter interface of the filter is isolated from the thread pool. A single-node service provides a maximum of 10 threads, and 40 machines provide a total of 400 threads to meet the needs of the simulation simulator and avoid slow interface response that affects other filtering services.

[0158] 3) During the recall phase of the NAF simulator, an ES query will be performed, and a separate Hystrix thread pool will be set up for the simulator to use to avoid affecting the business during peak traffic periods.

[0159] 508. Perform comparative analysis based on the business opportunity detail data after actual simulation and the business opportunity detail data after simulated simulation; summarize the business opportunity detail data after actual simulation, the business opportunity detail data after simulated simulation and the comparative analysis results in combination with the data of each dimension to obtain the final business opportunity conversion data.

[0160] The data of each dimension includes at least one of the following: agent dimension data, store dimension data, and brand dimension data.

[0161] Among them, the business opportunity conversion data corresponding to the brand dimension includes at least one of the following: business opportunity volume, transaction volume, fluctuation in business opportunity volume ratio, business opportunity conversion rate, and business opportunity conversion rate fluctuation; which are used to evaluate the brand's influence and competitiveness in the market.

[0162] The business opportunity conversion data corresponding to the store dimension includes at least one of the following: actual business opportunity quantity, simulated business opportunity quantity, actual number of divided customers, simulated number of divided customers, actual per capita business opportunities, simulated per capita business opportunities, per capita business opportunity fluctuations, actual transaction volume, simulated transaction volume; and is used to evaluate the overall performance and market performance of the store.

[0163] The business opportunity conversion data corresponding to the broker dimension includes at least one of the following: actual transaction volume, simulated transaction volume; and is used to evaluate the performance and efficiency of brokers.

[0164] The method of the embodiment of the present invention can also achieve the following technical effects: Improve decision-making efficiency and accuracy: Through automated traffic simulation methods, enterprises can quickly simulate the effects of different strategies and obtain accurate data support in a short period of time. This efficient decision-making process reduces the risk of relying on experience and intuition, making decisions more scientific and data-driven.

[0165] Reduce business risks: Before implementing a new strategy, simulation testing can predict potential risks and problems. This early assessment can help companies identify possible failure points, thereby avoiding losses in actual business and reducing the risk of strategy implementation.

[0166] Optimize resource allocation: Simulation results provide companies with in-depth insights into the distribution of business opportunities, enabling them to allocate resources more rationally. By analyzing the performance of brokers and stores, companies can ensure efficient use of resources and maximize business opportunity conversion rates.

[0167] Improve business performance: By comparing the effects of actual solutions and simulated solutions, companies can identify more effective business strategies. This continuous optimization process helps improve overall business performance, increase transaction volume and customer satisfaction.

[0168] Reduce manpower and time costs: The automated simulation process significantly reduces the need for manual operations, reducing manpower costs and time investment. Enterprises can invest more resources in strategic planning and customer service instead of tedious data processing and analysis.

[0169] The following describes an automatic traffic simulation system provided by an embodiment of the present invention. The automatic traffic simulation system described below and the automatic traffic simulation method described above can be referenced to each other.

[0170] The embodiment of the present invention provides a system for automatic flow simulation. Figure 6 ,include: The simulation scheme generating module 601 is used to receive the configuration instruction of the user, generate the traffic simulation scheme, and generate the corresponding SQL statement to be executed after the configuration is completed and store it in the first database; wherein the traffic simulation scheme includes parameters and rules for defining the simulation task, and the broker operation data and the interaction data between the broker and the customer corresponding to the traffic simulation scheme are stored in the second database; The weight value calculation module 602 is used to read the SQL statement to be executed in the first database through the first scheduled task, and load the corresponding broker job data in the second database for calculation, obtain the broker weight value and the store weight value, and update the solution status of the traffic simulation solution in the first database; The simulation execution module 603 is used to scan the first database through the second scheduled task, and if it is monitored that the scheme status has been updated, generate an executable simulation task according to the traffic simulation scheme, determine the target broker based on the broker weight value and the store weight value, obtain the interaction data between the target broker and the customer, execute the simulation task through the predefined business opportunity allocation strategy, and obtain the simulated business opportunity detail data; The data calculation module 604 is used to summarize the simulated business opportunity detail data in combination with the data of each dimension to obtain the final business opportunity conversion data.

[0171] The automatic traffic simulation system provided by the embodiment of the present invention automatically receives user configuration instructions and generates a traffic simulation plan, then generates corresponding SQL statements to be executed and stores them in a first database, automatically reads the SQL statements to be executed from the first database through a first scheduled task, and loads the corresponding broker job data in the second database for calculation to obtain broker weight values ​​and store weight values, which not only improves the accuracy and efficiency of data calculation, but also ensures dynamic monitoring and timely response of the simulation process by updating the traffic simulation plan status in the first database in real time; then scans the first database through a second scheduled task, and once the plan status update is monitored, an executable simulation task is generated according to the traffic simulation plan, and a target broker is determined based on the broker weight value and the store weight value, and the interaction data between the target broker and the customer is obtained to execute the simulation task, and the simulated business opportunity detail data is obtained, thereby effectively solving the problems of cumbersome manual operation and low efficiency in the traditional traffic simulation process.

[0172] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7As shown, the electronic device may include: a processor (processor) 710 , a communication interface (Communications Interface) 720 , a memory (memory) 730 and a communication bus 740 , wherein the processor 710 , the communication interface 720 , and the memory 730 communicate with each other through the communication bus 740 . The processor 1210 can call the logic instructions in the memory 730 to execute the method of automatic traffic simulation, which includes: receiving the configuration instructions of the user, generating a traffic simulation plan, and generating corresponding SQL statements to be executed after the configuration is completed and storing them in the first database; wherein the traffic simulation plan includes parameters and rules for defining simulation tasks, and the broker operation data corresponding to the traffic simulation plan and the interaction data between the broker and the customer are stored in the second database; reading the SQL statements to be executed in the first database through the first scheduled task, and loading the corresponding broker operation data in the second database for calculation, obtaining the broker weight value and the store weight value, and updating the plan status of the traffic simulation plan in the first database; scanning the first database through the second scheduled task, if it is monitored that the plan status has been updated, generating an executable simulation task according to the traffic simulation plan, and determining the target broker based on the broker weight value and the store weight value, obtaining the interaction data between the target broker and the customer, executing the simulation task through the predefined business opportunity allocation strategy, and obtaining the simulated business opportunity detail data; according to the simulated business opportunity detail data, combining the data of each dimension for aggregation, obtaining the final business opportunity conversion data.

[0173] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0174] On the other hand, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for automatic traffic simulation provided by the above methods is implemented, the method comprising: receiving a user's configuration instruction, generating a traffic simulation plan, and generating a corresponding SQL statement to be executed after the configuration is completed and storing it in a first database; wherein the traffic simulation plan includes parameters and rules for defining simulation tasks, and the broker operation data corresponding to the traffic simulation plan and the interaction data between the broker and the customer are stored in a second database; reading the SQL statement to be executed in the first database through a first scheduled task, and loading the corresponding broker operation data in the second database for calculation, obtaining the broker weight value and the store weight value, and updating the solution status of the traffic simulation plan in the first database; scanning the first database through a second scheduled task, if it is monitored that the solution status has been updated, generating an executable simulation task according to the traffic simulation plan, and determining the target broker based on the broker weight value and the store weight value, obtaining the interaction data between the target broker and the customer, executing the simulation task through a predefined business opportunity allocation strategy, and obtaining the simulated business opportunity detail data; according to the simulated business opportunity detail data, combining the data of each dimension for aggregation, obtaining the final business opportunity conversion data.

[0175] On the other hand, the present invention also provides a computer program product, on which a computer program is stored, and when the computer program is executed by a processor, the method for automatic traffic simulation provided by the above methods is implemented, the method comprising: receiving a user's configuration instruction, generating a traffic simulation plan, and generating a corresponding SQL statement to be executed after the configuration is completed and storing it in a first database; wherein the traffic simulation plan includes parameters and rules for defining simulation tasks, and the broker operation data corresponding to the traffic simulation plan and the interaction data between the broker and the customer are stored in a second database; reading the SQL statement to be executed in the first database through a first scheduled task, and loading the corresponding broker operation data in the second database for calculation, obtaining the broker weight value and the store weight value, and updating the plan status of the traffic simulation plan in the first database; scanning the first database through a second scheduled task, if it is monitored that the plan status has been updated, generating an executable simulation task according to the traffic simulation plan, and determining the target broker based on the broker weight value and the store weight value, obtaining the interaction data between the target broker and the customer, executing the simulation task through a predefined business opportunity allocation strategy, and obtaining the simulated business opportunity detail data; according to the simulated business opportunity detail data, combining the data of each dimension for aggregation, obtaining the final business opportunity conversion data.

[0176] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0177] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for automatic flow simulation, characterized in that: A system for flow simulation, the method comprising: Receive the configuration instructions of the user, generate a traffic simulation plan, and generate corresponding SQL statements to be executed after the configuration is completed and store them in the first database; wherein the traffic simulation plan includes parameters and rules for defining simulation tasks, and the broker operation data corresponding to the traffic simulation plan and the interaction data between the broker and the customer are stored in the second database; The SQL statement to be executed in the first database is read through the first scheduled task, and the corresponding broker job data is loaded into the second database for calculation to obtain the broker weight value and the store weight value, and the solution status of the traffic simulation solution in the first database is updated; Scan the first database through the second scheduled task, if it is monitored that the scheme status has been updated, generate an executable simulation task according to the traffic simulation scheme, determine the target broker based on the broker weight value and the store weight value, obtain the interaction data between the target broker and the customer, execute the simulation task through the predefined business opportunity allocation strategy, and obtain the simulated business opportunity detail data; Based on the detailed business opportunity data after simulation, combined with the data of each dimension, the final business opportunity conversion data is obtained.

2. The method for automatic flow simulation according to claim 1, characterized in that: The traffic simulation scheme includes: simulation scheme parameters, simulation scheme status, and configuration information required to trigger the execution of the simulation task; Among them, the status of the newly created traffic simulation plan is waiting; After generating the broker weight value and the store weight value, updating the status of the traffic simulation scheme to be simulating; After the traffic simulation is completed, the status of the traffic simulation scheme is updated to simulated.

3. The method for automatic flow simulation according to claim 1, characterized in that: After obtaining the broker weight value and the store weight value, the method further includes: The broker weight value, store weight value and the store's historical business opportunity details data are used as generated material data and synchronized to the search engine.

4. The method for automatic flow simulation according to claim 3, characterized in that: The second database stores historical business opportunity detail data corresponding to the simulation task; Generate an executable simulation task according to the traffic simulation scheme, and determine the target broker based on the broker weight value and the store weight value, specifically including: Generate an executable target simulation task according to the traffic simulation scheme through a second scheduled task, and store it in the first database; Read the target simulation task in the first database through the first scheduled task, obtain the historical business opportunity detail data corresponding to the target simulation task from the second database, and write it into the stream processing platform; Monitoring the historical business opportunity detail data in the stream processing platform through the supplier platform, and when the historical business opportunity detail data is monitored, retrieving the broker data related to the target simulation task, as well as the broker weight value and store weight value corresponding to the broker data in the search engine through the supplier platform; After the broker data is determined, the broker weight value and store weight value are screened according to predefined screening rules to determine the target broker.

5. The method for automatic flow simulation according to claim 1, characterized in that: The traffic simulation scheme includes: an actual scheme and a simulation scheme; the simulation task includes: an actual scheme simulation task and a simulation scheme simulation task; the predefined business opportunity allocation strategy is used to simulate the actual business opportunity allocation logic; According to the interaction data between the target broker and the customer, the simulation task is executed through the predefined business opportunity allocation strategy to obtain the simulated business opportunity detail data, specifically including: The supplier platform uses multiple threads to execute in parallel the steps of executing the simulation task according to the interaction data between the target broker and the customer through a predefined business opportunity allocation strategy to obtain the simulated business opportunity detail data, wherein each thread performs simulation in the order of the actual scenario simulation task and the simulated scenario simulation task.

6. The method for automatic flow simulation according to claim 5, characterized in that: The predefined business opportunity allocation strategies include: formula gain, theoretical gain, business gain and actual gain; For each thread, the supplier platform uses multiple threads to execute the steps of performing the simulation task according to the broker weight value, the store weight value and the broker operation data through the predefined business opportunity allocation strategy in parallel, and obtains the simulated business opportunity detail data, specifically including: For each thread, according to the broker weight value, store weight value and broker operation data, the actual scheme simulation task is first performed based on the allocation strategies of formula gain, theoretical gain, business gain and actual gain to obtain the detailed data of business opportunities after actual simulation; then the simulation scheme simulation task is performed based on the allocation strategies of formula gain, theoretical gain, business gain and actual gain to obtain the detailed data of business opportunities after simulation.

7. The method for automatic flow simulation according to claim 6, characterized in that: According to the detailed business opportunity data after simulation, combined with the data of each dimension, the final business opportunity conversion data is obtained, including: Conduct comparative analysis based on the detailed business opportunity data after actual simulation and the detailed business opportunity data after simulated simulation; According to the business opportunity detail data after actual simulation, the business opportunity detail data after simulation and the comparative analysis results, the data of each dimension are summarized to obtain the final business opportunity conversion data, wherein the data of each dimension includes at least one of the following: broker dimension data, store dimension data, and brand dimension data; The business opportunity conversion data corresponding to the brand dimension includes at least one of the following: business opportunity volume, transaction volume, fluctuation in business opportunity volume ratio, business opportunity conversion rate, and business opportunity conversion rate fluctuation; The business opportunity conversion data corresponding to the store dimension includes at least one of the following: actual business opportunity volume, simulated business opportunity volume, actual number of customers, simulated number of customers, actual per capita business opportunities, simulated per capita business opportunities, per capita business opportunity fluctuations, actual transaction volume, simulated transaction volume; The business opportunity conversion data corresponding to the broker dimension includes at least one of the following: actual transaction volume and simulated transaction volume.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for automatic traffic simulation as claimed in any one of claims 1 to 7 is implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for automatic traffic simulation as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program is implemented to perform the method for automatic traffic simulation as claimed in any one of claims 1 to 7.

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