Parking charging simulation sandbox system and application method thereof
By constructing a parking billing simulation sandbox system, the accuracy and reliability issues of parking billing rules in complex scenarios were resolved, achieving efficient rule verification and management, and ensuring user experience and operational benefits.
Patent Information
- Application Number
- CN202511195413.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-26
- Publication Date
- 2025-11-21
AI Technical Summary
The accuracy and reliability of existing parking billing rules are difficult to guarantee in complex scenarios, which affects user trust and parking lot operation efficiency.
Design a parking billing simulation sandbox system, including a billing engine module, a sandbox simulation module, a data interface module, and a visualization analysis module. Construct a virtual operating environment, realize rule version binding, automatic batch simulation and difference comparison, support multi-source data inflow and debugging output, and provide in-depth visualization analysis.
Without affecting actual business operations, we will fully verify the accuracy of parking billing rules, reduce the risk of rule implementation, improve system reliability and management efficiency, and protect the economic interests and user experience of operators.
Smart Images
Figure CN120997916A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of smart parking, and more particularly to a parking billing simulation sandbox system. Background Technology
[0003] Currently, parking billing rules are mostly deployed and put into operation through configuration, meaning updates are completed by directly entering or modifying rule parameters in the business system. There are significant shortcomings in the verification process before rule deployment, making it difficult to guarantee the accuracy and reliability of parking billing rules in complex scenarios. This not only affects user trust in parking services but may also cause economic losses for parking lot operators and inefficiency in urban traffic management. Summary of the Invention
[0004] This invention provides a parking billing simulation sandbox system and its application method, which can effectively solve the problems in the background art.
[0005] To achieve the above objectives, in a first aspect, the present invention provides a parking billing simulation sandbox system, including a billing engine module, a sandbox simulation module, a data interface module, and a visualization analysis module. The billing engine module is connected to the sandbox simulation module and the data interface module, respectively. The sandbox simulation module is connected to the visualization analysis module, and the data interface module is connected to the sandbox simulation module.
[0006] The billing engine module is used to implement dynamic time segmentation, cross-record accumulation, tiered billing formula calculation, free policy judgment, limit processing, precondition expression parsing, and path recording functions;
[0007] The sandbox simulation module is used to build a virtual runtime environment, enabling rule version binding, automatic batch simulation execution, and difference comparison.
[0008] The data interface module is used to support multi-source data inflow and debugging output;
[0009] The visualization analysis module is used to handle the sandbox results output and provides in-depth visualization capabilities.
[0010] In conjunction with the first aspect, in one possible design, the dynamic time segmentation function of the billing engine module is specifically to automatically split the parking duration according to the reset point or time period rules;
[0011] The cross-record accumulation function specifically merges multiple records within a natural day or a custom period for tiered judgment.
[0012] The tiered pricing formula calculation function uses the formula: C=∑(L i / G)·R i L i G represents the length of each step, in units G and R. iUnit price;
[0013] The free policy judgment function is used to determine whether parking orders should be included in the fee base.
[0014] The limit processing function uses formula C. final =min(C,C max ), where C is the total cost of tiered pricing, C max This is the maximum limit;
[0015] The precondition expression parsing function specifically means that the component is activated only when Trigger = Evaluate(P) = True, where P is the precondition parameter;
[0016] The path recording function is used to form a component hit tree.
[0017] In conjunction with the first aspect, in one possible design, the virtual runtime environment of the sandbox simulation module is specifically that all order processing is carried out in isolated memory, without affecting the actual database;
[0018] The rule version binding feature allows you to specify a particular rule version for each test, simulating its actual performance during the effective period.
[0019] The automatic batch execution engine function specifically supports batch importing of orders and concurrent execution of simulations;
[0020] The difference comparison tool specifically provides reports on cost and path differences for a particular order under different rule versions.
[0021] In conjunction with the first aspect, in one possible design, the data inflow function of the data interface module includes API methods and parameter overriding mechanisms;
[0022] The API method specifically involves providing a POST interface to receive parking orders in JSON format.
[0023] The parameter overriding mechanism specifically supports adding test flags to order fields to replace real attributes;
[0024] The data output function specifically includes the ability to output summary costs, optionally output detailed path trees, and generate test reports in PDF or chart formats.
[0025] In conjunction with the first aspect, in one possible design, the path visualization function of the visualization analysis module specifically involves constructing a path graph with components as nodes to display the hit status of each component.
[0026] The component debugging indicator function specifically uses different colors to indicate the status of successful hit, failed hit, and condition not met;
[0027] The cost breakdown view function specifically displays the cost percentages through bar charts, including free deductions, tiered accumulation, and discount coefficients.
[0028] In conjunction with the first aspect, in one possible design, the preconditions in the precondition expression parsing function of the billing engine module include vehicle attribute conditions, parking space conditions, parking behavior conditions, and time conditions.
[0029] Vehicle attribute conditions include whether the vehicle is a new energy vehicle or a taxi;
[0030] Parking space requirements include whether it is a charging station;
[0031] Parking behavior conditions include whether or not there is charging activity;
[0032] The time condition includes whether it is the first time parking.
[0033] In conjunction with the first aspect, in one possible design, the rule version binding function of the sandbox simulation module supports binding multiple different rule versions simultaneously, and each rule version runs independently in the virtual runtime environment.
[0034] In conjunction with the first aspect, in one possible design, the data inflow function of the data interface module supports batch import of historical parking order data, and the imported historical parking order data can have its attributes modified through a parameter overwrite mechanism.
[0035] In conjunction with the first aspect, in one possible design, the component hit status path diagram and cost composition bar chart generated by the visualization analysis module can be exported as image formats, including PNG and JPEG.
[0036] Secondly, the present invention also provides an application method for a parking billing simulation sandbox system, comprising:
[0037] Step 1: Import the parking order data required for testing through the data interface module. The order attributes can be modified using the parameter overriding mechanism according to the testing requirements.
[0038] Step 2: Select the rule version to be tested in the sandbox simulation module and bind it. The sandbox simulation module calls the billing engine module to process the imported parking order data.
[0039] Step 3: The billing engine module executes dynamic time segmentation, cross-record accumulation, tiered billing formula calculation, free policy judgment, limit processing, and precondition expression parsing operations in sequence according to the bound rule version, and records the path to form a component hit tree;
[0040] Step 4: Obtain the processing results from the billing engine module through the sandbox simulation module. If multiple versions need to be compared, repeat steps 2 to 3 and select different rule versions for processing.
[0041] Step 5: The sandbox simulation module sends the processing results to the visualization analysis module. The visualization analysis module performs graphical processing on the processing results, generating a component hit status path diagram, a cost composition bar chart, and a test report.
[0042] Step Six: View the test results through the visualization analysis module. If the test results do not meet expectations, adjust the rule version and repeat steps two through five until the test results meet expectations.
[0043] The above technical solution can achieve the following technical effects:
[0044] By constructing a parking billing simulation sandbox system that integrates a billing engine, sandbox simulation, data interface, and visualization analysis, and based on core billing functions such as dynamic time segmentation and cross-record accumulation, the system enables rule version binding, batch simulation, and comparison of differences between multiple versions in an isolated virtual environment. Combined with multi-source data import debugging, visualization paths, and cost analysis capabilities, the system can comprehensively verify the accuracy of parking billing rules in complex scenarios without affecting actual business operations. This effectively reduces the risk of rule deployment, improves the reliability and management efficiency of the parking billing system, and safeguards the economic interests of operators and the travel experience of users. Attached Figure Description
[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0046] Figure 1 This is a schematic diagram of the parking fee simulation sandbox system in this invention;
[0047] Figure 2 This is a flowchart of the application method of the parking billing simulation sandbox system in this invention; Detailed Implementation
[0048] This application will now be described with reference to the accompanying drawings.
[0049] refer to Figure 1 The parking billing simulation sandbox system 100 of the present invention includes:
[0050] Billing engine module 101 is used to implement dynamic time segmentation, cross-record accumulation, tiered billing formula calculation, free policy judgment, limit processing, precondition expression parsing and path recording functions;
[0051] Sandbox simulation module 102 is used to build a virtual runtime environment to achieve rule version binding, automatic batch simulation execution, and difference comparison.
[0052] Data interface module 103 is used to support multi-source data inflow and debugging output;
[0053] The visualization analysis module 104 is used to receive the sandbox results output and provides in-depth visualization capabilities.
[0054] It should be noted that the parking billing simulation sandbox system of this invention includes multiple modules for simulating, testing, analyzing, and optimizing parking billing rules. The billing engine module, as the core processing unit, achieves accurate execution of complex billing rules through dynamic time segmentation and tiered billing calculations, while recording component hit paths to ensure traceability of billing logic. The sandbox simulation module constructs an isolated virtual operating environment, supporting parallel testing and difference comparison of multiple rule versions, avoiding impact on actual business data. The data interface module is responsible for connecting to multi-source test data and outputting debugging results, providing data support for simulation testing. The visualization analysis module graphically displays the billing path and cost composition, helping users intuitively understand the rule execution effect and optimize billing strategies.
[0055] Specifically, the billing engine module's dynamic time segmentation function can automatically split parking durations according to preset reset points or time period rules, ensuring that fee calculations for different time periods comply with the rules. The reset point can be midnight of a calendar day, and the time period rules can be divided according to peak / off-peak periods. The cross-record accumulation function can merge multiple parking records within a calendar day or a custom period, providing a data foundation for determining the tiered billing level. The tiered billing formula is calculated using C = ∑(L i / G)·R i L i G represents the length of each step, in units G and R. i To correspond to tiered unit prices, the total cost is obtained by summing the fees for each tier; the free policy judgment function can determine whether an order should be included in the cost base based on vehicle attributes, parking behavior, and other conditions; the limit processing function is implemented through C... final =min(C,C max ), where C is the total cost of tiered pricing, C maxThe maximum limit is set to ensure that the actual charge does not exceed the maximum limit set by the rules; the precondition expression parsing function is only activated when Trigger = Evaluate(P) = True, where P includes conditional parameters such as vehicle attributes, parking space type, and time; the path recording function records the hit status of each component during the billing process in a tree structure, providing a basis for rule debugging.
[0056] The sandbox simulation module's virtual runtime environment confines all order processing within isolated memory. All data operations are only effective within the simulation environment and do not affect the actual database, ensuring business data security. The rule version binding function allows specifying a particular rule version for each test, simulating the billing performance of that version on historical or simulated orders during the effective period, facilitating rule verification before deployment. The automatic batch simulation function can receive batch-imported parking order data and efficiently execute simulation calculations through a concurrent processing mechanism, improving testing efficiency. The difference comparison tool can generate detailed reports on the differences in cost amount and path hit rate for the same order under different rule versions, accurately pinpointing the impact of rule modifications on the results.
[0057] The data interface module supports both API and parameter overriding mechanisms for data inflow. The API method receives parking order data in JSON format via a standard POST interface, including basic fields such as license plate, entry time, and exit time. The parameter overriding mechanism allows the addition of test flags to order fields, such as forcibly marking them as new energy vehicles, to replace real attributes and meet the needs of specific test scenarios. The data output function can output core results such as summary costs and detailed path trees, and supports the generation of PDF test reports or charts, such as cost composition charts, to facilitate the archiving and analysis of test results.
[0058] The visualization analysis module's path graph function constructs a path diagram with components as nodes, connecting them with arrows to show the execution flow of billing rules and clearly presenting the hit status of each component. The component debugging indicator function uses different colors to distinguish the component's hit success (green), hit failure (red), and condition not met (gray), intuitively reflecting the anomalies in rule execution. The cost composition view function displays the cost percentage of each part through bar charts, including free deduction amount, cumulative cost of each tier, and the impact of discount coefficients, helping users quickly identify key aspects of cost calculation.
[0059] Preferably, the precondition expression parsing function of the billing engine module can be expanded to support more complex combination conditions, such as nested logic - new energy vehicle and first parking or dynamic parameter - dynamic parameter, improving the flexibility of the rules; the path recording function can add timestamp information to record the trigger time of each component, which is convenient for analyzing the rule execution efficiency; the rule version binding function of the sandbox simulation module can support binding multiple different versions of rules for parallel testing at the same time, and ensure that the operation of each version does not interfere with each other through a resource isolation mechanism; the automatic batch simulation function can introduce a priority queue to adjust the processing order according to the order complexity or test urgency; the data interface module can add a data validation function to perform format validation and logic validation on the imported order data, such as the exit time being later than the entry time, to avoid invalid data affecting the test results; the output format can be expanded to support more types such as Excel and JSON to meet the data needs of different scenarios; the visualization analysis module can add interactive operation functions, such as clicking on path graph nodes to view details and filtering dimensions of the cost composition graph, improving the convenience of analysis; at the same time, it supports custom chart styles to adapt to the display needs of different users.
[0060] In some embodiments, the prerequisites in the prerequisite expression parsing function of the billing engine module include vehicle attribute conditions, parking space conditions, parking behavior conditions, and time conditions; the vehicle attribute conditions include whether the vehicle is a new energy vehicle or a taxi; the parking space conditions include whether it is a charging space; the parking behavior conditions include whether there is charging behavior; and the time conditions include whether it is the first time parking.
[0061] It should be noted that the precondition expression parsing function of the billing engine module achieves precise triggering of billing rules through multi-dimensional condition judgments, covering core influencing factors such as vehicle, parking space, behavior, and time. Vehicle attribute conditions implement differentiated billing strategies for different vehicle types; for example, new energy vehicles may enjoy discounts, while taxis may be subject to specific billing standards. Parking space conditions differentiate the impact of parking space type on billing; for example, parking fees for charging spaces may include a basic parking fee and a charging surcharge. Parking behavior conditions adjust the billing logic based on the user's actual behavior; for example, orders with charging behavior may trigger fee reductions during the charging period. Time conditions combine the user's parking history to determine whether time-limited preferential policies such as free first parking apply. Through the combination of multiple conditions, the system can accurately identify the applicable rules for orders, ensuring the compliance of billing results.
[0062] Specifically, vehicle attribute conditions trigger corresponding rules by parsing the vehicle type field in the order, such as the "new energy" or "taxi" identifier. When the vehicle is a new energy vehicle, the new energy exclusive discount is activated. Parking space conditions are determined by the parking space type data associated with the parking space number, such as the "charging space" marker. If the parking space is a charging space and the order contains a charging record, the charging space billing rule is executed. Parking behavior conditions determine whether there is charging behavior by checking whether the order is associated with a charging record, such as the charging start / end time, and thus trigger rules such as "free parking for 1 hour while charging". Time conditions determine whether it is the first parking by querying the user's historical parking records. If there is no parking record within 30 days, the first parking free policy is activated. These condition parameters are logically calculated using the Evaluate(P) function. When the result is True, the corresponding component is executed to ensure the accurate application of billing rules.
[0063] Preferably, the preconditions can be further expanded to include more dimensions, such as user membership level conditions, parking duration conditions, and weather conditions, to improve the coverage of the rules; the condition expressions support custom function extensions, allowing users to write complex judgment logic according to business needs; at the same time, condition weight settings can be added, and when multiple conditions are met at the same time, the corresponding rules are executed according to the weight priority to resolve the rule conflict problem.
[0064] In some embodiments, the rule version binding function of the sandbox simulation module supports binding multiple different rule versions simultaneously, and each rule version runs independently in the virtual runtime environment.
[0065] It should be noted that the multi-version rule parallel testing capability of the sandbox simulation module is the core function for rule optimization and verification. This function allows users to bind multiple different rule versions simultaneously in the same test task. Each version runs in an independent virtual environment, using the same test order data for simulation calculations, ensuring the comparability of test results. The running processes of each version are isolated from each other, without sharing memory data or interfering with each other, ensuring the independence and accuracy of test results. This design enables users to efficiently compare the billing differences between different rule versions, quickly assess the impact of rule modifications, and provide data support for rule iteration.
[0066] Specifically, users select multiple rule versions to be tested, such as V1.0, V2.0, and V3.0, through the version management interface of the sandbox simulation module. The system allocates independent memory space and computing resources for each version. After importing test order data, the system sends order copies to the runtime environment of each version. Each version's rules process the billing of the order according to its own logic, and the intermediate and result data generated during the process are only stored in the isolated space of the corresponding version. After the test is completed, the system collects the billing results of each version, analyzes the cost differences and path differences of the same order under different versions through the difference comparison tool, and generates a comparison report. For example, comparing the billing results of the "2-hour parking for new energy vehicles" order between versions V1.0 and V2.0 can clearly show the cost changes caused by the "adjustment of the new energy discount coefficient" and the corresponding component hit differences.
[0067] Preferably, the sandbox simulation module can support branch management of rule versions, allowing users to create and modify branches based on existing versions and perform parallel testing. The differences between branches and the main version can be displayed through a visual interface. At the same time, it can add version runtime resource monitoring function to display the CPU and memory usage of each version in real time, avoiding resource competition from affecting testing efficiency. For large-scale rule version testing, distributed computing capabilities can be introduced to allocate test tasks of different versions to different nodes for execution, improving the processing speed of batch testing.
[0068] In some embodiments, the data inflow function of the data interface module supports batch import of historical parking order data, and the imported historical parking order data can have its attributes modified through a parameter overriding mechanism.
[0069] It should be noted that the data interface module's support for batch importing and modifying attributes of historical order data is key to improving the realism and flexibility of testing. Historical parking order data contains real parking scenario information, such as parking records for different time periods and different vehicle types. Batch import can simulate the rule execution effect under real business scenarios, ensuring that the test results are close to actual applications. The parameter coverage mechanism allows users to temporarily adjust order attributes without modifying the original historical data, such as marking ordinary vehicles as new energy vehicles or changing the entry time to peak hours, thereby testing the performance of rules in special scenarios or edge cases.
[0070] Specifically, the data interface module provides a batch import tool that supports importing historical parking order data via Excel templates or JSON files, including fields such as license plate number, vehicle type, entry time, exit time, and parking space number. During the import process, the system validates the data format to ensure that required fields are complete and the time logic is reasonable. Users can add test attributes to orders through the parameter overriding interface, such as adding the "IsNewEnergy:True" field to overridden the original vehicle type, or adding the "OverrideTime:2024-05-01 08:00" field to modify the entry time. When the system passes the orders to the sandbox simulation module, it prioritizes reading the overriding parameters for billing calculations, while retaining the original data for comparative analysis. For example, after importing historical orders from 2023, some orders can be marked as new energy vehicles through parameter overriding to test the applicability of newly launched new energy vehicle preferential rules to historical scenarios.
[0071] Preferably, the data interface module supports incremental import functionality, importing only newly added historical order data to avoid duplicate imports; the parameter overriding mechanism supports batch rule settings, such as "marking all Monday orders as new energy vehicles," improving the efficiency of attribute modification; it can also add a data anonymization function to anonymize sensitive information such as real license plates in imported historical orders, protecting user privacy; the imported data supports version management, allowing users to save different test datasets for easy repeated testing and traceability.
[0072] In some embodiments, the component hit status path diagram and cost composition bar chart generated by the visualization analysis module can be exported as image formats, including PNG and JPEG.
[0073] It should be noted that the results export function of the visualization analysis module facilitates the archiving and sharing of test reports; the component hit status path diagram intuitively displays the execution process of billing rules and component hit status, and the cost composition bar chart clearly presents the proportion of each part of the cost. These graphical results are important basis for rule verification and optimization; it supports exporting mainstream image formats such as PNG and JPEG, ensuring that the results can be easily used in various scenarios such as documents, slides, and reports, facilitating internal team communication and rule review.
[0074] Specifically, after viewing the path chart and bar chart in the visualization analysis interface, users can select PNG or JPEG format via the "Export" button. The system renders the graphics to ensure that the clarity of the exported image is consistent with the interface display. The exported image contains complete elements such as chart title, legend, and data labels, ensuring the integrity of the information. For example, after exporting the component hit path chart of an order as a PNG format, it can be inserted into the rule test report to intuitively explain the rule execution logic. After exporting the cost composition bar chart of different rule versions as a JPEG format, changes in the cost structure can be quickly identified through image comparison.
[0075] Preferably, the visualization analysis module supports more export formats, such as PDF and SVG. The SVG format ensures that the image does not lose quality when scaled at any time, making it suitable for professional report production. The export function supports batch operations, allowing users to export analysis charts for multiple orders at once and package them into a compressed file for download. At the same time, an image watermark function can be added to the exported image to add watermarks such as test time and version information, which facilitates result traceability.
[0076] The above embodiments of the present invention have the following beneficial effects: The billing engine module of the present invention achieves accurate execution of complex billing rules through functions such as dynamic time segmentation and tiered billing formula calculation; the path recording function ensures the traceability of billing logic and improves the reliability of rules; the sandbox simulation module constructs an isolated virtual running environment, supports parallel testing and difference comparison of multiple version rules, avoids impacting actual business, and improves testing efficiency through automatic batch simulation running function; the data interface module supports multi-source data inflow and flexible parameter coverage, providing rich data sources for simulation testing, and the debugging output function facilitates rule problem localization; the visualization analysis module helps users intuitively understand the rule execution effect by graphically displaying path and cost composition, and the export function facilitates result sharing and archiving.
[0077] The billing engine module's precondition expression parsing function covers multiple dimensions such as vehicle, parking space, behavior, and time, ensuring the accuracy of rule triggering; the combination of tiered billing and limit processing formulas ensures compliance in cost calculation; the sandbox simulation module's multi-version independent operation mechanism makes rule comparison testing efficient and reliable, and the difference comparison tool accurately locates the impact of modifications; the data interface module's ability to import historical orders in batches enhances test realism, and the parameter coverage mechanism enhances the flexibility of test scenarios; the visualization analysis module's path diagrams and bar charts intuitively present the details of rule execution, and the multi-format export function meets the needs of different scenarios.
[0078] refer to Figure 2 The application method 200 of the parking billing simulation sandbox system of the present invention includes:
[0079] Step 1: Import the parking order data required for testing through the data interface module. The order attributes can be modified using the parameter overriding mechanism according to the testing requirements.
[0080] Step 2: Select the rule version to be tested in the sandbox simulation module and bind it. The sandbox simulation module calls the billing engine module to process the imported parking order data.
[0081] Step 3: The billing engine module executes dynamic time segmentation, cross-record accumulation, tiered billing formula calculation, free policy judgment, limit processing, and precondition expression parsing operations in sequence according to the bound rule version, and records the path to form a component hit tree;
[0082] Step 4: Obtain the processing results from the billing engine module through the sandbox simulation module. If multiple versions need to be compared, repeat steps 2 to 3 and select different rule versions for processing.
[0083] Step 5: The sandbox simulation module sends the processing results to the visualization analysis module. The visualization analysis module performs graphical processing on the processing results, generating a component hit status path diagram, a cost composition bar chart, and a test report.
[0084] Step Six: View the test results through the visualization analysis module. If the test results do not meet expectations, adjust the rule version and repeat steps two through five until the test results meet expectations.
[0085] It is understandable that the steps and references described in the application method 200 of this parking billing simulation sandbox system are... Figure 1 The modules described correspond to those in the parking fee simulation sandbox system. Therefore, the modules, features, and beneficial effects described above for the parking fee simulation sandbox system also apply to the application method 200 of the parking fee simulation sandbox system and the operations contained therein, and will not be repeated here.
[0086] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. A parking fee simulation sandbox system, characterized in that, It includes a billing engine module, a sandbox simulation module, a data interface module, and a visualization analysis module. The billing engine module is connected to the sandbox simulation module and the data interface module, respectively. The sandbox simulation module is connected to the visualization analysis module, and the data interface module is connected to the sandbox simulation module. The billing engine module is used to implement dynamic time segmentation, cross-record accumulation, tiered billing formula calculation, free policy judgment, limit processing, precondition expression parsing and path recording functions. The sandbox simulation module is used to build a virtual running environment to achieve rule version binding, automatic batch simulation, and difference comparison. The data interface module is used to support multi-source data inflow and debugging output; The visualization analysis module is used to receive the sandbox results and provide in-depth visualization capabilities.
2. The parking fee simulation sandbox system according to claim 1, characterized in that, The dynamic time segmentation function of the billing engine module specifically involves automatically splitting the parking duration according to the reset point or time period rules; The cross-record accumulation function specifically merges multiple records within a natural day or a custom period for tiered judgment. The tiered pricing formula calculation function uses the formula: C=∑(L i / G)·R i L i G represents the length of each step, in units G and R. i Unit price; The free policy judgment function is used to determine whether parking orders should be included in the fee base. The limit processing function uses formula C. final =min(C,C max ), where C is the total cost of tiered pricing, C max This is the maximum limit; Specifically, the precondition expression parsing function is that the component is activated only when Trigger = Evaluate(P) = True, where P is the precondition parameter; The path recording function is used to form a component hit tree.
3. The parking fee simulation sandbox system according to claim 1, characterized in that, The virtual operating environment of the sandbox simulation module is that all order processing is carried out in isolated memory, without affecting the actual database; The rule version binding function specifically allows a specific rule version to be specified for each test, simulating its actual performance during the effective period; The automatic batch execution engine function specifically supports batch importing of orders and concurrent execution of simulations; The difference comparison tool specifically provides reports on cost and path differences for a given order under different rule versions.
4. The parking fee simulation sandbox system according to claim 1, characterized in that, The data inflow function of the data interface module includes API method and parameter overriding mechanism; Specifically, the API method involves providing a POST interface to receive parking orders in JSON format. The parameter overriding mechanism specifically supports adding test flags to order fields to replace real attributes; The data output function specifically includes the ability to output summary costs, optionally output detailed path trees, and generate test reports in PDF or chart formats.
5. The parking fee simulation sandbox system according to claim 1, characterized in that, The path visualization function of the visualization analysis module specifically involves constructing a path graph with components as nodes to display the hit status of each component; The component debugging identification function specifically uses different colors to indicate the status of successful hit, failed hit, and condition not met. The cost composition view function specifically displays the cost percentage through a bar chart, including free deductions, tiered accumulation, and discount coefficients.
6. The parking fee simulation sandbox system according to claim 1, characterized in that, The preconditions in the precondition expression parsing function of the billing engine module include vehicle attribute conditions, parking space conditions, parking behavior conditions, and time conditions. The vehicle attribute conditions include whether the vehicle is a new energy vehicle or a taxi; The parking space conditions include whether it is a charging parking space; The parking behavior conditions include whether there is charging activity; The time condition includes whether it is the first time parking.
7. The parking fee simulation sandbox system according to claim 1, characterized in that, The rule version binding function of the sandbox simulation module supports binding multiple different rule versions at the same time, and each rule version runs independently in the virtual runtime environment.
8. The parking fee simulation sandbox system according to claim 1, characterized in that, The data inflow function of the data interface module supports batch import of historical parking order data, and the imported historical parking order data can be modified in terms of attributes through the parameter overwrite mechanism.
9. The parking fee simulation sandbox system according to claim 1, characterized in that, The component hit status path diagram and cost composition bar chart generated by the visualization analysis module can be exported as image formats, including PNG and JPEG.
10. A method for applying a parking fee simulation sandbox system, applied to the parking fee simulation sandbox system described in any one of claims 1 to 9 as above, characterized in that, Includes the following steps: Step 1: Import the parking order data required for testing through the data interface module. The order attributes can be modified using the parameter overriding mechanism according to the testing requirements. Step 2: Select the rule version to be tested in the sandbox simulation module and bind it. The sandbox simulation module calls the billing engine module to process the imported parking order data. Step 3: The billing engine module executes dynamic time segmentation, cross-record accumulation, tiered billing formula calculation, free policy judgment, limit processing, and precondition expression parsing operations in sequence according to the bound rule version, and records the path to form a component hit tree; Step 4: Obtain the processing results from the billing engine module through the sandbox simulation module. If multiple versions need to be compared, repeat steps 2 to 3 and select different rule versions for processing. Step 5: The sandbox simulation module sends the processing results to the visualization analysis module. The visualization analysis module performs graphical processing on the processing results, generating a component hit status path diagram, a cost composition bar chart, and a test report. Step Six: View the test results through the visualization analysis module. If the test results do not meet expectations, adjust the rule version and repeat steps two through five until the test results meet expectations.