Wharf charging data processing method and system
By adopting dynamic optimization and visual pruning technology of rule networks in the dock billing system, the complexity problem of existing systems when dealing with different business types and contract discounts is solved, and more efficient billing calculation and system use are achieved.
Patent Information
- Application Number
- CN202411673955.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-21
- Publication Date
- 2025-03-11
AI Technical Summary
When handling different business types and contract discounts, the existing dock billing system needs to reconfigure billing rules, resulting in complex billing management and difficulty for front-line workers to master the system operation, which affects the calculation and use efficiency.
Dynamic optimization and visual pruning technology based on rule networks are adopted to define the billing dimensions of business data, and the billing dimensions are generated by visually configuring contract data, establish rule network models, dynamic optimization and pruning rule networks are improved to improve computing efficiency.
The setting and optimization of the rule network is simplified, the computing efficiency is improved when processing complex and large numbers of rules is improved, the data volume is reduced, and the system's usage efficiency is improved.
Smart Images
Figure CN119599744A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of port digital technology, and in particular to a terminal billing data processing method and system. Background Art
[0002] According to relevant regulations, major ports along my country's coast, the Yangtze River main line and all other open ports provide services such as ship entry and exit, anchoring, berthing, passenger embarkation and disembarkation, cargo loading and unloading, lighterage, storage and port security. Port operators and pilotage agencies and other units collect port operating service fees from shipowners, cargo owners or their agents.
[0003] At present, most ports have transferred billing work online, so the correctness of the billing contract configuration determines whether the terminal billing system can accurately complete the billing work.
[0004] Terminal billing work generally uses the form filling method to configure the contract content. The user abstracts the contract content into different billing scenarios, fills in the form, and stores it in the database. When billing, the system matches the appropriate billing scenarios through background logic to calculate the relevant fees.
[0005] The existing terminal billing system has the following shortcomings:
[0006] 1. Different business types of the same customer entity are configured with independent billing contracts, and the billing rules need to be reconfigured;
[0007] 2. When the contract details change or there are discounts or offers, you need to re-edit the contract or create a new contract and configure and describe the discount or offer rules.
[0008] All of the above will make billing management complicated. At the same time, due to the uneven educational level of some front-line workers at the docks and their lack of familiarity with the system, it is relatively easy for them to find it difficult to master the operation methods of contract configuration, affecting the efficient calculation and use of the system. Summary of the invention
[0009] In order to improve the smoothness of terminal billing management and enhance the calculation and use efficiency of the corresponding system, the present application provides a terminal billing data processing method and system.
[0010] In a first aspect, the present application provides a terminal billing data processing method, which adopts the following technical solution:
[0011] A method for processing terminal billing data, comprising:
[0012] S1. Define the billing dimensions of business data;
[0013] S2. Visually configure contract data and generate corresponding billing dimensions to establish a visual billing dimension streamlined process;
[0014] S3, rule network processing; and,
[0015] S4. Generate a billing result based on the obtained rule network and save it as historical data; wherein the historical data is used to adjust the rule network processing result.
[0016] Optionally, S1, defining billing dimensions of service data, includes:
[0017] S11. Obtain all billing elements with non-zero probability involving multiple business types; wherein one billing element is defined as one billing dimension and the total number of billing elements obtained is M, the number is M, and the billing elements include judgment elements and workload elements; the judgment element is used to make conditional judgments during billing to determine the applicable billing rules; the workload element is used to calculate the billing results according to the billing rules;
[0018] S12. Configure the associated settings of the billing elements.
[0019] Optionally, the step S2, establishing a visualized billing dimension streamlining process, includes:
[0020] S21. Obtaining a billing contract;
[0021] S22. Analyze and process the billing contract based on the predetermined mind map sample to obtain node data; wherein any branch in the map represents a billing scenario under a condition;
[0022] S23. Perform visualization based on node data, generate a mind map of billing rules, and complete the simplification of billing dimensions.
[0023] Optionally, the S3, rule network processing, includes:
[0024] S31, define a tree structure as the current rule network model, and generate a billing rule tree; a rule network model includes several execution routes, and there is only one execution route that can calculate the correct billing result;
[0025] The billing rule tree contains relationship nodes, judgment nodes and calculation nodes; among them, the relationship node is used to summarize the execution results of the rule network sub-route and calculate the output of the node and includes ANY, AND, ALL and NONE; the judgment node is used to determine the execution direction of the branch in the rule tree, and the billing node is used to calculate the final billing result;
[0026] S32. Obtain business data, analyze matching billing dimensions, and extract fact data of corresponding billing elements according to the billing dimensions. Input the extracted billing elements into the rule network model for calculation to obtain billing results.
[0027] S31, define a rule tree as a sample;
[0028] S32. Based on the billing rule mind map, the relationship nodes, judgment nodes and calculation nodes of the rule tree are obtained, and a billing rule tree is created; wherein the relationship nodes are used to summarize the execution results of the rule network sub-routes and calculate the output of the node and include ANY, AND, ALL and NONE; the judgment nodes are used to determine the execution direction of the branches in the rule tree, and the billing nodes are used to calculate the final billing results.
[0029] Optionally, also include:
[0030] Define ANY and / or AND as root node one;
[0031] Obtain historical data and statistically analyze the hit rate of the branch corresponding to the root node;
[0032] Define the execution order of branches according to their hit rates; and,
[0033] The generated billing rule tree is split and executed according to the execution order of the branches.
[0034] Optionally, also include:
[0035] Define ALL and / or NONE as root node 2;
[0036] Let the branches corresponding to root node 2 be executed in parallel, and the corresponding data are not saved.
[0037] Optionally, also include:
[0038] Obtain the source of business data and identify the corresponding user;
[0039] The corresponding historical billing data is called according to the user identity, and a comparison is made to determine whether there are similar records. If so, the fact data corresponding to the billing elements in the current business data is extracted, and the corresponding historical billing rule tree is imported to generate the billing result.
[0040] Optionally, it also includes:
[0041] If a smart collaboration request is received, the identity information of the requesting party is obtained;
[0042] Retrieve corresponding historical business data based on identity information; wherein the business data includes the types of goods;
[0043] Use the preset AI prediction model to analyze current time and historical business data, predict the current highest probability business type and output the corresponding business data, assign modification permissions, receive feedback after modification and generate current business data.
[0044] In a second aspect, the present application provides a terminal billing data processing system, which adopts the following technical solution:
[0045] A terminal billing data processing system comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute any of the terminal billing data processing methods described above.
[0046] In summary, the present application includes the following beneficial technical effects: converting and processing the rule network description language, supporting a flexible and highly readable graphical rule network setting method, allowing staff to formulate concise and efficient rule networks; at the same time, improving the network structure and matching order of the rule network through dynamic rule optimization and pruning technology, greatly improving the computing efficiency when processing complex and large numbers of rules. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] Figure 1 It is a schematic diagram of the main process of the method of the present application;
[0048] Figure 2 It is a visualization diagram of the network rules of the method of the present application. DETAILED DESCRIPTION
[0049] The following is combined with Figure 1 This application is described in further detail.
[0050] The embodiment of the present application discloses a method for processing terminal billing data.
[0051] Reference Figure 1 , the terminal billing data processing method includes:
[0052] S1. Define the billing dimensions of business data;
[0053] S2. Visually configure contract data and generate corresponding billing dimensions to establish a visual billing dimension streamlined process;
[0054] S3, rule network processing; and,
[0055] S4. Generate a billing result based on the obtained rule network and save it as historical data; wherein the historical data is used to adjust the rule network processing result.
[0056] The purpose of this method is to propose a data processing method for the dock billing system based on the dynamic optimization of the rule network and visual pruning technology in view of the current status of the dock billing business. It improves the network structure and matching order of the rule network through dynamic rule optimization and pruning technology, greatly improving the calculation efficiency when processing complex and large numbers of rules; at the same time, the rule network description language is converted and processed to support a flexible and highly readable graphical rule network setting method, so that staff can formulate concise and efficient rule networks. The following is a detailed explanation.
[0057] S1 above defines the billing dimensions of business data, which include:
[0058] S11. (Based on the data uploaded by the staff) obtain all billing elements with non-zero probability involving multiple business types; wherein one billing element is defined as one billing dimension and the total number of billing elements obtained is M.
[0059] In this embodiment, the charging elements include:
[0060] 1) Judgment factor, which is used to make conditional judgments during billing and determine the appropriate billing rules; it can be understood that the billing rules are pre-entered and uploaded by the staff according to the business type;
[0061] 2) Workload factor, which is used to calculate the billing results according to the billing rules.
[0062] S12. Configure the associated settings of the billing elements; example: rule code, billing unit, billing method.
[0063] Reference Figure 2 , the above S2, visually configures the contract data and generates the corresponding billing dimensions to establish a visual billing dimension streamlining process, which aims to allow users to intuitively delete unnecessary billing dimensions by establishing a visual mind map, that is, the billing dimension M1 required by the contract is less than or equal to all billing dimensions M. It is conducive to reducing the scale of the rule network; it includes:
[0064] S21. Obtain business data, and extract fact data corresponding to billing elements from the business data to generate a billing contract. Obtain a billing contract; this part of data can be obtained and uploaded from the customer based on past and current needs when creating a system / platform corresponding to this method;
[0065] S22. Analyze and process the billing contract based on a predetermined mind map sample to obtain node data; wherein any branch in the diagram represents a billing scenario under a condition.
[0066] S23. Visualize the node data to generate a mind map of billing rules and simplify the billing dimensions. Specifically:
[0067] Based on the node data, the number of billing rules is configured through visual means, the required billing elements are selected, the redundant billing elements are cut, the billing dimension is streamlined, and a billing rule mind map is generated.
[0068] If the number of billing elements involved in a billing contract is defined as M1, then M1<M, so the above steps can trim unnecessary billing dimensions and reduce the amount of data.
[0069] The above S3, rule network processing, includes:
[0070] S31, define a tree structure as the current rule network model, and generate a billing rule tree; a rule network model includes several execution routes, and there is only one execution route that can calculate the correct billing result;
[0071] The billing rule tree contains relationship nodes, judgment nodes and calculation nodes; among them, the relationship node is used to summarize the execution results of the rule network sub-route and calculate the output of the node and includes ANY, AND, ALL and NONE; the judgment node is used to determine the execution direction of the branch in the rule tree, and the billing node is used to calculate the final billing result;
[0072] S32. Obtain business data, analyze matching billing dimensions, and extract fact data of corresponding billing elements according to the billing dimensions. Input the extracted billing elements into the rule network model for calculation to obtain billing results.
[0073] S31, define the rule tree as a sample; Figure 2 As shown;
[0074] S32. Obtain relationship nodes, judgment nodes, and calculation nodes of a rule tree based on the billing rule mind map, and create a billing rule tree.
[0075] Among them, the relationship node is used to summarize the execution results of the rule network sub-routes and calculate the output of the node, which includes ANY, AND, ALL and NONE; the judgment node is used to judge the execution direction of the branch in the rule tree, and the billing node is used to calculate the final billing result.
[0076] Regarding relationship nodes, examples:
[0077] ANY means that if one of the child nodes returns TRUE, the node will also be TRUE. If all are FALSE, the node will be FALSE. The execution will terminate at the point where TRUE is reached, which is similar to the "or" logic.
[0078] AND means that if one of all child nodes returns FALSE, the node will also be FALSE. It will be TRUE only if all are TRUE. Execution will be terminated when FALSE is reached, which is similar to the "AND" logic.
[0079] ALL means that all child nodes will be executed. If any one of them returns TRUE, the node will also be TRUE. If none of them is TRUE and one of them is FALSE, the node will be FALSE. If none of them is TRUE and none of them is FALSE, the node will be FALSE. If none of them is TRUE and FALSE, the node will be NONE. The execution of all child nodes is terminated.
[0080] NONE means that all child nodes will be executed, and NONE will be returned regardless of what the child node returns.
[0081] In one embodiment of the method, it also includes:
[0082] Define ANY and / or AND as root node one;
[0083] Obtain historical data and statistically analyze the hit rate of the branch corresponding to the root node;
[0084] Define the execution order of branches according to their hit rates; and,
[0085] The generated billing rule tree is split and executed according to the execution order of the branches. Example: Prioritize the execution of routes with high hit rates, thereby reducing the number of execution steps of the rule network.
[0086] In this embodiment, the hit rate refers to the situation that the staff accepts the rule tree of S3 or the unmodified tree and only modifies some parameters, which is called a hit. The hit rate can be obtained by calculating the ratio of the number of hits to the total number. In another embodiment of the present application, a related design for improving the hit rate is made from another perspective.
[0087] In another embodiment of the method, it also includes:
[0088] Define ALL and / or NONE as root node 2;
[0089] The branches corresponding to the root node 2 are executed in parallel, and the corresponding data are not saved, thereby reducing the execution time of the rule network without affecting the execution result; at the same time, it saves storage space and improves the efficiency of historical data query.
[0090] In another embodiment of the method, it also includes:
[0091] Obtain the source of business data and identify the corresponding user identity; it is understandable that any business data has a corresponding owner / unit, that is, the customer, so the data obtained in the past is bound to the identity to form a record.
[0092] The corresponding historical business data is called according to the user identity, and a comparison is made to determine whether there are similar records. If so, the fact data corresponding to the billing elements in the current business data is extracted, and the corresponding historical billing rule tree is imported to generate the billing result.
[0093] According to the above settings, for the same user, if this method is not used for the first time, the corresponding historical data can be called. If the same business structure / contract structure has been used before, and only a situation such as a change in the number of containers occurs, this method will no longer go through the process again but directly call the historical billing rule tree to save computing power and improve efficiency.
[0094] In another embodiment of the method, it also includes:
[0095] If a smart collaboration request is received, the identity information of the requesting party is obtained;
[0096] Retrieve corresponding historical business data based on identity information; wherein the business data includes the types of goods;
[0097] Use the preset AI prediction model to analyze current time and historical business data, predict the current highest probability business type and output the corresponding business data, assign modification permissions, receive feedback after modification and generate current business data.
[0098] Example: Taking seasonal goods such as mango, watermelon, and swimming crab as an example, the time when the corresponding business data appears is relatively regular, so the business data that may be matched can be predicted by the current time, reducing the input and import work of the requester's business data. On the above basis, only the historical data needs to be modified. At the same time, it can also directly call the aforementioned branches obtained from the history to perform sorting, ensure the hit rate, and efficiently complete the relative task.
[0099] The above-mentioned AI prediction model can be trained with the existing expected algorithm, and the accuracy of the training results meets the user's expected goals; it can also directly extract non-rule item information of historical business data and import it into the current generative AI prediction results.
[0100] The embodiment of the present application also discloses a terminal billing data processing system.
[0101] A terminal billing data processing system comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute any of the terminal billing data processing methods described above.
[0102] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.
Claims
1. A method for processing terminal billing data, characterized in that: include: S1. Define the billing dimensions of business data; S2. Visually configure contract data and generate corresponding billing dimensions to establish a visual billing dimension streamlined process; S3, rule network processing; as well as, S4. Generate a billing result based on the obtained rule network and save it as historical data; wherein the historical data is used to adjust the rule network processing result.
2. The method for processing terminal billing data according to claim 1, characterized in that: S1: defining the billing dimensions of the service data, which includes: S11. Obtain all billing elements with non-zero probability involving multiple business types; wherein one billing element is defined as one billing dimension, and the total number of billing elements obtained is M, and the billing elements include judgment elements and workload elements; the judgment elements are used to make conditional judgments during billing to determine the appropriate billing rules; and the workload elements are used to calculate the billing results according to the billing rules; S12. Configure the associated settings of the billing elements.
3. The method for processing terminal billing data according to claim 2, characterized in that: S2, establishing a visual billing dimension streamlined process, which includes: S21. Obtaining a billing contract; S22. Analyze and process the billing contract based on the predetermined mind map sample to obtain node data; wherein any branch in the map represents a billing scenario under a condition; S23. Perform visualization based on node data, generate a mind map of billing rules, and complete the simplification of billing dimensions.
4. The method for processing terminal billing data according to claim 3, characterized in that: S3, rule network processing, includes: S31. Define the tree structure as the current rule network model and generate a billing rule tree; generate a rule network model according to the billing rule tree. A rule network model contains several execution routes, and there is only one execution route that can calculate the correct billing result; The billing rule tree contains relationship nodes, judgment nodes and calculation nodes. Among them, the relationship node is used to summarize the execution results of the rule network sub-routes and calculate the output of the node and includes ANY, AND, ALL and NONE; the judgment node is used to determine the execution direction of the branch in the rule tree, and the billing node is used to calculate the final billing result; S32. Obtain business data, analyze the matching billing dimensions, and extract the factual data of the corresponding billing elements according to the billing dimensions. According to S2, obtain the corresponding billing rule mind map, input the extracted billing elements into the rule network model for calculation, and obtain the billing results.
5. The terminal billing data processing method according to claim 4, characterized in that: Also includes: Define ANY and / or AND as root node one; Obtain historical data and statistically analyze the hit rate of the branch corresponding to the root node; Define the execution order of branches according to their hit rates; as well as, The generated billing rule tree is split and executed according to the execution order of the branches.
6. The terminal billing data processing method according to claim 4, characterized in that: Also includes: Define ALL and / or NONE as root node 2; Let the branches corresponding to root node 2 be executed in parallel, and the corresponding data are not saved.
7. The terminal billing data processing method according to claim 1, characterized in that: Also includes: Obtain the source of business data and identify the corresponding user; According to the user identity, the corresponding historical billing business data is called, and it is compared and determined whether there are similar records. If so, the fact data corresponding to the billing elements in the current business data is extracted, and the corresponding historical billing rule tree is imported to generate the billing result.
8. The terminal billing data processing method according to claim 1, characterized in that: It also includes: If a smart collaboration request is received, the identity information of the requesting party is obtained; Retrieve corresponding historical business data based on identity information; wherein the business data includes the types of goods; Use the preset AI prediction model to analyze current time and historical business data, predict the current highest probability business type and output the corresponding business data, assign modification permissions, receive feedback after modification and generate current business data.
9. A terminal billing data processing system, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the terminal billing data processing method as described in any one of claims 1 to 8.