Rate fast searching method based on multi-attribute record weight weighting algorithm

Abstract

The invention belongs to the technical field of service charging, and particularly relates to a rate quick searching method based on a multi-attribute record weight weighting algorithm, which comprises a plurality of stages of data preparation, weight calculation and rate determination, and compared with a traditional rate searching method, the rate quick searching method has the advantages that the rate can be quickly searched through clear weight setting and a comprehensive evaluation mechanism; the corresponding rate can be accurately determined according to the actual attributes of the goods, so that the rate searching accuracy is greatly improved; meanwhile, the algorithm abandons traditional low-efficiency traversal search, the search range is quickly narrowed through weight setting, and the search efficiency is remarkably improved; besides, the mode based on attribute matching and weight setting enables the system to have stronger adaptability and flexibility when facing different business scenes and cargo types, and enterprises can conveniently adjust attribute requirements in rate configuration according to actual business conditions, thereby realizing more reasonable charging.

Description

Technical Field

[0001] This application relates to the technical field of business billing, and specifically provides a fast rate search method based on a multi-attribute record weight weighting algorithm. Background Art

[0002] In today's increasingly complex business environment, the billing systems of enterprises face many challenges. Taking the goods transportation industry as an example, for the same transportation fee item, there are multiple different rate settings due to differences in the different attributes of the goods (such as weight, volume, transportation distance, transportation time limit requirements, goods value, etc.) and business scenarios (such as domestic transportation, international transportation, general goods transportation, special goods transportation, etc.).

[0003] In traditional rate search methods, simple conditional judgments or linear search algorithms are often used; for example, early billing systems may only determine the rate based on the weight of the goods. This method can meet the requirements in relatively simple business scenarios; however, with the expansion and diversification of business, this simple method becomes inadequate; especially when multiple attributes affect the rate simultaneously, simple conditional judgments cannot comprehensively and accurately match the appropriate rate.

[0004] When using a linear search algorithm, the system needs to traverse the entire rate data list and compare each attribute condition corresponding to each rate setting one by one until a match is found; assuming there are hundreds or thousands of rate records in a billing system, each search requires traversing all records, which will undoubtedly consume a large amount of system resources and time; especially during peak business periods, when a large number of billing requests flood in simultaneously, this linear search method will cause the system response speed to be extremely slow, seriously affecting the normal development of business.

[0005] In addition, traditional methods lack reasonable consideration of attribute weights; when different attributes determine the rate, their importance degrees are often different; for example, in the transportation of some high-value goods, the impact of the goods value on the rate may be much greater than the volume, but traditional methods are difficult to accurately reflect this difference, resulting in inaccurate rate matching, which may cause economic losses or customer loss to the enterprise. At the same time, due to the lack of a unified and efficient rate search mechanism, different business departments may use different methods to determine the rate when dealing with similar billing operations, which leads to inconsistent billing standards, easily causing internal management chaos and customer doubts about the fairness of billing. Summary of the Invention

[0006] The purpose of this application is to provide a fast rate search method based on a multi-attribute record weight weighting algorithm to solve the technical problems proposed in the above background art.

[0007] To achieve the above object, the present application provides the following technical solution: A fast rate search method based on a multi-attribute record weight weighting algorithm, comprising the following steps: S1. In the data preparation stage, all the goods attributes in the system are sorted out and defined, and the rate configuration data is sorted out to clarify the various attribute requirements and value ranges corresponding to each rate configuration record. The sorted goods attributes and rate configuration data are incorporated into the system; S2. In the weight calculation stage, the system extracts the various attribute information of the goods for which the rate is to be determined. The system matches the goods attributes with the rate configuration attributes, sets the weight value according to the matching result, and adds up multiple weight values to obtain the total weight score; S3. In the rate determination stage, all the total weight scores are sorted, the rate configuration record with the highest score is found, and the rate value corresponding to this record is selected as the rate of the current goods; The determination method of the weight value is: match the goods attribute information with the attributes in the rate configuration record one by one. When the match is successful, the weight value is 1. When the match fails, the weight value is -1. When there are no specific requirements for the goods attribute information, the weight value is 0 and it is defaulted to the hit state.

[0008] Preferably, the goods attributes include the physical attributes, commercial attributes and transportation-related attributes of the goods.

[0009] Preferably, the physical attributes include the goods weight and the goods volume, the commercial attributes include the goods value and whether the goods are fragile, and the goods transportation-related attributes include the transportation distance and the transportation timeliness.

[0010] Preferably, the system adopts a data caching mechanism for fast search. The data caching mechanism is: cache the frequently used rate configuration records and their weight scores. When searching for the rate of similar goods next time, data can be preferentially obtained from the cache. If there is no matching data in the cache, then perform the complete weight calculation and search process.

[0011] Preferably, the system also has a data monitoring function, and the data monitoring function is used to regularly count the rate matching result logs. The statistics of the data monitoring function include querying all the log records with the failure of the hit result, the rate search serial number, arranging the final matching record numbers from largest to smallest, distinguishing the summary information and the detailed information, the serial number and the matching rate record number, the goods information, the multi-rate record details, and the matching weight value.

[0012] Preferably, the system can also establish a 1-to-1 display style according to the attributes of the goods information and the rate attributes.

[0013] Preferably, the log includes the recorded rate configuration ID, the goods information in JSON format to be billed, the matching time, the hit result, and the reason for non - matching.

[0014] Preferably, the system can also analyze and provide feedback on the rationality of the weight setting according to the rate matching result. The analysis and feedback method is as follows: search in the matching exception statistics result. The search object is the same goods for which there are multiple rates meeting the conditions and the weight values are the same. When the above - mentioned situation exists, adjust the weight setting rule and update it in the master data management system.

[0015] Preferably, the method of adjusting the weight setting rule is to modify the default value of a certain rate attribute or narrow the range of the attribute value.

[0016] Compared with the prior art, the beneficial effects of this application are as follows: 1) In terms of accuracy, by carefully matching the goods attributes and rate configuration attributes and setting weights, this application can accurately reflect the true rate requirements of different goods in different business scenarios. Compared with the traditional method that may determine the rate only based on one or two main attributes, this application comprehensively considers many relevant attributes, avoiding rate deviations caused by ignoring important attributes. For example, in some complex logistics billing scenarios, the traditional method may only focus on the goods weight and transportation distance, while ignoring the impact of the goods value attribute on the rate, which may result in an overly low rate for transporting high - value goods and bring potential risks to the enterprise. However, this application can accurately match the rate that conforms to the actual situation by comprehensively considering multiple attributes such as the goods value and assigning corresponding weights, improving the accuracy of billing, helping the enterprise formulate more reasonable and fair price strategies, and enhancing customer satisfaction.

[0017] 2) In terms of efficiency improvement, abandoning the traditional linear traversal search method, this application adopts a data search model based on weight indexing, greatly reducing the search range. The system does not need to compare each rate record one by one, but can quickly locate the subset of rates that may match according to the weight, significantly reducing the time and computing resources required for searching. Especially in the case of large enterprises with a large amount of rate configuration data, the traditional search method may take several seconds or even longer to determine the rate, while this application can complete the search in a very short time to meet the business requirements of real - time billing. For example, during an e - commerce promotion event, when a large number of orders generate billing requirements simultaneously, this application can respond quickly to ensure the efficient and smooth billing process, avoiding order processing backlogs caused by billing delays and improving the overall operation efficiency of the enterprise.

[0018] 3) From the aspects of flexibility and adaptability, the configurable weight adjustment mechanism of this application provides great convenience for enterprises. Due to the continuous changes in factors such as market environment, business requirements, and cost structure, enterprises need to flexibly adjust the rate calculation method. This application allows enterprises to easily modify the weights of various attributes according to the actual situation to adapt to the business development strategies at different stages. For example, when an enterprise hopes to expand its business in a specific region, it can appropriately reduce the weight of the transport distance attribute in that region, thereby reducing the freight rate for goods transported in that region and attracting more customers. This flexible adjustment ability enables enterprises to quickly respond to market changes and maintain their competitive advantages. Brief Description of the Drawings

[0019] Figure 1 It is a schematic diagram of the overall process of this application. Detailed Implementation Manner

[0020] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0021] Please refer to Figure 1 , this application provides a technical solution: a method for quickly finding rates based on a multi-attribute record weight weighting algorithm, including the following steps: S1. In the data preparation stage, all the goods attributes in the system (physical attributes of goods: goods weight, goods volume, etc.; commercial attributes: goods value, whether the goods are fragile, etc.; transport-related attributes: transport distance, transport timeliness, etc.) are sorted out and defined, and the rate configuration data is sorted out to clarify the attribute requirements and value ranges corresponding to each rate configuration record. The sorted goods attributes and rate configuration data are incorporated into the system (the master data management system is responsible for uniformly managing, maintaining, and updating these core data to ensure the consistency, accuracy, and integrity of the data. For example, when a new type of goods appears or the rate policy is adjusted, the relevant attributes and rate configurations can be conveniently modified in the master data management system). S2. Weight calculation stage: The system extracts the attribute information of the goods for which the rate is to be determined. The system matches the goods attributes with the rate configuration attributes, sets the weight values according to the matching results, and adds up multiple weight values to obtain the total weight score (after matching each attribute of the goods with the attributes in the rate configuration one by one and setting the weights, next, a comprehensive evaluation of the final weight score is performed for each rate configuration record. The comprehensive evaluation method is that the system accumulates the attribute weight values in each rate configuration record to obtain the final weight score of each rate configuration record). The determination method of the weight value is as follows: Match the goods attribute information with the attributes in the rate configuration record one by one. When a match is hit (when a certain attribute of the goods exactly matches the corresponding attribute in the rate configuration), the weight value is 1. When a match is not hit (when a certain attribute of the goods does not match the corresponding attribute in the rate configuration), the weight value is -1. When there are no specific requirements for the goods attribute information (considering that in the rate configuration, there may be some attributes with no clear requirements, that is, the attribute value is empty, representing that any attribute is adaptable), in this case, the weight value is set to 0 and it is defaulted to the hit state.

[0022] For example, when there is a specific good for which the corresponding rate needs to be found, the system will automatically extract the attribute information of the good. Taking a batch of goods to be transported as an example, assume its weight is 1000 kg, volume is 5 cubic meters, goods value is 100,000 yuan, transportation distance is 1000 km, and the transportation time limit requirement is to be delivered within 3 days.

[0023] The system matches the attribute information of the goods with the attributes in the rate configuration record one by one. For each rate configuration record, for example, a certain rate configuration requires the weight to be between 500 kg and 1500 kg, the volume to be between 3 cubic meters and 8 cubic meters, the goods value to be between 50,000 yuan and 150,000 yuan, the transportation distance to be unlimited, and the transportation time limit to be within 5 days. Then, the weight attribute matches, and the weight is set to 1; the volume attribute matches, and the weight is set to 1; the goods value attribute matches, and the weight is set to 1; the transportation distance attribute has no specific requirements, so the weight is set to 0; the transportation time limit attribute matches, and the weight is set to 1. The initial weight total of this rate configuration record is 4.

[0024] If another rate configuration requires the weight to be between 800 kg and 1200 kg, the volume to be between 4 cubic meters and 6 cubic meters, the goods value to be between 80,000 yuan and 120,000 yuan, the transportation distance to be between 800 km and 1200 km, and the transportation time limit to be within 2 days. Then, the weight attribute matches, and the weight is set to 1; the volume attribute matches, and the weight is set to 1; the goods value attribute matches, and the weight is set to 1; the transportation distance attribute matches, and the weight is set to 1; the transportation time limit attribute does not match, and the weight is set to -1. The initial weight total of this rate configuration record is 3.

[0025] S3. In the rate determination stage, sort the sum of all weight scores, find the rate configuration record with the highest score, and select the rate value corresponding to this record as the rate for the current goods (among numerous rate configuration records, the rate value corresponding to the record with the highest score is the rate value hit by the current goods).

[0026] By adopting the above method of the present application, this approach has significant advantages compared with traditional rate search methods. Traditional methods often cannot comprehensively and accurately consider the comprehensive impact of numerous attributes of goods on the rate, nor can they quickly screen out the most suitable rate among numerous rate configurations. However, through clear weight setting and comprehensive evaluation mechanisms, this algorithm can accurately determine the corresponding rate according to the actual attributes of the goods, greatly improving the accuracy of rate search. At the same time, this algorithm abandons the traditional inefficient traversal search, quickly narrows the search scope through weight setting, and significantly improves the search efficiency. In addition, this method based on attribute matching and weight setting enables the system to have stronger adaptability and flexibility in the face of different business scenarios and goods types. Enterprises can conveniently adjust the attribute requirements in the rate configuration according to the actual business situation, so as to achieve more reasonable billing.

[0027] To further improve the search efficiency, the system adopts a data caching mechanism for quick search. The data caching mechanism is as follows: Cache the frequently used rate configuration records and their weight scores. When searching for the rate of similar goods next time, data can be preferentially obtained from the cache. If there is no matching data in the cache, then perform the complete weight calculation and search process.

[0028] The system also has a data monitoring function, and the data monitoring function is used to regularly count the rate matching result logs (the logs include recording the rate configuration ID, the goods information in JSON format to be billed, the matching time, the hit result, and the reason for non-matching). The statistics of the data monitoring function include querying all log records with a failed hit result, the rate search serial number, sorting the final number of matching records from largest to smallest, distinguishing summary information and detailed information, the serial number and the number of matching rate records, the goods information, the details of multiple rate records, and the matching weight value.

[0029] The system can also establish a one-to-one display style according to each attribute of the goods information and the rate attributes. The specific display style is shown in the following table:

[0030] The system can also analyze and provide feedback on the rationality of weight settings based on the rate matching results. The analysis and feedback method is as follows: Search in the matching anomaly statistics results. The search target is the same goods for which multiple rates meet the conditions and have the same weight value (normally, only one rate is precisely matched. If multiple rates meet the conditions simultaneously, it is an anomaly. This anomaly can reflect that some attributes are not reasonably set in the weight settings, which will lead to inaccurate rate matching results). When the above situation exists, adjust the weight setting rules (by modifying the default value of a certain rate attribute or narrowing the range of attribute values), and update in the master data management system to continuously optimize the accuracy and adaptability of the rate search algorithm.

[0031] The foregoing has shown and described the basic principles, main features, and advantages of the present application. For those skilled in the art, it is obvious that the present application is not limited to the details of the above-described exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present application is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present application, and any reference signs in the claims should not be regarded as limiting the claims involved.

[0032] Although the embodiments of the present application have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present application. The scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A fast rate lookup method based on a multi-attribute record weight weighting algorithm, characterized in that: It includes the following steps: S1. In the data preparation stage, sort out and define all the goods attributes in the system, and organize the rate configuration data to clarify the attribute requirements and value ranges corresponding to each rate configuration record. Incorporate the sorted goods attributes and rate configuration data into the system; S2. In the weight calculation stage, the system extracts the attribute information of the goods for which the rate is to be determined. The system matches the goods attributes with the rate configuration attributes, sets the weight values according to the matching results, and adds up multiple weight values to obtain the total weight score; S3. In the rate determination stage, sort the total sum of all weight scores, find the rate configuration record with the highest score, and select the rate value corresponding to this record as the rate of the current goods; The determination method of the weight value is as follows: Match the goods attribute information with the attributes in the rate configuration record one by one. When the match is successful, the weight value is 1. When the match fails, the weight value is -1. When there are no specific requirements for the goods attribute information, the weight value is 0 and it is defaulted to the hit state.

2. The rate quick search method based on the multi-attribute record weight weighting algorithm according to claim 1, characterized in that: The goods attributes include the physical attributes, commercial attributes, and transportation-related attributes of the goods.

3. The rate quick search method based on the multi-attribute record weight weighting algorithm according to claim 2, characterized in that: The physical attributes include the goods weight and the goods volume. The commercial attributes include the goods value and whether the goods are fragile. The goods transportation-related attributes include the transportation distance and the transportation timeliness.

4. A rapid rate lookup method based on a multi-attribute record weight weighting algorithm according to claim 1, characterized in that: The system adopts a data caching mechanism for quick search. The data caching mechanism is as follows: Cache the frequently used rate configuration records and their weight scores. When looking up the rate for similar goods next time, data can be preferentially obtained from the cache. If there is no matching data in the cache, then perform the complete weight calculation and search process.

5. A rapid rate lookup method based on a multi-attribute record weight weighting algorithm according to claim 1 or 4, characterized in that: The system also has a data monitoring function, and the data monitoring function is used to regularly count the rate matching result logs. The statistics of the data monitoring function include querying all the log records with the query hit result being failed, the rate search serial number, arranging the final number of matching records from largest to smallest, distinguishing the summary information and the detailed information, the serial number and the number of matching rate records, the goods information, the details of multiple rate records, and the matching weight value.

6. A rapid rate lookup method based on a multi-attribute record weight weighting algorithm according to claim 5, characterized in that: The system can also establish a one-to-one display style according to the attributes of the goods information and the rate attributes.

7. A rapid rate lookup method based on a multi-attribute record weight weighting algorithm according to claim 6, characterized in that: The log includes recording the rate configuration ID, the goods information to be billed in JSON format, the matching time, the hit result, and the non-matching reason.

8. A fast rate lookup method based on a multi-attribute record weight weighting algorithm according to claim 7, characterized in that: The system can also analyze and feedback on the rationality of the weight setting according to the rate matching result. The analysis and feedback method is as follows: Search in the matching exception statistical results. The search object is the same goods for which there are multiple rates meeting the conditions and the weight values are the same. When it is found that there are multiple rates meeting the conditions and the weight values are the same for the same goods, adjust the weight setting rules and update them in the master data management system.

9. A fast rate lookup method based on a multi-attribute record weight weighting algorithm according to claim 8, characterized in that: The method of adjusting the weight setting rules is to modify the default value of a certain rate attribute or narrow the range of the attribute value.

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