Historical data-based intelligent decision-making method for point location contract renewal

Abstract

The invention discloses a historical data-based point location contract renewal intelligent decision-making method, and the method comprises the steps: constructing a multi-source data collection and hierarchical storage system, designing a weighted comprehensive scoring model which comprises the daily average income, operation efficiency, location value and potential growth four-dimension, and combining a time series prediction and clustering analysis algorithm, thereby achieving the intelligent decision-making of point location contract renewal. According to the method, centralized management and real-time calculation of historical data of point locations can be realized, quantitative evaluation and grading of the point locations can be realized, and closed-loop management from'data analysis' to'intelligent decision 'can be realized by automatically outputting extension suggestions through trend prediction and a rule engine. Compared with an existing contract management system, the contract extension decision-making efficiency and accuracy are improved, strategies can be dynamically optimized according to trend changes, low-efficiency or risk point locations can be recognized in advance, and therefore operation risks are reduced, resource configuration is optimized, and digital and intelligent transformation of the contract extension process is achieved.

Description

Technical Field

[0001] This application relates to the field of intelligent decision-making technology for contract renewal, and in particular to an intelligent decision-making method for point-based contract renewal based on historical data. Background Technology

[0002] In industries heavily reliant on physical locations, such as retail, vending machines, and advertising, acquiring, operating, and maintaining these locations are core elements of business success. Especially in business models where locations are leased or operated through contracts, the ability to scientifically assess location value and make renewal decisions directly impacts revenue stability and resource utilization efficiency. With increasing industry competition and the rapid accumulation of location operation data, intelligent decision-making for location contract renewals based on historical data has become a clear technological development need.

[0003] While current contract management technologies have made significant progress in contract lifecycle management and project process control, they still lack specific solutions for the particular scenario of "location-based contract renewal." For example, while reference patent CN119539912A provides an efficient method covering the entire contract lifecycle and demonstrates good results in process control and contract archiving, it does not delve into the renewal decisions for location-based contracts, particularly lacking concrete and feasible technical guidance on how to conduct quantitative analysis and intelligent evaluation based on historical operational data. Reference patent CN119721959A focuses on the entire project lifecycle management and proposes an effective mechanism for project planning and resource coordination. While it has some guiding significance for project-based businesses, it still has significant shortcomings in renewal decisions for atypical and unstructured contract types like location-based contracts. Specifically, this patent also fails to provide effective technical means for comprehensive analysis and renewal recommendation formulation based on dimensions such as average daily sales, operational efficiency, and growth potential of locations.

[0004] Therefore, in site contract renewal management, existing technologies generally suffer from problems such as a lack of data-driven evaluation capabilities, reliance on human experience for decision-making, long response cycles, and poor accuracy. In particular, how to build scoring models and trend prediction models based on historical data and form automated renewal suggestions has become an urgent problem that restricts the intelligent development of the industry. Summary of the Invention

[0005] This application provides an intelligent decision-making method for site contract renewal based on historical data, aiming to solve the problems that existing technologies in site contract renewal management generally lack data-driven evaluation capabilities, rely on human experience for decision-making, and have long response cycles and poor accuracy.

[0006] On the one hand, a smart decision-making method for location contract renewal based on historical data is provided, the method comprising:

[0007] S1. Collect historical transaction data, contract performance information, operational status data, and geographical location data of the target location to construct a historical dataset of the location;

[0008] S2. Store the structured data in the dataset in a relational database, store the access data and intermediate calculation results in a cache database, and store the unstructured operation reports and multimedia data in a document database.

[0009] S3. Calculate the daily average revenue score R, operational efficiency score O, location value score L, and potential growth score P based on the historical data, and apply the formula... Calculate the overall score S of the points, where , , , The weighting coefficients and ;

[0010] S4. Based on the comprehensive score S, the locations are divided into different levels, and the levels A, B, C or D are determined.

[0011] S5. Based on historical sales data, establish a time series forecasting model to predict future sales trends, obtain trend information, and use a clustering algorithm to perform feature clustering analysis on the locations to form location clustering results.

[0012] S6. Based on the comprehensive score S, trend information and operation status, generate renewal decision suggestions according to preset decision rules;

[0013] S7. Output renewal recommendations and decision-making basis, forming a visual report or renewal list.

[0014] Optionally, in the above scheme, step S2 includes:

[0015] A MySQL database is used to store basic location information, transaction records, and contract information.

[0016] The Redis database is used to cache hot data, real-time statistics, and intermediate calculation results;

[0017] The system uses MongoDB to store operational reports, multimedia materials, and unstructured data, enabling the integrated storage of multiple types of information.

[0018] Optionally, in step S3 of the above scheme:

[0019] R is the standardized score of the average daily sales revenue of the location;

[0020] O represents the operational efficiency score, which is calculated by weighting indicators such as equipment uptime, replenishment timeliness, and fault response time.

[0021] L represents the comprehensive location value score calculated based on geographical popularity, passenger flow density, and rental return ratio.

[0022] P is the potential growth score calculated based on sales trend forecast results.

[0023] In the above scheme, optionally, the grading standard for step S4 is:

[0024] Grade A location: S≥90;

[0025] Grade B location: 75≤S<90;

[0026] C-level location: 60≤S<75;

[0027] Level D location: S < 60;

[0028] A renewal priority list is generated based on different levels for subsequent strategy decisions.

[0029] In the above scheme, optionally, the trend prediction in step S5 adopts the seasonal ARIMA extended model (SARIMAX), with its parameters set to order=(1,1,1) and seasonal_order=(1,1,1,12), to predict the sales change trend in the next 12 periods.

[0030] The feature clustering uses the KMeans clustering algorithm, with the number of clusters set to 4 and the random seed set to 42. The input features include geographic coordinates, average sales, operational efficiency, and location popularity.

[0031] Optionally, in the above scheme, the renewal decision rule in step S6 includes:

[0032] When the overall score S≥90 and the trend information is rising, output "Strongly recommend renewal, rent can be appropriately increased";

[0033] When the overall score is 75≤S<90 and trend is not decreasing, output "It is recommended to renew the contract and maintain the current conditions";

[0034] When the overall score is 60≤S<75 and the operational status is stable, output "The contract can be renewed, but the operation needs to be optimized";

[0035] When the overall score S < 60, output "It is recommended to terminate the contract and find an alternative location".

[0036] Optionally, in the above scheme, the renewal result output in step S7 includes:

[0037] The results, including site level distribution maps, renewal lists, trend prediction curves, and risk warning lists, are displayed through a visual interface or reports, and support management personnel for review and batch decision-making.

[0038] Optionally, the method further includes the following steps:

[0039] S8. Based on the results of manual review and actual contract renewal feedback, dynamically adjust the weight parameters. , , , and the threshold for level classification;

[0040] S9. Write the corrected parameters and results back to the database for model adaptive training and optimization to improve the accuracy and stability of renewal decisions.

[0041] Optionally, the above solution can be applied to contract management scenarios centered on geographical locations, such as retail, vending machines, and advertising. It can automatically complete data collection, model calculation, renewal suggestion generation, and risk warning, thereby realizing the digital and intelligent management of location contract renewals.

[0042] On the other hand, a location contract renewal intelligent decision-making system based on historical data is provided, characterized by comprising:

[0043] The data access module is used to collect transaction records, contract performance information, operational status data, and geographic location data of the target location.

[0044] The data storage module is used to store the collected data in a MySQL database, a Redis cache database, and a MongoDB document database, respectively.

[0045] The indicator calculation module is used to calculate the daily average revenue score R, operational efficiency score O, location value score L, and potential growth score P based on the collected data, and to calculate the comprehensive score. ;

[0046] The prediction and clustering module is used to predict sales trend information using the SARIMAX model and to perform point clustering using the KMeans algorithm.

[0047] The decision rule engine module is used to generate renewal decision suggestions based on the comprehensive score S, trend information, and operational status.

[0048] The visualization output module is used to output renewal suggestions, grade distribution charts, and trend prediction results;

[0049] The self-learning module is used to dynamically adjust weight parameters and scoring thresholds based on actual renewal results, thereby achieving model optimization and continuous learning.

[0050] Compared with the prior art, this application has at least the following beneficial effects:

[0051] Based on further analysis and research into existing technological problems, this application recognizes that in site contract renewal management, existing technologies generally suffer from a lack of data-driven evaluation capabilities, reliance on human experience in decision-making, long response cycles, and poor accuracy. By constructing a multi-source data collection and hierarchical storage system, designing a weighted comprehensive scoring model encompassing four dimensions—daily revenue, operational efficiency, location value, and potential growth—and combining time series forecasting and clustering analysis algorithms, a data-driven intelligent decision-making mechanism for renewal is formed. This fundamentally solves the problems of scattered multi-source data, lack of evaluation systems, and reliance on human experience in existing technologies. This method enables centralized management and real-time calculation of historical site data, quantitative evaluation and grading of sites, and automatic output of renewal suggestions through trend prediction and rule engines, achieving closed-loop management from "data analysis" to "intelligent decision-making." Compared with existing contract management systems, this invention not only improves the efficiency and accuracy of contract renewal decisions, but also dynamically optimizes strategies based on trend changes, identifies inefficient or risky points in advance, thereby reducing operational risks, optimizing resource allocation, and realizing the digital and intelligent transformation of the contract renewal process. It effectively solves the core technical problems pointed out in the background technology, such as "difficulty in unifying data, lack of evaluation system and insufficient intelligent decision-making". Attached Figure Description

[0052] Figure 1 A schematic diagram of a location contract renewal intelligent decision-making method based on historical data provided in one embodiment of this application;

[0053] Figure 2 This application provides an embodiment of a smart decision-making device for location contract renewal based on historical data;

[0054] Figure 3 A flowchart illustrating the point level classification process provided in one embodiment of this application;

[0055] Figure 4 This is a data processing flowchart provided for one embodiment of this application. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] In the description of this application: unless otherwise stated, "a plurality of" means two or more. The terms "first," "second," "third," etc., in this application are intended to distinguish the objects referred to and do not have any special meaning in terms of technical connotation (e.g., they should not be construed as an emphasis on importance or order). Expressions such as "including," "comprising," and "having" also mean "not limited to" (certain units, components, materials, steps, etc.).

[0058] In one embodiment, such as Figure 1 As shown, a smart decision-making method for location contract renewal based on historical data is provided, the method comprising:

[0059] S1. Collect historical transaction data, contract performance information, operational status data, and geographical location data of the target location to construct a historical dataset of the location;

[0060] S2. Store the structured data in the dataset in a relational database, store the access data and intermediate calculation results in a cache database, and store the unstructured operation reports and multimedia data in a document database.

[0061] S3. Calculate the daily average revenue score R, operational efficiency score O, location value score L, and potential growth score P based on the historical data, and apply the formula... Calculate the overall score S of the points, where , , , The weighting coefficients and ;

[0062] S4. Based on the comprehensive score S, the locations are divided into different levels, and the levels A, B, C or D are determined.

[0063] S5. Based on historical sales data, establish a time series forecasting model to predict future sales trends, obtain trend information, and use a clustering algorithm to perform feature clustering analysis on the locations to form location clustering results.

[0064] S6. Based on the comprehensive score S, trend information and operation status, generate renewal decision suggestions according to preset decision rules;

[0065] S7. Output renewal recommendations and decision-making basis, forming a visual report or renewal list.

[0066] In this embodiment, step S2 includes:

[0067] A MySQL database is used to store basic location information, transaction records, and contract information.

[0068] The Redis database is used to cache hot data, real-time statistics, and intermediate calculation results;

[0069] The system uses MongoDB to store operational reports, multimedia materials, and unstructured data, enabling the integrated storage of multiple types of information.

[0070] In this embodiment, in step S3:

[0071] R is the standardized score of the average daily sales revenue of the location;

[0072] O represents the operational efficiency score, which is calculated by weighting indicators such as equipment uptime, replenishment timeliness, and fault response time.

[0073] L represents the comprehensive location value score calculated based on geographical popularity, passenger flow density, and rental return ratio.

[0074] P is the potential growth score calculated based on sales trend forecast results.

[0075] In this embodiment, the grading criteria for step S4 are as follows:

[0076] Grade A location: S≥90;

[0077] Grade B location: 75≤S<90;

[0078] C-level location: 60≤S<75;

[0079] Level D location: S < 60;

[0080] A renewal priority list is generated based on different levels for subsequent strategy decisions.

[0081] In this embodiment, the trend prediction in step S5 adopts the seasonal ARIMA extended model (SARIMAX), with parameters set to order = (1,1,1) and seasonal_order = (1,1,1,12), to predict the sales change trend in the next 12 periods.

[0082] The feature clustering uses the KMeans clustering algorithm, with the number of clusters set to 4 and the random seed set to 42. The input features include geographic coordinates, average sales, operational efficiency, and location popularity.

[0083] In this embodiment, the renewal decision rule in step S6 includes:

[0084] When the overall score S≥90 and the trend information is rising, output "Strongly recommend renewal, rent can be appropriately increased";

[0085] When the overall score is 75≤S<90 and trend is not decreasing, output "It is recommended to renew the contract and maintain the current conditions";

[0086] When the overall score is 60≤S<75 and the operational status is stable, output "The contract can be renewed, but the operation needs to be optimized";

[0087] When the overall score S < 60, output "It is recommended to terminate the contract and find an alternative location".

[0088] In this embodiment, the renewal result output in step S7 includes:

[0089] The results, including site level distribution maps, renewal lists, trend prediction curves, and risk warning lists, are displayed through a visual interface or reports, and support management personnel for review and batch decision-making.

[0090] In this embodiment, the method further includes the following steps:

[0091] S8. Based on the results of manual review and actual contract renewal feedback, dynamically adjust the weight parameters. , , , and the threshold for level classification;

[0092] S9. Write the corrected parameters and results back to the database for model adaptive training and optimization to improve the accuracy and stability of renewal decisions.

[0093] In this embodiment, the method is applicable to contract management scenarios centered on geographical locations, such as retail, vending machines, and advertising. It can automatically complete data collection, model calculation, renewal suggestion generation, and risk warning, thereby realizing the digital and intelligent management of location contract renewals.

[0094] The intelligent decision-making method for site contract renewal based on historical data provided in this embodiment has significant technological breakthroughs and practical application value in solving common industry problems such as "lack of data-driven evaluation capabilities, reliance on manual decision-making, slow response and poor accuracy".

[0095] In one embodiment, such as Figure 2 As shown, a site contract renewal intelligent decision-making system based on historical data is provided, the system comprising:

[0096] The data access layer is used to access historical operational data related to the locations, including basic location information, transaction records, contract information, and operational documents.

[0097] The data storage layer is used to store the incoming data. The data storage layer includes a structured data storage module, a cache module, and an unstructured data storage module.

[0098] A data processing layer is used to analyze and process the historical operational data. The data processing layer includes:

[0099] The location comprehensive scoring model module is used to assign weighted scores to each location based on multiple set dimensions;

[0100] The site grading module is used to classify sites into different grades based on the scoring results;

[0101] The trend prediction module is used to predict the operational trends of locations based on historical data.

[0102] The decision rule engine module is used to generate renewal suggestions based on scoring results, operational trends, and operational status.

[0103] The data application layer is used to provide analysis results and renewal recommendations to users or business systems to assist in contract renewal decisions.

[0104] This embodiment provides an intelligent decision-making system for site contract renewal based on historical data. Its overall architecture includes a data access layer, a data storage layer, a data processing layer, and a data application layer. The modules in each layer of the system work together to achieve intelligent evaluation and automatic decision support for whether to renew commercial site contracts.

[0105] In practical implementation, the data access layer can access historical data from multiple systems through API interfaces, data synchronization tools, or middleware platforms. This data includes, but is not limited to, basic location information (such as location, region, type, etc.), transaction records (such as daily sales data, number of transactions, etc.), historical contract information (such as contract start and end dates, rental terms, default records, etc.), and operation-related documents (such as inspection reports, video materials, customer reviews, etc.). All accessed data is preprocessed according to formatting standards to ensure the readability and consistency of subsequent calculation modules.

[0106] The data storage layer is divided into three parts: the structured data storage module uses a MySQL database to store basic location information tables, transaction record tables, and contract information tables, supporting efficient relational query operations; the caching module uses a Redis database to cache real-time data of hot locations, frequently accessed historical calculation results, and certain temporary indicators, improving system response speed; and the unstructured data storage module uses a MongoDB database to store document-type data such as inspection photos, operation report PDFs, and multimedia presentation materials, providing multi-dimensional data support for the system.

[0107] The data processing layer, as the core analysis engine of the system, integrates multiple functional modules. First, the comprehensive scoring model module calculates the comprehensive score S for each location based on the set scoring criteria. Its scoring model is as follows:

[0108] Where R represents the average daily revenue score, calculated using standardized sales data; O represents the operational efficiency score, such as equipment online rate and fault response time; L represents the location value score, scored using indicators such as regional customer traffic and surrounding commercial density; and P represents the potential growth score, quantified by combining information such as revenue growth rate and operational improvement in recent months. to These are preset weighting coefficients, which can be dynamically adjusted based on industry characteristics or historical validation experience. This model can comprehensively reflect the current and future operational value of a location.

[0109] Next, the location grading module classifies locations into four levels—A, B, C, and D—based on the aforementioned scoring results S, forming a clear operational classification standard. The trend prediction module, based on historical transaction data, sales figures, and other time-series data, uses the SARIMAX model to model trends and predict the future performance of locations over a period of time. This module supports modeling and processing continuous anomalies, short-term fluctuations, and cyclical changes, improving the accuracy of predictions.

[0110] After completing the scoring and trend analysis, the decision rule engine module combines the score, trend results, and operational status (such as stability and the presence of unforeseen problems) to make a comprehensive judgment. The rule engine internally configures several decision paths; for example, a score ≥ 90 with an upward trend strongly recommends renewal, while a score < 60 with unstable operations suggests contract termination. This module allows the system to output standardized, structured renewal recommendations without relying on manual intervention.

[0111] The data application layer outputs the above analysis results and recommendations to the management interface or integrates with the contract management system, presenting them through visual charts, list prompts, or API responses. For example, the system can generate location heatmaps, contract expiration date sorting lists, and risk point reminder lists in the management backend, assisting the operations department in efficiently completing the decision-making process for contract renewal, optimization, or replacement.

[0112] In this embodiment, the structured data storage module includes a MySQL database for storing a location basic information table, a transaction record table, and a contract information table.

[0113] In this embodiment, the caching module includes a Redis database for caching hot data, real-time statistics, and intermediate calculation results.

[0114] In this embodiment, the unstructured data storage module includes a MongoDB database for storing site operation reports, multimedia materials, and other unstructured data.

[0115] In this embodiment, the location comprehensive scoring model module scores based on the following formula:

[0116] Where: S is the overall score; R is the average daily revenue score; O is the operational efficiency score; L is the location value score; P is the potential growth score; , , , Weighting coefficients, satisfying .

[0117] In this embodiment, the location classification module divides locations into four levels, including:

[0118] Grade A locations: Overall score S ≥ 90;

[0119] Grade B location: 75 ≤ S < 90;

[0120] C-level location: 60 ≤ S < 75;

[0121] Level D location: S < 60.

[0122] In this embodiment, the trend prediction module makes predictions based on a time series model, which includes the SARIMAX model.

[0123] In this embodiment, the data processing layer further includes a point clustering module, which classifies and manages points based on the KMeans clustering algorithm.

[0124] In this embodiment, the decision rule engine module generates one of the following suggestions based on the scoring result S, operational trend status, and operating status according to preset rules:

[0125] Renewal is strongly recommended; renewal is recommended; renewal is possible but operational optimization is required; termination of the contract is recommended.

[0126] In this embodiment, the data application layer is connected to external business methods through an API interface, and the renewal suggestion is displayed to administrators in a visual interface or returned to the backend method through the interface.

[0127] In one embodiment, a location contract renewal intelligent decision-making system based on historical data is provided, comprising:

[0128] The data access module is used to collect transaction records, contract performance information, operational status data, and geographic location data of the target location.

[0129] The data storage module is used to store the collected data in a MySQL database, a Redis cache database, and a MongoDB document database, respectively.

[0130] The indicator calculation module is used to calculate the daily average revenue score R, operational efficiency score O, location value score L, and potential growth score P based on the collected data, and to calculate the comprehensive score. ;

[0131] The prediction and clustering module is used to predict sales trend information using the SARIMAX model and to perform point clustering using the KMeans algorithm.

[0132] The decision rule engine module is used to generate renewal decision suggestions based on the comprehensive score S, trend information, and operational status.

[0133] The visualization output module is used to output renewal suggestions, grade distribution charts, and trend prediction results;

[0134] The self-learning module is used to dynamically adjust weight parameters and scoring thresholds based on actual renewal results, thereby achieving model optimization and continuous learning.

[0135] In one embodiment, such as Figure 2 and Figure 3 As shown, a smart decision-making method for location contract renewal based on historical data is proposed, including:

[0136] The overall architecture of the method consists of four main layers:

[0137] Data access layer; data storage layer; data processing layer; data application layer;

[0138] The core algorithm model is: a comprehensive point scoring model, where the comprehensive point score (S) is calculated using a weighted scoring method. ;

[0139] Where: R: Average daily revenue score; O: Operational efficiency score; L: Location value score; P: Potential growth score. , , , : Corresponding weight coefficients, and ;

[0140] Based on the overall score S, the locations are divided into the following levels:

[0141] Grade A location: S ≥ 90;

[0142] Grade B location: 75 ≤ S < 90;

[0143] C-level location: 60 ≤ S < 75;

[0144] Level D location: S < 60;

[0145] Method implementation details, data storage layer implementation: MySQL database:

[0146] Basic location information table; Transaction record table; Contract information table;

[0147] Redis caching: caching hot data; real-time statistics; temporary calculation results;

[0148] MongoDB: Operational reports and documents; multimedia materials; unstructured data;

[0149] Data processing layer algorithm implementation, such as Figure 4 As shown, it specifically includes:

[0150] Time series forecasting model implementation code:

[0151] from statsmodels.tsa.statespace.sarimax import SARIMAX

[0152] def sales_forecast(historical_data):

[0153] model = SARIMAX(historical_data,

[0154] order=(1, 1, 1),

[0155] seasonal_order=(1, 1, 1, 12))

[0156] return model.fit();

[0157] Point clustering algorithm implementation code:

[0158] from sklearn.cluster import KMeans;

[0159] def location_clustering(features):

[0160] kmeans = KMeans(n_clusters=4, random_state=42)

[0161] return kmeans.fit_predict(features);

[0162] Decision rule engine implementation code:

[0163] def decision_rules(score, trend, operation_status):

[0164] if score >= 90 and trend == 'up':

[0165] "Renewal is strongly recommended; a reasonable increase in rent may be considered."

[0166] elif score >= 75 and trend != 'down':

[0167] The return message reads: "We recommend renewing the contract and maintaining the existing terms."

[0168] elif score >= 60 and operation_status == 'stable':

[0169] The response was: "The contract can be renewed, but operational optimization is needed."

[0170] else:

[0171] The response reads: "It is recommended to terminate the contract and find an alternative entry point."

[0172] Significantly improve the efficiency of contract renewal decisions through automated data analysis and decision support. Provide more accurate renewal recommendations based on historical data and multi-dimensional analysis. Help enterprises optimize resource allocation through a scientific site evaluation system. Identify potential risk sites early through predictive analytics. Achieve digital and intelligent transformation of site management.

[0173] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A smart decision-making method for location contract renewal based on historical data, characterized in that, The method includes: S1. Collect historical transaction data, contract performance information, operational status data, and geographical location data of the target location to construct a historical dataset of the location; S2. Store the structured data in the dataset in a relational database, store the access data and intermediate calculation results in a cache database, and store the unstructured operation reports and multimedia data in a document database. S3. Calculate the daily average revenue score R, operational efficiency score O, location value score L, and potential growth score P based on the historical data, and apply the formula... Calculate the overall score S of the points, where , , , The weighting coefficients and ; S4. Based on the comprehensive score S, the locations are divided into different levels, and the levels A, B, C or D are determined. S5. Based on historical sales data, establish a time series forecasting model to predict future sales trends, obtain trend information, and use a clustering algorithm to perform feature clustering analysis on the locations to form location clustering results. S6. Based on the comprehensive score S, trend information and operation status, generate renewal decision suggestions according to preset decision rules; S7. Output renewal recommendations and decision-making basis, forming a visual report or renewal list.

2. The method according to claim 1, characterized in that, Step S2 includes: A MySQL database is used to store basic location information, transaction records, and contract information. The Redis database is used to cache hot data, real-time statistics, and intermediate calculation results; The system uses MongoDB to store operational reports, multimedia materials, and unstructured data, enabling the integrated storage of multiple types of information.

3. The method according to claim 1, characterized in that, In step S3: R is the standardized score of the average daily sales revenue of the location; O represents the operational efficiency score, which is calculated by weighting indicators such as equipment uptime, replenishment timeliness, and fault response time. L represents the comprehensive location value score calculated based on geographical popularity, passenger flow density, and rental return ratio. P is the potential growth score calculated based on sales trend forecast results.

4. The method according to claim 1, characterized in that, The grading criteria for step S4 are as follows: Grade A location: S≥90; Grade B location: 75≤S<90; C-level location: 60≤S<75; Level D location: S < 60; A renewal priority list is generated based on different levels for subsequent strategy decisions.

5. The method according to claim 1, characterized in that, The trend prediction in step S5 uses the seasonal ARIMA extended model (SARIMAX), with parameters set to order = (1,1,1) and seasonal_order = (1,1,1,12), to predict the sales trend over the next 12 periods. The feature clustering uses the KMeans clustering algorithm, with the number of clusters set to 4 and the random seed set to 42. The input features include geographic coordinates, average sales, operational efficiency, and location popularity.

6. The method according to claim 1, characterized in that, The renewal decision rules in step S6 include: When the overall score S≥90 and the trend information is rising, output "Strongly recommend renewal, rent can be appropriately increased"; When the overall score is 75≤S<90 and trend is not decreasing, output "It is recommended to renew the contract and maintain the current conditions"; When the overall score is 60≤S<75 and the operational status is stable, output "The contract can be renewed, but the operation needs to be optimized"; When the overall score S < 60, output "It is recommended to terminate the contract and find an alternative location".

7. The method according to claim 1, characterized in that, The renewal results output in step S7 include: The results, including site level distribution maps, renewal lists, trend prediction curves, and risk warning lists, are displayed through a visual interface or reports, and support management personnel for review and batch decision-making.

8. The method according to claim 1, characterized in that, The method further includes the following steps: S8. Based on the results of manual review and actual contract renewal feedback, dynamically adjust the weight parameters. , , , and the threshold for level classification; S9. Write the corrected parameters and results back to the database for model adaptive training and optimization to improve the accuracy and stability of renewal decisions.

9. The method according to claim 1, characterized in that, The method is applicable to contract management scenarios centered on geographical locations, such as retail, vending machines, and advertising. It can automatically complete data collection, model calculation, renewal suggestion generation, and risk warning, realizing the digital and intelligent management of location contract renewals.

10. A location contract renewal intelligent decision-making system based on historical data, characterized in that, include: The data access module is used to collect transaction records, contract performance information, operational status data, and geographic location data of the target location. The data storage module is used to store the collected data in a MySQL database, a Redis cache database, and a MongoDB document database, respectively. The indicator calculation module is used to calculate the daily average revenue score R, operational efficiency score O, location value score L, and potential growth score P based on the collected data, and to calculate the comprehensive score. ; The prediction and clustering module is used to predict sales trend information using the SARIMAX model and to perform point clustering using the KMeans algorithm. The decision rule engine module is used to generate renewal decision suggestions based on the comprehensive score S, trend information, and operational status. The visualization output module is used to output renewal suggestions, grade distribution charts, and trend prediction results; The self-learning module is used to dynamically adjust weight parameters and scoring thresholds based on actual renewal results, thereby achieving model optimization and continuous learning.

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