Big Data-Based Intelligent Field Search Methods, Devices, Equipment, and Media

Abstract

This application relates to the field of business management system technology, and discloses a method, apparatus, device, and medium for intelligent site search based on big data. The method includes: acquiring a site search task sent by an external terminal; acquiring pre-stored first site resources from a preset system resource pool; sending the site search task to a user terminal and receiving second site resources sent by the user terminal; performing multi-dimensional comparisons between the first and second site resources and the site search task based on a preset matching algorithm to generate a list of successfully matched candidate sites; performing hierarchical evaluation on each candidate site in the candidate site list, determining the target site based on the hierarchical evaluation results of each candidate site, and updating the status information of each candidate site in the system resource pool; generating an intelligent site search result corresponding to the site search task based on the site information corresponding to the target site, thus completing the intelligent site search. This achieves collaborative interaction between "existing resource search" and "external recommendation site search" modes.

Description

Technical Field

[0001] This application relates to the field of business management system technology, and in particular to a method, apparatus, device and medium for intelligent site search based on big data. Background Technology

[0002] With the nationwide expansion of the express delivery and logistics network, the requirements for timeliness, cost control, and compliance in the selection of branch and distribution station sites are becoming increasingly stringent. Traditional site selection methods mainly rely on manual screening of existing resources or offline surveys, which have the following significant drawbacks:

[0003] 1. Inefficient data integration and matching: The existing system only supports single-mode address matching (such as internal database retrieval), cannot integrate newly added site resources provided by third parties, and lacks the ability to automatically compare multi-dimensional data such as location, type, and area, resulting in time-consuming and lengthy screening of effective site resources.

[0004] 2. Lack of intelligent and standardized evaluation process: Site evaluation relies on human experience and judgment, lacks systematic and quantitative evaluation of geographical location suitability, operating costs, compliance and expansion potential, and has not established a digital integration mechanism for expert decision-making rules such as historical contract data and regional policies, resulting in highly subjective and inconsistent evaluation results.

[0005] 3. Insufficient system scalability and reliability: Traditional systems often adopt a monolithic architecture, which makes it difficult to support tens of thousands of concurrent nationwide search requests. Furthermore, they lack an automatic filtering mechanism for invalid data (such as resources that have not been updated for a long time), resulting in a decline in data matching quality and high system response latency.

[0006] 4. Lack of closed-loop business processes: The entire process, from task release and resource matching to final decision-making, lacks systematic support and requires manual intervention in many aspects (such as contract process triggering, progress notification, etc.), which can easily lead to process gaps and information delays, and cannot meet the real-time needs of rapid expansion of the express delivery network.

[0007] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0008] This application provides a big data-based intelligent site selection method, apparatus, device, and medium to address the problem that existing geographic information-based site selection systems have failed to achieve collaborative interaction between "existing resource search" and "external recommendation site selection," and have not constructed a closed-loop process that includes intelligent verification, multi-dimensional hierarchical evaluation, and distributed high-concurrency support.

[0009] Firstly, this application provides a big data-based intelligent field-finding method, the method comprising:

[0010] Obtain a site search task sent by an external terminal, the site search task including at least the site purpose, target address, library type requirements, required area and reward criteria; obtain the first pre-stored site resource from the preset system resource pool;

[0011] The site search task is sent to the user terminal, and the second site resource sent by the user terminal is received. Based on the preset matching algorithm, the first site resource and the second site resource are compared with the site search task in multiple dimensions to generate a list of candidate sites that have been successfully matched.

[0012] Each candidate site in the candidate site list is evaluated in a hierarchical manner. The target site is determined based on the hierarchical evaluation results of each candidate site, and the status information of each candidate site in the system resource pool is updated.

[0013] Based on the site information corresponding to the target site, generate the intelligent site search result corresponding to the site search task, and complete the intelligent site search.

[0014] In some embodiments, before performing a graded evaluation on each candidate site in the candidate site list, the method further includes: performing intelligent verification processing on the first site resource and the second site resource respectively, verifying the compliance of the site address through a preset address validity verification algorithm, verifying the integrity of the site ownership certificate through a property rights information matching model, and evaluating the reasonableness of the site rental quotation through a rental data regression model, so as to ensure that the site resources included in the graded evaluation meet the preset reliability standards.

[0015] In some embodiments, the tiered evaluation of each candidate site in the candidate site list includes: quantitatively scoring the candidate sites based on a multi-dimensional evaluation index system, wherein the multi-dimensional evaluation index includes at least: geographical location suitability: calculating the straight-line distance between the candidate site and the target address, traffic accessibility, and regional express delivery business density through a geofencing algorithm; operating cost suitability: performing economic calculations based on rental data, site area, and a preset unit area cost threshold; compliance suitability: verifying whether the property rights, fire safety qualifications, and environmental approval documents of the candidate site meet business operation standards; and expansion potential suitability: analyzing the surrounding road network planning, regional commercial planning, and express delivery business volume growth forecast data for the next three years.

[0016] The scores corresponding to the multi-dimensional evaluation indicators are weighted and calculated using a machine learning classification model to generate a comprehensive evaluation score for each candidate site.

[0017] In some embodiments, determining the target site based on the graded evaluation results corresponding to each candidate site includes: sorting the comprehensive evaluation scores according to preset business strategy priorities, wherein the business strategy priorities include at least timeliness priority strategy, cost priority strategy, and service scope priority strategy; for the top preset number of candidate sites in terms of comprehensive evaluation scores, introducing an expert decision rule base for secondary verification, wherein the expert decision rule base includes historical contracted site characteristic parameters, regional policy restrictions, and customized screening rules of the business competent department; and determining the target site based on the secondary verification results.

[0018] In some embodiments, generating intelligent site search results corresponding to the site search task based on the site information corresponding to the target site includes: generating a structured site search report based on the basic information corresponding to the site information, multi-dimensional evaluation reports, recommended strategy basis, and preset contract signing guidelines; automatically triggering the site on-site inspection process or contract pre-approval process through a preset task closed-loop management module, and sending process progress notifications to relevant business departments.

[0019] In some embodiments, obtaining the pre-stored first site resources from the preset system resource pool includes: filtering the site resources stored in the system resource pool based on their timeliness, automatically excluding site resources whose most recent valid data update time is more than three months, and only retaining site resources with business interaction records or data update records within the last three months as the first site resources.

[0020] In some embodiments, the method further includes: implementing system support through a distributed deployment architecture, deploying the core business processing layer, data service layer, and data backend support module on a distributed server cluster using container orchestration technology, configuring a load balancer to achieve concurrent processing of tens of thousands of QPS query requests; and monitoring the operating status of each module in real time through a full-link monitoring system, wherein the full-link monitoring system includes at least a log collection component, a performance indicator analysis component, and an automatic fault switching component, and when a single node performance abnormality is detected, automatically routing business requests to a backup node and triggering a fault node repair process.

[0021] Secondly, this application provides a big data-based intelligent field-finding device, the device comprising:

[0022] The task acquisition unit is used to acquire site search tasks sent by external terminals. The site search tasks include at least the site purpose, target address, library type requirements, required area, and reward criteria. The unit also acquires pre-stored first site resources from a preset system resource pool.

[0023] The task sending unit is used to send the site search task to the user terminal and receive the second site resource sent by the user terminal; based on a preset matching algorithm, the first site resource and the second site resource are compared with the site search task in multiple dimensions to generate a list of candidate sites that have been successfully matched.

[0024] The hierarchical evaluation unit is used to perform hierarchical evaluation on each candidate site in the candidate site list, determine the target site based on the hierarchical evaluation result of each candidate site, and update the status information of each candidate site in the system resource pool.

[0025] The site search completion unit is used to generate an intelligent site search result corresponding to the site search task based on the site information corresponding to the target site, and to complete the intelligent site search.

[0026] Thirdly, this application provides a computer device, which includes a memory and a processor;

[0027] The memory is used to store computer programs;

[0028] The processor is used to execute the computer program and, when executing the computer program, implement any of the big data-based intelligent field-finding methods provided in the embodiments of this application.

[0029] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to implement any of the big data-based intelligent field-finding methods provided in the embodiments of this application.

[0030] This application discloses a method, apparatus, device, and medium for intelligent site search based on big data. It receives site search tasks (including information such as site purpose, target address, library type requirements, required area, and reward criteria) from an external terminal and extracts pre-stored first site resources from a preset system resource pool. The site search task is pushed to a user terminal, and second site resources are collected based on user feedback, forming an integrated data source of system resources and user-contributed resources. A preset matching algorithm performs multi-dimensional comparisons (such as purpose, address, library type, and area) between the first and second site resources and the site search task, generating a list of successfully matched candidate sites. Candidate sites are evaluated hierarchically (e.g., comprehensive scoring, priority ranking) to determine the optimal target site, and the status of candidate sites in the system resource pool is updated (e.g., marked as "matched" or "pending review"). Intelligent site search results are generated based on the target site information, completing the site search process. Combining pre-stored system resources (first site resources) and user-contributed resources (second site resources) expands the data source coverage and improves the resource matching breadth of the site search task. Collaborative resource collection through user terminals forms a two-way data input mode of "system + user," reducing the limitations of relying solely on system data. A multi-dimensional matching algorithm ensures precise matching of site resources with task requirements, reducing manual screening costs and improving matching efficiency. A tiered evaluation mechanism further optimizes candidate site ranking, determining the optimal target site based on preset rules (such as reward criteria and demand priority), improving the practicality and accuracy of matching results. Updating site status information in the system resource pool ensures real-time data, avoids duplicate matching or invalid resource occupation, and enhances the dynamism and reliability of system resource management. The site search task includes reward criteria, potentially incentivizing users to contribute high-quality site resources, fostering a healthy data ecosystem, and continuously enriching the system resource pool.

[0031] In summary, this method achieves efficient and accurate matching of venue resources through data integration, intelligent matching, dynamic management, and user collaboration. It is suitable for scenarios that require rapid response to venue needs (such as warehousing, leasing, and event venues), and has both technological innovation and practical application value.

[0032] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1This is a schematic flowchart illustrating the steps of a big data-based intelligent field-finding method provided in an embodiment of this application;

[0035] Figure 2 This is a schematic block diagram of a big data-based intelligent field-finding device provided in an embodiment of this application;

[0036] Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0037] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0040] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0041] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0042] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0043] With the nationwide expansion of the express delivery and logistics network, the requirements for timeliness, cost control, and compliance in the selection of branch and distribution station sites are becoming increasingly stringent. Traditional site selection methods mainly rely on manual screening of existing resources or offline surveys, which have the following significant drawbacks:

[0044] 1. Inefficient data integration and matching: The existing system only supports single-mode address matching (such as internal database retrieval), cannot integrate newly added site resources provided by third parties, and lacks the ability to automatically compare multi-dimensional data such as location, type, and area, resulting in time-consuming and lengthy screening of effective site resources.

[0045] 2. Lack of intelligent and standardized evaluation process: Site evaluation relies on human experience and judgment, lacks systematic and quantitative evaluation of geographical location suitability, operating costs, compliance and expansion potential, and has not established a digital integration mechanism for expert decision-making rules such as historical contract data and regional policies, resulting in highly subjective and inconsistent evaluation results.

[0046] 3. Insufficient system scalability and reliability: Traditional systems often adopt a monolithic architecture, which makes it difficult to support tens of thousands of concurrent nationwide search requests. Furthermore, they lack an automatic filtering mechanism for invalid data (such as resources that have not been updated for a long time), resulting in a decline in data matching quality and high system response latency.

[0047] 4. Lack of closed-loop business processes: The entire process, from task release and resource matching to final decision-making, lacks systematic support and requires manual intervention in many aspects (such as contract process triggering, progress notification, etc.), which can easily lead to process gaps and information delays, and cannot meet the real-time needs of rapid expansion of the express delivery network.

[0048] Although existing location selection systems based on geographic information exist, none have achieved collaborative interaction between the "existing resource search" and "external recommendation site selection" modes, nor have they constructed a complete closed-loop process that includes intelligent verification, multi-dimensional hierarchical evaluation, and distributed high-concurrency support. Therefore, how to achieve high efficiency, intelligence, and scalability of the site selection process through big data and intelligent algorithms has become an urgent technical problem to be solved in this field.

[0049] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating a big data-based intelligent field-finding method provided in an embodiment of this application. The method is applied to computer equipment, which can be deployed on a single server or a server cluster. It can also be deployed on handheld terminals, laptops, wearable devices, or robots, etc.

[0050] It should be noted that the acquisition of any information mentioned in the provided methods is in compliance with relevant regulations and is carried out with the user's consent, and will not infringe on the user's privacy or violate relevant laws and regulations.

[0051] like Figure 1 As shown, the specific steps of this big data-based intelligent field-finding method include: steps S101 to S104.

[0052] S101. Obtain a site search task sent by an external terminal. The site search task includes at least the site purpose, target address, library type requirements, required area, and reward standard. Obtain the first pre-stored site resource from the preset system resource pool.

[0053] Specifically, this step realizes the initial reception of site search tasks and the preloading of internal existing resources. The core includes: site search task parsing: receiving structured site search tasks from external terminals (such as logistics enterprise management systems, business middleware), including site use (such as distribution centers, warehousing nodes), target address (latitude and longitude or administrative region), warehouse type requirements (automated warehouse, flat warehouse, etc.), required area (range or precise value) and reward standards (incentive mechanism for external recommendations).

[0054] System resource pool data acquisition: Extract the first site resource from the preset system resource pool, that is, the historically accumulated stock data, including site address, area, library type, compliance status, historical usage records, etc. in the internal database.

[0055] Data interaction is achieved through API interfaces to connect with external terminals, and HTTPS protocol is used to ensure data security. Task parameters are parsed in a standardized JSON format. The system resource pool is stored based on distributed databases (such as MySQL clusters and MongoDB) and supports multi-dimensional indexes (such as indexes by region, database type, and area) to accelerate retrieval.

[0056] Before acquiring data from external terminals, compliance is ensured by verifying the legality of the data source through an access control module and anonymizing sensitive information such as target addresses (retaining only the geofence area) to ensure compliance with user privacy protection and data security regulations.

[0057] Breaking away from the limitations of traditional systems that rely solely on internal databases, this approach synchronously loads the search requirements of external terminals with existing system resources, laying the foundation for subsequent collaborative matching of internal and external resources and resolving the problem of "low data integration efficiency." Through structured parsing, non-standard business requirements are transformed into quantifiable algorithmic inputs (such as converting target addresses into geographic coordinates and mapping database type requirements to attribute tags), improving the processing efficiency of subsequent matching algorithms.

[0058] S102. Send the site search task to the user terminal and receive the second site resource sent by the user terminal; based on the preset matching algorithm, compare the first site resource and the second site resource with the site search task in multiple dimensions to generate a list of candidate sites that have been successfully matched.

[0059] Specifically, this step achieves a dual-mode collaboration between "existing resource search" and "external recommendation site search," with core components including: External resource collection: Pushing site search tasks to user terminals (such as partner apps or internal employee terminals), supporting manual input or photo upload of second site resources (adding external site information, such as address, contact person, ownership certificate, etc.), forming a data source integrating internal and external resources. Multi-dimensional matching algorithm: Based on a preset matching algorithm, the first and second site resources are compared with the site search task in multiple dimensions, including: Geographic adaptation: Calculating the straight-line distance and traffic accessibility between the site and the target address (based on GIS road network data); Attribute matching: Precise / fuzzy matching of hard indicators such as library type, area, and load-bearing capacity (e.g., area allows ±10% fluctuation); Compliance pre-screening: Automatically verifying basic compliance data such as site ownership certificates and fire safety acceptance documents (connecting to third-party compliance databases); Reward association: If it is an externally recommended resource, verifying whether it meets the reward criteria (e.g., if the recommended site area meets the standard, triggering points rewards). Candidate list generation: Filtering sites with scores ≥ a threshold based on matching degree scores (e.g., weighted summation model) to generate a candidate site list.

[0060] User terminal interaction enables lightweight task push via mini-programs or H5 pages, supports GPS positioning for quick acquisition of site coordinates, and integrates OCR technology to automatically recognize ownership certificate text information, reducing manual data entry costs. The matching algorithm adopts a rule engine (such as Drools) combined with spatial analysis algorithms (such as buffer analysis and Thiessen polygons) to process geographic and attribute data in layers. Rule weights can be configured in the backend (such as geographic distance accounting for 40%, area matching accounting for 30%, and compliance accounting for 30%). Concurrency support: The matching module is deployed based on a microservice architecture, and task distribution is decoupled through load balancing (such as Nginx) and message queues (such as Kafka), supporting parallel processing of tens of thousands of site search requests.

[0061] Breaking away from the limitations of traditional manual offline surveys, external resources are collected through crowdsourcing via user terminals and combined with existing internal data to form a "data loop," solving the problem of "insufficient integration of new resources" and effectively shortening the screening time. By replacing manual comparison with algorithms, the system achieves automated verification of indicators such as location, type of warehouse, and area, improving matching efficiency by more than 80%, and supports dynamic adjustment of matching rules to adapt to different business scenarios (such as focusing on geographical distance in urgent recruitment scenarios and focusing on unit price per square meter in cost-price scenarios).

[0062] S103. Perform a graded evaluation on each candidate site in the candidate site list, determine the target site based on the graded evaluation results of each candidate site, and update the status information of each candidate site in the system resource pool.

[0063] Specifically, this step constructs an intelligent evaluation system, the core of which includes: a tiered evaluation model: based on the Analytic Hierarchy Process (AHP) or machine learning algorithms (such as random forest), a quantitative indicator system is established from dimensions such as geographical location suitability (surrounding road network density, distance to express delivery hubs), operating costs (rental unit price, water and electricity consumption), compliance (land use, environmental approval), and expansion potential (surrounding expandable area, policy planning), outputting an evaluation score and grade (e.g., S / A / B / C) for each candidate site. Expert rule integration: historical contract data and regional policies (such as logistics park subsidy policies) are digitized and embedded into the evaluation model (e.g., setting a compliance "veto" rule, or automatically adding points for policy subsidy areas). Dynamic status management: the site status in the system resource pool is updated according to the evaluation results (e.g., "under evaluation," "rejected," "recommended for signing") to avoid duplicate evaluations and interference from invalid data.

[0064] The evaluation index library supports different evaluation templates for different business types (distribution stations / warehousing centers) by establishing a configurable index weight library; it also connects with third-party data (such as land use data from the Ministry of Natural Resources interface and rental data from commercial real estate platform API) to achieve dynamic data updates.

[0065] Intelligent verification verifies the authenticity of compliance documents using AI image recognition technology (such as OCR+NLP) and calculates commuting efficiency by combining geographical location adaptability with real-time traffic data (such as Gaode Map API).

[0066] Distributed storage stores site status information in a distributed key-value database (such as Redis), supporting millisecond-level read and write operations to ensure real-time status updates in high-concurrency scenarios.

[0067] By replacing manual experience-based judgment with quantitative models, the consistency of assessments is improved, solving the problems of "high subjectivity and poor consistency" in traditional systems. Invalid resources that have not been updated for a long time (such as sites that have not been matched for more than 6 months and marked as "dormant") are automatically filtered out, and the quality of the resource pool is dynamically updated in combination with the assessment results, thereby improving the accuracy of system data matching.

[0068] S104. Generate the intelligent site search result corresponding to the site search task based on the site information corresponding to the target site, and complete the intelligent site search.

[0069] Specifically, this step achieves a closed-loop business process, with core components including: Result Generation: Based on the tiered evaluation results, the site with the highest overall score is selected as the target site, generating a structured report containing site details, evaluation score, and reasons for recommendation; if multiple sites of the same level exist, automatic sorting based on additional conditions (such as reward priority, expansion potential) is supported. Process Triggering: Subsequent processes are automatically triggered via smart contracts or workflow engines (such as Activiti), including contract approval processes, progress notifications (SMS / email push to relevant personnel), and resource pool status synchronization (marking the target site as "occupied"). Data Accumulation: Site search tasks, matching process data, and evaluation results are stored in a historical database for subsequent algorithm optimization (e.g., training evaluation model weights using historical successful cases).

[0070] Visual output generates site search result dashboards through BI tools, intuitively displaying information such as the geographical location of the target site, comparison of key indicators, and summary of compliance documents; process automation is achieved by establishing an event-driven mechanism, such as automatically feeding back candidate results to the user terminal after the evaluation is completed, and automatically synchronizing to the enterprise ERP system after the contract is confirmed.

[0071] High concurrency support is achieved by adopting a distributed architecture (such as Spring Cloud microservices + Docker containers) and using a circuit breaker mechanism (such as Hystrix) to prevent the failure of a single node from affecting the overall process, ensuring a second-level response time for tens of thousands of search tasks.

[0072] The system provides comprehensive support for the entire process from task release to decision implementation, reducing manual intervention (such as contract triggering and improved efficiency of progress notifications) and solving the traditional problems of "process gaps and information lag". Through distributed architecture and intelligent process engine, the system can support the needs of nationwide network expansion, and the efficiency and concurrency performance can be linearly scaled with the growth of business volume, meeting the real-time site selection requirements of the express delivery industry.

[0073] In some embodiments, before performing a graded evaluation on each candidate site in the candidate site list, the method further includes: performing intelligent verification processing on the first site resource and the second site resource respectively, verifying the compliance of the site address through a preset address validity verification algorithm, verifying the integrity of the site ownership certificate through a property rights information matching model, and evaluating the reasonableness of the site rental quotation through a rental data regression model, so as to ensure that the site resources included in the graded evaluation meet the preset reliability standards.

[0074] Before the tiered assessment, three layers of intelligent verification are added to ensure that candidate sites meet basic reliability standards: Address validity verification: The authenticity of the site address (e.g., whether it exists in the map service database), format compliance (whether it conforms to administrative division regulations), and geographic positioning accuracy (whether it is within the preset radius of the target address) are verified through a preset algorithm. Property rights integrity verification: Using a property rights information matching model, key information (owner, term of use, land nature) in ownership documents (such as real estate ownership certificates, lease agreements) is identified by OCR and logically verified to determine whether the documents are complete and consistent. Rent reasonableness assessment: A regression model is trained based on historical rent data, combined with features such as the site's location, area, and warehouse type, to predict a reasonable rent range and verify whether the quoted price is within a reasonable fluctuation range (e.g., ±20%).

[0075] Address verification standardizes and parses addresses by connecting to a pre-defined map API, validates the address format using regular expressions, converts the address to latitude and longitude using geocoding services, and calculates whether the spatial distance to the target address exceeds the limit.

[0076] Property rights verification extracts textual information from ownership documents by integrating OCR technology, constructs logical relationships of ownership through knowledge graphs (such as whether the owner is consistent with the site registration), and connects to the enterprise credit information disclosure system to verify the legality of the owner.

[0077] The rental assessment uses linear regression or random forest models, inputting features such as region, area, and floor level to train the model, setting thresholds (e.g., Z-score > 2 is considered an abnormal quote), and automatically marking venues with unreasonable rents.

[0078] By employing a three-tiered verification system to filter out invalid, false, or abnormal data, unreliable resources are avoided from being included in subsequent assessments, reducing unnecessary computational waste and improving the efficiency of the assessment process. Addressing issues, property disputes, and inflated rents are preemptively ruled out, controlling compliance and economic risks from the outset and preventing rework in later stages.

[0079] In some embodiments, the tiered evaluation of each candidate site in the candidate site list includes: quantifying the candidate sites based on a multi-dimensional evaluation index system, wherein the multi-dimensional evaluation index includes at least: geographical location suitability: calculating the straight-line distance, traffic accessibility, and regional express delivery business density between the candidate site and the target address using a geofencing algorithm; operating cost suitability: performing economic calculations based on rental data, site area, and a preset unit area cost threshold; compliance suitability: verifying whether the property rights, fire safety qualifications, and environmental approval documents of the candidate site meet business operation standards; expansion potential suitability: analyzing the surrounding road network planning, regional commercial planning, and express delivery business volume growth forecast data for the next three years; and generating a comprehensive evaluation score for each candidate site by weighting the scores corresponding to the multi-dimensional evaluation indexes using a machine learning classification model.

[0080] A four-layer evaluation index system is constructed, combined with machine learning to achieve quantitative scoring: Geographical location suitability: Geofencing algorithm calculates straight-line distance (with the target address as the center, weighted scoring within a 5km radius); Transportation accessibility (connecting to map API to obtain road level and distance from main roads); Regional express delivery business density (calculating the number of express delivery outlets within 3km based on historical order data). Operating cost suitability: Comparison of rental unit price × area with preset cost thresholds, allowing ±15% fluctuation, deducting points if exceeding. Compliance suitability: Verifying property rights nature (whether industrial / commercial land meets business requirements), validity of fire safety acceptance documents, and environmental approval status (connecting to government public databases). Expansion potential suitability: Analyzing surrounding road network planning (whether new highway entrances will be added in the future), regional commercial planning (whether it is planned as a logistics park), and business volume forecasting (time series model predicts order growth over the next 3 years). Comprehensive score: Weighted scoring of each dimension is achieved through machine learning models such as XGBoost, outputting a comprehensive score of 0-100.

[0081] Geographic data is acquired through OpenStreetMap or commercial map APIs, business density data comes from the company's internal order system, and policy planning data is captured from government public documents. Model training uses historical contracted site data as positive samples and uncontracted sites as negative samples to train the classification model and determine the weights of each indicator (e.g., geographical location 40%, compliance 30%). Business personnel can adjust indicator weights through the backend (e.g., reducing the weight of transportation accessibility for remote areas).

[0082] By transforming subjective experience into a quantifiable indicator system, the consistency of results from different assessors is improved, resolving the problem of "inconsistent standards" in traditional assessments. Through expansion potential analysis, sites with long-term development potential are prioritized, avoiding the site selection trap of short-term compliance but lack of scalability.

[0083] In some embodiments, determining the target site based on the graded evaluation results corresponding to each candidate site includes: sorting the comprehensive evaluation scores according to preset business strategy priorities, wherein the business strategy priorities include at least timeliness priority strategy, cost priority strategy, and service scope priority strategy; for the top preset number of candidate sites in terms of comprehensive evaluation scores, introducing an expert decision rule base for secondary verification, wherein the expert decision rule base includes historical contracted site characteristic parameters, regional policy restrictions, and customized screening rules of the business competent department; and determining the target site based on the secondary verification results.

[0084] Select a sorting strategy based on business needs (such as "timeliness priority" which focuses on geographical distance and transportation accessibility, and "cost priority" which focuses on the cost-effectiveness of rent and area), and adjust the overall score accordingly.

[0085] The expert rule base undergoes secondary verification, including: historical contract characteristics (e.g., successful sites in a certain region generally require a courier distribution center within 500m); regional policy restrictions (e.g., some cities prohibit the establishment of distribution stations within 500m of residential areas); and customized rules (e.g., the business management department requires priority to be given to sites owned by state-owned enterprises). Target site determination: For the top N (e.g., 3) candidate sites, expert rules are applied to exclude sites that violate mandatory rules, and the site with the highest remaining score is selected as the target.

[0086] A visual interface is provided for business departments to configure strategy weights (e.g., increasing the geographical adaptability weight to 50% in a time-priority strategy). Expert rules are loaded using the Drools or Aviator engine to verify candidate sites one by one (e.g., "If the land use of the site is residential, then exclude it directly"). The expert rule base supports regular updates, and historical contract data is automatically synchronized to the rule base, forming a "data + experience" dual-driven decision-making system.

[0087] It supports differentiated site selection strategies for different business scenarios (such as prioritizing timeliness during e-commerce promotions and prioritizing cost during regular expansion), adapting to the diverse needs of the express delivery industry. It transforms tacit expert experience into a reusable rule base, avoiding the loss of decision-making capabilities due to staff turnover, and iteratively optimizes rule accuracy through historical data.

[0088] In some embodiments, generating intelligent site search results corresponding to the site search task based on the site information corresponding to the target site includes: generating a structured site search report based on the basic information corresponding to the site information, multi-dimensional evaluation reports, recommended strategy basis, and preset contract signing guidelines; automatically triggering the site on-site inspection process or contract pre-approval process through a preset task closed-loop management module, and sending process progress notifications to relevant business departments.

[0089] The site search report is generated by integrating basic site information (address, contact person), multi-dimensional evaluation report (radar chart of scores for each indicator), recommendation strategy basis (such as "cost priority recommendation because the rent is 20% lower than the regional average"), and contract signing guidelines (compliance document list, negotiation points).

[0090] The closed-loop process includes: on-site inspection: automatically creating inspection tasks and assigning them to regional teams, with an assessment report attached as an inspection reference; contract pre-approval: connecting to the enterprise OA system, generating a pre-approval form and synchronizing assessment data; progress notification: pushing process nodes (such as "Site X has entered the contract approval stage") to the person in charge via WeChat / email.

[0091] The report template system utilizes preset report templates from Apache POI or JasperReports, supporting dynamic population of evaluation data and recommendation reasons to generate PDF / HTML format reports. The workflow engine defines process nodes based on Activiti or Camunda, automatically triggering the next stage upon completion of the evaluation, with process status synchronized to the system dashboard in real time. The notification mechanism decouples the notification service through a message queue (such as RabbitMQ), supporting multi-channel (SMS, email, IM) push notifications, and includes process links for quick navigation.

[0092] The structured report clearly presents the site selection logic, facilitating rapid decision-making by management and reducing cross-departmental communication costs (improved information transmission efficiency). The seamless transition from assessment to execution eliminates issues such as missed manual triggers and delayed notifications, shortening the process cycle and meeting the express delivery industry's need for "rapid implementation."

[0093] In some embodiments, obtaining the pre-stored first site resources from the preset system resource pool includes: filtering the site resources stored in the system resource pool based on their timeliness, automatically excluding site resources whose most recent valid data update time is more than three months, and only retaining site resources with business interaction records or data update records within the last three months as the first site resources.

[0094] When acquiring the first site resource from the system resource pool, a timeliness filtering rule is added: Data update time judgment: Filter sites with data update records (such as ownership document updates, rental price adjustments) or business interaction records (such as being included in the candidate list or having been inspected on-site) within the last 3 months. Invalid data exclusion: Sites that have not been updated for more than 3 months and have no interaction records are marked as "expired" and will not be included in the first resource (they are only retained in the historical database and can be manually activated).

[0095] Timestamp Index: Add "Last Update Time" and "Last Interaction Time" fields to the database table to create a composite index for faster queries. Scheduled Task Cleanup: Run a script every morning to scan the resource pool and add status tags (such as "Dormant") to venues that meet the expiration criteria. Queries will automatically filter data with this status. Manual Activation Mechanism: Provide a management interface to allow manual restoration of expired venues, preventing accidental deletion of valid resources (such as venues in remote areas that have not been updated for a long time but are actually usable).

[0096] Automatically filters outdated data to avoid invalid matches (such as venues with changed contact numbers), improving the success rate of first-resource matching. Reduces computational resource consumption for invalid data, increasing database query speed by 30%, especially noticeable with tens of thousands of data points.

[0097] In some embodiments, the method further includes: implementing system support through a distributed deployment architecture, deploying the core business processing layer, data service layer, and data backend support module on a distributed server cluster using container orchestration technology, configuring a load balancer to achieve concurrent processing of tens of thousands of QPS query requests; and monitoring the operating status of each module in real time through a full-link monitoring system, wherein the full-link monitoring system includes at least a log collection component, a performance indicator analysis component, and an automatic fault switching component, and when a single node performance abnormality is detected, automatically routing business requests to a backup node and triggering a fault node repair process.

[0098] The distributed architecture design includes: a core business layer (matching algorithm, evaluation model), a data service layer (third-party API integration, database access), and a backend support layer (logs, monitoring), all deployed via containerization (Docker); Kubernetes is used for container orchestration to achieve dynamic scaling and support tens of thousands of concurrent QPS; and an Nginx load balancer is configured to distribute requests to different server nodes according to traffic weight.

[0099] End-to-end monitoring system: Log collection: ELK Stack (Elasticsearch + Logstash + Kibana) centrally stores and analyzes logs from each module; Performance metrics: Prometheus + Grafana monitors CPU / memory usage, interface response time, and queue backlog; Fault switching: Self-developed scripts detect node anomalies (such as 5 consecutive request timeouts), automatically trigger Nginx routing switching, and send alarms to the operations team via Prowler, while simultaneously initiating the restart / repair process for the faulty node.

[0100] Best practices for containerization implementation: Each microservice is packaged as an independent image, and inter-service dependencies are defined using Docker Compose. Kubernetes automatically scales the number of replicas based on resource utilization (e.g., the matching module scales to 10 instances during peak periods). Customized monitoring metrics: Set SLAs (response time <500ms) for core interfaces (e.g., resource matching interface), and automatically apply circuit breakers when thresholds are triggered. Disaster recovery in remote locations: Critical data is synchronized to a remote data center, and remote nodes are prioritized during failover.

[0101] The distributed architecture increases system throughput, easily handling simultaneous site selection requests from a nationwide network and avoiding the response latency issues of traditional monolithic architectures. End-to-end monitoring enables fault detection within seconds and recovery within minutes, reducing system downtime, ensuring the real-time needs of express delivery network expansion, and preventing site selection progress from stalling due to system failures.

[0102] In some embodiments, NLP technology is introduced to automatically extract key information for unstructured site search needs (such as email body or free text in business documents) that may be provided by external terminals. This includes: Entity recognition and relation extraction: using a pre-trained language model (such as BERT) to identify entities in the text such as site use, target address, library type requirements, required area, and reward criteria, and parsing implicit constraints (such as converting "near Metro Line 1" into a geographic adaptation condition). Intent classification and rule mapping: performing intent classification on ambiguous expressions (such as "high cost-effectiveness") and mapping them to preset quantitative rules (such as rent ≤ 110% of the average price in the area and area ≥ 500㎡), realizing the transformation of unstructured needs into structured parameters.

[0103] Customized NLP model training fine-tunes the BERT model using historical field-finding text annotation data (approximately 100,000 records) from the express delivery industry, improving entity recognition accuracy (e.g., F1 score ≥ 92% for terms in fields such as "distribution center" and "automated warehouse"). Multimodal input support integrates OCR technology to process scanned documents, extracting key information through text semantic analysis, and supports parsing requirement documents in formats such as PDF and images. The rule engine is linked by establishing a "fuzzy expression - quantified rule" mapping dictionary (e.g., "convenient transportation" corresponds to "distance from main road ≤ 500m and road network density ≥ 3 roads / km²"), automatically generating computable matching conditions through the rule engine.

[0104] In some embodiments, federated learning technology is used to achieve collaborative matching that keeps data within its domain by utilizing site resource data from external partners (such as logistics parks and real estate agencies). The core of this approach includes:

[0105] Multi-party collaborative data modeling: Without sharing original site data, each partner trains a matching model locally (e.g., a local model for geographical distance and area matching), and generates a global matching model through encrypted parameter aggregation (e.g., the FedAvg algorithm), improving the matching accuracy of external resources. Privacy protection mechanism: Homomorphic encryption is used to encrypt interaction parameters, and differential privacy is combined with noise to ensure that the data privacy of partners is not leaked.

[0106] Federated Learning Framework Setup: A distributed training platform is built based on TensorFlow Federated (TFF), connecting to 100+ partner terminals. Each terminal deploys a lightweight matching model (such as logistic regression). Matching Process Transformation: The system sends encrypted site-finding task parameters to partners, who then locally filter sites that meet the criteria (only returning matching scores, without disclosing specific addresses). Finally, the results from all partners are aggregated to generate a candidate list. Security Authentication System: Blockchain technology is used to record model update records of each participant, ensuring the traceability of the parameter aggregation process and preventing malicious attacks.

[0107] In some embodiments, reinforcement learning is introduced to automate the optimization of weights by dynamically adjusting the weights of evaluation indicators under different business scenarios. The core of this approach includes: State definition and reward function: State: The type of the current site search task (distribution center / warehouse node), regional characteristics (city level, business volume growth rate); Action: Adjusting the weights of each evaluation indicator (e.g., increasing the weight of "geographical fit" from 40% to 50%); Reward: Positive rewards are given based on historical site search results (e.g., no operational efficiency or compliance issues at the contracted site), and negative penalties are imposed otherwise. Weight strategy generation: A policy network is trained using deep reinforcement learning algorithms (e.g., PPO, DQN) to automatically generate a combination of evaluation indicator weights adapted to the current scenario.

[0108] The search results from the past three years are labeled as "successful" (no abnormalities in operation for 6 months after signing) or "failed" (no signing due to compliance issues or operating costs exceeding expectations) to build a reward dataset; the model is fine-tuned online based on real-time business data during daily use, and batch trained at night using offline historical data to balance model stability and adaptability; an intervention interface for business personnel is provided, and when reinforcement learning results conflict with business experience, the weights can be manually locked and used as expert data to feed back into the model.

[0109] In some embodiments, generative artificial intelligence (such as GPT-4) is used to automatically generate site search reports and decision support strategies. The core of these strategies includes: automatically generating natural language reports containing advantages analysis, risk warnings, and negotiation strategies based on multi-dimensional evaluation data of candidate sites (e.g., "The rent of this site is 15% lower than the average price in the area, but the remaining validity period of the fire safety inspection documents is less than 1 year. It is recommended to prioritize negotiating the renewal terms"); and customized strategy recommendations: generating differentiated strategies based on business objectives (e.g., "rapid implementation within 3 months").

[0110] The GPT-4 model is fine-tuned using historical site search reports (50,000+) from the express delivery industry to ensure that the generated content conforms to industry terminology standards (such as accurately distinguishing the compliance differences between "flat warehouse" and "retractable warehouse"); preset report templates (such as compliance document lists and rent negotiation scripts) are converted into prompt word input models to control the structure and focus of the generated content; business personnel can make modification requests for the generated content (such as "supplementing comparisons of surrounding competitor sites"), and incremental generation is achieved through dialogue history memory.

[0111] In some embodiments, by introducing a spatiotemporal prediction model during the resource matching phase and dynamically selecting sites in conjunction with future business needs, the core includes:

[0112] Spatiotemporal forecasting of business volume: Utilizing an LSTM+Graph Neural Network (GNN) model, based on historical order data, regional population growth, e-commerce promotional events, and other factors, the system predicts the trend of express delivery volume changes around the target address over the next 6-12 months. Dynamic resource adaptation: The system conducts spatiotemporal assessments of the area and traffic capacity of candidate sites (e.g., "Current area meets demand, but predicted business volume growth in 10 months will lead to insufficient area"), prioritizing sites with dynamic adaptation capabilities (e.g., expandable or near backup warehousing resources).

[0113] Multi-source data fusion, including population statistics (National Bureau of Statistics API), e-commerce platform promotional calendars, and regional infrastructure planning (public government data), is used to construct a spatiotemporal feature vector input prediction model. Dynamic indicator calculation is performed by defining a "spatiotemporal fit" indicator, calculated using the formula: Spatiotemporal fit = α * current matching degree + ( *The predicted fit for the next 6 months; where α is the business demand time preference coefficient (α=0.8 for urgent recruitment scenarios and α=0.5 for long-term planning scenarios); Prediction model deployment: Minute-level prediction updates are achieved through the Flink real-time computing framework, and the prediction error rate is controlled within 15% (30% improvement in accuracy compared to traditional linear prediction).

[0114] In some embodiments, a compliance knowledge graph for the express delivery industry is constructed to achieve intelligent reasoning and risk prediction of site compliance. The core of this includes: Compliance knowledge modeling: constructing a relational graph between regulations such as land use, fire safety standards, and environmental protection requirements, and site attributes (e.g., "Storing lithium batteries requires Class A fire safety certification"). Intelligent reasoning engine: performing compliance verification based on the graph (e.g., "The site's land use is agricultural land → prohibited from being used as a distribution center"), and identifying potential risks (e.g., "Fire safety acceptance date is more than 3 years ago → re-verification recommended").

[0115] Graph Construction Tool: Using the Neo4j graph database, relevant regulatory documents are extracted to construct a compliance graph containing 100,000+ entities and 300,000+ relationships; Inference Rule Engine: Combining SWRL rules (e.g., site.warehouse type = automated warehouse ∧ site.floor height < 10m → non-compliant), compliance conclusions are automatically derived through the Pellet inference engine; Dynamic Update Mechanism: Connecting to regulatory databases (e.g., Peking University Law Database) to achieve annual regulatory updates, and the entity relationships in the graph are automatically synchronized to ensure that the compliance verification logic is consistent with the latest policies.

[0116] In some embodiments, a big data intelligent site search system is created, including a front-end H5, a mobile APP, a management platform, and a data service back-end, to support a complete digital operation process encompassing address collection, intelligent matching, tiered evaluation, bonus setting, and batch distribution.

[0117] Detailed implementation: Key processes and technical modules include: A. Decision execution layer: Core business flow, data consistency, notifications and announcements serve as information flow support modules; recommendation evaluation is the core node, with input being the entire business process, and output being: Recommendation successful → On-site verification → Contract signing; Recommendation failed → Re-recommendation; Not approved → Re-recommendation.

[0118] B. Core Business Processing Layer: Microservice clusters, API gateways, distributed transactions, approval workflows, and real-time computing form the technical foundation; the business process includes publishing site search tasks → contract governance → governance algorithms; the complete output returns the recommendation evaluation results; two scenario modes are presented: 1) Site search personnel directly search for addresses from existing resources for matching; 2) Site search personnel publish site search bonus tasks through the system, and third-party drivers or local personnel provide addresses for matching; address matching logic: based on 6 basic information inputs (province, city, district / county, street, library type, area), the existing address resource library is matched and called; address: requires precise matching; library type, area: compared within the agreed range; requests for resources that have not been effectively loaded for more than 3 months are not matched, and invalid data analysis is restricted.

[0119] C. Data Service Layer: Input System: Backend Management System, Resource Platform, Signing System → Customer Addition → Data Governance; Intelligent Resource Matching: Spatial Data + Geographic Resources → Intelligent Recommendation Engine → BI Analysis → Visual Reports.

[0120] D. Data backend support technologies: Distributed deployment: Kubernetes + FastAPI; Performance metrics: Supports tens of thousands of QPS queries, with an average response time of < 300ms; Security monitoring: ELK + Grafana full-link monitoring, automatic notifications, and fault redirection.

[0121] Compared to traditional manual judgment or static systems, this invention has the following technical advantages:

[0122] (1) Significantly improved matching efficiency: The structured matching algorithm enables automated comparison of fields such as input region, library type, and area, greatly reducing manual screening time and shortening the average matching time to 1 / 5 of the original time;

[0123] (2) Automatic filtering mechanism for effective resources: By introducing the mechanism of "automatic exclusion of resources that have not been updated for more than 3 months", the quality of resource matching is effectively improved and the generation of invalid tasks is reduced;

[0124] (3) Support for high-concurrency request and response: The system supports tens of thousands of QPS query requests and controls the average response time of core operations to within 300ms, which is suitable for simultaneous field search operations in multiple locations across the country;

[0125] (4) Improve the success rate of finding venues and the signing conversion rate: After the system was implemented, the recommendation success rate was significantly improved. Combined with manual review and bonus reward mechanism, the final signing rate increased by about 37%;

[0126] (5) Achieve end-to-end closed-loop control: From data access, task release, resource governance, intelligent recommendation to contract signing, the entire process system supports the formation of a closed-loop business chain, significantly enhancing the flexibility of capacity expansion;

[0127] (6) Promote intelligent decision-making and standardized platform governance: Through the BI analysis platform and visual screen feedback, the business team can gain real-time insight into the site search effect, assist in strategic site selection, and standardize internal collaboration and assessment mechanisms.

[0128] As the central engine for future new regional site selection, this system can be widely applied to various business scenarios such as express logistics, retail warehousing, and urban distribution, and has extremely strong promotion and replication value.

[0129] The proposed method utilizes two scenario modes—"existing resource search" and "bonus task offering"—covering both internal retrieval and external collaboration paths. This overcomes the limitations of traditional single-matching models, significantly expanding address source channels and improving the response speed of search tasks. Through hierarchical matching logic based on address (exact matching), library type, and area (range comparison), combined with an "automatic exclusion of resources not updated for 3 months" mechanism, it achieves efficient data filtering and accurate matching, reducing invalid data analysis and improving resource utilization quality. An end-to-end closed-loop process is constructed, from task acquisition, resource matching, hierarchical evaluation to result generation. Through intelligent verification, quantitative scoring, and secondary validation using an expert rule base, the evaluation process is standardized and the decision-making is scientific, reducing the cost of manual intervention and error rates. Through a distributed deployment architecture and load balancing technology, it supports tens of thousands of QPS query requests and controls response time. Combined with end-to-end monitoring and automatic fault switching, it solves the problem of insufficient scalability in existing systems and is suitable for large-scale needs of simultaneous search across multiple locations nationwide.

[0130] Please see Figure 2 , Figure 2 An embodiment of this application also provides a schematic block diagram of a big data-based intelligent field-finding device 200, which is used to execute the aforementioned big data-based intelligent field-finding method. This big data-based intelligent field-finding device can be configured in a server or terminal.

[0131] The server can be a standalone server, a server cluster, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal can be an electronic device such as a mobile phone, tablet, laptop, desktop computer, user digital assistant, or wearable device.

[0132] like Figure 2 As shown, the big data-based intelligent field-finding device 200 includes:

[0133] The task acquisition unit 201 is used to acquire a site search task sent by an external terminal. The site search task includes at least the site purpose, target address, library type requirements, required area and reward standard; and to acquire the first pre-stored site resources from the preset system resource pool.

[0134] The task sending unit 202 is used to send the site search task to the user terminal and receive the second site resource sent by the user terminal; based on a preset matching algorithm, the first site resource and the second site resource are compared with the site search task in multiple dimensions to generate a list of candidate sites that have been successfully matched.

[0135] The hierarchical evaluation unit 203 is used to perform hierarchical evaluation on each candidate site in the candidate site list, determine the target site based on the hierarchical evaluation result of each candidate site, and update the status information of each candidate site in the system resource pool.

[0136] The site search completion unit 204 is used to generate an intelligent site search result corresponding to the site search task based on the site information corresponding to the target site, and complete the intelligent site search.

[0137] In some embodiments, before performing a graded evaluation on each candidate site in the candidate site list, the method further includes: performing intelligent verification processing on the first site resource and the second site resource respectively, verifying the compliance of the site address through a preset address validity verification algorithm, verifying the integrity of the site ownership certificate through a property rights information matching model, and evaluating the reasonableness of the site rental quotation through a rental data regression model, so as to ensure that the site resources included in the graded evaluation meet the preset reliability standards.

[0138] In some embodiments, the tiered evaluation of each candidate site in the candidate site list includes: quantitatively scoring the candidate sites based on a multi-dimensional evaluation index system, wherein the multi-dimensional evaluation index includes at least: geographical location suitability: calculating the straight-line distance between the candidate site and the target address, traffic accessibility, and regional express delivery business density through a geofencing algorithm; operating cost suitability: performing economic calculations based on rental data, site area, and a preset unit area cost threshold; compliance suitability: verifying whether the property rights, fire safety qualifications, and environmental approval documents of the candidate site meet business operation standards; and expansion potential suitability: analyzing the surrounding road network planning, regional commercial planning, and express delivery business volume growth forecast data for the next three years.

[0139] The scores corresponding to the multi-dimensional evaluation indicators are weighted and calculated using a machine learning classification model to generate a comprehensive evaluation score for each candidate site.

[0140] In some embodiments, determining the target site based on the graded evaluation results corresponding to each candidate site includes: sorting the comprehensive evaluation scores according to preset business strategy priorities, wherein the business strategy priorities include at least a time-priority strategy, a cost-priority strategy, or a service scope-priority strategy; for the top preset number of candidate sites in terms of comprehensive evaluation scores, introducing an expert decision rule base for secondary verification, wherein the expert decision rule base includes historical contracted site characteristic parameters, regional policy restrictions, and customized screening rules of the business competent department; and determining the target site based on the secondary verification results.

[0141] In some embodiments, generating intelligent site search results corresponding to the site search task based on the site information corresponding to the target site includes: generating a structured site search report based on the basic information corresponding to the site information, multi-dimensional evaluation reports, recommended strategy basis, and preset contract signing guidelines; automatically triggering the site on-site inspection process or contract pre-approval process through a preset task closed-loop management module, and sending process progress notifications to relevant business departments.

[0142] In some embodiments, obtaining the first pre-stored site resources from the preset system resource pool includes: performing timeliness filtering on the site resources stored in the system resource pool, automatically excluding site resources whose most recent valid data update time is more than three months, and only retaining site resources with business interaction records or data update records within the last three months to include in the first site resource set.

[0143] In some embodiments, the method further includes: implementing system support through a distributed deployment architecture, deploying the core business processing layer, data service layer, and data backend support module on a distributed server cluster using container orchestration technology, configuring a load balancer to achieve concurrent processing of tens of thousands of QPS query requests; and monitoring the operating status of each module in real time through a full-link monitoring system, wherein the full-link monitoring system includes at least a log collection component, a performance indicator analysis component, and an automatic fault switching component, and when a single node performance abnormality is detected, automatically routing business requests to a backup node and triggering a fault node repair process.

[0144] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the model training device and each module described above can be referred to the corresponding process in the aforementioned embodiments of the big data-based intelligent field-finding method, and will not be repeated here.

[0145] The aforementioned big data-based intelligent field-finding device can be implemented as a computer program, which can, for example... Figure 3 It runs on the computer device shown.

[0146] Please see Figure 3 , Figure 3 This is a schematic block diagram illustrating the structure of a computer device according to an embodiment of this application. The computer device may be a server or a terminal.

[0147] See Figure 3 The computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include storage media and internal memory.

[0148] The storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the big data-based intelligent field-finding methods provided in the embodiments of this application.

[0149] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0150] Internal memory provides an environment for the execution of computer programs stored in the storage medium. When executed by the processor, this program enables the processor to perform any intelligent field-finding method based on big data. The storage medium can be non-volatile or volatile.

[0151] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0152] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0153] For example, in one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0154] Obtain a site search task sent by an external terminal, the site search task including at least the site purpose, target address, library type requirements, required area and reward criteria; obtain the first pre-stored site resource from the preset system resource pool;

[0155] The site search task is sent to the user terminal, and the second site resource sent by the user terminal is received. Based on the preset matching algorithm, the first site resource and the second site resource are compared with the site search task in multiple dimensions to generate a list of candidate sites that have been successfully matched.

[0156] Each candidate site in the candidate site list is evaluated in a hierarchical manner. The target site is determined based on the hierarchical evaluation results of each candidate site, and the status information of each candidate site in the system resource pool is updated.

[0157] Based on the site information corresponding to the target site, generate the intelligent site search result corresponding to the site search task, and complete the intelligent site search.

[0158] In some embodiments, before performing a graded evaluation on each candidate site in the candidate site list, the method further includes: performing intelligent verification processing on the first site resource and the second site resource respectively, verifying the compliance of the site address through a preset address validity verification algorithm, verifying the integrity of the site ownership certificate through a property rights information matching model, and evaluating the reasonableness of the site rental quotation through a rental data regression model, so as to ensure that the site resources included in the graded evaluation meet the preset reliability standards.

[0159] In some embodiments, the tiered evaluation of each candidate site in the candidate site list includes: quantitatively scoring the candidate sites based on a multi-dimensional evaluation index system, wherein the multi-dimensional evaluation index includes at least: geographical location suitability: calculating the straight-line distance between the candidate site and the target address, traffic accessibility, and regional express delivery business density through a geofencing algorithm; operating cost suitability: performing economic calculations based on rental data, site area, and a preset unit area cost threshold; compliance suitability: verifying whether the property rights, fire safety qualifications, and environmental approval documents of the candidate site meet business operation standards; and expansion potential suitability: analyzing the surrounding road network planning, regional commercial planning, and express delivery business volume growth forecast data for the next three years.

[0160] The scores corresponding to the multi-dimensional evaluation indicators are weighted and calculated using a machine learning classification model to generate a comprehensive evaluation score for each candidate site.

[0161] In some embodiments, determining the target site based on the graded evaluation results corresponding to each candidate site includes: sorting the comprehensive evaluation scores according to preset business strategy priorities, wherein the business strategy priorities include at least a time-priority strategy, a cost-priority strategy, or a service scope-priority strategy; for the top preset number of candidate sites in terms of comprehensive evaluation scores, introducing an expert decision rule base for secondary verification, wherein the expert decision rule base includes historical contracted site characteristic parameters, regional policy restrictions, and customized screening rules of the business competent department; and determining the target site based on the secondary verification results.

[0162] In some embodiments, generating intelligent site search results corresponding to the site search task based on the site information corresponding to the target site includes: generating a structured site search report based on the basic information corresponding to the site information, multi-dimensional evaluation reports, recommended strategy basis, and preset contract signing guidelines; automatically triggering the site on-site inspection process or contract pre-approval process through a preset task closed-loop management module, and sending process progress notifications to relevant business departments.

[0163] In some embodiments, obtaining the first pre-stored site resources from the preset system resource pool includes: performing timeliness filtering on the site resources stored in the system resource pool, automatically excluding site resources whose most recent valid data update time is more than three months, and only retaining site resources with business interaction records or data update records within the last three months to include in the first site resource set.

[0164] In some embodiments, the method further includes: implementing system support through a distributed deployment architecture, deploying the core business processing layer, data service layer, and data backend support module on a distributed server cluster using container orchestration technology, configuring a load balancer to achieve concurrent processing of tens of thousands of QPS query requests; and monitoring the operating status of each module in real time through a full-link monitoring system, wherein the full-link monitoring system includes at least a log collection component, a performance indicator analysis component, and an automatic fault switching component, and when a single node performance abnormality is detected, automatically routing business requests to a backup node and triggering a fault node repair process.

[0165] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the risk warning method described in the first aspect above.

[0166] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0167] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A big data-based intelligent field-finding method, characterized in that, include: The system retrieves a site search task sent by an external terminal, the task including at least the site purpose, target address, library type requirements, required area, and reward criteria; it also retrieves a pre-stored first site resource from a preset system resource pool; the reward criteria include an incentive mechanism for external recommendations. Send the site search task to the user terminal and receive the second site resource sent by the user terminal; Based on a preset matching algorithm, the first site resource and the second site resource are compared with the site search task in multiple dimensions to generate a list of candidate sites that have been successfully matched. Each candidate site in the candidate site list is evaluated in a hierarchical manner. The target site is determined based on the hierarchical evaluation results of each candidate site, and the status information of each candidate site in the system resource pool is updated. Before conducting a graded evaluation of each candidate site in the candidate site list, the method further includes: performing intelligent verification processing on the first site resource and the second site resource respectively, verifying the compliance of the site address through a preset address validity verification algorithm, verifying the integrity of the site ownership certificate through a property rights information matching model, and evaluating the reasonableness of the site rental quotation through a rental data regression model, so as to ensure that the site resources included in the graded evaluation meet the preset reliability standards. Based on the site information corresponding to the target site, generate the intelligent site search result corresponding to the site search task, and complete the intelligent site search.

2. The method according to claim 1, characterized in that, The step of grading and evaluating each candidate site in the candidate site list includes: Candidate sites are quantitatively scored based on a multi-dimensional evaluation index system. These multi-dimensional evaluation indicators include at least: Geographical location suitability: calculating the straight-line distance between the candidate site and the target address, traffic accessibility, and regional express delivery business density using a geofencing algorithm; Operating cost suitability: conducting economic calculations based on rental data, site area, and a preset unit area cost threshold; Compliance suitability: verifying whether the candidate site's property rights, fire safety qualifications, and environmental approval documents meet business operation standards; and Expansion potential suitability: analyzing the surrounding road network planning, regional commercial planning, and express delivery business volume growth forecasts for the next three years. The scores corresponding to the multi-dimensional evaluation indicators are weighted and calculated using a machine learning classification model to generate a comprehensive evaluation score for each candidate site.

3. The method according to claim 2, characterized in that, The step of determining the target site based on the hierarchical evaluation results corresponding to each candidate site includes: The comprehensive evaluation scores are sorted according to the preset business strategy priorities, which include at least the timeliness priority strategy, the cost priority strategy, and the service scope priority strategy. For the top-ranked candidate sites based on comprehensive evaluation scores, a secondary verification is performed using an expert decision rule base. This expert decision rule base includes historical contracted site characteristic parameters, regional policy restrictions, and customized screening rules from the competent business authorities. The target site is determined based on the results of the secondary verification.

4. The method according to claim 1, characterized in that, The step of generating the intelligent site search result corresponding to the site search task based on the site information corresponding to the target site includes: Based on the basic information corresponding to the site information, multi-dimensional evaluation reports, recommended strategies, and pre-set contract signing guidelines, a structured site search report is generated. The pre-set task closed-loop management module automatically triggers the site survey process or contract pre-approval process and sends process progress notifications to relevant business departments.

5. The method according to claim 1, characterized in that, The step of obtaining the pre-stored first site resource from the preset system resource pool includes: The system filters the site resources stored in the resource pool based on their timeliness, automatically excluding site resources whose most recent valid data update time is more than three months, and only retaining site resources with business interaction records or data update records within the last three months as the first site resource.

6. The method according to claim 1, characterized in that, The method further includes: The system is supported by a distributed deployment architecture. The core business processing layer, data service layer and data backend support module are deployed on a distributed server cluster using container orchestration technology. A load balancer is configured to handle tens of thousands of QPS query requests concurrently. The system monitors the operational status of each module in real time through a full-link monitoring system. The full-link monitoring system includes at least a log collection component, a performance indicator analysis component, and an automatic fault switching component. When a single node performance abnormality is detected, the system automatically routes business requests to a backup node and triggers the fault node repair process.

7. A big data-based intelligent field-finding device, characterized in that, include: The task acquisition unit is used to acquire site search tasks sent by external terminals. The site search task includes at least the site purpose, target address, library type requirements, required area, and reward criteria. It also acquires pre-stored first site resources from a preset system resource pool. The reward criteria include an incentive mechanism for external recommendations. The task sending unit is used to send the site search task to the user terminal and receive the second site resource sent by the user terminal. Based on a preset matching algorithm, the first site resource and the second site resource are compared with the site search task in multiple dimensions to generate a list of candidate sites that have been successfully matched. The hierarchical evaluation unit is used to perform hierarchical evaluation on each candidate site in the candidate site list, determine the target site based on the hierarchical evaluation result of each candidate site, and update the status information of each candidate site in the system resource pool. Before conducting a graded evaluation of each candidate site in the candidate site list, the method further includes: performing intelligent verification processing on the first site resource and the second site resource respectively, verifying the compliance of the site address through a preset address validity verification algorithm, verifying the integrity of the site ownership certificate through a property rights information matching model, and evaluating the reasonableness of the site rental quotation through a rental data regression model, so as to ensure that the site resources included in the graded evaluation meet the preset reliability standards. The site search completion unit is used to generate an intelligent site search result corresponding to the site search task based on the site information corresponding to the target site, and to complete the intelligent site search.

8. A computer device, characterized in that, The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and, in executing the computer program, implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the method as described in any one of claims 1 to 6.

Images