A service provider dynamic matching method and system fusing credit and load constraints

Abstract

The application provides a service dynamic matching method and system fusing credit and load constraints; demand information of a demand party is acquired to generate a first structured label set, a service party information base and a second structured label set are maintained, and a credit level label is calculated for the service party, and current load indexes are collected in real time; load pruning is performed on an initial candidate set according to a credit level-load threshold mapping; similarity is calculated for a candidate set after pruning, and a recommendation list is reordered and output according to a comprehensive score = similarity x credit weight coefficient + load penalty term; the scheme reduces invalid calculation, improves concurrency and enforceability. Through "credit level driven load pre-pruning + credit / load joint reordering after similarity calculation", the application significantly reduces invalid calculation, reduces system response time delay, improves concurrent throughput capacity while ensuring matching quality and enforceability.

Description

Technical Field

[0001] This invention relates to the field of information technology, and specifically to a method and system for dynamic matching of service providers that integrates credit and load constraints. Background Technology

[0002] In internet service platforms, under the conditions of both real-time capacity constraints (current load indicators) and service quality differences (credit rating labels) of service providers, efficient and accurate matching of demanders and service providers can be achieved. This is especially suitable for local life service scenarios such as home decoration and repair, which require consideration of the real-time availability, historical reputation and demand matching of service providers.

[0003] In internet-based supply and demand matching platforms, the platform typically maintains a large database of service providers. When a demander submits a request, it needs to quickly select service providers from a large pool of candidates that are both "highly matched" and "able to respond and fulfill their obligations immediately" in order to reduce system response delays and improve the reliability of matching results.

[0004] The shortcomings of existing technology: 1. Matching schemes that rely solely on semantic similarity ignore real-time status and reputation differences, resulting in insufficient fulfillment of obligations. One typical approach focuses on converting the descriptions of demanders and service providers into vectors and calculating cosine similarity, using similarity as the primary matching criterion. However, it fails to incorporate the service provider's current order load / service availability and long-term accumulated reputation differences (credit rating) as core decision factors into the matching process. This may result in recommending service providers that are "semantically matched but are already at full capacity and unable to respond in a timely manner" or "have a poor historical service quality," thereby reducing the fulfillment and reliability of the matching results.

[0005] 2. Introducing reputation information after similarity calculation can easily lead to a large amount of invalid calculations, affecting high-concurrency performance. Another type of improvement introduces reputation evaluation information such as user ratings and complaint rates during the recommendation list sorting stage, but this usually occurs in the post-processing or static weighting stage "after the similarity calculation of all candidate sets is completed". This will still perform the complete high-complexity vector similarity calculation for service providers including those whose "real-time status is clearly unavailable or overloaded", resulting in a large number of invalid calculations and affecting the system response performance in scenarios with large-scale service providers and high-concurrency requests.

[0006] 3. Reputation (a long-term static indicator) and load (a short-term dynamic indicator) are treated separately, lacking a coordination mechanism, making it difficult to achieve coordinated optimization of resource allocation and quality assurance. Existing solutions often handle reputation and load separately: first calculate similarity, then weight reputation, and then (or simultaneously) filter load; there is a lack of a collaborative mechanism that can dynamically adjust the tolerable load threshold based on the service provider's credit rating, making it difficult to achieve coordinated optimization of system resources and service quality while ensuring matching quality.

[0007] 4. The lack of a hard-line avoidance mechanism in the "early matching stage" makes it difficult to guarantee the immediate availability of recommendation results from the source. Therefore, existing technologies have shortcomings and need further improvement. Summary of the Invention

[0008] To address the problems existing in the prior art, this invention provides a method and system for dynamic matching of service providers that integrates credit and load constraints.

[0009] To achieve the above objectives, the specific solution of the present invention is as follows: This invention provides a method for dynamic matching of service providers that integrates credit and load constraints, comprising: S1. Obtain the demand information submitted by the demand party, perform structured parsing on the demand information to generate a first structured tag set; maintain a service provider information database, and maintain a second structured tag set for each service provider, the second structured tag set including at least a credit rating tag calculated based on historical performance data; and simultaneously obtain the current load index of each service provider in real time. S2. Based on the preset credit rating-load threshold mapping relationship, determine the corresponding load threshold of each service provider according to the credit rating label; compare the current load index of each service provider with its corresponding load threshold, and remove the service providers whose current load index reaches or exceeds the load threshold from the initial candidate service provider set of the current matching task to obtain the pruned candidate set. S3. Vectorize the first structured tag set and the second structured tag sets of each service provider in the pruned candidate set respectively, calculate the similarity between the demander and each service provider in the pruned candidate set, and obtain the similarity result. S4. Adjust the similarity results based on the credit rating label and the current load index to obtain a comprehensive score for each service provider, and sort the service providers in the pruned candidate set according to the comprehensive score to output a recommendation list.

[0010] Furthermore, the current load metric is at least one of the following: concurrent order count, task queue length, or resource utilization rate; the real-time acquisition of the current load metric of each service provider includes: the real-time status monitoring module collecting the heartbeat reporting information and / or order status synchronization information of the service provider's client, and writing the current load metric into a low-latency cache for the matching process to read.

[0011] Furthermore, the credit rating-load threshold mapping relationship satisfies the following: the higher the credit rating, the larger the corresponding load threshold; and the credit rating-load threshold mapping relationship is stored in the form of a configuration file or database table and supports hot updates.

[0012] Further, step S3 includes: encoding the first structured label set and the second structured label set into dense feature vectors respectively; performing batch similarity calculation or Top-K retrieval on the pruned candidate set using approximate nearest neighbor retrieval technology, and using cosine similarity as the similarity.

[0013] Further, step S4 includes: A credit weighting coefficient is determined based on the credit rating label; the higher the credit rating, the greater the credit weighting coefficient. Set a warning ratio and calculate the load rate = current load index / load threshold; when the load rate exceeds the warning ratio, set the load penalty item to a negative value, otherwise set the load penalty item to 0; The comprehensive score is calculated using the following formula: Comprehensive score = Similarity × Credit weight coefficient + Load penalty item.

[0014] Furthermore, the credit rating label is dynamically calculated and updated based on the service provider's historical performance data, which includes at least one or more of the following: completion rate, positive review rate, and complaint rate. When the service provider's historical performance data does not meet the requirements corresponding to its current credit rating within a continuous monitoring period, a credit rating downgrade is triggered, and the updated credit rating label is written back to the service provider's information database.

[0015] Furthermore, the current load index includes business load index and physiological load index. The real-time acquisition of the current load index of each service provider also includes: collecting at least one physiological parameter among heart rate, heart rate variability (HRV), and skin temperature through wearable devices worn by the service providers, and calculating a physiological load index characterizing the degree of fatigue based on the physiological parameters; fusing the business load index and the physiological load index according to a preset weight to obtain a fused load value, and using the fused load value as the load threshold comparison object for step S2 and / or as the load input for determining the load penalty item in step S4.

[0016] Furthermore, the current load index includes the remaining working hours load estimated based on the tool-side sensors. The real-time acquisition of the current load index of each service provider includes: deploying inertial measurement unit (IMU) sensors and acoustic emission sensors on the construction tools used by the service providers to collect the vibration signals and / or acoustic emission signals of the tools; extracting features from the vibration signals or acoustic emission signals and inputting them into a pre-trained working hours estimation model to output the work intensity of the task being performed by the service provider and the estimated remaining working hours; using the estimated remaining working hours as the current load index, or fusing the estimated remaining working hours with the number of concurrent orders to obtain a fused load value, and using the current load index or fused load value for load pruning judgment in step S2 and / or determining the load penalty item in step S4.

[0017] Furthermore, it also includes a feedback update step for closed-loop optimization: after outputting the recommendation list and generating matching results, performance data corresponding to the matching results is collected. The performance data includes at least one or more of the following: order acceptance rate, completion time, positive review rate, complaint rate, and timeout rate. Based on the performance data, the stable load level that the service provider can handle under preset service quality constraints is statistically analyzed according to credit level. The statistical value of the stable load level is used as the basis for updating the credit level-load threshold mapping relationship and / or the credit weight coefficient. The updated mapping relationship and / or credit weight coefficient are written into the configuration file or database table and a hot update is triggered so that the updated parameters are used in step S2 or step S4 of the subsequent matching task.

[0018] This invention also provides a dynamic service provider matching system that integrates credit and load constraints to implement the above method, comprising: The tag management and generation module is used to parse the demand information of the demand party into a first structured tag set and maintain the second structured tag set of each service party in the service party information database. The second structured tag set includes at least a credit rating tag. The real-time status monitoring module is used to collect and update the current load indicators of each service provider in real time, and write the current load indicators into a low-latency cache. The intelligent matching engine is communicatively connected to the tag management and generation module and the real-time status monitoring module. The intelligent matching engine includes: The load pruning unit is configured with a credit rating-load threshold mapping relationship. It is used to filter the initial candidate service provider set based on the credit rating label and the current load index before vector similarity calculation, and output the pruned candidate set. The vector similarity calculation unit is used to perform vectorization processing on the pruned candidate set and calculate the similarity. The multi-factor re-ranking unit is used to adjust the similarity based on the credit rating label and the current load index, calculate the comprehensive score, and sort and output the recommendation list.

[0019] The technical solution of this invention has the following beneficial effects: 1. Reduce unnecessary computation, reduce computational complexity and shorten response latency: Before similarity calculation, candidate service providers are pre-pruned based on the "credit rating-load threshold mapping relationship". Vector similarity calculation is only performed on the pruned candidate set. This reduces the high-complexity vector calculation for "overloaded / unavailable" service providers from the algorithm level and reduces end-to-end response latency.

[0020] 2. Optimize system resource usage, improve concurrency throughput and scalability: Since the range of high-density vector operations is compressed to a high-quality subset with reasonable load, the ineffective use of computing resources such as CPU and memory is significantly reduced, enabling the system to support higher concurrency and be more easily horizontally scaled under the same hardware conditions.

[0021] 3. Improve the fulfillment, reliability and user satisfaction of matching results: First, eliminate unfulfillable (overloaded) service providers through the "hard constraint" of pre-pruning, and then use the "soft adjustment" of joint re-ranking to integrate semantic matching degree, long-term credit and short-term availability to ensure that the recommendation list simultaneously meets the requirements of "high matching relevance, high service credibility and high response timeliness".

[0022] 4. Form a technical system that can be parameterized and optimized in a closed loop: Credit rating threshold mapping, credit weight coefficient, load penalty trigger conditions / penalty values, etc. are all configurable and measurable parameters. They can be optimized offline or online based on the platform's historical performance data, thereby building a closed loop of continuous self-optimization. Attached Figure Description

[0023] Figure 1 This is a flowchart of the matching method of the present invention; Figure 2 This is a logical schematic diagram of the credit-driven dynamic load pruning of the present invention; Figure 3 This is a schematic diagram of the computational model for joint reordering in this invention; Figure 4 This is a system architecture block diagram of the present invention; Figure 5 This is an experimental comparison curve of the present invention. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, and not all of them.

[0025] In this specification, the following terms have the following meanings: (1) First set of structured tags: refers to the set of tags obtained after structured parsing of the demand information submitted by the demand party, which includes at least service category, geographical range, time window, budget range, skills / style and other tag items. The tag items can be represented in the form of key-value pairs, sparse vectors or dense vectors.

[0026] (2) Second structured tag set: refers to the tag set maintained for each service provider, which includes at least static attribute tags and credit rating tags for the service provider; the static attribute tags include at least service category, geographical coverage, qualification / job type, available service time period and other tag items; the credit rating tags are calculated from historical performance data and can be updated over time.

[0027] (3) Current load index: an index used to characterize the real-time available capacity of the service provider at the matching time, preferably including the number of concurrent orders, task queue length, resource utilization, fusion load value and their combination; the current load index and the load threshold are in the same dimension or can be converted to the same dimension for comparison.

[0028] (4) Load threshold: Based on the credit rating-load threshold mapping relationship, the upper limit of tasks that the service provider can be assigned is determined by the credit rating label; the higher the credit rating, the larger the corresponding load threshold.

[0029] (5) Pruned candidate set: refers to the candidate set obtained after removing the service providers whose current load index reaches or exceeds their corresponding load threshold from the initial candidate service provider set.

[0030] (6) Similarity: refers to the similarity result calculated after vectorizing the first structured label set and the second structured label set. Cosine similarity is preferred and normalized to [0,1].

[0031] (7) Comprehensive score: refers to the score obtained after adjusting the similarity based on the similarity, combined with the credit weight coefficient and the load penalty term, which is used to rank the service providers in the pruned candidate set and output the recommendation list.

[0032] Combination Figures 1-5 As shown, this invention provides a dynamic matching method for service providers that integrates credit and load constraints, comprising: S1. Obtain the demand information submitted by the demand party, perform structured parsing on the demand information to generate a first structured tag set; maintain a service provider information database, and maintain a second structured tag set for each service provider, the second structured tag set including at least a credit rating tag calculated based on historical performance data; and simultaneously obtain the current load index of each service provider in real time. S2. Based on the preset credit rating-load threshold mapping relationship, determine the corresponding load threshold of each service provider according to the credit rating label; compare the current load index of each service provider with its corresponding load threshold, and remove the service providers whose current load index reaches or exceeds the load threshold from the initial candidate service provider set of the current matching task to obtain the pruned candidate set. S3. Vectorize the first structured tag set and the second structured tag sets of each service provider in the pruned candidate set respectively, calculate the similarity between the demander and each service provider in the pruned candidate set, and obtain the similarity result. S4. Adjust the similarity results based on the credit rating label and the current load index to obtain a comprehensive score for each service provider, and sort the service providers in the pruned candidate set according to the comprehensive score to output a recommendation list.

[0033] The current load metric is at least one of the following: number of concurrent orders, task queue length, or resource utilization. The real-time acquisition of the current load metric of each service provider includes: the real-time status monitoring module collecting the heartbeat reporting information and / or order status synchronization information of the service provider's client, and writing the current load metric into a low-latency cache for the matching process to read.

[0034] The credit rating-load threshold mapping relationship satisfies the following: the higher the credit rating, the larger the corresponding load threshold; and the credit rating-load threshold mapping relationship is stored in the form of a configuration file or database table and supports hot updates.

[0035] Step S3 includes: encoding the first structured label set and the second structured label set into dense feature vectors respectively; performing batch similarity calculation or Top-K retrieval on the pruned candidate set using approximate nearest neighbor retrieval technology, and using cosine similarity as the similarity.

[0036] Step S4 includes: A credit weighting coefficient is determined based on the credit rating label; the higher the credit rating, the greater the credit weighting coefficient. Set a warning ratio and calculate the load rate = current load index / load threshold; when the load rate exceeds the warning ratio, set the load penalty item to a negative value, otherwise set the load penalty item to 0; The comprehensive score is calculated using the following formula: Comprehensive score = Similarity × Credit weight coefficient + Load penalty item.

[0037] The credit rating label is dynamically calculated and updated based on the service provider's historical performance data, which includes at least one or more of the following: completion rate, positive review rate, and complaint rate. When the service provider's historical performance data does not meet the requirements corresponding to its current credit rating within a continuous monitoring period, a credit rating downgrade is triggered, and the updated credit rating label is written back to the service provider's information database.

[0038] The current load indicators include business load indicators and physiological load indicators. The real-time acquisition of the current load indicators of each service provider also includes: collecting at least one physiological parameter among heart rate, heart rate variability (HRV), and skin temperature through wearable devices worn by the service providers, and calculating a physiological load indicator characterizing the degree of fatigue based on the physiological parameters; fusing the business load indicators and the physiological load indicators according to preset weights to obtain a fused load value, and using the fused load value as the load threshold comparison object for step S2 and / or as the load input for determining the load penalty item in step S4.

[0039] The current load index includes the remaining working hours load estimated based on the tool-side sensors. The real-time acquisition of the current load index of each service provider includes: deploying inertial measurement unit (IMU) sensors and acoustic emission sensors on the construction tools used by the service providers to collect the vibration signals and / or acoustic emission signals of the tools; extracting features from the vibration signals or acoustic emission signals and inputting them into a pre-trained working hours estimation model to output the work intensity of the task being performed by the service provider and the estimated remaining working hours; using the estimated remaining working hours as the current load index, or fusing the estimated remaining working hours with the number of concurrent orders to obtain a fused load value, and using the current load index or fused load value for load pruning judgment in step S2 and / or determining the load penalty item in step S4.

[0040] It also includes a feedback update step for closed-loop optimization: after outputting the recommendation list and generating matching results, it collects performance data corresponding to the matching results. The performance data includes at least one or more of the following: order acceptance rate, completion time, positive review rate, complaint rate, and timeout rate. Based on the performance data, it calculates the stable load level that the service provider can handle under preset service quality constraints according to credit rating. The preset service quality constraints are that at least one of the performance data meets the corresponding preset threshold condition. The statistical measure of the stable load level is the sliding window mean and / or quantile. The statistical measure is used as the basis for updating the credit rating-load threshold mapping relationship and / or the credit weight coefficient. The updated mapping relationship and / or credit weight coefficient are written into the configuration file or database table and a hot update is triggered so that the updated parameters are used in step S2 or step S4 of the subsequent matching task.

[0041] This invention also provides a dynamic service provider matching system that integrates credit and load constraints to implement the above method, comprising: The tag management and generation module is used to parse the demand information of the demand party into a first structured tag set and maintain the second structured tag set of each service party in the service party information database. The second structured tag set includes at least a credit rating tag. The real-time status monitoring module is used to collect and update the current load indicators of each service provider in real time, and write the current load indicators into a low-latency cache. The intelligent matching engine is communicatively connected to the tag management and generation module and the real-time status monitoring module. The intelligent matching engine includes: The load pruning unit is configured with a credit rating-load threshold mapping relationship. It is used to filter the initial candidate service provider set based on the credit rating label and the current load index before vector similarity calculation, and output the pruned candidate set. The vector similarity calculation unit is used to perform vectorization processing on the pruned candidate set and calculate the similarity. The multi-factor re-ranking unit is used to adjust the similarity based on the credit rating label and the current load index, calculate the comprehensive score, and sort and output the recommendation list.

[0042] Example 1: Dynamic Matching Process for Home Decoration Service Providers I. System Deployment and Data Objects 1. System Components and Deployment Methods The platform adopts a microservice architecture, including: Tag management and generation module: includes a requirements analysis submodule (NLP / CV), a service provider information database (service provider tag library), and a credit calculation engine; Real-time status monitoring module: includes data collector, stream processing layer, and status publishing center (low-latency cache); Intelligent matching engine: includes load pruning unit, vector similarity calculation unit, reordering unit, result assembly and return; 2. Service Provider Information Database and Tag Structure For each service provider, a second structured tag set is maintained, which includes at least: service type, region, style / skill tags, and credit rating tags derived from historical performance data; the credit rating tags are periodically updated by the credit calculation engine and synchronized to the service provider information database and cache.

[0043] 3. Definition and Acquisition of Current Load Metrics In this embodiment, the current load metric is the number of projects in progress that have not yet entered the acceptance phase. The load is calculated by the stream processing layer by aggregating order status change events and writing the load into a low-latency cache with a time-to-live setting to prevent historical data from being retained; the matching engine reads the load value through a batch query interface.

[0044] II. Parameter Configuration parameter: Credit rating set: A, B, C, D (A is the highest).

[0045] Credit Rating - Load Threshold Mapping Table (Supports Hot Updates): Credit rating-load threshold mapping: A=10, B=8, C=6, D=2; Credit weight coefficient mapping table: Credit weight coefficient mapping: A=1.2, B=1.0, C=0.8, D=0.5; Load penalty trigger condition: The load rate r equals the current load metric divided by the load threshold corresponding to the credit rating; a penalty is triggered when r > 0.8. The penalty value P is -0.05; P is 0 when no penalty is triggered. The comprehensive scoring formula is: Score = Sim × W + P, where Sim is the cosine similarity normalized to [0,1]. Vector retrieval: ANN retrieval is performed using Faiss; Top-K=50; Embedded vector dimension d can be selected as 256 / 384 / 768 (consistent with the Embedding model used).

[0046] III. Implementation Steps S1: Structured and Vectorized Preparation of Demand and Service Provider Information 1. Request Submission: Homeowners submit their text requirements and optional images (such as floor plans) on the platform. Example text: "Modern minimalist style, three bedrooms and two living rooms, budget 200,000-250,000 RMB, hoping for smart home experience."

[0047] 2. Multimodal parsing generates the first structured tag set: Text path: Use NLP models (such as BERT / ERNIE) for entity recognition and intent classification to obtain labels (style, apartment type, budget range, smart home experience, etc.). Image path: Upload the floor plan and use a CV model (such as ResNet / ViT) to extract the number of rooms, area estimation, and spatial structure information; Back-off supplementation mechanism: When the image recognition confidence is lower than the threshold (e.g., 0.6), template matching, speech recognition or manual input are enabled to supplement the labels to ensure that the first structured label set is complete and consistent.

[0048] 3. Service provider candidate set and the second structured tag set: The service provider information database is initially screened by region, service category, etc., to obtain an initial candidate set (e.g., 1000 providers); each service provider has a second set of structured tags, including credit rating tags.

[0049] 4. Vectorization: The first and second structured tag sets are converted into dense vectors (Embedding) by a vectorization service and then L2 normalized to prepare for cosine similarity calculation.

[0050] S2: Credit-driven dynamic load pruning (pre-filtering to avoid invalid computation) 1. Perform batch load reading on the initial candidate set: The matching engine performs batch queries on the current load metrics of the candidate service providers through a low-latency cache to obtain the current_load of the candidate service providers.

[0051] 2. Perform pruning judgment for each service provider: Read its credit rating (credit), and look up the threshold T = threshold_map[credit] in the table; If current_load >= T, then remove it from the candidate set; otherwise, keep it and add it to the pruned candidate set.

[0052] 3. Example: "Company Alpha" credit A, threshold 10, current load 8, satisfies 8<10, retain; "Company Beta" credit B, threshold 8, current load 9, satisfies 9>=8, is removed and not involved in any subsequent vector calculations.

[0053] 4. Example of pruning results scale: Remove about 200 overloaded service providers from 1000 to obtain a pruned candidate set M=800.

[0054] S3: Calculate vector similarity for the pruned candidate set. 1. Only build / maintain vector indexes (not full indexes) for the pruned candidate set, perform ANN retrieval through Faiss, calculate the cosine similarity Sim between the demand vector and the candidate service direction quantity and normalize it to [0,1]; 2. Example: Batch similarity calculation for 800 candidates takes about 1 second; the similarity Sim for "Company Alpha" is 0.92.

[0055] S4: Joint reordering based on credit and load (resulting in the final recommendation list) 1. Calculate for each candidate service provider: Credit weight: W = W_map[credit]; Load ratio: r = current_load / threshold_map[credit]; Load penalty: If r > 0.8, then P = -0.05; otherwise, P = 0. Overall score: Score = Sim × W + P.

[0056] 2. Example calculation: "Company Alpha": Credit A, W=1.2; Load factor 8 / 10=0.8, not exceeding the trigger line, P=0; Final score Score=0.92×1.2+0=1.104; Another company, with credit rating B, Sim=0.95, and load factor 7 / 8=0.875, triggered a penalty, resulting in a score of 0.95×1.0-0.05=0.90, thus ranking lower than "Company Alpha".

[0057] 3. Output: Sort the pruned candidate set in descending order of Score and return a Top-K recommendation list (e.g., K=20 / 50), along with explanatory fields such as score, credit rating, and load factor; the end-to-end return time can be on the order of approximately 2 seconds.

[0058] 1. Credit rating update mechanism: The credit calculation engine recalculates credit scores and maps them to credit ratings based on time decay weighting for consumer order completion events, evaluation events, and complaint events; when the performance data does not meet the requirements of the current rating within a continuous monitoring period, the credit rating is downgraded and synchronized to the service provider's information database and cache.

[0059] 2. Thresholds and weights are configurable: threshold_map, W_map, warning ratio, and penalty value are all managed by configuration files or database tables, supporting hot updates, which facilitates offline analysis / online optimization based on historical performance data.

[0060] Example 2: Dynamic matching process for home decoration service providers incorporating wearable physiological load Based on Example 1, this embodiment uses physiological parameters collected by wearable devices worn by the service provider to calculate physiological load indicators, and merges them with business load indicators to form a fused load value. This fused load value is then used in the load pruning judgment in step S2 and the load penalty calculation in step S4 to achieve fulfillable matching that "considers both order volume and service provider fatigue status".

[0061] I. System Structure Supplement Based on the system architecture (tag management and generation module, real-time status monitoring module, intelligent matching engine, etc.), the following sub-modules are added / expanded: 1. Wearable data acquisition submodule, which is part of the real-time status monitoring module; Data collection targets: wristbands / watches worn by service providers; Communication: Bluetooth BLE connects to the service provider's client application (App), and the App reports to the platform using HTTPS / TLS; Data collection fields: Heart rate HR (bpm), Heart rate variability HRV (ms), Skin temperature Tskin (°C), at least one.

[0062] 2. Physiological load calculation submodule (deployed in the stream processing layer or as a standalone service) Input: HR / HRV / Tskin time series; Output: Standardized physiological load index phys_load, with a value range of [0,1]. The larger the value, the more fatigued / higher the risk. Output write: Write to the cache (such as Redis) in the same way as the business load, with the key service_id:phys_load, and set TTL to prevent dirty data from being retained.

[0063] 3. Integrate load generation logic (implemented within the load pruning unit and / or multi-factor reordering unit). The order_load (business load) and phys_load (physiological load) are read in batches from the cache and fused according to the preset weight to obtain the fused load value fused_load, which is used for load threshold comparison and penalty calculation.

[0064] II. Key Parameter Settings Parameter configuration: 1. Credit Rating - Load Threshold Mapping Table {Grade A: 10, Grade B: 8, Grade C: 6, Grade D: 2} is used to determine the maximum load that a service provider can accept.

[0065] 2. Credit weight and load penalty trigger line W={A:1.2,B:1.0,C:0.8,D:0.5}; Load penalty trigger line: load / threshold > 0.8; penalty value P = -0.05, otherwise P = 0; comprehensive score Score = Sim × W + P.

[0066] 3. Physiological data sampling and windowing HR sampling frequency: 1Hz; HRV calculation window: sliding window of 5 minutes, step size of 30 seconds; Tskin sampling frequency: 0.2Hz (once every 5 seconds).

[0067] 4. Calculation of physiological load indicators Using RMSSD as the representative value of HRV, we first normalize it: HR_norm=clamp((HR-60) / 60,0,1) (60~120bpm is mapped to 0~1); HRV_norm=clamp(RMSSD / 80,0,1) (0~80ms is mapped to 0~1); Temp_norm=clamp((Tskin-33) / 4,0,1) (33~37℃ is mapped to 0~1); Physiological load indicators: phys_load=clamp(0.5HR_norm+0.4(1-HRV_norm)+0.1Temp_norm,0,1); The value (1-HRV_norm) reflects the principle that "the lower the HRV, the higher the fatigue level".

[0068] 5. Merging business load and physiological load (forming a merged load value) Business load metric: order_load = number of orders currently in progress; Combined load value (in the form of "equivalent number of orders" for easy comparison with the threshold): fused_load=order_load+β×phys_load×T_credit; Where T_credit is the load threshold corresponding to the service provider's credit rating, and β is the weighting coefficient (β=0.3 in the example).

[0069] When phys_load increases, the equivalent load increases, which in turn makes the pruning threshold judgment more conservative or increases the penalty term.

[0070] 6. Missing Data Handling If phys_load data is missing or expired (e.g., not updated for more than 180 seconds), phys_load=0.5 is set as a conservative default value, and a "physiological data missing" flag is recorded in the log; it is automatically updated after the TTL expires.

[0071] III. Implementation Steps S1: Data Preparation (Requirement Tags and Service Provider Tags) The first set of structured tags is generated from the demand information of the demand party and quantified; the candidate service provider maintains a second set of structured tags and includes credit rating tags; the initial candidate screening yields an initial candidate set N (e.g., 1000 companies).

[0072] S1': Real-time status acquisition (extended: physiological status) 1. The service provider's client application (App) reports a "wearable data packet" every 30 seconds, which includes service_id, timestamp, HR, RMSSD, and Tskin; 2. The stream processing layer aggregates and calculates phys_load by service_id and writes it to Redis: service_id:order_load (obtained from order flow aggregation); service_id:phys_load (obtained from wearable stream aggregation); all are set to TTL (e.g., 300 seconds).

[0073] S2: Dynamic load pruning (using merged load values) 1. The matching engine reads the order_load and phys_load of candidate service providers in batches, and reads the credit rating-threshold mapping table; 2. For each candidate service provider: Obtain the credit rating (credit) and find the threshold value (T_credit); Calculate the converged load value The fused load value is calculated as: fused_load = order_load + 0.3 × phys_load × T_credit; If fused_load >= T_credit, then it is pruned and removed directly; otherwise, it is retained and added to the candidate set after pruning.

[0074] S3: Vector similarity calculation (only for pruned candidate sets) Perform vector similarity calculation (e.g., FaissANN retrieval) on the pruned candidate set to obtain the similarity Sim between each candidate service provider and the demand.

[0075] S4: Joint Reordering (Integrating Load Input Penalty) 1. For each candidate service provider, read the fused_load and threshold T_credit again, and calculate the fused load ratio: r_fused=fused_load / T_credit; 2. Load penalty term: If r_fused > 0.8, then P = -0.05; otherwise, P = 0. 3. Final score: Score = Sim × W_credit + P, and output the recommendation list in descending order of Score.

[0076] IV. Numericalization Examples Assume the candidate service provider "Company Alpha" has a credit rating of A, a threshold of T_A=10, and a business load of order_load=8 (8 orders in progress).

[0077] Scenario 1: Physiological state is good, phys_load=0.2 fused_load = 8 + 0.3 × 0.2 × 10 = 8 + 0.6 = 8.6 < 10, so pruning is used; Assuming similarity Sim=0.92, W_A=1.2, and r_fused=0.86, the penalty is P=-0.05; Score = 0.92 × 1.2 - 0.05 = 1.054.

[0078] Scenario 2: Significant physiological fatigue, phys_load=0.7 fused_load=8+0.3×0.7×10=8+2.1=10.1>=10, so it is directly pruned and removed in stage S2; This exclusion ensures that the system will not consume any subsequent vector computing resources on that service provider, and avoids the risks of timeouts, cancellations, and complaints caused by high fatigue.

[0079] V. Technical Effects 1. Expand the "fulfillability" constraint from pure order load to "order load + physiological fatigue", which is closer to construction capacity; 2. Without changing the existing overall framework of "similarity calculation after pruning", further reduce the invalid calculation and reassignment of high-risk candidates; 3. Improve the stability and security of recommendation results (reduce quality fluctuations and accident probability caused by fatigue construction) and maintain low response latency in high-concurrency scenarios.

[0080] Example 3: Dynamic matching process for estimating actual remaining working hours based on tool-side IMU / acoustic emission signals I. System Structure and Hardware Layout Based on the "real-time status monitoring module", the "tool-side sensor acquisition link" is extended. The overall aggregation is still achieved through the stream processing layer, and the load is written to the global cache (such as Redis Cluster) with service_id:load_value, and TTL is set to prevent dirty data from being retained.

[0081] 1. Tool-side sensor configuration IMU sensor: 6-axis (accelerometer + gyroscope), fixed inside the casing or handle area of ​​the construction tool (electric hammer / angle grinder / cutting machine / electric drill, etc.); Acoustic emission sensor (AE): piezoelectric type, attached to the tool housing or key stress-bearing parts, and fixed by a coupling agent; Deploy the IMU or deploy the AE.

[0082] 2. Tool Gateway and Client The tool-side sensors are connected to the tool gateway (integrated inside or external to the tool), and the gateway has local feature extraction capabilities; The tool gateway connects to the service provider's mobile client application (App) via BLE. The App reports data to the platform's "data collector" via HTTPS / TLS. (The collector is deployed using an Agent / subscription Binlog method and reports via REST.) 3. Platform-side processing link The data acquisition unit receives data streams from the tool; The stream processing layer (Flink or Spark Streaming) performs feature aggregation and time estimation on the tool's data stream; The status publishing center writes the estimated load value to a low-latency cache for the matching engine to read; The load pruning unit and multi-factor reordering unit of the matching engine both read the load in batches from the cache and execute the filtering / scoring logic.

[0083] II. Key Parameter Settings 1. Credit Rating - Load Threshold Mapping (in "Equivalent Order Count") Example: {A:10,B:8,C:6,D:2}.

[0084] 2. Credit Weight and Load Penalty W={A:1.2,B:1.0,C:0.8,D:0.5}; Trigger line is “load / threshold>0.8”, penalty value P=-0.05; Overall score Score=Sim×W+P.

[0085] 3. Sensing Sampling and Window IMU sampling frequency: 100Hz; AE raw sampling frequency: 200kHz (the raw waveform is not directly sent to the platform, but only the window feature is extracted at the tool gateway and then sent up). Feature window: 1-second sliding window with a step size of 1 second; an additional 10-second sliding average is set to suppress jitter.

[0086] 4. Equivalent order quantity conversion factor Let the platform define “standard equivalent working hours” H_ref = 8 hours / order (used to convert the remaining working hours into the equivalent number of orders, so as to facilitate direct comparison with the threshold table).

[0087] If the working hours of platform business vary greatly, set H_ref(category) according to the service category. In this embodiment, it is fixed at 8 hours.

[0088] III. Data Format and Model 1. Report data packets (Tools / Gateway → App → Platform) Feature packets are reported once per second: service_id, tool_id, timestamp; IMU characteristics: acc_rms (triaxial acceleration RMS), gyro_rms, acc_energy_10_40Hz (10–40Hz energy), acc_kurtosis; AE features: ae_rms, ae_peak, ae_counts (threshold counts), ae_centroid_freq (spectral centroids).

[0089] If an IMU is deployed, the AE feature field is set to empty; if an AE is deployed, the IMU feature field is set to empty; the platform selects the model input based on the existing fields.

[0090] 2. Work time estimation model (pre-trained and then inferred online) Model type: Gradient Boosting Tree (GBDT) or Lightweight Neural Network (MLP), with the above feature vector as input; Output: work_intensity∈[0,1] (work intensity); t_remain_hours (estimated remaining hours, in hours, non-negative real number).

[0091] Training data acquisition (offline): The actual working hours of each order record from "start to completion" are used as labels, and tool feature sequences during the period are collected. After training, the model version is fixed and published to the stream processing layer for online inference.

[0092] 3. Multiple tasks are aggregated at the service provider level (remaining workload). For all ongoing orders / processes under the same service_id, maintain their respective remaining_hours_i and calculate: H_remain=Σremaining_hours_i (total remaining working hours of the service provider); eq_orders = H_remain / H_ref (equivalent number of orders); Write eq_orders as the "current load metric (load_value)" to Redis: service_id:eq_orders, TTL=300 seconds.

[0093] IV. Implementation Steps S1: Requirements Analysis and Candidate Set Generation Obtain the demand from the demand side and generate a first structured tag set through NLP and / or multimodal extraction; maintain the service provider information database and maintain a second structured tag set (including credit rating tags) for the service provider; obtain an initial candidate set N by region / category, etc.

[0094] S1': Real-time acquisition of tool signals and estimation of remaining working hours 1. The tool gateway acquires raw IMU data at 100Hz and raw AE data at 200kHz; 2. Calculate features on the gateway side and report the "feature packet" every second; 3. The platform's stream processing layer receives the feature packets and calls the online inference model, outputting the work_intensity and t_remain_hours for each ongoing task; 4. Aggregate by service_id to obtain H_remain and eq_orders, and write eq_orders to Redis (TTL=300 seconds).

[0095] S2: Load pruning (with the load being "equivalent number of orders converted from estimated remaining working hours") 1. The matching engine reads the eq_orders of candidate service providers in batches from Redis; at the same time, it reads the "credit rating-threshold mapping table" from local memory or cache; 2. For each candidate service provider: Read the credit rating (credit) and retrieve the threshold value (T_credit); Get load_value=eq_orders; If load_value >= T_credit, then it is pruned and removed; otherwise, it is retained and added to the pruned candidate set M.

[0096] S3: Vector similarity calculation (only for pruned candidate sets) Vector similarity calculation is performed only on the pruned candidate set of size M. It is preferable to use Faiss to build a vector index for the "pruned candidate set" and retrieve the Top-K similarity results.

[0097] S4: Joint Reordering (using actual workload as a penalty) 1. The multi-factor reordering unit reads load_value=eq_orders from the cache again and calculates the load ratio r=load_value / T_credit; 2. If r > 0.8, then P = -0.05; otherwise, P = 0. Calculate the overall score according to Score = Sim × W + P, and then sort and output the results.

[0098] V. Numericalization Examples The service provider "Company Alpha" has a credit rating of A, corresponding to a threshold of T_A=10; Alpha currently has two ongoing orders. The tool estimates the remaining man-hours for the two orders to be 12 hours and 8 hours respectively. Therefore: H_remain=20h, H_ref=8h, eq_orders=20 / 8=2.5; Pruning criteria: If 2.5 < 10, prune the 2.5 and add it to the candidate set. If the similarity Sim=0.92, the credit weight W_A=1.2, the load ratio r=2.5 / 10=0.25 does not trigger a penalty, P=0, and the score Score=0.92×1.2=1.104 (consistent with the formula).

[0099] The other service provider, Credit A, estimates the total remaining working hours H_remain=90h on the tool side. Therefore, eq_orders=90 / 8=11.25, which satisfies 11.25>=10. In stage S2, it is directly pruned and no longer participates in any vector calculation.

[0100] VI. Anomaly and Consistency Handling 1. Missing / Expired Sensor Data: If eq_orders in Redis is missing or has not been updated beyond TTL, the rollback will use the number of concurrent orders on the business side as a temporary load. The rollback load can be multiplied by an amplification factor (1.2) before participating in pruning to reduce the risk of mis-assigned orders.

[0101] 2. Parallel use of multiple tools: When multiple tools are used simultaneously for the same order, aggregate by order dimension (take the maximum remaining working hours or weighted average) to generate remaining_hours_i, and then summarize to H_remain to ensure consistency of dimensions.

[0102] 3. Model version consistency: The online inference service is managed by model version number, and the stream processing layer only uses the currently effective version to avoid inconsistencies in load balancing caused by different service providers using different models at the same time.

[0103] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of the present invention.

Claims

1. A dynamic matching method for service providers that integrates credit and load constraints, characterized in that, include: S1. Obtain the demand information submitted by the demand party, and perform structured parsing on the demand information to generate a first set of structured tags; Maintain a service provider information database and maintain a second structured tag set for each service provider, the second structured tag set including credit rating tags calculated based on historical performance data; at the same time, obtain the current load indicators of each service provider in real time; S2. Based on the preset credit rating-load threshold mapping relationship, determine the corresponding load threshold of each service provider according to the credit rating label; compare the current load index of each service provider with its corresponding load threshold, and remove the service providers whose current load index reaches or exceeds the load threshold from the initial candidate service provider set of the current matching task to obtain the pruned candidate set. S3. Vectorize the first structured tag set and the second structured tag sets of each service provider in the pruned candidate set respectively, calculate the similarity between the demander and each service provider in the pruned candidate set, and obtain the similarity result. S4. Adjust the similarity results based on the credit rating label and the current load index to obtain a comprehensive score for each service provider, and sort the service providers in the pruned candidate set according to the comprehensive score to output a recommendation list.

2. The method as described in claim 1, characterized in that, The current load metric is at least one of the following: number of concurrent orders, task queue length, or resource utilization. The real-time acquisition of the current load metric of each service provider includes: the real-time status monitoring module collecting the heartbeat reporting information and order status synchronization information of the service provider's client, and writing the current load metric into a low-latency cache for the matching process to read.

3. The method as described in claim 1, characterized in that, The credit rating-load threshold mapping relationship satisfies the following: the higher the credit rating, the larger the corresponding load threshold; and the credit rating-load threshold mapping relationship is stored in the form of a configuration file or database table and supports hot updates.

4. The method as described in claim 1, characterized in that, Step S3 includes: encoding the first structured label set and the second structured label set into dense feature vectors respectively; performing batch similarity calculation or Top-K retrieval on the pruned candidate set using approximate nearest neighbor retrieval technology, and using cosine similarity as the similarity.

5. The method as described in claim 1, characterized in that, Step S4 includes: A credit weighting coefficient is determined based on the credit rating label; the higher the credit rating, the greater the credit weighting coefficient. Set a warning ratio and calculate the load rate = current load index / load threshold; when the load rate exceeds the warning ratio, set the load penalty item to a negative value, otherwise set the load penalty item to 0; The comprehensive score is calculated using the following formula: Comprehensive score = Similarity × Credit weight coefficient + Load penalty item.

6. The method as described in claim 1, characterized in that, The credit rating label is dynamically calculated and updated based on the service provider's historical performance data, which includes one or more of the following: completion rate, positive review rate, and complaint rate. When the service provider's historical performance data does not meet the requirements corresponding to its current credit rating within a continuous monitoring period, a credit rating downgrade is triggered, and the updated credit rating label is written back to the service provider's information database.

7. The method as described in claim 5, characterized in that, The current load indicators include business load indicators and physiological load indicators. The real-time acquisition of the current load indicators of each service provider also includes: collecting at least one physiological parameter among heart rate, heart rate variability (HRV), and skin temperature through wearable devices worn by the service providers, and calculating a physiological load indicator representing the degree of fatigue based on the physiological parameters; fusing the business load indicators and the physiological load indicators according to preset weights to obtain a fused load value, and using the fused load value as the load threshold comparison object for step S2 or as the load input for determining the load penalty item in step S4.

8. The method as described in claim 5, characterized in that, The current load index includes the remaining working hours load estimated based on the tool-side sensors. The real-time acquisition of the current load index of each service provider includes: deploying inertial measurement unit (IMU) sensors and acoustic emission sensors on the construction tools used by the service providers to collect the vibration signals and acoustic emission signals of the tools; extracting features from the vibration signals or acoustic emission signals and inputting them into a pre-trained working hours estimation model to output the work intensity of the task being performed by the service provider and the estimated remaining working hours; using the estimated remaining working hours as the current load index, or fusing the estimated remaining working hours with the number of concurrent orders to obtain a fused load value, and using the current load index or fused load value for load pruning judgment in step S2 and determining the load penalty item in step S4.

9. The method as described in claim 5, characterized in that, It also includes a feedback update step for closed-loop optimization: after outputting the recommendation list and generating matching results, it collects performance data corresponding to the matching results. The performance data includes at least one or more of the following: order acceptance rate, completion time, positive review rate, complaint rate, and timeout rate. Based on the performance data, it statistically analyzes the stable load level that the service provider can handle under preset service quality constraints according to credit rating. The preset service quality constraints are that at least one of the performance data meets the corresponding preset threshold condition. The statistical measures of the stable load level are the sliding window mean and quantiles. The statistical measures are used as the basis for updating the credit rating-load threshold mapping relationship and the credit weight coefficient. The updated mapping relationship or credit weight coefficient is written into the configuration file or database table and a hot update is triggered so that the updated parameters are used in step S2 or step S4 of the subsequent matching task.

10. A dynamic service provider matching system integrating credit and load constraints, used to implement the method described in any one of claims 1-9, characterized in that, include: The tag management and generation module is used to parse the demand information of the demand party into a first structured tag set and maintain the second structured tag set of each service party in the service party information database. The second structured tag set includes at least a credit rating tag. The real-time status monitoring module is used to collect and update the current load indicators of each service provider in real time, and write the current load indicators into a low-latency cache. The intelligent matching engine is communicatively connected to the tag management and generation module and the real-time status monitoring module. The intelligent matching engine includes: The load pruning unit is configured with a credit rating-load threshold mapping relationship. It is used to filter the initial candidate service provider set based on the credit rating label and the current load index before vector similarity calculation, and output the pruned candidate set. The vector similarity calculation unit is used to perform vectorization processing on the pruned candidate set and calculate the similarity. The multi-factor re-ranking unit is used to adjust the similarity based on the credit rating label and the current load index, calculate the comprehensive score, and sort and output the recommendation list.

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