A method and operation system for matching supply and demand of housekeeping service based on multi-parameter quantification model

Abstract

The application discloses a kind of housekeeping service supply and demand matching method and operating system based on multi-parameter quantification model, collection business data, order interaction data, settlement data, account risk control data, signing robot service ability data and cloud warehouse resource data, order demand information is standardized, and order characteristic data and service execution option analysis result are formed;According to service adaptation, performance, security and privacy constraints, the initial screening is carried out on service personnel, and when the candidate is insufficient, start hierarchical shared routing;Combined with desensitization direct dialogue, event state diagram, covariance constraint matching, confirmation entropy correction and security risk fusion generate posterior matching probability;Then output recommended result, when suitable for robot execution, synchronize robot service option output, and dynamically adjust cashing amount, solve the problem that supply and demand matching accuracy is insufficient, shared scheduling is disordered and performance and settlement risk control are disconnected.

Description

Technical Field

[0001] This invention relates to the field of supply and demand matching for domestic services, specifically to a method and operating system for supply and demand matching of domestic services based on a multi-parameter quantitative model. Background Technology

[0002] With the continuous development of the domestic service industry, users' demand for various types of services, such as cleaning, nannies, maternal and infant care, companionship, in-home services, and temporary jobs, is constantly increasing. Platform-based matching is gradually becoming the main way to connect the supply and demand of domestic services. When distributing orders and recommending service personnel, existing domestic service platforms usually rely on keyword search, regional filtering, manual order assignment, or single rating ranking to complete the initial matching. Although these methods can achieve a certain degree of rapid connection between supply and demand, they often struggle to ensure matching accuracy, sufficient communication and confirmation, stable performance, and subsequent financial security when order requirements are complex, service content varies greatly, performance conditions are changeable, and risk control requirements are high.

[0003] Specifically, domestic service orders typically include a variety of information such as service category, service time, service area, budget range, type of service recipient, additional needs, and privacy restrictions. However, when actually entering orders, the demand side often uses colloquial, unstructured, or incomplete expressions, making it difficult for the platform to accurately extract key fields and establish a unified matching basis. On the other hand, in addition to basic skill tags, service personnel are also affected by factors such as service radius, available service time, current workload, agency support capabilities, historical performance, and the degree of communication and confirmation. If recommendations are made solely based on static tags, it is easy to encounter problems such as superficial matching but insufficient actual capacity, repeated service confirmations, or unstable performance after signing the contract.

[0004] In addition, existing housekeeping platforms typically only match and recommend human service providers. For tasks that are highly standardized, repetitive, have clear pre-set processes, or are more suitable for robots to perform, there is a lack of a unified identification, configuration, and recommendation mechanism, making it difficult to make a reasonable choice between human and robot services based on actual needs.

[0005] Furthermore, existing platforms typically lack clear hierarchical routing mechanisms for shared scheduling and expanded matching. This can lead to situations where the search scope is directly expanded when local candidates are insufficient, resulting in increased candidate numbers but excessively high arrival costs, unstable cross-institutional connections, or insufficient guarantee capabilities. Meanwhile, while many platforms offer online communication functions, they often treat them merely as ordinary communication tools. They fail to transform processes such as confirming service time, service content, budget, and additional conditions into effective data that can be used to correct matches. This results in a disconnect between communication and recommendation results, failing to fully leverage the role of communication confirmation in improving matching accuracy.

[0006] Furthermore, the domestic service industry, from order generation, onboarding review, online signing, service execution, evaluation feedback, complaint handling to settlement and withdrawal, actually constitutes a continuous process chain. However, existing technologies often handle recommendations, fulfillment, complaints, and withdrawal risk control separately, lacking a unified data loop. This makes it difficult for the platform to promptly adjust recommendations and withdrawal controls based on abnormal statuses during the service process, unresolved complaints, account behavior risks, and settlement consistency. Consequently, fulfillment management and fund control become disconnected, affecting the platform's operational stability and user experience.

[0007] In view of the above problems, a method and operating system for matching the supply and demand of domestic services based on a multi-parameter quantization model are proposed. Summary of the Invention

[0008] The purpose of this invention is to provide a method and operating system for matching the supply and demand of domestic services based on a multi-parameter quantization model, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for matching the supply and demand of domestic services based on a multi-parameter quantification model, comprising collecting business data related to supply and demand matching, institutional guarantee data, order interaction data, settlement data, account risk control data, contracted robot service capability data, and cloud warehouse resource data; standardizing the order demand information to form order feature data; and generating service execution option analysis results based on the order feature data, contracted robot service capability data, and cloud warehouse resource data; initially screening service personnel according to service adaptation constraints, performance constraints, guarantee constraints, and privacy constraints to obtain an initial candidate set; when the candidate size or overall matching quality of the initial candidate set is lower than preset conditions, a hierarchical shared routing is initiated to expand the matching of candidate service personnel according to the order of the institution, the region, and the cross-institutional shared resource pool to form shared routing data; and establishing an anonymized direct dialogue channel within the platform between the demand side and the candidate service personnel to extract demand confirmation events and interaction behavior features to form dialogue confirmation data. The system records and associates status nodes during the onboarding review, online order signing, service execution, evaluation feedback, complaint handling, and settlement processing processes to construct an event state diagram and form a closed-loop data flow. Based on order feature data, shared routing data, dialogue confirmation data, and closed-loop data flow, a multi-parameter quantitative model is constructed. Through covariance constraint matching, confirmation entropy correction, and the fusion of guarantee path and risk information, the posterior matching probability of each candidate service provider is obtained. Recommendation results are output based on the posterior matching probability and service execution option analysis results. When conditions are met, phased information release and online order signing are executed. Specifically, when the service execution option analysis results indicate that the order demand is suitable for robot execution, robot service options are simultaneously output to the demand side for confirmation. After online order signing is completed, risk linkage control is implemented based on the platform service fee validity status, commission disabling status, settlement consistency information, and account risk control data to dynamically adjust withdrawal limits. Limit, freeze, and unfreeze controls are implemented when the risk reaches the corresponding threshold.

[0010] This invention also provides a domestic service supply and demand matching operating system based on a multi-parameter quantification model, including a data acquisition module, a standardization processing module, an initial screening module, a shared routing module, a dialogue confirmation module, a process construction module, a matching calculation module, a result control module, and a risk control processing module. The data acquisition module collects business data related to supply and demand matching, institutional support data, order interaction data, settlement data, account risk control data, contracted robot service capability data, and cloud warehouse resource data. The standardization processing module standardizes order demand information to form order feature data and generates service execution option analysis results. The initial screening module completes the selection of candidate service personnel. The initial screening and shared routing modules are used to perform hierarchical expansion matching of shared resource pools within the organization, the region, and across organizations. The dialogue confirmation module is used to extract the characteristics of demand confirmation events and interactive behaviors. The process construction module is used to construct an event state diagram. The matching calculation module is used to obtain the posterior matching probability of candidate service personnel based on a multi-parameter quantification model. The result control module is used to output recommendation results and control information release and online order signing. When the service execution option analysis results indicate that the robot is more suitable for execution, the module outputs robot service options for the demand side to confirm and select. The risk control processing module is used to perform withdrawal limit adjustment, limit control, account freezing, and account unfreezing based on settlement consistency information and account risk control data.

[0011] Preferably, the standardization processing of order demand information includes at least service category mapping, service sub-item extraction, service area identification, service time period identification, budget range extraction, service object type extraction, service frequency extraction, and additional demand field identification, and converts unstructured descriptions into order feature data that can be used for calculation and constraint comparison.

[0012] Preferably, the initial screening is based at least on service category consistency, service area accessibility, service time period overlap, service load status, institutional guarantee capacity satisfaction, and information openness level compatibility, so as to eliminate candidate service personnel who do not meet the basic performance conditions before matching calculation.

[0013] Preferably, the hierarchical shared routing adopts a step-by-step expansion method. When the number of candidates within the candidate range of the organization is lower than a preset number threshold or the overall matching quality is lower than a preset quality threshold, the matching is expanded to the candidate range within the region to which the order belongs. When the expanded candidate results within the region still do not meet the preset matching conditions, the matching is further expanded to the cross-organization shared resource pool.

[0014] Preferably, the dialogue confirmation data includes at least the following demand confirmation events: service time confirmation, service location confirmation, service content confirmation, service duration confirmation, budget range confirmation, and additional conditions confirmation, as well as the first response duration, number of effective interactions, consistency of confirmation information before and after, and completeness of message logs.

[0015] Preferably, the closed-loop process data is formed by associating the status nodes in the onboarding review, online order signing, service execution, evaluation feedback, complaint handling, and settlement process with time and objects, and is used to characterize the service process integrity, processing responsiveness, and anomaly traceability.

[0016] Preferably, the multi-parameter quantification model includes at least three calculation stages: basic adaptation value acquisition, confirmation correction, and risk fusion. The basic adaptation value is obtained through the difference constraint relationship between the demand feature vector and the service feature vector. The confirmation correction corrects the basic adaptation value through the degree of effective confirmation of the demand confirmation event and the degree of communication consistency. The risk fusion generates the posterior matching probability by jointly generating the guarantee path information and the account risk control information.

[0017] Preferably, the risk linkage control includes at least the generation of direct settlement control value, the generation of risk linkage value, and the calculation of the daily withdrawable amount, and performs limited withdrawal, account freezing, and account unfreezing operations based on the relationship between the risk linkage value and the preset risk threshold.

[0018] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention transforms order requirements, service personnel capabilities, institutional guarantees, dialogue confirmation, process closure, and account risk control into computable data. It first completes standardized order processing and initial screening, then combines hierarchical shared routing, anonymized direct dialogue, confirmation entropy correction, guarantee path analysis, and risk linkage control to generate a post-hoc matching probability. Based on this, it outputs recommendation results, releases control information, and dynamically adjusts withdrawal limits. This allows the platform to improve the accuracy of supply and demand matching while also considering recommendation response speed, order conversion rate, fulfillment stability, and fund control security. Furthermore, it can continuously correct matching results based on candidate size, communication confirmation level, guarantee strength, and risk status, avoiding mismatches, repeated communication, unstable fulfillment, and disconnected settlement risk control caused by relying solely on static tags, human experience, or single scoring. This solves the problems of insufficient accuracy in domestic service supply and demand matching, disordered shared scheduling, insufficient utilization of process confirmation, and separation of fulfillment and withdrawal control in existing technologies. It also provides robot service options in scenarios suitable for robot execution, expanding service execution methods and improving the processing efficiency of standardized, repetitive, and pre-set process tasks. Attached Figure Description

[0019] Figure 1 This is an overall block diagram of the operating system of the present invention;

[0020] Figure 2 This is a flowchart of the method of the present invention;

[0021] Figure 3 This is a flowchart illustrating the standardized order processing and initial screening procedures of this invention.

[0022] Figure 4 This is a flowchart of the hierarchical shared routing process of the present invention;

[0023] Figure 5 This is a flowchart of the dialogue confirmation and posterior matching probability generation process of the present invention;

[0024] Figure 6 This is a flowchart for the construction of the event state diagram and the risk linkage control of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figure 1-6 This invention provides a technical solution: a method and operating system for matching supply and demand in domestic services based on a multi-parameter quantitative model. The platform operates primarily along the lines of order standardization, candidate screening, shared expansion, dialogue confirmation, closed-loop recording, post-matching, and settlement risk control linkage. On the demand side, order information is entered through applications, mini-programs, web pages, or management terminals. Service personnel and agencies complete onboarding, order acceptance, communication, contract signing, fulfillment, and settlement through corresponding ports. The platform server continuously collects business data, guarantee data, order interaction data, settlement data, and account risk control data, and performs structured processing, constraint judgment, and dynamic calculation on a unified data foundation. Unlike methods that rely solely on keyword retrieval, manual recommendations, or single rating values, this invention does not directly perform a rough ranking of all service personnel. Instead, it first transforms order expression, personnel capabilities, agency guarantees, communication processes, and financial behavior into calculable data objects. Then, through multi-stage constraint contraction and multi-parameter fusion calculation, it obtains the post-matching probability, thereby improving matching accuracy, contract signing success rate, and subsequent contract fulfillment stability.

[0027] Specifically, during the order entry stage, the platform first preprocesses the text, options, and behavioral data submitted by the demand side to form order feature data, and simultaneously collects service capability data, service scope data, service time data, performance status data, guarantee-related data, and account risk control data from the service personnel side.

[0028] The order feature data includes at least the fields of service category, service sub-item, service area, service time period, budget range, service object type, service frequency, and additional requirements. The service category is uniformly mapped to one of the following: cleaning service, nanny service, maternal and infant service, casual labor, door-to-door service, and agency franchise. Then, corresponding sub-item tags, constraint tags, and privacy tags are generated, thereby transforming the original information that was originally scattered on the demand side, service personnel side, institution side, and account side into a unified data object that can be compared, constrained, and calculated.

[0029] Subsequently, the platform conducts an initial screening of service personnel based on service adaptation constraints, performance constraints, guarantee constraints, and privacy constraints to obtain an initial candidate set. When the candidate size or overall matching quality of the initial candidate set is lower than preset conditions, it initiates hierarchical shared routing in the order of shared resource pools within the organization, within the region, and across organizations, forming shared routing data. On this basis, the platform establishes an anonymized direct dialogue channel between the demand side and the candidate service personnel, extracts demand confirmation events such as service time confirmation, service location confirmation, service content confirmation, service duration confirmation, budget range confirmation, and additional conditions confirmation, and combines them with the first response time, the number of effective interactions, the consistency of confirmation information before and after, and the completeness of message traces to form dialogue confirmation data.

[0030] After generating order feature data, the platform can also combine contracted robot service capability data and cloud warehouse resource data to generate service execution option analysis results for the work content in the order requirements that is suitable for robots to perform. In the result output stage, the robot service option is displayed to the demand side as an option alongside the candidate service personnel recommendation results, so that the demand side can confirm whether to select the robot service.

[0031] The platform further unifies and records the status nodes in the processes of onboarding review, online order signing, service execution, evaluation feedback, complaint handling, and settlement processing, constructing an event status diagram between orders, customers, service personnel, institutions, and accounts to form closed-loop process data. Based on order feature data, shared routing data, dialogue confirmation data, and closed-loop process data, a multi-parameter quantitative model is constructed. The basic adaptation value M0 is obtained through covariance constraint matching, and the corrected adaptation value M1 is obtained through confirmation entropy correction. The guaranteed routing value G0, risk value R0, and posterior matching probability P0 are generated by fusing the guaranteed path and risk information. The recommendation result is then output based on the posterior matching probability, and phased information release and online order signing are executed when the conditions are met.

[0032] Furthermore, in the outcome control phase, in addition to outputting candidate service personnel recommendations, the platform also analyzes service execution options to determine whether to simultaneously output robot service options. When the service execution option analysis indicates that there are tasks more suitable for robot execution within the order requirements, the outcome control module displays the robot service options alongside the candidate service personnel recommendations to the demand side, allowing the demand side to confirm whether to select the robot service. After the demand side selects the robot service, the platform, based on the contracted robot service capability data and cloud warehouse resource data, enters the corresponding robot execution process; the robot execution process includes at least one of the following: robot entry, installation, operation management, maintenance, or inventory transfer. If the demand side does not select the robot service, the platform continues to execute subsequent information release, online order signing, and fulfillment processes according to the candidate service personnel recommendations. In this way, the platform does not treat robot services as an additional function independent of the supply and demand matching of domestic services, but rather as a type of service execution option formed after further analysis of order requirements, incorporating it into the outcome control phase along with the candidate service personnel recommendations, thereby improving the matching flexibility and execution efficiency of standardized, repetitive, and pre-set process-based tasks.

[0033] After the online order is signed, the platform combines the platform service fee validity status, commission disabling status, settlement consistency information and account risk control data to generate the direct settlement control value Z0, risk linkage value R1 and the daily withdrawal limit Q1, and then implements limit withdrawal, account freezing and account unfreezing control based on the risk threshold.

[0034] Compared to matching methods that rely solely on static tags or single scoring values, this invention utilizes core calculation results such as the basic adaptation value M0, the corrected adaptation value M1, the guaranteed routing value G0, the risk value R0, the posterior matching probability P0, and the withdrawal limit Q1. This allows the matching process to simultaneously consider the order demand itself, the service personnel's capabilities, the strength of institutional guarantees, the quality of communication and confirmation, and the account's risk status. Consequently, it improves the overall accuracy of supply and demand matching, the order conversion rate, the stability of contract fulfillment, and the security of fund control.

[0035] Specifically, when standardizing order demand information in S1, the platform first receives raw order data entered by the demand side through applications, mini-programs, web pages, or management terminals. The raw order data can be a combination of structured options, semi-structured forms, and natural language descriptions. The platform converts the raw order data into a unified order raw record package and then splits and extracts it according to service category, service sub-item, service area, service time period, budget range, service object type, service frequency, and additional demand fields.

[0036] For content entered in the form of text descriptions, the colloquial content is first transformed into standard fields that can be used for subsequent calculations through keyword dictionary matching, synonym merging, time expression normalization, address area mapping, and amount range conversion. Among them, the service category is preferentially mapped to one of the six main business tags: cleaning service, nanny service, maternal and infant service, casual labor, door-to-door service, and agency franchise. Then, the corresponding service sub-item tags are extracted under the main business tags.

[0037] Among them, cleaning services can be categorized into daily cleaning, deep cleaning, hourly cleaning, and post-construction cleaning; nanny services can be categorized into live-in nannies, day-shift nannies, and caregivers; maternal and infant services can be categorized into postpartum nannies, childcare nannies, and postpartum care; odd jobs and casual work can be categorized into temporary moving, temporary purchasing, short-term care, and errand running; in-home services can be categorized into in-home cooking, in-home cleaning, in-home cleaning, medical accompaniment, and daily care; and agency / franchise services are used to mark order requests with institutional access, business franchising, or operational cooperation attributes. After completing the mapping of the main business tags, the platform continues to generate service sub-item tags, constraint tags, and privacy tags by combining the service recipient, service action, service duration, and additional requirements, thereby ensuring that order request information can not only be categorized but also enter subsequent constraint comparison and matching calculations.

[0038] For orders with ambiguous mapping results, the platform determines the main business tag according to the rule of "service object first, service action second, service duration third" to avoid the same order being classified into multiple categories at the same time, which would affect the subsequent screening results.

[0039] Furthermore, the platform verifies the completeness and consistency of the order fields and generates a standardized order value B0. Let the total number of target fields specified in the template be N0, the number of valid fields directly identified be N1, the number of completed fields obtained through rule completion be N2, and the number of fields that still have conflicts or ambiguities be N3. Then the standardized order value B0 satisfies: B0 = (N1 + 0.8N2) / (N0 + N3 + 1).

[0040] Among them, N1 is used to reflect the clarity of the original order information, N2 is used to reflect the platform's ability to complete the information, and N3 is used to reflect the degree of unresolved conflicts in the order.

[0041] When B0 is greater than or equal to the preset standardized threshold, the order is determined to be able to directly enter the subsequent screening calculation; when B0 is lower than the preset standardized threshold but higher than the supplementary entry trigger threshold, the platform sends a secondary confirmation guide to the demand side, requiring the supplementation of service period, budget range, service object type or additional demand fields;

[0042] When B0 falls below the supplementary entry trigger threshold, the platform pauses candidate generation and retains only the order draft status. To improve the accuracy of the main business tag mapping, the platform also generates a business classification confidence value C0. Let the keyword matching score be U1, the semantic similarity score be U2, and the rule consistency score be U3. Then the business classification confidence value C0 satisfies: C0 = δ1U1 + δ2U2 + δ3U3, where δ1, δ2, and δ3 are weight coefficients, and δ1 + δ2 + δ3 = 1.

[0043] The platform selects the category with the highest confidence value among the six main business tags as the final service category tag, and writes it together with the sub-item tags, constraint tags, and privacy tags into the order feature data.

[0044] Furthermore, order standardization processing goes beyond simply generating labels; it aims to create order feature data that can be directly used for subsequent constraint comparisons and matching calculations.

[0045] After determining the main business tags and sub-item tags, the platform also simultaneously generates service area constraint tags, service time constraint tags, budget range constraint tags, service recipient constraint tags, and privacy level tags. Among them, the service area constraint tag is used to limit the service scope of candidate service personnel, the service time constraint tag is used to determine whether it overlaps with the available order time, the budget range constraint tag is used to determine the suitability of the price, the service recipient constraint tag is used to identify whether it involves the care of the elderly, patients, pregnant women, or infants, and the privacy level tag is used to determine the openness level of order address, contact information, and family information before, during, and after communication.

[0046] For agency and franchise orders, the platform also generates agency matching tags, franchise level tags, and operational support tags, giving them priority in entering the agency candidate range in subsequent processes; for on-site service orders, the platform adds arrival time tags and on-site condition tags to make more accurate judgments on accessibility and fulfillment timeliness in the future.

[0047] After the above processing, the originally scattered, vague, and colloquial order requirements are uniformly transformed into order feature data with category labels, sub-item labels, constraint labels, and privacy labels, thus providing a consistent data foundation for subsequent initial screening, hierarchical shared routing, dialogue confirmation correction, and posterior matching calculation.

[0048] Furthermore, after generating order characteristic data, the platform also combines contracted robot service capability data and cloud warehouse resource data to generate service execution option analysis results. Contracted robot service capability data includes at least robot type, work categories, standardized operation capabilities, repetitive operation capabilities, installation capabilities, operation control capabilities, maintenance capabilities, and inventory transfer capabilities; cloud warehouse resource data includes at least robot inventory status, warehouse location, availability status, delivery range, installation and maintenance support status, and inventory transfer route information. Based on the service category, service sub-item, additional requirements, on-site conditions, timeliness requirements, and execution standards in the order characteristic data, the platform identifies and analyzes standardized work, repetitive work, pre-defined process work, installation work, control work, maintenance work, or inventory transfer work suitable for robot execution in the order requirements, and generates service execution option analysis results. The service execution option analysis results include at least candidate service personnel recommendation options and robot service options; when the order requirement is more suitable for human completion, the platform prioritizes retaining candidate service personnel recommendation options; when the order requirement contains work content suitable for robot execution, the platform simultaneously generates robot service options in addition to candidate service personnel recommendation options for output in the subsequent result control stage.

[0049] Furthermore, the configuration methods for robot services include at least robot type configuration, functional module configuration, and service scenario configuration. Robot type configuration allows selection of one or more of the following based on order requirements: cleaning robot, handling robot, delivery robot, inspection robot, installation assistance robot, maintenance assistance robot, or composite service robot. Functional module configuration allows loading cleaning modules, grasping and handling modules, path navigation modules, remote communication modules, image recognition modules, status monitoring modules, installation assistance modules, or maintenance assistance modules based on order requirements. Service scenario configuration determines the applicable indoor environment, building environment, shop environment, warehouse environment, or community environment for the robot based on order requirements. The platform combines the service category, service sub-item, execution frequency, operation standards, operation duration, on-site conditions, and timeliness requirements from the order characteristic data to determine the applicability of the robot service. Among these, work content with high standardization, high repetition, clear preset processes, fixed manual operation steps, and that can be completed by a preset program or supported by remote control is prioritized for robot execution. The platform also combines contracted robot service capability data and cloud warehouse resource data to judge the feasibility of robot execution. Feasibility includes at least robot type availability, functional module matching, on-site accessibility, battery life or power supply, installation and maintenance support accessibility, and cloud warehouse allocation timeliness. Robot service options are only generated in the service execution option analysis results when the applicable scope and feasibility of robot execution meet the preset conditions.

[0050] Furthermore, to improve the accuracy of converting order demand information from natural language to structured tags, the platform sets up a lightweight semantic understanding model port in the order standardization process. This model port is used to receive text descriptions, speech-to-text transcriptions, and supplementary notes input from the demand side, and performs semantic decomposition and weight classification on the service actions, service objects, labor intensity, duration, working environment, and special requirements involved.

[0051] For natural language fields, the model port first identifies the main service action, and then performs secondary subdivision by combining modifiers, scene words and object words to generate task labels and intensity labels;

[0052] For example, when "cleaning" appears in an order, the platform further identifies whether it includes floor cleaning, kitchen grease removal, bathroom deep cleaning, glass wiping, and whether tools are required, and generates a cleaning task intensity value Iq.

[0053] When the order includes "caring for the elderly's daily life", the platform further identifies whether it includes content such as assisting with getting up, toileting, feeding and administering medication, wheelchair transfer, rehabilitation walking assistance, or nighttime care. It also generates a care task intensity value Ih based on factors such as the age, mobility, self-care ability, weight range, presence of underlying diseases, and whether nighttime response is required.

[0054] For orders containing multiple tasks, the platform combines the intensity values ​​of each sub-task to calculate the overall task intensity value It, which satisfies: It = λ1Iq + λ2Ih + λ3Ic + λ4If + λ5Ie, where Iq is the cleaning task intensity value, Ih is the care task intensity value, Ic is the cooking task intensity value, If is the carrying or physical assistance task intensity value, Ie is the nighttime response or special environment additional intensity value, and λ1, λ2, λ3, λ4 and λ5 are the corresponding task weight coefficients.

[0055] In this way, the platform can transform vague, colloquial order descriptions into strength parameters that can participate in subsequent matching calculations, thereby improving the accuracy of order demand understanding and processing efficiency.

[0056] Furthermore, the lightweight semantic understanding model port is preferably deployed as an edge inference interface or a cloud microservice interface. Its inputs are order text fields, historical correction fields, and user supplementary confirmation fields, and its outputs are main business category labels, service sub-item labels, task intensity labels, and special constraint labels.

[0057] To avoid overly rigid recognition due to reliance on fixed rules, the platform fuses the rule dictionary matching results with the output of a small semantic classification model to generate a field understanding confidence value V0. V0 satisfies: V0 = (r1K0 + r2S0 + r3A0) / (r1 + r2 + r3), where K0 is the rule dictionary matching score, S0 is the semantic classification model output score, A0 is the historical manual correction consistency score, and r1, r2, and r3 are fusion weight coefficients.

[0058] When V0 is higher than the preset understanding threshold, the platform directly uses the main business category and sub-item labels output by the model; when V0 is lower than the preset understanding threshold but higher than the review threshold, the platform sends a secondary confirmation prompt to the demand side to ask supplementary questions about ambiguous fields in time, location, budget, service object and special requirements; when V0 is lower than the review threshold, the platform marks the order as a low-confidence order and suspends it from entering the subsequent matching process, thereby improving processing efficiency while ensuring that the field understanding results do not deviate from the actual business logic.

[0059] Furthermore, to improve the compatibility between service personnel and orders in terms of physical strength, endurance, and psychological resilience, the platform sets up an encrypted service capability collection module when service personnel join or when their information is updated periodically. This module authorizes the collection, encrypted storage, and quantification of information on the basic physical condition, work capacity, and psychological stability of service personnel.

[0060] Basic physical conditions include at least age range, height range, body type range, weight range, previous experience in heavy physical labor, sustainable working hours, and historical high-intensity order completion status on the platform. The labor endurance includes at least the carrying ability label, the ability to stand for long periods of time label, the ability to bend over frequently label, the tolerance for night service label, and the continuous service recovery label. Psychological stability information includes at least the communication stability, the amplitude of emotional fluctuations, the ability to cope with emergencies, the sensitivity to complaints, and the level of patience for continuous care.

[0061] To avoid directly exposing raw sensitive information, the platform does not display the service personnel's raw age, height, weight, and psychological test results on the front end. Instead, the relevant data is anonymized, segmented, and encrypted before being converted into physical fitness index Ep, endurance fitness index En, and psychological fitness index Es. Ep satisfies: Ep = m1B1 + m2B2 + m3B3 + m4B4; En satisfies: En = n1C1 + n2C2 + n3C3; and Es satisfies: Es = p1D1 + p2D2 + p3D3 + p4D4. In the table, B1 is the body size carrying capacity label value, B2 is the handling capacity label value, B3 is the sustainable working hours label value, B4 is the historical high-intensity order completion rate, C1 is the continuous work tolerance label value, C2 is the night service tolerance label value, C3 is the recovery efficiency label value, D1 is the communication stability label value, D2 is the emotional fluctuation control label value, D3 is the emergency response label value, and D4 is the long-term care patience level value. m1, m2, m3, m4, n1, n2, n3, p1, p2, p3, and p4 are all weighting coefficients.

[0062] In the subsequent matching stage, the platform does not directly call the original physical information and psychological test results of service personnel. Instead, it calls Ep, En and Es to participate in constraint judgment and matching calculation. This improves the matching satisfaction of service personnel and demand side in terms of actual labor intensity and service experience while protecting sensitive personal information.

[0063] Furthermore, after calculating the overall task intensity value It for an order, the platform can perform pre-adaptation calculations between It and the physical fitness index Ep, endurance fitness index En, and psychological fitness index Es on the service personnel side to generate a task acceptance fitness value At. At satisfies: At = μ1 × Ep + μ2 × En + μ3 × Es - μ4 × |It - Ep|, where μ1, μ2, μ3, and μ4 are weighting coefficients. The task acceptance fitness value At is used to preemptively eliminate service personnel who are clearly unable to undertake high-intensity care tasks, heavy physical cleaning tasks, or high-emotional-stress companionship tasks before subsequent initial screening.

[0064] For elderly care orders that include moving, nighttime response, and long-term care requirements, the platform increases the weight of Ih and Ie, and prioritizes screening service personnel with higher Ep, En, and Es scores. For orders that mainly involve regular hourly cleaning, the platform reduces the weight of endurance and psychological stress tolerance, and gives more consideration to regional accessibility and time overlap.

[0065] Through the above processing, the platform can make an initial judgment on the workload, stability requirements, and physical and psychological endurance needs during the order standardization stage, thereby significantly reducing mismatches in the subsequent matching stage and improving demand-side satisfaction and service personnel's contract fulfillment stability.

[0066] Specifically, when initial screening of service personnel in S2 based on service compatibility constraints, performance constraints, security constraints, and privacy constraints, the platform first retrieves the order feature data generated after processing in S1, and simultaneously reads the service personnel's service capability data, service scope data, service time period data, performance status data, security-related data, as well as the aforementioned physical fitness compatibility index Ep, endurance compatibility index En, psychological compatibility index Es, and task acceptance compatibility value At.

[0067] The initial screening does not directly rank all service personnel. Instead, it performs pre-filtering based on constraints, eliminating service personnel who clearly do not meet the order requirements before proceeding to complex matching calculations. This reduces subsequent computational load and improves the accuracy of the candidate set. The platform breaks down the initial screening into four levels: service compatibility constraint judgment, performance constraint judgment, security constraint judgment, and privacy constraint judgment. Only when the minimum requirements of all four levels are met will the corresponding service personnel enter the initial candidate set.

[0068] During the service compatibility constraint judgment phase, the platform first compares whether the main business category tag, service sub-item tag and service object tag of the order are consistent with the service capability tag and historical order acceptance tag on the service personnel side.

[0069] When the category, sub-item, or object is obviously mismatched, it is directly filtered. To avoid situations where the labels are consistent but the actual capacity is insufficient, the platform further combines the comprehensive task intensity value It obtained from S1 with the task acceptance adaptation value At on the service personnel side for adaptation judgment. At satisfies: At=μ1×Ep+μ2×En+μ3×Es-μ4×|It-Ep|, where μ1, μ2, μ3, and μ4 are weight coefficients;

[0070] When At is lower than the task acceptance threshold Tt corresponding to the current order, it indicates that although the service personnel are matched in terms of the surface business category, they are not strong enough in terms of physical strength, endurance or psychological capacity to reliably accept the current order, and the platform will remove them from the initial screening.

[0071] For orders involving tasks such as heavy physical cleaning, assisting bedridden elderly to turn over, responding to nighttime activities, providing continuous care, or engaging in high-frequency emotional communication, the platform increases the weight of the corresponding task item (It) and simultaneously increases (Tt) to prioritize retaining service personnel with higher task-acceptance suitability values ​​(At).

[0072] In determining service area accessibility, the platform generates an area accessibility value Ar based on the order service location, the service radius registered by the service personnel, historical order hotspots, and the mobile range.

[0073] Let Ld be the distance from the order service location to the service personnel's permanent service center, Rd be the service personnel's registered service radius, and Kd be the regional commuting congestion correction factor. Then the regional reachability value Ar satisfies: Ar = Rd / (Ld × Kd + 1).

[0074] When Ar is lower than the regional reachability threshold Tr, it indicates that the arrival cost is too high, the commuting stability is insufficient, or the service radius is exceeded, and the platform will filter out the corresponding service personnel.

[0075] When Ar reaches or exceeds Tr, the corresponding service personnel enter the next judgment stage. For on-site services and casual jobs, the platform can further increase the weight of regional accessibility to prioritize retaining service personnel who are geographically closer and more suitable for rapid response.

[0076] During the performance constraint assessment phase, the platform focuses on comparing the overlap between the order service time window and the service personnel's available time window, and conducts a comprehensive screening based on the current service load status and historical performance stability. Let the length of the order's required service time window be To, and the overlap length between the service personnel's available service time window and the order time window be Tp. Then the time overlap Ot satisfies: Ot = Tp / To.

[0077] When Ot is lower than the preset overlap threshold T0, it indicates insufficient time adaptation, and the platform directly filters it. Further, the platform retrieves the number of orders Nc that the service personnel have currently accepted, the current service duration Tc to be completed, and the average cancellation rate Xc in the recent period to generate a fulfillment stability value Ac, which satisfies: Ac=v1×Ot+v2×(1 / (Nc+1))+v3×(1 / (Tc+1))+v4×(1-Xc), where v1, v2, v3 and v4 are weighting coefficients.

[0078] When Ac is lower than the fulfillment threshold Ta, it indicates that although the service personnel may overlap in local time, their actual load is too high, their service continuity is insufficient, or their historical fulfillment is unstable. The platform will not include them in the initial candidate set, thereby avoiding situations where the time is barely matched but the actual fulfillment quality is poor.

[0079] During the constraint assessment phase, the platform reads the support capability data of the service personnel's affiliated or related institutions from the institution side, including accident response capability, backup deployment capability, complaint handling timeliness, regional support capability, and settlement cooperation capability, and converts it into a support capability value Ag.

[0080] Let G1 be the accident response capability score, G2 be the backup deployment capability score, G3 be the complaint handling timeliness score, G4 be the regional support capability score, and G5 be the settlement coordination capability score. Then the guarantee capability value Ag satisfies: Ag = s1×G1 + s2×G2 + s3×G3 + s4×G4 + s5×G5, where s1, s2, s3, s4, and s5 are weighting coefficients. For general cleaning and short-term casual labor orders, Ag only needs to reach the basic guarantee threshold.

[0081] For orders related to elderly care, maternal and infant companionship, patient assistance, and long-term home care, the platform has increased the coverage threshold Tb. Service providers will only be included in the initial candidate set if Ag is greater than or equal to Tb. If a service provider is part-time and not affiliated with an organization that can provide accident sharing or emergency replacement, and the order carries a high care risk or requires continuous service, the service provider will be filtered out due to insufficient coverage, even if their service capability tags match.

[0082] During the privacy constraint assessment phase, the platform compares the order privacy level label with the information openness level settings on the service personnel side for compatibility.

[0083] Order privacy levels can be divided into three levels: basic open, controlled open, and highly sensitive restricted. The information openness level on the service personnel side corresponds to the scope of openness for viewing address accuracy, contact information accuracy, family member information, and special health information.

[0084] The platform sets the order privacy requirement level to Lo, the service personnel's current access level to Lp, and the privacy compatibility value Ap satisfies: Ap = 1 - |Lo - Lp| / Lmax, where Lmax is the maximum level difference set by the platform.

[0085] When Ap is lower than the privacy compatibility threshold Tp, the platform believes that the two parties were unable to complete an effective exchange of information without violating the privacy control rules during the initial communication process, and therefore will not include the corresponding service personnel in the initial candidate set.

[0086] When Ap reaches or exceeds Tp, the platform allows it to enter the subsequent candidate range, and further releases information step by step through de-identified direct dialogue in the S4 stage, thereby ensuring that subsequent communication is carried out within the controllable information boundary and avoiding the leakage of too much sensitive information before the candidates are stable.

[0087] In terms of identifying the employment status of service personnel, the platform further combines order activity, continuous service duration, institution binding status, insurance coverage status, and historical performance to distinguish between full-time and part-time service personnel.

[0088] Let H1 be the weekly order duration, H2 be the number of consecutive active days, H3 be the institutional binding strength, H4 be the guarantee coverage level, and H5 be the historical default rate. Then the employment status identification value Ps satisfies: Ps=k1×H1+k2×H2+k3×H3+k4×H4-k5×H5, where k1, k2, k3, k4 and k5 are weighting coefficients.

[0089] When Ps is higher than the first identification threshold, the platform marks the service personnel as full-time service personnel; when Ps is between the first and second identification thresholds, they can be marked as stable part-time service personnel; when Ps is lower than the second identification threshold, they are marked as ordinary part-time service personnel.

[0090] During the initial screening phase, the platform categorizes service personnel into full-time and part-time service providers and sets different thresholds based on the level of protection and continuous service intensity required for the order. For example, full-time service providers are given priority for continuous care orders, while part-time service providers who meet the regional and time period requirements can be retained for high-frequency, short-time orders.

[0091] After completing the above four types of constraint judgments, the platform generates an initial screening result value A0. Let the service adaptation judgment result be E1, the performance judgment result be E2, the guarantee judgment result be E3, and the privacy judgment result be E4. When each judgment meets the condition, it is set to 1, and when the condition is not met, it is set to 0. Then A0 satisfies: A0 = E1 × E2 × E3 × E4.

[0092] The corresponding service personnel are included in the initial candidate set only when A0 equals 1; if any of the conditions are not met, the corresponding service personnel are filtered out.

[0093] To further evaluate the overall quality of the initial candidate set before entering S3, the platform can also calculate the average initial candidate quality Aq for the service personnel included in the set. Aq satisfies: Aq = (ΣAi) / N, where Ai is the comprehensive screening score of the i-th selected service personnel in terms of adaptation, performance, protection, and privacy, and N is the number of service personnel in the initial candidate set. When Aq is low or N is lower than a preset threshold, the platform triggers the hierarchical shared routing corresponding to claim 4.

[0094] Through the above screening method, the initial candidate set is no longer a simple tag retrieval result, but a high-quality candidate set after being constrained by order task intensity, physical endurance and psychological adaptation, regional accessibility, performance load, institutional protection and privacy compatibility, thus providing more stable and accurate input data for subsequent sharing expansion, dialogue confirmation and post-matching calculation.

[0095] Specifically, when tiered shared routing is initiated in S3, the platform does not immediately expand the service personnel across the entire platform indiscriminately after the initial candidate set is formed. Instead, it first triggers a judgment on the candidate size and overall matching quality of the initial candidate set.

[0096] The platform sets the number of service personnel in the initial candidate set to N0, the average matching quality of the initial candidate set to Aq, the candidate quantity trigger threshold to Tn, and the matching quality trigger threshold to Tq. When N0 is less than Tn or Aq is less than Tq, it is determined that the current order cannot obtain a sufficiently stable matching result within the local candidate range, and the platform starts hierarchical shared routing.

[0097] When N0 is greater than or equal to Tn and Aq is greater than or equal to Tq, the platform continues to perform subsequent dialogue confirmation and post-hoc matching calculations within the current candidate range, without expanding the search scope. The platform can also adaptively adjust Tn and Tq based on the order's main business category, task intensity value It, and guarantee level requirements. For example, for orders related to elderly care, maternal and infant care, and continuous home-based services, Tq and Tn can be appropriately increased; for casual work, short-term in-home services, and general hourly cleaning services, Tq can be appropriately decreased while maintaining a low Tn, in order to improve matching efficiency while considering candidate quality.

[0098] In the first-level shared routing, the platform prioritizes expanding the candidate pool based on the candidate range corresponding to the service personnel's organization. This candidate pool includes available service personnel of the same category or similar sub-items as the current order, backup service personnel, and service personnel who have completed onboarding review but are currently below their service load limit. Within the organization's scope, the platform performs the same basic constraint review as in S2 on newly added service personnel entering the candidate pool, and calculates the number of newly available service personnel N1 after expansion, the overall quality average of the expanded candidate set A1, and the organization's expansion conversion rate U1. U1 satisfies: U1 = N1 / M1, where M1 is the total number of service personnel entering the organization's expanded search range. When A1 is greater than or equal to Tq and the number of candidates after merging the initial candidate set with the organization's expanded candidates reaches Tn, the platform stops expanding to the next level and uses the organization's expansion result as the first-level output of the shared routing data. When A1 is still lower than Tq or the number of candidates after merging is still lower than Tn, the platform proceeds to the second-level shared routing. The purpose of this approach is to prioritize the use of stable internal resources and existing management chains, minimize unnecessary cross-organizational scheduling, and thereby reduce subsequent service handover costs and management complexity.

[0099] In the second-level shared routing, the platform expands the scope to match candidate areas within the order's region. The order's region can be at the street level, district / county level, or a grid service area preset by the platform. Within this region, the platform calls upon service personnel of the same category, personnel awaiting assignment from nearby institutions, and service personnel with regional support permissions as the regional expansion candidate pool. At this level, the platform not only records the number of candidates but also focuses on recording the route distance cost D2, estimated arrival time cost T2, and regional guarantee adaptation value B2 after regional expansion. The route distance cost D2 is preferably formed by the distance between the service personnel's current location or permanent service center and the order's service location, commuting time, and route complexity. The estimated arrival time cost T2 is preferably formed by real-time traffic conditions, peak-hour correction coefficients, and historical arrival fluctuation coefficients. The regional guarantee adaptation value B2 is determined based on whether institutions within the region can provide accident support, backup services, complaint handling, and settlement assistance. To comprehensively evaluate the effectiveness of the expanded region, the platform calculates the regional shared routing value G2, which satisfies: G2 = c1 × U2 + c2 × B2 + c3 × 1 / (1 + D2) + c4 × 1 / (1 + T2), where U2 is the region expansion conversion rate, and c1, c2, c3, and c4 are weighting coefficients. When G2 is greater than or equal to the preset regional routing threshold Tg, and the candidate size or overall matching quality after region expansion meets preset conditions, the platform stops further expansion. When G2 is lower than Tg or the matching requirements are still not met after region expansion, the platform enters the third-level shared routing stage. By introducing D2 and T2 at this level, the platform can avoid situations where the number of candidates in the region increases but the actual arrival cost is too high, leading to untimely fulfillment.

[0100] In the third-level shared routing, the platform further extends to a cross-organizational shared resource pool. This pool consists of dispatchable service personnel, substitute service personnel, and emergency support personnel authorized and shared by multiple organizations. When the platform invokes this resource pool, it simultaneously extracts the cross-organizational hop count J3, the cross-organizational assurance connection value B3, the candidate conversion rate U3, and the cross-organizational comprehensive cost C3. The cross-organizational hop count J3 characterizes the number of scheduling levels an order goes through when switching from the current organization's link to another organization's link. The cross-organizational assurance connection value B3 characterizes the compatibility between the current order's requirements and the external organization's capabilities in accident sharing, substitute support, complaint handling, and settlement cooperation. The cross-organizational comprehensive cost C3 comprehensively considers cross-organizational communication costs, service handover costs, responsibility boundary confirmation costs, and remote scheduling costs. The platform calculates the cross-organizational shared routing value G3, which satisfies: G3 = d1 × U3 + d2 × B3 + d3 × 1 / (1 + C3) + d4 × 1 / (1 + J3), where d1, d2, d3, and d4 are weighting coefficients. When G3 reaches the cross-institutional access threshold Tc, the platform incorporates the cross-institutional candidate results into the shared routing data. When G3 remains below Tc, the platform can retain the order draft, but will lower the automatic recommendation priority and issue a candidate shortage warning to the demand side or guide adjustments to some constraints of the order. In this way, the cross-institutional shared resource pool is no longer an unconditional safety net, but is only activated under the premise of ensuring seamless connection and acceptable overall costs, thereby avoiding the introduction of subsequent performance and responsibility allocation risks simply to increase the number of candidates.

[0101] After completing the three-level shared routing, the platform merges the results of its own organization's expansion, regional expansion, and cross-organizational expansion to form a shared routing dataset Rg. The shared routing dataset Rg includes at least the expansion level identifier, the number of new candidates at each level, the conversion rate at each level, the route distance cost at each level, the estimated arrival time cost, the guaranteed fit value, the number of cross-organizational hops, and the comprehensive cross-organizational cost. To facilitate direct participation in matching calculations in S6, the platform further normalizes the shared routing data to generate a comprehensive shared routing value Gr. Let the institution's routing value be G1, the regional shared routing value be G2, and the cross-institutional shared routing value be G3. Then, the comprehensive shared routing value Gr satisfies: Gr = r1 × G1 + r2 × G2 + r3 × G3, where r1, r2, and r3 are hierarchical weight coefficients, dynamically adjusted according to order type, task intensity value It, and guarantee level requirements. For high-risk care orders, the platform increases r1 and r2 and decreases r3 to prioritize more stable candidates within the institution and region. For on-site service orders with high timeliness requirements but low labor intensity, the platform can appropriately increase r2 and decrease r1 to improve rapid response capabilities within the region. For agency / franchise or special orders where institutional resources are scarce and sharing and collaboration are required, the platform can increase r3 while ensuring sufficient connection to make fuller use of the cross-institutional shared resource pool. Through the above methods, shared routing is no longer just a functional action of "whether or not there is sharing," but is transformed into quantifiable, comparable, and directly applicable data input for posterior matching probability calculation, thereby improving the rationality and feasibility of the final recommendation results.

[0102] Specifically, when extracting demand confirmation events and interaction behavior features in S4, the platform establishes an anonymized direct dialogue channel between the demand side and candidate service providers. This anonymized direct dialogue channel does not directly reveal complete contact information and detailed address details. Instead, it first reveals necessary fields according to the order's privacy level and the current matching stage. This allows the demand side to see the service category, service experience, service area, available service time period, and some capability tags. Service providers can see the service category, order area, service time period, budget range, and an overview of additional needs. Precise addresses, complete contact information, detailed health information of the service recipient, and sensitive family information remain hidden until preset communication and confirmation conditions are met. In this way, the platform can control the upfront exposure of sensitive information while ensuring effective communication, and ensure that subsequent demand confirmation behavior is conducted within controlled data boundaries.

[0103] During the conversation, the platform uniformly collects text messages, voice transcriptions, structured confirmation button operations, and system pop-up confirmation results from both parties. The original interaction content is first cleaned, removing greetings, repetitive phrases, invalid emojis, irrelevant chatter, and non-task-related links, retaining only service time, service location, service content, service duration, budget range, and additional conditions directly related to order fulfillment. The platform then segments the retained interaction data into several confirmation event units, each including at least the event type, event initiator, event time, event content, event confirmation status, and related fields. The event type includes at least service time confirmation, service location confirmation, service content confirmation, service duration confirmation, budget range confirmation, and additional conditions confirmation. Additional conditions confirmation may further include whether the client brings their own tools, whether they accept additional services, whether they require caregiver experience, whether they accept nighttime service, whether they can be moved or assisted, and any special considerations for specific service recipients. For the same field confirmation content that appears repeatedly in the same order, the platform generates an event sequence in chronological order so that it can identify whether the confirmations are consistent and whether there are repeated modifications.

[0104] To improve the accuracy of demand confirmation event extraction, the platform uses a three-tiered approach—rule triggering, semantic recognition, and contextual reference—to determine the content of conversations. Rule triggering primarily identifies clearly structured confirmation expressions such as "what time will you arrive?", "how many hours will you work?", "can you cook?", "do you need to bring tools?", and "what is your budget?". Semantic recognition primarily identifies colloquial expressions such as "coming in the morning is fine", "helping the elderly to the toilet", "the kitchen needs to be greasy", and "it would be best if you can take care of bedridden patients". Contextual reference primarily identifies content omitted in the preceding and following sentences but actually referring to the same field. For service time confirmation, the platform can extract time windows from content such as "tomorrow morning from 8:00 to 11:00" or "Saturday afternoon after 2:00." For service location confirmation, the platform can extract regional features from content such as "near Tiexi Square," "five minutes from the subway station," or "in a residential compound next to a hospital." For service content confirmation, the platform can extract service action combinations from content such as "tidying the bedroom and kitchen," "helping the elderly wash and feed themselves," or "picking up children from school and cooking dinner." The same structured extraction method is used for service duration confirmation, budget range confirmation, and additional condition confirmation. After the above processing, the platform forms a set of demand confirmation events Ce categorized by event type, providing raw input for subsequent confirmation coefficient calculation and confirmation entropy calculation.

[0105] In addition to request confirmation events, the platform also extracts interactive behavior features to measure the communication quality and response reliability between candidate service personnel and the requesting party. Interactive behavior features include at least the initial response duration, the number of effective interactions, the consistency of confirmation information before and after the request, and the completeness of message logs. The initial response duration characterizes the speed at which service personnel respond to order communication requests. The platform denotes the time when the requesting party sends its first valid inquiry as t0, and the time when the service personnel make their first response directly related to the order as t1. The initial response duration Tr satisfies: Tr = t1 - t0. The number of effective interactions characterizes the number of effective rounds of communication between the two parties regarding key order fields. The platform records each interaction that changes the event status from "unconfirmed" to "preliminary confirmation" or from "preliminary confirmation" to "definite confirmation" as a valid interaction, accumulating the total number of valid interactions Ne. Consistency of confirmation information before and after is used to determine whether the same field remains stable in multiple rounds of dialogue. Let Ns be the number of times the confirmation results for the same field are consistent and Nc be the number of times they are inconsistent. Then the field consistency value Yc satisfies: Yc = Ns / (Ns + Nc + 1). The platform takes a weighted average of the consistency values ​​of all key fields to obtain the overall consistency degree Y of confirmation information before and after. Message trace completeness is used to measure whether the dialogue process forms a complete record within the platform. Let Nq be the total number of key confirmation events and Nr be the number of events that are completely recorded by the platform and can be traced back. Then the message trace completeness L satisfies: L = Nr / (Nq + 1). Through the joint extraction of Tr, Ne, Y, and L, the platform no longer judges the communication status solely based on "whether there was a chat," but forms a data set that can be directly used for subsequent matching and correction.

[0106] After the characteristics of demand confirmation events and interaction behaviors are formed, the platform further generates dialogue confirmation data (Cd). Dialogue confirmation data (Cd) includes at least the event category number, the number of field confirmations, the number of valid confirmations, the number of conflicting confirmations, the initial response duration, the number of valid interactions, the field consistency value, and the message trace completeness, and is stored in association by order number, candidate service personnel number, and time sequence. For each type of demand confirmation event, the platform classifies it into four states based on the event status evolution: unconfirmed, preliminary confirmed, explicit confirmed, and conflicting confirmed. When the same field is raised in the initial dialogue but does not receive a explicit response, it is recorded as unconfirmed; when the field receives a relatively clear but still limited description, it is recorded as preliminary confirmed; when both parties complete a structured and consistent confirmation of the field, it is recorded as explicit confirmed; when the same field shows inconsistencies, mutual negation, or changes in conditions in subsequent dialogues, it is recorded as conflicting confirmed. The platform preferentially assigns higher event weights to explicit confirmed events and negative impact weights to conflicting confirmed events, thereby making subsequent dialogue confirmation corrections more consistent with the actual communication process.

[0107] Furthermore, the platform can also set up a proactive confirmation trigger mechanism during the conversation. When the system identifies high-intensity tasks, high-risk care recipients, or highly sensitive additional conditions in an order, it automatically pushes standardized confirmation question templates to both parties, guiding them to reconfirm key fields. For example, for elderly care orders, the platform automatically displays confirmation templates such as "Does it involve turning over assistance?", "Does it require nighttime assistance?", and "Is there a need for wheelchair transfer?"; for maternal and infant service orders, the platform automatically displays confirmation templates such as "Does it require nighttime feeding?", "Does it require years of childcare experience?", and "Does it require postpartum care?"; for in-home deep cleaning orders, the platform automatically displays confirmation templates such as "Does it involve wiping high places?", "Is there heavy oil stain?", and "Are you required to bring your own tools?" Through template-based guidance, the platform can significantly reduce the lack of confirmations caused by incomplete expression of needs or misunderstandings by service personnel, thereby increasing the effective confirmation frequency and overall consistency of needs confirmation events. The platform can also write the template trigger results into the conversation confirmation data (Cd) as supplementary input for subsequent confirmation corrections.

[0108] Through the above methods, the dialogue confirmation data generated in the S4 stage is no longer just ordinary chat logs, but a structured set of confirmation results and interaction behavior results generated around the key fields of the order. This data can be used, on the one hand, to confirm and correct the basic fit values ​​in the subsequent S6 stage, improving the accuracy of the posterior matching probability calculation; on the other hand, it can provide traceable evidence in the subsequent order signing, complaint tracking, and settlement processing stages, enabling the platform to determine whether a decline in the fit of a candidate service provider is due to insufficient service capabilities, inadequate communication and confirmation, or changes in the required information. Thus, the direct dialogue function is further transformed into a core data source for improving matching accuracy, reducing misunderstanding rates, and enhancing subsequent traceability, rather than merely being an independent communication interface.

[0109] Specifically, when constructing the event state graph in S5, the platform does not record the onboarding review, online order signing, service execution, evaluation feedback, complaint handling, and settlement processing separately. Instead, it treats orders, customers, service personnel, institutions, and accounts as different object nodes in the same business process and associates them uniformly to form an event state graph that is continuously updated, traceable, and can be used for subsequent matching and risk control. The platform first uses the order number as the main index and then sequentially attaches customer nodes, service personnel nodes, institution nodes, and account nodes under the same order link. The behavior or state change of each object in the business process is defined as a state event. Among them, state events include at least the following events: onboarding review passed, identity verification completed, candidate recommendation, dialogue establishment, online order signing, service start, service completion, evaluation submission, complaint initiation, complaint feedback, settlement confirmation, withdrawal application, limit trigger, freeze, and unfreeze. The platform writes each status event into an event sequence according to the order of occurrence, and records the event type, triggering object, trigger time, completion time, feedback time, associated order number, upstream event number, and downstream event number for each event. This allows any link to locate its preceding source and subsequent destination in the event graph, avoiding the status break caused by the scattered recording of review, order signing, service, and funds in traditional platforms.

[0110] Regarding object association, the platform establishes an object association set Ao for each order. Ao includes at least a customer identifier, a service personnel identifier, an organization identifier, and an account identifier, and further records the relationships between objects. For example, a customer node corresponds to an order initiation relationship with an order node, a service personnel node corresponds to an acceptance relationship with an order node, an organization node corresponds to an attribution or guarantee relationship with a service personnel node, and an account node corresponds to a payment and receipt relationship with a customer node or a service personnel node. To quantify the stability of object association, the platform can calculate the object association value Ro. Let N1 be the number of valid associations between a customer and an order, N2 be the number of valid associations between a service personnel and an order, N3 be the number of valid associations between an organization and a service personnel guarantee relationship, and N4 be the number of valid associations between an account and a settlement link. Then the object association value Ro satisfies: Ro = t1 × N1 + t2 × N2 + t3 × N3 + t4 × N4, where t1, t2, t3, and t4 are weighting coefficients. The higher the object association value Ro, the clearer the binding relationship between objects in the current event state diagram and the more complete the link. When the object association value Ro is too low, it indicates that a certain object relationship is missing, the connection is incomplete, or there is a break in the link. The platform can mark the corresponding order as having insufficient closed-loop integrity and reduce its process closed-loop weight in subsequent steps.

[0111] Regarding time correlation, the platform records the trigger time Ts, completion time Te, and feedback time Tf for each state node, and generates continuous time data based on the time connection relationship between adjacent events. For events with a clear sequential relationship, such as online order signing and service start events, service completion and evaluation submission events, and complaint initiation and complaint feedback events, the platform calculates the time interval value ΔT between adjacent events. Let the completion time of an event Ei be Tei, and the trigger time of the next event Ei+1 be Tsi+1, then the corresponding time connection value ΔTi satisfies: ΔTi = Tsi+1 - Tei. The platform further normalizes all time connection values ​​in the same order chain to generate a continuous time value Rt. Let there be m time connection values ​​in the order chain, then the continuous time value Rt satisfies: Rt = 1 / [1 + (ΣΔTi) / m]. When Rt is high, it indicates that the connection between review, order signing, service execution, evaluation, complaint, and settlement is smooth and the node response is timely; when Rt is low, it indicates that there are obvious delays, status stagnation, or processing gaps in certain stages. The platform can directly write Rt into the closed-loop data of the process, which can be used to judge whether the processing responsiveness of a candidate service personnel or an organization in the actual business chain meets the requirements.

[0112] To reflect the completeness of the entire order process, the platform further constructs a process loop completeness value Rc. Let M0 be the total number of key nodes the current order should traverse in the platform's preset process template, M1 be the number of key nodes with valid records, and M2 be the number of nodes with conflicts, missing nodes, or abnormal rollbacks. Then, the process loop completeness value Rc satisfies: Rc = (M1 - 0.5M2) / (M0 + 1). Here, M1 represents the degree to which the current order process has formed a closed loop record, and M2 represents the degree of conflict, backtracking, state overwriting, or missing records. When Rc is higher than the preset completeness threshold, the platform determines that the order's process loop data can directly participate in subsequent matching and risk control calculations. When Rc is lower than the preset completeness threshold but higher than the repair threshold, the platform sends a request to the corresponding institution or management to supplement or review missing nodes or correct conflicting nodes. When Rc is lower than the repair threshold, the platform marks the order process as unreliable and restricts its related data from directly entering high-weighted post-hoc matching calculations. In this way, the platform can effectively avoid the event chain being broken due to manual intervention, offline customer service coordination, or failure to promptly record fund processing back into the system, ensuring that the event status diagram can accurately reflect the business process.

[0113] In terms of responsiveness, the platform can also calculate node response values ​​Rh to measure the timeliness of feedback from various objects in the event chain. Let L0 be the standard response time limit for a certain state event, and L1 be the actual response time. Then the event response value Hi satisfies: Hi = L0 / (L1 + 1). The platform weights and summarizes the response values ​​of the review response, order signing response, service start response, complaint feedback response, and settlement feedback response in the order chain to obtain the node response value Rh, which satisfies: Rh = u1 × H1 + u2 × H2 + u3 × H3 + u4 × H4 + u5 × H5, where u1, u2, u3, u4, and u5 are response weight coefficients. For complaint, dispute, and financial events, the platform can appropriately increase the corresponding weights so that the event state diagram not only reflects "whether these nodes have passed through" but also "whether the processing of these nodes has been timely." In subsequent use, if an organization has basic guarantee capabilities but its complaint feedback is delayed for a long time and its settlement feedback efficiency is low, its Rh will decrease, thereby affecting its priority in subsequent shared routing and post-matching.

[0114] To enhance anomaly tracing capabilities, the platform adds an anomaly flag Ae to each state event in the event state diagram. This flag indicates at least several issues, including missing nodes, inverted timelines, duplicate submissions, incorrect object assignments, inconsistent fund status, and unclosed complaint states. When an anomaly flag is triggered, the platform generates an anomaly tracing value Re. Let the number of anomaly events be K1, the number of serious anomalies be K2, and the number of resolved anomalies be K3. Then, the anomaly tracing value Re satisfies: Re = (K1 + 2K2 - K3) / (M0 + 1). A higher Re indicates a higher number of anomalies in the order chain requiring manual review or system restrictions. The platform will automatically reduce the credibility weight of the closed-loop process data in subsequent post-test matching and increase the risk linkage sensitivity in settlement and withdrawal control. A lower Re indicates a relatively stable and complete event chain, which can be directly used as reliable business process data in subsequent calculations. Therefore, the event state diagram not only serves as a process recorder but also directly quantifies anomaly tracing capabilities.

[0115] Based on the aforementioned object association value Ro, time continuity value Rt, process loop integrity value Rc, node response value Rh, and anomaly tracing value Re, the platform ultimately generates process loop data Cg. Let the comprehensive process loop value be Cg, then Cg satisfies: Cg = q1×Ro + q2×Rt + q3×Rc + q4×Rh - q5×Re, where q1, q2, q3, q4, and q5 are weighting coefficients. A higher comprehensive process loop value Cg indicates a more complete business chain formed by the order and its associated objects within the platform, more timely responses, fewer anomalies, and clearer tracing. In the subsequent S6 stage, the platform can assign a higher process credibility weight to this order. Conversely, when Cg is low, even if the order appears to have completed signing and service execution, the platform will reduce its calculation weight as a high-quality sample or highly credible order due to its poor loop quality. By unifying object association, time association, process integrity, response efficiency, and anomaly tracing into an event state diagram and transforming it into closed-loop process data, the platform can integrate the previously fragmented processes of review, order signing, service, evaluation, complaint, and settlement into a unified data structure that can participate in matching and risk control decisions. This allows the generation of subsequent post-matching probabilities to no longer rely solely on the order and service personnel themselves, but to simultaneously reflect the stability and reliability of the entire business process.

[0116] Specifically, the basic adaptation value acquisition stage in S6 is mainly used to quantify the static matching degree between order requirements and candidate service personnel before introducing dialogue correction and risk correction, thus providing basic input for subsequent confirmation correction and posterior matching probability generation. The platform first reads the order feature data formed in stage S1 and extracts core fields directly related to matching to construct an order requirement feature vector D. The order requirement feature vector D preferably includes at least service category features, service sub-item features, service area features, service time period features, budget range features, service target features, task intensity features, and privacy level features. Simultaneously, the platform reads the service capability data, service range data, service time period data, historical performance data, and task acceptance capability data of candidate service personnel to construct a service feature vector P. The service feature vector P preferably includes at least capability category features, capability sub-item features, serviceable area features, available order time period features, price range features, service target experience features, task acceptance adaptation features, and information openness level features. In this way, the order side and the service side are uniformly expressed as feature vectors that can directly participate in matrix operations under the same dimensional system, thereby avoiding the rough matching problem caused by relying on a single label or human experience judgment.

[0117] To ensure that different fields can enter a unified computational space, the platform first normalizes the data of each dimension in the order demand feature vector D and the service feature vector P. For categorical fields, one-hot encoding or multi-label encoding is preferred to form the corresponding dimensions; for interval fields, the interval center value and interval width are preferred to be used together; for ordinal fields, a graded quantification method is preferred; for fields related to task intensity, physical fitness, endurance, and psychological fitness, the platform's internal unified interval mapping rules are preferred to convert them into standard scores. After normalization, each dimension feature is compressed into a preset computational range to reduce the impact of different units on the difference calculation. The platform then obtains the order demand feature vector D and the service feature vector P in the same dimensional order, and then subtracts the two to obtain the difference Δ, where Δ = D - P. The difference Δ is used to represent the degree of deviation between the order and the candidate service personnel in each key feature dimension. The smaller the deviation in a certain dimension, the closer the candidate service personnel are to the current order requirements in that dimension; the larger the deviation, the more obvious the mismatch.

[0118] To avoid simply summing the deviations of each dimension as independent terms, this invention further introduces a covariance matrix Σ constructed based on historical matching samples. The platform extracts corresponding demand feature vectors and service feature vectors from historical orders and historical service completion samples, and statistically analyzes the joint change relationships between each feature dimension to form the covariance matrix Σ. The covariance matrix Σ is mainly used to characterize the correlation between different feature dimensions. For example, service area and service time period have a linkage relationship in certain business scenarios, and task intensity and task undertaking capacity also have a significant correlation. If a simple linear weighting method is used, it is easy to repeatedly amplify the influence of certain related dimensions. By introducing the covariance matrix Σ, the matching calculation can automatically correct the repeated contributions between highly correlated dimensions while considering the differences of each dimension, thereby improving the calculation accuracy of the basic fit value. After obtaining the covariance matrix Σ, the platform uses the difference quantity Δ and the covariance matrix Σ to calculate the difference constraint value Q, which satisfies: Q=Δ^T×Σ^-1×Δ, where Δ^T represents the transpose of the difference quantity Δ, and Σ^-1 represents the inverse matrix of the covariance matrix Σ. The smaller the difference constraint value Q, the smaller the overall difference between the order demand feature vector D and the service feature vector P after considering dimensional correlation; the larger the difference constraint value Q, the more obvious the overall difference.

[0119] After obtaining the difference constraint value Q, the platform continues to calculate the basic adaptation value M0. The basic adaptation value M0 satisfies: M0 = 1 / (1 + Q). This expression ensures that the basic adaptation value M0 remains within a stable range, monotonically decreasing as the difference constraint value Q increases, and monotonically increasing as the difference constraint value Q decreases. When candidate service providers are highly similar to order requirements in terms of multi-dimensional features such as service category, service sub-item, region, time, budget, task intensity, and privacy conditions, Q is small, and M0 is close to 1; when candidate service providers differ significantly from the order in multi-dimensional features, Q increases significantly, and M0 decreases accordingly. In this way, the platform can compress the originally complex multi-dimensional static matching relationship into a single basic adaptation value M0 that can directly participate in subsequent calculations. The basic fit value M0 mainly reflects the original static matching degree between the candidate service personnel and the current order before considering communication confirmation, security links and account risks. Therefore, in the confirmation and correction stage in S6, the platform will further combine the dialogue confirmation data to correct M0 to the correction fit value M1, so that the matching result is closer to the actual order signing scenario.

[0120] In actual operation, the platform can also assign basic participation weights to each dimension in the order demand feature vector D and the service feature vector P to control the degree of influence of key dimensions on the difference Δ under different business scenarios. For example, in orders for elderly care, patient companionship, and maternal and infant care, the platform appropriately increases the participation weights of service recipient characteristics, task intensity characteristics, and service time characteristics to strengthen the impact of care experience, continuous service capability, and time continuity. In orders for cleaning services, in-home organization, and odd jobs, the platform appropriately increases the participation weights of regional characteristics, service content characteristics, and immediate accessibility characteristics to strengthen the role of arrival efficiency and task completion efficiency. For orders with special additional requirements, such as requiring nighttime response, requiring the ability to lift and move objects, or requiring the ability to withstand high-intensity housework, the platform can also simultaneously increase the participation ratio of dimensions related to the physical fitness index Ep, endurance fitness index En, and task acceptance fitness value At in the service feature vector P, so that the basic fitness value M0 more realistically reflects the actual static acceptance capability between service personnel and orders.

[0121] Furthermore, to improve the interpretability of the basic fit value M0 across different order types, the platform can maintain different historical matching sample sets and corresponding covariance matrices Σ for each order business category. For example, for cleaning service orders, the covariance matrix is ​​preferably constructed using historical matching samples from cleaning services; for maternal and infant service orders, it is preferably constructed using historical matching samples from maternal and infant services; and for elderly care orders, it is preferably constructed using historical matching samples from care services. This ensures that the basic fit value M0 under different business scenarios better reflects the actual matching patterns of that type of business. For cross-category or composite orders, the platform can construct a combined covariance matrix by prioritizing the primary business category and compensating with secondary business categories, thus balancing the matching accuracy for mixed needs of multiple business types. Through these methods, the basic fit value M0 not only has a unified calculation form but also maintains good applicability and discriminative ability based on different service scenarios.

[0122] Through this stage of processing, the platform ultimately obtains a basic fit value M0 for each candidate service provider corresponding to the current order. The basic fit value M0 serves as the first-level input for subsequent confirmation, correction, and posterior probability calculation. Its role is to first statically categorize "who is objectively closer to the current order requirements," thus laying the foundation for subsequent dynamic adjustments based on communication to confirm quality, ensure path stability, and assess risk status. In this way, the platform's matching process no longer relies on a single identical label for fit determination, nor is it a simple linear weighted summation of multiple fields. Instead, considering the correlation across various dimensions, it establishes a more accurate static matching benchmark through the difference constraint value Q and the basic fit value M0, thereby improving the accuracy and stability of the entire supply-demand matching process.

[0123] Specifically, in S6, when confirming and correcting the basic fit value, the platform does not directly use the basic fit value M0 as the final matching result. Instead, it further combines the dialogue confirmation data generated in S4 to quantitatively correct the communication confirmation quality between the candidate service personnel and the demand side. The basic fit value M0 reflects the degree of similarity between the order demand characteristics and service capability characteristics under static conditions. However, in real matching scenarios, even if a candidate service personnel is highly similar to the order in terms of category, region, time period, and budget, if there are misunderstandings, repeated modifications, insufficient confirmation of key information, or slow response during the actual communication process, it may still lead to subsequent order failure, performance deviation, or increased complaint risk. Therefore, this stage sets up a confirmation and correction process to further correct the "static matching degree" to the "actual matching degree after communication confirmation," thereby obtaining the corrected fit value M1. M1 will participate in the generation of the posterior matching probability P0 together with the guaranteed routing value G0 and the risk value R0 in subsequent stages. Therefore, the processing result of this stage directly affects the recommendation ranking and order control.

[0124] Before confirming and correcting, the platform first merges events and categorizes fields in the dialogue confirmation data. Each order establishes a set of demand confirmation events around at least six key fields: service time, service location, service content, service duration, budget range, and additional conditions. If an order also involves elderly care, maternal and infant care, patient companionship, heavy physical labor, nighttime services, bringing your own tools, or temporary add-ons, the platform adds extended event categories such as care level confirmation, labor intensity confirmation, tool requirement confirmation, and special precaution confirmation, in addition to the six basic fields. The platform arranges events in each category chronologically and sequentially determines whether they ultimately result in clear confirmation, whether there are any conflicts, whether they remain only in vague expressions, or whether they are covered by subsequent information. For any demand confirmation event of category j, the platform counts its valid confirmation count n1j and conflict confirmation count n2j. The number of valid confirmations (n1j) indicates the number of times both parties reach a clear, consistent, and actionable confirmation regarding this type of field. For example, if the requester states "from 8:00 AM to 11:00 AM," and the service personnel clearly reply "can arrive before 8:30 AM," and both parties confirm again that there are no changes, this is counted as one valid confirmation. The number of conflicting confirmations (n2j) indicates the number of times both parties have inconsistencies, repeated modifications, or mutual denials regarding the same field. For example, if the initial conversation confirmed a budget of 300 yuan, but later changed it to 200 yuan, and no new stable and consistent conclusion was reached, this is counted as one conflicting confirmation.

[0125] After obtaining n1j and n2j, the platform generates the confirmation coefficient Cj for the j-th type of requirement confirmation event. The confirmation coefficient Cj satisfies: Cj = n1j / (n1j + n2j + 1). A preset correction value of 1 is added to the denominator, primarily to avoid a denominator of 0 when both n1j and n2j are 0, and to automatically maintain a low confirmation weight for fields that have never been effectively discussed in the calculation. Therefore, the higher the number of effective confirmations for a field and the fewer the number of conflicting confirmations, the closer Cj is to 1; when a field is rarely confirmed or repeatedly conflicts, Cj decreases significantly. For example, if the service time field is effectively confirmed 3 times and conflicts 0 times, the time confirmation coefficient is 3 / (3 + 0 + 1) = 0.75; if the budget field is effectively confirmed 1 time and conflicts 1 time, the budget confirmation coefficient is 1 / (1 + 1 + 1) = 0.333; if the additional condition field is not effectively confirmed and has no conflicts, the additional condition confirmation coefficient is 0 / (0 + 0 + 1) = 0. In this way, the platform can use a unified method to express the degree to which different fields have been confirmed in communication.

[0126] To further reflect the concentration and dispersion of various confirmation events in the overall communication, the platform normalizes the confirmation coefficients after obtaining each Cj, generating a normalized confirmation percentage pj. The normalized confirmation percentage pj preferably satisfies: pj = Cj / ΣCj, where ΣCj represents the sum of the confirmation coefficients of all demand confirmation events. When some fields in an order are sufficiently and stably confirmed, while other fields are almost unconfirmed or repeatedly conflicting, the differences between different pj will be significantly widened. Based on this, the platform further calculates the confirmation entropy H, which satisfies: H = -Σ(pj × lnpj). The confirmation entropy H reflects the degree of orderliness and information uncertainty in the confirmation of key fields for the entire order. When the vast majority of confirmations are concentrated on a few highly reliable fields, and the status of each key field is stable, H is relatively low, indicating a clear communication structure and concentrated confirmation results; when the distribution of key field confirmations is chaotic, and some fields are repeatedly communicated but it is always difficult to reach a stable conclusion, H is relatively high, indicating strong uncertainty in the communication process. The purpose of introducing H on the platform is to avoid relying solely on the "total number of confirmations" for judgment, because although some conversations may have a lot of back-and-forth exchanges, a large number of these exchanges revolve around vague expressions or repeated modifications, which does not necessarily help improve the accuracy of the actual matching.

[0127] After generating the confirmation coefficient Cj and confirmation entropy H, the platform further reads the initial response duration T, message trace completeness L, and consistency of confirmation information before and after Y, and jointly generates a dialogue correction value Sd. The initial response duration T comes from the time taken for the first effective response recorded in stage S4; a faster response indicates a stronger focus on the order requirement and a greater willingness to accept the order by the service personnel. The message trace completeness L reflects the proportion of traceable messages in all key confirmation events; a higher L indicates a more complete communication process and easier tracing of subsequent disputes. The consistency of confirmation information before and after Y is obtained by comparing the consistency of the same field across multiple rounds of confirmation; a higher Y indicates stronger communication stability. The platform generates the dialogue correction value Sd according to the following formula: Sd = [Σ(ωj×Cj) + L + Y] / [1 + ηH + μT]. Where ωj represents the weight of the j-th type of request confirmation event, the weight can be dynamically adjusted according to the order type. For example, in elderly care orders, the weight of service duration confirmation, care content confirmation, and additional condition confirmation can be higher than the weight of region confirmation. η and μ are adjustment coefficients used to control the influence of confirmation entropy and response time in the overall correction. From this relationship, it can be seen that the higher the weighted result of the confirmation coefficient, the more complete the traceability, and the higher the consistency, the larger Sd will be; the higher the confirmation entropy and the longer the average response time, the smaller Sd will be. This can both reward high-quality communication and suppress seemingly active but actually unstable communication processes.

[0128] After obtaining the dialogue correction value Sd, the platform uses Sd to correct the basic adaptation value M0, resulting in the corrected adaptation value M1, which satisfies: M1 = M0 × (1 + Sd). Here, M0 mainly reflects the degree of matching under static conditions, Sd mainly reflects the strengthening or weakening effect of communication confirmation quality on the matching relationship, and M1 represents the overall degree of adaptation between the candidate service personnel and the current order after completing the actual communication confirmation. For example, if a candidate service provider's base fit value M0 is 0.78, it means they are already quite close to the order requirements in terms of category, region, time period, and budget. However, if during the conversation, the service time, budget, and additional conditions are fully confirmed, the message log is complete, consistency is high, and the response is timely, resulting in an Sd of 0.42, then M1 = 0.78 × (1 + 0.42) = 1.1076, indicating that their fit has significantly improved after communication and confirmation. Conversely, if another candidate service provider also has an M0 of 0.78, but during the communication process, the service time is modified multiple times, the budget changes repeatedly, the initial response is slow, and the key field logs are incomplete, resulting in an Sd of only 0.08, then M1 = 0.78 × (1 + 0.08) = 0.8424, indicating that although their static conditions are similar, their reliable match after actual communication is significantly lower than the former. Therefore, M1 is not a simple amplification value, but a key intermediate quantity that truly reflects the reliability of communication in the matching calculation.

[0129] To enhance the applicability of confirmation adjustments across different order scenarios, the platform prioritizes scenario-based settings for ωj. For cleaning service orders, higher weights are preferred for service content confirmation, service duration confirmation, and budget range confirmation, as these orders are sensitive to defining work boundaries and labor intensity. For elderly care orders, higher weights are preferred for service recipient care requirements confirmation, nighttime service confirmation, additional conditions confirmation, and service time confirmation, as these orders rely more heavily on care details, duration, and responses to special circumstances. For maternal and infant service orders, higher weights are preferred for service recipient type confirmation, service time period confirmation, and experience requirement confirmation, emphasizing the assessment of professional competence and time continuity. In this way, when adjusting confirmations for different orders, Sd will not become a fixed template, but can be dynamically strengthened around the core risk points of the order, making M1 more suitable for the actual matching needs of different business scenarios.

[0130] To improve the stability of data in this stage, the platform can also set upper and lower correction limits. For example, when Sd exceeds the preset upper limit Ts, Sd can be limited to Ts to prevent individual candidate service personnel from being overvalued due to extremely high communication activity; when Sd is below the preset lower limit Tb, Sd can be limited to Tb to ensure that the matching value in extremely low-quality communication scenarios does not lose its comparative significance due to excessive compression. The platform can also link the process loop comprehensive value Cg obtained from M1 and S5 stages. When a certain order or a certain service personnel performs poorly in the historical process loop, the positive amplification effect of the corresponding dialogue correction value can be appropriately suppressed, thereby avoiding overvaluing a candidate service personnel based solely on the performance in the current round of dialogue.

[0131] Through the above steps, this stage completes the transformation from the basic adaptation value M0 to the corrected adaptation value M1. M1, as a crucial input for the subsequent integration of assurance paths and risk information, not only retains the benchmark of static condition matching but also introduces a dynamic correction dimension: dialogue confirmation quality. Thus, when generating the posterior matching probability P0, the platform no longer uses the static matching value calculated solely based on order and service capability vectors. Instead, it uses the corrected adaptation value M1, which is more realistic and closer to the signing and fulfillment results, after being jointly corrected by key requirement confirmation, consistency verification, response efficiency, and message logging. This further improves the accuracy and stability of the entire supply and demand matching process.

[0132] Specifically, in the S6 stage of integrating the guarantee path and risk information, after obtaining the corrected fit value M1, the platform does not directly use M1 as the final recommendation basis. Instead, it continues to incorporate the stability of the guarantee chain and the account risk status corresponding to the actual fulfillment process of the order into the calculation to generate a posterior matching probability P0 that is closer to the actual order signing success rate and subsequent fulfillment stability. The core purpose of this stage is to address the following situation: although a candidate service provider performs well in terms of order characteristics, service capabilities, and dialogue confirmation, if the guarantee connection capability of the institution behind them is insufficient, the cost of cross-institutional scheduling is too high, the account behavior is obviously abnormal, or the potential risks in the order chain are large, then even if the static fit and communication confirmation are high, they should not be given priority by the platform. To this end, the platform further constructs the guarantee association path based on the event state graph and simultaneously extracts risk control features, so that the matching result is further improved from "whether it is suitable" to "whether it is suitable and worth prioritizing".

[0133] When constructing the assurance path, the platform starts with the current order node and sequentially connects candidate service personnel nodes, affiliated institution nodes, regional support nodes, and settlement support nodes to form at least one fulfillable, reliable, and settlement-enabled assurance path. For candidates within the same institution, the assurance path typically appears as "order node—service personnel node—affiliated institution node"; for candidates entering the candidate range through regional shared routing or cross-institutional shared resource pools, the assurance path can be further extended to "order node—service personnel node—source institution node—regional support institution node—assurance processing node" or "order node—service personnel node—cross-institutional collaboration node—settlement support node". The platform assigns reliability tags to the edge relationships on each assurance path, and these edge relationships include at least service capacity acceptance relationships, institution affiliation relationships, assurance support relationships, substitute allocation relationships, complaint response relationships, and settlement collaboration relationships. If a certain relationship has a high success rate in fulfilling orders, a short incident response time, a high complaint handling closure rate, or a high stability in settlement cooperation in historical orders, then its edge reliability coefficient will be high; conversely, if the performance of such a relationship has large fluctuations, slow guarantee response, or many settlement disputes in historical data, then its edge reliability coefficient will be low.

[0134] After obtaining the reliability coefficients of each edge, the platform first calculates the path reliability product. Let π be the guarantee path corresponding to the current candidate service personnel, and ri,e be the reliability coefficient of each edge in the path. Then the path reliability product satisfies Πri,e, which represents the overall stability of the path under the continuous transmission of multi-layer relationships. To avoid relying solely on the path reliability product and ignoring the actual fulfillment costs caused by long-distance scheduling and multi-layer transfers, the platform further introduces the routing distance cost Li and the number of cross-organization hops Ji. The routing distance cost Li preferably takes into account the spatial distance of the service personnel to the order location, the expected commuting time, the complexity of the route, and the time loss caused by remote scheduling. The number of cross-organization hops Ji is used to characterize the number of organizational levels that the order passes through in the guarantee chain. The more hops, the longer the communication chain, the more complex the responsibility boundary, and the higher the risk of temporary allocation. Based on this, the platform calculates the guarantee route value G0, which satisfies: G0 = max Πri,e × exp(-αLi-βJi). Here, max Πri,e represents the path with the highest reliability product value selected by the platform from all feasible assurance paths corresponding to the current candidate service personnel as the primary assurance path. α and β are penalty coefficients used to control the compression effect of distance cost and hop count on the assurance route value. Therefore, G0 is larger when the assurance path corresponding to a candidate service personnel has high reliability, low distance cost, and few cross-institutional hops; G0 will decrease significantly when the assurance chain exists but has complex layers, excessive distance, or high cross-institutional coordination costs. After this processing, the platform can further refine "whether there is assurance" into "whether the assurance chain is short, stable, and can be quickly deployed."

[0135] In the risk information fusion section, the platform extracts five risk characteristics from account risk control data and event status graphs: abnormal withdrawal frequency, abnormal account association, abnormal device login, unresolved complaint status, and abnormal order status changes, denoted as F1, F2, F3, F4, and F5, respectively. Abnormal withdrawal frequency (F1) indicates whether a service personnel account has made frequent abnormal withdrawal requests or premature withdrawals that are clearly inconsistent with service completion confirmation within a unit period; abnormal account association (F2) indicates whether a service personnel account shares payment media, settlement paths, or frequently crosses fund flows with other accounts; abnormal device login (F3) indicates whether an account frequently logs in at abnormal locations, times, or on uncommon devices; unresolved complaint status (F4) indicates whether there are unresolved complaints, appeals, or disputes in the current order or related historical orders; and abnormal order status changes (F5) indicate whether there are reverse jumps, duplicate submissions, or status conflicts in the order during the service start, service completion, evaluation submission, and settlement confirmation stages. To uniformly incorporate the above five risk characteristics into the calculation, the platform uses an S-shaped risk compression method to generate a risk value R0, which satisfies: R0 = 1 / [1 + exp(-(γ0 + γ1F1 + γ2F2 + γ3F3 + γ4F4 + γ5F5))]. Here, γ0 is the basic bias coefficient, and γ1, γ2, γ3, γ4, and γ5 are the adjustment coefficients for each risk characteristic. In this way, the platform can uniformly compress risk characteristics with different distribution ranges and dimensions into a stable range. When the overall risk characteristic is weak, R0 tends to a lower value; when account anomalies, complaint anomalies, equipment anomalies, and order status anomalies overlap, R0 rises rapidly, thus significantly suppressing high-risk candidates in subsequent matching decisions.

[0136] After obtaining the corrected adaptation value M1, the guaranteed routing value G0, and the risk value R0, the platform further generates the posterior matching probability P0 for the candidate service personnel corresponding to the current order. The posterior matching probability P0 satisfies: P0 = 1 / [1 + exp(-(θ0 + θ1M1 + θ2G0 - θ3R0))]. Where θ0 is the basic bias coefficient, and θ1, θ2, and θ3 are the adjustment coefficients corresponding to the corrected adaptation value, the guaranteed routing value, and the risk value, respectively. This relationship shows that M1 and G0 have a positive effect on improving the posterior matching probability P0, while R0 has a negative inhibitory effect on P0. That is, if a candidate service personnel obtains a high M1 under static conditions and after communication confirmation, and their corresponding guarantee chain is short, stable, and can be quickly implemented, then G0 is large, and both increase P0; if their account, device, complaint, or order status is abnormal, then R0 increases, and the platform will suppress P0 through risk factors to prevent them from being mistakenly promoted to the top of the recommendation list simply because of their good service capabilities. The P0 generated in this way is no longer a ranking value that simply depends on label similarity or communication popularity, but a final posterior matching probability that comprehensively reflects "whether it matches, whether it is reliable, and whether it is safe".

[0137] To ensure the posterior matching probability P0 has good scenario adaptability under different order types, the platform dynamically adjusts θ1, θ2, and θ3 based on the order category and task intensity value It. For orders related to elderly care, patient companionship, and maternal and infant care, the platform appropriately increases θ2 and θ3 to highlight the impact of routing and risk values ​​on the final recommendation results, as these orders rely more on institutional support, backup capabilities, and complaint risk control. For general cleaning, hourly services, and short-term in-home orders, the platform can appropriately increase θ1, giving the correction adaptation value M1 a greater weight in the recommendation, thereby improving the sensitivity to direct matching of region, time period, and service content. For agency / franchise and cross-institutional collaboration orders, the platform can simultaneously increase θ2 and the Li and Ji related penalty coefficients, ensuring that cross-institutional links are only prioritized when the connection is fully guaranteed. Through this scenario-based dynamic weight allocation method, P0 can maintain good interpretability and ranking effectiveness under different business types.

[0138] Furthermore, to prevent an abnormally high P0 due to an extreme prominence of a single candidate service provider's value, the platform can set normalization boundaries and upper / lower limit correction mechanisms before M1, G0, and R0 are included in the P0 calculation. For example, if M1 is too high but originates from an abnormally short-term, high-frequency confirmation dialogue, the platform can prune its upper limit by combining confirmation entropy H and message trace completeness L; if G0 is high but there are incompletely closed guarantee collaboration nodes in the path, the platform can appropriately lower it using a path integrity correction factor; if R0 is low but there are duplicate submissions and state conflicts in the event state graph that have not yet been reviewed, the platform can also compensatorily raise it using an anomaly traceability value Re. This prevents individual candidates from being excessively amplified due to a local advantage, while ensuring that P0 maintains a more stable and realistic response to the global business state.

[0139] After completing the above calculations, the platform ranks all candidate service providers for the current order according to their P0 value from highest to lowest, forming a final ranking list. Candidates with higher P0 values ​​are prioritized for the results output stage. The platform prioritizes displaying their service category, capability tags, regional scope, guarantee overview, and partial dialogue confirmation results to the demand side. When preset recommendation conditions are met, the platform proceeds to the phased information release and online order signing process. Candidates with lower P0 values ​​but still within the candidate range are retained in the backup list. If a higher-ranked candidate rejects the order, fails to respond within the time limit, or subsequent dialogue confirmation fails, the platform will then fill the vacancy by selecting candidates in order of P0. In this way, the platform no longer relies on a single recommendation value for one-time decisions. Instead, it establishes a progressive, substitute, and fallback recommendation mechanism centered on P0, thereby ensuring both computational accuracy and business execution flexibility in the entire supply and demand matching process.

[0140] Through this stage of processing, the platform ultimately integrates the relationship between static orders and service capabilities, dynamic dialogue confirmation results, the stability of institutional support paths, and account risk status into a unified posterior matching probability P0. P0 is both the comprehensive output of the aforementioned M0, M1, G0, and R0, and a key input for the subsequent S7 recommendation output and S8 settlement risk control linkage. Thus, the candidate service providers ultimately recommended by the platform to the demand side are no longer simply "people with similar conditions," but rather, after multiple constraints including static matching, communication confirmation, support assessment, and risk filtering, they are more suitable for undertaking the task in the current order scenario and deserve priority recommendation. This improves the overall accuracy of supply and demand matching, the success rate of order signing, and the stability of contract fulfillment.

[0141] Specifically, the risk linkage control in S8 is mainly used to dynamically manage order settlement, service personnel payment, and account withdrawal after online order signing. This allows the platform to maintain its operational goals of "platform service fees being valid annually, no commission being deducted from single service fees, and employer payments reaching service personnel as stably and directly as possible," while also mitigating risks in a timely manner when abnormal withdrawals, abnormal account associations, abnormal device logins, unresolved complaints, or abnormal changes in order status occur. Unlike methods that only set fixed withdrawal limits on the account side or rely solely on manual review, this stage incorporates the platform service fee validity status, commission disabling status, consistency between order payment amount and service personnel payment amount, service completion confirmation status, and account behavior risk status into the calculation. This ensures that settlement control and risk control are based on the joint establishment of the actual order fulfillment chain and the capital flow chain, thereby improving the accuracy, real-time nature, and explainability of capital control.

[0142] During the direct settlement control phase, the platform first reads the platform service fee validity status F0, commission disabling status J0, consistency status between order payment amount and service personnel's received amount Y0, and service completion confirmation status W0. The platform service fee validity status F0 indicates whether the demand side or institution side is within a valid platform service fee period. If it is still within the valid period, it means that the order meets the prerequisite for directly entering the service fee flow and matching settlement according to platform rules. The commission disabling status J0 indicates whether the current order complies with the platform's control rule of no longer charging additional commissions on single service fees. If the order has special cash flows such as specific cashback, franchise profit sharing, default compensation, or abnormal difference adjustments, the platform can adjust J0 accordingly. The consistency status between order payment amount and service personnel's received amount Y0 is used to determine whether the actual payment amount from the demand side in the current order is consistent with the amount that should enter the service personnel's receiving side according to the rules. If the difference is within the allowable range, Y0 is higher; if there is a significant deviation, Y0 is lower. The service completion confirmation status W0 indicates whether the order has passed the service completion confirmation node, evaluation feedback node, or at least formed a valid chain of evidence for service completion. The platform generates a direct settlement control value Z0 based on the four statuses mentioned above. Z0 satisfies: Z0 = a1×F0 + a2×J0 + a3×Y0 + a4×W0, where a1, a2, a3, and a4 are adjustment coefficients. This formula shows that if the platform service fee status is valid, the commission disabling status is normal, the payment consistency is good, and the service completion confirmation is sufficient, then Z0 is high, indicating that the order has strong direct settlement stability in the settlement chain. If any of these conditions are abnormal, Z0 decreases, and the platform will automatically reduce the corresponding available amount in subsequent withdrawal controls.

[0143] To ensure that the values ​​of F0, J0, Y0, and W0 better reflect actual business conditions, the platform prioritizes assigning tiered values ​​to each status. The platform service fee validity status F0 is divided into three levels: fully valid, nearing expiration, and invalid, corresponding to high, medium, and low values ​​respectively. The commission-disabled status J0 is assigned a value based on whether the order has platform deductions, penalty deductions, or special fund transfers. A higher value is used when there are no single commission deductions, a medium value is used when there are special deductions confirmed by both parties, and a low value is used if the fund path is abnormal and it's impossible to determine whether there are hidden commission deductions. The consistent payment status Y0 is assigned a value based on the consistency between the order payment amount, the amount recorded by the platform, and the amount actually received by the service personnel; the higher the consistency, the higher the value. The service completion confirmation status W0 is assigned a value based on whether there is service completion confirmation, whether there are evaluation records, whether there are valid traces, or whether there are unresolved disputes. After being assigned a tiered value, Z0 is no longer a simple "can I withdraw cash?" switch, but has become a continuous control value that can precisely reflect the reliability of the current order's funds, thereby making subsequent withdrawal limit control smoother and closer to the actual risk level.

[0144] During the risk linkage control phase, the platform further extracts the following characteristic values ​​from account risk control data and event status graphs: abnormal withdrawal frequency (F1), abnormal account association (F2), abnormal device login (F3), unresolved complaint (F4), and abnormal order status change (F5). The abnormal withdrawal frequency characteristic value F1 characterizes the number of withdrawal requests, the intervals between them, and their advancement relative to order completion within a certain period. If withdrawals are frequently initiated in concentrated bursts before or after service completion confirmation, or if large withdrawal requests are repeatedly initiated within a short period, F1 increases. The abnormal account association characteristic value F2 characterizes whether there are abnormal shared payment methods, abnormal payment switching, abnormal device sharing, or abnormal fund transfers between the current account and other accounts. If the relationship is complex and lacks a reasonable business explanation, F2 increases. The abnormal device login characteristic value F3 characterizes whether the logged-in device has undergone abnormal changes. Furthermore, the risk level F3 increases if there is frequent switching between abnormal locations or concurrent multi-device transactions in different locations within a short period. The unresolved complaint characteristic value F4 indicates the number and severity of complaints, appeals, breach of contract disputes, or settlement disputes in the current order or related historical orders that have not yet been closed out. The more numerous and severe the unresolved issues, the higher F4. The abnormal order status change characteristic value F5 indicates abnormal jumps in the order's status chain, such as service start, service completion, evaluation feedback, and settlement confirmation. If there are repeated rollbacks, duplicate submissions, status inversions, or settlement before service completion, F5 increases. Based on these five risk characteristics, the platform generates a risk linkage value R1, which satisfies the following equation: R1 = b1 × F1 + b2 × F2 + b3 × F3 + b4 × F4 + b5 × F5, where b1, b2, b3, b4, and b5 are adjustment coefficients. A higher R1 indicates a greater overall risk to the current order and the corresponding service personnel account in terms of fund security, complaint status, and behavioral consistency.

[0145] After obtaining the direct settlement control value Z0 and the risk linkage value R1, the platform further calculates the daily withdrawable amount Q1, which satisfies: Q1 = Qb × Z0 / (1 + R1), where Qb is the basic withdrawal amount. This calculation relationship shows that when the order's direct settlement chain is more complete, Z0 is higher, and the risk linkage value R1 is lower, the daily withdrawable amount Q1 for service personnel is closer to the basic withdrawal amount Qb. When Z0 decreases or R1 increases, Q1 is automatically compressed. For example, when the platform service fee is valid, commissions are disabled, payments are consistent, service completion confirmation is sufficient, and account risk is low, Z0 is close to a high level, R1 is close to a low level, and Q1 can remain within a range close to Qb. If the order has been signed but service completion confirmation is insufficient, or there are unresolved complaints, abnormal device logins, or abnormal withdrawal frequencies, even if the basic withdrawal amount is high, Q1 will decrease significantly due to the increase in R1. As a result, the platform achieves a coordinated control effect: "the more genuine the order, the more stable the connection, and the smoother the withdrawal; the higher the risk, the more unstable the connection, and the more restricted the withdrawal."

[0146] To establish clear control layers in business operations, the platform sets a first risk threshold T1 and a second risk threshold T2, where T1 is less than T2. ​​When R1 is less than T1, the platform determines that the current order and account are in a low-risk state, allowing normal withdrawals within Q1. When R1 is greater than or equal to T1 and less than T2, the platform determines that the current order and account are in a medium-risk state, and implements withdrawal limit control, specifically allowing only partial withdrawals, delaying partial withdrawals, or requiring supplementary materials such as order completion evidence, conversation confirmation records, and settlement confirmation records. When R1 is greater than or equal to T2, the platform determines that the current order and account are in a high-risk state, immediately triggering account freezing or at least freezing the withdrawal permissions associated with the current abnormal order, and simultaneously writing the current order, account, device, and complaint status into the abnormal review list. Freezing does not mean permanent unavailability. During the risk review stage, the platform can read the latest status in the event status diagram, complaint handling results, device review results, and settlement consistency correction results. When the cause of the abnormality is eliminated, the complaint loop is closed, the device environment is restored to credibility, and the settlement chain is restored to consistency, the account is unfrozen and withdrawal permissions are restored. By using this two-threshold hierarchical control method, the platform avoids the overly rigid approach of "completely freezing the system as soon as a risk occurs," and also avoids the problem of risk accumulation caused by "completely releasing the system as long as it has not reached the most serious level."

[0147] Furthermore, to ensure that the platform's service fee validity and commission disabling status truly serve the purpose of "improving satisfaction and directness," the platform can also link Z0 with demand-side experience data and service personnel settlement experience data. For example, when a certain type of order historically experiences issues such as "delayed payment after order completion," "unresolved complaints preventing service personnel from withdrawing funds for extended periods," or "discrepancies between the amount received and expectations due to cashback or promotions," the platform can adjust the values ​​of a1, a2, a3, and a4 based on historical feedback. This further enhances the sensitivity of the direct settlement control value Z0 to payment consistency and service completion confirmation. In this way, Z0 becomes not just a financial control value, but a comprehensive control measure that considers matching rules, direct fund transfer experience, and platform management objectives.

[0148] Furthermore, the platform can also adjust the risk linkage value R1 based on order business scenarios. For example, for order types that are more sensitive to continuous performance and complaint handling, such as maternal and infant care, elderly companionship, and patient assistance, the platform can appropriately increase the weight of b4 and b5, making unresolved complaints and abnormal changes in order status have a greater impact on R1. For casual labor, short-term on-site services, and general hourly cleaning orders, the platform can appropriately increase the weight of b1 and b3 to more sensitively reflect abnormal withdrawal frequency and abnormal device login behavior. In this way, risk linkage control under different business scenarios can be targeted to adjust the factors that are most likely to cause funding and performance problems in that scenario, thereby avoiding the control imbalance caused by applying a fixed set of risk control parameters to all businesses.

[0149] Through the above steps, stage S8 ultimately completes a closed loop, from order settlement chain consistency assessment to account risk linkage assessment, and then to withdrawal limit calculation, limit control, freezing, and unfreezing. The platform's output Q1 at this stage is not only a fund control result, but also a management result resulting from the combined effects of order authenticity, settlement consistency, complaint closure, and account risk status. Thus, the platform achieves dynamic linkage across the entire chain of "service matching—order signing—fulfillment—complaint—settlement—withdrawal," ensuring that order fund flows no longer operate independently of the matching and fulfillment process, but rather, together with the previously generated order characteristic data, dialogue confirmation data, process closure data, and post-matching probability, constitute a complete supply and demand matching and management control system.

[0150] Specifically, the domestic service supply and demand matching operating system of this invention can be deployed on a cloud server, edge computing node, or platform-dedicated data processing terminal, and communicate with demand-side terminals, service personnel terminals, agency management terminals, and platform settlement terminals, respectively. Demand-side terminals are used for order entry, demand confirmation, evaluation feedback, and complaint submission; service personnel terminals are used for registration, order response, dialogue confirmation, service execution feedback, and withdrawal applications; agency management terminals are used for personnel management, capacity maintenance, anomaly handling feedback, and shared resource pool maintenance; and platform settlement terminals are used for order settlement confirmation, payment verification, and risk control review. The system as a whole includes a data acquisition module, a standardization processing module, an initial screening module, a shared routing module, a dialogue confirmation module, a process construction module, a matching calculation module, a result control module, and a risk control processing module.

[0151] The data acquisition module is used to uniformly receive, cache, synchronize, number, and aggregate business data, institutional support data, order interaction data, settlement data, and account risk control data. The standardization processing module is used to perform field splitting, category mapping, sub-item classification, conflict verification, and rule completion on order demand information, converting natural language descriptions into computable order feature data and generating category labels, sub-item labels, constraint labels, and privacy labels. The initial screening module is used to initially screen service personnel based on service adaptation constraints, performance constraints, support constraints, and privacy constraints, and to distinguish between full-time and part-time services based on order activity, continuous service duration, institutional binding status, and support coverage status, thereby forming an initial candidate set. The shared routing module is used to initiate hierarchical expansion matching of shared resource pools within the institution, region, and across institutions when the candidate size or overall matching quality of the initial candidate set is lower than preset conditions, and to record candidate conversion results, routing distance costs, cross-institution hop counts, and institutional support adaptation results to form shared routing data.

[0152] The dialogue confirmation module establishes anonymized direct dialogue channels between the demand side and candidate service personnel within the platform. It extracts demand confirmation events such as service time confirmation, service location confirmation, service content confirmation, service duration confirmation, budget range confirmation, and additional conditions confirmation. It also records the initial response time, number of effective interactions, consistency of confirmation information before and after, and completeness of message logs to form dialogue confirmation data. The process construction module associates and records the status nodes in the processes of onboarding review, online order signing, service execution, evaluation feedback, complaint handling, and settlement processing. It constructs an event state diagram with orders, customers, service personnel, organizations, and accounts as object nodes, forming closed-loop process data. Matching... The calculation module constructs a multi-parameter quantitative model based on order feature data, shared routing data, dialogue confirmation data, and process closed-loop data. It then obtains the posterior matching probability of candidate service personnel through basic adaptation calculations, confirmation corrections, and the fusion of guarantee paths and risk information. The result control module outputs recommendation results based on the posterior matching probability and executes phased information release and online contract signing when conditions are met. The risk control module, after online contract signing, performs risk linkage control based on the platform service fee validity status, commission disabling status, settlement consistency information, and account risk control data to dynamically adjust withdrawal limits and implement limit, freeze, and unfreeze controls when the risk reaches the corresponding threshold. Through the coordinated operation of these modules, the system unifies order standardization processing, candidate screening, shared expansion, dialogue confirmation, closed-loop recording, matching decisions, and settlement risk control into a single operational chain, improving the accuracy of domestic service supply and demand matching, contract conversion rate, performance stability, and fund control security.

[0153] Specifically, the matching calculation module serves as the core computing unit during system operation. However, this module does not operate in isolation; rather, it operates within a parallel mechanism of sequential and coordinated scheduling among the data acquisition module, standardization processing module, initial screening module, shared routing module, dialogue confirmation module, process construction module, result control module, and risk control processing module. The data acquisition module first writes order data, service personnel data, institutional support data, dialogue interaction data, settlement data, and account risk control data into their respective caches. Then, the standardization processing module converts the raw input into structured order feature data and service capability feature data. After standardization, the initial screening module prioritizes basic constraint filtering, pre-eliminating service personnel who clearly do not meet the requirements for service category, regional accessibility, time overlap, support capability, and privacy compatibility. Only the reduced initial candidate set is submitted to the matching calculation module. Through this sequential scheduling method, the system completes data cleaning and candidate shrinking before entering high-complexity calculations, thus avoiding excessive computational burden on the matching calculation module by directly dealing with all service personnel. This demonstrates the scheduling characteristic of first compressing the search space and then performing refined calculations.

[0154] Specifically, the matching calculation module is further divided into a basic adaptation calculation submodule, a confirmation and correction submodule, and a risk fusion submodule. These three submodules operate in a progressive manner, with the result of the previous stage serving as the input for the next stage. The basic adaptation calculation submodule is executed first. Its input mainly comes from the standardization processing module and the initial screening module, and its output is the basic adaptation value of each candidate service personnel for the current order. This stage only processes static features and does not handle dialogue confirmation or risk factors. Therefore, it can complete the first round of numerical differentiation of the initial candidate set in a relatively short time, quickly widening the gap between high-fit and low-fit candidates. After calculating the basic adaptation value, the system does not immediately output recommendations. Instead, it sends the basic adaptation value to the confirmation and correction submodule. Simultaneously, the dialogue confirmation module provides data such as demand confirmation events, first response duration, number of effective interactions, consistency of confirmation information before and after, and message log completeness to perform a second correction on the basic adaptation value. The key feature of this scheduling sequence is that it first uses static conditions to perform a broad-based coarse screening, and then uses communication quality to perform a fine-tuning. This ensures both computational speed and avoids wasting computational resources by prematurely introducing complex dialogue factors into all candidates. After the correction is confirmed, the system schedules the risk fusion submodule to call the shared routing data output by the shared routing module, the process closed-loop data output by the process construction module, and the account risk control data synchronously output by the risk control processing module to perform a final fusion calculation on the confirmed and corrected candidate objects, thereby obtaining the posterior matching probability.

[0155] Specifically, during module scheduling, to balance matching accuracy and processing speed, the system employs different scheduling priorities for different stages. For the initial screening module and the basic adaptation calculation submodule, a high-priority, fast execution mechanism is used to ensure that once an order is standardized, a candidate set and basic adaptation values ​​suitable for subsequent calculations are formed in the shortest possible time. For the confirmation and correction submodule, an event-triggered scheduling mechanism is used. This means that the adaptation values ​​of the corresponding candidate objects are only corrected when the dialogue confirmation module detects a key requirement confirmation event, a change in confirmation status, or a significant change in consistency, rather than recalculating the entire dataset after each ordinary message is sent, thus reducing unnecessary repetitive calculations. For the risk fusion submodule, a phased merging scheduling mechanism is used. This means that unified fusion calculations are performed only after shared routing data, process loop data, and account risk control data meet preset update conditions, thus avoiding frequent partial refreshes that reduce overall computational efficiency. Through this "static fast calculation, dynamic triggering, and phased merging" scheduling structure, the system can maintain the front-end recommendation response speed while promptly correcting matching results when key events occur, demonstrating a scheduling feature that emphasizes both accuracy and speed.

[0156] Specifically, the basic adaptation calculation submodule prioritizes working on the reduced initial candidate set during scheduling. Its key feature is the centralized quantification of differences between order demand characteristics and service capability characteristics using covariance constraints, moving away from the simple approach of considering matching based solely on identical labels. Since the pre-screening has already eliminated candidates that clearly do not meet the constraints, this submodule deals with a candidate set that already possesses basic comparability. Performing vector difference calculations and covariance constraint calculations on this basis allows for higher discrimination accuracy within a smaller computational scale. In other words, the system does not sacrifice accuracy for speed; instead, it eliminates "invalid objects" through pre-filtering, allowing remaining computational resources to be focused on "potentially correct objects," thereby simultaneously improving matching speed and accuracy. The rationale is that if fine-grained matching is performed directly on all objects without initial screening, a large number of clearly mismatched objects will still consume computational resources, prolonging processing time and increasing noise in subsequent sorting. However, by compressing the candidate space through initial screening, fine-grained calculations only apply to true candidate objects, resulting in more stable and faster results.

[0157] Specifically, the scheduling focus of the confirmation and correction submodule is to transform "dialogue content" into "confirmation information that can participate in calculations," and to correct only necessary nodes through an event-driven approach. Confirmations of service time, service location, service content, service duration, budget range, and additional conditions formed during the anonymized direct dialogue between the demand side and candidate service personnel are not all treated the same for correction. Instead, they are processed in layers based on event type, number of confirmations, number of conflicts, degree of consistency, and completeness of record keeping. For repetitive interactions without new information, the system only records them without triggering recalculation; only interactions that cause key fields to change from "unconfirmed" to "explicitly confirmed" or from "consistent" to "conflicting" trigger the confirmation and correction submodule to update the correction adaptation value of the corresponding candidate object. In this way, the system avoids the inefficient method of "recalculating a full match for every message" in terms of calculation speed, while ensuring that key information that truly affects the signing and performance results is included in the calculation in a timely manner in terms of matching accuracy. The basis for this argument is that in domestic service matchmaking scenarios, much of the chat content consists of small talk, confirmation of receipt, or repetitive expressions. These contents have minimal impact on the actual matching relationship. If all of them trigger calculation updates, it will significantly slow down the system response. However, by only allowing key confirmation events to trigger calculations, limited computing power can be concentrated on changes in variables that truly affect the quality of matchmaking, thus forming a more efficient and accurate scheduling mechanism.

[0158] Specifically, the scheduling of the risk fusion submodule is characterized by "post-composite decision-making," meaning that instead of simultaneously incorporating all risk and protection factors into the calculation during the early stages of candidate generation, a comprehensive fusion is performed after basic adaptation and confirmation correction. This design prioritizes answering "Is this person a basic match?" in the initial stage, "Is this person still a match after communication?" in the middle stage, and only finally "Is this person worthy of priority recommendation and suitable for a safe transaction?" The routing hierarchy, conversion results, distance costs, and institutional protection adaptation results output by the shared routing module, the event status diagram and process closed-loop data output by the process construction module, and the account anomaly, device anomaly, unresolved complaints, and status anomaly change data output by the risk control processing module are all uniformly fed into the risk fusion submodule at this stage, forming the final correction to the post-matching probability. The advantage of this approach is that the judgment of protection and risk no longer exists in isolation from the order and communication process, but is based on the premise that "this is a genuine candidate," thus making risk control more focused and protection judgments closer to the actual performance scenario. The basis for this argument is that if all protection and risk control factors are included in the full calculation at the very beginning, not only will the computational cost be high, but many objects that would not have entered the candidate sequence will also be subject to complex risk assessments, resulting in a large amount of invalid calculations. However, if protection and risk fusion is performed only on high-potential candidates in the later stage, the overall processing efficiency can be significantly improved, and the credibility of the final recommendation results can be increased.

[0159] Specifically, two-way linked scheduling is adopted between the result control module and the risk control processing module. The result control module outputs recommendation results according to the posterior matching probability, and controls phased information release and online order signing; after the order signing is completed, the risk control processing module reads settlement consistency, complaint handling status and account risk control data, generates results of withdrawal limit adjustment, limit control, freezing and thawing, and writes the results back to the result control module in reverse. That is, if a candidate service provider has a high posterior matching probability but subsequently has abnormalities in settlement consistency, account behavior or closed-loop complaint handling, the risk control processing module can feed back the risk status to the result control module, so that the system reduces the recommendation priority of the candidate service provider in subsequent similar orders, or restricts the candidate service provider's information release and automatic order signing permissions. Through this reverse writing-back mechanism, the recommendation results of the system are not one-time static output, but can be continuously updated with performance performance and risk status. It is characterized in that the recommendation module and the risk control module are no longer two isolated units disconnected from each other, but form a scheduling cycle of "accurate matching at the front end, closed-loop correction at the back end, and continuous result writing back". The demonstration basis is that: if the recommendation results do not receive subsequent performance and risk feedback, the system can only continuously repeatedly recommend candidates that are superficially matched in history but have poor actual performance; after writing back the risk control and performance feedback to the result control module, the system can continuously correct the real priority of candidate service providers, and the overall matching accuracy, order signing conversion rate and satisfaction will be significantly improved after long-term operation.

[0160] Specifically, from the perspective of the overall system, the reason why the scheduling method of the above modules can simultaneously take into account matching accuracy and processing speed is that the system does not adopt a single-stage, full-volume, static calculation mode, but adopts a scheduling mode that combines hierarchical compression, hierarchical triggering, phased fusion and result writing back. The front section quickly compresses the candidate range through standardized processing and initial screening, the middle section drives local correction through key confirmation events, and the rear section completes high-reliability recommendation through joint decision-making of guarantee and risk, and meanwhile, the risk control processing result continuously feeds back the result control. This not only reduces invalid calculations, but also ensures that factors that really affect order signing and performance can be gradually included, so that at the system operation level, the multi-level scheduling feature of "the closer to the back end, the less data participating in the calculation but the higher the quality, the slower the decision-making but the more accurate" is formed. Therefore, the system of the present invention not only has the function of completing supply and demand matching for housekeeping services, but also embodies the remarkable characteristics of taking accurate matching as the core, rapid response as the requirement, and closed-loop correction as the guarantee in the module collaboration mode.

[0161] Embodiment 1: Complete processing procedure of a day-shift care order for a semi-independent elderly in Daqing

[0162] 1. Original input data of the order

[0163] The demand side enters the following order on the platform:

[0164] Service location: Near Wanda Plaza, Dongfeng New Village, Sartu District, Daqing City, Heilongjiang Province;

[0165] Target client: A 78-year-old woman who is semi-dependent;

[0166] Service period: May 6, 2026 to May 10, 2026, from 08:00 to 18:00 daily;

[0167] Services include: assisting with getting up, toileting, feeding, walking, and short walks within the community;

[0168] Service frequency: 5 consecutive days;

[0169] Budget: 150 yuan / day;

[0170] Additional requirements: Priority will be given to those with experience in elderly care, stable communication skills, and the ability to accept short-distance accompaniment when going out.

[0171] II. Standardized Processing of S1 Order Request Information

[0172] 1. Business Category Mapping

[0173] The platform maps this order as:

[0174] Main business category: Nanny services;

[0175] Service sub-item: Daily living care for elderly people working day shifts;

[0176] Service area: Dongfeng New Village area, Sartu District, Daqing City;

[0177] Service hours: 08:00 to 18:00;

[0178] Service recipient type: Semi-independent elderly;

[0179] Additional needs tags: assisting with getting up, toileting assistance, feeding, walking companionship, short-distance accompaniment on outings.

[0180] 2. Task Intensity Breakdown

[0181] After the platform decomposes the order task through a lightweight semantic understanding model, it obtains:

[0182] The cleaning task intensity value Iq is 1.0;

[0183] The care task intensity value (Ih) is 8.0;

[0184] The cooking assistance task intensity value (Ic) is 0.8;

[0185] The intensity value If for the lifting and assisting task is 4.0;

[0186] The additional intensity value Ie for special scenarios is 1.8.

[0187] Among them, Iq corresponds to the labor intensity of daily tidying and simple cleaning; Ih corresponds to the main care intensity of assisting to get up, toileting, feeding, and walking; If corresponds to the physical exertion of support, mobility assistance, and short-distance walking accompaniment; and Ie corresponds to the additional requirements of 5 consecutive days of service and short-distance accompaniment outings.

[0188] 3. Calculation of the comprehensive task intensity value It

[0189] The background records show that the weight coefficients λ1 is 0.05, λ2 is 0.65, λ3 is 0.05, λ4 is 0.15, and λ5 is 0.10.

[0190] The calculation process for the overall task intensity value It of an order is as follows:

[0191] It=λ1Iq+λ2Ih+λ3Ic+λ4If+λ5Ie

[0192] It=0.05×1.0+0.65×8.0+0.05×0.8+0.15×4.0+0.10×1.8

[0193] It=0.05+5.20+0.04+0.60+0.18

[0194] It = 6.07

[0195] This indicates that the order is a medium-to-high intensity daytime care order for the elderly, with the workload mainly concentrated on care and assistance with moving and lifting.

[0196] 4. Calculation of Standardized Order Value B0

[0197] The platform uses an 8-target field template for this type of order. The backend records the total number of target fields N0 of the template as 8, the number of directly identified valid fields N1 as 7, the number of fields completed by rules N2 as 1, and the number of conflicting fields N3 as 0.

[0198] The calculation process for the standardized order value B0 is as follows:

[0199] B0=(N1+0.8N2) / (N0+N3+1)

[0200] B0=(7+0.8×1) / (8+0+1)

[0201] B0 = 7.8 / 9

[0202] B0 = 0.867

[0203] The results indicate that the order is highly structured and can proceed directly to the subsequent screening process.

[0204] 5. Field Understanding and Confidence Value V0 Calculation

[0205] The background record shows that the rule dictionary matching score K0 is 0.87, the semantic classification model output score S0 is 0.89, the historical manual correction consistency score A0 is 0.85, and the fusion weights r1, r2, and r3 are 0.40, 0.40, and 0.20, respectively.

[0206] The calculation process for the field understanding confidence value V0 is as follows:

[0207] V0=(r1K0+r2S0+r3A0) / (r1+r2+r3)

[0208] V0=(0.40×0.87+0.40×0.89+0.20×0.85) / 1

[0209] V0 = 0.348 + 0.356 + 0.170

[0210] V0 = 0.874

[0211] The result indicates that the text fields of the order are clear and reliable, and can proceed directly to the subsequent matching process.

[0212] III. S2 Initial Screening

[0213] The platform selected two candidate service personnel from the pool of local service personnel in Daqing who were in the same business category as the order.

[0214] 1. Service personnel A registers data in the background.

[0215] The service area covers Sartu District and Longfeng District;

[0216] Service hours are from 07:30 to 19:00;

[0217] Four years of experience in elderly care;

[0218] Physical fitness index (Ep) is 7.1;

[0219] The endurance compatibility index (En) is 7.4.

[0220] The psychological fit index Es is 7.8;

[0221] The organization's binding status is now set to "Organization Exclusive".

[0222] The coverage level is high.

[0223] 2. Service personnel B register data in the background.

[0224] The service area covers Ranghulu District and the western part of Sartu District;

[0225] Service hours are from 09:00 to 17:30;

[0226] One year of experience in elderly care;

[0227] The physical fitness index (Ep) is 5.8.

[0228] The endurance compatibility index (En) is 5.9.

[0229] The psychological fit index Es is 6.4;

[0230] The organization's registration status is "part-time";

[0231] The coverage level is medium.

[0232] 3. Calculation of Task Acceptance Adaptation Value At

[0233] The formula for recording task acceptance and adaptation values ​​in the background is:

[0234] At=μ1Ep+μ2En+μ3Es-μ4×|It-Ep|

[0235] The background records show that the weight coefficients μ1 is 0.40, μ2 is 0.35, μ3 is 0.25, and μ4 is 0.20.

[0236] The calculation process for the task acceptance fit value of service personnel A is as follows:

[0237] At=0.40×7.1+0.35×7.4+0.25×7.8-0.20×|6.07-7.1|

[0238] At=2.84+2.59+1.95-0.206

[0239] At = 7.174

[0240] The calculation process for the task acceptance and fit value of service personnel B is as follows:

[0241] At=0.40×5.8+0.35×5.9+0.25×6.4-0.20×|6.07-5.8|

[0242] At=2.32+2.065+1.60-0.054

[0243] At = 5.931

[0244] The background system records a task acceptance threshold Tt of 6.80. Therefore, service provider A is approved, while service provider B is filtered out.

[0245] 4. Calculation of Regional Reachability Value Ar

[0246] The background records show that the distance Ld from the order location to the service center where service personnel A is stationed is 3.8 kilometers, the registered service radius Rd is 10 kilometers, and the regional commuting congestion correction factor Kd is 1.05.

[0247] The calculation process for the regional reachability value Ar is as follows:

[0248] Ar = Rd / (Ld × Kd + 1)

[0249] Ar = 10 / (3.8 × 1.05 + 1)

[0250] Ar = 10 / 4.99

[0251] Ar = 2.004

[0252] The threshold for the backend area is 1.20, and service personnel A has passed.

[0253] 5. Calculation of time overlap Ot and performance stability value Ac

[0254] The backend records an order service time window length To of 10 hours, and the service time Tp for service personnel A that can overlap is 9.5 hours.

[0255] The calculation process for time overlap Ot is as follows:

[0256] Ot = Tp / To

[0257] Ot = 9.5 / 10

[0258] Ot = 0.95

[0259] The backend records that service personnel A has received 1 order, Nc, and the current service duration Tc is 2.5 hours. The recent average cancellation rate Xc is 0.03, and the weights for calculating the fulfillment stability value are v1 0.40, v2 0.20, v3 0.20, and v4 0.20.

[0260] The calculation process for the performance stability value Ac is as follows:

[0261] Ac=v1Ot+v2×1 / (Nc+1)+v3×1 / (Tc+1)+v4×(1-Xc)

[0262] Ac=0.40×0.95+0.20×1 / 2+0.20×1 / 3.5+0.20×0.97

[0263] Ac=0.38+0.10+0.057+0.194

[0264] Ac = 0.731

[0265] The backend fulfillment threshold is 0.68, and service personnel A has passed.

[0266] 6. Calculation of the guarantee capability value Ag

[0267] The backend records show that service personnel A's organization has the following performance scores: Incident Response Capability (G1) 0.89, Backup Deployment Capability (G2) 0.84, Complaint Handling Timeliness (G3) 0.87, Regional Support Capability (G4) 0.82, and Settlement Cooperation Capability (G5) 0.90. The corresponding weights are s1 0.25, s2 0.20, s3 0.20, s4 0.15, and s5 0.20.

[0268] The calculation process for the protection capability value Ag is as follows:

[0269] Ag=s1G1+s2G2+s3G3+s4G4+s5G5

[0270] Ag=0.25×0.89+0.20×0.84+0.20×0.87+0.15×0.82+0.20×0.90

[0271] Ag=0.2225+0.168+0.174+0.123+0.180

[0272] Ag = 0.8675

[0273] The threshold for elderly care insurance in the background is 0.80, and service personnel A has passed the assessment.

[0274] 7. Privacy-compatible value Ap calculation

[0275] The backend records that the order privacy requirement level Lo is 2, the current open permission level Lp for service personnel A is 2, and the maximum level difference Lmax is 3.

[0276] The calculation process for the privacy compatibility value Ap is as follows:

[0277] Ap = 1 - |Lo - Lp| / Lmax

[0278] Ap = 1 - 0 / 3

[0279] Ap = 1.00

[0280] Service personnel A used privacy restrictions.

[0281] 8. Initial screening result A0

[0282] The background record service adaptation judgment result E1 is 1, the performance judgment result E2 is 1, the protection judgment result E3 is 1, and the privacy judgment result E4 is 1.

[0283] The calculation process for the initial screening result A0 is as follows:

[0284] A0 = E1 × E2 × E3 × E4

[0285] A0 = 1 × 1 × 1 × 1

[0286] A0 = 1

[0287] Therefore, service personnel A is included in the initial candidate set. At this time, the background records show that the initial candidate count N0 is 1.

[0288] IV. S3 Hierarchical Shared Routing

[0289] The background candidate quantity threshold Tn is 2, and the overall matching quality threshold Tq is 0.70. Since N0 = 1, which is less than 2, the system triggers hierarchical shared routing.

[0290] 1. Extended Results of This Organization

[0291] The agency has added service personnel C to its candidate pool, but their service hours are only from 10:00 to 16:00, which does not cover the entire daytime care time required by the order. Therefore, their service hours have not passed the time period review.

[0292] 2. Results of regional expansion

[0293] The platform expanded its candidate pool to include the region where the order was located, adding service provider E. The backend records show that service provider E's service area covers Ranghulu District and Sartu District, with service hours from 08:00 to 18:00. They have 2.5 years of experience in elderly care, a physical fitness index (Ep) of 6.8, an endurance fitness index (En) of 6.9, a psychological fitness index (Es) of 7.2, and a coverage level of medium to high. After reviewing the basic constraints, service provider E was added to the shared expanded candidate pool.

[0294] Therefore, the candidates for entering the subsequent detailed calculation stage include service personnel A and service personnel E.

[0295] V. S4 Dialogue Confirmation

[0296] After the demand side and service personnel A established a direct, anonymized communication channel within the platform, the backend generated the following key confirmation event records:

[0297] Service time confirmed valid 3 times, with 0 conflicts;

[0298] Service location confirmed valid 2 times, with 0 conflicts;

[0299] Service content was confirmed valid 4 times and there were 0 conflicts.

[0300] Service duration confirmed valid 2 times, with 0 conflicts;

[0301] The budget scope was confirmed valid once, with zero conflicts.

[0302] The additional conditions were confirmed to be valid 2 times and there were 0 conflicts.

[0303] The confirmation coefficients for each requirement confirmation event are calculated as follows:

[0304] C1 = 3 / (3 + 0 + 1) = 0.75

[0305] C2=2 / (2+0+1)=0.667

[0306] C3 = 4 / (4 + 0 + 1) = 0.80

[0307] C4=2 / (2+0+1)=0.667

[0308] C5 = 1 / (1 + 0 + 1) = 0.50

[0309] C6=2 / (2+0+1)=0.667

[0310] The background event weights are recorded as ω1 = 0.20, ω2 = 0.10, ω3 = 0.25, ω4 = 0.15, ω5 = 0.10, and ω6 = 0.20.

[0311] The calculation process for the weighted confirmation result Σ(ωj×Cj) is as follows:

[0312] Σ(ωj×Cj)

[0313] = 0.20 × 0.75 + 0.10 × 0.667 + 0.25 × 0.80 + 0.15 × 0.667 + 0.10 × 0.50 + 0.20 × 0.667

[0314] =0.63375

[0315] The background system also records the average response time T of the conversation as 0.10, the message trace completeness L as 0.96, the consistency of the confirmation information before and after as 0.92, the confirmation entropy H as 1.70, and the adjustment coefficients η and μ as 0.35 and 0.40, respectively.

[0316] The calculation process for the dialogue correction value Sd is as follows:

[0317] Sd=[Σ(ωj×Cj)+L+Y] / [1+ηH+μT]

[0318] Sd=(0.63375+0.96+0.92) / (1+0.35×1.70+0.40×0.10)

[0319] Sd = 2.51375 / 1.635

[0320] Sd = 1.537

[0321] VI. S5 Event State Diagram

[0322] The backend generates the following event chain for the order corresponding to service personnel A:

[0323] Onboarding approval → Dialogue establishment → Online order signing → Service commencement → Service completion → Feedback submission → Settlement confirmation

[0324] Currently, there are no complaints, no reverse state transitions, and no duplicate submissions, indicating that this order chain is a highly reliable closed-loop chain.

[0325] VII. S6 calculates the basic adaptation value M0, the corrected adaptation value M1, the guaranteed route value G0, the risk value R0, and the posterior matching probability P0.

[0326] 1. Calculation of basic adaptation value M0

[0327] The backend performs a difference constraint calculation on the order demand feature vector D and the service feature vector P, and obtains a difference constraint value Q of 0.24.

[0328] The calculation process for the basic adaptation value M0 is as follows:

[0329] M0 = 1 / (1 + Q)

[0330] M0 = 1 / 1.24

[0331] M0 = 0.806

[0332] 2. Calculation of calibration fit value M1

[0333] The calculation process for the calibration fit value M1 is as follows:

[0334] M1 = M0 × (1 + Sd)

[0335] M1 = 0.806 × (1 + 1.537)

[0336] M1 = 0.806 × 2.537

[0337] M1 = 2.045

[0338] 3. Ensure the calculation of routing value G0

[0339] The background records show that the reliability coefficients of the best-safety path for service personnel A are 0.93, 0.90, and 0.88, and the path reliability product is:

[0340] 0.93 × 0.90 × 0.88 = 0.7366

[0341] The background records show that the routing distance cost Li is 0.70, the number of cross-institution hops Ji is 0, and the penalty coefficients α and β are 0.15 and 0.25.

[0342] The calculation process for the guaranteed routing value G0 is as follows:

[0343] G0=maxΠri,e×exp(-αLi-βJi)

[0344] G0=0.7366×exp(-0.15×0.70)

[0345] G0=0.7366×exp(-0.105)

[0346] G0 = 0.7366 × 0.900

[0347] G0 = 0.663

[0348] 4. Calculation of Risk Value R0

[0349] The backend records show the following abnormal withdrawal frequency (F1) as 0.04, abnormal account association (F2) as 0.03, abnormal device login (F3) as 0.04, unresolved complaints (F4) as 0, abnormal order status changes (F5) as 0.03, and risk parameters γ0 as -2.20, γ1 as 0.80, γ2 as 0.70, γ3 as 0.60, γ4 as 1.10, and γ5 as 0.90.

[0350] The calculation process for the risk value R0 is as follows:

[0351] R0=1 / [1+exp(-(γ0+γ1F1+γ2F2+γ3F3+γ4F4+γ5F5))]

[0352] R0=1 / [1+exp(-(-2.20+0.80×0.04+0.70×0.03+0.60×0.04+1.10×0+0.90×0.03))]

[0353] The result in parentheses is -2.096.

[0354] R0 = 0.109

[0355] 5. Calculation of posterior matching probability P0

[0356] The backend records the posterior matching probability parameters θ0 as -1.45, θ1 as 1.00, θ2 as 0.90, and θ3 as 1.10.

[0357] The calculation process for the posterior matching probability P0 is as follows:

[0358] P0=1 / [1+exp(-(θ0+θ1M1+θ2G0-θ3R0))]

[0359] P0=1 / [1+exp(-(-1.45+1.00×2.045+0.90×0.663-1.10×0.109))]

[0360] The result in parentheses is 1.072.

[0361] P0 = 0.745

[0362] The background recommendation threshold is 0.72. Therefore, service personnel A meets the recommendation criteria and is listed as the first recommendation position by the result control module.

[0363] VIII. S8 Risk Linkage Control and Withdrawal Limit Calculation

[0364] After the service is completed, the platform enters the risk linkage control phase. The total service fee for this order is 150 yuan per day, for 5 consecutive days, totaling 750 yuan.

[0365] 1. Calculation of direct settlement control value Z0

[0366] The backend records the platform service fee validity status F0 as 1, the commission disabled status J0 as 1, the payment consistency status Y0 as 0.98, and the service completion confirmation status W0 as 1.

[0367] The calculation process for the direct settlement control value Z0 is as follows:

[0368] Z0=a1F0+a2J0+a3Y0+a4W0Z0=0.25×1+0.25×1+0.25×0.98+0.25×1

[0369] Z0 = 0.995

[0370] 2. Calculation of Risk Linkage Value R1

[0371] The backend records show the following abnormal withdrawal frequency (F1) as 0.03, abnormal account association (F2) as 0.02, abnormal device login (F3) as 0.02, unresolved complaints (F4) as 0, abnormal order status changes (F5) as 0.02, and risk linkage parameters (b1) as 0.35, b2 as 0.15, b3 as 0.15, b4 as 0.20, and b5 as 0.15.

[0372] The calculation process for the risk linkage value R1 is as follows:

[0373] R1=b1F1+b2F2+b3F3+b4F4+b5F5

[0374] R1=0.35×0.03+0.15×0.02+0.15×0.02+0.20×0+0.15×0.02

[0375] R1=0.0105+0.003+0.003+0+0.003

[0376] R1 = 0.0195

[0377] 3. Calculation of daily cash withdrawal limit Q1

[0378] The system uses the current settlementable balance of 750 yuan as the basic withdrawal limit Qb.

[0379] The calculation process for the daily withdrawable amount Q1 is as follows:

[0380] Q1 = Qb × Z0 / (1 + R1)

[0381] Q1 = 750 × 0.995 / 1.0195

[0382] Q1 = 731.9 yuan

[0383] The first risk threshold recorded in the backend is 0.12, and the second risk threshold is 0.30. Since R1 = 0.0195 is less than 0.12, this transaction will proceed normally and will not trigger any limits or freeze controls.

[0384] Example 2: Complete Processing of Daytime Care Orders for Semi-Independent Elderly in Harbin

[0385] I. Original Order Input Data

[0386] The demand side enters the following orders on the platform:

[0387] Service location: Near Wanda Plaza, Nangang District, Harbin, Heilongjiang Province;

[0388] Target client: An 81-year-old male, partially self-reliant;

[0389] Service hours: May 12, 2026 to May 16, 2026, from 08:30 to 18:30 daily;

[0390] Services include: assisting with getting up, toileting, feeding, walking indoors, accompanying someone for short activities downstairs, and accompanying someone to a community clinic once.

[0391] Service frequency: 5 consecutive days;

[0392] Budget: 220 yuan / day;

[0393] Additional requirements: Priority will be given to candidates with experience in elderly care, who can accompany patients to medical appointments, and who are patient and stable in their communication.

[0394] II. Standardized Processing of S1 Order Request Information

[0395] 1. Business Category Mapping

[0396] The platform maps this order as:

[0397] Main business category: Nanny services;

[0398] Service sub-item: Daily living care for elderly people working day shifts;

[0399] Service area: Haxi area, Nangang District, Harbin City;

[0400] Service hours: 08:30 to 18:30;

[0401] Service recipient type: Semi-independent elderly;

[0402] Additional needs tags: assisting with getting up, toileting assistance, feeding, walking companion, short-term medical accompaniment.

[0403] 2. Task Intensity Breakdown

[0404] After the platform decomposes the order task through a lightweight semantic understanding model, it obtains:

[0405] The cleaning task intensity value Iq is 1.1;

[0406] The care task intensity value (Ih) is 8.3.

[0407] The cooking assistance task intensity value (Ic) is 0.7;

[0408] The intensity value If for the lifting and assisting task is 4.6;

[0409] The additional intensity value Ie for special scenarios is 2.2.

[0410] Among them, Iq corresponds to the labor intensity of daily environmental tidying and simple incidental cleaning; Ih corresponds to the main care intensity formed by assisting to get up, toileting, feeding, and walking; If corresponds to the physical exertion formed by accompanying walking, supporting, and assisting movement; and Ie corresponds to the additional requirements brought about by accompanying patients to community clinics and providing services for 5 consecutive days.

[0411] 3. Calculation of the comprehensive task intensity value It

[0412] The background records show that the weight coefficients λ1 is 0.05, λ2 is 0.65, λ3 is 0.05, λ4 is 0.15, and λ5 is 0.10.

[0413] The calculation process for the overall task intensity value It of an order is as follows:

[0414] It=λ1Iq+λ2Ih+λ3Ic+λ4If+λ5Ie

[0415] It=0.05×1.1+0.65×8.3+0.05×0.7+0.15×4.6+0.10×2.2

[0416] It=0.055+5.395+0.035+0.690+0.220

[0417] It = 6.395

[0418] This indicates that the order is a medium-to-high intensity daytime care order for the elderly, with the workload mainly concentrated on care and assistance with moving and lifting.

[0419] 4. Calculation of Standardized Order Value B0

[0420] The platform uses an 8-target field template for this type of order. The backend records the total number of target fields N0 of the template as 8, the number of directly identified valid fields N1 as 7, the number of fields completed by rules N2 as 1, and the number of conflicting fields N3 as 0.

[0421] The calculation process for the standardized order value B0 is as follows:

[0422] B0=(N1+0.8N2) / (N0+N3+1)

[0423] B0=(7+0.8×1) / (8+0+1)

[0424] B0 = 7.8 / 9

[0425] B0 = 0.867

[0426] The result indicates that the order is highly structured and can proceed directly to the subsequent screening process.

[0427] 5. Field Understanding and Confidence Value V0 Calculation

[0428] The background record shows that the rule dictionary matching score K0 is 0.86, the semantic classification model output score S0 is 0.90, the historical manual correction consistency score A0 is 0.84, and the fusion weights r1, r2, and r3 are 0.40, r3, and r4, respectively.

[0429] The calculation process for the field understanding confidence value V0 is as follows:

[0430] V0=(r1K0+r2S0+r3A0) / (r1+r2+r3)

[0431] V0=(0.40×0.86+0.40×0.90+0.20×0.84) / 1

[0432] V0 = 0.344 + 0.360 + 0.168

[0433] V0 = 0.872

[0434] The result indicates that the text fields of the order are clear and reliable, and can proceed directly to the subsequent matching process.

[0435] III. S2 Initial Screening

[0436] The platform selected two candidate service personnel from the pool of local service personnel in Harbin who were in the same business category as the order.

[0437] 1. Service personnel A registers data in the background.

[0438] The service area covers Nangang District and Daoli District;

[0439] Service hours are from 08:00 to 19:00;

[0440] The elderly care experience is 3.5 years;

[0441] Physical fitness index (Ep) is 7.0;

[0442] The endurance compatibility index (En) is 7.3.

[0443] The psychological fit index Es is 7.6;

[0444] The organization's binding status is now set to "Organization Exclusive".

[0445] The coverage level is high.

[0446] 2. Service personnel B register data in the background.

[0447] The service area covers Xiangfang District and the eastern part of Nangang District;

[0448] Service hours are from 09:30 to 17:30;

[0449] The elderly care experience is 1.5 years;

[0450] Physical fitness index (Ep) is 6.0;

[0451] The endurance compatibility index (En) is 6.1.

[0452] The psychological fit index Es is 6.5;

[0453] The organization's registration status is "part-time";

[0454] The coverage level is medium.

[0455] 3. Calculation of Task Acceptance Adaptation Value At

[0456] The formula for recording task acceptance and adaptation values ​​in the background is:

[0457] At=μ1Ep+μ2En+μ3Es-μ4×|It-Ep|

[0458] The background records show that the weight coefficients μ1 is 0.40, μ2 is 0.35, μ3 is 0.25, and μ4 is 0.20.

[0459] The calculation process for the task acceptance fit value of service personnel A is as follows:

[0460] At=0.40×7.0+0.35×7.3+0.25×7.6-0.20×|6.395-7.0|

[0461] At=2.80+2.555+1.90-0.121

[0462] At = 7.134

[0463] The calculation process for the task acceptance and fit value of service personnel B is as follows:

[0464] At=0.40×6.0+0.35×6.1+0.25×6.5-0.20×|6.395-6.0|

[0465] At=2.40+2.135+1.625-0.079

[0466] At = 6.081

[0467] The background system records a task acceptance threshold Tt of 6.90. Therefore, service provider A is approved, while service provider B is filtered out.

[0468] 4. Calculation of Regional Reachability Value Ar

[0469] The background records show that the distance Ld from the order location to the service center where service personnel A is stationed is 4.6 kilometers, the registered service radius Rd is 11 kilometers, and the regional commuting congestion correction factor Kd is 1.08.

[0470] The calculation process for the regional reachability value Ar is as follows:

[0471] Ar = Rd / (Ld × Kd + 1)

[0472] Ar = 11 / (4.6 × 1.08 + 1)

[0473] Ar = 11 / 5.968

[0474] Ar = 1.843

[0475] The threshold for the backend area is 1.20, and service personnel A has passed.

[0476] 5. Calculation of time overlap Ot and performance stability value Ac

[0477] The backend records an order service time window length To of 10 hours, and the service time Tp for service personnel A that can overlap is 9.5 hours.

[0478] The calculation process for time overlap Ot is as follows:

[0479] Ot = Tp / To

[0480] Ot = 9.5 / 10

[0481] Ot = 0.95

[0482] The backend records show that service personnel A has received 2 orders (Nc), has a current service duration of 4 hours (Tc), a recent average cancellation rate (Xc) of 0.04, and has performance stability value calculation weights of 0.40 for v1, 0.20 for v2, 0.20 for v3, and 0.20 for v4.

[0483] The calculation process for the performance stability value Ac is as follows:

[0484] Ac=v1Ot+v2×1 / (Nc+1)+v3×1 / (Tc+1)+v4×(1-Xc)

[0485] Ac=0.40×0.95+0.20×1 / 3+0.20×1 / 5+0.20×0.96

[0486] Ac=0.38+0.0667+0.04+0.192

[0487] Ac = 0.6787

[0488] The backend fulfillment threshold is 0.66, and service personnel A has passed.

[0489] 6. Calculation of the guarantee capability value Ag

[0490] The backend records show that service personnel A's organization has the following performance scores: Incident Response Capability (G1) 0.87, Backup Deployment Capability (G2) 0.82, Complaint Handling Timeliness (G3) 0.84, Regional Support Capability (G4) 0.80, and Settlement Cooperation Capability (G5) 0.88. The corresponding weights are s1 0.25, s2 0.20, s3 0.20, s4 0.15, and s5 0.20.

[0491] The calculation process for the protection capability value Ag is as follows:

[0492] Ag=s1G1+s2G2+s3G3+s4G4+s5G5

[0493] Ag=0.25×0.87+0.20×0.82+0.20×0.84+0.15×0.80+0.20×0.88

[0494] Ag=0.2175+0.164+0.168+0.120+0.176

[0495] Ag = 0.8455

[0496] The threshold for elderly care insurance in the background is 0.80, and service personnel A has passed the assessment.

[0497] 7. Privacy-compatible value Ap calculation

[0498] The backend records that the order privacy requirement level Lo is 2, the current open permission level Lp for service personnel A is 2, and the maximum level difference Lmax is 3.

[0499] The calculation process for the privacy compatibility value Ap is as follows:

[0500] Ap = 1 - |Lo - Lp| / Lmax

[0501] Ap = 1 - 0 / 3

[0502] Ap = 1.00

[0503] Service personnel A used privacy restrictions.

[0504] 8. Initial screening result A0

[0505] The background record service adaptation judgment result E1 is 1, the performance judgment result E2 is 1, the protection judgment result E3 is 1, and the privacy judgment result E4 is 1.

[0506] The calculation process for the initial screening result A0 is as follows:

[0507] A0 = E1 × E2 × E3 × E4

[0508] A0 = 1 × 1 × 1 × 1

[0509] A0 = 1

[0510] Therefore, service personnel A is included in the initial candidate set. At this time, the background records show that the initial candidate count N0 is 1.

[0511] IV. S3 Hierarchical Shared Routing

[0512] The background candidate quantity threshold Tn is 2, and the overall matching quality threshold Tq is 0.70. Since N0 = 1, which is less than 2, the system triggers hierarchical shared routing.

[0513] 1. Extended Results of This Organization

[0514] The organization has added service personnel C to its candidate pool, but their service area is mainly located in Hulan District, and the actual cost of reaching them is too high. After regional accessibility verification, they have been filtered out.

[0515] 2. Results of regional expansion

[0516] The platform expanded its candidate pool to include the region where the order was located, adding service provider E. Backend records show that service provider E's service area covers Nangang and Daoli districts, with service hours from 8:30 AM to 6:30 PM. They have 2.8 years of experience in elderly care, a physical fitness index (Ep) of 6.8, an endurance fitness index (En) of 6.9, a psychological fitness index (Es) of 7.2, and a coverage level of medium to high. After reviewing the basic constraints, service provider E was added to the shared expanded candidate pool.

[0517] Therefore, the candidates for entering the subsequent detailed calculation stage include service personnel A and service personnel E.

[0518] V. S4 Dialogue Confirmation

[0519] After the demand side and service personnel A established a direct, anonymized communication channel within the platform, the backend generated the following key confirmation event records:

[0520] Service time confirmed valid 3 times, with 0 conflicts;

[0521] Service location confirmed valid 2 times, with 0 conflicts;

[0522] Service content was confirmed valid 3 times and there were 0 conflicts;

[0523] Service duration confirmed valid 2 times, with 0 conflicts;

[0524] The budget scope was confirmed valid twice and there was one conflict.

[0525] The additional conditions were confirmed to be valid 2 times and there were 0 conflicts.

[0526] The confirmation coefficients for each requirement confirmation event are calculated as follows:

[0527] C1 = 3 / (3 + 0 + 1) = 0.75

[0528] C2=2 / (2+0+1)=0.667

[0529] C3 = 3 / (3 + 0 + 1) = 0.75

[0530] C4=2 / (2+0+1)=0.667

[0531] C5 = 2 / (2 + 1 + 1) = 0.50

[0532] C6=2 / (2+0+1)=0.667

[0533] The background event weights are recorded as ω1 = 0.20, ω2 = 0.10, ω3 = 0.25, ω4 = 0.15, ω5 = 0.10, and ω6 = 0.20.

[0534] The calculation process for the weighted confirmation result Σ(ωj×Cj) is as follows:

[0535] Σ(ωj×Cj)

[0536] = 0.20 × 0.75 + 0.10 × 0.667 + 0.25 × 0.75 + 0.15 × 0.667 + 0.10 × 0.50 + 0.20 × 0.667

[0537] =0.68715

[0538] The background system also recorded the average response time T of the conversation as 0.18, the message trace completeness L as 0.91, the consistency of the confirmation information Y as 0.84, the confirmation entropy H as 1.76, and the adjustment coefficients η and μ as 0.35 and 0.40, respectively.

[0539] The calculation process for the dialogue correction value Sd is as follows:

[0540] Sd=[Σ(ωj×Cj)+L+Y] / [1+ηH+μT]

[0541] Sd=(0.68715+0.91+0.84) / (1+0.35×1.76+0.40×0.18)

[0542] Sd = 2.43715 / 1.688

[0543] Sd = 1.444

[0544] VI. S5 Event State Diagram

[0545] The backend generates the following event chain for the order corresponding to service personnel A:

[0546] Onboarding approval → Dialogue established → Online order confirmation → Service commencement → Service completion → Complaint initiated → Settlement pending confirmation

[0547] Because a complaint was triggered after the service was completed due to the escort's return time being later than expected by the demand side, and the complaint is not yet closed, the order chain has formed a main event chain, but it is not a completely closed chain.

[0548] VII. S6 calculates the basic adaptation value M0, the corrected adaptation value M1, the guaranteed route value G0, the risk value R0, and the posterior matching probability P0.

[0549] 1. Calculation of basic adaptation value M0

[0550] The backend performs a difference constraint calculation on the order demand feature vector D and the service feature vector P, and obtains a difference constraint value Q of 0.27.

[0551] The calculation process for the basic adaptation value M0 is as follows:

[0552] M0 = 1 / (1 + Q)

[0553] M0 = 1 / 1.27

[0554] M0 = 0.787

[0555] 2. Calculation of calibration fit value M1

[0556] The calculation process for the calibration fit value M1 is as follows:

[0557] M1 = M0 × (1 + Sd)

[0558] M1 = 0.787 × (1 + 1.444)

[0559] M1 = 0.787 × 2.444

[0560] M1 = 1.923

[0561] 3. Ensure the calculation of routing value G0

[0562] The background records show that the reliability coefficients of the best-safety path for service personnel A are 0.91, 0.88, and 0.86, and the path reliability product is:

[0563] 0.91 × 0.88 × 0.86 = 0.688

[0564] The backend records that the routing distance cost Li is 0.90, the number of cross-institution hops Ji is 0, and the penalty coefficients α and β are 0.15 and 0.25.

[0565] The calculation process for the guaranteed routing value G0 is as follows:

[0566] G0=maxΠri,e×exp(-αLi-βJi)

[0567] G0=0.688×exp(-0.15×0.90)

[0568] G0 = 0.688 × exp(-0.135)

[0569] G0 = 0.688 × 0.874

[0570] G0 = 0.601

[0571] 4. Calculation of Risk Value R0

[0572] The backend records show the following abnormal withdrawal frequency (F1) as 0.05, abnormal account association (F2) as 0.03, abnormal device login (F3) as 0.04, unresolved complaints (F4) as 0.22, abnormal order status changes (F5) as 0.06, and risk parameters γ0 as -2.05, γ1 as 0.80, γ2 as 0.70, γ3 as 0.60, γ4 as 1.10, and γ5 as 0.90.

[0573] The calculation process for the risk value R0 is as follows:

[0574] R0=1 / [1+exp(-(γ0+γ1F1+γ2F2+γ3F3+γ4F4+γ5F5))]

[0575] R0=1 / [1+exp(-(-2.05+0.80×0.05+0.70×0.03+0.60×0.04+1.10×0.22+0.90×0.06))]

[0576] The result in parentheses is -1.669.

[0577] R0 = 0.159

[0578] 5. Calculation of posterior matching probability P0

[0579] The backend records the posterior matching probability parameters θ0 as -1.45, θ1 as 1.00, θ2 as 0.90, and θ3 as 1.10.

[0580] The calculation process for the posterior matching probability P0 is as follows:

[0581] P0=1 / [1+exp(-(θ0+θ1M1+θ2G0-θ3R0))]

[0582] P0=1 / [1+exp(-(-1.45+1.00×1.923+0.90×0.601-1.10×0.159))]

[0583] The result in parentheses is 0.839.

[0584] P0 = 0.698

[0585] The background recommendation threshold is 0.68. Therefore, service personnel A meets the recommendation criteria and is listed as the first recommendation position by the result control module.

[0586] VIII. S8 Risk Linkage Control and Withdrawal Limit Calculation

[0587] After the service is completed, the platform enters the risk linkage control phase. The total service fee for this order is 220 yuan per day, for 5 consecutive days, totaling 1100 yuan.

[0588] 1. Calculation of direct settlement control value Z0

[0589] The backend records the platform service fee validity status F0 as 1, the commission disabled status J0 as 1, the payment consistency status Y0 as 0.95, and the service completion confirmation status W0 as 0.85.

[0590] The calculation process for the direct settlement control value Z0 is as follows:

[0591] Z0=a1F0+a2J0+a3Y0+a4W0

[0592] Z0=0.25×1+0.25×1+0.25×0.95+0.25×0.85

[0593] Z0 = 0.9625

[0594] 2. Calculation of Risk Linkage Value R1

[0595] The backend records show the following abnormal withdrawal frequency (F1) as 0.05, abnormal account association (F2) as 0.03, abnormal device login (F3) as 0.04, unresolved complaints (F4) as 0.35, abnormal order status changes (F5) as 0.08, and risk linkage parameters (b1) as 0.35, b2 as 0.15, b3 as 0.15, b4 as 0.20, and b5 as 0.15.

[0596] The calculation process for the risk linkage value R1 is as follows:

[0597] R1=b1F1+b2F2+b3F3+b4F4+b5F5

[0598] R1=0.35×0.05+0.15×0.03+0.15×0.04+0.20×0.35+0.15×0.08

[0599] R1=0.0175+0.0045+0.006+0.070+0.012

[0600] R1 = 0.110

[0601] 3. Calculation of daily cash withdrawal limit Q1

[0602] The system uses the current settlement balance of 1100 yuan as the basic withdrawal limit Qb.

[0603] The calculation process for the daily withdrawable amount Q1 is as follows:

[0604] Q1 = Qb × Z0 / (1 + R1)

[0605] Q1 = 1100 × 0.9625 / 1.110

[0606] Q1 = 953.8 yuan

[0607] The first risk threshold recorded in the backend is 0.10, and the second risk threshold is 0.30. Since R1 = 0.110 is greater than or equal to 0.10 and less than 0.30, this order has entered a limited withdrawal state, allowing an initial withdrawal of 953.8 yuan. The remaining amount will be released after the complaint is closed.

[0608] Example 3: Complete Processing of Deep Cleaning Orders for New Homes in Daqing

[0609] I. Original Order Input Data

[0610] The demand side enters the following orders on the platform:

[0611] Service Location: Jingliu Street Area, Dongfeng New Village, Sartu District, Daqing City, Heilongjiang Province;

[0612] Building area: 118 square meters;

[0613] Property status: New home, ready for occupancy;

[0614] Appointment service period: 08:30 to 17:30 on May 18, 2026;

[0615] Services include: whole house floor cleaning, kitchen grease removal, bathroom deep cleaning, glass wiping, cabinet surface dusting, and cleaning of door and window corners;

[0616] Additional conditions: Service personnel must bring their own basic tools, have priority given to those with post-construction cleaning experience, and must complete the work on the same day;

[0617] Order payment budget: 420 yuan / order.

[0618] II. Standardized Processing of S1 Order Request Information

[0619] 1. Business Category Mapping

[0620] The platform maps this order as:

[0621] Main business category: Cleaning services;

[0622] Service Sub-item: Deep Cleaning of New Homes;

[0623] Service area: Dongfeng New Village area, Sartu District, Daqing City;

[0624] Service hours: 08:30 to 17:30;

[0625] Budget range: 420 yuan per order;

[0626] Additional requirements tags: glass cleaning, heavy oil stain removal, corner cleaning, bring your own tools, same-day completion.

[0627] 2. Task Intensity Breakdown

[0628] After the platform decomposes the order task through a lightweight semantic understanding model, it obtains:

[0629] The cleaning task intensity value (Iq) is 8.9;

[0630] The care task intensity value Ih is 0;

[0631] The cooking assistance task intensity value Ic is 0;

[0632] The intensity value If for carrying or assisting in physical labor tasks is 3.2;

[0633] The additional strength value Ie for special environments is 2.4.

[0634] Among them, Iq corresponds to the main labor intensity brought about by deep cleaning of new homes, kitchen grease removal, deep cleaning of bathrooms, and glass wiping; If corresponds to the physical exertion of moving tools, adjusting ladders and stools, and continuous cleaning of multiple rooms; Ie corresponds to the additional requirements brought about by cleaning corners and dead corners and completing the task on the same day.

[0635] 3. Calculation of the comprehensive task intensity value It

[0636] The background records show that the weight coefficients λ1 is 0.75, λ2 is 0.05, λ3 is 0.05, λ4 is 0.10, and λ5 is 0.05.

[0637] The calculation process for the overall task intensity value It of an order is as follows:

[0638] It=λ1Iq+λ2Ih+λ3Ic+λ4If+λ5Ie

[0639] It=0.75×8.9+0.05×0+0.05×0+0.10×3.2+0.05×2.4

[0640] It=6.675+0+0+0.320+0.120

[0641] It = 7.115

[0642] This indicates that the order is a high-intensity cleaning order, with the task intensity mainly concentrated on deep cleaning, oil stain removal, and cleaning of corners and details.

[0643] 4. Calculation of Standardized Order Value B0

[0644] The platform uses an 8-target field template for this type of order. The backend records the total number of target fields N0 of the template as 8, the number of directly identified valid fields N1 as 7, the number of completed fields N2 as 1 after rule completion, and the number of conflicting fields N3 as 0.

[0645] The calculation process for the standardized order value B0 is as follows:

[0646] B0=(N1+0.8N2) / (N0+N3+1)

[0647] B0=(7+0.8×1) / (8+0+1)

[0648] B0 = 7.8 / 9

[0649] B0 = 0.867

[0650] The results indicate that the order is highly structured and can proceed directly to the next screening stage.

[0651] 5. Field Understanding and Confidence Value V0 Calculation

[0652] The background record shows that the rule dictionary matching score K0 is 0.90, the semantic classification model output score S0 is 0.92, the historical manual correction consistency score A0 is 0.88, and the fusion weights r1, r2, and r3 are 0.40, 0.40, and 0.20, respectively.

[0653] The calculation process for the field understanding confidence value V0 is as follows:

[0654] V0=(r1K0+r2S0+r3A0) / (r1+r2+r3)

[0655] V0=(0.40×0.90+0.40×0.92+0.20×0.88) / 1

[0656] V0 = 0.360 + 0.368 + 0.176

[0657] V0 = 0.904

[0658] The result indicates that the text fields of the order are clear and reliable, and can proceed directly to the subsequent matching process.

[0659] III. S2 Initial Screening

[0660] The platform selected two candidate service personnel from the pool of local service personnel in Daqing who were in the same business category as the order.

[0661] 1. Service personnel A registers data in the background.

[0662] The service area covers Sartu District and Longfeng District;

[0663] Service hours are from 08:00 to 18:00;

[0664] Deep cleaning experience: 3.8 years;

[0665] The physical fitness index (Ep) is 7.9;

[0666] The endurance compatibility index (En) is 7.6.

[0667] The psychological fit index Es is 7.1;

[0668] The organization's binding status is now set to "Organization Exclusive".

[0669] The coverage level is medium to high.

[0670] 2. Service personnel B register data in the background.

[0671] The service area covers Ranghulu District and the western part of Sartu District;

[0672] Service hours are from 09:00 to 17:00;

[0673] Deep cleaning experience: 1.6 years;

[0674] The physical fitness index (Ep) is 6.4.

[0675] The endurance compatibility index (En) is 6.1.

[0676] The psychological fit index Es is 6.8;

[0677] The organization's registration status is "part-time";

[0678] The coverage level is medium.

[0679] 3. Calculation of Task Acceptance Adaptation Value At

[0680] The formula for recording task acceptance and adaptation values ​​in the background is:

[0681] At=μ1Ep+μ2En+μ3Es-μ4×|It-Ep|

[0682] The background records show that the weight coefficients μ1 is 0.45, μ2 is 0.35, μ3 is 0.20, and μ4 is 0.20.

[0683] The calculation process for the task acceptance fit value of service personnel A is as follows:

[0684] At=0.45×7.9+0.35×7.6+0.20×7.1-0.20×|7.115-7.9|

[0685] At=3.555+2.660+1.420-0.157

[0686] At = 7.478

[0687] The calculation process for the task acceptance and fit value of service personnel B is as follows:

[0688] At=0.45×6.4+0.35×6.1+0.20×6.8-0.20×|7.115-6.4|

[0689] At=2.880+2.135+1.360-0.143

[0690] At = 6.232

[0691] The background system records a task acceptance threshold Tt of 7.00. Therefore, service provider A is approved, while service provider B is filtered out.

[0692] 4. Calculation of Regional Reachability Value Ar

[0693] The background records show that the distance Ld from the order location to the service center where service personnel A is stationed is 4.2 kilometers, the registered service radius Rd is 11 kilometers, and the regional commuting congestion correction factor Kd is 1.05.

[0694] The calculation process for the regional reachability value Ar is as follows:

[0695] Ar = Rd / (Ld × Kd + 1)

[0696] Ar = 11 / (4.2 × 1.05 + 1)

[0697] Ar = 11 / 5.41

[0698] Ar = 2.033

[0699] The threshold for the backend area is 1.20, and service personnel A has passed.

[0700] 5. Calculation of time overlap Ot and performance stability value Ac

[0701] The backend records an order service time window length To of 9 hours, and the service time Tp for service personnel A that can overlap is 8.5 hours.

[0702] The calculation process for time overlap Ot is as follows:

[0703] Ot = Tp / To

[0704] Ot = 8.5 / 9

[0705] Ot = 0.944

[0706] The backend records that service personnel A has received 1 order, Nc, and the current service duration Tc is 2 hours. The recent average cancellation rate Xc is 0.02, and the weights for calculating the fulfillment stability value are v1 0.40, v2 0.20, v3 0.20, and v4 0.20.

[0707] The calculation process for the performance stability value Ac is as follows:

[0708] Ac=v1Ot+v2×1 / (Nc+1)+v3×1 / (Tc+1)+v4×(1-Xc)

[0709] Ac=0.40×0.944+0.20×1 / 2+0.20×1 / 3+0.20×0.98

[0710] Ac=0.3776+0.1000+0.0667+0.196

[0711] Ac = 0.7403

[0712] The backend fulfillment threshold is 0.68, and service personnel A has passed.

[0713] 6. Calculation of the guarantee capability value Ag

[0714] The backend records show that service personnel A's organization has the following performance scores: Incident Response Capability (G1) 0.83, Backup Deployment Capability (G2) 0.81, Complaint Handling Timeliness (G3) 0.84, Regional Support Capability (G4) 0.80, and Settlement Cooperation Capability (G5) 0.88. The corresponding weights are s1 0.25, s2 0.20, s3 0.20, s4 0.15, and s5 0.20.

[0715] The calculation process for the protection capability value Ag is as follows:

[0716] Ag=s1G1+s2G2+s3G3+s4G4+s5G5

[0717] Ag=0.25×0.83+0.20×0.81+0.20×0.84+0.15×0.80+0.20×0.88

[0718] Ag=0.2075+0.162+0.168+0.120+0.176

[0719] Ag = 0.8335

[0720] The threshold for back-office cleaning services is 0.75, and service personnel A has passed the test.

[0721] 7. Privacy-compatible value Ap calculation

[0722] The backend records that the order privacy requirement level Lo is 1, the current open permission level Lp for service personnel A is 1, and the maximum level difference Lmax is 3.

[0723] The calculation process for the privacy compatibility value Ap is as follows:

[0724] Ap = 1 - |Lo - Lp| / Lmax

[0725] Ap = 1 - 0 / 3

[0726] Ap = 1.00

[0727] Service personnel A used privacy restrictions.

[0728] 8. Initial screening result A0

[0729] The background record service adaptation judgment result E1 is 1, the performance judgment result E2 is 1, the protection judgment result E3 is 1, and the privacy judgment result E4 is 1.

[0730] The calculation process for the initial screening result A0 is as follows:

[0731] A0 = E1 × E2 × E3 × E4

[0732] A0 = 1 × 1 × 1 × 1

[0733] A0 = 1

[0734] Therefore, service personnel A is included in the initial candidate set. At this time, the background records show that the initial candidate count N0 is 1.

[0735] IV. S3 Hierarchical Shared Routing

[0736] The background candidate quantity threshold Tn is 2, and the overall matching quality threshold Tq is 0.70. Since N0 = 1, which is less than 2, the system triggers hierarchical shared routing.

[0737] 1. Expanded Results of This Organization

[0738] The organization has added service personnel C to its candidate pool, but their service hours are only from 10:00 to 16:00, and they have not registered their ability to bring their own tools. Therefore, they did not pass the time period and additional conditions review.

[0739] 2. Results of regional expansion

[0740] The platform expanded its candidate pool to include service personnel E within the order's region. Backend records show that service personnel E's service area covers Sartu District and Longfeng District, with service hours from 08:30 to 18:00. They have 2.9 years of deep cleaning experience, a physical fitness index (Ep) of 7.1, an endurance fitness index (En) of 7.0, a psychological fitness index (Es) of 7.0, and a coverage level of medium-high. After basic constraint review, service personnel E was added to the shared expanded candidate pool.

[0741] Therefore, the candidates for entering the subsequent detailed calculation stage include service personnel A and service personnel E.

[0742] V. S4 Dialogue Confirmation

[0743] After the demand side and service personnel A established a direct, anonymized communication channel within the platform, the backend generated the following key confirmation event records:

[0744] Service time confirmed valid 2 times, with 0 conflicts;

[0745] Service location confirmed valid 2 times, with 0 conflicts;

[0746] Service content was confirmed valid 4 times and conflicted 1 time;

[0747] Service duration confirmed valid 2 times, with 0 conflicts;

[0748] The budget scope was confirmed valid twice and there were zero conflicts.

[0749] The additional conditions were confirmed to be valid 3 times and there were 0 conflicts.

[0750] The confirmation coefficients for each requirement confirmation event are calculated as follows:

[0751] C1=2 / (2+0+1)=0.667

[0752] C2=2 / (2+0+1)=0.667

[0753] C3=4 / (4+1+1)=0.667

[0754] C4=2 / (2+0+1)=0.667

[0755] C5=2 / (2+0+1)=0.667

[0756] C6 = 3 / (3 + 0 + 1) = 0.75

[0757] The background event weights are recorded as ω1 = 0.15, ω2 = 0.10, ω3 = 0.30, ω4 = 0.15, ω5 = 0.10, and ω6 = 0.20.

[0758] The calculation process for the weighted confirmation result Σ(ωj×Cj) is as follows:

[0759] Σ(ωj×Cj)

[0760] = 0.15 × 0.667 + 0.10 × 0.667 + 0.30 × 0.667 + 0.15 × 0.667 + 0.10 × 0.667 + 0.20 × 0.75

[0761] =0.6501

[0762] The background system also recorded the average response time T of the conversation as 0.14, the message trace completeness L as 0.93, the consistency of the confirmation information Y as 0.88, the confirmation entropy H as 1.79, and the adjustment coefficients η and μ as 0.35 and 0.40, respectively.

[0763] The calculation process for the dialogue correction value Sd is as follows:

[0764] Sd=[Σ(ωj×Cj)+L+Y] / [1+ηH+μT]

[0765] Sd=(0.6501+0.93+0.88) / (1+0.35×1.79+0.40×0.14)

[0766] Sd = 2.4601 / 1.6825

[0767] Sd = 1.462

[0768] VI. S5 Event State Diagram

[0769] The backend generates the following event chain for the order corresponding to service personnel A:

[0770] Onboarding approval → Dialogue establishment → Online order signing → Service commencement → Service completion → Feedback submission → Settlement confirmation

[0771] Currently, there are no complaints, no reverse state transitions, and no duplicate submissions, indicating that this order chain is a highly reliable closed-loop chain.

[0772] VII. S6 calculates the basic adaptation value M0, the corrected adaptation value M1, the guaranteed route value G0, the risk value R0, and the posterior matching probability P0.

[0773] 1. Calculation of basic adaptation value M0

[0774] The backend performs a difference constraint calculation on the order demand feature vector D and the service feature vector P, and obtains a difference constraint value Q of 0.23.

[0775] The calculation process for the basic adaptation value M0 is as follows:

[0776] M0 = 1 / (1 + Q)

[0777] M0 = 1 / 1.23

[0778] M0 = 0.813

[0779] 2. Calculation of calibration fit value M1

[0780] The calculation process for the calibration fit value M1 is as follows:

[0781] M1 = M0 × (1 + Sd)

[0782] M1 = 0.813 × (1 + 1.462)

[0783] M1 = 0.813 × 2.462

[0784] M1 = 2.001

[0785] 3. Ensure the calculation of routing value G0

[0786] The background records show that the reliability coefficients of the best-safety path for service personnel A are 0.90, 0.88, and 0.86, and the path reliability product is:

[0787] 0.90 × 0.88 × 0.86 = 0.681

[0788] The backend records that the routing distance cost Li is 0.60, the number of cross-institution hops Ji is 0, and the penalty coefficients α and β are 0.15 and 0.25.

[0789] The calculation process for the guaranteed routing value G0 is as follows:

[0790] G0=maxΠri,e×exp(-αLi-βJi)

[0791] G0=0.681×exp(-0.15×0.60)

[0792] G0 = 0.681 × exp (-0.09)

[0793] G0 = 0.681 × 0.914

[0794] G0 = 0.622

[0795] 4. Calculation of Risk Value R0

[0796] The backend records show the following abnormal withdrawal frequency (F1) as 0.03, abnormal account association (F2) as 0.02, abnormal device login (F3) as 0.03, unresolved complaints (F4) as 0, abnormal order status changes (F5) as 0.03, and risk parameters γ0 as -2.15, γ1 as 0.80, γ2 as 0.70, γ3 as 0.60, γ4 as 1.10, and γ5 as 0.90.

[0797] The calculation process for the risk value R0 is as follows:

[0798] R0=1 / [1+exp(-(γ0+γ1F1+γ2F2+γ3F3+γ4F4+γ5F5))]

[0799] R0=1 / [1+exp(-(-2.15+0.80×0.03+0.70×0.02+0.60×0.03+1.10×0+0.90×0.03))]

[0800] The result in parentheses is -2.067.

[0801] R0 = 0.112

[0802] 5. Calculation of posterior matching probability P0

[0803] The backend records the posterior matching probability parameters θ0 as -1.45, θ1 as 1.00, θ2 as 0.90, and θ3 as 1.10.

[0804] The calculation process for the posterior matching probability P0 is as follows:

[0805] P0=1 / [1+exp(-(θ0+θ1M1+θ2G0-θ3R0))]

[0806] P0=1 / [1+exp(-(-1.45+1.00×2.001+0.90×0.622-1.10×0.112))]

[0807] The result in parentheses is 0.988.

[0808] P0 = 0.729

[0809] The background recommendation threshold is 0.70. Therefore, service personnel A meets the recommendation criteria and is listed as the first recommendation position by the result control module.

[0810] VIII. S8 Risk Linkage Control and Withdrawal Limit Calculation

[0811] After the service is completed, the platform enters the risk linkage control phase. The total service fee for this order is 420 yuan.

[0812] 1. Calculation of direct settlement control value Z0

[0813] The backend records the platform service fee validity status F0 as 1, the commission disabled status J0 as 1, the payment consistency status Y0 as 0.98, and the service completion confirmation status W0 as 1.

[0814] The calculation process for the direct settlement control value Z0 is as follows:

[0815] Z0=a1F0+a2J0+a3Y0+a4W0

[0816] Z0=0.25×1+0.25×1+0.25×0.98+0.25×1

[0817] Z0 = 0.995

[0818] 2. Calculation of Risk Linkage Value R1

[0819] The backend records show the following abnormal withdrawal frequency (F1) as 0.03, abnormal account association (F2) as 0.02, abnormal device login (F3) as 0.02, unresolved complaints (F4) as 0, abnormal order status changes (F5) as 0.02, and risk linkage parameters (b1) as 0.35, b2 as 0.15, b3 as 0.15, b4 as 0.20, and b5 as 0.15.

[0820] The calculation process for the risk linkage value R1 is as follows:

[0821] R1=b1F1+b2F2+b3F3+b4F4+b5F5

[0822] R1=0.35×0.03+0.15×0.02+0.15×0.02+0.20×0+0.15×0.02

[0823] R1=0.0105+0.003+0.003+0+0.003

[0824] R1 = 0.0195

[0825] 3. Calculation of daily cash withdrawal limit Q1

[0826] The system uses the current settlementable balance of 420 yuan as the basic withdrawal limit Qb.

[0827] The calculation process for the daily withdrawable amount Q1 is as follows:

[0828] Q1 = Qb × Z0 / (1 + R1)

[0829] Q1 = 420 × 0.995 / 1.0195

[0830] Q1 = 409.9 yuan

[0831] The first risk threshold recorded in the backend is 0.12, and the second risk threshold is 0.30. Since R1 = 0.0195 is less than 0.12, this transaction will proceed normally and will not trigger any limits or freeze controls.

[0832] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for matching the supply and demand of domestic services based on a multi-parameter quantization model, characterized in that, Includes the following steps: S1. Collect business data related to supply and demand matching, institutional support data, order interaction data, settlement data, account risk control data, contract robot service capability data, and cloud warehouse resource data. Standardize the order demand information to form order feature data, and generate service execution option analysis results based on the order feature data, contract robot service capability data, and cloud warehouse resource data. S2. Perform initial screening of service personnel based on service adaptation constraints, performance constraints, guarantee constraints, and privacy constraints to obtain an initial candidate set. The employment status of service personnel should be at least distinguished as full-time or part-time. S3. When the candidate size or overall matching quality of the initial candidate set is lower than preset conditions, initiate hierarchical shared routing. Expand the matching of candidate service personnel according to the order of their own institution, region, and cross-institutional shared resource pool to form shared routing data. S4. Establish an anonymized direct dialogue channel within the platform between the demand side and candidate service personnel, extract demand confirmation events and interaction behavior characteristics to form dialogue confirmation data. S5. Conduct onboarding review and online... The status nodes in the order signing, service execution, evaluation feedback, complaint handling, and settlement processing processes are linked and recorded to construct an event state diagram, forming a closed-loop process data; S6. Based on the order feature data, shared routing data, dialogue confirmation data, and closed-loop process data, a multi-parameter quantitative model is constructed. Through covariance constraint matching, confirmation entropy correction, and fusion of guarantee path and risk information, the posterior matching probability corresponding to each candidate service personnel is obtained; S7. Based on the posterior matching probability and the service execution option analysis results, a recommendation result is output, and phased information release and online order signing are executed when the conditions are met; wherein, when the service execution option analysis results indicate that the order demand is suitable for robot execution, robot service options are simultaneously output to the demand side for confirmation of whether to select robot service; S8. After completing the online order signing, risk linkage control is performed based on the platform service fee validity status, commission disabling status, settlement consistency information, and the account risk control data to dynamically adjust the withdrawal limit, and limit, freeze, and unfreeze controls are executed when the risk reaches the corresponding threshold.

2. The method for matching the supply and demand of domestic services based on a multi-parameter quantization model according to claim 1, characterized in that, S1 standardizes order demand information, including: extracting service category, service sub-item, service area, service time period, budget range, service object type, service frequency, and additional demand fields from the order; mapping the service category to a business classification, at least to one of the following: cleaning service, nanny service, maternal and infant service, casual labor, door-to-door service, and agency / franchise; completing, validating, and transforming missing fields, conflicting fields, and unstructured descriptions to generate order feature data containing category tags, sub-item tags, constraint tags, and privacy tags; and combining the contracted robot service capability data and cloud warehouse resource data to identify and analyze standardized work, repetitive work, preset process work, installation work, control work, maintenance work, or goods transfer work suitable for robot execution in the order demand, generating service execution option analysis results, which include at least candidate service personnel recommendation options and robot service options.

3. The method for matching supply and demand of domestic services based on a multi-parameter quantization model according to claim 1, characterized in that, In step S2, initial screening of service personnel is performed based on service adaptation constraints, performance constraints, guarantee constraints, and privacy constraints. This includes: comparing the order feature data with the service personnel's corresponding service capability data, service scope data, service time period data, performance status data, and guarantee-related data; the service adaptation constraints include at least service category consistency and service area accessibility; the performance constraints include at least service time period overlap and service load status; the guarantee constraints include at least institutional guarantee capability satisfaction and anomaly handling support; and the privacy constraints include at least information openness level compatibility. Service personnel who meet all constraints are included in the initial candidate set, while service personnel who do not meet any of the constraints are filtered out.

4. The method for matching supply and demand of domestic services based on a multi-parameter quantization model according to claim 1, characterized in that, The step S3 involves initiating a tiered shared routing mechanism, which includes: prioritizing matching within the candidate range corresponding to the organization to which the service personnel belong; expanding the matching range to the candidate range within the order's region when the number of candidates in the initial candidate set is lower than a preset quantity threshold or the overall matching quality is lower than a preset quality threshold; further expanding the matching range to the cross-organizational shared resource pool when the expanded candidate results within the region still do not meet the preset matching conditions; and recording the candidate conversion results, routing distance costs, and organization guarantee adaptation results during each level of expanded matching process, and using these as the shared routing data.

5. The method for matching supply and demand of domestic services based on a multi-parameter quantization model according to claim 1, characterized in that, The S4 process extracts demand confirmation events and interaction behavior features, including: establishing an anonymized direct dialogue channel within the platform between the demand side and candidate service personnel; extracting events related to service time confirmation, service location confirmation, service content confirmation, service duration confirmation, budget range confirmation, and additional condition confirmation during the dialogue process; simultaneously recording the first response duration, number of effective interactions, consistency of confirmation information before and after, and completeness of message traces; and generating dialogue confirmation data that reflects the degree of demand confirmation and communication reliability.

6. The method for matching supply and demand of domestic services based on a multi-parameter quantization model according to claim 1, characterized in that, The S5 constructs an event state diagram, including: performing time-based and object-based associations on state nodes during the onboarding review, online order signing, service execution, evaluation feedback, complaint handling, and settlement processing processes; wherein, object associations include at least the relationships between orders, customers, service personnel, institutions, and accounts, and time associations include at least the trigger time, completion time, and feedback time of each state node; the state change results, processing results, and associated objects of each state node are mapped to a unified state chain to form the closed-loop process data used to characterize the integrity, responsiveness, and anomaly traceability of the service process.

7. The method for matching supply and demand of domestic services based on a multi-parameter quantization model according to claim 1, characterized in that, The basic adaptation value in S6 is obtained through the following steps: constructing order demand features as a demand feature vector D, constructing candidate service personnel service capability features as a service feature vector P, constructing a covariance matrix Σ based on historical matching samples, and calculating the difference Δ between the demand feature vector D and the service feature vector P, wherein the difference Δ satisfies: Δ = D - P; using the formula Q = × The difference constraint value Q is obtained by multiplying Δ; then, the basic adaptation value M0 corresponding to the current order for the candidate service personnel is obtained by using the formula M0 = 1 / (1 + Q), where, This is the transpose of the difference Δ. 1 is the inverse of the covariance matrix Σ.

8. The method for matching supply and demand of domestic services based on a multi-parameter quantization model according to claim 7, characterized in that, The confirmation and correction of the basic adaptation value in S6 is obtained through the following steps: The number of valid confirmations and conflicting confirmations for each requirement confirmation event are counted respectively; the confirmation coefficient of the j-th requirement confirmation event is obtained using the formula Cj = n1j / (n1j + n2j + 1); the normalized confirmation ratio of each requirement confirmation event is obtained using the formula pj = Cj / ΣCj; and the confirmation entropy H is obtained using the formula H = -Σ(pj × lnpj); then, combined with the average response time T, message trace completeness L, and previous... After confirming the consistency of the information Y, the dialogue correction value Sd is obtained using the formula Sd = [Σ(ωj×Cj) + L + Y] / [1 + ηH + μT]. Then, the correction adaptation value M1 is obtained using the formula M1 = M0 × (1 + Sd), where n1j is the number of valid confirmations for the j-th requirement confirmation event, n2j is the number of conflict confirmations for the j-th requirement confirmation event, pj is the normalized confirmation ratio for the j-th requirement confirmation event, ωj is the weight of the j-th requirement confirmation event, and η and μ are both adjustment coefficients.

9. The method for matching supply and demand of domestic services based on a multi-parameter quantization model according to claim 1, characterized in that, The fusion of assurance path and risk information in S6 is obtained through the following steps: Construct assurance association paths between order nodes, service personnel nodes, institution nodes, and account nodes; obtain the reliability coefficients of each edge on the assurance path corresponding to the candidate service personnel; obtain the path reliability product value; and combine the routing distance cost Li and the number of hops across institutions Ji, using the formula G0 = maxΠri,e × exp(-αLi-βJi) to obtain the assurance routing value G0 of the candidate service personnel; then, combine the abnormal withdrawal frequency, abnormal account association, abnormal device login, unresolved complaint status, and abnormal order status change information, using the formula R0 = 1 / [1 + exp(-(γ0 + γ1F1 + γ2F2 + γ3F3 + γ4F4 + γ5F5))] to obtain the assurance routing value G0 of the candidate service personnel. The risk value R0 of the selected service personnel is used; then, the posterior matching probability P0 of the candidate service personnel corresponding to the current order is obtained using the formula P0 = 1 / [1 + exp(-(θ0 + θ1M1 + θ2G0 - θ3R0))], where ri,e are the reliability coefficients of each associated edge in the guarantee path, Li is the routing distance cost, Ji is the number of cross-institution hops, α and β are both penalty coefficients, γ0 is the basic bias coefficient, γ1, γ2, γ3, γ4 and γ5 are all risk adjustment coefficients, θ0 is the basic bias coefficient, θ1, θ2 and θ3 are all posterior matching adjustment coefficients, F1 is the abnormal withdrawal frequency feature value, F2 is the abnormal account association feature value, F3 is the abnormal device login feature value, F4 is the unresolved complaint feature value, and F5 is the abnormal change in order status feature value.

10. The method for matching supply and demand of domestic services based on a multi-parameter quantization model according to claim 1, characterized in that, The risk linkage control in S8 is obtained through the following steps: Obtaining the platform service fee validity status F0, commission disable status J0, consistency status between order payment amount and service personnel's received amount Y0, and service completion confirmation status W0; using the formula Z0 = a1F0 + a2J0 + a3Y0 + a4W0 to obtain the direct settlement control value Z0; then combining the abnormal withdrawal frequency characteristic value F1, abnormal account association characteristic value F2, abnormal device login characteristic value F3, unresolved complaint characteristic value F4, and abnormal order status change characteristic value F5, using the formula R1 = b The risk linkage value R1 is obtained by combining 1F1 + b2F2 + b3F3 + b4F4 + b5F5. Then, the daily withdrawal limit Q1 is obtained using the formula Q1 = Qb × Z0 / (1 + R1), where a1, a2, a3, a4, b1, b2, b3, b4, and b5 are adjustment coefficients, and Qb is the basic withdrawal limit. When the risk linkage value R1 is greater than or equal to the first risk threshold and less than the second risk threshold, the withdrawal limit is controlled. When the risk linkage value R1 is greater than or equal to the second risk threshold, the account is frozen. The account is unfrozen after the risk review is completed.

11. A domestic service supply and demand matching operating system based on a multi-parameter quantization model, characterized in that, include: The data acquisition module is used to collect business data related to supply and demand matching, institutional support data, order interaction data, settlement data, account risk control data, contract robot service capability data, and cloud warehouse resource data. The standardization processing module is used to standardize order demand information to form order feature data, and generate service execution option analysis results based on the order feature data, contract robot service capability data and cloud warehouse resource data. The initial screening module is used to initially screen service personnel based on service compatibility constraints, performance constraints, security constraints, and privacy constraints, obtaining an initial candidate set. The employment status of service personnel must be categorized at least as full-time or part-time. The shared routing module is used to initiate hierarchical shared routing when the candidate size or overall matching quality of the initial candidate set falls below preset conditions. This expands the matching of candidate service personnel according to the order of their organization, region, and cross-organizational shared resource pools, forming shared routing data. The dialogue confirmation module is used to establish an anonymized direct dialogue channel within the platform between the demand side and candidate service personnel, extracting demand confirmation events and interaction behavior characteristics to form dialogue confirmation data. The process construction module is used to associate and record the status nodes in the processes of onboarding review, online order signing, service execution, evaluation feedback, complaint handling, and settlement processing, constructing an event state diagram to form closed-loop process data. The matching calculation module uses… A multi-parameter quantitative model is constructed based on the order feature data, shared routing data, dialogue confirmation data, and process closed-loop data. Through covariance constraint matching, confirmation entropy correction, and fusion of guarantee path and risk information, the posterior matching probability of each candidate service provider is obtained. The result control module outputs recommendation results based on the posterior matching probability and the service execution option analysis results, and performs phased information release and online order signing when conditions are met. Specifically, when the service execution option analysis results indicate that the order demand is suitable for robot execution, robot service options are simultaneously output to the demand side for confirmation of whether to select robot service. The risk control processing module, after completing online order signing, performs risk linkage control based on the platform service fee validity status, commission disabling status, settlement consistency information, and the account risk control data to dynamically adjust the withdrawal limit, and performs limit, freeze, and unfreeze controls when the risk reaches the corresponding threshold.

12. The domestic service supply and demand matching operating system based on a multi-parameter quantization model according to claim 11, characterized in that, The matching calculation module includes a basic adaptation calculation submodule, a confirmation correction submodule, and a risk fusion submodule. The basic adaptation calculation submodule calculates a basic adaptation value based on order feature data and service capability feature data. The confirmation correction submodule corrects the basic adaptation value based on dialogue confirmation data to obtain a corrected adaptation value. The risk fusion submodule combines shared routing data, process closed-loop data, and account risk control data to generate a posterior matching probability. The result control module controls the output of recommendation results, phased information release, and online order signing based on the posterior matching probability and service execution option analysis results. When the service execution option analysis results indicate that the order demand is suitable for robot execution, it outputs robot service options for the demand side to confirm and select. The risk control processing module performs withdrawal limit adjustments, limit control, account freezing, and account unfreezing based on the posterior matching probability, settlement consistency information, complaint handling status, and account risk control data.

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