Unlocking platform scheduling and settlement control method and system and readable storage medium

Abstract

The invention relates to the technical field of unlocking platforms, in particular to an unlocking platform scheduling and settlement control method and system and a readable storage medium, a service state parameter corresponding to a single unlocking service is acquired through a multi-source state parameter acquisition module, the corresponding service is marked as a controlled state and enters a parameter correction or re-checking process, and the service state parameter corresponding to the single unlocking service is acquired. Constructing a service state feature combination, and generating a control parameter set for controlling a platform scheduling and settlement process according to the service state feature combination; and after the settlement result is generated, the service state parameter, the control parameter set and the settlement result are bound, and evidence storage is completed through a credible evidence storage module for subsequent consistency verification and process tracing. According to the invention, a platform computer system can automatically generate a control parameter set based on complete and credible service state information, so that cooperative control of a scheduling path and a settlement calculation path is realized, a system settlement result is more reasonable and stable, and the problem of disorder of an industry commission limit is avoided.

Description

Technical Field

[0001] This invention relates to the field of lock-picking platform technology, and in particular to a lock-picking platform scheduling and settlement control method, system, and readable storage medium. Background Technology

[0002] With the widespread adoption of internet platform technology in residential services, locksmith services are gradually shifting from a traditional offline model to an online platform model. Existing locksmith service platforms typically rely on user terminals to initiate service requests, with the platform system handling processes such as service information collection, service personnel matching, and service fee settlement.

[0003] In existing technologies, most lock-unlocking service platforms only perform scheduling and settlement processing based on service type or fixed services. When the service status changes, the platform system lacks a unified status fusion mechanism, which can easily lead to scheduling paths and settlement processes failing to dynamically adjust with the service status, resulting in problems such as system response delays or abnormal settlements. Summary of the Invention

[0004] The purpose of this invention is to solve the problems pointed out in the background art, and to propose a method, system and readable storage medium for scheduling and settlement control of unlocking platform.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A lock-unlocking platform scheduling and settlement control method, applied to the computer system of a lock-unlocking service platform, includes the following steps:

[0007] a. The service status parameters corresponding to a single unlocking service are collected through a multi-source status parameter acquisition module. The service status parameters include at least the following:

[0008] Lock status parameters characterizing the technical features and usage status of locks;

[0009] Time period coefficient parameters characterizing the timing of service occurrence;

[0010] Regional coefficient parameters characterizing the regional features of the service occurrence area;

[0011] An emergency demand level coefficient that characterizes the urgency of a service;

[0012] Supply and demand status parameters characterizing the platform's real-time service capabilities;

[0013] The historical average order price parameter that represents the platform's prices over a recent period;

[0014] Personnel qualification status parameters that characterize the capabilities of the service provider;

[0015] b. Perform integrity verification, consistency verification and anomaly detection on the service status parameters. When an abnormal status parameter is detected, the corresponding service is marked as controlled and enters the parameter correction or review process.

[0016] c. Based on the verified service status parameters, construct a service status feature combination, and generate a set of control parameters for controlling the platform scheduling and settlement process according to the service status feature combination;

[0017] d. Based on the set of control parameters, automatically control the scheduling path, settlement calculation path, settlement triggering conditions, and settlement restriction conditions of the single unlocking service to generate a settlement result;

[0018] e. After the settlement result is generated, the service status parameters, control parameter set and settlement result are bound together and stored through the trusted evidence storage module for subsequent consistency verification and process traceability.

[0019] The present invention proposes a lock-unlocking platform scheduling and settlement control method, which has the following advantages: By collecting and integrating multi-source service status parameters such as lock status, time status, regional status, demand status, supply and demand status, historical average price status, and personnel qualification status, the present invention provides unified control over the scheduling and settlement process corresponding to a single lock-unlocking service. This enables the platform's computer system to automatically generate a set of control parameters based on complete and reliable service status information, thereby achieving coordinated control of the scheduling path and the settlement calculation path. This makes the system's settlement results more reasonable and stable, avoiding the problem of disordered commission amounts in the industry.

[0020] A lock-unlocking platform scheduling and settlement system includes a multi-source status parameter acquisition module, a status verification and anomaly detection module, a control parameter generation module, a scheduling and settlement control module, and a trusted evidence storage module, wherein each module is configured to execute the above-described methods.

[0021] The lock-unlocking platform scheduling and settlement control system proposed in this invention has the following advantages: By setting up a multi-source status parameter acquisition module, a status verification and anomaly detection module, a control parameter generation module, a scheduling and settlement control module, and a trusted evidence storage module, the platform computer system can complete various functions of lock-unlocking service scheduling and settlement control in a modular manner, reducing the coupling between system functions, improving the scalability and maintainability of the system structure, and enabling the lock-unlocking service platform to achieve automated scheduling and settlement control of complex service scenarios without relying on manual intervention, thereby improving the overall operating efficiency and stability of the system.

[0022] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0023] The computer-readable storage medium proposed in this invention has the following advantages: by storing a computer program for executing the unlocking platform scheduling and settlement control method in the computer-readable storage medium, the relevant technical solutions can be deployed in the form of software on different computer devices or server systems. When the computer program is executed by the processor, it can automatically realize the collection, verification, anomaly detection, control parameter generation, and scheduling and settlement process control of multi-source service status parameters. Attached Figure Description

[0024] Figure 1 This is a control flowchart of one embodiment of the control method of the present invention;

[0025] Figure 2 This is a block diagram of one embodiment of the control system of the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0027] Reference Figures 1-2 A method for scheduling and settling control of a lock-unlocking platform, applied to the computer system of a lock-unlocking service platform, includes the following steps:

[0028] a. The service status parameters corresponding to a single unlocking service are collected through a multi-source status parameter acquisition module. The service status parameters include at least the following:

[0029] Lock status parameters characterize the technical features and usage status of locks; the lock status parameters include at least basic lock parameters and lock aging coefficient, wherein the lock aging coefficient is determined by dual verification through user-uploaded lock appearance photos and lock brand, model and year of registration data; if verification is not possible, the coefficient corresponding to the average service life of similar locks in the region is used.

[0030] Time period coefficient parameters characterize the time characteristics of service occurrence; for example, normal time period (6:00-22:00, non-holidays), nighttime period (22:00-6:00 the next day), holiday period, etc.

[0031] Regional coefficient parameters characterizing the regional features of the service; for example, dividing cities into first-tier, new first-tier, second-tier, third-tier, fourth-tier and below.

[0032] An emergency demand level coefficient characterizes the urgency of a service; the emergency demand level coefficient is determined jointly by the service demand information submitted by the user and the confirmation result of the platform. Before the confirmation is completed, the system restricts the execution of the scheduling or settlement process of the corresponding service.

[0033] Supply and demand status parameters characterizing the platform's real-time service capabilities; these parameters are generated based on the ratio of the number of available service personnel in the region to the number of service requests to be processed within a unit of time, and the ratio of the number of service personnel with corresponding emergency level service qualifications to the total number of online service personnel in the region, and are used to adjust the scheduling or settlement control parameters in the control parameter set.

[0034] The historical average order price parameter that represents the platform's prices over a recent period;

[0035] Personnel qualification status parameters characterize the capabilities of the service execution entity; when a mismatch is detected between the personnel qualification status parameters and the emergency demand level coefficient, the system imposes restrictions or delays on the scheduling or settlement process of the corresponding service.

[0036] By using the above-mentioned multi-source state parameter acquisition methods, the computer system can comprehensively characterize the actual state of a single unlocking service from multiple technical dimensions, avoiding reliance on a single parameter for scheduling or settlement control, thereby improving the completeness and accuracy of service state modeling.

[0037] b. Perform integrity verification, consistency verification, and anomaly detection on the service status parameters. When an abnormal status parameter is detected, the corresponding service is marked as controlled and enters the parameter correction or review process. Anomaly detection includes a parameter distribution model built based on historical service data. When the combination of service status features corresponding to the current service exceeds a preset threshold range, the system automatically adjusts the control parameter set or triggers a manual review process.

[0038] Through the above processing mechanism, abnormal or distorted state parameters will not directly participate in scheduling and settlement calculations, reducing the risk of unreasonable scheduling or settlement results in abnormal service scenarios and improving the stability and reliability of the platform system.

[0039] c. Based on the verified service status parameters, construct a service status feature combination, and generate a set of control parameters for controlling the platform scheduling and settlement process according to the service status feature combination;

[0040] This step ensures that the scheduling and settlement processes are no longer executed independently, but are uniformly controlled by the same set of service state characteristics. This facilitates collaborative control of the platform under complex service states and enhances the system's adaptability to changes in service states.

[0041] The service status feature combination is a comprehensive characterization of the actual status of a single unlocking service. It is formed by combining multi-source service status parameters (7 categories including lock status parameters, time period coefficient parameters, and regional coefficient parameters) according to preset rules after passing integrity, consistency verification and anomaly detection. It is the basis for generating the control parameter set. It adopts the combination method of "core parameters + auxiliary parameters". Among them, lock status parameters, emergency demand level parameters, and regional coefficient parameters are core parameters, and the rest are auxiliary parameters. After combination, it must be able to uniquely represent the scenario characteristics of a single service to ensure the relevance and rationality of the control parameter set.

[0042] d. Based on the control parameter set, automatically control the scheduling path, settlement calculation path, settlement triggering conditions, and settlement restriction conditions of the single unlocking service to generate a settlement result; the settlement restriction conditions include at least a settlement upper limit limit. When the settlement result exceeds the corresponding settlement upper limit, the system outputs the settlement result at the settlement upper limit, or the system interrupts the automatic settlement process and enters a risk control state; the settlement result is only confirmed and effective after the service status parameters and control parameter set have completed trusted storage and passed consistency verification.

[0043] Through the above control methods, the computer system can technically constrain abnormal results during the settlement stage, avoid uncontrolled settlement output under abnormal conditions, and improve the security and controllability of the platform system in complex service scenarios.

[0044] Among them, the core indicator of settlement restrictions refers to the maximum settlement amount threshold preset by the platform for different service scenarios (classified by region, lock type, and emergency level). This threshold is used to constrain the reasonableness of settlement results, avoid abnormally high settlements, and protect the rights and interests of users and the platform.

[0045] The setting basis and application rules are as follows: The average price of the most effective orders in the past 90 days in the same region, with the same lock type and the same emergency level is used as the benchmark. Combined with the service cost limit and industry pricing standards, the settlement limit is set (usually 3 times the benchmark average price, which can be dynamically adjusted to 5 times the benchmark average price in extreme emergency scenarios).

[0046] In application, in the settlement control of step d of this invention, after the system generates the initial settlement result, it compares it with the settlement limit of the corresponding scenario; if the initial settlement result exceeds the settlement limit and the extreme emergency risk control review process is not triggered, the settlement limit is used as the final settlement result output; if an extreme emergency scenario is triggered (such as unlocking for fire rescue), the settlement limit can be exceeded after the platform's risk control review is passed, and the review record needs to be stored in the trusted evidence storage module at the same time.

[0047] The control parameter set connects the service status parameters with the core intermediate parameter set of the scheduling and settlement control process. It is generated based on the combination of verified service status features and is used to uniformly control the scheduling path, settlement calculation path, settlement triggering conditions and settlement restriction conditions of a single unlocking service, so as to realize the coordinated control of scheduling and settlement.

[0048] It includes at least four categories: dynamic price parameters, scheduling priority parameters, commission calculation parameters, and settlement restriction parameters. Among them, the dynamic price parameters can be generated by the formula P1=P0×α×θ×β×γ×λ×ε (P0 is the historical average order price parameter, α is the basic lock parameter, θ is the lock aging coefficient, β is the regional coefficient parameter, γ is the time period coefficient parameter, λ is the emergency demand level coefficient, and ε is the supply and demand correction coefficient). The remaining parameters are dynamically adapted according to the service status characteristics.

[0049] e. After the settlement result is generated, the service status parameters, control parameter set and settlement result are bound together and stored through the trusted evidence storage module for subsequent consistency verification and process traceability.

[0050] Through a trusted evidence storage mechanism, the platform can manage the entire process of scheduling and settlement for a single unlocking service in a traceable manner, thereby improving the credibility of the settlement results and reducing the technical difficulty of dispute resolution and data verification.

[0051] Among them, the trusted evidence storage module is used to realize the function of traceable and tamper-proof data throughout the service process. It is built with a consortium blockchain architecture and integrates data encryption, hash operation and multi-node evidence storage functions. It is used to store key data of a single unlocking service and provide a trusted basis for subsequent consistency verification and dispute resolution.

[0052] The trusted evidence storage module adopts a consortium blockchain architecture, with participating nodes including platform servers, regulatory agency servers, and third-party notary office servers. Only authorized nodes can participate in data reading, writing, and verification to ensure data authority. Before data is uploaded to the blockchain, key data such as service status parameters, control parameter sets, and settlement results are encrypted using the AES-256 encryption algorithm. The encrypted data is then used to generate a unique hash identifier using the SHA-256 hash algorithm. The hash value is bound to the original data for subsequent consistency verification (if the original data is tampered with, the hash value will change significantly). The evidence storage content includes the original service status parameters, parameter verification records, control parameter sets, settlement results, scheduling records, and audit records for a single service. Its purpose is to achieve traceability of the entire scheduling and settlement process, ensure the credibility of settlement results, reduce the technical difficulty of dispute resolution and data verification, and meet regulatory compliance requirements.

[0053] In this control method, after verification and anomaly screening, the multi-source state parameters do not directly participate in the settlement calculation. Instead, they first form a combination of service state features and then further transform them into a set of control parameters. The set of control parameters simultaneously acts on the scheduling path selection, settlement calculation path, settlement triggering conditions, and settlement restriction conditions, thus maintaining the technical constraint logic that keeps the scheduling behavior and settlement behavior consistent at the system level.

[0054] Meanwhile, by introducing a trusted evidence storage and consistency verification mechanism before the settlement results take effect, the service status parameters, control parameter sets and settlement results are bound together in an inseparable way, which technically prevents the possibility of directly outputting settlement results in abnormal states, parameter distortion or process inconsistencies.

[0055] As one implementation method, the trusted evidence storage module can adopt a consortium blockchain architecture, with nodes including platform servers, regulatory agency servers, and third-party notary office servers. Before data is uploaded to the blockchain, it is encrypted using AES-256 and the hash algorithm uses SHA-256 to ensure data security and immutability.

[0056] Therefore, this invention forms a closed-loop control mechanism at the computer system level, which includes "state awareness, unified control, restricted settlement, and traceable verification." This enables the platform to achieve coordinated control of the scheduling and settlement processes in complex and ever-changing unlocking service scenarios. It effectively solves the problems of system instability caused by the dispersion of service status parameters, difficulty in identifying abnormal states, and lack of constraints on the settlement process in existing technologies. This makes the system settlement results more reasonable, stable, and controllable, and avoids the problem of disordered commission amounts in the industry.

[0057] This solution uses at least 100,000 real historical locksmith service order data from the platform as a sample, covering cities from first-tier to fourth-tier, all time periods, all lock types, and all emergency levels. Invalid data such as canceled orders and disputed orders are removed to ensure the comprehensiveness and effectiveness of the sample.

[0058] This solution employs a normal distribution analysis algorithm to fit the numerical distribution curve of a single service status parameter (such as the lock aging coefficient and time period coefficient) and determine the reasonable threshold range (mean ± 3 times standard deviation) for each parameter. Combined with the K-means clustering algorithm, it performs cluster analysis on multi-parameter combinations (such as lock status + time period + emergency level) to divide the reasonable range of parameter combinations under different service scenarios.

[0059] One implementation method:

[0060] The platform system, through its multi-source status parameter acquisition module, collects and determines the following multi-dimensional service status parameters for a single user-initiated unlocking service request:

[0061] Lock status parameters: The system determines the status parameters related to the lock, including at least:

[0062] Lock basic parameters (α): Quantified according to the lock's technical type and encryption level. As an example, the parameter for a mechanical lock can be set to 1.0; smart locks can be set to different parameter values ​​depending on their encryption level, such as 1.5, 1.7, or 2.0.

[0063] Lock Aging Coefficient (θ): This is quantified based on the lock's age and wear level. As an example, the value range of the aging coefficient is determined based on statistics from 100,000 historical lock-picking service data points. Locks older than 10 years are 90% more difficult to unlock than new locks, therefore the corresponding coefficient is set to 1.9-2.3. Locks ≤ 1 year have a coefficient of 1.0, 1-5 years have a coefficient of 1.2-1.4, 5-10 years have a coefficient of 1.5-1.8, and > 10 years have a coefficient of 1.9-2.3. The degree of wear can be verified by users uploading images of the lock's appearance, combined with the lock's brand and model registration data.

[0064] For example, when using this solution:

[0065] Service scenarios for older smart locks (service life > 8 years + smart lock type)

[0066] (1) Traditional model: Settlement consistency compliance rate is 45.2% (the price deviation of the same type of order is up to 120%).

[0067] (2) Optimized mode: Settlement consistency compliance rate of 94.8% (price deviation of the same type of order ≤8%).

[0068] (3) Results: Settlement consistency improved by 110%, completely solving the problem of price confusion for similar services.

[0069] Time period coefficient parameter (γ): The system determines this parameter based on the time the service request is initiated. For example, the parameter for the time period from 6:00 to 22:00 (excluding holidays) can be set to 1.0 (normal time period), the parameter for the time period from 22:00 to 6:00 the next day can be set to 1.8-2.2 (nighttime period), and the parameter for holidays can be set to 1.5-1.7 (holiday period).

[0070] For example, when using this solution:

[0071] Nighttime emergency service scenario (22:00-6:00+ high emergency demand)

[0072] (1) Traditional model: Abnormal settlement rate 32.7% (price overvaluation accounted for 28.3%), commission dispute rate 29.5%.

[0073] (2) Optimized model: Abnormal settlement rate 4.1% (price overvaluation rate 1.8%), commission dispute rate 3.3%.

[0074] (3) Results: Abnormal settlement rate decreased by 87.5%, and commission dispute rate decreased by 88.8%.

[0075] Regional coefficient parameter (β): The system determines this parameter based on the regional characteristics of the service location. For example, the average industry wage in a specific benchmark city can correspond to a parameter of 1.0, and different parameters can be set according to the commercial development level of other cities (such as first-tier, new first-tier, second-tier, etc.), for example, 1.3-1.5 for first-tier cities, 1.2-1.3 for new first-tier cities, etc.

[0076] Emergency Request Level Coefficient (λ): The system determines the urgency level based on the service request description submitted by the user and the platform's review results (e.g., manual review can be completed within 3 minutes). For example, it can be divided into four levels: ordinary, general emergency, high emergency, and extreme emergency, each corresponding to a quantified coefficient, such as 1.0, 1.3-1.5, 1.6-1.9, and 2.0-2.5. Before review and confirmation, the system can restrict subsequent processes.

[0077] Supply and demand status parameter (ε): A dynamic parameter reflecting the platform's service capacity, collected and calculated by the system in real time. For example, this parameter can be generated based on the real-time ratio K (number of online service personnel in the region per unit time / number of service requests currently pending) and the qualification matching degree M (number of service personnel with the emergency qualifications required for the current service / total number of online service personnel in the region). This parameter is used to dynamically adjust the control logic.

[0078] For example, when using this solution:

[0079] Supply and demand imbalance scenario (regional service personnel shortage > 50%)

[0080] (1) Traditional model: Commission calculation error rate of 27.8% (due to failure to dynamically adjust supply and demand coefficients).

[0081] (2) Optimization mode: Commission calculation error rate 1.5% (supply and demand status parameter real-time adjustment control parameter set).

[0082] (3) Effect: The commission calculation error rate was reduced by 94.6%, avoiding commission amount disorder caused by supply and demand imbalance.

[0083] Historical Order Average Price Parameter (P0): The system retrieves recent completed orders from the historical database that are identical to the current service in terms of region, lock type, and emergency level, and calculates their average price. Before calculation, the system uses an algorithm (such as setting a deviation threshold from the average) to remove abnormal order data;

[0084] As an example: Take the average price of valid orders within the same region, lock type, and emergency level on the platform in the past 90 days (valid orders refer to normally completed orders excluding canceled orders and disputed orders); use an abnormal order elimination algorithm to filter valid orders. Abnormally high prices are defined as "more than 3 times the average price of orders of the same type and emergency level in the same region or more than the price threshold P_max", and abnormally low prices are defined as "less than 50% of the average price of orders of the same type and emergency level in the same region or less than P_min". The reasons for eliminating orders must be recorded (such as malicious ordering, data error) and kept for future reference.

[0085] Personnel qualification status parameters: The system obtains the qualification information of personnel who can currently provide services, including their star rating, historical violation records, and emergency service qualification level certification.

[0086] The system performs integrity and consistency checks and anomaly detection on the collected service status parameters. For example, the system can build a parameter distribution model based on historical data. If the parameter values ​​of the current combination exceed a preset reasonable threshold range, it is considered abnormal. When an anomaly is detected, the system marks the service as under control and triggers a parameter correction process or transfers it to a manual review process to avoid abnormal parameters from participating in subsequent calculations and causing misjudgments.

[0087] Based on the verified service status parameters, the system constructs a combination of current service status characteristics and automatically generates a set of control parameters for precisely controlling the service scheduling and settlement process. The dynamic price parameter P1 is calculated using a product form because the impact of each service status parameter on the unlocking service cost is multiplicative. For example, when nighttime hours (γ=2.0) and high emergency demand (λ=1.8) coexist, the service cost increases by 3.6 times, which aligns with the cost variation patterns in actual service scenarios. As a concrete example, this control parameter set may include the dynamic price parameter P1 used for settlement calculation. P1 can be generated using a combination formula, for example: P1 = P0 × α × θ × β × γ × λ × ε. Where P0 is the historical average order price parameter, α is the basic lock parameter, θ is the lock aging coefficient, β is the regional coefficient parameter, γ is the time period coefficient parameter, λ is the emergency demand level coefficient, and ε is the supply and demand correction coefficient generated based on the aforementioned supply and demand status parameters (K and M).

[0088] Based on the set of control parameters, the system automatically coordinates and controls the scheduling path, settlement calculation path, settlement triggering conditions, and settlement restriction conditions for this unlocking service, and generates the settlement result.

[0089] Dispatch control: For example, when the system detects that the emergency qualification in the personnel qualification status parameter does not match the emergency demand level coefficient of this service, it can impose restrictions on the dispatch process (such as not assigning orders to that personnel) or delay the trigger.

[0090] Settlement Calculation and Constraints: The system uses a set of control parameters to perform settlement calculations and generate an initial settlement value. Simultaneously, the settlement process is constrained by predefined settlement constraints. As an example, these constraints include at least a settlement upper limit. The system compares the initial settlement value with the settlement upper limit. If the upper limit is exceeded, the system can automatically output the settlement upper limit value as the final settlement result; alternatively, it can interrupt the automatic process and enter a risk control state awaiting manual review (in extreme emergency scenarios, this upper limit can be exceeded after risk control review). The principle for the settlement lower limit is similar.

[0091] Commission Settlement Example: After the final settlement result P is generated, the system further combines the personnel qualification status parameters to complete the commission settlement for the service provider (locksmith). For example, the commission rate R can be calculated using the formula R = (R0 × (1 - δ)) + μ. Where R0 is the basic commission rate corresponding to the locksmith's star rating, δ is the violation coefficient, and μ is the emergency service incentive coefficient (normal demand μ=0, general emergency μ=0.5%-1%, high emergency μ=1.5%-2.5%, extreme emergency μ=3%-4%). ​​When R < 1%, 1% is used as the minimum commission rate (ensuring the locksmith's basic income). When the locksmith has no violation records and the emergency order completion rate is ≥ 95%, an additional 0.3% quarterly incentive commission is added, directly included in the quarterly settlement total. The final system generates the commission amount C = P × R0;

[0092] After the settlement result is generated, the system binds all key data from this service, including the collected original service status parameters, the generated control parameter set, the settlement result, and records of key processes such as scheduling and review, and completes tamper-proof notarization through an integrated trusted notarization module (such as a blockchain module). A unique hash identifier is generated after notarization, which can be queried and verified by users, service personnel, and regulators. The settlement result is only finally confirmed and effective after the relevant data is successfully notarized and passes consistency verification.

[0093] To illustrate this more clearly, let's assume a specific service request is as follows:

[0094] The service location is Beijing (a first-tier city), the time is 23:00 (night), the lock is a high-end smart lock that has been used for 8 years (encryption level 3), and the need is "an elderly person trapped" (approved as a high-level emergency).

[0095] Parameter Acquisition and Determination: The system determines the following exemplary parameters: regional coefficient β=1.4, time period coefficient γ=2.0, lock basic parameter α=2.0, lock age coefficient θ=1.7, emergency demand level coefficient λ=1.8, and historical order average price parameter P0=450 yuan. Real-time calculation yields supply and demand status parameters K≈0.67 (<1), M=0.6;

[0096] Generate control parameter set: Based on the above parameters, the system generates a supply and demand correction coefficient ε=1.05, and calculates the initial dynamic price parameter P1 = 450 × 2.0 × 1.7 × 1.4 × 2.0 × 1.8 × 1.05 ≈ 10319.4 yuan;

[0097] Settlement control and output: The system calls the settlement upper limit limit (e.g., P_max = 1125 yuan). Since P1 > P_max, the system automatically adopts the upper limit value and outputs the final settlement result P = 1125 yuan;

[0098] Related Settlement: This service was undertaken by a five-star locksmith (R0=10%), who has no recent violations (δ=0), and this was a highly emergency service (μ=2.2%). The system calculates their commission rate as R=12.2%, and the commission amount as C=137.25 yuan;

[0099] Evidence storage: The parameter collection results, emergency review records, pricing results, and commission amount are stored on the blockchain and a unique hash value is generated: 0x7f3d21... (example) for query verification; the system can define the locksmith as having no violations and an emergency order completion rate of 100%, for example, he / she can enjoy an additional incentive of 0.3% per quarter.

[0100] It should be noted that the specific parameter values, calculation formulas, and judgment thresholds in the above embodiments are merely examples to clearly illustrate how the technical solution of the present invention operates in practice, and do not constitute a limitation on the scope of protection of the present invention. Within the framework defined by the claims of the present invention, those skilled in the art can make adjustments and changes according to the actual situation.

[0101] After adopting the technical solution of this invention, the abnormal settlement rate of the lock-unlocking platform is reduced from 15% of the existing technology to 3%, the dispatch response delay is shortened from 20 minutes to 8 minutes, the dispute rate between service personnel and users is reduced by 70%, and the problem of disordered commission amounts in the industry is effectively solved.

[0102] One implementation method:

[0103] Suppose a specific service request is as follows, with the following service status parameters: New first-tier city (Hangzhou), holiday (Spring Festival), mechanical lock (3 years of use), general emergency (child alone waiting) service;

[0104] α=1.0, θ=1.3 (3-year use), β=1.25, γ=1.6 (Spring Festival), λ=1.4 (general emergency), P0=180 yuan, K=30 / 32≈0.94<1, M=0.5, ε=1.03; Pricing P=180×1.0×1.3×1.25×1.6×1.4×1.03= 180×3.7696=678.53 yuan; 3-star locksmith accepted (R0=6%), no violations (δ=0), μ=0.8%, commission R=6.8%, C=678.53×6.8%≈46.14 yuan; Data synchronized and stored on the blockchain.

[0105] One implementation method:

[0106] Suppose a specific service request is as follows, with the following service status parameters: fourth-tier city (Yichang), normal time period (14:00), ordinary brand smart lock (encryption level 2, 1 year of use), ordinary service needs;

[0107] α=1.7, θ=1.0 (for 1 year), β=0.85, γ=1.0, λ=1.0, P0=200 yuan, K=15 / 10=1.5, M=0.7, ε=1.0; Pricing P=200×1.7×1.0×0.85×1.0×1.0×1.0=289 yuan; 5-star locksmith accepted (R0=10%), 2 valid complaints (δ=0.1), μ=0, commission R=9%, C=26.01 yuan; Data synchronized and stored on the blockchain.

[0108] One implementation method:

[0109] Suppose a specific service request is as follows, with the following service status parameters: second-tier city (Wuhan), nighttime (02:00), old mechanical lock (12 years old), extreme emergency (fire rescue lock opening service);

[0110] α=1.0, θ=2.2 (12 years of use), β=1.15, γ=2.1 (nighttime), λ=2.3 (extreme emergency), P0=300 yuan, K=12 / 25=0.48<1, M=0.3<0.4, ε=1.05; Pricing P=300×1.0×2.2×1.15×2.1×2.3×1.05=300×13.2955=3988.65 yuan; Extreme emergency exceeds P_max (300×3=900 yuan), platform risk control review passed; 5-star locksmith accepted (R0=10%), no violations (δ=0), μ=3.5%, commission R=13.5%, C=3988.65×13.5%≈538.47 yuan; priority dispatch followed by review, all data is stored on the blockchain.

[0111] refer to Figure 2A lock-unlocking platform scheduling and settlement system includes a multi-source status parameter acquisition module, a status verification and anomaly detection module, a control parameter generation module, a scheduling and settlement control module, and a trusted evidence storage module, wherein each module is configured to execute the above-described methods.

[0112] This invention, by setting up a multi-source status parameter acquisition module, a status verification and anomaly detection module, a control parameter generation module, a scheduling and settlement control module, and a trusted evidence storage module, enables the platform computer system to complete various functions of lock unlocking service scheduling and settlement control in a modular manner. This reduces the coupling between system functions, improves the scalability and maintainability of the system structure, and allows the lock unlocking service platform to achieve automated scheduling and settlement control of complex service scenarios without relying on manual intervention, thereby improving the overall operating efficiency and stability of the system.

[0113] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0114] By storing a computer program in the computer-readable storage medium for executing the unlocking platform scheduling and settlement control method, the relevant technical solutions can be deployed in software form on different computer devices or server systems. When the computer program is executed by the processor, it can automatically realize the collection, verification, anomaly detection, control parameter generation, and scheduling and settlement process control of multi-source service status parameters.

[0115] Table 1 is a table of regional coefficient parameters for one implementation method.

[0116]

[0117] Table 2 is a table of lock status parameters for one embodiment.

[0118]

[0119] Table 3 is a time period coefficient parameter table for one implementation method.

[0120]

[0121] Table 4 is a comparison table of core performance indicators for one implementation method.

[0122]

[0123] Table 5 is a comparison table of business operation performance indicators for one implementation method.

[0124]

[0125] Table 6 is a comparison table of user and service provider experience indicators for one implementation method.

[0126]

[0127] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technical solution, concept, or design obtained by those skilled in the art by making equivalent substitutions or changes to the technical solution and inventive concept of the present invention within the scope of the technology disclosed in the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for scheduling and settling control of a lock-unlocking platform, applied to a computer system of a lock-unlocking service platform, characterized in that, Includes the following steps: a. The service status parameters corresponding to a single unlocking service are collected through the multi-source status parameter acquisition module. The service status parameters include: Lock status parameters characterizing the technical features and usage status of locks; Time period coefficient parameters characterizing the timing of service occurrence; Regional coefficient parameters characterizing the regional features of the service occurrence area; An emergency demand level coefficient that characterizes the urgency of a service; Supply and demand status parameters characterizing the platform's real-time service capabilities; The historical average order price parameter that represents the platform's prices over a recent period; Personnel qualification status parameters that characterize the capabilities of the service provider; b. Perform integrity verification, consistency verification and anomaly detection on the service status parameters. When an abnormal status parameter is detected, the corresponding service is marked as controlled and enters the parameter correction or review process. c. Based on the verified service status parameters, construct a service status feature combination, and generate a set of control parameters for controlling the platform scheduling and settlement process according to the service status feature combination; d. Based on the set of control parameters, automatically control the scheduling path, settlement calculation path, settlement triggering conditions, and settlement restriction conditions of the single unlocking service to generate a settlement result; e. After the settlement result is generated, the service status parameters, control parameter set and settlement result are bound together and stored through the trusted evidence storage module for subsequent consistency verification and process traceability.

2. The unlocking platform scheduling and settlement control method according to claim 1, characterized in that, The lock status parameters include at least the lock's basic parameters and the lock's aging coefficient. The lock's aging coefficient is determined through dual verification using photos of the lock's appearance uploaded by the user and the lock's brand, model, and year of registration data. If verification is not possible, the coefficient corresponding to the average service life of similar locks in the region is used.

3. The unlocking platform scheduling and settlement control method according to claim 1, characterized in that, The emergency demand level coefficient is determined by the service demand information submitted by the user and the confirmation result of the platform. Before the confirmation is completed, the system restricts the execution of the scheduling or settlement process of the corresponding service.

4. The unlocking platform scheduling and settlement control method according to claim 1, characterized in that, The supply and demand status parameters are generated based on the ratio of the number of available service personnel in the region to the number of service requests to be processed within a unit of time, and the ratio of the number of service personnel with corresponding emergency level service qualifications to the total number of online service personnel in the region. They are used to adjust the scheduling or settlement control parameters in the control parameter set.

5. The unlocking platform scheduling and settlement control method according to claim 1, characterized in that, When a mismatch is detected between the personnel qualification status parameters and the emergency demand level coefficient, the system imposes restrictions or delays on the scheduling or settlement process of the corresponding service.

6. The unlocking platform scheduling and settlement control method according to claim 1, characterized in that, The anomaly detection in step b includes a parameter distribution model built based on historical service data. When the combination of service status features corresponding to the current service exceeds a preset threshold range, the system automatically adjusts the control parameter set or triggers a manual review process.

7. The unlocking platform scheduling and settlement control method according to claim 1, characterized in that, The settlement restrictions include at least a settlement upper limit. When the settlement result exceeds the corresponding settlement upper limit, the system outputs the settlement result at the settlement upper limit, or the system interrupts the automatic settlement process and enters a risk control state.

8. The unlocking platform scheduling and settlement control method according to claim 1, characterized in that, The settlement result is only confirmed and effective after the service status parameters and control parameter set have been trusted and verified for consistency.

9. A lock-unlocking platform scheduling and settlement system, characterized in that, It includes a multi-source state parameter acquisition module, a state verification and anomaly detection module, a control parameter generation module, a scheduling and settlement control module, and a trusted evidence storage module, wherein each module is configured to perform the method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which, when executed by a processor, implements the method described in any one of claims 1 to 8.

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