Cloud platform-based financial leasing system and method

Abstract

This invention relates to the field of financial leasing system technology, and more specifically, to a cloud-based financial leasing system and method. The system includes: a leasing personnel input unit for acquiring leasing demand information; a leasing equipment data acquisition unit for collecting equipment status information; a leasing business management unit for handling leasing matching and risk control; a demand unit for analyzing leasing demand; a leasing personnel registration unit for managing leasing personnel information; and a leasing platform resource library for storing system data. The leasing business management unit implements leasing matching and risk management through a dynamic multi-dimensional leasing matching and risk control algorithm. The dynamic time-series matching model, by combining historical data and real-time market demand, greatly improves the accuracy of leasing matching. The multi-dimensional leasing scoring system further considers multiple factors such as lessee credit and equipment condition, providing a comprehensive evaluation basis for subsequent decision-making.

Description

Technical Field

[0001] This invention relates to the field of financial leasing system technology, and more specifically, to a cloud-based financial leasing system and method. Background Technology

[0002] With the deepening of economic globalization and the rapid development of technology, the financial leasing industry, as an important component of modern service industries, is undergoing unprecedented changes. Traditional financial leasing models are gradually revealing their inherent limitations in the face of an increasingly complex market environment and customer demands. Although the industry has begun to try to introduce information technology into leasing management systems in recent years, these efforts often remain at the level of simply digitizing manual operating processes, failing to truly achieve intelligent and refined management of leasing operations.

[0003] Currently, the more advanced financial leasing systems on the market typically employ rule-based matching algorithms and static risk assessment models. While these systems perform reasonably well when handling structured data and fixed scenarios, their performance often falls short when facing rapidly changing market environments and diverse customer needs. For example, in equipment matching, existing systems rely primarily on pre-set matching rules, making it difficult to adapt to dynamic changes in customer demand and rapid shifts in market trends. In risk assessment, most systems still use static models based on historical data, failing to reflect real-time changes in market and credit risk. Furthermore, existing systems often separate leasing matching, risk control, and resource optimization functions, allowing each to operate independently without effective coordination mechanisms.

[0004] These problems have led to a series of practical difficulties: inefficient leasing matching and low customer satisfaction; delayed risk assessment, making it difficult to effectively prevent bad debts; poor equipment utilization, resulting in resource waste; and slow system response speed, making it difficult to meet the needs of rapid decision-making. More seriously, these problems are interconnected, forming a vicious cycle that is difficult to break through, severely restricting the further development of the financial leasing industry.

[0005] Faced with these challenges, the industry urgently needs an intelligent financial leasing system that can integrate advanced mathematical models, machine learning algorithms, and big data analytics. This system should be able to accurately match leasing demand, dynamically assess risk, optimize resource allocation, and ensure close collaboration between its various functional modules to form an organic whole. Summary of the Invention

[0006] To address the aforementioned technical challenges, this invention provides a cloud-based financial leasing system and method. It aims to resolve a series of problems inherent in traditional financial leasing systems, such as inaccurate lease matching, untimely risk assessment, unreasonable resource allocation, and slow system response. By introducing dynamic, multi-dimensional lease matching and risk control algorithms, this invention achieves intelligent management of the entire leasing business process, significantly improving the accuracy of lease matching, the real-time nature of risk control, and the efficiency of resource allocation.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] A cloud-based financial leasing system, comprising:

[0009] The rental personnel input unit is used to obtain rental demand information;

[0010] The rental equipment data acquisition unit is used to collect equipment status information;

[0011] The leasing business management unit is used to handle lease matching and risk control;

[0012] Demand unit, used to analyze leasing demand;

[0013] The leasing personnel registration unit is used to manage leasing personnel information;

[0014] The rental platform's resource library is used to store system data;

[0015] The leasing business management unit achieves leasing matching and risk management through dynamic multi-dimensional leasing matching and risk control algorithms.

[0016] Preferably, the dynamic multi-dimensional lease matching and risk control algorithm includes the following steps:

[0017] Acquire historical leasing data, real-time market demand data, and equipment status data;

[0018] Based on the acquired data, a prediction matrix is ​​generated using a dynamic time-series matching model;

[0019] Based on the prediction matrix, a rating matrix is ​​generated using a multi-dimensional rental rating system;

[0020] Based on the rating matrix, a recommendation matrix is ​​generated through a personalized rental recommendation engine;

[0021] Based on the recommendation matrix, a risk assessment matrix is ​​generated through a dynamic risk assessment and control system.

[0022] Based on the risk assessment matrix, optimization strategies are generated through the leased ecosystem optimizer.

[0023] Preferably, the algorithm formula for the dynamic time-series matching model is as follows:

[0024] Where, P∈R n×m Let W be the prediction matrix, and W be the wavelet transform operator. -1 Let T(H) be the inverse wavelet transform operator, where T(H) is the historical rental data matrix H∈R. n The Toeplitz matrix is ​​constructed from ×m, where Φ is the nonlinear activation function, and R∈R. n Let S ∈ R be the real-time market demand vector. m This is the device state vector. This is the Kronecker product, where α is the weighting coefficient.

[0025] Preferably, the algorithm formula for the multi-dimensional rental rating system is as follows: Where S∈R n ×m is the rating matrix, v ij =[P ij C i D j ] T P is the eigenvector. ij To predict the elements of the matrix, C i D represents the elements of the lessee's credit vector. j d represents an element of the device status vector. g (x,y) represents the geodesic distance on the Riemannian manifold, μ is the ideal point, and σ is the scale parameter.

[0026] Preferably, the algorithm formula for the personalized rental recommendation engine is: R = D -1 / 2 LD -1 / 2 PageRank(G), where R∈R n×m Let L be the recommendation matrix, DS be the graph Laplacian matrix, S be the rating matrix, D be the degree matrix, G = (V, E) be the historical rental graph, and PageRank(G) be the result of applying the PageRank algorithm to graph G.

[0027] Preferably, the algorithm formula for the dynamic risk assessment and control system is as follows: Where, V∈R n×m Let (C)∫ be the risk assessment matrix, and let f be the Choquet integral. ij Let x be the risk assessment function, and let x be the fuzzy measure. (t) Let X represent the set of risk factors X = {x1, ..., x2} k The t-th largest element in}, A (t) ={x (t) ,…,x (k)}

[0028] Preferably, the algorithm formula for the rental ecosystem optimizer is: Where, ξ∈R d Let f:R be the system state vector. d →R is the Morse function, defined as: f(ξ)=∑ i,j V ij ·exp(-∥ξ-ξ ij ∥ 2 / 2σ 2 ), where V ij ξ is an element of the risk assessment matrix. ij Let σ be the state point of the ideal system, and σ be the smoothing parameter.

[0029] Preferably, the rental personnel input unit includes an identity information confirmation module, a rental equipment type confirmation module, and a demand information confirmation module, used to obtain the rental personnel's identity information, the type of rental equipment required, and specific demand information.

[0030] Preferably, the rental equipment data acquisition unit includes a rental equipment status acquisition module and a rental equipment usage acquisition module, wherein the rental equipment usage acquisition module further includes a rental equipment usage time recording module, a rental equipment usage location recording module, a rental equipment usage amount recording module, and a rental equipment fault recording module.

[0031] The cloud-based intelligent financial leasing method based on the system includes the following steps:

[0032] Rental demand information is obtained through the rental personnel input unit;

[0033] Equipment status information is collected through the data acquisition unit of the leased equipment;

[0034] Input the rental demand information and equipment status information into the rental business management unit;

[0035] The process is handled through a dynamic, multi-dimensional lease matching and risk control algorithm within the lease business management unit.

[0036] Based on the processing results, lease matching schemes and risk control strategies are generated;

[0037] The rental matching scheme and risk control strategy are stored in the rental platform's resource library.

[0038] Compared with the prior art, the beneficial effects of the present invention are reflected in the following aspects:

[0039] From a macro perspective, this invention provides a comprehensive solution for the digital transformation of the financial leasing industry. It not only improves the operational efficiency of individual leasing companies but also helps optimize the allocation of resources across the entire industry, promoting its healthy development. Through more precise demand matching and more effective risk control, this system can expand the service scope of financial leasing, enabling more SMEs to benefit from flexible equipment financing solutions, thereby promoting the development of the real economy.

[0040] At the system architecture level, this invention adopts a modular design, where each functional unit is both relatively independent and closely collaborative. The rental personnel input unit, rental equipment data acquisition unit, rental business management unit, demand unit, rental personnel registration unit, and rental platform resource library form a complete information loop. This design not only improves the system's maintainability and scalability but also enables full data sharing and utilization, avoiding the formation of information silos.

[0041] In terms of core algorithms, the dynamic multi-dimensional rental matching and risk control algorithm of this invention is a highly innovative design. It comprises five interconnected components: a dynamic time-series matching model, a multi-dimensional rental scoring system, a personalized rental recommendation engine, a dynamic risk assessment and control system, and a rental ecosystem optimizer. These algorithm modules form a progressive processing chain, with the output of each step providing important input for the next, achieving full utilization of information and layer-by-layer optimization of decision-making.

[0042] Specifically, the dynamic time-series matching model significantly improves the accuracy of lease matching by combining historical data with real-time market demand. The multi-dimensional lease scoring system further considers multiple factors such as lessee credit and equipment condition, providing a comprehensive evaluation basis for subsequent decisions. Building on this, the personalized lease recommendation engine utilizes advanced machine learning algorithms to provide customers with lease solutions that better meet their needs, improving customer satisfaction. The dynamic risk assessment and control system enables real-time monitoring of the entire leasing process, greatly reducing the risk of bad debts. Finally, the lease ecosystem optimizer, from a global perspective, achieves optimal resource allocation across the entire leasing ecosystem.

[0043] These algorithm modules are not simply linearly superimposed, but rather form an organic whole that promotes mutual benefit and synergistic effects. For example, more accurate lease matching not only improves customer satisfaction but also reduces default risk; more effective risk control provides a guarantee for bolder leasing plans; and global resource optimization further enhances the accuracy of matching and the effectiveness of risk control. This virtuous cycle ultimately manifests as a significant improvement in lease matching accuracy, an extension of the average lease period, an increase in equipment utilization, a substantial decrease in bad debt rates, and improved customer satisfaction.

[0044] Furthermore, this invention cleverly resolves some technical contradictions present in traditional systems. For example, in traditional systems, improving matching accuracy often means longer processing time, while this system, through advanced algorithm design, significantly shortens system response time while improving matching accuracy. As another example, personalized services and risk control are often mutually restrictive in traditional systems, while this system, through dynamic risk assessment, achieves an organic unity between personalized services and strict risk control.

[0045] In summary, this invention not only achieves significant improvements in each individual indicator, but more importantly, it realizes a synergistic effect where the whole is greater than the sum of its parts through the organic combination of various modules and algorithms. This comprehensive optimization enables this invention to bring a qualitative leap to the financial leasing industry, driving the entire industry towards a more intelligent and refined direction. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the overall system flow of the present invention.

[0047] Figure 2 This is a logic block diagram of the rental equipment data acquisition unit of the present invention.

[0048] Figure 3 This is a logic block diagram of the leasing business management unit of the present invention. Detailed Implementation

[0049] The solutions in 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. All other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0050] like Figure 1-3 As shown, this invention provides an intelligent financial leasing system and method based on a cloud platform. This system aims to address problems in traditional financial leasing operations such as information asymmetry, inaccurate risk assessment, and inefficient resource allocation. By introducing advanced data analysis and machine learning technologies, this system can achieve precise matching of leasing needs, dynamic risk assessment, and optimized resource allocation.

[0051] First, the intelligent financial leasing system of this invention includes a leasing personnel input unit 1, a leasing equipment data acquisition unit 2, a leasing business management unit 3, a demand unit 4, a leasing personnel registration unit 5, and a leasing platform resource library 6. These units are connected and interact with each other through a cloud platform, forming a complete financial leasing ecosystem.

[0052] The rental personnel input unit 1 is primarily used to obtain rental demand information. For example, when a construction company needs to rent an excavator, the company's purchasing personnel can input specific equipment requirements, expected rental period, budget, and other information through this unit. This information will provide an important basis for subsequent rental matching.

[0053] The rental equipment data acquisition unit 2 is responsible for collecting real-time status information of the equipment. For example, for excavators that have already been rented out, this unit can use IoT technology to monitor data such as the equipment's working hours, geographical location, and fuel consumption in real time. This data not only helps with equipment management but also provides important references for subsequent rental decisions.

[0054] The Leasing Business Management Unit 3 is the core of this system. It handles lease matching and risk management through a dynamic, multi-dimensional lease matching and risk control algorithm. The innovation of this algorithm lies in its ability to comprehensively consider multiple factors, such as equipment availability, lessee credit, and market demand, thereby making more accurate leasing decisions.

[0055] Demand Unit 4 is primarily responsible for analyzing and classifying rental demands. For example, it can segment demands based on factors such as different equipment types, rental durations, and geographical locations, laying the foundation for accurate matching in the future.

[0056] Unit 5, the registration unit for lessees, is used to manage information about lessees. This includes not only basic identity information but also important data such as credit records and historical rental behavior. This information is crucial for risk assessment and personalized recommendations.

[0057] Finally, the rental platform's resource repository 6 serves as the data center for the entire system, storing various types of data, including historical transaction data, equipment information, and user information. This resource repository provides data support for the operation of the entire system.

[0058] Next, we will introduce in detail the dynamic multi-dimensional lease matching and risk control algorithm in Leasing Business Management Unit 3. This algorithm includes five key steps, each building upon the previous one, forming a complete data processing and decision-making chain.

[0059] The first step is to generate the prediction matrix using a dynamic temporal matching model. The algorithm formula for this model is as follows:

[0060]

[0061] Where, P∈R n×m Let n be the prediction matrix, n be the number of renters, and m be the number of devices. W is the wavelet transform operator. - 1 represents the inverse wavelet transform operator. T(H) is the historical rental data matrix H∈R.n×m The constructed Toeplitz matrix. Φ is the nonlinear activation function; in this embodiment, we choose the ReLU function. R∈R n Let S ∈ R be the real-time market demand vector. m This is the device state vector. This is the Kronecker product, where α is the weighting coefficient.

[0062] In practical applications, we typically choose α = 0.5 as the initial value and then dynamically adjust it using machine learning algorithms to achieve the best results. The advantage of this model is that it can simultaneously consider the periodicity of historical data and the dynamic changes in real-time market demand, thus generating more accurate predictions. The second step is to generate a rating matrix through a multi-dimensional rental rating system. The algorithm formula for this system is as follows:

[0063]

[0064] Where S∈R n×m For the rating matrix, v i j = [P] ij C i D j ] T P is the eigenvector. i j is an element of the prediction matrix, C i D represents the elements of the lessee's credit vector. j These are elements of the device status vector. g (x,y) represents the geodesic distance on the Riemannian manifold, μ is the ideal point, and σ is the scale parameter.

[0065] In practice, we typically set σ to one-third of the standard deviation of the eigenvectors to ensure a reasonable distribution of scores. The innovation of this scoring system lies in its use of Riemannian geometry concepts, which better capture the non-linear characteristics of the data, resulting in more accurate scoring results.

[0066] The third step is to generate a recommendation matrix using a personalized rental recommendation engine. The algorithm formula for this engine is:

[0067] The third step is to generate a recommendation matrix using a personalized rental recommendation engine. The algorithm formula for this engine is as follows:

[0068] R=D -1 / 2 LD -1 / 2 PageRank(G)

[0069] Where, R∈R n×m Let L be the recommendation matrix, DS be the graph Laplacian matrix, S be the rating matrix, D be the degree matrix, G = (V, E) be the historical rental graph, and PageRank(G) be the result of applying the PageRank algorithm to graph G.

[0070] This recommendation engine combines the advantages of spectral clustering and PageRank algorithms. Spectral clustering captures the global similarity structure, while PageRank considers the influence of historical leasing behavior. In the financial leasing scenario, this combination can effectively balance the needs of new customers and the preferences of existing customers.

[0071] The fourth step is to generate a risk assessment matrix through a dynamic risk assessment and control system. The algorithm formula for this system is:

[0072]

[0073] Where, V∈R n×m Let (C)∫ be the risk assessment matrix, and let f be the Choquet integral. i j is the risk assessment function, μ is the fuzzy measure, and x (t) Let X represent the set of risk factors X = {x1, ..., x2} k The t-th largest element in}, A (t) ={x (t) ,…,x (k)}

[0074] In the financial leasing sector, risk factors typically include credit risk, market risk, and operational risk. The use of Choquet integrals allows us to consider the interactions between these risk factors, resulting in a more comprehensive and accurate risk assessment. The final step is to generate an optimization strategy using the leasing ecosystem optimizer. The algorithm for this optimizer is as follows:

[0075]

[0076] in, Let f:R be the system state vector. d →R is the Morse function, defined as:

[0077]

[0078] Among them, V ij ξ is an element of the risk assessment matrix. ij Let σ be the state point of the ideal system, and σ be the smoothing parameter.

[0079] This optimizer uses Morse theory and the concept of gradient flow to effectively find the optimal state of the system. In financial leasing systems, this can help us optimize resource allocation and balance risk and return.

[0080] Through the five steps outlined above, the intelligent financial leasing system of this invention enables efficient and accurate leasing matching and risk control. For example, when a construction company needs to lease an excavator, the system first predicts the market demand for this type of equipment, then performs a multi-dimensional scoring based on the company's creditworthiness and the status of available equipment. Next, the system generates personalized recommendations based on historical leasing data, while simultaneously assessing potential risks. Finally, the system provides an optimized leasing plan that meets the company's needs while controlling potential risks.

[0081] The system of this invention has significant advantages over traditional financial leasing systems. First, it can more accurately predict market demand and assess risks, thereby reducing the bad debt rate. Second, through personalized recommendations, the system can improve customer satisfaction and equipment utilization. Finally, through global optimization, the system can improve the efficiency of the entire leasing ecosystem.

[0082] In practical applications, this system can be flexibly adjusted according to different business scenarios. For example, for leasing high-value equipment, we can increase the weight of risk assessment; for short-term leasing, we can give more consideration to the impact of real-time market demand. This flexibility enables the system to adapt to various complex financial leasing scenarios.

[0083] In summary, the intelligent financial leasing system provided by this invention offers a comprehensive, efficient, and intelligent solution for the financial leasing industry by combining advanced mathematical models and machine learning algorithms. It not only improves the accuracy and efficiency of lease matching but also effectively controls risk and optimizes resource allocation, thereby promoting innovation and development throughout the financial leasing industry.

[0084] Next, we will describe in detail the specific implementation of the dynamic risk assessment and control system in the intelligent financial leasing system of this invention. This system is a crucial part of the entire leasing process, enabling real-time assessment and control of various risks that may arise in the leasing business.

[0085] In one embodiment of the present invention, the core of the dynamic risk assessment and control system is an algorithm based on Choquet integrals. The formula for this algorithm is:

[0086]

[0087] In this formula, V∈R n×m This is a risk assessment matrix, where n represents the number of lessees and m represents the number of leaseable devices. (C)∫ symbol represents the Choquet integral, a non-linear integral that effectively handles the interactions between various risk factors. ijThis is a risk assessment function that maps various risk factors to a specific risk value. μ is a fuzzy measure used to represent the importance of different combinations of risk factors. (t) Let X represent the set of risk factors X = {x1, ..., x2} k The t-th largest element in}, and A (t) ={x (t) ,…,x (k)} represents the set consisting of risk factors from the t-th largest to the largest.

[0088] In practical applications, the risk factors we typically consider include, but are not limited to, credit risk, market risk, operational risk, and liquidity risk. For example, when a construction company leases a large excavator, we might pay particular attention to the company's credit history (credit risk), the current state of the construction market (market risk), the company's experience in operating large equipment (operational risk), and the company's cash flow situation (liquidity risk).

[0089] A significant advantage of using the Choquet integral is its ability to capture the non-linear interactions between these risk factors. For example, even if a company has a good credit history, its overall risk may be significantly increased if it lacks experience operating specific equipment. Traditional linear weighting methods may fail to accurately reflect this situation, while the Choquet integral can.

[0090] In practice, we typically set certain risk thresholds. For example, we might set V... ij The values ​​are standardized to a range of 0 to 1, with 0.7 set as the high-risk threshold and 0.4 as the medium-risk threshold. These threshold values ​​can be adjusted based on the company's risk appetite and industry standards. When the system detects that a leasing transaction's risk score exceeds the high-risk threshold, it may automatically trigger additional review procedures or require a higher margin.

[0091] In one embodiment of the invention, we will discuss the rental ecosystem optimizer, which is the last key component of the entire system. This optimizer uses a gradient flow algorithm based on Morse theory, and its core formula is:

[0092]

[0093] Here, ξ∈R d f:R is the system state vector, where d is the dimension of the system state, which may include indicators such as total leased amount, average lease term, and equipment utilization rate. d →R is the Morse function, defined as:

[0094]

[0095] Among them, V ij These are elements of the risk assessment matrix mentioned earlier, ξ ij σ is the state point of the ideal system, and σ is the smoothing parameter.

[0096] This optimizer works by continuously adjusting the system state ξ, moving it along the gradient direction until it reaches a local minimum of the Morse function f(ξ). In the context of financial leasing, this means the system automatically adjusts various parameters (such as lease pricing, risk thresholds, and recommendation strategies) to achieve the optimal balance between risk and return.

[0097] For example, if the system detects a sudden increase in rental demand for a certain type of equipment, the optimizer might suggest appropriately raising the rental price of that type of equipment while increasing inventory. However, it will also consider the risks of raising prices (such as customer churn) and the costs of increasing inventory, thus providing a balanced optimization solution.

[0098] In practical applications, we typically set σ to be between 1 / 5 and 1 / 3 of the system state standard deviation. This range achieves a good balance between smoothness and accuracy. We also set constraints, such as limiting rental price fluctuations to a certain percentage, to avoid drastic fluctuations during the optimization process.

[0099] It's worth noting that the leasing ecosystem optimizer is not just a passive optimization tool; it can also proactively provide strategic advice. For example, by analyzing long-term leasing data and market trends, the optimizer might suggest that a company explore new equipment categories or enter new geographic markets.

[0100] Next, let's discuss the composition of the rental personnel input unit in detail. This unit includes an identity information verification module, a rental equipment type verification module, and a demand information verification module. These three modules work together to obtain comprehensive information about the rental personnel, providing a foundation for subsequent rental matching and risk assessment.

[0101] The identity verification module is responsible for verifying the identity of the leasing personnel. In the financial leasing sector, accurate identity information is crucial for risk control. This module may require users to provide information such as their ID card number and business license, and verifies this in real time through integration with public security or industrial and commercial systems. In some high-risk leasing transactions, facial recognition or other biometric authentication may also be required.

[0102] The equipment type confirmation module allows users to select the type of equipment they need to rent. This module typically provides a well-categorized and easy-to-navigate equipment catalog. For example, for construction equipment rental, there might be broad categories such as excavators, bulldozers, and cranes, with specific models available within each category. This module may also intelligently recommend possible equipment types based on the user's rental history or industry.

[0103] The demand information confirmation module is used to collect more specific rental needs. This may include information such as the expected rental duration, location of use, and budget range. For example, a construction company may need to use an excavator at a specific construction site for three months, with a budget of less than 100,000 yuan per month. This detailed demand information is crucial for subsequent accurate matching and pricing.

[0104] The information from these three modules will be integrated to form a complete description of leasing demand, which will then be passed to the leasing business management unit for further processing.

[0105] Finally, let's look at the specific composition of the rental equipment data acquisition unit. This unit includes a rental equipment status acquisition module and a rental equipment usage data acquisition module. The latter further includes a rental equipment usage time recording module, a rental equipment usage location recording module, a rental equipment usage amount recording module, and a rental equipment fault recording module.

[0106] The equipment status acquisition module for leased equipment is primarily responsible for real-time monitoring of the equipment's operational status. This may involve the application of Internet of Things (IoT) technology. For example, for large construction equipment, various sensors can be installed to monitor operating parameters such as engine temperature, oil pressure, and fuel consumption. This data can be transmitted to a cloud platform in real time, helping to promptly identify potential equipment problems and improve equipment reliability and utilization efficiency.

[0107] The rental equipment usage data collection module focuses more on the actual usage of the equipment. The usage time recording module precisely records the equipment's startup time and actual working time. This not only helps in accurately calculating rental fees but also allows for assessing equipment usage intensity, providing a basis for maintenance planning.

[0108] The location recording module uses GPS technology to track the device's location. This is crucial for preventing the device from being illegally moved or used in unauthorized locations. Simultaneously, this location data can also be used to analyze device demand in different regions, providing a reference for resource allocation.

[0109] The usage fee recording module is primarily responsible for recording various expenses related to equipment use, including rental fees, fuel costs, and maintenance fees. This data is crucial for financial accounting and pricing strategy development.

[0110] Finally, the fault logging module records in detail all faults that occur during equipment use. These records are not only used for timely fault handling but also serve as an important basis for assessing equipment reliability and developing preventative maintenance plans. For example, if the system detects that a certain model of equipment frequently experiences specific faults, it may recommend adjusting the leasing strategy for that model or strengthening the maintenance of related components.

[0111] These modules work together to provide the leasing business management unit with comprehensive, real-time equipment usage data, thereby supporting more accurate decision-making and more effective resource management.

[0112] In summary, the intelligent financial leasing system of this invention, through these meticulously designed modules and advanced algorithms, achieves intelligent management of the entire process, from leasing demand collection, equipment matching, risk assessment to resource optimization. This not only significantly improves the efficiency and accuracy of leasing operations but also provides leasing companies with powerful decision support tools, helping them maintain a competitive edge in a highly competitive market.

[0113] In one embodiment of the present invention, a cloud-based intelligent financial leasing method based on the system is also disclosed, the method comprising the following steps:

[0114] Rental demand information is obtained through the rental personnel input unit 1;

[0115] The equipment status information is collected through the data acquisition unit 2 of the leased equipment;

[0116] Input the rental demand information and equipment status information into the rental business management unit 3;

[0117] The process is handled through the dynamic multi-dimensional leasing matching and risk control algorithm of the leasing business management unit 3.

[0118] Based on the processing results, lease matching schemes and risk control strategies are generated;

[0119] The rental matching scheme and risk control strategy are stored in the rental platform resource library 6.

[0120] Before delving into the embodiments and comparative examples of this invention, we must first clarify that the core innovation of this intelligent financial leasing system lies in its dynamic multi-dimensional leasing matching and risk control algorithm. To verify the superiority of this system, we designed a series of detailed experiments, comparing the performance of the embodiments of this invention with that of traditional leasing systems.

[0121] Example: We selected a large construction equipment leasing company as the implementing entity of this invention. This company owns over 1000 pieces of various construction equipment, including excavators, bulldozers, and cranes. Its customer base mainly consists of small and medium-sized construction companies and engineering contractors. We deployed the intelligent financial leasing system of this invention on the company's cloud platform for a period of 6 months.

[0122] Comparative Analysis: For comparison, we selected the six months prior to the company's adoption of this system as the baseline period. During this period, the company used a traditional leasing management system, relying primarily on manual experience for equipment matching and risk assessment.

[0123] Test metrics and methods:

[0124] 1. Leasing matching accuracy: Evaluated by customer feedback and the proportion of subsequent equipment replacements.

[0125] 2. Average lease term: The average term of all lease contracts.

[0126] 3. Equipment utilization rate: Calculate the ratio of actual usage time to available time of equipment.

[0127] 4. Bad debt ratio: Calculate the proportion of uncollectible rent to total rent.

[0128] 5. Customer Satisfaction: Assessed through customer surveys (out of 1-10).

[0129] 6. System response time: Records the average time from when a customer submits a rental request to when the system provides a matching solution.

[0130] Now, let's look at the specific test results:

[0131]

[0132]

[0133] This data clearly demonstrates the significant advantages of our invention's system compared to traditional systems. Let's analyze these results item by item:

[0134] First, the rental matching accuracy improved from 72% to 94%, a significant increase of 30.56%. This means customers received equipment that better met their needs, greatly reducing replacements and complaints caused by equipment mismatch. This result directly reflects the effectiveness of our dynamic time-series matching model and multi-dimensional rental scoring system.

[0135] Secondly, the average rental period increased from 45 days to 62 days, a rise of 37.78%. This data reflects increased customer satisfaction with their rented equipment and a willingness to extend the rental period. This may also be due to our personalized rental recommendation engine's ability to more accurately predict customers' long-term needs.

[0136] The improvement in equipment utilization was also significant, rising from 65% to 83%, an increase of 27.69%. This demonstrates that our system can allocate resources more effectively and reduce equipment idle time. This not only increases the company's revenue but also improves resource utilization efficiency.

[0137] The significant decrease in the bad debt ratio (from 3.5% to 1.2%, a reduction of 65.71%) highlights the powerful capabilities of our dynamic risk assessment and control system. Through real-time monitoring and multi-dimensional risk assessment, the system can effectively identify high-risk customers and take corresponding preventative measures.

[0138] The improvement in customer satisfaction (from 7.2 to 9.1, an increase of 26.39%) is the result of multiple factors. More accurate equipment matching, more efficient service processes, and more reasonable leasing recommendations all contributed to this improvement.

[0139] Finally, the significant reduction in system response time (from 2 hours to 5 minutes, a decrease of 95.83%) demonstrates the efficiency of our system. This not only improves the customer experience but also enables the company to respond more quickly to changes in market demands.

[0140] This data fully demonstrates the superiority of the system of this invention. It not only shows significant improvements in each individual indicator, but more importantly, these improvements are interconnected and mutually reinforcing. For example, more accurate lease matching leads to longer lease periods and higher customer satisfaction; more effective risk control reduces the bad debt rate and improves the company's profitability.

[0141] It is worth noting that these improvements are not isolated, but rather the result of the collaborative work of various modules in the system of this invention. The dynamic time-series matching model provides the foundation for rental matching; the multi-dimensional rental scoring system and personalized rental recommendation engine further improve the accuracy of matching; the dynamic risk assessment and control system effectively reduces operational risks; and finally, the rental ecosystem optimizer optimizes resource allocation from a global perspective.

[0142] In summary, these test results strongly demonstrate the significant advantages of the system of this invention in improving leasing business efficiency, enhancing risk control, and optimizing resource allocation. It not only brings direct economic benefits to leasing companies but also provides a successful example for the intelligent transformation of the entire industry. We have reason to believe that with further system optimization and continuous data accumulation, its performance will be further improved.

[0143] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto; any substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the scheme and improved concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A cloud-based financial leasing system, characterized in that: The system includes: The rental personnel input unit is used to obtain rental demand information; The rental equipment data acquisition unit is used to collect equipment status information; The leasing business management unit is used to handle lease matching and risk control; Demand unit, used to analyze leasing demand; The leasing personnel registration unit is used to manage leasing personnel information; The rental platform's resource library is used to store system data; The leasing business management unit implements leasing matching and risk management through a dynamic multi-dimensional leasing matching and risk control algorithm. This algorithm includes the following steps: Acquire historical leasing data, real-time market demand data, and equipment status data; Based on the acquired data, a prediction matrix is ​​generated using a dynamic time-series matching model. The algorithm formula for the dynamic time-series matching model is as follows: ； in: For the prediction matrix, For the number of lessees, For the number of devices, For wavelet transform operators, It is the inverse wavelet transform operator. For historical rental data matrix The constructed Toeplitz matrix, It is a non-linear activation function. As a vector of real-time market demand, This is the device state vector. For Kronecker product, These are the weighting coefficients; Based on the prediction matrix, a rating matrix is ​​generated using a multi-dimensional rental rating system. The algorithm formula for the multi-dimensional rental rating system is as follows: ； in: For the rating matrix The Line number Column elements, For the rating matrix, For feature vectors, For the prediction matrix, the first Line number Column elements, Credit Vector for Lessees The One element, Device state vector The One element, Let be the geodesic distance on the Riemannian manifold. For the ideal point, For scale parameters; Based on the rating matrix, a recommendation matrix is ​​generated using a personalized rental recommendation engine. The algorithm formula for the personalized rental recommendation engine is as follows: ； in: For the recommendation matrix, For the graph Laplace matrix, For the rating matrix, For degree matrix, Historical leasing map, Let the node set represent the lessee and the equipment. Let the edge set represent historical lease relationships. For the image Results of applying the PageRank algorithm; Based on the recommendation matrix, a risk assessment matrix is ​​generated through a dynamic risk assessment and control system. The algorithm formula for the dynamic risk assessment and control system is as follows: ； in: Risk assessment matrix The Line number Column elements, For risk assessment matrix, Represents the Choquet integral. For risk assessment function, For fuzzy measure, Represents the set of risk factors The Middle Large elements, For the number of risk factors, For the first The set of factors ranging from the largest to the most significant; Based on the risk assessment matrix, an optimization strategy is generated using a leasing ecosystem optimizer. The algorithm formula for the leasing ecosystem optimizer is as follows: ； in: Let be the system state vector. For the system state dimension, The Morse function is defined as follows: ； in: For the risk assessment matrix, the first Line number Column elements, For the ideal system state point, For smoothing parameters.

2. The system according to claim 1, characterized in that, The rental personnel input unit includes an identity information confirmation module, a rental equipment type confirmation module, and a demand information confirmation module, which are used to obtain the rental personnel's identity information, the type of rental equipment required, and specific demand information.

3. The system according to claim 1, characterized in that, The rental equipment data acquisition unit includes a rental equipment status acquisition module and a rental equipment usage acquisition module, wherein the rental equipment usage acquisition module further includes a rental equipment usage time recording module, a rental equipment usage location recording module, a rental equipment usage amount recording module, and a rental equipment fault recording module.

4. A cloud-based financial leasing method based on the system described in any one of claims 1-3, characterized in that, The method includes the following steps: Rental demand information is obtained through the rental personnel input unit; Equipment status information is collected through the data acquisition unit of the leased equipment; Input the rental demand information and equipment status information into the rental business management unit; The process is handled through a dynamic, multi-dimensional lease matching and risk control algorithm within the lease business management unit. Based on the processing results, lease matching schemes and risk control strategies are generated; The rental matching scheme and risk control strategy are stored in the rental platform's resource library.

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