Financial leasing system and method based on cloud platform

Abstract

The invention relates to the technical field of financial leasing systems, in particular to a financial leasing system and method based on a cloud platform, and the system comprises a leasing personnel input unit which is used for obtaining leasing demand information; the rental equipment data acquisition unit is used for acquiring equipment state information; the lease service management unit is used for processing lease matching and risk control; the demand unit is used for analyzing the lease demand; the leasing personnel registration unit is used for managing leasing personnel information; the rental platform resource library is used for storing system data; wherein the lease service management unit realizes lease matching and risk management through a dynamic multi-dimensional lease matching and risk control algorithm, and a state time sequence matching model greatly improves the accuracy of lease matching by combining historical data and real-time market demands; the multi-dimensional lease scoring system further considers a plurality of factors such as lessee credit and equipment conditions, and provides a comprehensive evaluation basis for subsequent decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of financial leasing systems, and more specifically, to a financial leasing system and method based on a cloud platform. Background Art

[0002] With the deepening development of economic globalization and the rapid development of science and technology, the financial leasing industry, as an important part of the modern service industry, is undergoing unprecedented changes. The traditional financial leasing model has gradually revealed its inherent limitations in the face of increasingly complex market environments and customer needs. 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 operation processes and have failed to truly achieve intelligent and refined management of leasing business.

[0003] At present, the more advanced financial leasing systems on the market usually use rule-based matching algorithms and static risk assessment models. Such systems perform well when processing structured data and fixed scenarios, but their performance is often unsatisfactory when faced with rapidly changing market environments and diverse customer needs. For example, in terms of equipment matching, the existing system mainly relies on preset matching rules, which makes it difficult to adapt to the dynamic changes in customer needs and the rapid changes in market trends. In terms of risk assessment, most systems still use static models based on historical data, which cannot reflect changes in market risk and credit risk in real time. In addition, the existing systems usually separate functions such as leasing matching, risk control, and resource optimization, and operate them independently, lacking an effective coordination mechanism.

[0004] These problems have led to a series of practical difficulties: low efficiency in leasing matching and low customer satisfaction; delayed risk assessment and inability to effectively prevent bad debts; poor equipment utilization and waste of resources; slow system response and difficulty in meeting the needs of rapid decision-making. More seriously, these problems have influenced each other, forming a vicious cycle that is difficult to break, which has seriously restricted the further development of the financial leasing industry.

[0005] In the face of these challenges, the industry urgently needs an intelligent financial leasing system that can integrate advanced mathematical models, machine learning algorithms and big data analysis technologies. This system should be able to achieve accurate matching of leasing needs, dynamic risk assessment, optimal allocation of resources, and close collaboration between various functional modules to form an organic whole. Summary of the invention

[0006] In the face of the above technical problems, the present invention provides a financial leasing system and method based on a cloud platform, which aims to solve a series of problems existing in the traditional financial leasing system, such as inaccurate leasing matching, untimely risk assessment, unreasonable resource allocation, slow system response speed, etc. By introducing a dynamic multi-dimensional leasing matching and risk control algorithm, the present invention realizes the intelligent management of the entire leasing business process, significantly improving the accuracy of leasing matching, the real-time nature of risk control, and the efficiency of resource allocation.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The financial leasing system based on the cloud platform includes:

[0009] A leasing personnel input unit is used to obtain leasing demand information;

[0010] Rental equipment data collection unit, used to collect equipment status information;

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

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

[0013] Rental personnel registration unit, used to manage rental personnel information;

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

[0015] Among them, the leasing business management unit realizes leasing matching and risk management through dynamic multi-dimensional leasing matching and risk control algorithms.

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

[0017] Obtain historical rental data, real-time market demand data, and equipment status data;

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

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

[0020] Based on the rating matrix, generating a recommendation matrix 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, an optimization strategy is generated by the leasing ecosystem optimizer.

[0023] Preferably, the algorithm formula of the dynamic timing matching model is:

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

[0025] Preferably, the algorithm formula of the multi-dimensional rental scoring system is: Among them, S∈R n ×m is the rating matrix, v ij =[P ij ,C i ,D j ] T is the feature vector, P ij is the element of the prediction matrix, C i is the element of the lessee's credit vector, D j is the element of the equipment status vector, d g (x, y) is the geodesic distance on the Riemann manifold, μ is the ideal point, and σ is the scale parameter.

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

[0027] Preferably, the algorithm formula of the dynamic risk assessment and control system is: Where V∈R n×m is the risk assessment matrix, (C)∫ represents the Choquet integral, f ij is the risk assessment function, is the fuzzy measure, x (t) Denotes the risk factor set X = {x1,…,x k}, the tth largest element in A (t) ={x (t) ,…,x (k)}

[0028] Preferably, the algorithm formula of the rental ecosystem optimizer is: Among them, ξ∈R d is the system state vector, f:R d →R is the Morse function, defined as: f(ξ)=∑ i,j V ij ·exp(-∥ξ-ξ ij ∥ 2 / 2σ 2 ), where V ij is the element of the risk assessment matrix, ξ ij is the ideal system state point, and σ is 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, which are used to obtain the rental personnel's identity information, the required rental equipment type and specific demand information.

[0030] Preferably, the rental equipment data collection unit includes a rental equipment status collection module and a rental equipment usage collection module, wherein the rental equipment usage collection 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] A cloud-based intelligent financial leasing method based on the system, the method comprising the following steps:

[0032] Acquire leasing demand information through leasing personnel input unit;

[0033] Collect equipment status information through the rental equipment data collection unit;

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

[0035] Processing is performed through the dynamic multi-dimensional leasing matching and risk control algorithm of the leasing business management unit;

[0036] Generate lease matching solutions and risk control strategies based on the processing results;

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

[0038] Compared with the prior art, the beneficial effects of the present invention are embodied 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 industry, promoting the healthy development of the industry. Through more accurate demand matching and more effective risk control, this system can expand the service scope of financial leasing, enabling more small and medium-sized enterprises to benefit from flexible equipment financing solutions, thereby promoting the development of the real economy.

[0040] At the system architecture level, the present invention adopts a modular design, and each functional unit is relatively independent and closely cooperates with each other. The rental personnel input unit, rental equipment data collection unit, rental business management unit, demand unit, rental personnel registration unit and rental platform resource library form a complete information closed loop. This design not only improves the maintainability and scalability of the system, but also realizes the full sharing and utilization of data, avoiding the formation of information islands.

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

[0042] Specifically, the dynamic time series matching model greatly improves the accuracy of leasing matching by combining historical data and real-time market demand. The multi-dimensional leasing scoring system further considers multiple factors such as the credit of the lessee and the condition of the equipment, providing a comprehensive evaluation basis for subsequent decision-making. On this basis, the personalized leasing recommendation engine uses advanced machine learning algorithms to provide customers with leasing solutions that are more in line with their needs, thereby improving customer satisfaction. The dynamic risk assessment and control system realizes real-time monitoring of the entire leasing process, greatly reducing the risk of bad debts. Finally, the leasing ecosystem optimizer realizes the optimal allocation of resources for the entire leasing ecosystem from a global perspective.

[0043] These algorithm modules are not simply linear superpositions, but form an organic whole that promotes each other and synergizes efficiency. For example, more accurate leasing matching not only improves customer satisfaction, but also reduces the risk of default; more effective risk control provides a guarantee for bolder leasing plans; and global resource optimization further improves the accuracy of matching and the effectiveness of risk control. This virtuous cycle is ultimately reflected in a significant improvement in the accuracy of leasing matching, an extension of the average leasing cycle, an increase in equipment utilization, a significant reduction in the bad debt rate, and an increase in customer satisfaction.

[0044] In addition, the present invention also cleverly solves some technical contradictions existing in traditional systems. For example, in traditional systems, improving matching accuracy often means longer processing time, while this system, through advanced algorithm design, improves matching accuracy while greatly shortening system response time. For another example, personalized services and risk control in traditional systems are often mutually constrained, while this system achieves the organic unity of personalized services and strict risk control through dynamic risk assessment.

[0045] In short, the present invention has not only achieved significant improvements in each individual indicator, but more importantly, it has achieved a synergistic effect of the whole being greater than the sum of its parts through the organic combination of various modules and algorithms. This all-round optimization enables the present invention to bring a qualitative leap to the financial leasing industry and promote the entire industry to develop in a more intelligent and refined direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the overall system 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 DESCRIPTION

[0049] The scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. The embodiments in the present invention and all other embodiments obtained by ordinary persons in the art without making creative work are within the scope of protection of the present invention.

[0050] like Figure 1-3 As shown, the present invention provides an intelligent financial leasing system and method based on a cloud platform. The system aims to solve the problems of information asymmetry, inaccurate risk assessment, and inefficient resource allocation in traditional financial leasing business. By introducing advanced data analysis and machine learning technologies, the system can achieve accurate matching of leasing needs, dynamic risk assessment, and optimal resource allocation.

[0051] First, the intelligent financial leasing system of the present invention includes a leasing personnel input unit 1, a leasing equipment data collection 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 data exchanged through a cloud platform, forming a complete financial leasing ecosystem.

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

[0053] The rental equipment data collection unit 2 is responsible for collecting real-time status information of the equipment. For example, for an excavator that has been rented out, the unit can monitor the equipment's working hours, geographical location, fuel consumption and other data in real time through the Internet of Things technology. These data are not only helpful for equipment management, but also provide important reference for subsequent rental decisions.

[0054] The leasing business management unit 3 is the core of this system. It handles leasing matching and risk management by implementing a dynamic multi-dimensional leasing matching and risk control algorithm. The innovation of this algorithm is that it can comprehensively consider factors in multiple dimensions, such as equipment availability, lessee credit, market demand, etc., so as to make more accurate leasing decisions.

[0055] Demand unit 4 is mainly responsible for analyzing and classifying rental demand. For example, it can segment demand according to different equipment types, rental duration, geographical location and other factors, laying the foundation for subsequent accurate matching.

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

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

[0058] Next, we will introduce in detail the dynamic multi-dimensional leasing matching and risk control algorithm in leasing business management unit 3. The algorithm includes five key steps, each of which is built on the previous step, forming a complete data processing and decision-making chain.

[0059] The first step is to generate a prediction matrix through a dynamic time series matching model. The algorithm formula of this model is:

[0060]

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

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

[0063]

[0064] Among them, S∈R n×m is the rating matrix, v i j=[P ij ,C i ,D j ] T is the feature vector, P i j is the element of the prediction matrix, C i is the element of the lessee's credit vector, D j is the element of the equipment status vector. g (x, y) is the geodesic distance on the Riemann manifold, μ is the ideal point, and σ is the scale parameter.

[0065] In practice, we usually set σ to 1 / 3 of the standard deviation of the eigenvector, which can ensure a reasonable distribution of the ratings. The innovation of this rating system is that it uses the concept of Riemannian geometry to better capture the nonlinear characteristics of the data, thereby obtaining more accurate rating results.

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

[0067] The third step is to generate a recommendation matrix through a personalized rental recommendation engine. The algorithm formula of this engine is:

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

[0069] Where R∈R n×m is the recommendation matrix, L=DS is the graph Laplacian matrix, S is the rating matrix, D is the degree matrix, G=(V,E) is the historical rental graph, and PageRank(G) is 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 can capture the global similarity structure, while PageRank takes into account the impact of historical leasing behavior. In the financial leasing scenario, this combination can effectively balance the needs of new customers and the preferences of old customers.

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

[0072]

[0073] Where V∈R n×m is the risk assessment matrix, (C)∫ represents the Choquet integral, f i j is the risk assessment function, μ is the fuzzy measure, x (t) Denotes the risk factor set X = {x1,…,x k}, the tth largest element in A (t) ={x (t) ,…,x (k)}.

[0074] In the field of financial leasing, risk factors usually include credit risk, market risk, operational risk, etc. The use of Choquet integral allows us to consider the interaction between these risk factors, thereby obtaining a more comprehensive and accurate risk assessment result. The last step is to generate an optimization strategy through the leasing ecosystem optimizer. The algorithm formula of the optimizer is:

[0075]

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

[0077]

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

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

[0080] Through the above five steps, the intelligent financial leasing system of the present invention can achieve efficient and accurate leasing matching and risk control. For example, when a construction company needs to lease an excavator, the system will first predict the market demand for this type of equipment, and then perform multi-dimensional scoring based on the company's credit status and the status of available equipment. Next, the system will generate personalized recommendations based on historical leasing data and assess possible risks. Finally, the system will provide an optimized leasing plan that not only meets the company's needs but also controls possible risks.

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

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

[0083] In general, the intelligent financial leasing system provided by the present invention provides a comprehensive, efficient and intelligent solution for the financial leasing industry by combining advanced mathematical models and machine learning algorithms. It can not only improve the accuracy and efficiency of leasing matching, but also effectively control risks and optimize resource allocation, thereby promoting innovation and development of the entire financial leasing industry.

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

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

[0086]

[0087] In this formula, V∈R n×m is the risk assessment matrix, where n represents the number of lessees and m represents the number of equipment that can be rented. (C) The ∫ symbol represents the Choquet integral, which is a nonlinear integral that can effectively handle the interaction between various risk factors. ijis 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 risk factor combinations. (t) Denotes the risk factor set X = {x1,…,x k}, and A (t) ={x (t) ,…,x (k)} represents the set of risk factors from the tth largest to the largest.

[0088] In practical applications, the risk factors we usually consider include but are not limited to: credit risk, market risk, operational risk, liquidity risk, etc. For example, in the case of a construction company leasing a large excavator, we may pay special attention to the company's credit history (credit risk), the current prosperity 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 Choquet integrals is that it can capture the nonlinear interactions between these risk factors. For example, even if a company has a good credit history, if it lacks experience in operating a particular piece of equipment, the overall risk may be greatly increased. Traditional linear weighting methods may not accurately reflect this situation, but Choquet integrals can.

[0090] In practice, we usually set some risk thresholds. For example, we may set V ij The value range is standardized to between 0 and 1, and then 0.7 is set as the high risk threshold and 0.4 is set as the medium risk threshold. The specific values ​​of these thresholds can be adjusted according to the company's risk appetite and industry standards. When the system detects that the risk score of a rental transaction exceeds the high risk threshold, it may automatically trigger additional review procedures or require a higher deposit.

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

[0092]

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

[0094]

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

[0096] The working principle of this optimizer is to continuously adjust the system state ξ so that it moves along the gradient direction and eventually reaches the local minimum of the Morse function f(ξ). In the context of financial leasing, this means that the system automatically adjusts various parameters (such as leasing pricing, risk thresholds, recommended strategies, etc.) to achieve the best 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 may recommend an appropriate increase in the rental price of that type of equipment and an increase in inventory. However, it will also take into account the risks that may arise from raising prices (such as customer churn) and the costs of increasing inventory, thus providing a balanced optimization solution.

[0098] In practical applications, we usually set σ to between 1 / 5 and 1 / 3 of the standard deviation of the system state. This range can achieve a good balance between smoothness and accuracy. At the same time, we also set some constraints, such as the change in rental price cannot exceed a certain percentage, to avoid drastic fluctuations during the optimization process.

[0099] It is worth noting that the Rental Ecosystem Optimizer is not just a passive optimization tool, it can also proactively provide some strategic suggestions. For example, by analyzing long-term rental data and market trends, the optimizer may suggest that the company expand into 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 the identity information confirmation module, the rental equipment type confirmation module, and the demand information confirmation module. These three modules work together to obtain comprehensive information about the rental personnel, providing a basis for subsequent rental matching and risk assessment.

[0101] The identity information confirmation module is responsible for verifying the identity of the leasing personnel. In the field of financial leasing, accurate identity information is crucial for risk control. This module may require users to provide information such as ID number and business license, and conduct real-time verification through connection with the public security system or industrial and commercial system. In some high-risk leasing businesses, facial recognition or other biometric authentication may also be required.

[0102] The rental equipment type confirmation module allows users to select the type of equipment they need to rent. This module usually provides a clearly classified and easy-to-navigate equipment catalog. For example, for construction equipment rental, there may be major categories such as excavators, bulldozers, cranes, etc., and each major category has specific models to choose from. The module may also intelligently recommend some types of equipment that may be needed based on the user's historical rental records or the industry they are in.

[0103] The demand information confirmation module is used to collect more specific rental requirements. 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 very important for subsequent accurate matching and pricing.

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

[0105] Finally, let's take a look at the specific composition of the rental equipment data collection unit. This unit includes a rental equipment status collection module and a rental equipment usage collection module, the latter of which 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 failure recording module.

[0106] The rental equipment status acquisition module is mainly responsible for real-time monitoring of the working status of the equipment. This may involve the application of Internet of Things (IoT) technology. For example, for large-scale construction equipment, various sensors can be installed to monitor the operating parameters of the equipment, such as engine temperature, oil pressure, fuel consumption, etc. These data can be transmitted to the cloud platform in real time to help timely discover potential equipment problems and improve the reliability and efficiency of equipment use.

[0107] The rental equipment usage collection module focuses more on the actual usage of the equipment. Among them, the usage time recording module accurately records the equipment's startup time, actual working time, etc. This not only helps to accurately calculate the rental fee, but can also be used to evaluate the equipment's usage intensity and provide a basis for maintenance plans.

[0108] The location record module uses GPS technology to track the location of the device. This is very important to prevent the device from being illegally transferred or used in unauthorized locations. At the same time, these location data can also be used to analyze the equipment demand in different regions and provide reference for resource allocation.

[0109] The usage amount recording module is mainly responsible for recording various expenses related to equipment use, including rental, fuel costs, maintenance costs, etc. These data are very important for financial accounting and pricing strategy formulation.

[0110] Finally, the fault record module will record in detail the various faults that occur during the use of the equipment. These records are not only used to handle faults in a timely manner, but also an important basis for evaluating equipment reliability and formulating preventive maintenance plans. For example, if the system finds that a certain model of equipment frequently has a specific fault, it may recommend adjusting the rental strategy of this model of equipment or strengthening the maintenance of related parts.

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

[0112] In general, the intelligent financial leasing system of the present invention realizes intelligent management of the entire process from leasing demand collection, equipment matching, risk assessment to resource optimization through these well-designed modules and advanced algorithms. This not only greatly improves the efficiency and accuracy of the leasing business, but also provides leasing companies with a powerful decision-making support tool, which helps them maintain their dominant position in the fiercely competitive market.

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

[0114] Obtaining rental demand information through rental personnel input unit 1;

[0115] Collecting equipment status information through the rental equipment data collection unit 2;

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

[0117] Processing is performed through the dynamic multi-dimensional leasing matching and risk control algorithm of the leasing business management unit 3;

[0118] Generate lease matching solutions and risk control strategies based on the processing results;

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

[0120] Before we delve into the embodiments and comparative examples of the present 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. In order to verify the superiority of the system, we designed a set of detailed experiments to compare the performance of the embodiments of the present invention with the traditional leasing system.

[0121] Embodiment: We selected a large construction equipment leasing company as the implementation subject of the present invention. The company has more than 1,000 construction equipment of various types, including excavators, bulldozers, cranes, etc. The company's customer base is mainly small and medium-sized construction companies and engineering contractors. We deployed the intelligent financial leasing system of the present invention on the company's cloud platform for a period of 6 months.

[0122] Comparison: For comparison, we selected the six months before the company adopted this system as the benchmark period. During this period, the company used a traditional rental management system, mainly relying on manual experience for equipment matching and risk assessment.

[0123] Test indicators and methods:

[0124] 1. Lease matching accuracy: evaluated through customer feedback and the proportion of subsequent equipment replacement.

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

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

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

[0128] 5. Customer satisfaction: scored through customer questionnaire (1-10 points).

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

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

[0131]

[0132]

[0133] From this set of data, we can clearly see the significant advantages of the system of the present invention over the traditional system. Let us analyze these results item by item:

[0134] First, the rental matching accuracy rate increased from 72% to 94%, an increase of 30.56%. This means that customers get equipment that better meets 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, an increase of 37.78%. This data reflects that customers are more satisfied with the rented equipment and are willing to extend the use time. At the same time, this may also be due to our personalized rental recommendation engine that can more accurately predict customers' long-term needs.

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

[0137] The significant drop in bad debt ratio (from 3.5% to 1.2%, a decrease of 65.71%) highlights the powerful function 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 preventive measures.

[0138] The improvement in customer satisfaction (from 7.2 points to 9.1 points, an increase of 26.39%) is the result of the combined effect of many factors. More accurate equipment matching, more efficient service processes, and more reasonable leasing recommendations have 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 customer experience, but also enables the company to respond more quickly to changes in market demand.

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

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

[0142] In general, this set of test results strongly proves the significant advantages of the system of the present invention in improving leasing business efficiency, enhancing risk control, optimizing resource allocation, etc. 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 the further optimization of the system and the continuous accumulation of data, its performance will be further improved.

[0143] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto; any person familiar with the art who, within the scope disclosed by the present invention, makes substitutions or changes based on the scheme and improved concepts of the present invention shall be covered within the protection scope of the present invention.

Claims

1. The financial leasing system based on the cloud platform is characterized by: The system includes: A leasing personnel input unit is used to obtain leasing demand information; Rental equipment data collection unit, used to collect equipment status information; The leasing business management unit is used to handle leasing matching and risk control; Demand unit, used to analyze rental demand; Rental personnel registration unit, used to manage rental personnel information; The rental platform resource library is used to store system data; Among them, the leasing business management unit realizes leasing matching and risk management through dynamic multi-dimensional leasing matching and risk control algorithms.

2. The intelligent financial leasing system according to claim 1, characterized in that: The dynamic multi-dimensional leasing matching and risk control algorithm includes the following steps: Obtain historical rental data, real-time market demand data, and equipment status data; Based on the acquired data, a prediction matrix is ​​generated through a dynamic time series matching model; Based on the prediction matrix, a scoring matrix is ​​generated through a multi-dimensional rental scoring system; Based on the rating matrix, generating a recommendation matrix through a personalized rental recommendation engine; Based on the recommendation matrix, a risk assessment matrix is ​​generated through a dynamic risk assessment and control system; Based on the risk assessment matrix, an optimization strategy is generated by the leasing ecosystem optimizer.

3. The intelligent financial leasing system according to claim 2, characterized in that: The algorithm formula of the dynamic timing matching model is: Where P∈R n×m is the prediction matrix, W is the wavelet transform operator, W -1 is the inverse wavelet transform operator, T(H) is the historical rental data matrix H∈R n ×m constructed Toeplitz matrix, Φ is a nonlinear activation function, R∈R n is the real-time market demand vector, S∈R m is the device state vector, is the Kronecker product, and α is the weight coefficient.

4. The intelligent financial leasing system according to claim 3, characterized in that: The algorithm formula of the multi-dimensional rental scoring system is: Among them, S∈R n ×m is the rating matrix, v ij =[P ij , C i , D j ] T is the feature vector, P ij is the element of the prediction matrix, C i is the element of the lessee's credit vector, D j is the element of the equipment status vector, d g (x, y) is the geodesic distance on the Riemann manifold, μ is the ideal point, and σ is the scale parameter.

5. The intelligent financial leasing system according to claim 4, characterized in that: The algorithm formula of the personalized rental recommendation engine is: R = D -1 / 2 LD -1 / 2 PageRank(G), where R∈R n×m is the recommendation matrix, L=DS is the graph Laplacian matrix, S is the rating matrix, D is the degree matrix, G=(V, E) is the historical rental graph, and PageRank(G) is the result of applying the PageRank algorithm to graph G.

6. The intelligent financial leasing system according to claim 5, characterized in that: The algorithm formula of the dynamic risk assessment and control system is: Where V∈R n×m is the risk assessment matrix, (C)∫ represents the Choquet integral, f ij is the risk assessment function, μ is the fuzzy measure, x (t) Denote the risk factor set X = {x1, ..., x k }, the tth largest element in A (t) ={x (t) , ..., x (k) }.

7. The intelligent financial leasing system according to claim 6, characterized in that: The algorithm formula of the rental ecosystem optimizer is: Among them, ξ∈R d is the system state vector, f:R d →R is the Morse function, defined as: f(ξ)=∑ i，j V ij ·exp(-‖ξ-ξ ij ‖ 2 / 2σ 2 ), where V ij is the element of the risk assessment matrix, ξ ij is the ideal system state point, and σ is the smoothing parameter.

8. The intelligent financial leasing 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 required rental equipment type and specific demand information.

9. The intelligent financial leasing system according to claim 1, characterized in that: The rental equipment data collection unit includes a rental equipment status collection module and a rental equipment usage collection module, wherein the rental equipment usage collection 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.

10. A cloud platform-based intelligent financial leasing method based on the system according to any one of claims 1 to 9, characterized in that: The method comprises the following steps: Acquire leasing demand information through leasing personnel input unit; Collect equipment status information through the rental equipment data collection unit; Inputting the rental demand information and equipment status information into a rental business management unit; Processing is performed through the dynamic multi-dimensional leasing matching and risk control algorithm of the leasing business management unit; Generate lease matching solutions and risk control strategies based on the processing results; The leasing matching scheme and risk control strategy are stored in the leasing platform resource library.

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