Intelligent automobile evaluation transaction system based on Internet big data, readable storage medium and computer program product

Abstract

The invention provides an intelligent automobile evaluation transaction system based on Internet big data, and provides intelligent and informatization support for automobile evaluation transaction. According to the intelligent automobile assessment transaction system, in the process of executing automobile big data assessment, comprehensive analysis of an assessment object in the prior art is expanded into a diversified assessment model which takes an assessment target object vehicle as a first-level assessment object and takes an assessment group composed of related vehicles as a second-level assessment object based on a collaborative assessment function; and an evaluation bus is introduced to execute data interconnection processing from informatization intelligent evaluation to an Internet of Things transaction platform. And meanwhile, the same evaluation group is borne by the Internet of Things subnet, and the annular evaluation model and the bus type big data evaluation transaction chain are subjected to information collaboration by using the big data intersection, so that the data systematization degree of the automobile evaluation transaction system based on the Internet big data is improved, the user-oriented evaluation and transaction information feedback accuracy of the system is improved, and the user-oriented evaluation and transaction information feedback accuracy of the system is improved. And user experience is improved.

Description

Technical Field

[0001] This invention belongs to the field of next-generation information technology, and in particular relates to an intelligent vehicle evaluation and transaction system based on Internet big data, a readable storage medium, and a computer program product. Background Technology

[0002] With the rapid development of IoT and big data technologies, more and more physical transactions and information exchanges are being carried out conveniently through big data platforms, extending to basic livelihood scenarios such as housing rental and sales, and used car transaction evaluation.

[0003] The Internet of Things (IoT) is a technological system that connects the physical world to the internet through information sensing devices. Its core lies in using technologies such as sensors, radio frequency identification (RFID), and global positioning systems (GPS) to collect various data from the physical world in real time, such as temperature, humidity, pressure, and location. This data is transmitted to a data processing center via wireless or wired networks, analyzed and processed, and then fed back to the corresponding devices or users to achieve intelligent management and control. An IoT system typically consists of a sensing layer, a transmission layer, and an application layer. The sensing layer is responsible for data acquisition, the transmission layer for data transmission, and the application layer for data processing and business logic implementation. The realization of the IoT relies on the integration of multiple technologies, including but not limited to low-power wide-area network (LPWAN), Bluetooth, Wi-Fi, ZigBee, and other wireless communication technologies, as well as cloud computing, big data, and artificial intelligence data processing technologies. Through the synergistic effect of these technologies, the IoT enables interconnection between devices, providing users with more convenient, efficient, and intelligent services.

[0004] In keeping with the times, IoT technology has been widely applied in various fields, driving the digital transformation of traditional industries. In the smart home sector, by embedding sensors and communication modules into home appliances, users can remotely control devices such as lights, air conditioners, and curtains via mobile applications, achieving intelligent home environment control. In industrial manufacturing, IoT technology is used for remote equipment monitoring and fault prediction. By installing sensors on production equipment, real-time operational data is collected, equipment health is analyzed, potential faults are detected early, and downtime and maintenance costs are reduced. In agriculture, IoT technology, through sensor networks deployed in farmland, monitors soil moisture, temperature, nutrients, and other information in real time. Combined with meteorological data, this enables precise irrigation and fertilization, improving agricultural production efficiency and crop yield. Furthermore, IoT technology plays a crucial role in transportation and logistics, healthcare, and environmental protection. For example, intelligent transportation systems optimize traffic flow through the interconnection of vehicles and road infrastructure; and medical IoT uses remote monitoring devices to acquire patients' vital signs data in real time, assisting doctors in remote diagnosis and treatment.

[0005] With continuous technological advancements, the Internet of Things (IoT) is expanding towards greater efficiency and intelligence. On one hand, the power consumption of IoT devices is constantly decreasing, enabling more devices to operate stably for extended periods. Simultaneously, improvements in communication technologies have enhanced the reliability and speed of data transmission. For example, the low latency and high bandwidth of 5G technology provide stronger communication support for the IoT, enabling more complex real-time interactions. On the other hand, the integration of IoT with technologies such as artificial intelligence and big data is deepening. Through machine learning algorithms analyzing massive amounts of data, IoT systems can achieve more accurate predictions and decisions. However, IoT technology also faces challenges, such as data security and privacy protection. Due to the large number and wide distribution of IoT devices, data is vulnerable to attacks during collection, transmission, and storage, leading to user privacy leaks and data tampering. Furthermore, the standardization and interoperability of IoT devices are urgent issues to be addressed. Compatibility problems often exist between devices from different manufacturers, limiting the widespread application of IoT systems. In the future, with continuous technological progress and the gradual improvement of standards, the IoT is expected to achieve breakthroughs in more fields, bringing greater convenience to people's lives and work.

[0006] Meanwhile, big data technology is a complex technological system for processing massive and diverse data, changing traditional data processing methods and mindsets. In today's digital age, the speed and scale of data generation are exploding, ranging from user behavior records on internet platforms and internal enterprise transaction data to sensor data generated by IoT devices. Data sources are wide-ranging and diverse in form. The core of big data technology lies in its ability to efficiently collect, store, manage, and analyze this massive amount of data. Data collection is the fundamental step, using various data acquisition tools and technologies, such as web crawlers and data interfaces, to obtain data from different data sources. In terms of storage, traditional database systems are no longer sufficient to meet the needs of big data, leading to the emergence of distributed storage architectures. These architectures distribute data across multiple nodes, increasing storage capacity while ensuring data security and reliability. At the management level, big data technology requires data classification, cleaning, and integration. Invalid or erroneous data is removed, and data from different sources is correlated and merged to form structured datasets, providing a high-quality data foundation for subsequent analysis. Data analysis is a crucial aspect of big data technology. It utilizes advanced algorithms and models, such as machine learning algorithms and data mining techniques, to extract valuable information and patterns from massive amounts of data. For example, by analyzing user purchasing behavior data, businesses can accurately predict consumer preferences and needs, thereby enabling personalized marketing strategies and improving sales efficiency and customer satisfaction. Big data technology has extremely wide applications, covering multiple industries such as finance, healthcare, transportation, and education. In the financial sector, big data can be used for risk assessment and fraud detection. Through real-time analysis of massive amounts of transaction data, abnormal transaction behavior can be detected promptly, ensuring the security of financial transactions. In the healthcare industry, big data technology can integrate patient medical records, examination results, and other data to assist doctors in disease diagnosis and treatment plan development, improving the quality and efficiency of medical services. With continuous technological development, big data technology is also constantly innovating and optimizing, such as strengthening data privacy protection technologies to ensure the protection of user privacy information during data processing; and combining edge computing with big data, pushing some data processing functions to the edge devices that generate data, reducing data transmission volume, and improving the real-time performance of data processing. The development of big data technology has brought tremendous changes and opportunities to society. It has driven the digital transformation of various industries, enhanced the competitiveness of enterprises, and brought more convenience and intelligent experiences to people's lives.

[0007] The development of big data technology is also reflected in its in-depth mining and utilization of data value. Data itself is a resource, but only through effective processing and analysis can it be transformed into valuable information and knowledge. In the era of big data, the value of data is no longer limited to traditional statistical analysis, but can be used through complex algorithms and models to discover hidden patterns and trends within the data. For example, in the field of marketing, by analyzing social media data, companies can understand consumers' emotional tendencies and brand awareness, thereby adjusting brand strategies and product positioning. Big data technology also promotes cross-domain data integration and innovative applications. Data from different industries can be interconnected and integrated to generate new value. For example, the transportation sector can combine meteorological data with traffic flow data to predict traffic congestion in advance and formulate reasonable traffic management plans. Driven by big data technology, data sharing and openness are gradually becoming a trend. Enterprises and institutions are beginning to realize that the value of data lies not only in their own use, but also in achieving data value-added and innovative applications through cooperation and sharing with other organizations. At the same time, the development of big data technology also faces some challenges, such as data quality control, data security, and privacy protection. Data quality directly affects the accuracy of analysis results, therefore, a strict data quality management mechanism needs to be established. Data security and privacy protection are crucial safeguards for the development of big data technology. With the continuous improvement of data regulations, enterprises and institutions need to adopt effective technical means and management measures to ensure the legal and compliant use of data. The future development prospects of big data technology are broad; it will continue to drive the digital transformation and innovative development of various industries, creating more value and opportunities for society.

[0008] Leveraging the rapid development of IoT and big data technologies, information-based transaction systems and platforms are increasingly being implemented in various sectors such as housing, labor, and automobile trading. A big data-driven automobile trading platform is an innovative business model that integrates modern information technology with the characteristics of the automotive industry. It optimizes transaction processes, enhances user experience, and improves platform operational efficiency by collecting, analyzing, and utilizing massive amounts of data. In traditional automobile trading, information asymmetry is a long-standing problem; consumers often struggle to obtain comprehensive and accurate vehicle information, while dealers find it difficult to accurately grasp market demand. The application of big data technology effectively solves this problem. The platform first collects data through multiple channels, including user browsing behavior, search history, and inquiry content, as well as detailed vehicle parameters, historical maintenance records, and market price fluctuations. This data comes from a wide range of sources and is diverse in form, covering comprehensive information from the user end to the vehicle end. Based on data collection, the platform uses advanced data processing technologies to clean, classify, and integrate this massive amount of data. By removing invalid or erroneous data, scattered information is correlated and merged to form a structured dataset, providing a solid foundation for subsequent analysis and applications. In the data analysis phase, big data-driven car trading platforms can utilize machine learning algorithms and data mining techniques to conduct in-depth analysis of user behavior patterns and preferences. For example, by analyzing users' browsing paths and dwell time, the platform can accurately determine users' interest in different types of cars, thereby enabling personalized vehicle recommendations. Simultaneously, the platform can provide users with accurate vehicle valuations and price trend predictions based on historical transaction data and market dynamics, helping users make more informed purchasing decisions. Furthermore, big data technology can monitor and analyze dealer inventory in real time, helping dealers optimize inventory management and reduce operating costs. Through accurate demand forecasting, dealers can adjust vehicle procurement plans in advance, avoiding inventory backlogs or stockouts. This refined management based on big data not only improves dealer operational efficiency but also provides consumers with a wider and more timely selection of vehicles.

[0009] Big data-driven car trading platforms not only demonstrate significant advantages in user service and dealer operations, but also play a crucial role in transaction security and market transparency. Regarding transaction security, the platform integrates vehicle maintenance records, accident histories, and other data to provide consumers with comprehensive vehicle background information, effectively preventing information fraud and concealment of vehicle defects. Simultaneously, the platform can identify abnormal transaction behavior through data analysis, promptly detecting and preventing potential transaction risks, and protecting the legitimate rights and interests of both parties. In terms of market transparency, big data technology makes vehicle price information more open and transparent. By collecting and analyzing vehicle transaction prices in different regions and time periods, the platform provides users with real-time market price references. This transparent price information not only helps consumers reasonably assess vehicle value but also encourages dealers to standardize pricing practices, maintaining a fair competitive market environment. Furthermore, big data-driven car trading platforms can promote resource integration and collaborative development within the automotive industry. The platform can integrate resources from car manufacturers, dealers, financial institutions, and after-sales service providers to form a complete car trading ecosystem. Through data sharing and collaborative cooperation, all parties can better understand market demand, optimize resource allocation, and improve the operational efficiency and service level of the entire industry. For example, the platform can provide users with personalized financial loan solutions based on their car purchase intentions and credit status; it can also offer convenient after-sales service appointments and maintenance suggestions, enhancing the user's post-purchase experience. With continuous technological advancements, big data-driven car trading platforms will further expand their functions and service scope. For instance, by combining artificial intelligence and IoT technologies, the platform can achieve remote vehicle monitoring and intelligent diagnostics, providing users with more intelligent vehicle management services. Simultaneously, the platform can also provide automakers with market feedback and product improvement suggestions through big data analysis, contributing to the innovative development of the automotive industry.

[0010] However, existing car trading platforms mainly focus on the target objects to be valued, and their valuation functions are relatively simple. The system valuation architecture is usually simple in chain and lacks data collaboration and integration of information from multiple objects. As a result, on the one hand, the valuation accuracy is insufficient, and on the other hand, it does not reasonably consider the needs of users to provide diversified valuation and trading space.

[0011] This invention proposes an intelligent vehicle evaluation and transaction system based on internet big data. Leveraging internet big data applications in vehicle transactions, it provides intelligent and information-based support for vehicle evaluation and transactions. During the execution of vehicle big data evaluation, the intelligent vehicle evaluation and transaction system expands the comprehensive analysis of evaluation objects in existing technologies, based on a group collaborative evaluation loop, into a diversified product valley model with the target vehicle as the primary evaluation object and preset evaluation groups as secondary evaluation objects. An evaluation bus is introduced to perform data interconnection and information processing from information-based intelligent evaluation to the IoT transaction platform. Based on this, the intelligent vehicle evaluation and transaction system claimed in this invention uses an IoT subnet to carry the same evaluation group, realizing an intelligent vehicle evaluation architecture corresponding to a hierarchical IoT structure. It utilizes big data convergence points to coordinate information between the ring-shaped evaluation model and the bus-type big data evaluation and transaction chain, improving the data systematization level of the internet big data-based vehicle evaluation and transaction system, improving the accuracy of user-facing evaluation and transaction information feedback, and enhancing the user experience. Summary of the Invention

[0012] The present invention aims to provide an intelligent vehicle evaluation and transaction system based on Internet big data that is superior to existing technologies.

[0013] To achieve the above objectives, the technical solution of the present invention is as follows: A smart vehicle evaluation and trading system based on Internet big data, the system comprising at least an evaluation target object, an evaluation vehicle self-organizing object, an evaluation bus, a group collaborative evaluation loop, and an Internet of Things trading platform, wherein: The target of the evaluation is the vehicle to be evaluated and traded as a smart car; The self-organizing objects of the evaluation vehicles are the vehicle objects that need to establish an IoT subnet based on the association of the group collaborative evaluation loop; The evaluation bus runs through the evaluation and transaction chain of the intelligent vehicle evaluation and transaction system. It is used to establish an interconnected Internet of Things (IoT) subnetwork for the evaluation target and each self-organizing evaluation vehicle. It also uses the IoT networking valuation configuration module, the first big data valuation interval module, and the second big data valuation interval module to intelligently realize collaborative evaluation of the same valuation group to obtain the first group valuation; and the vehicle evaluation of the evaluation target to obtain the first target valuation; and establishes the transmission bandwidth to the IoT transaction platform. The evaluation bus has a big data convergence point with the group collaborative evaluation loop. The big data convergence point is used to synchronize the vehicle evaluation big data and sensor acquisition data of the evaluation bus and the group collaborative evaluation loop. The group collaborative evaluation ring, based on the evaluation target object ID and the valuation group configuration ID of the IoT network valuation configuration module, detects evaluation vehicle self-organizing objects that match the valuation group configuration ID in the intelligent vehicle evaluation IoT, and establishes interconnected IoT subnets for the evaluation target object and each evaluation vehicle self-organizing object. The group collaborative evaluation ring also establishes a ring-shaped data structure based on the Internet of Things subnet, which is used to solidify the subnet parameters of the Internet of Things subnet established for the evaluation target object and the self-organizing objects of each evaluation vehicle. The Internet of Things (IoT) trading platform receives a first target valuation and a first set of valuations through an evaluation bus. Based on the user's set of used car models intended for sale, the platform provides the system user with a first target valuation for the corresponding car model that meets the user's requirements.

[0014] Preferably, the evaluation bus includes an IoT networking valuation configuration module, a first big data valuation intervalization module, and a second big data valuation intervalization module; in: The IoT network valuation configuration module is used to confirm the vehicle-to-vehicle association parameters based on each vehicle object that has entered the IoT network, and based on a specific vehicle-to-vehicle association parameter threshold, associate the vehicle IDs to be evaluated with vehicle-to-vehicle association parameters above the threshold with the evaluation target object ID to the same evaluation group, and set the same valuation group configuration ID. The first big data valuation interval module is used to perform information-based valuation of the vehicle of the target object and give a first target valuation. The second big data valuation interval module is used to perform information-based collaborative evaluation for the same valuation group and provide a first set of valuations, which includes the valuations of all other vehicle objects in the group.

[0015] Preferably, after the user provides feedback that the valuation of the first target is too high or too low, the IoT trading platform also detects valuations of vehicle objects within the first group of valuations that meet the user's valuation adjustment direction requirements, and feeds back both the first group of valuations and the valuations of vehicle objects within the group that meet the user's valuation adjustment direction requirements to the user.

[0016] Preferably, the first big data valuation interval module is used to perform an information-based assessment of the vehicle of the target object and provide a first target valuation, specifically: The first big data valuation interval module determines the first target valuation based on the vehicle model, new car price, new car condition parameters pre-configured by the system according to the vehicle condition, and used car transaction frequency parameters of the target object, using the first valuation algorithm.

[0017] Preferably, the second big data valuation intervalization module is used to perform information-based collaborative evaluation for the same valuation group, providing a first set of valuations. This first set of valuations includes the valuations of all evaluated vehicle objects within the group, specifically: The second big data valuation interval module determines the second target valuation based on the vehicle model, new car price, new car condition parameters pre-configured by the system according to the vehicle condition, and used car transaction frequency parameters of all other vehicles in the target evaluation group, using the second valuation algorithm.

[0018] Preferably, the subnet parameters of the IoT subnet include at least: Subnet ID of the IoT subnet The evaluation target object ID within the IoT subnet, The self-organizing object IDs of each evaluation vehicle within the IoT subnet, IoT subnet network configuration parameters.

[0019] Preferably, the big data convergence point is used to synchronously evaluate the vehicle evaluation big data and sensor-collected data of the bus and group collaborative evaluation loop, specifically as follows: The big data convergence point will coordinate the IoT vehicle valuation cycle fluctuation information big data collected by the IoT network valuation configuration module through the synchronous evaluation bus to the group collaborative evaluation ring, and coordinate the vehicle sensing information of each IoT sub-network collected by the group collaborative evaluation ring to the synchronous evaluation bus. The vehicle valuation cycle fluctuation information big data characterization is a curve representing the change of the valuation base value of a specific model within the first specific cycle, collected from multiple information trading centers.

[0020] Preferably, the valuation base value for a specific vehicle model is the used valuation benchmark value of the specific vehicle model in brand new condition, which is dynamically configured by the system or obtained by averaging the transaction data of the specific vehicle model in brand new condition within a second specific period, and the second specific period is different from the first specific period.

[0021] Meanwhile, the present invention also proposes a computer-readable storage medium storing a processor-executable program, characterized in that the processor-executable program, when executed by a processor, is used to perform the corresponding functions of the intelligent vehicle evaluation and transaction system based on Internet big data as described in any of the above.

[0022] Meanwhile, the present invention also proposes a computer program product, which includes computer instructions that, when executed by a processor, perform the corresponding functions of the intelligent vehicle evaluation and transaction system based on Internet big data as described above.

[0023] This invention proposes an intelligent vehicle evaluation and transaction system based on internet big data. Leveraging internet big data applications in vehicle transactions, it provides intelligent and information-based support for vehicle evaluation and transactions. During the execution of vehicle big data evaluation, the intelligent vehicle evaluation and transaction system expands the comprehensive analysis of evaluation objects in existing technologies, based on a group collaborative evaluation loop, into a diversified product valley model with the target vehicle as the primary evaluation object and preset evaluation groups as secondary evaluation objects. An evaluation bus is introduced to perform data interconnection and information processing from information-based intelligent evaluation to the IoT transaction platform. Based on this, the intelligent vehicle evaluation and transaction system claimed in this invention uses an IoT subnet to carry the same evaluation group, realizing an intelligent vehicle evaluation architecture corresponding to a hierarchical IoT structure. It utilizes big data convergence points to coordinate information between the ring-shaped evaluation model and the bus-type big data evaluation and transaction chain, improving the data systematization level of the internet big data-based vehicle evaluation and transaction system, improving the accuracy of user-facing evaluation and transaction information feedback, and enhancing the user experience. Attached Figure Description

[0024] Figure 1 This is a basic example diagram of an intelligent vehicle evaluation and transaction system based on Internet big data as shown in this invention; Figure 2 This is a basic example diagram of a collaborative evaluation loop in an intelligent vehicle evaluation and transaction system based on Internet big data, as shown in this invention. Figure 3 This is an example diagram of an evaluation bus in the intelligent vehicle evaluation and trading system based on Internet big data, which is the subject of this invention. Figure 4 This is one embodiment of the evaluation bus-related module in the intelligent vehicle evaluation and transaction system based on Internet big data, which is the subject of this invention. Figure 5 This is one of the specific embodiments of the first big data valuation interval module and the second big data valuation interval module in the intelligent vehicle evaluation and transaction system based on Internet big data that is claimed in this invention. Detailed Implementation

[0025] The following describes in detail several embodiments and beneficial effects of the intelligent vehicle evaluation and transaction system and method based on Internet big data that are claimed in this invention, in order to facilitate a more detailed examination and breakdown of this invention.

[0026] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0029] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0030] It should be understood that although the terms "first," "second," etc., may be used to describe the methods and corresponding apparatus in the embodiments of the present invention, these terms should not be limited to these terms. These terms are only used to distinguish the terms from each other. For example, without departing from the scope of the embodiments of the present invention, the first big data valuation interval module, the first target valuation, etc., may also be referred to as the second big data valuation interval module, the second target valuation, etc., and the second big data valuation interval module, the second target valuation, etc., may also be referred to as the first big data valuation interval module, the first target valuation.

[0031] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0032] As per the instruction manual Figure 1 The diagram shown is a basic example of an intelligent vehicle evaluation and transaction system based on Internet big data according to the present invention. As a preferred embodiment that can be superimposed, each node or module can preferably interconnect with other nodes or modules for data and instruction transmission. Of course, as another preferred embodiment that can be superimposed, some nodes may not have interconnection with some other nodes, or may be allowed to disable or enable interconnection with other nodes.

[0033] The intelligent vehicle evaluation and trading system based on Internet big data claimed in this invention includes at least an evaluation target object, an evaluation vehicle self-organizing object, an evaluation bus, a group collaborative evaluation loop, and an Internet of Things trading platform, wherein: The target of the evaluation is the vehicle to be evaluated and traded as a smart car; The self-organizing objects of the evaluation vehicles are the vehicle objects that need to establish an IoT subnet based on the association of the group collaborative evaluation loop; As per the instruction manual Figure 2 As shown, Figure 2 This is a basic example diagram of a collaborative evaluation loop in the intelligent vehicle evaluation and transaction system based on Internet big data, as shown in this invention. (See attached specification.) Figures 3-4 As shown, Figures 3-4 This is an example diagram of an evaluation bus in the intelligent vehicle evaluation and trading system based on Internet big data, as claimed in this invention. The evaluation bus runs through the evaluation and trading chain of the intelligent vehicle evaluation and trading system, establishing interconnected IoT subnetworks for the evaluation target objects and various self-organizing evaluation vehicles. Utilizing an IoT networking valuation configuration module, a first big data valuation intervalization module, and a second big data valuation intervalization module, it intelligently achieves collaborative evaluation of the same valuation group to obtain a first group valuation; and vehicle evaluation of the evaluation target object to obtain a first target valuation; simultaneously establishing transmission bandwidth to the IoT trading platform. The evaluation bus has a big data convergence point with the group collaborative evaluation loop. The big data convergence point is used to synchronize the vehicle evaluation big data and sensor acquisition data of the evaluation bus and the group collaborative evaluation loop. As a preferred, superimposed embodiment, the use of the IoT network valuation configuration module, the first big data valuation intervalization module, and the second big data valuation intervalization module to achieve intelligent collaborative evaluation of the same valuation group and vehicle evaluation of the target object specifically includes: Step S202: The evaluation bus obtains the IoT network valuation configuration module. Based on each evaluation vehicle object that has entered the IoT network, after confirming the vehicle-to-vehicle association parameters, the module associates the vehicle IDs to be evaluated with vehicle-to-vehicle association parameters above the threshold with the evaluation target object ID to the valuation group configuration ID after the same evaluation group. Step S204: The evaluation bus obtains the first big data valuation interval module through the information evaluation of the vehicle of the evaluation target object. The first target valuation is the vehicle evaluation of the evaluation target object realized by the Internet of Things networking valuation configuration module, the first big data valuation interval module and the second big data valuation interval module. Step S206: The evaluation bus obtains the second big data valuation interval module through information-based collaborative evaluation of the same valuation group, and gives the first set of valuations. The first set of valuations includes the valuations of all other evaluation vehicle objects in the group. This set of valuations is the intelligent collaborative evaluation of the same valuation group achieved by using the IoT network valuation configuration module, the first big data valuation interval module and the second big data valuation interval module.

[0034] The specific functions and operating procedures of the IoT networking valuation configuration module, the first big data valuation interval module, and the second big data valuation interval module are detailed below.

[0035] The group collaborative evaluation ring, based on the evaluation target object ID and the valuation group configuration ID of the IoT network valuation configuration module, detects evaluation vehicle self-organizing objects that match the valuation group configuration ID in the intelligent vehicle evaluation IoT, and establishes interconnected IoT subnets for the evaluation target object and each evaluation vehicle self-organizing object. The group collaborative evaluation ring also establishes a ring-shaped data structure based on the Internet of Things subnet, which is used to solidify the subnet parameters of the Internet of Things subnet established for the evaluation target object and the self-organizing objects of each evaluation vehicle. The Internet of Things (IoT) trading platform receives a first target valuation and a first set of valuations through an evaluation bus. Based on the user's set of used car models intended for sale, the platform provides the system user with a first target valuation for the corresponding car model that meets the user's requirements.

[0036] As a preferred embodiment that can be superimposed, the evaluation bus includes an IoT networking valuation configuration module, a first big data valuation intervalization module, and a second big data valuation intervalization module; in: The IoT network valuation configuration module is used to confirm the vehicle-to-vehicle association parameters based on each vehicle object that has entered the IoT network, and based on a specific vehicle-to-vehicle association parameter threshold, associate the vehicle IDs to be evaluated with vehicle-to-vehicle association parameters above the threshold with the evaluation target object ID to the same evaluation group, and set the same valuation group configuration ID. As a preferred, superimposed embodiment, the IoT network valuation configuration module is used to confirm vehicle-to-vehicle association parameters based on the vehicles currently in the IoT network. Based on a specific vehicle-to-vehicle association parameter threshold, it associates vehicle IDs whose vehicle-to-vehicle association parameter thresholds are above the threshold with the target vehicle ID into the same valuation group and sets the same valuation group configuration ID. Specifically, after obtaining the user's public consent and usage agreement, the intelligent vehicle valuation and transaction system based on internet big data allows the user to fill in a set of intended used car models. This set of intended used car models represents the user's preferred used car models. The IoT network valuation configuration module searches for each vehicle currently in the IoT network, obtaining parameters such as model, new car price, pre-configured newness / oldness parameters based on vehicle condition, and number of used transactions. It also collects the intended used car model set data from each user and calculates the frequency of pairwise associations between specific models in all users' intended used car model set data, using this as the vehicle-to-vehicle association parameter for the two models. As another preferred embodiment that can be superimposed, for example, if the system has 1000 users with the intention to buy used cars, then at least 1000 sets of used car transaction intention data are registered. Among them, there are 350 sets of used car transaction intention data that simultaneously show Qin and Seagull models, and 107 sets of used car transaction intention data that simultaneously show Sylphy and Tiggo models. In this case, the Qin-Seagull vehicle-to-vehicle association parameter is 0.35; and the Sylphy-Tiggo vehicle-to-vehicle association parameter is 0.107. The system pre-configures the vehicle condition's newness parameter, which is the vehicle's newness parameter obtained by the system through a known model algorithm, ranging from 0 to 1, with 1 representing brand new and 0 representing scrap and unusable. As another preferred embodiment that can be superimposed, the IoT network valuation configuration module associates the vehicle IDs of the model corresponding to the evaluation target ID with vehicle-to-vehicle association parameters above a certain value with all vehicle IDs of that model to the same evaluation group, and removes vehicle IDs with newness parameters outside the specific range, setting the same valuation group configuration ID. Vehicles with the same model as the target object ID may or may not be included in the same valuation group configuration ID.

[0037] As per the instruction manual Figure 5 As shown, Figure 5 This is one specific embodiment of the first big data valuation intervalization module and the second big data valuation intervalization module in the intelligent vehicle evaluation and transaction system based on Internet big data claimed in this invention. The first big data valuation intervalization module is used to perform an information-based evaluation of the vehicle of the evaluation target and provide a first target valuation; The second big data valuation interval module is used to perform information-based collaborative evaluation for the same valuation group and provide a first set of valuations, which includes the valuations of all other vehicle objects in the group.

[0038] As a preferred embodiment that can be superimposed, the IoT trading platform also detects the valuation of the vehicle object within the first group of valuations that meets the user's valuation adjustment direction requirements after the user's feedback on the first target valuation is too high or too low, and feeds back both the first group of valuations and the valuation of the vehicle object within the group that meets the user's valuation adjustment direction requirements to the user.

[0039] As a preferred embodiment that can be superimposed, the first big data valuation intervalization module is used to perform an information-based assessment of the vehicle of the target object and provide a first target valuation, specifically: The first big data valuation interval module determines a first target valuation based on the vehicle model, new car price, pre-configured newness / wear parameters based on vehicle condition, and used car transaction frequency parameters of the target vehicle using a first valuation algorithm. Alternatively, as a preferred embodiment that can be overlaid, the first valuation algorithm can be calculated as follows: Valuation = (New car price) * Pre-configured newness / wear parameters based on vehicle condition * (0.91 raised to the power of "used car transaction frequency parameters"), used to determine the valuation of the target vehicle.

[0040] As a preferred embodiment that can be overlaid, the second big data valuation intervalization module is used to perform information-based collaborative evaluation for the same valuation group, providing a first set of valuations. This first set of valuations includes the valuations of all evaluated vehicle objects within the group, specifically: The second big data valuation interval module determines the second target valuation based on the vehicle model, new car price, new car condition parameters pre-configured by the system according to the vehicle condition, and used car transaction frequency parameters of all other vehicles in the target evaluation group, using the second valuation algorithm.

[0041] As a preferred embodiment that can be superimposed, the first big data valuation interval module determines a first target valuation based on the vehicle model, new car price, new / used condition parameters pre-configured by the system according to the vehicle condition, and used transaction frequency parameters of the evaluation target object, using a first valuation algorithm. The second big data valuation interval module determines a second target valuation based on the vehicle model, new car price, new / used condition parameters pre-configured by the system according to the vehicle condition, and used transaction frequency parameters of all other vehicles in the evaluation target object group, using a second valuation algorithm. Specifically, both the first and second valuation algorithms may be specific valuation algorithms in the prior art, such as existing valuation algorithms in online car trading platforms. Alternatively, as another preferred embodiment that can be superimposed, the first and / or second valuation algorithms can be calculated as follows: Valuation = (New car price) * New / used condition parameters pre-configured by the system according to the vehicle condition * (0.91 raised to the power of "used transaction frequency parameters"), used to determine the valuation of the evaluation target vehicle.

[0042] As a preferred embodiment that can be superimposed, the subnet parameters of the IoT subnet include at least: the subnet ID of the IoT subnet, the evaluation target object ID within the IoT subnet, the self-organizing object IDs of each evaluation vehicle within the IoT subnet, and the IoT subnet network configuration parameters.

[0043] As a preferred embodiment that can be superimposed, the big data convergence point is used to synchronously evaluate the vehicle evaluation big data and sensor-collected data of the bus and group collaborative evaluation loop, specifically: The big data convergence point will coordinate the IoT vehicle valuation cycle fluctuation information big data collected by the IoT network valuation configuration module through the synchronous evaluation bus to the group collaborative evaluation ring, and coordinate the vehicle sensing information of each IoT sub-network collected by the group collaborative evaluation ring to the synchronous evaluation bus. The vehicle valuation cycle fluctuation information big data characterization is a curve representing the change of the valuation base value of a specific model within the first specific cycle, collected from multiple information trading centers.

[0044] As a preferred embodiment that can be superimposed, the valuation base value of the specific model is the used valuation benchmark value of the specific model in brand new condition, which is dynamically configured by the system or obtained by taking the average of the transaction data of the specific model in brand new condition within a second specific period, and the second specific period is different from the first specific period.

[0045] Meanwhile, the present invention also proposes a computer-readable storage medium storing a processor-executable program, characterized in that the processor-executable program, when executed by a processor, is used to perform the corresponding functions of the intelligent vehicle evaluation and transaction system based on Internet big data as described in any of the above.

[0046] Meanwhile, the present invention also proposes a computer program product, which includes computer instructions that, when executed by a processor, perform the corresponding functions of the intelligent vehicle evaluation and transaction system based on Internet big data as described above.

[0047] This invention proposes an intelligent vehicle evaluation and transaction system based on internet big data. Leveraging internet big data applications in vehicle transactions, it provides intelligent and information-based support for vehicle evaluation and transactions. During the execution of vehicle big data evaluation, the intelligent vehicle evaluation and transaction system expands the comprehensive analysis of evaluation objects in existing technologies, based on a group collaborative evaluation loop, into a diversified product valley model with the target vehicle as the primary evaluation object and preset evaluation groups as secondary evaluation objects. An evaluation bus is introduced to perform data interconnection and information processing from information-based intelligent evaluation to the IoT transaction platform. Based on this, the intelligent vehicle evaluation and transaction system claimed in this invention uses an IoT subnet to carry the same evaluation group, realizing an intelligent vehicle evaluation architecture corresponding to a hierarchical IoT structure. It utilizes big data convergence points to coordinate information between the ring-shaped evaluation model and the bus-type big data evaluation and transaction chain, improving the data systematization level of the internet big data-based vehicle evaluation and transaction system, improving the accuracy of user-facing evaluation and transaction information feedback, and enhancing the user experience.

[0048] In all the above embodiments, in order to achieve certain special data transmission and read / write function requirements, the above methods and corresponding devices can be expanded by adding devices, modules, components, hardware, pin connections or memory, processor differences during operation.

[0049] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the methods, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0050] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of method steps is only a logical or functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0051] The units described as separate components of the method and apparatus may or may not be logically or physically separate, and may not be physical units. That is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0052] Furthermore, the method steps and their implementations, as well as the functional units, in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0053] The aforementioned methods and apparatus can be implemented as integrated units in the form of software functional units, which can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), NVRAM, magnetic disks, or optical disks.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0055] It should be noted that the above embodiments are only used to more clearly explain and illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart vehicle evaluation and trading system based on Internet big data, the system comprising at least an evaluation target object, an evaluation vehicle self-organizing object, an evaluation bus, a group collaborative evaluation loop, and an Internet of Things trading platform, wherein: The target of the evaluation is the vehicle to be evaluated and traded as a smart car; The self-organizing objects of the evaluation vehicles are the vehicle objects that need to establish an IoT subnet based on the association of the group collaborative evaluation loop; The evaluation bus runs through the evaluation and transaction chain of the intelligent vehicle evaluation and transaction system. It is used to establish an interconnected Internet of Things (IoT) subnetwork for the evaluation target and each self-organizing evaluation vehicle. It also uses the IoT networking valuation configuration module, the first big data valuation interval module, and the second big data valuation interval module to intelligently realize collaborative evaluation of the same valuation group to obtain the first group valuation; and the vehicle evaluation of the evaluation target to obtain the first target valuation; and establishes the transmission bandwidth to the IoT transaction platform. The evaluation bus has a big data convergence point with the group collaborative evaluation loop. The big data convergence point is used to synchronize the vehicle evaluation big data and sensor acquisition data of the evaluation bus and the group collaborative evaluation loop. The group collaborative evaluation ring, based on the evaluation target object ID and the valuation group configuration ID of the IoT network valuation configuration module, detects evaluation vehicle self-organizing objects that match the valuation group configuration ID in the intelligent vehicle evaluation IoT, and establishes interconnected IoT subnets for the evaluation target object and each evaluation vehicle self-organizing object. The group collaborative evaluation ring also establishes a ring-shaped data structure based on the Internet of Things subnet, which is used to solidify the subnet parameters of the Internet of Things subnet established for the evaluation target object and the self-organizing objects of each evaluation vehicle. The Internet of Things (IoT) trading platform receives a first target valuation and a first set of valuations through an evaluation bus. Based on the user's set of used car models intended for sale, the platform provides the system user with a first target valuation for the corresponding car model that meets the user's requirements.

2. The intelligent vehicle evaluation and transaction system based on Internet big data as described in claim 1, characterized in that: The evaluation bus includes an IoT network valuation configuration module, a first big data valuation intervalization module, and a second big data valuation intervalization module. in: The IoT network valuation configuration module is used to confirm the vehicle-to-vehicle association parameters based on each vehicle object that has entered the IoT network, and based on a specific vehicle-to-vehicle association parameter threshold, associate the vehicle IDs to be evaluated with vehicle-to-vehicle association parameters above the threshold with the evaluation target object ID to the same evaluation group, and set the same valuation group configuration ID. The first big data valuation interval module is used to perform information-based valuation of the vehicle of the target object and give a first target valuation. The second big data valuation interval module is used to perform information-based collaborative evaluation for the same valuation group and provide a first set of valuations, which includes the valuations of all other vehicle objects in the group.

3. The intelligent vehicle evaluation and transaction system based on Internet big data as described in claim 1, characterized in that: The IoT trading platform also detects valuations of vehicle objects within the first group that meet the user's valuation adjustment needs after the user's feedback on the valuation of the first target being too high or too low. It then feeds back both the first group valuation and the valuations of vehicle objects within the group that meet the user's valuation adjustment needs to the user.

4. The intelligent vehicle evaluation and transaction system based on Internet big data as described in claim 2, characterized in that: The first big data valuation interval module is used to perform an information-based assessment of the vehicle of the target object and provide a first target valuation, specifically: The first big data valuation interval module determines the first target valuation based on the vehicle model, new car price, new car condition parameters pre-configured by the system according to the vehicle condition, and used car transaction frequency parameters of the target object, using the first valuation algorithm.

5. The intelligent vehicle evaluation and transaction system based on Internet big data as described in claim 1, characterized in that: The second big data valuation interval module is used to perform information-based collaborative evaluation for the same valuation group, providing a first set of valuations. This first set of valuations includes the valuations of all evaluated vehicles within the group. Specifically: The second big data valuation interval module determines the second target valuation based on the vehicle model, new car price, new car condition parameters pre-configured by the system according to the vehicle condition, and used car transaction frequency parameters of all other vehicles in the target evaluation group, using the second valuation algorithm.

6. The intelligent vehicle evaluation and transaction system based on Internet big data as described in claim 1, characterized in that: The subnet parameters of the IoT subnet include at least: Subnet ID of the IoT subnet The evaluation target object ID within the IoT subnet, The self-organizing object IDs of each evaluation vehicle within the IoT subnet, IoT subnet network configuration parameters.

7. The intelligent vehicle evaluation and transaction system based on Internet big data as described in claim 4, characterized in that: The big data convergence point is used to synchronously evaluate the vehicle evaluation big data and sensor-collected data of the bus and group collaborative evaluation loop, specifically: The big data convergence point will coordinate the IoT vehicle valuation cycle fluctuation information big data collected by the IoT network valuation configuration module through the synchronous evaluation bus to the group collaborative evaluation ring, and coordinate the vehicle sensing information of each IoT sub-network collected by the group collaborative evaluation ring to the synchronous evaluation bus. The vehicle valuation cycle fluctuation information big data characterization is a curve representing the change of the valuation base value of a specific model within the first specific cycle, collected from multiple information trading centers.

8. The intelligent vehicle evaluation and transaction system based on Internet big data as described in claim 7, characterized in that: The valuation base value for a specific vehicle model is the benchmark value for a used vehicle model in brand-new condition. It is dynamically configured by the system or obtained by averaging the transaction data of the specific vehicle model in brand-new condition within a second specific period. The second specific period is different from the first specific period.

9. A computer-readable storage medium storing a processor-executable program, characterized in that, The program executable by the processor is used to perform the corresponding functions of the intelligent vehicle evaluation and trading system based on Internet big data as described in any one of claims 1-8 when executed by the processor.

10. A computer program product comprising computer instructions, wherein the computer instructions, when executed by a processor, perform the corresponding functions of the intelligent vehicle evaluation and trading system based on Internet big data as described in any one of claims 1-8.

Images