Competition Results Scoring System Based on Cloud Computing Technology

Abstract

This invention discloses a competition score scoring system based on cloud computing technology, belonging to the field of scoring calculation technology. It includes an optimization module, an integration module, and a scoring module. The optimization module utilizes machine learning algorithms and big data analysis technology from an intelligent scoring system to process score data and optimize the processed data. The integration module employs a data routing algorithm based on network protocols to transmit the optimized score data of participants. The scoring module utilizes an intelligent alarm system within the synchronous score update network to provide a view of the competition status and complete the competition score scoring process. This invention can be dynamically adjusted according to actual needs, ensuring high availability and high scalability. Secure encryption mechanisms and integrity checks ensure the secure and reliable transmission of data. Overall, it not only improves the efficiency of competition management but also provides strong technical support for administrative, commercial, and financial fields, ensuring that every competition is conducted in a highly accurate and transparent environment.

Description

Technical Field

[0001] This invention relates to the field of scoring calculation technology, and in particular to a competition score scoring system based on cloud computing technology. Background Technology

[0002] In today's information and globalized world, various competitions are increasingly appearing across various fields as important platforms for showcasing individual talents, promoting teamwork, and fostering socio-cultural exchange. Whether it's sports events, academic competitions, or cultural and artistic exhibitions, these activities not only enhance professional skills but also provide opportunities for businesses, governments, and organizations to promote their brands and build their image. However, traditional methods of processing results rely on local computing resources and manual operation, which limits the flexibility and scalability of scoring systems and makes it difficult to meet the needs of competitions held simultaneously in multiple locations and time zones in a globalized context. Especially in large-scale international events, ensuring the real-time nature and accuracy of data is crucial for maintaining fair competition. Cloud computing technology, with its powerful computing capabilities and high level of security, offers new possibilities for solving these problems. By utilizing cloud platforms to achieve rapid processing and instant updates of results data, the transparency of competitions and the audience's participation experience are significantly improved.

[0003] Existing technologies have significant shortcomings in processing competition results scoring, especially under the requirements of key areas such as administration, commerce, finance, management, and supervision. Traditional methods struggle to handle sudden high-concurrency access, potentially leading to slow response times or service interruptions during large-scale events, affecting the timeliness and accuracy of scoring. Furthermore, traditional systems perform poorly in synchronizing results data across different regions, failing to guarantee real-time updates of results for participants worldwide, which is particularly important for cross-border competitions. Additionally, traditional scoring processes require significant manual intervention, increasing the risk of human error, and lack effective countermeasures in the face of server failures or other technical problems, resulting in lost or delayed results. This invention addresses these issues through a competition results scoring system based on cloud computing technology, employing flexible resource allocation and enhanced security measures. Summary of the Invention

[0004] In view of the problems existing in the competition score scoring system based on cloud computing technology, this invention is proposed.

[0005] Therefore, this invention addresses the problem that existing technologies may cause slow response or service interruptions during large-scale events, affecting the timeliness and accuracy of scoring. This invention adopts a competition score scoring method based on cloud computing technology to solve this problem.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a competition score scoring method based on cloud computing technology, comprising: deploying an intelligent scoring system on a cloud computing platform, processing score data using machine learning algorithms and big data analysis technology of the intelligent scoring system, and optimizing the processed score data; constructing a synchronous score update network, integrating the optimized participant score data through the intelligent scoring system, and transmitting the optimized participant score data using a data routing algorithm of a network protocol; establishing an intelligent monitoring and prediction platform, synchronizing the optimized participant score data after transmission to the score update network based on the intelligent scoring system, and using an intelligent alarm system in the synchronous score update network to provide a competition status view, thereby completing the competition score scoring process.

[0008] As a preferred embodiment of the competition score scoring method based on cloud computing technology described in this invention, the step of deploying the intelligent scoring system on a cloud computing platform includes providing elastic computing resources and distributed storage on the cloud computing platform, selecting the extended functions of the distributed storage, and dynamically adjusting the elastic computing resources based on the storage requirements arising from the extended functions.

[0009] The machine learning algorithms of the intelligent scoring system include a deep neural network model, a support vector machine model, and a random forest model. The deep neural network model utilizes dynamically adjusted elastic computing resources for learning and applying scoring rules, capturing the nonlinear relationships of scoring data within the scoring rules through a multi-layered neuron structure to evaluate participants' performance. The support vector machine model performs classification regression on the evaluated participants' performance, separating different categories of scoring data by finding a hyperplane to predict the participants' final scores. The random forest model fits the participants' final scores and processes missing final score data through a voting mechanism.

[0010] As a preferred embodiment of the competition score scoring method based on cloud computing technology described in this invention, the big data analysis technology includes: utilizing the high throughput and low latency of the scoring data to collect the participant score data received by the intelligent scoring system; preprocessing the participant score data; the preprocessing includes inputting the raw score data into the machine learning model of the intelligent scoring system; and the intelligent scoring system performing raw data cleaning, which includes removing outliers and filling in missing data in the participant score data.

[0011] The high throughput includes using a distributed message queue method to request participant score data, which includes horizontal scaling of participant score data, storage of participant score data, and partitioning of participant score data.

[0012] The expansion of participant score data includes increasing the number of cloud computing nodes in the intelligent scoring system to expand its processing capacity and process multiple scoring data during peak periods.

[0013] The data storage of participants' scores includes storing the scores using cloud computing nodes of the intelligent scoring system during the transmission of the optimized scores data of the participants.

[0014] The participant score data partitioning involves dividing the score data stored on cloud computing nodes into data streams, and assigning the score data in different regions to different partitions to complete the aggregation of participant score data in different geographically distributed regions.

[0015] As a preferred embodiment of the competition score scoring method based on cloud computing technology described in this invention, the low latency state includes data localization of the scoring data in different partitions and the local participant score data. The data localization includes automatically selecting the nearest cloud computing node to process the participant score data based on the participant's geographical location, detecting the transmission distance of the participant score data in the network, and managing the network latency state through the transmission distance.

[0016] The method of managing network latency status through transmission distance includes accessing the stored participant performance data using an in-memory database, and the access to the stored participant performance data includes cache preheating and data fragmentation.

[0017] The cache preheating includes loading the scoring data to be used into the memory database in advance before the start of the competition for cache preheating. During the competition, the intelligent scoring system can respond to scoring requests and handle delays caused by data loading.

[0018] The data sharding involves extending the in-memory database, sharding the scoring data, and distributing different types of data across N in-memory instances to achieve high-concurrency in-memory processing capabilities.

[0019] As a preferred embodiment of the competition score scoring method based on cloud computing technology described in this invention, the construction of the synchronous score update network includes deploying a cloud computing network topology in all participating regions, interconnecting them through fiber optic networks, and introducing a spanning tree algorithm in the cloud computing network topology to determine the connection path between cloud computing nodes.

[0020] The formula for the spanning tree algorithm is:

[0021] ;

[0022] in, Indicates the spanning tree threshold. Represents the set of spanning trees. Represents cloud computing nodes and Link weights between This represents the set of parameters that allow the objective function to reach its minimum value. Represents the candidate spanning tree;

[0023] When a cloud computing node detects that network latency exceeds the spanning tree threshold When this happens, the intelligent scoring system automatically triggers the cloud computing network topology adjustment mechanism to recalculate the minimum spanning tree;

[0024] When a cloud computing node detects that the network latency does not exceed the spanning tree threshold In this case, the intelligent scoring system dynamically adjusts the connection relationships of other cloud computing nodes based on the current traffic situation, predicts future traffic changes based on historical data, and performs preventative topology optimization in advance.

[0025] As a preferred embodiment of the competition score scoring method based on cloud computing technology described in this invention, the step of integrating the optimized participant score data through an intelligent scoring system includes encrypting the participant data after data localization and the participant data after data stream division. After receiving the data from both sources, the receiver verifies the hash value and detects whether the data has been tampered with.

[0026] When a hash value is appended to each piece of data, the data is considered tamper-proof; otherwise, if no hash value is appended, the integration of the participants' scores is rejected.

[0027] The process of transmitting the optimized score data of the participants includes analyzing the shortest path from the cloud computing node to the target cloud computing node based on the new score data received by the intelligent scoring system, and selecting the optimal transmission path.

[0028] As a preferred embodiment of the competition score scoring method based on cloud computing technology described in this invention, the establishment of the intelligent monitoring and prediction platform includes the process of integrating the score data of rejected participants and triggering a security alarm mechanism.

[0029] The provision of the competition status view includes, in the event of triggering a security alarm mechanism, the intelligent monitoring and prediction platform will pop up an event page, which includes the competition rules, scoring criteria and the scores of the participants;

[0030] When a user clicks on a specific time point, the intelligent monitoring and prediction platform will pop up an event page, through which the user can learn about the competition requirements and scoring details for each stage.

[0031] When a user selects a time period, the intelligent monitoring and prediction platform will generate a summary report of the scores for that time period, showing the score changes and highlights of the competition during that time period.

[0032] When a user selects a participant, the intelligent monitoring and prediction platform will mark the participant's events on the timeline and complete the competition score scoring process based on the provided competition status view.

[0033] Secondly, this invention provides a competition score scoring system based on cloud computing technology, comprising: an optimization module, which deploys the intelligent scoring system on a cloud computing platform, processes score data using machine learning algorithms and big data analysis technology of the intelligent scoring system, and optimizes the processed score data; an integration module, which constructs a synchronous score update network, integrates the optimized participant score data through the intelligent scoring system, and transmits the optimized participant score data using a data routing algorithm of a network protocol; and a scoring module, which establishes an intelligent monitoring and prediction platform, synchronizes the transmitted optimized participant score data to the score update network based on the intelligent scoring system, and provides a competition status view using an intelligent alarm system in the synchronous score update network to complete the competition score scoring process.

[0034] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described competition score scoring method based on cloud computing technology.

[0035] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the above-described competition score scoring method based on cloud computing technology.

[0036] The beneficial effects of this invention are as follows: By deploying the intelligent scoring system on a cloud computing platform, this invention utilizes machine learning algorithms and big data analysis technology to achieve efficient processing and optimization of participant performance data, ensuring the accuracy and fairness of the scoring results. Through the construction of a globally synchronized performance update network, the system can update performance data synchronously across multiple locations and time zones, providing millisecond-level response speeds, greatly enhancing the transparency of the competition and the audience's participation experience. The intelligent monitoring and prediction platform combines real-time data monitoring and predictive analysis, enabling early identification of potential problems and triggering intelligent alarms to ensure the smooth running of the competition. Furthermore, the system's elastic computing resources and distributed storage capabilities can be dynamically adjusted according to actual needs, ensuring high availability and scalability. Secure encryption mechanisms and integrity verification ensure the secure and reliable transmission of data. Overall, this invention not only improves the efficiency of competition management but also provides strong technical support for administrative, commercial, and financial fields, ensuring that every competition is conducted in a highly accurate and transparent environment. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0038] Figure 1 A flowchart illustrating a competition score scoring method based on cloud computing technology, provided as an embodiment of the present invention.

[0039] Figure 2 An internal structural diagram of a computer device for a competition score scoring method based on cloud computing technology, provided in one embodiment of the present invention. Detailed Implementation

[0040] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0041] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0043] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0044] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0045] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0046] Example 1: Refer to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a competition score scoring method based on cloud computing technology, including:

[0047] S1: Deploy the intelligent scoring system on a cloud computing platform, utilize the machine learning algorithms and big data analytics of the intelligent scoring system to process the score data, and optimize the processed score data.

[0048] Deploying the intelligent scoring system on a cloud computing platform includes providing elastic computing resources and distributed storage on the cloud computing platform, selecting the extended functions of distributed storage, and dynamically adjusting the elastic computing resources based on the storage needs arising from the extended functions.

[0049] The machine learning algorithms of the intelligent scoring system include deep neural network models, support vector machine models, and random forest models. The deep neural network model uses dynamically adjusted elastic computing resources for learning and applying scoring rules. Through a multi-layered neuron structure, it captures the non-linear relationships of scoring data in the scoring rules and evaluates the participants' performance. The support vector machine model performs classification and regression on the evaluated participants' performance. It finds a hyperplane to separate the scoring data of different categories and predicts the participants' final scores. The random forest model fits the participants' final scores and processes the missing final score data through a voting mechanism.

[0050] Furthermore, to achieve efficient and accurate competition scoring, the intelligent scoring system is deployed on a high-performance cloud computing platform. This platform provides elastic computing resources and distributed storage, which can dynamically adjust computing power and storage capacity according to actual needs. Specifically, the cloud computing platform supports automatic expansion, which can automatically add computing nodes during peak periods to ensure high availability and responsiveness of the system. In a large international competition with 10,000 participants, the system can automatically expand to 200 computing nodes during the competition, processing more than 10,000 scoring requests per second, ensuring that each participant's score is processed within milliseconds.

[0051] S1.1: Big data analytics technology includes using the high throughput and low latency of scoring data to collect participant score data received by the intelligent scoring system, preprocessing the participant score data, including inputting the raw scoring data into the machine learning model of the intelligent scoring system, and the intelligent scoring system cleaning the raw data, including removing outliers and filling in missing data in the participant score data.

[0052] High throughput includes using a distributed message queue method to request participant score data. The distributed message queue method includes horizontal scaling of participant score data, storage of participant score data, and partitioning of participant score data.

[0053] Expanding the level of participant score data includes increasing the number of cloud computing nodes in the intelligent scoring system to expand its processing capacity and process multiple scoring data during peak periods.

[0054] The data storage of participants' scores includes storing the scores using the cloud computing nodes of the intelligent scoring system during the transmission of the optimized scores data of the participants.

[0055] The participant score data partitioning involves dividing the score data stored on cloud computing nodes into data streams, and assigning the score data in different regions to different partitions to complete the aggregation of participant score data in different geographically distributed regions.

[0056] Furthermore, the low-latency state includes data localization of the scoring data in different partitions and the local participant performance data. Data localization includes automatically selecting the nearest cloud computing node to process the participant performance data based on the participant's geographical location, detecting the transmission distance of the participant performance data in the network, and managing the network latency state through the transmission distance.

[0057] Managing network latency status through transmission distance includes accessing stored participant performance data using an in-memory database, which includes cache warm-up and data sharding.

[0058] Cache preheating includes loading the scoring data to be used into the memory database in advance before the start of the competition for cache preheating. During the competition, the intelligent scoring system can respond to scoring requests and handle delays caused by data loading.

[0059] Data sharding involves extending the in-memory database, sharding the scoring data, and distributing different types of data across N in-memory instances to achieve high-concurrency in-memory processing capabilities.

[0060] S2: Construct a synchronous score update network to integrate the optimized participant score data through the intelligent scoring system, and use a data routing algorithm based on network protocols to transmit the optimized participant score data.

[0061] The construction of a synchronous score update network includes deploying a cloud computing network topology in all participating regions, interconnecting them through fiber optic networks, and introducing the spanning tree algorithm in the cloud computing network topology to determine the connection paths between cloud computing nodes.

[0062] The formula for the spanning tree algorithm is:

[0063] ;

[0064] in, Indicates the spanning tree threshold. Represents the set of spanning trees. Represents cloud computing nodes and Link weights between This represents the set of parameters that allow the objective function to reach its minimum value. Represents the candidate spanning tree;

[0065] When a cloud computing node detects that network latency exceeds the spanning tree threshold When this happens, the intelligent scoring system automatically triggers the cloud computing network topology adjustment mechanism to recalculate the minimum spanning tree;

[0066] When a cloud computing node detects that the network latency does not exceed the spanning tree threshold In this case, the intelligent scoring system dynamically adjusts the connection relationships of other cloud computing nodes based on the current traffic situation, predicts future traffic changes based on historical data, and performs preventative topology optimization in advance.

[0067] Furthermore, the optimized participant performance data will be integrated through an intelligent scoring system, including encrypting the participant data after data localization and the participant data after data stream segmentation. After receiving both data, the recipient will verify the hash value and detect whether the data has been tampered with.

[0068] When a hash value is appended to each piece of data, the data is considered tamper-proof; otherwise, if no hash value is appended, the integration of the participant's score data is rejected.

[0069] Transmitting the optimized score data of the participants includes analyzing the shortest path from the cloud computing node to the target cloud computing node based on the new score data received by the intelligent scoring system, and selecting the optimal transmission path.

[0070] S3: Establish an intelligent monitoring and prediction platform. The platform will transmit optimized participant performance data, which will be synchronized to the performance update network based on the intelligent scoring system. The intelligent alarm system will be used in the synchronized performance update network to provide a view of the competition status and complete the competition performance scoring process.

[0071] The establishment of an intelligent monitoring and prediction platform includes the process of integrating the performance data of rejected participants and triggering a security alarm mechanism.

[0072] The competition status view includes an event page that pops up on the intelligent monitoring and prediction platform when a security alarm mechanism is triggered. The event page includes the competition rules, scoring criteria, and participants' scores.

[0073] When a user clicks on a specific time point, the intelligent monitoring and prediction platform will pop up an event page, through which the user can learn about the competition requirements and scoring details for each stage.

[0074] When a user selects a time period, the intelligent monitoring and prediction platform will generate a summary report of the scores for that time period, showing the score changes and highlights of the competition during that time period.

[0075] When a user selects a participant, the intelligent monitoring and prediction platform will mark that participant's events on the timeline and complete the competition score scoring process based on the provided competition status view. The performance parameters of the intelligent scoring system on the high-performance cloud computing platform are shown in Table 1 below:

[0076] Table 1 Performance parameters of the intelligent scoring system on a high-performance cloud computing platform

[0077] parameter Numerical Number of participants 10,000 people Number of computing nodes (peak period) 200 Number of rating requests processed per second More than 10,000 Response time millisecond level Single node storage capacity Approximately 500GB Total storage capacity Approximately 100TB Cache preheating data volume Approximately 1TB Number of in-memory database instances 100 Query processing capacity per second More than 100,000 times

[0078] As can be seen from the data in Table 1 above, the deployment of the intelligent scoring system on a high-performance cloud computing platform can not only handle large-scale participant data, but also maintain extremely low latency under high concurrency, ensuring that each participant's score can be processed within milliseconds. This efficient resource allocation and processing capability provides strong technical support for large-scale international competitions, ensuring the smooth progress of the competition and the accuracy of the scoring results.

[0079] In a preferred embodiment, a competition score scoring system based on cloud computing technology includes an optimization module, which deploys the intelligent scoring system on a cloud computing platform and uses machine learning algorithms and big data analysis technology of the intelligent scoring system to process score data and optimize the processed score data; an integration module, which constructs a synchronous score update network to integrate the optimized participant score data through the intelligent scoring system and uses a data routing algorithm of a network protocol to transmit the optimized participant score data; and a scoring module, which establishes an intelligent monitoring and prediction platform to synchronize the transmitted optimized participant score data to the score update network based on the intelligent scoring system. Within the synchronous score update network, an intelligent alarm system is used to provide a competition status view, completing the competition score scoring process.

[0080] Please see Figure 2 The computer device can be a terminal, and includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0081] In summary, this invention deploys an intelligent scoring system on a cloud computing platform, utilizing machine learning algorithms and big data analytics to achieve efficient processing and optimization of participant performance data, ensuring the accuracy and fairness of scoring results. By constructing a globally synchronized performance update network, the system can update scores simultaneously across multiple locations and time zones, providing millisecond-level response times, greatly enhancing the transparency of the competition and the audience's participation experience. The intelligent monitoring and prediction platform combines real-time data monitoring and predictive analysis, enabling early identification of potential problems and triggering intelligent alarms to ensure the smooth running of the competition. Furthermore, the system's elastic computing resources and distributed storage capabilities can be dynamically adjusted according to actual needs, ensuring high availability and scalability. Secure encryption mechanisms and integrity verification ensure the secure and reliable transmission of data. Overall, this invention not only improves the efficiency of competition management but also provides strong technical support for administrative, commercial, and financial fields, ensuring that every competition is conducted in a highly accurate and transparent environment.

[0082] Example 2: Refer to Figure 1 and Figure 2 This is the second embodiment of the present invention, which provides a competition score scoring method based on cloud computing technology. In order to verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0083] To verify the effectiveness and performance of a cloud-based competition scoring system, a series of rigorous experiments were designed and implemented. The experimental environment simulated a large-scale international competition with 15,000 participants distributed across five different geographical regions. The uneven distribution of participants across each region tested the system's synchronous processing capabilities. During the experiments, the system was deployed on a high-performance cloud computing platform that provided elastic computing resources and distributed storage, dynamically adjusting computing power and storage capacity according to actual needs. To simulate high concurrency requests during peak periods, an additional 30% of scoring requests were artificially added during critical phases of the competition, requiring the system to handle over 15,000 scoring requests per second at peak times. Simultaneously, the system automatically scaled to 250 computing nodes. This ensures high system availability and response speed. To test the system's low-latency performance, 10 cloud computing nodes were set up in different geographical regions. Each node is responsible for processing scoring data for a specific region. Through a data localization strategy, the system automatically selects the nearest cloud computing node for data processing based on the participant's geographical location, ensuring that the data transmission distance does not exceed 200 kilometers and controlling network latency to within 5 milliseconds. In addition, the system uses an in-memory database to accelerate data access, preloading approximately 1.5TB of historical scoring data for cache warm-up, reducing query response time to within 0.5 milliseconds. To cope with high concurrency access, the system adopts data sharding technology, distributing the scoring data across 150 memory instances, supporting more than 200,000 query requests per second. The experimental data of this invention are shown in Table 2 below:

[0084] Table 2 Experimental data of the present invention

[0085] parameter Numerical Number of participants 15,000 Geographical distribution Five regions (Asia-Pacific, Europe, North America, South America, and Africa). Additional rating requests increase during peak periods 30% Number of rating requests processed per second (peak hours) More than 15,000 Number of computing nodes (peak period) 250 Number of cloud computing nodes (different regions) 10 Data transmission distance control No more than 200 kilometers Network latency control Within 5 milliseconds Cache preheating data volume Approximately 1.5TB Query response time Within 0.5 milliseconds Number of in-memory database instances 150 Query processing capacity per second More than 200,000 times Detected abnormal scoring events 5 cases Smart alarm trigger time Within 2 seconds

[0086] Table 2 shows that all performance indicators during the experiment were superior to the previous descriptions, verifying the stability and efficiency of the system under larger scale and higher concurrency conditions. A comparison between this invention and existing technologies is shown in Table 3 below:

[0087] Table 3 Comparison of the present invention with the prior art

[0088] ;

[0089] As can be seen from the comparison in Table 3, the present invention is significantly superior to the prior art in many aspects, providing a more efficient, accurate and secure competition score scoring solution.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A competition score scoring method based on cloud computing technology, characterized by: include: The intelligent scoring system is deployed on a cloud computing platform, and the machine learning algorithms and big data analysis technology of the intelligent scoring system are used to process the score data, and the processed score data is optimized. A synchronous score update network is constructed to integrate the optimized participant score data through the intelligent scoring system, and a data routing algorithm based on the network protocol is used to transmit the optimized participant score data; An intelligent monitoring and prediction platform will be established, which will transmit optimized participant performance data, based on an intelligent scoring system and synchronize it to the performance update network. The intelligent alarm system in the synchronized performance update network will provide a view of the competition status and complete the competition performance scoring process. The deployment of the intelligent scoring system on a cloud computing platform includes providing elastic computing resources and distributed storage on the cloud computing platform, selecting the extended functions of distributed storage, and dynamically adjusting the elastic computing resources based on the storage requirements arising from the extended functions. The machine learning algorithms of the intelligent scoring system include a deep neural network model, a support vector machine model, and a random forest model. The deep neural network model utilizes dynamically adjusted, elastic computing resources for learning and applying scoring rules, capturing the non-linear relationships in the scoring data through a multi-layered neuron structure to evaluate participants' performance. The support vector machine model performs classification and regression on the evaluated participants' scores, separating different categories of scoring data by finding a hyperplane to predict the participants' final scores. The random forest model fits the participants' final scores and processes missing final score data through a voting mechanism. The big data analysis technology includes using the high throughput and low latency of the scoring data to collect the participant score data received by the intelligent scoring system, preprocessing the participant score data, and inputting the raw scoring data into the machine learning model of the intelligent scoring system. The intelligent scoring system then performs raw data cleaning, which includes removing outliers and filling in missing data in the participant score data. The high throughput includes using a distributed message queue method to request participant score data, which includes horizontal scaling of participant score data, storage of participant score data, and partitioning of participant score data. The expansion of participant score data includes increasing the number of cloud computing nodes in the intelligent scoring system to expand its processing capacity and process multiple scoring data during peak periods. The data storage of participants' scores includes storing the scores using cloud computing nodes of the intelligent scoring system during the transmission of the optimized scores data of the participants. The participant score data partitioning includes dividing the score data stored using cloud computing nodes into data streams, and assigning the score data in different regions to different partitions to complete the aggregation of participant score data in different geographically distributed regions. The low latency state includes localizing the scoring data in different partitions and the local participant performance data. The data localization includes automatically selecting the nearest cloud computing node to process the participant performance data based on the participant's geographical location, detecting the transmission distance of the participant performance data in the network, and managing the network latency state through the transmission distance. The method of managing network latency status through transmission distance includes accessing the stored participant performance data using an in-memory database, and the access to the stored participant performance data includes cache preheating and data fragmentation. The cache preheating includes loading the scoring data to be used into the memory database in advance before the start of the competition for cache preheating. During the competition, the intelligent scoring system can respond to scoring requests and handle delays caused by data loading. The data sharding involves extending the in-memory database, sharding the scoring data, and distributing different types of data across N in-memory instances to achieve high-concurrency in-memory processing capabilities.

2. The competition score scoring method based on cloud computing technology as described in claim 1, characterized in that: The construction of the synchronous score update network includes deploying a cloud computing network topology in all participating regions, interconnecting them through fiber optic networks, and introducing the spanning tree algorithm in the cloud computing network topology to determine the connection paths between cloud computing nodes. The formula for the spanning tree algorithm is: ； in, Indicates the spanning tree threshold. Represents the set of spanning trees. Represents cloud computing nodes and Link weights between This represents the set of parameters that allow the objective function to reach its minimum value. Represents the candidate spanning tree; When a cloud computing node detects that network latency exceeds the spanning tree threshold When this happens, the intelligent scoring system automatically triggers the cloud computing network topology adjustment mechanism to recalculate the minimum spanning tree; When a cloud computing node detects that the network latency does not exceed the spanning tree threshold In this case, the intelligent scoring system dynamically adjusts the connection relationships of other cloud computing nodes based on the current traffic situation, predicts future traffic changes based on historical data, and performs preventative topology optimization in advance.

3. The competition score scoring method based on cloud computing technology as described in claim 2, characterized in that: The process of integrating the optimized participant performance data through the intelligent scoring system includes encrypting the participant data that has been localized and the participant data after data stream segmentation. After receiving the data from both sources, the receiver verifies the hash value and detects whether the data has been tampered with. When a hash value is appended to each piece of data, the data is considered tamper-proof; otherwise, if no hash value is appended, the integration of the participants' scores is rejected. The process of transmitting the optimized score data of the participants includes analyzing the shortest path from the cloud computing node to the target cloud computing node based on the new score data received by the intelligent scoring system, and selecting the optimal transmission path.

4. The competition score scoring method based on cloud computing technology as described in claim 3, characterized in that: The establishment of the intelligent monitoring and prediction platform includes the process of integrating the performance data of rejected participants and triggering a security alarm mechanism. The provision of the competition status view includes, in the event of triggering a security alarm mechanism, the intelligent monitoring and prediction platform will pop up an event page, which includes the competition rules, scoring criteria and the scores of the participants; When a user clicks on a specific time point, the intelligent monitoring and prediction platform will pop up an event page, through which the user can learn about the competition requirements and scoring details for each stage. When a user selects a time period, the intelligent monitoring and prediction platform will generate a summary report of the scores for that time period, showing the score changes and highlights of the competition during that time period. When a user selects a participant, the intelligent monitoring and prediction platform will mark the participant's events on the timeline and complete the competition score scoring process based on the provided competition status view.

5. A competition score scoring system based on cloud computing technology, based on the competition score scoring method based on cloud computing technology according to any one of claims 1 to 4, characterized in that: include: The optimization module deploys the intelligent scoring system on a cloud computing platform, uses the machine learning algorithms and big data analysis technology of the intelligent scoring system to process the score data, and optimizes the processed score data. The integration module constructs a synchronous score update network, integrates the optimized participant score data through the intelligent scoring system, and uses a data routing algorithm based on network protocols to transmit the optimized participant score data. The scoring module establishes an intelligent monitoring and prediction platform. It transmits optimized participant performance data, which is then synchronized to the performance update network based on the intelligent scoring system. The intelligent alarm system in the synchronized performance update network provides a view of the competition status and completes the competition performance scoring process.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the competition score scoring method based on cloud computing technology as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the competition score scoring method based on cloud computing technology as described in any one of claims 1 to 4.

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