Sports event score real-time data processing method and system

Abstract

The invention provides a real-time data processing method and system for sports event scores, and relates to the technical field of data processing.The method comprises the steps that analysis and structured conversion are conducted on event source data collected in real time, and a multi-region structured event data set is generated; and performing cross-region association verification and identification on the multi-region structured event data set, generating an identified multi-region structured event data set, and creating and initializing an event core database. According to the invention, full-process closed-loop processing of the competition data from collection and analysis, credibility verification, dynamic calculation, real-time distribution to storage and tracing is realized, the real-time performance, credibility, safety and traceability of the competition data are guaranteed, and efficient and standard management of the competition data is supported.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for real-time data processing of sports event results. Background Technology

[0002] In the field of real-time data processing for sports events, existing systems typically rely on collecting data from multiple independent areas or subsystems (such as timing, sensors, and manual data entry terminals) and then centrally aggregating and processing it. These methods can meet basic requirements when the data sources are stable and data conflicts are not significant. However, when some data sources experience temporary anomalies due to the on-site environment, equipment status, or transmission links, the reliability of the data will fluctuate dynamically, and the limitations of existing solutions may become apparent.

[0003] Taking the real-time results processing of a road cycling race as an example, the system needs to integrate time and location information from multiple data sources, such as athletes' onboard GPS, fixed timing points along the route, and high-speed cameras at the finish line. A common existing approach is to assign fixed priorities to different data sources (e.g., prioritizing data from fixed timing points) or set simple verification thresholds (such as time difference tolerance). However, in actual races, onboard GPS signals may be lost in tunnels, and fixed timing points may be subject to occasional radio interference. These factors can cause a temporary decrease in the reliability of a single data source at a specific time. Existing processing models based on fixed rules or weights typically do not fully consider this dynamic reliability relationship that changes with the progress of the race. When instantaneous contradictions occur in the data, they may not be able to adaptively adjust the dependence on different data sources, which may affect the accuracy and stability of intermediate results calculations or final rankings. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for real-time data processing of sports event results, which realizes refined access control of event data and effectively ensures the security of access to event data and the compliance of its use.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A first aspect includes a method for processing real-time data on sports event results, the method comprising: Step 1: Analyze and transform the real-time collected event source data into a structured data set to generate a multi-regional structured event data set; Step 2: Perform cross-regional correlation verification and identification on the multi-regional structured event data set, generate the identified multi-regional structured event dataset, and create and initialize the event core database; Step 3: Based on the identified multi-region structured event dataset, establish a set of nonlinear differential equations describing the evolution of the data's reliable state and solve for the dynamic equilibrium point; generate a three-dimensional virtual ellipsoidal surface model based on the dynamic equilibrium point, divide the three-dimensional virtual ellipsoidal surface model into reliability regions, and calculate a reliability convergence factor for each region; Step 4: Filter basic data from the identified multi-region structured competition dataset, map each piece of basic data to the corresponding confidence region and associate it with the corresponding confidence convergence factor, so as to process and calculate the basic data, generate individual scores, team rankings and comprehensive competition statistics, and dynamically update the core competition database. Step 5: The updated individual scores, team rankings, and comprehensive event statistics in the core event database are distributed in real time to the graphic packaging module, broadcast data link, and authorized query terminal, and access control is performed according to the preset data hierarchy and permission matrix. Step 6: Store all data processing elements in the historical data warehouse, and build a traceable data processing consistency evidence chain based on the global transaction identifier and data version hash.

[0006] Secondly, a real-time data processing system for sports event results includes: The parsing module is used to parse and transform the real-time collected event source data into a structured form, generating a multi-regional structured event data set. The verification module is used to perform cross-regional correlation verification and identification on multi-regional structured event data sets, generate identified multi-regional structured event datasets, and create and initialize the event core database. The modeling module is used to establish a set of nonlinear differential equations describing the evolution of the data's reliable state based on the identified multi-region structured event dataset and solve for the dynamic equilibrium point; it generates a three-dimensional virtual ellipsoidal surface model based on the dynamic equilibrium point, divides the three-dimensional virtual ellipsoidal surface model into reliability regions, and calculates a reliability convergence factor for each region. The calculation module is used to filter basic data from the identified multi-region structured event dataset, map each piece of basic data to the corresponding confidence region and associate it with the corresponding confidence convergence factor, so as to process and calculate the basic data, generate individual scores, team rankings and comprehensive event statistics, and dynamically update the core event database. The distribution module is used to synchronize and distribute the updated individual scores, team rankings and comprehensive event statistics from the core event database to the graphic packaging module, broadcast data link and authorized query terminal in real time, and to perform access control according to the preset data hierarchy and permission matrix. The traceability module is used to store all elements of data processing in a historical data warehouse and to build a traceable data processing consistency evidence chain based on global transaction identifiers and data version hashes.

[0007] The above-described solution of the present invention has at least the following beneficial effects: By establishing a system of nonlinear differential equations to describe the evolution of data credibility, and combining dynamic equilibrium point solving with the construction of a three-dimensional virtual ellipsoidal surface model, the limitations of fixed weights and fixed verification rules are overcome. This approach can adaptively match the credibility changes of various data sources during the competition process, effectively avoiding the impact of instantaneous anomalies from a single data source on score calculation, and improving the accuracy and stability of individual scores, team rankings, and comprehensive statistical indicators. Based on credibility region division and convergence factor correlation, refined processing of basic data is achieved. By mapping data to corresponding credibility regions and assigning appropriate convergence factors, targeted weighted calculations are performed on action timing signals and raw score pulses, ensuring a high degree of compatibility between the data processing process and the data's inherent credibility. This further guarantees the objectivity of the core competition data calculation results and meets the stringent requirements of sports events for the accuracy of performance data. An integrated core competition data processing and distribution system is constructed, from multi-region structured data generation to the core database. Dynamic updates and real-time data synchronization and distribution across multiple terminals form an efficient end-to-end data processing chain, ensuring that individual scores, rankings, and statistical indicators can be quickly synchronized to event-related terminals such as graphic packaging and broadcast links, meeting the real-time data requirements of scenarios such as live event broadcasts and on-site displays. Simultaneously, combined with preset data hierarchies and permission matrices for access control, refined permission management of event data is achieved, effectively ensuring the security of event data access and compliance of its use. A comprehensive and traceable event data processing and evidence storage system has been established, uniformly storing all elements in the entire data processing process in a historical data warehouse. A consistent evidence storage chain is constructed through global transaction identifiers and data version hashes, realizing end-to-end data traceability and consistency verification from event source data parsing to data distribution. This not only quickly locates problem nodes in the data processing process but also provides complete and effective data evidence for disputes over event results, ensuring the fairness and authority of sports events. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating a real-time data processing method for sports event results provided by an embodiment of the present invention.

[0009] Figure 2 This is a schematic diagram of a real-time data processing system for sports event results provided by an embodiment of the present invention. Detailed Implementation

[0010] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0011] like Figure 1 As shown in the figure, an embodiment of the present invention proposes a method for real-time data processing of sports event results, the method comprising the following steps: Step 1: Analyze and transform the real-time collected event source data into a structured data set to generate a multi-regional structured event data set; Step 2: Perform cross-regional correlation verification and identification on the multi-regional structured event data set, generate the identified multi-regional structured event dataset, and create and initialize the event core database; Step 3: Based on the identified multi-region structured event dataset, establish a set of nonlinear differential equations describing the evolution of the data's reliable state and solve for the dynamic equilibrium point; generate a three-dimensional virtual ellipsoidal surface model based on the dynamic equilibrium point, divide the three-dimensional virtual ellipsoidal surface model into reliability regions, and calculate a reliability convergence factor for each region; Step 4: Filter basic data from the identified multi-region structured competition dataset, map each piece of basic data to the corresponding confidence region and associate it with the corresponding confidence convergence factor, so as to process and calculate the basic data, generate individual scores, team rankings and comprehensive competition statistics, and dynamically update the core competition database. Step 5: The updated individual scores, team rankings, and comprehensive event statistics in the core event database are distributed in real time to the graphic packaging module, broadcast data link, and authorized query terminal, and access control is performed according to the preset data hierarchy and permission matrix. Step 6: Store all data processing elements in the historical data warehouse, and build a traceable data processing consistency evidence chain based on the global transaction identifier and data version hash.

[0012] In this embodiment of the invention, by establishing a system of nonlinear differential equations to describe the evolution of the data credibility state, and combining dynamic equilibrium point solving with the construction of a three-dimensional virtual ellipsoidal surface model, the limitations of fixed weights and fixed verification rules are overcome. This allows for adaptive matching of credibility changes in various data sources during the competition process, effectively avoiding the impact of instantaneous anomalies in a single data source on score calculation, and improving the accuracy and stability of individual scores, team rankings, and comprehensive statistical index calculations. Based on credibility region division and convergence factor correlation, refined processing of basic data is achieved. By mapping data to corresponding credibility regions and assigning appropriate convergence factors, targeted weighted calculations are performed on action timing signals and original score pulses, ensuring a high degree of compatibility between the data processing process and the data's inherent credibility. This further guarantees the objectivity of the core competition data calculation results and meets the stringent requirements of sports events for the accuracy of score data. An integrated core competition data processing and distribution system is constructed, from multi-region structured data generation to… From dynamic updates to the core database to real-time data synchronization and distribution across multiple terminals, an efficient end-to-end data processing chain is formed. This ensures that individual scores, rankings, and statistical indicators can be quickly synchronized to event-related terminals such as graphic packaging and broadcast links, meeting the real-time data requirements of scenarios such as live event broadcasts and on-site presentations. Simultaneously, combined with pre-defined data hierarchies and permission matrices for access control, refined permission management of event data is achieved, effectively ensuring the security of event data access and compliance of its use. A comprehensive and traceable event data processing and evidence storage system has been established, storing all elements throughout the data processing process in a unified historical data warehouse. A consistent evidence storage chain is constructed using global transaction identifiers and data version hashes, enabling end-to-end data traceability and consistency verification from event source data parsing to data distribution. This not only allows for rapid identification of problem nodes in the data processing process but also provides complete and effective data evidence for disputes over event results, ensuring the fairness and authority of sports events.

[0013] In a preferred embodiment of the present invention, step 1 includes: The time-synchronized data stream of the event source data is parsed. Based on a predefined unified data model, the parsed identity code, action timing signal, and original performance pulse are mapped into structured identity data, structured action data, and structured performance data, respectively, and integrated into a multi-region structured event data set. The acquisition of the event source data includes: real-time collection of event source data containing identity code, action timing signal, and original performance pulse through sensing and timing devices deployed in the athlete identification area, competition action execution area, and performance record determination area, forming event source data, specifically including: First, the event data is collected. In the three core competition areas—athlete identification area, competition action execution area, and score recording and judgment area—dedicated sensing and timing devices are deployed according to the data collection needs of each area. The sensing and timing devices in each area work collaboratively according to the requirements of real-time data collection for sports events, and synchronously collect raw data during the event. Specifically, the devices in the athlete identification area collect unique identification codes, the devices in the competition action execution area collect action timing signals that reflect the entire process of the athlete's competition actions, and the devices in the score recording and judgment area collect raw score pulses used to record the basic data of the competition results. The sensing and timing devices in each area transmit the identification codes, action timing signals, and raw score pulses they have collected in real time, and converge to form a multi-source, continuous event data stream. Subsequently, the aggregated event source data stream is processed for time synchronization. A unified time synchronization mechanism is used to calibrate the event source data collected and transmitted by different regions and different types of sensors and timing devices, so that all event source data corresponds to a unified time dimension reference standard. This completely eliminates the time deviation caused by differences in the acquisition response of different devices and differences in the data transmission links of different regions, resulting in an event source data stream that has completed time dimension calibration and has consistent time information. Next, a full analysis of the event source data stream that has completed time synchronization processing is performed. The data stream is analyzed frame by frame according to the transmission sequence. From each frame of data, three types of core data are accurately extracted: identity code, action timing signal, and original score pulse. During the analysis process, the integrity of each type of data is verified, and invalid or incomplete data is removed to ensure that each identity code, action timing signal, and original score pulse extracted in the end is complete and without any missing key content.

[0014] Subsequently, a mapping and transformation process was carried out based on a unified data model predefined before the event data processing, taking into account the event type, data collection dimensions, and subsequent data processing requirements. This unified data model builds a dedicated data structure framework around three core data categories: identity, action, and results. For each data category, a complete field specification containing basic information, collection association information, and regional affiliation information is preset. Specifically, the preset field specification for structured identity data includes athlete identity code, athlete name, athlete's competition event, athlete's team, unique identifier of the collection device, collection timestamp, and collection region code. The preset field specification for structured action data includes action timing signal value, action occurrence timestamp, action feature description, unique identifier of the collection device, device operating status, and collection region code. The preset field specification for structured result data includes raw result pulse value, pulse collection timestamp, cumulative pulse count, unique identifier of the collection device, device calibration status, and collection region code. Based on this unified data model, a one-to-one mapping transformation is performed on the three types of core data extracted from the parsing. The extracted identity code is used as the core primary key, and the corresponding athlete basic information, the unique identifier of the device that collected the code, the collection timestamp, and the collection area code are supplemented according to the preset field specifications, thus completely converting it into structured identity data. The extracted action timing signal is used as the core primary key, and the corresponding action occurrence timestamp, action feature description, the unique identifier of the device that collected the signal, and the collection area code are supplemented according to the preset field specifications, thus completely converting it into structured action data. The extracted raw performance pulse is used as the core primary key, and the corresponding pulse collection timestamp, cumulative pulse count, the unique identifier of the device that collected the pulse, and the collection area code are supplemented according to the preset field specifications, thus completely converting it into structured performance data. The entire mapping transformation process strictly follows the field definition requirements, data format standards, and data association rules of the unified data model, ensuring that the structured identity data, structured action data, and structured performance data obtained after the transformation all meet the standardization and normalization requirements and can be directly used for subsequent data processing.

[0015] Finally, the multi-regional structured event data was categorized and integrated. Using the actual data collection area as the sole basis for attribution, all structured identity data was uniformly collected into structured data for athlete identification, all structured action data was uniformly collected into structured data for competition action execution, and all structured score data was uniformly collected into structured data for score recording and judgment. The structured data in the three areas were then sorted and verified separately, duplicate data was removed, and correlation identifiers were added. Finally, a multi-regional structured event data set with clear data classification, clear regional attribution, and consistent data format throughout the process was formed. This completed the entire process of transforming the event source data from its original unstructured form to a standardized structured form, providing a standardized data source foundation that is standardized, unified, and directly callable for subsequent cross-regional event data correlation verification.

[0016] In this embodiment, source data is collected by deploying dedicated sensing and timing devices in three core competition areas, achieving targeted and precise collection of core competition data. This ensures the comprehensiveness and relevance of the source data, fully capturing the three core raw data categories of identity, actions, and results during the competition. Unified time synchronization processing of the competition source data stream eliminates time discrepancies between data collected from different devices and regions, ensuring consistency of various source data in the time dimension and preventing deviations in subsequent data association and processing due to time asynchrony. A predefined unified data model is used to complete the mapping and conversion from raw data to structured data, achieving standardization and normalization of non-standardized competition source data. This unifies the format and structure of different types and regions of data, solving the integration and processing difficulties caused by inconsistent formats of multi-source data. Finally, the three types of structured data are integrated according to the collection area to form a multi-regional structured competition data set, achieving regional classification and standardized aggregation of competition data, making the regional affiliation of the data clearly identifiable.

[0017] In a preferred embodiment of the present invention, step 2 includes: Step 200: For the multi-regional structured event data set, perform cross-regional association verification based on the preset coding mapping relationship, temporal continuity conditions, and pulse validity intervals to obtain the cross-regional association verification results. Specifically, this includes: First, performing cross-regional association verification on the multi-regional structured event data set. This involves retrieving three types of core verification rules preset before event data processing. The preset coding mapping relationship is the unique binding relationship between athlete identity codes and structured data in each region. This relationship is configured in advance based on athlete participation information to ensure that the data in the same athlete's identity recognition area, competition action execution area, and performance record determination area are all associated with the same identity code; the preset temporal continuity conditions are... The reasonable tolerance range for the timestamps of regional and athlete data collection is pre-set to 0 to 5 seconds based on the event type and real-time data collection requirements. The upper tolerance limit is 1 second for sprint racing events, 5 seconds for physical fitness events, and 3 seconds for skill-based events. The preset pulse validity range is the reasonable range of original performance pulse values ​​for each event. This range is pre-set based on the event data collection specifications and equipment measurement accuracy. The pulse value range is 0 to 9999 for racing events, 0 to 5000 for physical fitness events, and 0 to 1000 for skill-based events. During verification, the multi-region structured event data is first processed according to the athlete's identity code. All data in the set is grouped, ensuring that structured data from the same athlete's identification area, competition action execution area, and performance record determination area are grouped together. Then, three types of checks are performed on each data group sequentially. First, the coding mapping relationship is checked, verifying that the identification code characters, number of bits, and code segments of each area within the group are completely consistent with the preset coding mapping relationship. No character deviations, number of bits discrepancies, or code segment misalignments are considered a pass. Second, the temporal continuity is checked, calculating the time difference between the data collection timestamp of the competition action execution area and the athlete's identification area, and the time difference between the data collection timestamp of the performance record determination area and the competition action execution area. First, check whether the two time differences are both within the preset time tolerance range of the corresponding event type. If the difference is greater than 0 and less than or equal to the upper limit of the corresponding event tolerance, the verification is passed. Finally, check the pulse validity. Check whether the original score pulse value in the score record judgment area falls within the preset pulse validity range of the corresponding event. If the pulse value is greater than or equal to the lower limit of the range and less than or equal to the upper limit of the range, the verification is passed. Record the three types of verification results for each data group in real time. Completely record the encoding mapping verification result, time continuity verification result, and pulse validity verification result of each data group to form a full cross-regional correlation verification result. This result includes the verification pass status of each data group and the specific verification items that failed.

[0018] Step 201: Based on the results of cross-regional correlation verification, automatically attach consistent, pending arbitration, or conflict data consistency labels to each record in the multi-regional structured event dataset to generate a labeled multi-regional structured event dataset. Specifically, this includes: attaching data consistency labels based on the cross-regional correlation verification results; first, clarifying the criteria for the three types of data consistency labels; the consistency label criteria are that all three verification items—encoding mapping, temporal continuity, and impulse validity—of the data set meet the verification pass conditions, with no verification anomalies; the pending arbitration label criteria are that the encoding mapping verification of the data set passes, and the single time difference value of the temporal continuity verification is within 80% to 100% of the corresponding event's tolerance limit, or the original performance impulse value is within 10% of the upper and lower limits of the impulse validity range for the corresponding event, i.e., impulse values ​​for speed events are in the range of 8999 to 9999, impulse values ​​for physical fitness events are in the range of 4500 to 5000, and impulse values ​​for skill events are in the range of 900 to 1000. The data has minor anomalies but does not meet the invalidation standard. The conflict identification standard is that if any character, bit length, or code segment in the encoding mapping verification is mismatched, or any time difference in the time sequence continuity verification is less than 0 or greater than the corresponding event tolerance limit, or the original performance pulse value is less than the lower limit of the pulse interval of the corresponding event or greater than the upper limit of the interval, the data has a clear invalid or abnormal problem. According to this judgment standard, each data record in the multi-region structured event dataset after being grouped by athlete identity code is judged one by one. Based on the cross-regional association verification results of its data group, the corresponding consistency, pending arbitration, or conflict data consistency mark is automatically attached to each record. When attaching, the data consistency mark is associated and bound with the core field of the original data record to ensure that the mark and the data correspond one-to-one. After the mark is attached to all records, the full data is integrated and sorted out, retaining the original content of the structured data of each region and the newly added consistency mark information, and generating the marked multi-region structured event dataset.

[0019] Step 202: Using the identified multi-regional structured event dataset as the initial data content, create and initialize the event core database. Specifically, this includes: creating and initializing the event core database based on the identified multi-regional structured event dataset. First, build the overall architecture of the event core database according to the requirements of the entire event data processing process. This architecture includes four core data storage areas: regional structured data storage area, data consistency identifier storage area, real-time computing cache area, and event result storage area. The regional structured data storage area is used to store the original structured data of each region, the data consistency identifier storage area is used to store the consistency identifier information bound to the data, the real-time computing cache area is used to temporarily store the intermediate results of subsequent data processing, and the event result storage area is used to store the final event results and ranking data. After the database architecture is built, according to the functional positioning of each storage area, the identified multi-regional structured event dataset is split and classified for import. The regional structured data is imported into the corresponding storage area, the data consistency identifier information is imported into the data consistency identifier storage area, and an association index is established between the storage areas. The index uses the athlete identification code and data collection timestamp as the core association fields.

[0020] Subsequently, the core database of the competition underwent comprehensive initialization and configuration of basic parameters. For the real-time synchronization mechanism of data updates, real-time synchronization trigger rules matching the frequency of competition data collection were set, a 500-millisecond data synchronization response threshold was configured, and direct synchronization links between collection devices in each region and the database were established to ensure that competition data from the collection end can be pushed to the corresponding storage area of ​​the database in real time. Simultaneously, an automatic reconnection and logging mechanism for data synchronization anomalies was configured to ensure the continuity and stability of data synchronization. Regarding the basic rules for database access permissions, a three-level permission hierarchy was configured according to the operation role and business operation requirements: Level 1 is for system administrators, Level 2 for data processing operators, and Level 3 for data viewers. Each level was configured with a unique scope of operation permissions. Level 1 system administrators have full access to the database. Functional operation permissions are defined as follows: Level 2 data processing operators have permissions to import, process, and calculate data, but no permissions to modify or delete data; Level 3 data viewers only have viewing permissions for a specified data range. Each operation account is configured with unique authentication information, and full logging of operation activities is enabled to ensure traceability of database operations. Regarding the lifecycle management strategy for data storage, it is tiered according to data type and usage value. Core structured data for the competition is set with a permanent long-term storage period, while intermediate data processing results are set with a 72-hour temporary storage period. Automatic cleanup rules are configured to periodically clean up expired temporary data. A data backup mechanism is also established, with core data being backed up to multiple nodes in different locations at a fixed 24-hour interval to ensure the security and efficiency of data storage. After completing the full data classification and import and configuring all basic parameters, a comprehensive verification of the overall operational status and data association accuracy of the event's core database was conducted. On the one hand, by simulating data collection and synchronization operations, the operational status of the database's real-time synchronization mechanism, access control, and data lifecycle management was tested to confirm that all parameter configurations were functioning correctly and without any functional abnormalities or configuration failures. On the other hand, using athlete identification codes and data collection timestamps as core search conditions, event data from different athletes and regions were randomly selected, and the data association and matching were checked across storage areas. The accuracy of field association and index matching between the regional structured data storage area and the data consistency identifier storage area was verified one by one to ensure that the data association between each storage area was correct, the mapping was accurate, and there were no issues such as data misalignment or identifier disconnection. At this point, the creation and initialization of the event's core database was completed.

[0021] This embodiment, through multi-dimensional cross-regional correlation verification, comprehensively checks multi-regional data from three core dimensions: encoding binding, time sequence, and pulse value. It effectively identifies problematic data such as data mismatch, disordered time sequence, and invalid pulses, improving the accuracy and effectiveness of event data from the data correlation level. Based on the verification results, a standardized consistency identifier is attached to each data entry, enabling hierarchical and categorized management of event data. This allows for the rapid differentiation of valid and usable consistent data, data requiring further verification and awaiting arbitration, and unusable conflicting data. A dedicated architecture for the event's core database is built according to the event data processing flow, achieving categorized storage and correlation management of data and identifiers, ensuring the database structure is highly compatible with the needs of event data processing. Using the identified multi-regional structured event dataset as the initial data for the database ensures the accuracy and standardization of the initial data in the core event database, preventing problematic data from entering the core processing stage. This guarantees the reliability of subsequent event result calculations, ranking statistics, and other tasks from the data storage source, while the database initialization configuration ensures the real-time and smooth operation of data processing.

[0022] In a preferred embodiment of the present invention, step 3 includes: Step 300: Extract data quality indicators for the athlete identification area, competition action execution area, and performance record determination area within a continuous time window from the identified multi-region structured event dataset. Use these data quality indicators as state variables to describe system evolution. Specifically, this involves: setting a fixed-duration continuous time window (5 minutes based on the event data collection frequency) from the identified multi-region structured event dataset, using the data collection timestamp as the time axis. Within this time window, extract the full data for the athlete identification area, competition action execution area, and performance record determination area, respectively, and calculate the core number for each of the three areas. According to the quality indicators, all indicators are used as state variables describing the system's evolution. The indicators extracted and the calculation process are consistent across regions. Specific indicators and calculation methods are as follows: The number of data records with consistent identifiers within a given time window is divided by the total number of data records within that region's time window. The result is rounded to four decimal places. The formula is: Data Consistency Ratio = Number of Consistently Identified Records ÷ Total Number of Records in the Region. Using the original pulse signal intensity of the first data record within the time window as the baseline, the difference between the pulse signal intensity of each data record within the window and the baseline value is calculated. The arithmetic mean of all differences is calculated. The formula is: Signal Attenuation = (Single data pulse intensity - baseline pulse intensity) ÷ total number of records in the region; The time difference between the actual acquisition timestamp and the theoretical acquisition timestamp for each data point in the region within the statistical time window (the theoretical acquisition timestamp is generated sequentially at 100-millisecond intervals, starting from the actual acquisition timestamp of the first data point in the region within the time window); calculate the arithmetic mean of all time differences, in milliseconds, using the formula: Transmission delay value = (Actual collection timestamp - Theoretical collection timestamp) ÷ Total number of records in the region; The number of complete data records in the region without missing fields or null values ​​within the statistical time window is divided by the total number of data records in the region's time window. The result is rounded to 4 decimal places. The formula is: Data integrity rate = Number of complete data records ÷ Total number of records in the region; After extracting the indicators for the three regions, the 12 indicators of data consistency ratio, signal attenuation value, transmission delay value, and data integrity rate for the athlete identification area, competition action execution area, and performance record judgment area are used as all state variables for this system evolution analysis. The indicator values ​​are all rounded to 4 decimal places and stored in the state variable dataset.

[0023] Step 301: Based on the coupling relationship between the data consistency ratio, signal attenuation, and transmission delay in the state variables, establish a set of nonlinear differential equations describing the evolution of the data reliability state over time. Specifically, this includes: using all the state variables extracted in step 300 as a basis, focusing on the three core state variables—data consistency ratio, signal attenuation value, and transmission delay value—and establishing a set of nonlinear differential equations describing the evolution of the data reliability state over time based on the coupling relationship between them. The equation set uses time t as the independent variable and the data reliability state value of each of the three regions as the dependent variable. The core coupling relationship is that the data consistency ratio is positively correlated with the data reliability state, while the signal attenuation value and transmission delay value are negatively correlated with the data reliability state. Furthermore, the coupling effect among the three is nonlinear (i.e., a change in one variable will trigger a nonlinear change in the other two variables, thus jointly affecting the data reliability state). The construction of the equation set follows the following rules: establish an independent set of nonlinear differential equations for each of the athlete identification area, the competition action execution area, and the score recording and judgment area. The three sets of equations have a unified form, requiring only substitution. The equations for each region are as follows: the left side of the equation represents the rate of change of the data reliability state over time, and the right side represents a nonlinear coupled expression of the data consistency ratio, signal attenuation value, and transmission delay value. The expression includes first-order, second-order, and cross-terms of the variables, reflecting the nonlinear interaction among them. In the coupled expression, a positive coefficient is added to the data consistency ratio term, and negative coefficients are added to the signal attenuation and transmission delay terms. The coefficients of the cross-terms are set according to the coupling strength between the variables (positive coefficients indicate synergistic effects, and negative coefficients indicate antagonistic effects). All coefficients in the equations are set to fixed constants (range 0 to 1) according to the characteristics of the event data to ensure the rationality and convergence of the equation solution. The final nonlinear differential equation system describes the dynamic change of the data reliability state of each region over time t, meaning that the real-time data reliability state of each region at any time point can be derived from the equation system. Let the data reliability state values ​​of the athlete identification area, competition action execution area, and score recording judgment area be x(t), y(t), and z(t), respectively, and the data consistency ratio of each region be... The signal attenuation value is The transmission delay value is The fixed coefficients for each dimension are: to (Values ​​range from 0 to 1, positive coefficients indicate positive correlation, and negative coefficients indicate negative correlation), then the formulas for the nonlinear differential equation system in the three regions are: Athlete identification area: ; Competition action execution area: ; Grade Recording and Judgment Area: .

[0024] Step 302: Discretize the nonlinear differential equation system into a difference equation system, and set an initial iteration vector containing data quality indicators for the athlete identification area, competition action execution area, and performance record determination area; input the initial iteration vector into the difference equation system for iterative calculation. When the norm of the difference between the current iteration result and the previous iteration result is less than the preset tolerance, stop the iteration and obtain a converged numerical solution; use the converged numerical solution as the steady-state solution or periodic solution of the nonlinear differential equation system at the latest time point, and use the steady-state solution or periodic solution as the dynamic equilibrium point of data credibility for the athlete identification area, competition action execution area, and performance record determination area. Specifically, this includes: based on the nonlinear differential equation system constructed in step 301, discretize it into a difference equation system, solve the converged numerical solution through iterative calculation, and use this solution as the dynamic equilibrium point of data credibility for each area. The specific process is as follows: set an equal time step. =0.1 seconds, with discrete time steps Using the independent variable, the nonlinear differential equation system with continuous time t is transformed into a difference equation system. The discretization process preserves the nonlinear coupling relationship of the original equation system, ensuring that the solutions of the difference equation system are consistent with those of the original differential equation system. These are the discrete time steps. Real-time reliable status values ​​of data in the athlete identification area, competition action execution area, and performance record judgment area; The difference equation for the athlete identification zone is: ; The difference equation for the execution zone of the competition action is: ; The difference equation for the grade record determination zone is: ; The initial iteration vector is a three-dimensional column vector, with its dimensions matching the core state variables of the three regions. The elements within the vector are, in order, the comprehensive calculated values ​​of the latest time-point data quality indicators extracted in step 300 for the athlete identification region, the competition action execution region, and the performance record determination region. The values ​​are rounded to four decimal places and denoted as [value missing]. The L2 norm is used to calculate the norm of the difference between the current iteration result and the previous iteration result, with a preset norm tolerance of 10. -6When the calculated difference norm is less than the preset tolerance, the iteration stops immediately. The initial iteration vector is substituted into the discretized difference equation system, and the iteration result for each step is calculated sequentially according to the discrete time step. The iteration vector is updated sequentially. The difference norm is calculated once after each iteration, and the iteration result and norm value are recorded. After the iteration stops, the final convergent numerical solution is the stable solution of the difference equation system. Combining all the iteration results recorded during the iteration calculation, the solution is accurately determined as a steady-state solution or a periodic solution based on its characteristics. The specific determination method is as follows: If the convergent numerical solution is a unique, fixed value, and this value remains unchanged without any fluctuation in subsequent supplementary iterations (3 additional iterations), it is a steady-state solution; if the convergent numerical solution is a numerical sequence that fluctuates repeatedly according to a fixed time period, and the fluctuation period is fixed (period deviation does not exceed 0.001 seconds) and the fluctuation amplitude is stable (amplitude deviation does not exceed 10 seconds), it is a steady-state solution. -4 The convergent numerical solution is the periodic solution. After the determination is completed, the convergent numerical solution is assigned a value according to the region. The component corresponding to the athlete identification area is assigned the dynamic balance point of data credibility in the athlete identification area, the component corresponding to the competition action execution area is assigned the dynamic balance point of data credibility in the competition action execution area, and the component corresponding to the result record determination area is assigned the dynamic balance point of data credibility in the result record determination area. The balance point values ​​of the three regions are all retained to 4 decimal places and the values ​​are strictly controlled within the range of 0 to 1. The closer the value is to 1, the stronger the stability of data collection, transmission and processing in that region, and the higher the data credibility. The closer the value is to 0, the greater the probability of deviation and fluctuation in the data in that region, and the lower the data credibility.

[0025] Step 303: Using the components representing the confidence level of each region in the dynamic equilibrium point as parameters, substitute them into a parameterized three-dimensional ellipsoid equation to calculate and generate a three-dimensional virtual ellipsoid surface model with dynamically adjustable principal axis direction and semi-axis length in real time. Based on the curvature changes of discrete sampling points on the three-dimensional virtual ellipsoid surface model, the model is divided into a high-confidence convergence region and a low-confidence divergence region. Specifically, this includes: using the dynamic equilibrium point obtained in step 302 as the core parameter, constructing a parameterized three-dimensional virtual ellipsoid surface model, and completing the high-confidence convergence region and low-confidence divergence region based on the surface curvature changes. The division of the zones is as follows: The dynamic equilibrium point component values ​​of the data reliability of the athlete identification zone, the competition action execution zone, and the performance record determination zone are used sequentially as the length parameters of the three principal axes of the three-dimensional ellipsoid equation, denoted as A, B, and C, respectively, with values ​​ranging from 0 to 1. The origin of the three-dimensional coordinate system is taken as the center of the ellipsoid, and the three principal axes of the ellipsoid are set to coincide with the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system, respectively. The X-axis corresponds to the athlete identification zone, the Y-axis to the competition action execution zone, and the Z-axis to the performance record determination zone. Based on the determined core parameters, a parameterized three-dimensional ellipsoid surface equation is established, which is: This equation is used to calculate and generate a three-dimensional virtual ellipsoidal surface model in real time. The principal axis direction of the model is dynamically adjusted according to the changes in the semi-axis length parameters A, B, and C. Specifically, the three principal axes of the three-dimensional ellipsoid correspond one-to-one with the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system. The X-axis corresponds to the athlete identification area, the Y-axis corresponds to the competition action execution area, and the Z-axis corresponds to the score recording and judgment area. When any one or more of the semi-axis length parameters A, B, and C change, the length of the corresponding principal axis changes synchronously, thereby driving the overall principal axis direction of the ellipsoid to adaptively adjust, ensuring that the principal axis always remains consistent with the coordinate axes of the corresponding coordinate system and fits the data credibility status of each area. The semi-axis length is updated synchronously with the changes in the dynamic equilibrium point component values. That is, when the dynamic equilibrium point component values ​​of the athlete identification area are updated, the corresponding semi-axis length parameter A is updated synchronously; when the dynamic equilibrium point component values ​​of the competition action execution area are updated, the corresponding semi-axis length parameter B is updated synchronously; and when the dynamic equilibrium point component values ​​of the score recording and judgment area are updated, the corresponding semi-axis length parameter C is updated synchronously, realizing real-time linkage between the ellipsoidal surface model and the data credibility status of each area.

[0026] The generated 3D virtual ellipsoidal surface model is uniformly discretely sampled with a sampling point spacing of 0.01, covering the entire ellipsoidal surface in a 3D coordinate grid format. The 3D coordinates (xi, yi, zi) of all sampling points and their corresponding surface position information are recorded. The surface normal curvature is calculated for each discrete sampling point using the partial derivative method. First, the first and second partial derivatives of the ellipsoidal surface equation at that point are solved, and then the normal curvature value is calculated using the curvature formula: [Formula omitted for brevity]. ,in It is the first-order partial derivative. For the second-order partial derivative, the curvature values ​​at all sampling points are retained to 6 decimal places; Based on the accuracy requirements of the event data credibility analysis, a surface curvature threshold of 0.5 was set. This threshold serves as the curvature boundary between the high-confidence convergence region and the low-confidence divergence region. This threshold was determined through multiple iterations, taking into account the accuracy requirements of the event data credibility analysis, the sampling accuracy of the 3D virtual ellipsoid surface model (sampling point spacing of 0.01), and the numerical range of the dynamic equilibrium point (0 to 1) obtained in step 302. This ensures that the threshold effectively distinguishes between regions with gentle and steep surface curvature, accurately matches the core judgment requirement of stable high-confidence data and volatile low-confidence data, and balances computational efficiency with judgment accuracy. To avoid misjudging low-confidence divergence regions due to excessively high thresholds and missing high-confidence convergence regions due to excessively low thresholds, the curvature values ​​of all sampling points are compared with the division threshold. Ellipsoidal surface regions formed by sampling points with curvature values ​​less than or equal to 0.5 are identified as high-confidence convergence regions, characterized by gentle surface curvature and stable data confidence. Ellipsoidal surface regions formed by sampling points with curvature values ​​greater than 0.5 are identified as low-confidence divergence regions, characterized by steep surface curvature and fluctuating data confidence. After division, the spatial location and boundary range of the two regions on the 3D virtual ellipsoidal surface model are clearly defined, achieving precise region delineation.

[0027] Step 304: Based on the dynamic equilibrium point component values ​​of the athlete identification area, competition action execution area, and performance record determination area associated with each region in the high-confidence convergence region and the low-confidence divergence region, a confidence convergence factor is derived for each region in the high-confidence convergence region and the low-confidence divergence region through a normalized weighted calculation. Specifically, based on the high-confidence convergence region and the low-confidence divergence region divided in Step 303, and combining the dynamic equilibrium point component values ​​associated with each region, a unique confidence convergence factor is derived for each region through a normalized weighted calculation. The specific process is as follows: Extract all data confidence dynamic equilibrium point component values ​​of the athlete identification area, competition action execution area, and performance record determination area associated with the high-confidence convergence region and the low-confidence divergence region, denoted as... (High-confidence convergence region) and (Low confidence divergence region), all values ​​are rounded to 4 decimal places; the three dynamic equilibrium point components associated with each region are selected as the core indicators for weighted calculation. Fixed weights are set according to the importance of each region in the event data collection and processing, with athlete identification weighting at 0.4, competition action execution weighting at 0.3, and performance record judgment weighting at 0.3, and the sum of all weights being 1; linear normalization is performed on the core indicators in the high confidence convergence region and the low confidence divergence region to eliminate the influence of numerical dimensions, mapping the indicator values ​​to the 0-1 range. The normalization formula is: Normalized value = (Actual indicator value - Minimum indicator value) ÷ (Maximum indicator value - Minimum indicator value), where the minimum indicator value is 0 and the maximum indicator value is 1, and the calculation result is rounded to 4 decimal places; the normalized value of each indicator is multiplied by its corresponding weight, and the products are summed sequentially to obtain the initial weighted calculation value. The calculation formula is: Weighted initial value = (Normalized value of athlete identification discrimination × 0.4) + (Normalized value of competition action execution discrimination × 0.3) + (Normalized value of performance record judgment discrimination × 0.3); The initial weighted values ​​are used as the confidence convergence factors for the corresponding regions. The factor values ​​are retained to four decimal places and range from 0 to 1. The closer the value is to 1, the stronger the convergence of the data confidence in that region, the more stable the data state, and the stronger the anti-interference ability. The closer the value is to 0, the weaker the convergence of the data confidence in that region, the more prone the data state is to divergence, and the more necessary it is to optimize and adjust the data collection and processing. The calculated confidence convergence factors are uniquely bound to the high confidence convergence region and the low confidence divergence region, respectively. At the same time, the values ​​of the dynamic equilibrium point components, normalized values, and weighted calculation process data associated with each region are recorded to form a complete confidence convergence factor calculation archive.

[0028] This embodiment, through the precise extraction of data quality indicators and the setting of state variables, achieves a quantitative description of the data status in various regions of the event, breaking the qualitative evaluation mode of data quality analysis. It provides a standardized and calculable quantitative basis for the analysis of data credibility, improving the objectivity and accuracy of event data quality analysis. The nonlinear differential equation system constructed based on the coupling relationship of state variables can accurately characterize the dynamic evolution of the event data credibility status over time. Compared with linear analysis models, it better reflects the actual changing characteristics of event data, improving the scientific rigor of data credibility analysis. Solving the nonlinear differential equation system and cross-validating it through a discretized difference equation system effectively improves the accuracy and reliability of the steady-state / periodic solutions, ensuring the accuracy of the dynamic equilibrium point of data credibility. Determining the dynamic equilibrium point enables precise quantification of the data reliability in each area—athlete identification, competition action execution, and performance record determination—clarifying the differences in data reliability across areas. This provides clear direction for the tiered processing and focused verification of event data, enhancing the targeting and efficiency of event data processing. The calculation and regional division of the reliability convergence factor not only quantifies the convergence of data reliability in each area but also clearly distinguishes between high-reliability convergence zones and low-reliability divergence zones. This provides specific directions for optimizing the event data processing system: for low-reliability divergence zones, timely debugging of data acquisition equipment and optimization of transmission links can be carried out; for high-reliability convergence zones, existing acquisition and processing strategies can be maintained, achieving a reasonable allocation of event data processing resources and improving the overall operational efficiency and data processing quality of the system.

[0029] In a preferred embodiment of the present invention, step 4 includes: Step 400: From the identified multi-region structured competition dataset, records with consistent data consistency are selected as basic data. Each basic data record is mapped to a corresponding high-confidence convergence region or low-confidence divergence region on a 3D virtual ellipsoidal surface model based on its source information, and the corresponding region's confidence convergence factor is associated. Using the associated confidence convergence factor, the action time series signal in the basic data is weighted and smoothed to obtain the weighted smoothing result. Using the associated confidence convergence factor, the original performance pulses in the basic data are accumulated and transformed with confidence weight to obtain the confidence weighted calculation result, specifically including: From the identified multi-region structured sports event dataset, basic data screening, 3D model region mapping, credibility association, and weighted calculation of two types of core data are carried out. The specific process is as follows: The entire dataset of the multi-region structured sports event dataset is traversed, and all records marked with consistent data are selected. These records are used as the basic data for this data processing. All fields in the basic data, such as athlete identity information, competition action timing signals, original performance pulses, and data source region information, are retained to ensure data integrity. The source information of each piece of basic data is extracted to clarify its athlete identification area, competition action execution area, or performance record judgment area. Based on the spatial assignment of this area on the 3D virtual ellipsoidal surface model, each piece of basic data is accurately mapped to the corresponding high-credibility convergence area or low-credibility divergence area on the model. After completing the region mapping, the credibility convergence factor corresponding to the mapped region of the basic data is uniquely bound to the basic data, realizing a one-to-one association between the basic data and the credibility convergence factor.

[0030] For each piece of basic data containing the competition action time-series signal, a weighted smoothing process is performed using its associated confidence convergence factor. The specific calculation process is as follows: First, determine the sampling point sequence of the action time-series signal, denoted as S1, S2, S3, ..., Sm, where m is the total number of sampling points of the time-series signal. Then, extract the confidence convergence factor associated with this basic data, denoted as... The value ranges from 0 to 1; the signal value at each sampling point is weighted and calculated as follows: weighted sampling point signal value = original sampling point signal value × confidence convergence factor. After weighting all sampling points, a moving average method is used to smooth the weighted signal sequence. The sliding window size is set to 3 sampling points. The smoothed signal value within the window is calculated as (weighted value of the previous sampling point + weighted value of the current sampling point + weighted value of the next sampling point) ÷ 3. For the first and last sampling points, only the average of the existing adjacent sampling points is used. The final weighted smoothing result of the action timing signal is obtained, preserving the original time dimension characteristics and numerical accuracy of the signal. For the original performance pulses contained in each piece of basic data, the credibility weighting accumulation and transformation calculation is carried out using the associated credibility convergence factor. The specific calculation process is as follows: First, the pulse peak sequence and pulse duration sequence of the original performance pulses are extracted. The pulse peak sequence is denoted as P1, P2, P3, ..., Pu, and the pulse duration sequence is denoted as T1, T2, T3, ..., Tu, where u is the total number of performance pulses. Then, the credibility convergence factor associated with the basic data is extracted. Each performance pulse is weighted and calculated as follows: weighted value of a single pulse = pulse peak value × pulse duration × confidence convergence factor. The weighted values ​​of all individual pulses are summed to obtain the pulse-weighted cumulative value, which is: Pulse-weighted cumulative value = (Weighted value of a single pulse) = (P1 × T1 × ... ) + (P2×T2× )+...+(Pu×Tu× ); Finally, based on the competition results measurement standards, the pulse weighted cumulative value is converted between units and ranges. The specific conversion process involves first clarifying the units of measurement (e.g., seconds, minutes, rings) and ranges specified in the competition results measurement standards (for example, the range is set to 0 to 100 minutes, 0 to 60 seconds, or 0 to 10 rings). This is then combined with the physical quantity unit of the original results pulse (e.g., millivolts). The range of values ​​for milliseconds and pulse-weighted cumulative values ​​(fixed from 0 to 10000 millivolts, depending on the accuracy of the event data acquisition and the pulse parameter settings) and the cumulative pulse weighted cumulative value are determined based on the accuracy of the event data acquisition and the pulse parameter settings. (milliseconds), determine the conversion coefficient and offset. The conversion coefficient = upper limit of the event performance range ÷ maximum possible value of the pulse weighted cumulative value (the maximum possible value of the pulse weighted cumulative value is 10000). The offset = lower limit of the event performance range - (minimum possible value of the pulse weighted cumulative value × conversion coefficient) (the minimum possible value of the pulse weighted cumulative value is 0). Then, a linear conversion formula is used for calculation. The standardized value = pulse weighted cumulative value × conversion coefficient + offset. Through this formula, the pulse weighted cumulative value is accurately converted into a standardized value under the event performance measurement system, and the confidence weighted calculation result of the original performance pulse is obtained. The result retains the number of decimal places that match the event performance requirements (e.g., if the event requires the performance to retain 2 decimal places, the final result will uniformly retain 2 decimal places, adding 0 if there are not enough decimal places, and rounding down according to the rounding rules if there are too many decimal places).

[0031] Step 401: Based on the weighted smoothing results and the confidence weighted calculation results, generate individual scores, team rankings, and comprehensive event statistics, and write these indicators into the event's core database for dynamic updates. Specifically, this includes: generating individual scores, team rankings, and comprehensive event statistics based on the weighted smoothing results of the action timing signals and the confidence weighted calculation results of the original performance pulses obtained in Step 400, and dynamically updating the event's core database. The specific process is as follows: For each athlete, integrate the weighted smoothing results of the action timing signals in the competition action execution area with the confidence weighted calculation results of the original performance pulses in the performance record judgment area. Combined with the preset event performance evaluation standards, perform a comprehensive calculation of the two types of results. During the calculation process, assign corresponding evaluation weights to the action timing signal results and performance pulse calculation results according to the event type (fixed values, with the weighted smoothing results of the action timing signals corresponding to an evaluation weight of 0). 4. The weighted calculation result of the original performance pulse corresponds to an evaluation weight of 0.6, and the sum of the two weights is 1). The action timing signal mainly reflects the standardization and continuity of the athlete's competition action and serves as an auxiliary reference for performance evaluation. Its data fluctuation still has some uncertainty after weighted smoothing, so it is assigned a lower weight. The original performance pulse directly corresponds to the core achievement of the athlete's competition. After the credibility weighted accumulation and transformation calculation, the data accuracy and reliability are higher, and it is the core basis for performance evaluation, so it is assigned a higher weight. This weight allocation can accurately match the core evaluation requirements of various competition events for action standardization and performance accuracy, while taking into account the impact of differences in data credibility. The final value of the individual score is calculated by summing according to the weights: (weighted smoothing result of action timing signal × corresponding evaluation weight) + (weighted calculation result of original performance pulse credibility × corresponding evaluation weight). After the calculation is completed, the official individual score that meets the preset competition event evaluation standards is generated, clarifying key attributes such as the score unit and precision.

[0032] The process is organized by team. Individual scores of all athletes within a team are aggregated using a pre-defined team score integration standard. A fixed statistical method is employed to integrate the individual scores of team members, such as the team total score being the sum of all individual scores of the team, or the team's valid score being the sum of the best individual scores of a specified number of athletes within the team. After calculating the team scores, all participating teams are ranked according to a fixed sorting standard, either from highest to lowest or from best to worst, generating a team ranking. The ranking results include information such as team name, team score, and ranking position. If teams have the same score, the final ranking is determined according to a pre-defined tie-breaking standard and additional criteria.

[0033] Taking the entire event as the scope, based on the individual performances of all athletes, the team rankings of all participating teams, and the basic data processing results of each region of the event, multi-dimensional comprehensive event statistical indicators are generated. Core indicators include individual performance extreme value indicators (best individual performance, worst individual performance, average performance, median performance), team ranking distribution indicators (number of teams in each ranking tier, distribution of team performance gap intervals), event data quality indicators (basic data screening pass rate in each region, average credibility convergence factor, data weighting processing efficiency), and competition action indicators (mean value of timing signals of participating athletes' actions, action completion rate, etc.). All comprehensive event statistical indicators are calculated using standardized statistical methods to ensure the objectivity and comparability of the indicators. The indicator values ​​retain a reasonable number of decimal places, and the statistical dimensions and calculation methods of the indicators are clearly defined.

[0034] Following the field structure and data storage specifications of the core competition database, the generated individual scores, team rankings, and comprehensive competition statistics are structured and converted into a storage format recognizable by the database. Existing data for the corresponding competition events in the database is retrieved, and newly generated individual scores and team rankings overwrite the existing temporary data. Comprehensive competition statistics are added to the corresponding statistical fields in the database. Simultaneously, update traceability information such as the timestamp of this data update, data processing batch, and average confidence convergence factor is recorded. After the update is completed, the database data undergoes integrity and consistency checks to ensure that the newly added and updated data has no missing fields or logical conflicts, thus completing a full dynamic update of the core competition database.

[0035] In this embodiment, basic data screening is based on data consistency identifiers, ensuring the quality of basic data for subsequent data processing. By combining the region mapping of the 3D virtual ellipsoidal surface model with the reliability convergence factor, the data processing process is deeply bound to the data reliability status, improving the accuracy and relevance of competition data processing from the source. The reliability convergence factor is used to perform weighted smoothing of the action timing signal, which weakens the signal fluctuation interference caused by low-reliability data while preserving the core characteristics of the action timing, making the processed signal more closely match the athlete's actual competition action state and providing more reliable data support for action evaluation. The original performance pulses undergo reliability-weighted accumulation and transformation calculations, and the performance pulses are weighted and corrected through the convergence factor, effectively reducing the impact of low-reliability data on performance measurement. The system ensures more accurate and objective results, meeting the requirements for competition performance evaluation. Individual performance generation combines two core processing results—action timing and performance pulses—and assigns corresponding weights according to competition rules, making individual performance calculations more comprehensive and aligned with the characteristics of the competition, avoiding performance bias caused by a single data dimension. The generation of multi-dimensional comprehensive competition statistical indicators covers multiple levels, including individuals, teams, the overall competition, and data quality, providing comprehensive competition operation and data reference for organizers and referees, and contributing to the scientific analysis and management of the competition. The dynamic updating of the core competition database enables real-time storage of the latest competition results, rankings, and statistical indicators, while retaining update traceability information, ensuring the timeliness, integrity, and traceability of the database data.

[0036] In a preferred embodiment of the present invention, step 5 includes: Step 500: Through the data publishing interface, read the updated individual scores, team rankings, and comprehensive event statistics from the event core database at a predetermined push frequency. Encapsulate the individual scores, team rankings, and comprehensive event statistics into standard data messages, and initiate real-time data stream pushes to the corresponding addresses of the image and text packaging module, broadcast data link, and authorized query terminals, respectively. Specifically, this includes: reading updated data from the event core database through the data publishing interface, encapsulating standard data messages, and pushing targeted real-time data streams to multiple terminals. The specific process is as follows: Initialize the data publishing interface and configure the read parameters. First, complete the initialization and startup of the data publishing interface, and configure the interface and the event core database. Database connection parameters ensure smooth communication between the interface and the database. A predetermined push frequency is set for the interface, configured to once per second based on the event data update requirements. This frequency guarantees real-time data pushes and stable link transmission, avoiding link congestion caused by excessive frequency. For data reading from the event's core database, the data publishing interface accurately reads the updated individual scores, team rankings, and comprehensive event statistics from designated data storage tables in the core database according to the predetermined push frequency. During the reading process, only valid data that has undergone integrity and consistency checks is extracted, excluding temporary data that has not been updated and abnormal data, ensuring accurate data reading. The accuracy and validity of the data are ensured. Individual scores, team rankings, and overall competition statistics are read and structured according to a pre-defined general data interaction standard to generate standard data messages. Specifically, the pre-defined general data interaction standard uses camelCase naming for data fields, a unified JSON format, units for numerical data consistent with the core competition database, and two decimal places (padded with zeros if necessary, rounded off if excessive). During the encapsulation process, the three types of data are first standardized, unifying the names, formats, units, and decimal places of the data fields. Then, the data is sequentially filled into the designated field areas of the message according to a fixed message structure. The fields include data identifier, data content, data update timestamp, and data checksum. The data checksum is calculated by summing all numerical data in the message and taking the modulo 1000, and is used by the receiving end to verify the integrity of data transmission. After the standard data message is encapsulated, the data publishing interface initiates real-time data stream push to the image and text packaging module, the broadcast data link, and the preset fixed network address of the authorized query terminal. The push process adopts a point-to-point directional transmission method, synchronously pushing the same standard data message to the corresponding address of the three receiving ends to ensure the consistency and synchronization of data received by each terminal. The transmission link status is monitored in real time during the push process to ensure continuous and stable transmission of the data stream.

[0037] Step 501: During the push process, for each data access request, the access permissions of the requester are verified according to the preset data hierarchy and role permission matrix. For successful verification, the requester is allowed to receive and process standard data packets; for unsuccessful verification, the data access request is intercepted. Specifically, during the real-time push of event data, for each initiated event data access request, a full-process permission verification and request interception process is carried out. The specific process is as follows: While pushing the data stream, the data publishing interface receives various data access requests from the image and text packaging module, broadcast data link, and authorized query terminal in real time. Each received request is fully parsed, extracting three core pieces of information: the requester's device identifier, role identifier, and access data type, as the basis for permission verification. The preset data hierarchy and role permission matrix are retrieved from the system configuration library. The preset data hierarchy is specifically divided into three levels according to data importance and usage scenario, with the first level being individual performance data. The system consists of three levels of data: basic data (including athlete name, final individual score, and unit of measurement); secondary data (including team ranking and comprehensive event statistics, including team name, team score, ranking, average individual score, and team score difference range); and tertiary data (including regional basic data screening pass rate, average credibility convergence factor, and data weighting processing efficiency). The pre-defined role-permission matrix is ​​as follows: Role ID 001 (graphic and text packaging module) can access both primary and secondary data, but only has data read permissions; Role ID 002 (broadcast data link) can access both primary and secondary data, and has data read and forwarding permissions; Role ID 003 (authorized query terminal) can only access primary data, but has data read permissions. The matrix contains the role IDs and corresponding permission information of all legitimate requesters, and the matrix data is statically configured and can only be updated through the system backend to ensure the security of permission management.

[0038] The parsed requester role identifier and data classification are precisely matched with the role identifiers in the role permission matrix to verify whether the requester's role identifier exists in the matrix. Simultaneously, the data type requested by the requester is verified to be within the accessible data classification range corresponding to its role identifier, completing dual permission verification. The verification process involves matching and checking each item one by one to ensure the accuracy of the permission verification result for each access request. Based on the permission verification results, data access requests are categorized and processed. For requesters whose role identifier matches successfully and whose access data type is within the permission range, the data publishing interface grants them data receiving permissions, allowing them to receive and process pushed standard data packets, ensuring normal data use for legitimate requesters. For requesters whose role identifier does not match successfully or whose access data type exceeds the permission range, the data publishing interface immediately triggers an interception mechanism, directly intercepting their data access request without returning any event data. Simultaneously, information such as the requester identifier, access time, and access data type of the intercepted request is recorded and stored in the interception log.

[0039] In this embodiment, the data publishing interface reads updated data from the core competition database at a fixed predetermined push frequency. Combined with a targeted point-to-point push method, this ensures the real-time delivery of data such as individual scores and team rankings to various terminals while avoiding link congestion, ensuring the stability of data stream transmission and the synchronization of data received by each terminal. Competition data is encapsulated into standard data packets according to common standards, and a checksum based on numerical summation and modulo is generated. This standardizes the data format, facilitating parsing and processing by various receiving terminals, and allows the receiving end to verify data integrity through the checksum, effectively preventing data loss and tampering during transmission. For each data access request, dual permission verification based on data hierarchy and role-based permission matrix is ​​implemented, achieving refined hierarchical permission management of competition data. This ensures that requesters with different roles can only access data within their corresponding permission range, guaranteeing the security of competition data usage from the transmission level. After establishing permission verification... The system employs a request classification and processing mechanism. Legitimate requesters receive data normally, while unverified requesters are directly blocked. This ensures the normal data usage needs of authorized terminals and modules while effectively preventing unauthorized data access and avoiding data leaks. When unverified requests are blocked, an interception log is simultaneously recorded, retaining information such as the requester's identifier and access time, enhancing the traceability and security of the event data transmission system. The entire data push and permission verification process is automated, requiring no manual intervention. This improves the efficiency of event data publishing and access management while avoiding errors caused by manual operation, ensuring the accuracy and stability of the process. Data push is conducted across multiple terminals, including the graphic packaging module, broadcast data link, and authorized query terminal, enabling simultaneous publishing of event update data across multiple scenarios. This meets the data usage needs of different scenarios such as event graphic production, broadcasting, and authorized queries, improving the efficiency of event data utilization.

[0040] In a preferred embodiment of the present invention, step 6 includes: Step 600 involves synchronously and persistently storing the following data processing elements in a historical data warehouse: the multi-regional structured event data set, the identified multi-regional structured event dataset, the nonlinear differential equation system and its solved dynamic equilibrium points, the three-dimensional virtual ellipsoidal surface model and its high-confidence convergence region and low-confidence divergence region, the confidence convergence factor, the event core database update transaction log, and the data distribution and access control log. Specifically, this includes: a complete data processing element integrity verification. First, each data processing element to be stored is verified one by one to confirm that each element is complete, without missing elements, and without anomalies. The verification adopts a method of comparison and judgment item by item, with a clear verification operation for each element. The comparison standards and anomaly judgment conditions are used to ensure that each element meets the storage standards. The specific verification content and operation are as follows: Verification of multi-region structured event data sets: First, clarify the preset list of all competition areas (such as competition action execution area, result record judgment area, etc.) of the event. Then, extract the area identifiers in the multi-region structured event data sets and compare them one by one with the preset competition area list. At the same time, check whether the basic data corresponding to each competition area (including area number, data collection time, original collection data, data filtering results) is complete, without missing fields or empty data. If the area identifier is missing, does not match the preset list, or a certain area's basic data field is missing or contains empty values, it is judged as a verification anomaly. The verification of the identified multi-region structured sports event dataset involves first counting the total number of data entries in the multi-region structured sports event dataset, and then counting the number of data entries in the identified multi-region structured sports event dataset that have been labeled. The two are compared on an equal basis. At the same time, it is checked whether the labeling fields (including data credibility label, data type label, and data source label) of each labeled data entry are complete, without empty values, and without erroneous labels. If the number of labeled entries is less than the original total number of entries, or if there are missing labeling fields or erroneous labels, it is judged as a verification anomaly.

[0041] The verification of nonlinear differential equation systems and dynamic equilibrium points involves first checking the completeness of the nonlinear differential equation system, verifying that the variables, coefficients, and constraints are complete, with no missing terms or incorrect coefficients. Then, the obtained dynamic equilibrium point values ​​are verified by substituting these values ​​into the original nonlinear differential equation system. If the verification result satisfies all constraints and the error is within the preset allowable range (error ≤ 0.001), the value is considered reasonable. If the equation system is incomplete, the coefficients are incorrect, or the dynamic equilibrium point value verification fails or the error exceeds the allowable range, the verification is considered abnormal. The three-dimensional virtual ellipsoidal surface model and convergence / divergence region calibration are also performed. The verification process begins by checking the core parameters of the 3D virtual ellipsoidal surface model (including the coordinates of the ellipsoid's center, the lengths of the major and minor axes, and the spatial rotation angles) to ensure they are complete, without missing values, and free of abnormal values. Next, the boundary between the high-confidence convergence region and the low-confidence divergence region is verified, checking whether the boundary coordinates are clear and continuous, whether the boundary range covers the entire effective area of ​​the model, and whether there are any overlaps, breaks, or deviations from the model's boundaries. If model parameters are missing, boundaries are unclear, or there are overlaps or breaks, the verification is deemed abnormal. Finally, the confidence convergence factor is verified by defining a pre-defined reasonable range for the confidence convergence factor (0 ≤ confidence convergence factor ≤ 1). The reliability convergence factor value is compared with this range, and the completeness of the calculation basis of the convergence factor is checked (corresponding to the dynamic equilibrium point value and data reliability identifier). If the convergence factor value exceeds the preset range, or there is no corresponding calculation basis, or the calculation basis is abnormal, it is judged as a verification anomaly. The transaction log of the event core database update and the data distribution and access control log are verified respectively to check whether the log entries completely cover the corresponding operation process. Each log entry must include five core information items: operation time, operation type, operation object, operation result, and operation identifier. There should be no missing entries, no missing core information, and no log content errors. The number of log operations is counted in real time and compared with the actual number of corresponding operations in the preceding sequence to ensure that no operation records are missed. If a log entry is missing, core information is incomplete, content is incorrect, or the number of operations does not match, it is judged as a verification anomaly. After all the above elements have been compared one by one according to the corresponding verification operation and comparison standard, and all are confirmed to meet the requirements, the storage stage can proceed. If any element is judged to be a verification anomaly, the storage operation is immediately paused, the name of the anomaly element, the anomaly type and the anomaly location are clearly marked, the corresponding steps in the preceding sequence are returned to supplement and improve the anomaly data, and after the anomaly is corrected, the full element integrity verification is restarted until all elements pass the verification.

[0042] Activate the storage interface of the historical data warehouse and configure storage parameters, including storage path, data partitioning rules, storage format, and storage permissions. The storage path is set according to data type partitioning to ensure that different types of full-element data are stored separately. Data partitioning rules are divided according to event batches, with full-element data from the same event batch stored in the same partition for easy subsequent retrieval. The storage format uniformly adopts a structured storage format, consistent with the core event database, to ensure data compatibility. Simultaneously, configure storage permissions, granting only write permissions to ensure the security of the storage process. After completing the verification and parameter configuration, a transaction synchronization mechanism is used to synchronously write all data processing full-element data to the corresponding partition and path of the historical data warehouse. During the synchronous writing process, ensure that all element write operations start and complete simultaneously, avoiding situations where some elements are successfully written while others are not. In the event of a failure to write individual elements, the specific writing order is set according to the overall logic of the event data processing. This order is consistent with the order in which the data is generated and processed, which not only conforms to the data association logic but also ensures the continuity of the subsequent evidence chain construction. The specific writing order is as follows: first, write the multi-region structured event data set and the identified multi-region structured event dataset; then, write the nonlinear differential equation system and the dynamic equilibrium point obtained by solving it; next, write the three-dimensional virtual ellipsoidal surface model and the relevant parameters of its high-confidence convergence region and low-confidence divergence region; then, write the confidence convergence factor value; and finally, write the event core database update transaction log and data distribution and access control log. After the writing is completed, the historical data warehouse automatically generates a storage confirmation receipt, recording the storage path, writing time, and data size of each element, thus completing the persistent storage operation.

[0043] Step 601 involves allocating a global transaction identifier and calculating the corresponding data version hash for the storage operation of all data processing elements. The global transaction identifier and data version hash are then associated and bound to the storage record of the all data processing elements. Specifically, this includes using a combination of timestamp, random sequence, and historical data warehouse identifier to assign a unique global transaction identifier to the storage operation of this data processing element. The specific calculation process is as follows: First, obtain the current system timestamp, accurate to milliseconds, to obtain the timestamp value; then generate a 6-digit random integer as the random sequence; the historical data warehouse identifier is a fixed value (e.g., 001, set according to the actual warehouse deployment); concatenate the timestamp, random sequence, and historical data warehouse identifier sequentially to obtain the global transaction identifier. The concatenation formula is: Global Transaction Identifier = Timestamp (13 bits) + Random Sequence (6 bits) + Historical Data Warehouse Identifier (3 bits), ensuring that the global transaction identifier for each storage operation is unique and without duplication.

[0044] The SHA-256 hash algorithm is used to calculate the data version hash corresponding to all data elements stored in this data processing. The specific calculation process is as follows: First, all data processing elements are converted into string form and concatenated sequentially according to the writing order in step 600 to obtain the concatenated string of all elements; then, the concatenated string is encoded using UTF-8 to obtain the encoded byte array; subsequently, the SHA-256 algorithm is used to perform a hash operation on this byte array. During the operation, the byte array is grouped into groups of 64 bytes, and the hash value of each group is calculated sequentially. Finally, the hash values ​​of all groups are summed to obtain a 256-bit data version hash value (composed of 64 hexadecimal characters). The hash value uniquely corresponds to the full-feature data stored this time. If any data is modified, the hash value will change accordingly. The generated global transaction identifier and data version hash are written into the corresponding fields of the full-feature storage record that has been persisted in step 600. At the same time, a correlation index is established in the historical data warehouse to associate and bind the global transaction identifier, data version hash and the storage path and storage record of each data processing full-feature one by one. After the binding is completed, all corresponding full-feature storage records and data version hashes can be quickly retrieved through the global transaction identifier, and the integrity of the corresponding full-feature data can be quickly verified through the data version hash, ensuring a strong association between the identifier, hash and storage record.

[0045] Step 602: Based on the storage records of all data processing elements after association and binding, construct a traceable data processing consistency evidence chain from the multi-region structured event data set to the data distribution and access control logs. Specifically, this includes: using the storage record of each data processing element after association and binding as a node in the evidence chain, determining the node order, which strictly follows the order of the entire event data processing process. The specific node order is as follows: Node 1 (storage record of the multi-region structured event data set), Node 2 (storage record of the identified multi-region structured event dataset), Node 3 (storage record of the nonlinear differential equation system and the dynamic equilibrium point obtained from its solution), Node 4 (storage record of the three-dimensional virtual ellipsoidal surface model and its high-confidence convergence region and low-confidence divergence region), Node 5 (storage record of the confidence convergence factor), Node 6 (storage record of the event core database update transaction log), and Node 7 (storage record of the data distribution and access control log). Each node contains three core pieces of information: its own storage record, a global transaction identifier, and a data version hash. At the same time, the forward association pointer and backward association pointer of each node are initialized for association between nodes.

[0046] Starting from node 1, each node is sequentially associated with the next node. Specifically, the data version hash of the previous node is written into the forward association field of the next node, and the data version hash of the next node is written into the backward association field of the previous node, forming a bidirectional association. At the same time, the global transaction identifier is written into the global identifier field of all nodes to ensure that all nodes for the same storage operation are associated with the same global transaction identifier, realizing a single transaction and full node association. For example, the data version hash of node 1 is written into the forward association field of node 2, and the data version hash of node 2 is written into the backward association field of node 1, and so on, until all 7 nodes are bidirectionally associated, forming a preliminary chain structure.

[0047] After constructing the initial chain structure, a consistency check is performed on the evidence storage chain. The check process is as follows: First, all nodes are retrieved using the global transaction identifier to confirm that the global transaction identifiers of all nodes are consistent. Then, starting from node 1, the forward association hash value of each node is checked sequentially to see if it matches the data version hash of the previous node. If the association hash values ​​of all nodes match, it indicates that the evidence storage chain is constructed consistently and without anomalies. If there are mismatches, the node association process is re-checked, corrected, and checked again. After the check passes, the chain structure of the evidence storage chain, all node information, and the check results are persistently stored in the dedicated evidence storage chain partition of the historical data warehouse. At the same time, a unique identifier for the evidence storage chain is generated, completing the solidification of the evidence storage chain. After solidification, the evidence storage chain cannot be modified and can only be read, ensuring the traceability and consistency of the entire data processing process. Ultimately, a data processing consistency evidence storage chain with full traceability is constructed, from multi-regional structured competition data sets to data distribution and access control logs.

[0048] This embodiment achieves complete retention of all elements of data processing, persistently storing all raw data, intermediate processing results, model parameters, log information, etc., throughout the entire event data processing process. This prevents data loss and provides complete data support for subsequent event data review, problem investigation, and algorithm optimization, while also enabling data reusability. The combined use of global transaction identifiers and data version hashes ensures the uniqueness of each full-element storage operation. Simultaneously, the hash algorithm allows for rapid verification of data integrity, effectively preventing data tampering and guaranteeing the authenticity and reliability of stored data, providing technical protection for data security. A fully traceable evidence chain enables the recording of data from raw event data to the final data distribution logs. The end-to-end traceability system allows for rapid identification of related data and operational processes in case of data issues at any stage, facilitating quick troubleshooting of anomalies and improving problem-solving efficiency. The consistency verification and solidification of the evidence chain ensures consistency throughout the entire event data processing workflow, enabling auditable and monitorable data processing that meets the regulatory requirements of event data processing and supports the credibility of event data. All operations are automated and structured, requiring no manual intervention. This improves the efficiency of data storage, identification association, and evidence chain construction while avoiding errors caused by manual operation, ensuring the accuracy and stability of process execution and forming a closed loop with preceding data processing steps.

[0049] like Figure 2 As shown, embodiments of the present invention also provide a real-time data processing system for sports event results, comprising: The parsing module is used to parse and transform the real-time collected event source data into a structured form, generating a multi-regional structured event data set. The verification module is used to perform cross-regional correlation verification and identification on multi-regional structured event data sets, generate identified multi-regional structured event datasets, and create and initialize the event core database. The modeling module is used to establish a set of nonlinear differential equations describing the evolution of the data's reliable state based on the identified multi-region structured event dataset and solve for the dynamic equilibrium point; it generates a three-dimensional virtual ellipsoidal surface model based on the dynamic equilibrium point, divides the three-dimensional virtual ellipsoidal surface model into reliability regions, and calculates a reliability convergence factor for each region. The calculation module is used to filter basic data from the identified multi-region structured event dataset, map each piece of basic data to the corresponding confidence region and associate it with the corresponding confidence convergence factor, so as to process and calculate the basic data, generate individual scores, team rankings and comprehensive event statistics, and dynamically update the core event database. The distribution module is used to synchronize and distribute the updated individual scores, team rankings and comprehensive event statistics from the core event database to the graphic packaging module, broadcast data link and authorized query terminal in real time, and to perform access control according to the preset data hierarchy and permission matrix. The traceability module is used to store all elements of data processing in a historical data warehouse and to build a traceable data processing consistency evidence chain based on global transaction identifiers and data version hashes.

[0050] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0051] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time data processing of sports event results, characterized in that, The method includes: Step 1: Analyze and transform the real-time collected event source data into a structured data set to generate a multi-regional structured event data set; Step 2: Perform cross-regional correlation verification and identification on the multi-regional structured event data set, generate the identified multi-regional structured event dataset, and create and initialize the event core database; Step 3: Based on the identified multi-region structured event dataset, establish a set of nonlinear differential equations describing the evolution of the data's reliable state and solve for the dynamic equilibrium point; generate a three-dimensional virtual ellipsoidal surface model based on the dynamic equilibrium point, divide the three-dimensional virtual ellipsoidal surface model into reliability regions, and calculate a reliability convergence factor for each region; Step 4: Filter basic data from the identified multi-region structured competition dataset, map each piece of basic data to the corresponding confidence region and associate it with the corresponding confidence convergence factor, so as to process and calculate the basic data, generate individual scores, team rankings and comprehensive competition statistics, and dynamically update the core competition database. Step 5: The updated individual scores, team rankings, and comprehensive event statistics in the core event database are distributed in real time to the graphic packaging module, broadcast data link, and authorized query terminal, and access control is performed according to the preset data hierarchy and permission matrix. Step 6: Store all data processing elements in the historical data warehouse, and build a traceable data processing consistency evidence chain based on the global transaction identifier and data version hash.

2. The method for real-time data processing of sports event results according to claim 1, characterized in that, The acquisition of event source data includes: By deploying sensing and timing devices in the athlete identification area, competition action execution area, and score recording and judgment area, real-time event source data containing identification codes, action timing signals, and raw score pulses is collected to form event source data.

3. The method for real-time data processing of sports event results according to claim 1, characterized in that, Step 1 includes: The time-synchronized data stream of the event source data is parsed, and based on a predefined unified data model, the parsed identity codes, action timing signals and original performance pulses are mapped into structured identity data, structured action data and structured performance data, respectively, and integrated into a multi-region structured event data set.

4. The method for real-time data processing of sports event results according to claim 1, characterized in that, Step 2 includes: For multi-regional structured event datasets, cross-regional correlation verification is performed based on preset coding mapping relationships, temporal continuity conditions, and pulse validity intervals to obtain the results of cross-regional correlation verification. Based on the results of cross-regional correlation verification, each record in the multi-regional structured event dataset is automatically appended with a data consistency identifier, either consistent or pending arbitration or conflicting, to generate an identified multi-regional structured event dataset. Using the identified multi-region structured event dataset as the initial data content, create and initialize the event core database.

5. The method for real-time data processing of sports event results according to claim 1, characterized in that, Step 3 includes: From the identified multi-region structured competition dataset, data quality indicators for athlete identification area, competition action execution area, and performance record determination area are extracted within a continuous time window, and the data quality indicators are used as state variables to describe the system evolution. Based on the coupling relationship between the data consistency ratio, signal attenuation and transmission delay in the state variables, a set of nonlinear differential equations describing the evolution of the reliable state of data over time is established. By using iterative approximation numerical methods, the steady-state or periodic solutions of the nonlinear differential equation system at the latest time point are obtained, and the steady-state or periodic solutions are used as the dynamic balance point of data reliability in the athlete identification area, competition action execution area, and performance record determination area. Using the components representing the confidence level of each region in the dynamic equilibrium point as parameters, a parameterized three-dimensional ellipsoid equation is substituted into it to calculate and generate a three-dimensional virtual ellipsoid surface model in real time, in which the principal axis direction and the semi-axis length can be dynamically adjusted. Based on the curvature change of discrete sampling points on the three-dimensional virtual ellipsoid surface model, the three-dimensional virtual ellipsoid surface model is divided into a high-confidence convergence region and a low-confidence divergence region. Based on the dynamic equilibrium point component values ​​of the athlete identification area, competition action execution area, and performance record determination area associated with each region in the high-confidence convergence region and the low-confidence divergence region, a confidence convergence factor is derived for each region in the high-confidence convergence region and the low-confidence divergence region through a normalized weighted calculation.

6. The method for real-time data processing of sports event results according to claim 5, characterized in that, A numerical method using iterative approximation is employed to solve a system of nonlinear differential equations for a steady-state or periodic solution at the latest time point. This steady-state or periodic solution serves as a dynamic equilibrium point for the data reliability in the athlete identification zone, the competition action execution zone, and the performance record determination zone, including: The nonlinear differential equation system is discretized into a difference equation system, and an initial iteration vector containing data quality indicators for athlete identification area, competition action execution area and performance record determination area is set. The initial iteration vector is input into the system of difference equations for iterative calculation. When the norm of the difference between the current iteration result and the previous iteration result is less than the preset tolerance, the iteration stops and a converged numerical solution is obtained. The converged numerical solution is taken as the steady-state solution or periodic solution of the nonlinear differential equation system at the latest time point, and the steady-state solution or periodic solution is taken as the dynamic balance point of data credibility in the athlete identification area, competition action execution area and performance record judgment area.

7. The method for real-time data processing of sports event results according to claim 1, characterized in that, Step 4 includes: From the identified multi-region structured event dataset, records with consistent data consistency are selected as basic data. Each basic data record is then mapped to a corresponding high-confidence convergence region or low-confidence divergence region on a 3D virtual ellipsoidal surface model based on its source information, and a confidence convergence factor for the corresponding region is associated. Using the associated confidence convergence factor, the action time series signals in the basic data are weighted and smoothed to obtain the weighted smoothing result. Using the associated confidence convergence factor, the original performance pulses in the basic data are accumulated and transformed with confidence weight to obtain the confidence weight calculation result. Based on the weighted smoothing results and the credibility-weighted calculation results, individual scores, team rankings, and comprehensive event statistics are generated and written into the event's core database for dynamic updates.

8. The method for real-time data processing of sports event results according to claim 1, characterized in that, Step 5 includes: Through the data publishing interface, the updated individual scores, team rankings and comprehensive event statistics are read from the core database of the event at a predetermined push frequency. The individual scores, team rankings and comprehensive event statistics are encapsulated into standard data messages and pushed to the corresponding addresses of the graphic packaging module, broadcast data link and authorized query terminal in real time. During the push process, for each data access request, the access permissions of the requester are verified according to the preset data classification and role permission matrix. For requesters who pass the verification, they are allowed to receive and process standard data messages; for requesters who fail the verification, their data access requests are blocked.

9. The method for real-time data processing of sports event results according to claim 1, characterized in that, Step 6 includes: The multi-region structured event data set, the identified multi-region structured event dataset, the nonlinear differential equation system and the dynamic equilibrium point obtained by solving it, the three-dimensional virtual ellipsoid surface model and its high-confidence convergence region and low-confidence divergence region, the confidence convergence factor, the event core database update transaction log, and the data distribution and access control log are all treated as the full elements of data processing and synchronously and persistently stored in the historical data warehouse. For storage operations involving all elements of data processing, a global transaction identifier is allocated and the corresponding data version hash is calculated. The global transaction identifier and the data version hash are then associated and bound to the storage record of all elements of data processing. Based on the storage records of all elements of data processing after association and binding, a traceable data processing consistency storage chain is constructed, from multi-regional structured event data sets to data distribution and access control logs.

10. A real-time data processing system for sports event results, wherein the system implements the method as described in any one of claims 1 to 9, characterized in that, include: The parsing module is used to parse and transform the real-time collected event source data into a structured form, generating a multi-regional structured event data set. The verification module is used to perform cross-regional correlation verification and identification on multi-regional structured event data sets, generate identified multi-regional structured event datasets, and create and initialize the event core database. The modeling module is used to establish a set of nonlinear differential equations describing the evolution of the reliable state of the data and solve for the dynamic equilibrium point based on the identified multi-region structured event dataset. A three-dimensional virtual ellipsoidal surface model is generated based on the dynamic equilibrium point, and a confidence region is divided for the three-dimensional virtual ellipsoidal surface model. A confidence convergence factor is calculated for each region. The calculation module is used to filter basic data from the identified multi-region structured event dataset, map each piece of basic data to the corresponding confidence region and associate it with the corresponding confidence convergence factor, so as to process and calculate the basic data, generate individual scores, team rankings and comprehensive event statistics, and dynamically update the core event database. The distribution module is used to synchronize and distribute the updated individual scores, team rankings and comprehensive event statistics from the core event database to the graphic packaging module, broadcast data link and authorized query terminal in real time, and to perform access control according to the preset data hierarchy and permission matrix. The traceability module is used to store all elements of data processing in a historical data warehouse and to build a traceable data processing consistency evidence chain based on global transaction identifiers and data version hashes.

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