Statistical analysis method for campus statistical events

Abstract

The invention discloses a statistical analysis method for campus statistical events, and relates to the technical field of data statistical analysis. According to the scheme, on the basis of collecting expressions of participants to form an original data set Dat, the difference of different match scores is eliminated through a standardized result set Nor; the problem that fairness evaluation cannot be performed when part of the competitors only participate in a small number of competitions is solved in combination with a stability result set Sta, the performance of the competitors in a plurality of competitions can be uniformly measured by a finally generated cross-competition performance consistency index Ce, and a comprehensive feedback value Rf is backtracked and bound to a personal file after being further calculated; therefore, a comprehensive, comparable and personalized evaluation effect is realized. And meanwhile, the time sequence accumulation of the comprehensive feedback value Rf provides possibility for long-term tracking, so that a teacher or an organization party can clearly see a growth track of a certain student formed by multiple events in a semester.

Description

Technical Field

[0001] This invention relates to the field of data statistical analysis technology, specifically a method for statistical analysis of campus events. Background Technology

[0002] On campus, various statistical competitions have become an important way to measure students' comprehensive qualities and abilities. From subject-based modeling competitions to logic and expression debates, and physical fitness and endurance sports events, these diverse campus competitions provide students with platforms to showcase their talents and hone their skills. However, with the continuous increase in the types and number of competitions, how to scientifically statistically analyze and assess students' performance in different competitions has gradually become an urgent problem to be solved in educational management and competition organization.

[0003] Existing statistical methods for campus competitions mostly focus on scores, rankings, or time results within a single competition, lacking a systematic evaluation of the overall performance of participants across different competitions. For example, a student may excel in an academic competition but also perform well in an expressive or physical competition; existing methods cannot cross-reference or uniformly measure these results. This is especially true when students participate in only one or two competitions, making it easier for evaluation results to become distorted or incomparable. This situation limits the role of campus competitions in discovering students' multi-dimensional potential, promoting educational equity, and optimizing competition organization, highlighting the need for an innovative analytical method that can accommodate varying numbers of participants and provide tiered processing. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for statistical analysis of campus sports events, solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for statistical analysis of campus sports events, comprising the following steps:

[0006] S1. Collect performance data of participants in campus statistical competitions to form the original dataset Dat;

[0007] S2. Normalize the original dataset Dat to generate a standardized result set Nor, which is used to eliminate the differences in the dimensionality of the components between different events.

[0008] S3. Perform hierarchical calculations based on the original data set Dat and the standardized result set Nor to obtain the stability result set Sta;

[0009] S4, combine the normalized result set Nor with the stability result set Sta, and generate a cross-competition performance consistency index Ce using a hierarchical processing calculation method, for evaluating the overall performance of the contestant in different competitions;

[0010] S5, based on the cross-competition performance consistency index Ce, perform comprehensive operation to obtain a comprehensive feedback value Rf, and bind the contestant to perform comprehensive feedback.

[0011] Preferably, the S1 includes S11 and S12;

[0012] S11, through synchronous calling of a record interface of a competition management platform and a data interface of a competition judge scoring system, respectively collect performance data of the contestant in the subject competition, the expression competition and the physical competition, and perform data formatting and weighting processing, specifically including steps S111, S112 and S113;

[0013] S111, call a competition result record database, extract the final score of the contestant in the subject competition, and generate a subject performance value Es;

[0014] S112, call a judge scoring record system, collect multi-dimensional scores of the contestant in the expression competition, specifically including logic, language fluency, adaptability and persuasiveness dimensions, and perform weighted average on the scores of each dimension according to a preset proportion, to generate an expression performance value Ee;

[0015] S113, call a physical record competition system, collect the time and completion rate of the contestant in completing the competition, perform weighted calculation on the completion time score and the completion rate score, to generate a physical performance value Et, and then integrate the subject performance value Es and the expression performance value Ee to form a performance parameter set.

[0016] Preferably, S12, based on the performance parameter set, call a competition registration system and a competition result database to compare the actual competition records of the contestant in various competitions, count the total number of competitions actually participated by the contestant, and generate a competition participation quantity Num;

[0017] In the counting process, the records of contestants who have registered but have not actually participated are excluded; finally, the competition participation quantity Num, the subject performance value Es, the expression performance value Ee and the physical performance value Et are integrated together to form an original data set Dat.

[0018] Preferably, the S2 includes S21;

[0019] S21, based on the subject performance value Es, the expression performance value Ee and the physical performance value Et in the original data set Dat, respectively call the normalization processing function to convert the performance data in different dimensions to a unified numerical interval, obtain the subject normalized performance value Nes, the expression normalized performance value Nee and the physical normalized performance value Net, and synchronize integration to form the standardized result set Nor;

[0020] The normalization processing function is as follows:

[0021] ;

[0022] In the formula, Ni represents the i-th normalized performance value, including the subject normalized performance value Nes, the expression normalized performance value Nee and the physical normalized performance value Net; Ei represents the i-th original performance value, including the subject performance value Es, the expression performance value Ee and the physical performance value Et; Emin and Emax respectively represent the minimum performance value and the maximum performance value of all participants in the event.

[0023] Preferably, the S3 includes S31 and S32;

[0024] S31, according to the value of the event participation quantity Num, the hierarchical rule is determined to determine the stability calculation mode;

[0025] The hierarchical rule is as follows:

[0026] When the event participation quantity Num is 1, the participant only has single event results, and there is no need for stability calculation, and the standardized result set Nor is directly used as the stability input;

[0027] When the event participation quantity Num is 2, the difference degree of the standardized result set Nor between the two events of the participant is calculated;

[0028] When the event participation quantity Num is greater than 2, the variance calculation method is adopted to evaluate the volatility of the performance of the participant in the standardized result set Nor in multiple events.

[0029] Preferably, S32, on the basis of the hierarchical rule determination, the specific calculation of the stability calculation mode is executed to generate the stability result set Sta={Nor, Val} in a unified format, wherein Val represents the stability factor value, and the specific generation method is as follows:

[0030] When the event participation quantity Num is 1, the participant only has single event performance, and there is no difference in cross-event performance, so the standardized result set Nor is directly inherited, and the stability factor value Val is set to zero, forming the stability result set Sta={Nor, 0};

[0031] When the number of event participations Num is 2, based on the normalized score values in the normalized result set Nor, a pairwise difference traversal calculation is performed to obtain a double-event difference value Dif, and a stability factor value Val=Dif is set to form a stability result set Sta={Nor, Dif};

[0032] The double-event difference value Dif is calculated according to the following formula: ;

[0033] In the formula, Ni and Nj represent the i-th and j-th normalized score values of the two events respectively, and Num represents the number of event participations, and Num=2;

[0034] When the number of event participations Num>2, based on the normalized score values in the normalized result set Nor of each event of the participant, a variance-type stability factor Sd is calculated, and a stability factor value Val=Sd is set to form a stability result set Sta={Nor, Sd};

[0035] The variance-type stability factor Sd is calculated according to the following formula: ;

[0036] In the formula, represents the average normalized score value, Num represents the number of event participations, and Num=2.

[0037] Preferably, the S4 includes S41 and S42;

[0038] S41, based on the stability result set Sta, extracts the normalized result set Nor therein as a score input, and extracts the stability factor value Val therein, and then combines a preset event type weighting coefficient set to calculate a comprehensive score value of the numerator part, and the calculation method is as follows: ;

[0039] In the formula, Sum represents a weighted score value, Num represents the number of event participations, and Wti represents a weighting coefficient of the i-th event type.

[0040] Preferably, S42, based on the obtained weighted score value Sum and stability factor value Val, generates a cross-event performance consistency index Ce to reflect the score level and stability of the participant in cross-event;

[0041] The cross-event performance consistency index Ce is obtained by a calculation formula;

[0042] Wherein, the cross-competition performance consistency index Ce: the combination of the achievement performance part Sum and the stability factor value Val is calculated, which can comprehensively reflect the achievement level and stability of the participants in the cross-competition.

[0043] Preferably, the S5 comprises S51;

[0044] S51, on the basis of obtaining the cross-competition performance consistency index Ce, introducing the viewing correction factor Vf fitted based on the viewing data, thereby generating the comprehensive feedback value Rf, and synchronously tracing the comprehensive feedback value Rf back to the individual participants as the comprehensive performance index of the participants;

[0045] The comprehensive feedback value Rf is obtained by the calculation formula Rf=Ce×(1+Vf);

[0046] Wherein, the viewing correction factor Vf is obtained by steps S511 and S512;

[0047] S511, obtaining the on-site viewing number Vn and the online viewing number Vo from the competition data statistical platform;

[0048] S512, uniformly normalizing the on-site viewing number Vn and the online viewing number Vo, and substituting into the fitting formula to calculate the viewing correction factor Vf;

[0049] The viewing correction factor Vf calculation formula is obtained by the following calculation formula:

[0050] ;

[0051] In the formula, Vnmax represents the maximum on-site viewing number, Vomax represents the maximum online viewing number; v1 represents the adjustment coefficient of the on-site viewing number Vn and the maximum on-site viewing number Vnmax calculation result, v2 represents the adjustment coefficient of the online viewing number Vo and the maximum online viewing number Vomax calculation result, and v1+v2=1, the specific value is set by the user.

[0052] Preferably, S52, after obtaining the comprehensive feedback value Rf of each participant, the comprehensive feedback value Rf is bound with the unique identity information of the participants to form individualized data files, and the unique identity information includes student number, competition number and unique identity code generated by the competition system.

[0053] The application provides a campus statistical competition statistical analysis method, which has the following beneficial effects:

[0054] (1) On the basis of collecting the performance of contestants to form the original data set Dat, the differences in the dimensions of different competition scores are eliminated through the standardized result set Nor, and the problem that some contestants cannot be fairly evaluated when they only participate in a small number of competitions is solved by combining the stability result set Sta, and the cross-competition performance consistency index Ce generated finally makes the performance of contestants in multiple competitions be able to be uniformly measured, and after the comprehensive feedback value Rf is further calculated, it is bound to the personal archives, so as to realize the comprehensive, comparable and personalized evaluation effect. In line with the actual situation, at the same time, the time series accumulation of the comprehensive feedback value Rf makes it possible to track for a long time, so that teachers or organizers can clearly see the growth trajectory of a student in a semester composed of multiple competitions, thereby effectively solving the shortcomings of "data dispersion, unfair results, and lack of long-term tracking" in the past campus competition statistics.

[0055] (2) By unifying the score data to the standardized result set Nor, and then performing hierarchical rule judgment and stability calculation based on the number of competition participation Num, the stability result set Sta is generated, so as to ensure the comparability and uniformity of the results in single competition, double competition and multiple competition. Even if the number of competitions of contestants is different, the stability result set Sta can still get the same structure, which provides a fair calculation basis for subsequent evaluation, which is in line with the actual situation.

[0056] (3) First, the standardized result set Nor and the stability factor value Val in the stability result set Sta are extracted, and then the influence degree of different competition categories is differentiated by combining the competition type weighting coefficient Wti, to get the weighted score value Sum, and finally the cross-competition performance consistency index Ce is generated. The advantage of this design is that it not only integrates the performance of contestants in various competitions, but also introduces the correction of the stability factor value Val, which can avoid the distortion of the overall result caused by the too high or too low score of the contestant in individual competition. In line with the actual situation, it can truly reflect the overall stability, so as to ensure that the evaluation is more in line with the actual situation. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 It is a schematic diagram of the steps of the campus statistical competition statistical analysis method of the application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0059] Embodiment 1: The present application provides a campus statistical event statistical analysis method, please refer to Figure 1 , comprising the following steps:

[0060] S1, collecting the performance data of the contestants in the campus statistical events to form an original data set Dat;

[0061] S2, normalizing the original data set Dat to generate a standardized result set Nor, which is used to eliminate the dimensional difference of different events;

[0062] S3, based on the original data set Dat and the standardized result set Nor, the stability result set Sta is calculated by layering;

[0063] S4, combining the standardized result set Nor and the stability result set Sta, using the calculation method of layering to generate the cross-event performance consistency index Ce, which is used to evaluate the overall performance of the contestants in different events;

[0064] S5, based on the cross-event performance consistency index Ce, the comprehensive feedback value Rf is obtained by comprehensive operation, and then it is bound with the contestant for comprehensive feedback.

[0065] In this embodiment, on the basis of collecting the performance of the contestants to form the original data set Dat, the dimensional difference of different events is eliminated through the standardized result set Nor, and then the problem that some contestants cannot be fairly evaluated when they only participate in a small number of events is solved by combining the stability result set Sta. The cross-event performance consistency index Ce generated finally makes the performance of the contestants in multiple events be able to be measured uniformly, and after the comprehensive feedback value Rf is calculated, it is bound back to the personal archives, so as to realize the evaluation effect of comprehensive, comparable and personalized. In line with the actual situation, for example, in the scene where school games and academic competitions coexist, in the past, students with high stability in mathematics competitions were often ignored because they only participated in one competition, or students who participated in football competitions many times but had fluctuating performance were overestimated. The method avoids the accidental influence of single high score or low score by combining the stability result set Sta and the cross-event performance consistency index Ce. At the same time, the time series accumulation of the comprehensive feedback value Rf makes it possible to track for a long time, so that teachers or organizers can clearly see the growth trajectory of a student composed of multiple events in a semester, thereby effectively solving the problems of "data dispersion, unfair results, and lack of long-term tracking" in the past campus event statistics.

[0066] Embodiment 2: Specifically, the S1 includes S11 and S12;

[0067] S11, by synchronously calling the record interface of the competition management platform and the data interface of the competition judge scoring system, performance data of the contestants in the subject competition, the expression competition and the physical competition are collected respectively, and data formatting and weighting processing are performed, specifically including steps S111, S112 and S113;

[0068] S111, the competition result record database is called to extract the final score of the contestant in the subject competition, and the subject score value Es is generated;

[0069] S112, the judge scoring record system is called to collect the multi-dimensional scores of the contestant in the expression competition, specifically including the logic, language fluency, adaptability and persuasiveness dimensions, and the weighted average of the scores of each dimension is calculated according to the preset proportion, and the expression score value Ee is generated;

[0070] S113, the physical record competition system is called to collect the time and completion rate of the contestant completing the competition, and the completion time score and the completion rate score are weighted to generate the physical score value Et, which is then integrated with the subject score value Es and the expression score value Ee to form a score parameter set;

[0071] Among them: the subject score value Es: derived from the final score recorded in the competition database, directly reflecting the performance of the contestant in the academic competition;

[0072] The expression score value Ee: obtained by weighted average of the scoring results of the judges in the logic, language fluency, adaptability and persuasiveness dimensions, which can fully reflect the performance of the contestant in the expression competition;

[0073] The physical score value Et: composed of completion time score and completion rate score, which ensures that the evaluation of physical competition is closer to the actual performance of the contestant.

[0074] S12, on the basis of the score parameter set, the actual competition records of the contestants in various competitions are compared by calling the competition registration system and the competition result database, the total number of competitions actually participated by the contestant is counted, and the competition participation quantity Num is generated;

[0075] In the statistical process, the records of contestants who have registered but have not actually participated are excluded; finally, the competition participation quantity Num, the subject score value Es, the expression score value Ee and the physical score value Et are integrated together to form the original data set Dat;

[0076] Among them: the competition participation quantity Num: refers to the number of competitions actually completed by the contestant within the statistical period, which is obtained by cross verification of the registration data and the result data, if the contestant has not participated after registration, the competition participation quantity Num is not counted.

[0077] In this embodiment, through the processing of step S1, the subject performance value Es, the expression performance value Ee and the physical performance value Et are collected and weightedly integrated according to different data interfaces, and the number of event participations Num is introduced for comparison and verification, so as to finally form the original data set Dat, which not only ensures multi-dimensional coverage of the data, but also effectively eliminates false records of non-participation, and avoids distortion in the statistical process. In line with the actual situation, for example, in the scenario where the school sports meeting and the debate competition are parallel, some students may only register without participating, and if only relying on the registration list for statistics, the student's participation enthusiasm will be misjudged, but through the cross-verification of the number of event participations Num, it can be ensured that the statistical result truly reflects the actual participation situation. At the same time, since the expression performance value Ee combines the dimensions of logic, language fluency, adaptability and persuasiveness, and the physical performance value Et integrates the completion time and completion rate, which makes the original data set Dat not only contains academic performance, but also covers expression ability and physical performance, so that the contestant evaluation is more three-dimensional, avoiding the shortcomings of drawing one-sided conclusions only by single event performance in the past.

[0078] Embodiment 3: Specifically, the S2 includes S21;

[0079] S21, based on the subject performance value Es, the expression performance value Ee and the physical performance value Et in the original data set Dat, respectively calls a normalization processing function to convert the performance data in different dimensions to a unified numerical interval, obtains the subject normalized performance value Nes, the expression normalized performance value Nee and the physical normalized performance value Net, and synchronously integrates to form a standardized result set Nor;

[0080] The normalization processing function is as follows:

[0081] ;

[0082] In the formula, Ni represents the i-th normalized performance value, including the subject normalized performance value Nes, the expression normalized performance value Nee and the physical normalized performance value Net; Ei represents the i-th original performance value, including the subject performance value Es, the expression performance value Ee and the physical performance value Et; Emin and Emax respectively represent the minimum performance value and the maximum performance value of all participants in the event.

[0083] The S3 includes S31 and S32;

[0084] S31, according to the numerical value of the number of event participations Num, a hierarchical rule judgment is made to determine a stability calculation mode;

[0085] The hierarchical rule judgment is as follows:

[0086] When the number of event participation Num is 1, the contestant only has single event results, and there is no need for stability calculation, and the standardized result set Nor is directly used as the stability input;

[0087] When the number of event participation Num is 2, the difference between the standardized result set Nor of the contestant in the two events is calculated;

[0088] When the number of event participation Num is greater than 2, the variance calculation method is used to evaluate the volatility of the standardized result set Nor performance of the contestant in multiple events;

[0089] Among them, the hierarchical rule judgment: according to the number of contestants participating in the competition, different stability calculation methods are selected to ensure that the method is suitable for different situations of single event, double event and multiple events;

[0090] Effect description: Through the clear hierarchical rule, the calculation distortion problem caused by insufficient sample quantity can be avoided, and the rationality and universality of subsequent stability calculation are guaranteed.

[0091] S32, on the basis of the hierarchical rule judgment, the specific calculation of the stability calculation mode is executed, and a unified format stability result set Sta={Nor, Val} is generated, wherein Val represents the stability factor value, and the specific generation method is as follows:

[0092] When the number of event participation Num is 1, the contestant only has single event results, and there is no need for stability calculation, and the standardized result set Nor is directly inherited, and the stability factor value Val is set to zero, forming the stability result set Sta={Nor, 0};

[0093] When the number of event participation Num is 2, based on the normalized score value in the standardized result set Nor, two-way difference traversal calculation is performed to obtain the double event difference value Dif, and the stability factor value Val=Dif is set, forming the stability result set Sta={Nor, Dif};

[0094] The double event difference value Dif calculation formula is as follows: ;

[0095] In the formula, Ni and Nj represent the i-th and j-th normalized score values of the two events respectively, Num represents the number of event participation, and Num=2;

[0096] When the number of event participation Num is greater than 2, based on the normalized score value in the standardized result set Nor of each event of the contestant, the variance type stability factor Sd is calculated, and the stability factor value Val=Sd is set, forming the stability result set Sta={Nor, Sd};

[0097] The variance stability factor Sd is calculated as follows:

[0098] In the formula, Nor represents the average normalized score value, and Num represents the number of event participations, and Num > 2.

[0099] In the formula, Dif represents the difference between two events, and is calculated by traversing the normalized scores two by two and accumulating the difference values, and is used to measure the overall difference between the two events.

[0100] In the formula, Sd represents the variance stability factor, and is calculated by the variance formula to measure the volatility of the scores in multiple events. The smaller the value, the more stable the performance of the participant across events.

[0101] By unifying the stability results under different participation numbers into a binary set structure of the stability result set Sta, not only is the logical consistency under the three situations of single event, double event and multiple event ensured, but also a unified input format is provided for subsequent cross-event performance consistency index calculation, avoiding processing bias caused by inconsistent data structures.

[0102] In this embodiment, by the processing of steps S2 and S3, the score data under different dimensions is first unified to the standardized result set Nor using the normalization processing function, and then the stability calculation is performed based on the event participation number Num for hierarchical rule judgment, generating the stability result set Sta, thereby ensuring the comparability and uniformity of the results under the three different situations of single event, double event and multiple event. The special advantage is that even if the number of events of the participants is different, the structure-consistent input can still be obtained through the stability result set Sta, providing a fair calculation basis for subsequent evaluation. In line with the real situation, for example, within the same semester, some students only participate in a mathematics competition, while others participate in multiple sports events and speech competitions. If the original scores are directly compared, the results will be distorted due to the difference in the number of participations. Through this method, the results of students participating in a single event will form the stability result set Sta={Nor, 0}, the results of students participating in two events will form the stability result set Sta={Nor, Dif}, and the results of students participating in multiple events will form the stability result set Sta={Nor, Sd}, thereby performing unified processing under the same format, avoiding bias caused by inconsistent data structures, and making the final cross-event analysis more reasonable and reliable.

[0103] Embodiment 4: Specifically, the S4 includes S41 and S42.

[0104] ​​S41, based on the stability result set Sta, extracting the normalized result set Nor therein as the performance input, and extracting the stability factor value Val therein, then combining the preset event type weighting coefficient set, calculating the comprehensive performance value of the molecular part, the calculation method is as follows: ;

[0105] In the formula, Sum represents the weighted performance value, Num represents the number of event participation, Wti represents the weighting coefficient of the i-th event type, and the specific value is set by the user;

[0106] Event type weighting coefficient Wti: reflects the relative importance of different event types in the overall evaluation, which is preset according to the nature of the event.

[0107] S42, based on the weighted performance value Sum and the stability factor value Val, generate the cross-event performance consistency index Ce, reflecting the performance level and stability of the contestants in cross-event;

[0108] The cross-event performance consistency index Ce is obtained by The calculation formula, wherein 1+Val represents the introduction of stability correction, when Val becomes larger, the index value is suppressed, thereby weakening the influence of unstable performance on the overall performance;

[0109] Among them, the cross-event performance consistency index Ce: is calculated by combining the performance part Sum and the stability factor value Val, which can comprehensively reflect the performance level and stability of the contestants in cross-event.

[0110] In this embodiment, through the processing of step S4, the normalized result set Nor and the stability factor value Val in the stability result set Sta are extracted first, and then the influence degree of the event type weighting coefficient Wti on different event categories is calculated differently to obtain the weighted performance value Sum, and finally the cross-event performance consistency index Ce is generated. The advantage of this design is that it not only comprehensively reflects the performance of contestants in various events, but also introduces the correction effect of the stability factor value Val, which can avoid the distortion of the overall result caused by the high or low performance of the contestants in individual events. In line with the actual situation, for example, a student performs outstandingly in the school games short run but fluctuates greatly in other events, and if only the average performance is used, the overall performance of the student may be overestimated. Through this method, the stability factor value Val will suppress the performance with large fluctuations, so that the final cross-event performance consistency index Ce can not only reflect the performance advantage, but also truly reflect the overall stability, so as to ensure that the evaluation is more in line with the actual situation.

[0111] Embodiment 5: Specifically, the S5 includes S51 and S52;

[0112] S51, on the basis of obtaining the cross-event performance consistency index Ce, introducing the viewing correction factor Vf fitted based on the viewing data, thereby generating a comprehensive feedback value Rf;

[0113] The comprehensive feedback value Rf is obtained by the calculation formula Rf=Ce×(1+Vf);

[0114] Wherein, the viewing correction factor Vf is obtained by steps S511 and S512;

[0115] S511, obtaining the on-site viewing number Vn and the online viewing number Vo from the event data statistical platform;

[0116] S512, uniformly normalizing the on-site viewing number Vn and the online viewing number Vo, and substituting into the fitting formula to calculate the viewing correction factor Vf;

[0117] The viewing correction factor Vf calculation formula is obtained by the following calculation formula:

[0118] ;

[0119] In the formula, Vnmax represents the maximum on-site viewing number, which is normalized by the maximum on-site viewing number Vn in all events in the same statistical period (such as a semester or a season), Vomax represents the maximum online viewing number, which is normalized by the maximum online viewing number Vo in all events in the same statistical period (such as a semester or a season); v1 represents the adjustment coefficient of the on-site viewing number Vn and the maximum on-site viewing number Vnmax calculation result, v2 represents the adjustment coefficient of the online viewing number Vo and the maximum online viewing number Vomax calculation result, and v1+v2=1, the specific value is set by the user;

[0120] Wherein, each participant will obtain a corresponding comprehensive feedback value Rf, which reflects not only the cross-event performance (Ce), but also the comprehensive consideration of the influence of the event (Vf), realizing the final comprehensive evaluation of the individual;

[0121] Organizers can identify the mismatch between event performance and viewing attention by comparing the comprehensive feedback values Rf of participants in different events, which can be used for event organization and competition system optimization;

[0122] Long-term tracking: at the end of the statistical period (such as a semester or a season), the system will form a comprehensive feedback value Rf time series for each participant, which can be used to track the growth trajectory of the participant.

[0123] The S5 includes S52;

[0124] S52, after obtaining the comprehensive feedback value Rf of each participant, the comprehensive feedback value Rf is bound with the unique identity information of the participant to form an individualized data file, ensuring the traceability of the subsequent evaluation and optimization process, and the unique identity information includes student number, competition number and unique identity code generated by the competition system;

[0125] It should be noted that:

[0126] Individual evaluation and ranking: in the school competition statistical analysis system, the comprehensive feedback value Rf of each participant is bound with the individual file as the comprehensive performance index of the participant, and the system can automatically generate the comprehensive ranking of the participant to provide the basis for award evaluation, credit identification and incentive measures;

[0127] Competition organization and optimization: organizers can compare the comprehensive feedback value Rf of participants in different competitions to identify the performance difference of participants in high attention competitions and low attention competitions. When it is found that the competition performance does not match the attention level, the competition type setting, competition arrangement and evaluation system can be optimized to make the competition more in line with the actual teaching goal and the needs of students' development;

[0128] Long-term tracking and growth analysis: at the end of the statistical period (such as semester or season), the system will generate a time sequence of the comprehensive feedback value Rf for each participant, which is bound in the personal file of the participant for analyzing the growth trajectory and long-term performance stability of the participant. Through cross-cycle comparison, the progress and potential shortcomings of the participant can be identified to provide data support for the subsequent training plan.

[0129] In this embodiment, through the processing of step S5, the game watching correction factor Vf is introduced on the basis of the calculation of the cross-game performance consistency index Ce, the comprehensive feedback value Rf that can take into account the individual performance and the influence of the game is generated, and is further bound with the unique identity information of the participant to form the individualized data file, so that the traceable application of the result is realized. The special advantage is that the comprehensive feedback value Rf no longer stays in a single statistical result, but can be directly converted into the basis for the evaluation of the participants and the improvement of the game. For example, there are both high-attention sports games and relatively small academic competitions in schools. If a student performs excellently in the academic competition but the game attention is low, he is often overlooked in the past. Through the generation and binding of the comprehensive feedback value Rf, the student's strength in the academic game can be reflected, and the performance in other games can be compared. The organizers can identify the situation that the game attention and the performance do not match, and adjust the allocation of game resources. At the same time, at the end of the statistical period, the time sequence of the comprehensive feedback value Rf formed by the system for each participant can show the growth trajectory of the student in multiple periods, and the teachers can find that some students have low starting point but their comprehensive feedback value Rf increases year by year, reflecting their progress in learning and training, so as to provide scientific support for award evaluation, training plan and credit recognition.

[0130] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for statistical analysis of a campus statistical event, characterized in that: The method comprises the following steps: S1, collecting performance data of contestants in campus statistical events to form an original data set Dat; S2, normalizing the original data set Dat to generate a standardized result set Nor, which is used to eliminate the dimensional differences of different events; S3, based on the original data set Dat and the standardized result set Nor, hierarchical calculation is performed to obtain a stability result set Sta; S4, combining the standardized result set Nor and the stability result set Sta, using hierarchical processing calculation to generate a cross-event performance consistency index Ce, which is used to evaluate the overall performance of contestants in different events; S5, based on the cross-event performance consistency index Ce, a comprehensive feedback value Rf is obtained by comprehensive operation, and then the comprehensive feedback value Rf is bound with the contestant for comprehensive feedback.

2. The method of claim 1, wherein: The S1 comprises S11 and S12; S11, through synchronous calling of the record interface of the event management platform and the data interface of the event judge scoring system, performance data of contestants in subject events, expression events and physical events are collected respectively, and data formatting and weighting processing are performed, which specifically includes steps S111, S112 and S113; S111, calling the event result record database, extracting the final score of the contestant in the subject event, and generating a subject score value Es; S112, calling the judge scoring record system, collecting multi-dimensional scores of the contestant in the expression event, specifically including logicality, language fluency, adaptability and persuasiveness dimensions, and weighting and averaging the scores of each dimension according to the preset proportion to generate an expression score value Ee; S113, calling the physical record event system, collecting the time and completion rate of the contestant completing the event, weighting the completion time score and the completion rate score, generating a physical score value Et, and integrating the subject score value Es and the expression score value Ee to form a score parameter set.

3. The method of claim 2, wherein: S12, based on the score parameter set, the actual participation records of the contestant in various events are compared by calling the event registration system and the event result database, the total number of events actually participated by the contestant is counted, and the number of events participated Num is generated; In the statistical process, the records of contestants who have registered but have not actually participated are excluded; finally, the number of events participated Num, the subject score value Es, the expression score value Ee and the physical score value Et are integrated together to form the original data set Dat.

4. The method of claim 3, wherein: The S2 comprises S21; S21, based on the subject score value Es, the expression score value Ee and the physical score value Et in the original data set Dat, a normalization processing function is called respectively to convert the score data in different dimensions to a unified numerical interval, obtain the subject normalized score value Nes, the expression normalized score value Nee and the physical normalized score value Net, and integrate them synchronously to form the standardized result set Nor; The normalization processing function is as follows: ； In the formula, Ni represents the i-th normalized score value, including the subject normalized score value Nes, the expression normalized score value Nee, and the physical normalized score value Net; Ei represents the i-th original score value, including the subject score value Es, the expression score value Ee, and the physical score value Et; Emin and Emax respectively represent the minimum score value and the maximum score value of all participants in the event.

5. The method of claim 4, wherein: The S3 includes S31 and S32; S31, according to the value of the number of event participations Num, performs hierarchical rule determination to determine a stability calculation mode; The hierarchical rule determination is as follows: When the number of event participations Num is 1, the participant only has single event results, and there is no need for stability calculation, and the normalized result set Nor is directly taken as the stability input; When the number of event participations Num is 2, the difference degree of the normalized result set Nor of the participant between the two events is calculated; When the number of event participations Num is greater than 2, a variance calculation method is used to evaluate the volatility of the normalized result set Nor performance of the participant in multiple events.

6. The method of claim 5, wherein: S32, on the basis of the hierarchical rule determination, performs specific calculation of the stability calculation mode to generate a unified format stability result set Sta={Nor, Val}, wherein Val represents a stability factor value, and the specific generation mode is as follows: When the number of event participations Num is 1, the participant only has single event scores, and there is no difference in cross-event performance, so the normalized result set Nor is directly inherited, and the stability factor value Val is set to zero, forming the stability result set Sta={Nor, 0}; When the number of event participations Num is 2, based on the normalized score values in the normalized result set Nor, a pairwise difference traversal calculation is performed to obtain a double event difference value Dif, and the stability factor value Val is set to Dif, forming the stability result set Sta={Nor, Dif}; The double event difference value Dif calculation formula is as follows: ； In the formula, Ni and Nj respectively represent the i-th and j-th normalized score values of the two events, and Num represents the number of event participations, and Num=2; When the number of event participations Num is greater than 2, based on the normalized score values in the normalized result set Nor of the participant in each event, a variance type stability factor Sd is calculated, and the stability factor value Val is set to Sd, forming the stability result set Sta={Nor, Sd}; The variance type stability factor Sd calculation formula is as follows: ； In the formula, wherein, represents the average normalized performance value, Num represents the number of event participations, and Num >

2.

7. The method of claim 6, wherein: The S4 includes S41 and S42; S41, based on the stability result set Sta, extracts the normalized result set Nor therein as the score input, and extracts the stability factor value Val therein, and then combines a preset event type weighting coefficient set to calculate the comprehensive score value of the numerator part, and the calculation mode is as follows: ； In the formula, Sum represents the weighted score value, Num represents the number of event participations, and Wti represents the weighting coefficient of the i-th event type.

8. The method of claim 7, wherein: S42, based on the obtained weighted performance value Sum and stability factor value Val, generate a cross-event performance consistency index Ce, reflecting the performance level and stability of the participants in cross-event; The cross-event performance consistency index Ce is obtained by The calculation formula is obtained; Wherein, the cross-event performance consistency index Ce: through the combination of performance part Sum and stability factor value Val calculated, can fully reflect the performance level and stability of the participants in cross-event.

9. The method of claim 8, wherein: The S5 includes S51 and S52; S51, on the basis of obtaining the cross-event performance consistency index Ce, introduce the viewing correction factor Vf fitted based on the viewing data, thereby generating the comprehensive feedback value Rf; The comprehensive feedback value Rf is obtained by the calculation formula Rf=Ce×(1+Vf); Wherein, the viewing correction factor Vf is obtained by steps S511 and S512; S511, obtain the number of on-site viewers Vn and the number of online viewers Vo from the event data statistical platform; S512, the on-site viewers Vn and the number of online viewers Vo are normalized and substituted into the fitting formula to calculate the viewing correction factor Vf; The viewing correction factor Vf calculation formula is obtained by the following calculation formula: ； In the formula, Vnmax represents the maximum value of the number of on-site viewers, Vomax represents the maximum value of the number of online viewers; v1 represents the adjustment coefficient of the calculation result of the number of on-site viewers Vn and the maximum value of the number of on-site viewers Vnmax, v2 represents the adjustment coefficient of the calculation result of the number of online viewers Vo and the maximum value of the number of online viewers Vomax, and v1+v2=1, the specific value is set by the user.

10. The method of claim 9, wherein: The S5 includes S52; S52, after obtaining the comprehensive feedback value Rf of each participant, bind the comprehensive feedback value Rf with the unique identity information of the participants to form individualized data files, and the unique identity information includes student number, competition number and unique identity code generated by the event system.

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