Billiard user ability data identification and matching method and system

Abstract

The invention discloses a billiard user ability data identification and matching method and system, and relates to the technical field of data processing, and the method comprises the following steps: obtaining user login information of a plurality of billiard players; according to the user login information, historical match data of the corresponding billiard players are called; classifying the plurality of billiard players into corresponding matching regions according to the historical match data; according to a preset matching mechanism, performing battle matching grouping on the plurality of billiard players; acquiring completion data of the target billiard player after the current game is promoted; according to the game completion data, the multiple target billiard players are classified into corresponding matching regions again; and performing battle matching grouping on the multiple target billiard players again according to the matching mechanism. The method has the advantages that the reasonability of the billiard match matching mechanism is improved, and the competition experience of the players and the improvement of the competition level are promoted.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method and system for identifying and matching billiards user ability data. Background Art

[0002] In various large-scale billiards competitions, it is necessary to match and pair each participating player in groups of two. Currently, most of the matching mechanisms are to randomly match after entering the personal information of the players. For some players with relatively high levels, if they are directly grouped for a match, they may be eliminated early due to accidental losses, and it is also difficult to re-match according to the actual ability changes of the players during the competition, which affects the players' participation experience and the improvement of their competitive levels, and the user experience is poor. Summary of the Invention

[0003] The main purpose of this application is to provide a method and system for identifying and matching billiards user ability data, aiming to solve the technical problem that the existing billiards competition matching mechanism is unreasonable and affects the players' participation experience and the improvement of their competitive levels.

[0004] To achieve the above purpose, this application provides a method for identifying and matching billiards user ability data, including the following steps: Obtain the user login information of multiple billiards players; According to the user login information, retrieve the historical competition data of the corresponding billiards players; According to the historical competition data, classify multiple billiards players into corresponding matching positions; among them, the matching positions include the upper position area, the middle position area, and the lower position area; According to the preset matching mechanism, conduct a battle matching grouping for multiple billiards players; among them, the matching mechanism is that the billiards players in the upper position area are matched with the billiards players in the lower position area, and the billiards players in the middle position area are matched with each other; Obtain the completion data of the target billiards players after advancing to the current competition; According to the completion data, re-classify multiple target billiards players into corresponding matching positions; Re-conduct a battle matching grouping for multiple target billiards players according to the matching mechanism.

[0005] Optionally, according to the completion data, re-classifying multiple target billiards players into corresponding matching positions includes: Input the completion data into a preset multi-dimensional dynamic evaluation model to obtain the ability evaluation value of the corresponding target billiards player; According to the magnitude of the ability evaluation value, re-classify multiple target billiards players into corresponding matching positions.

[0006] Optionally, the expression of the multi-dimensional dynamic evaluation model is: E = K1 TE + K2 PR + K3 GP; Wherein, E is the ability evaluation value, TE is the technical execution degree, which is used to characterize the hitting accuracy and tactical implementation ability of the target billiards player, PR is the mental toughness index, which is used to quantify the coping ability of the target billiards player in an adversity game, GP is the growth potential coefficient, which is used to predict the technical progress speed of the target billiards player, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0007] Optionally, the expression of the technical execution degree is: TE = W1 J + W2 F + W3 T; ; Wherein, J is the effective hitting accuracy, N is the total number of hits, θ i is the hitting angle of the i-th hit, F is the defensive success rate, T is the tactical execution index, W1 is the first weight coefficient, W2 is the second weight coefficient, and W3 is the third weight coefficient.

[0008] Optionally, the expression of the mental toughness index is: PR = Y lnX + 0.7 G; G = σ / μ; Wherein, Y is the winning rate in a headwind game, X is the number of consecutive scoring games, G is the key ball handling coefficient, σ is the standard deviation of the hitting accuracy of key balls, and μ is the heart rate variability coefficient.

[0009] Optionally, the expression of the growth potential coefficient is: GP = 1.2 ΔTE GP'; ; Wherein, ΔTE is the growth rate of the technical execution degree, GP' is the historical baseline value, M is the number of historical game sample fields, e is the natural constant, GP t is the growth potential coefficient based on the t-th historical game.

[0010] Optionally, after reclassifying multiple target billiards players into corresponding matching positions according to the finish data, it further includes: Determine whether there are abnormal billiards players; among them, the abnormal billiards player is the target billiards player who drops from the upper area to the lower area; If not, enter the process of re - grouping multiple target billiards players for battle matching according to the matching mechanism; If so, input the completion data of the abnormal billiards player into a preset fluctuation index detection model to obtain an abnormal fluctuation index; Determine whether the abnormal fluctuation index is greater than a preset index threshold. If so, transfer the abnormal billiards player to the middle position area; if not, keep the abnormal billiards player grouped in the lower position area.

[0011] Optionally, the expression of the fluctuation index detection model is: Q = (λ 1 + λ 2 +... + λ n ) / n + 0.3 ε; In the formula, Q is the abnormal fluctuation index, λ n is the deviation degree of a single game, n is the number of valid games, and ε is the environmental interference coefficient.

[0012] Optionally, before re - grouping multiple target billiards players for battle matching according to the matching mechanism, it further includes: Obtain the number of promoted players among the target billiards players; Determine whether the number of promoted players is less than a preset number; If not, enter the process of re - grouping multiple target billiards players for battle matching according to the matching mechanism; If so, group the target billiards players for battle matching according to the random matching mechanism.

[0013] To achieve the above - mentioned purpose, the present application also provides a billiards user ability data recognition and matching system, including: An information acquisition module, used to acquire the user login information of multiple billiards players; A data retrieval module, used to retrieve the historical game data of the corresponding billiards player according to the user login information; An initial classification module, used to classify multiple billiards players into corresponding matching position areas according to the historical game data; among them, the matching position areas include the upper position area, the middle position area, and the lower position area; An initial matching module, used to group multiple billiards players for battle matching according to a preset matching mechanism; among them, the matching mechanism is that the billiards players in the upper position area are matched with the billiards players in the lower position area, and the billiards players in the middle position area are matched with each other; A data acquisition module, used to acquire the completion data of the target billiards players after advancing to the current game; A re - classification module, used to re - classify multiple target billiards players into corresponding matching position areas according to the completion data; The rematching module is used to regroup multiple target billiard players for battle matching according to the matching mechanism.

[0014] To achieve the above objectives, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0015] To achieve the above objectives, the present application also provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above method.

[0016] The beneficial effects that this application can achieve are as follows: This application is based on the user login information of the participating billiard players. By retrieving the historical game data of the corresponding billiard players, a preliminary evaluation of the strength of the participating billiard players can be made. Then, according to the evaluation results, multiple billiard players can be classified into corresponding matching areas. Here, the matching areas are divided into upper area, middle area and lower area, that is, billiard players with upper, middle and lower billiard strength levels respectively. According to the preset matching mechanism, multiple billiard players are grouped for battle matching. Here, the matching mechanism is that the billiard players in the upper area are matched with the billiard players in the lower area, and the billiard players in the middle area are matched with each other, so as to play a temporary protection role for the billiard players with stronger strength in the upper area, and to prevent premature elimination due to accidental failure as much as possible. Billiard players in the middle zone can compete fairly. After completing the current round of competition, the completion data of the target billiard players who have advanced to the current game can be obtained. At this time, the strength of the target billiard players can be re-evaluated according to the latest completion data, so that multiple target billiard players can be reclassified into corresponding matching positions, and multiple target billiard players can be re-grouped for battle matching according to the matching mechanism. Therefore, this application can dynamically update the billiard player strength evaluation data according to the completion data of each round, and perform dynamic matching and grouping based on a specific matching mechanism, thereby reducing the situation where relatively strong billiard players are accidentally defeated and eliminated prematurely, and can re-match and battle according to the changes in the players' actual abilities, thereby promoting the players' competition experience and improving their competitive level. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the prior art description. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0018] Figure 1It is a schematic flowchart of a method for identifying and matching billiards user ability data in an embodiment of the present application; Figure 2 It is a schematic framework diagram of a billiards user ability data identification and matching system in an embodiment of the present application.

[0019] The realization of the purpose of the present application, functional features and advantages will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific Embodiments

[0020] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application.

[0021] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present application are only used to explain the relative positional relationship and movement conditions between components in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0022] In the present application, unless otherwise clearly defined and limited, terms such as "connection" and "fixation" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0023] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the meaning of "and / or" appearing throughout the text includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution that satisfies both A and B at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0024] Embodiment 1: Reference Figure 1 This embodiment provides a billiards user ability data identification and matching method, comprising the following steps: Get user login information for multiple billiard players; According to the user login information, retrieve the historical game data of the corresponding billiards player; According to historical game data, multiple billiard players are classified into corresponding matching areas; wherein the matching areas include upper area, middle area and lower area; According to a preset matching mechanism, multiple billiard players are grouped for battle matching; wherein the matching mechanism is that billiard players in the upper zone are matched with billiard players in the lower zone, and billiard players in the middle zone are matched with each other; Get the completion data of the target billiards player who has advanced to the current game; According to the game completion data, multiple target billiard players are reclassified into corresponding matching positions; According to the matching mechanism, multiple target billiard players are regrouped for battle matching.

[0025] In this embodiment, based on the user login information of the participating billiard players, by retrieving the historical game data of the corresponding billiard players, a preliminary evaluation of the strength of the participating billiard players can be performed, and then multiple billiard players can be classified into corresponding matching areas according to the evaluation results. Here, the matching areas are divided into upper area, middle area and lower area, that is, billiard players with upper, middle and lower billiard strengths, respectively. According to the preset matching mechanism, multiple billiard players are grouped for battle matching. Here, the matching mechanism is that the billiard players in the upper area are matched with the billiard players in the lower area, and the billiard players in the middle area are matched with each other, so that the billiard players with stronger strength in the upper area can be temporarily protected, and the situation of premature elimination due to accidental failure can be prevented as much as possible. The billiard players in the middle zone can compete fairly. After completing the current round of competition, the completion data of the target billiard players who have advanced to the current game are obtained. At this time, the strength of the target billiard players can be re-evaluated according to the latest completion data, so that multiple target billiard players can be reclassified into corresponding matching positions, and multiple target billiard players can be re-grouped for battle matching according to the matching mechanism. Therefore, this embodiment can dynamically update the billiard player strength evaluation data according to the completion data of each round, and perform dynamic matching and grouping based on a specific matching mechanism, thereby reducing the situation where relatively strong billiard players are accidentally defeated and eliminated prematurely, and can re-match and fight according to the changes in the players' actual abilities, thereby promoting the players' competition experience and improving their competitive level.

[0026] It should be noted that there are many reference factors for historical competition data, such as the number of historical competitions, winning rate, event level, and personal relevant skill levels. Here, an initial evaluation model can be constructed, and each parameter of the historical competition data is input into this initial evaluation model, so as to quantitatively obtain the initial ability evaluation value of the corresponding billiards player, which is convenient for accurate subsequent zoning. When allocating the upper, middle, and lower zones, the number of people in the three zones can be set according to the mechanism of being as balanced as possible, and the number of people in the upper and lower zones should be equal for one-to-one matching, and the excess is allocated to the middle zone. Taking 100 billiards players as an example, the number of people in the upper and lower zones is 33 each, while the number of people in the middle zone is 34.

[0027] As an alternative implementation, according to the completion data, multiple target billiards players are reclassified into corresponding matching zones, including: Input the completion data into a preset multi-dimensional dynamic evaluation model to obtain the ability evaluation value of the corresponding target billiards player; According to the magnitude of the ability evaluation value, multiple target billiards players are reclassified into corresponding matching zones.

[0028] In this implementation, through the pre-constructed multi-dimensional dynamic evaluation model, the completion data of each target billiards player can be input into this multi-dimensional dynamic evaluation model, and the ability evaluation value of the target billiards player can be quantitatively calculated. According to the magnitude of its value, it is convenient to accurately reclassify multiple target billiards players into corresponding matching zones.

[0029] As an alternative implementation, the expression of the multi-dimensional dynamic evaluation model is: E = K1 TE + K2 PR + K3 GP; In the formula, E is the ability evaluation value, TE is the technical execution degree, and the technical execution degree is used to characterize the hitting accuracy and tactical implementation ability of the target billiards player. PR is the psychological resilience index, and the psychological resilience index is used to quantitatively measure the coping ability of the target billiards player in adversity games. GP is the growth potential coefficient, and the growth potential coefficient is used to predict the technical progress speed of the target billiards player. K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0030] In this implementation, the ability change level of the target billiards player is comprehensively evaluated from three dimensions: technical execution degree, psychological resilience index, and growth potential coefficient. The assessment is more comprehensive and can better reflect the comprehensive strength and real ability change of the player. And the three assessment indicators belong to parameters of different attributes. Therefore, they are respectively transformed and adjusted through the first adjustment coefficient K1, the second adjustment coefficient K2, and the third adjustment coefficient K3, so that each parameter can be quantitatively superimposed.

[0031] It should be noted that the parameter update conditions for each of the above dimensions are as follows: the technical execution degree is updated after each game ends, the mental toughness index is updated whenever there is a headwind game, and the growth potential coefficient is updated after every 3 games are completed, so as to dynamically update each parameter and dynamically evaluate the ability changes of each player.

[0032] As an optional implementation method, the expression of the technical execution degree is: TE = W1 J + W2 F + W3 T; ; In the formula, J is the effective hitting accuracy, N is the total number of hits, θ i is the hitting angle of the i-th hit, F is the defensive success rate, T is the tactical execution index, W1 is the first weight coefficient, W2 is the second weight coefficient, and W3 is the third weight coefficient.

[0033] In this implementation method, the technical execution degree is comprehensively considered from three dimensions: the effective hitting accuracy J, the defensive success rate F, and the tactical execution index T. Using the above calculation formula for the effective hitting accuracy J, the effective hitting accuracy J can be expressed as the average value of the comprehensive deviation of N hitting angles, and the data of each hitting angle deviation can be collected by sensors set inside the billiard table or analyzed and obtained by using machine vision recognition technology; the defensive success rate F is expressed as the proportion of defensive rounds that force the opponent to be unable to directly hit the target ball; the tactical execution index T can calculate the cosine similarity between the ideal hitting path and the actual path based on the reflection theorem. Combining the above three reference indicators of the effective hitting accuracy J, the defensive success rate F, and the tactical execution index T, the technical execution degree TE of the target billiards player can be reflected specifically and comprehensively. At the same time, considering the different influencing degrees of these three reference indicators on the technical execution degree TE, the corresponding weight coefficients (i.e., W1, W2, and W3) are respectively assigned. Preferably, it can be obtained by combining big data model training. Here, W1 takes 0.45, W2 takes 0.35, and W3 takes 0.2. Finally, the accurate and reliable technical execution degree TE can be calculated.

[0034] As an optional implementation method, the expression of the mental toughness index is: PR = Y lnX + 0.7 G; G = σ / μ; In the formula, Y is the winning rate in headwind games, X is the number of consecutive scoring games, G is the key ball handling coefficient, σ is the standard deviation of the hitting accuracy of key balls, and μ is the heart rate variability coefficient.

[0035] In this embodiment, the mental toughness index mainly quantifies the player's ability to cope with adversity. The data reference mainly comes from the adverse situation (for example, when the score lags behind by ≥ 3 games). The main data references can be the winning rate and the number of consecutive scoring games. In the above formula, the value corresponding to (Y lnX) is the player's ability to reverse the adverse situation. At the same time, the key ball handling coefficient G is also considered. The key ball handling coefficient G is comprehensively calculated from the standard deviation σ of the key ball hitting accuracy and the heart rate variability coefficient μ (which can characterize the degree of tension and relevant data can be obtained by wearing a heart rate monitoring bracelet). Among them, σ is directly proportional to G, and μ is inversely proportional to G. Finally, the accurate and reliable mental toughness index PR is calculated, which has strong reference and guidance.

[0036] As an alternative embodiment, the expression of the growth potential coefficient is: GP = 1.2 ΔTE GP'; ; In the formula, ΔTE is the growth rate of technical execution, GP' is the historical baseline value, M is the number of historical game samples, e is the natural constant (take 2.7), GP t is the growth potential coefficient based on the t-th historical game.

[0037] In this embodiment, the growth potential coefficient is considered from two reference factors: the growth rate of technical execution and the historical baseline value. Based on the above formula, the growth rate of technical execution can be calculated based on the exponential smoothing method, and the historical baseline value can be characterized as the average value of the growth potential coefficients of the player based on the M-field historical game sample data, which can be calculated by constructing an LSTM prediction model, so as to accurately calculate the value of the growth potential coefficient GP.

[0038] As an alternative embodiment, after reclassifying multiple target billiard players into corresponding matching positions according to the finish data, it further includes: Judging whether there are abnormal billiard players; among them, the abnormal billiard player is the target billiard player who drops from the upper area to the lower area; If not, then enter the step of re-matching and grouping multiple target billiard players according to the matching mechanism; If so, input the finish data of the abnormal billiard player into a preset fluctuation index detection model to obtain an abnormal fluctuation index; Judge whether the abnormal fluctuation index is greater than a preset index threshold. If so, transfer the abnormal billiard player to the middle area. If not, keep the abnormal billiard player grouped in the lower area.

[0039] In this embodiment, considering the abnormal situation where some players in the upper region are grouped into the lower region due to various accidental reasons and lose, these players are marked as abnormal billiards players, and the completion data of the abnormal billiards players is retrieved and input into a preset fluctuation index detection model to obtain an abnormal fluctuation index. According to the result of comparing the abnormal fluctuation index with a preset index threshold, if the abnormal fluctuation index is greater than the index threshold, it indicates that there are indeed abnormal factors causing the accidental loss of the player. At the same time, considering fairness, the abnormal billiards player can be transferred to the middle region. If not, it means that the lack of competitiveness of the abnormal billiards player itself, rather than abnormal factors, and the grouping result of the lower region can be maintained, thus having the effect of dynamically correcting abnormal grouping and improving the rationality and fairness of the matching grouping.

[0040] As an alternative embodiment, the expression of the fluctuation index detection model is: Q = (λ 1 + λ 2 +... + λ n ) / n + 0.3 ε; In the formula, Q is the abnormal fluctuation index, λ n is the deviation degree of a single game, n is the number of valid games, and ε is the environmental interference coefficient.

[0041] In this embodiment, when calculating the abnormal fluctuation index Q, the average value of the deviation degrees of a single game for multiple valid games can be calculated, that is, the calculated value of (λ 1 + λ 2 +... + λ n ). The deviation degree of a single game can be calculated based on the principle of conservation of momentum in billiard collisions for the deviation between the theoretical and actual trajectories. At the same time, an environmental interference coefficient ε is introduced, and the environmental interference coefficient ε can be quantitatively calculated based on parameters such as environmental light, humidity, and temperature, so as to quantitatively calculate the abnormal fluctuation index Q.

[0042] As an alternative embodiment, before re-grouping multiple target billiards players for battle matching according to the matching mechanism, it further includes: Obtaining the number of promoted players among the target billiards players; Judging whether the number of promoted players is less than the preset number; If not, then enter the step of re-grouping multiple target billiards players for battle matching according to the matching mechanism; If so, then group the target billiards players for battle matching according to the random matching mechanism.

[0043] In this embodiment, since the number of promoted billiards players becomes smaller and smaller after a certain number of rounds of the competition, and the players promoted to the later rounds are basically those with relatively strong strength and stable performance, it is no longer applicable to use the preset matching mechanism for grouping. At this time, a threshold of a preset number of people can be set. When the number of promoted people is less than the preset number, the grouping is directly carried out according to the random matching mechanism to improve the fairness of the competition.

[0044] Embodiment 2: Based on the same inventive concept as the foregoing embodiment, with reference to Figure 1 - Figure 2 , this embodiment further provides a billiards user ability data recognition and matching system, including: An information acquisition module, configured to acquire user login information of multiple billiards players; A data retrieval module, configured to retrieve historical game data of corresponding billiards players according to the user login information; An initial classification module, configured to classify multiple billiards players into corresponding matching regions according to the historical game data; wherein, the matching regions include an upper region, a middle region, and a lower region; An initial matching module, configured to perform battle matching grouping on multiple billiards players according to a preset matching mechanism; wherein, the matching mechanism is that the billiards players in the upper region are matched with the billiards players in the lower region, and the billiards players in the middle region are matched with each other; A data acquisition module, configured to acquire the completion data of the target billiards players after advancing to the current competition; A reclassification module, configured to reclassify multiple target billiards players into corresponding matching regions according to the completion data; A re-matching module, configured to re-perform battle matching grouping on multiple target billiards players according to the matching mechanism.

[0045] For the relevant explanations and examples of each module in the device of this embodiment, reference can be made to the method of the foregoing embodiment, which will not be elaborated here.

[0046] Embodiment 3: Based on the same inventive concept as the foregoing embodiment, this embodiment provides a computer device, which includes a memory and a processor. A computer program is stored in the memory, and the processor executes the computer program to implement the above method.

[0047] Embodiment 4: Based on the same inventive concept as the foregoing embodiment, this embodiment provides a computer-readable storage medium, on which a computer program is stored, and the processor executes the computer program to implement the above method.

[0048] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present application.

Claims

1. A billiards user ability data identification and matching method, characterized in that: The following steps are involved: Get user login information for multiple billiard players; According to the user login information, retrieve the historical game data of the corresponding billiards player; According to historical game data, multiple billiard players are classified into corresponding matching areas; wherein the matching areas include upper area, middle area and lower area; According to a preset matching mechanism, multiple billiard players are grouped for battle matching; wherein the matching mechanism is that billiard players in the upper zone are matched with billiard players in the lower zone, and billiard players in the middle zone are matched with each other; Get the completion data of the target billiards player who has advanced to the current game; According to the game completion data, multiple target billiard players are reclassified into corresponding matching positions; According to the matching mechanism, multiple target billiard players are regrouped for battle matching.

2. A method for identifying and matching billiards user ability data as claimed in claim 1, characterized in that: Based on the game completion data, multiple target billiard players are reclassified into corresponding matching positions, including: Input the completed game data into a preset multi-dimensional dynamic evaluation model to obtain the ability evaluation value of the corresponding target billiard player; According to the size of the ability evaluation values, multiple target billiard players are reclassified into corresponding matching positions.

3. A method for identifying and matching billiards user ability data as claimed in claim 2, characterized in that: The expression of the multidimensional dynamic evaluation model is: E=K1 TE+K2 PR+K3 GP; In the formula, E is the ability evaluation value, TE is the technical execution degree, which is used to characterize the target billiards player's hitting accuracy and tactical implementation ability, PR is the psychological toughness index, which is used to quantify the target billiards player's ability to cope with adversity, GP is the growth potential coefficient, which is used to predict the target billiards player's technical improvement speed, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

4. A method for identifying and matching billiards user ability data as claimed in claim 3, characterized in that: The expression of technical execution is: TE=W1 J+W2 F+W3 T; In the formula, J is the effective hitting accuracy, N is the total number of hits, θ i is the hitting angle of the ith hit, F is the defense success rate, T is the tactical execution index, W1 is the first weight coefficient, W2 is the second weight coefficient, and W3 is the third weight coefficient.

5. A method for identifying and matching billiards user ability data as claimed in claim 3, characterized in that: The expression of psychological resilience index is: PR=Y lnX+0.7 G; G = σ / μ; Where Y is the winning rate of adverse games, X is the number of consecutive scoring games, G is the key ball processing coefficient, σ is the standard deviation of the key ball hitting accuracy, and μ is the heart rate variability coefficient.

6. A method for identifying and matching billiards user ability data as claimed in claim 3, characterized in that: The expression of growth potential coefficient is: GP=1.2 ΔTE GP'; In the formula, ΔTE is the growth rate of technical execution, GP' is the historical baseline value, M is the number of historical game samples, e is a natural constant, GP t is the growth potential coefficient based on the tth historical game.

7. A method for identifying and matching billiards user ability data according to any one of claims 1 to 6, characterized in that: According to the game completion data, after reclassifying multiple target billiard players into corresponding matching positions, it also includes: Determine whether there is an abnormal billiard player; wherein the abnormal billiard player is a target billiard player who falls from the upper area to the lower area; If not, then enter the step of regrouping the multiple target billiard players for battle matching according to the matching mechanism; If yes, the completion data of the abnormal billiard player is input into a preset fluctuation index detection model to obtain an abnormal fluctuation index; It is determined whether the abnormal fluctuation index is greater than a preset index threshold. If so, the abnormal billiard player is moved to the middle area. If not, the abnormal billiard player is kept grouped in the lower area.

8. A method for identifying and matching billiards user ability data as claimed in claim 7, characterized in that: The expression of the volatility index detection model is: Q =(λ1+λ2+...+λ n ) / n + 0.3 e; Where Q is the abnormal fluctuation index, λ n is the single-game deviation, n is the number of effective games, and ε is the environmental interference coefficient.

9. A method for identifying and matching billiards user ability data as claimed in claim 1, characterized in that: Before regrouping multiple target billiard players for battle matching according to the matching mechanism, it also includes: Get the number of target billiard players who advance to the next round; Determine whether the number of people who advance is less than the preset number; If not, then enter the step of regrouping the multiple target billiard players for battle matching according to the matching mechanism; If so, the target billiard players are grouped for battle matching according to the random matching mechanism.

10. A billiards user ability data identification and matching system, characterized in that: include: An information acquisition module, used to acquire user login information of multiple billiard players; The data retrieval module is used to retrieve the historical game data of the corresponding billiards player according to the user login information; An initial classification module is used to classify multiple billiard players into corresponding matching areas according to historical game data; wherein the matching areas include upper area, middle area and lower area; The initial matching module is used to group multiple billiard players for battle matching according to a preset matching mechanism; wherein the matching mechanism is that billiard players in the upper zone are matched with billiard players in the lower zone, and billiard players in the middle zone are matched with each other; A data acquisition module, used to acquire the completion data of the target billiards player who has advanced to the current game; A reclassification module, used for reclassifying multiple target billiard players into corresponding matching positions according to the game completion data; The rematching module is used to regroup multiple target billiard players for battle matching according to the matching mechanism.

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