Game login concurrency control methods, devices, electronic equipment, media and products

Abstract

This disclosure presents a method, apparatus, electronic device, medium, and product for game login concurrency control. The method includes: acquiring hardware status data, operational status data, login-related data, and business scenario identifiers of a service node at the current moment; determining the predicted login request volume of the service node within a preset time period after the current moment based on the login-related data, business scenario identifiers, and a login request prediction model; determining the load assessment result corresponding to the service node based on the hardware status data, operational status data, login-related data, and business scenario identifiers; and determining the login control strategy corresponding to the service node based on the predicted login request volume and load assessment result, so as to perform login concurrency control on the service node according to the login control strategy. This technical solution achieves accurate prediction and adaptive login concurrency control of login requests based on multi-dimensional data and game business scenarios, effectively improving control response speed and accuracy.

Description

Technical Field

[0001] This disclosure relates to the fields of computer and communication technology, and in particular to a method, apparatus, electronic device, medium, and product for concurrent game login control. Background Technology

[0002] With the rapid development of the online gaming industry and the continuous expansion of the user base, scenarios such as new server launches, version updates, and holiday events can easily lead to a surge in instantaneous login traffic, causing problems such as server overload, increased login latency, and frequent login failures, severely impacting user experience and system stability. How to achieve efficient, stable, and adaptive concurrency control in high-concurrency, high-fluctuation login scenarios has become a critical technical problem that urgently needs to be solved in the field of game server architecture.

[0003] In related technologies, high-concurrency control methods typically employ passive threshold control based on a single metric. When the system detects that these metrics have reached a preset fixed threshold, it triggers protection mechanisms such as rate limiting or circuit breaking. However, this passive threshold control suffers from delayed response and insufficient prediction, often triggering protection only after a surge in game logins, leading to server overload and login lag. Furthermore, it has limitations, being prone to false triggers or missed triggers, and cannot adapt to sudden traffic fluctuations and complex business scenarios. Summary of the Invention

[0004] This invention provides a game login concurrency control method, device, electronic device, medium, and product to achieve accurate prediction and adaptive login concurrency control based on multi-dimensional data and game business scenarios, effectively improving control response speed and accuracy.

[0005] According to one aspect of the present invention, a game login concurrency control method is provided, the method comprising: Obtain the hardware status data, operational status data, login association data, and business scenario identifier of the service node at the current moment; the business scenario identifier is used to indicate the game business operation scenario in which the service node is at the current moment. Based on the login association data, the business scenario identifier, and the login request prediction model, the predicted number of login requests for the service node within a preset time period after the current time is determined. Based on the hardware status data, the operating status data, the login association data, and the business scenario identifier, determine the load assessment result of the service node at the current moment; Based on the predicted number of login requests and the load assessment results, the login control policy corresponding to the service node at the current time is determined, so as to perform login concurrency control on the service node according to the login control policy.

[0006] According to another aspect of the present invention, a game login concurrency control device is provided, the device comprising: The data acquisition module is used to acquire the hardware status data, operation status data, login association data, and business scenario identifier of the service node at the current moment; the business scenario identifier is used to indicate the game business operation scenario in which the service node is at the current moment. The login request prediction module is used to determine the predicted number of login requests for the service node within a preset time period after the current time based on the login association data, the business scenario identifier, and the login request prediction model. The evaluation result determination module is used to determine the load evaluation result of the service node at the current time based on the hardware status data, the operation status data, the login association data, and the business scenario identifier. The control strategy determination module is used to determine the login control strategy corresponding to the service node at the current time based on the predicted login request volume and the load assessment result, so as to perform login concurrency control on the service node according to the login control strategy.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: One or more processors; Storage device for storing one or more programs. When one or more programs are executed by one or more processors, the one or more processors implement any of the game login concurrency control methods as described in the embodiments of this disclosure.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement any of the game login concurrency control methods of the present invention.

[0009] According to another aspect of the present disclosure, a computer program product is provided, which, when executed by a processor, implements any of the game login concurrency control methods described in the embodiments of the present disclosure.

[0010] The technical solution of this disclosure, by acquiring the hardware status data, operational status data, login association data, and business scenario identifier of the service node at the current moment, can comprehensively collect the node's current operating environment, business status, and scenario information, providing a complete data foundation for subsequent prediction and evaluation. Furthermore, by determining the predicted login request volume of the service node within a preset time period after the current moment based on the login association data, business scenario identifier, and login request prediction model, the login traffic trend within the preset time period can be accurately predicted, providing a basis for advance management. Furthermore, by determining the load assessment result corresponding to the service node at the current moment based on the hardware status data, operational status data, login association data, and business scenario identifier, the overall carrying capacity and operational pressure of the service node can be accurately reflected, ensuring the authenticity and reliability of the load assessment. Furthermore, by determining the login control strategy corresponding to the service node at the current moment based on the predicted login request volume and load assessment result, and performing login concurrency control on the service node according to the login control strategy, refined concurrency control based on future predictions and current status can be achieved, ensuring stable operation of the service node and improving user experience. The technical solution of this disclosure solves the technical problems of lagging control response, insufficient prediction, certain limitations, and easy false triggering or missed triggering in related technologies. It realizes accurate prediction of login requests and adaptive login concurrency control based on multi-dimensional data and game business scenarios. By predicting the login request volume, it can sense load changes in advance and dynamically match control strategies, thereby effectively improving the control response speed and accuracy, avoiding false triggering or missed triggering, and significantly improving the operational stability and business carrying capacity of service nodes.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating a game login concurrency control method provided in an embodiment of this disclosure; Figure 2 A flowchart illustrating another game login concurrency control method provided in this embodiment of the disclosure; Figure 3This is a schematic diagram of the structure of a game login concurrency control device provided in an embodiment of the present disclosure; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0017] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0018] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0019] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0020] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0021] With the large-scale deployment of online games and online service systems, a surge of users logging in during new server launches, version updates, and major events can create instantaneous high-concurrency pressure on backend service nodes. The hardware resources, operational status, and login processing performance of these service nodes face severe challenges. How to stably and efficiently handle login surges and ensure the continuous and reliable operation of the system has become a key technical issue of concern in this field.

[0022] Currently, most high-concurrency control methods adopt passive threshold control mechanisms, which rely solely on a single hardware indicator for rate limiting, circuit breaking, or simple load balancing. These methods suffer from technical problems such as prediction lag, biased judgment, and low efficiency. Furthermore, they are difficult to make accurate and adaptive adjustments based on game business scenarios, and cannot fundamentally solve the problems of overload, latency, and instability caused by login surges.

[0023] To address the aforementioned issues, this embodiment employs an adaptive login concurrency control method based on login trend prediction and load assessment. By integrating business scenarios, hardware status, operational status, and login-related data for prediction and assessment, and dynamically generating matching login control strategies, this method achieves early prediction of login peaks, accurate assessment of system status, and adaptive regulation of login traffic. This effectively avoids passive response delays and biases in judging single indicators, significantly improving service node stability, resource utilization, and user login experience.

[0024] Figure 1 This is a flowchart illustrating a game login concurrency control method provided in an embodiment of this disclosure. This embodiment is applicable to processing game user login requests on game service nodes. The method can be executed by a game login concurrency control device, which can be implemented in hardware and / or software and can be configured in electronic devices such as computers or servers. Figure 1 As shown, the method in this embodiment includes: S110. Obtain the hardware status data, running status data, login association data, and business scenario identifier of the service node at the current moment.

[0025] In this context, a service node can be understood as a server, server cluster, or service instance deployed in the game's backend system to receive and process user login requests and provide game login-related services. As the core operating unit handling user login traffic and business computation, the processing capacity and operational status of the service node directly determine the user login experience and system stability. In this embodiment, the service node can be any service node deployed in the game's backend system. "Current moment" can refer to the real-time point in time at which data collection, prediction, evaluation, and strategy generation operations are performed; it is the baseline time corresponding to the execution of the login concurrency control process. Hardware status data can be understood as the real-time operation and resource usage data of the service node's hardware at the current moment, reflecting the hardware load and resource availability of the service node. Optionally, hardware status data includes, but is not limited to, CPU utilization, memory usage, network bandwidth utilization, and disk I / O status. Operational status data can be understood as the real-time operational metrics data at the software system level of the service node at the current moment, characterizing the operational health and business processing capabilities of the service node. Optionally, operational status data includes, but is not limited to, service process status, average response time, number of concurrent connections, request processing throughput, and number of exceptions / errors. Login-related data can be understood as real-time business data directly related to user login behavior at the current moment, used to reflect the traffic scale and processing status of current login requests. Optionally, login-related data includes, but is not limited to, the number of real-time login requests, login request rate, login queue length, login processing time, number of successful logins, and number of failed logins. Business scenario identifiers can be understood as unique identifiers indicating the game business operation scenario or stage type in which the service node is currently located, used to characterize the background characteristics of the current login traffic. It can be understood that the game business operation scenario refers to the business operation status and user activity environment of the game system at the current moment, used to distinguish the login traffic characteristics under different time periods and different operational activities. Optionally, game business operation scenarios include, but are not limited to, regular operations, new server launches, version updates, event launches, festival celebrations, tournaments, and server merge maintenance scenarios.

[0026] In one implementation, for multiple service nodes deployed in the game backend system, the hardware resource usage information, running status data, login behavior-related business data, and business scenario identifiers representing the current game business operation scenario of the service nodes can be read and collected in real time through a preset data collection interface and detection component, so as to obtain the hardware status data, running status data, login-related data, and business scenario identifiers of the service nodes at the current moment.

[0027] S120. Based on login association data, business scenario identifiers, and login request prediction models, determine the predicted number of login requests for the service node within a preset time period after the current moment.

[0028] The login request prediction model can be understood as a prediction model pre-trained with a large amount of historical data. This model can predict the number of login requests in the future based on input data. Optionally, the login request prediction model can be a mathematical model built based on data mapping relationships, or a deep learning model built based on neural network structures. This embodiment does not specifically limit this. For example, the login request prediction model can learn the correspondence between the number of historical login requests, the login request rate, and different game business operation scenarios to establish a mapping rule from current login-related data, business scenario identifiers, to the number of login requests in the future. When real-time login data in scenarios such as new server opening or version update is input, the model can output prediction results such as the trend of login requests and the estimated peak value within a preset time period based on the learned rules, thereby achieving advance prediction of login traffic. The preset time period can be understood as a pre-set future time period for which login traffic prediction needs to be performed, such as the next 5 minutes or 10 minutes. The predicted login request volume can be understood as the predicted value output by the login request prediction model, representing the number of login requests, request rate, and / or peak value that the service node is expected to receive within the predicted time period.

[0029] In this embodiment, the predicted login request volume of a service node within a preset time period after the current time is determined based on login association data, business scenario identifiers, and a login request prediction model. This includes at least one of the following: inputting login association data and business scenario identifiers into the login request prediction model to obtain the predicted login request volume of the service node within a preset time period after the current time; or inputting historical year-on-year login data, historical month-on-month login data, login association data, and business scenario identifiers corresponding to the current time into the login request prediction model to obtain the predicted login request volume of the service node within a preset time period after the current time. One of these determination methods will be described in detail below.

[0030] Optionally, based on login-related data, business scenario identifiers, and a login request prediction model, the predicted login request volume for the service node within a preset time period after the current moment is determined. This includes: obtaining the historical year-on-year and month-on-month login data for the service node at the current moment; and inputting the login-related data, business scenario identifiers, historical year-on-year and month-on-month login data into the login request prediction model to obtain the output predicted login request volume for the service node within a preset time period after the current moment. The advantage of this setup is that by introducing historical year-on-year and month-on-month login data, combined with real-time login data and business scenarios for prediction, the accuracy and stability of the predicted login request volume are significantly improved, better reflecting business fluctuation patterns and effectively reducing prediction errors.

[0031] Historical year-on-year login data can be understood as login data from the same or similar historical periods, reflecting the same or similar business scenarios, time attributes, and operational types as the current moment. Optionally, historical year-on-year login data includes, but is not limited to, the number of login requests, login request rate, peak login request count, login queue length, number of successful logins, number of failed logins, and login response time for the same historical period. For example, if the current moment is 10:00 AM on the day a new server opens, the corresponding historical year-on-year login data could be the number of login requests, login request rate, peak login request count, login queue length, number of successful logins, number of failed logins, and login response time generated around 10:00 AM on the same day last year, all within the same new server opening scenario. Historical month-on-month data can be understood as historical login data directly related to the current login business over a continuous period prior to the current moment, reflecting the continuous trend of recent login traffic changes. Optionally, historical month-on-month login data includes, but is not limited to: the number of login requests within multiple consecutive historical time units, the login request rate within multiple consecutive historical time units, the peak login request rate within multiple consecutive historical time units, the login queue length within multiple consecutive historical time units, the number of successful logins and the number of failed logins within multiple consecutive historical time units, and the login response time within multiple consecutive historical time units. For example, if the current time is 10:00, the corresponding historical month-on-month login data could be the number of login requests, login request rate, peak login request rate, login queue length, number of successful logins, number of failed logins, and login response time for consecutive or adjacent time periods such as 9:50-9:59 and 9:40-9:49.

[0032] In this embodiment, the login request prediction model can be used to quantitatively predict login requests over a future period based on real-time and historical data. The predicted login request quantity can be understood as the numerical result directly output by the login request prediction model, which represents the expected number of login requests, request rate, and / or peak request within a preset time period.

[0033] In one implementation, upon obtaining the login association data and business scenario identifier of the service node at the current moment, historical year-on-year login data and historical month-on-month login data corresponding to the current moment can be retrieved from the database based on the current moment and the business scenario identifier. Furthermore, the login association data, business scenario identifier, historical year-on-year login data, and historical month-on-month login data can be input into a pre-trained login request prediction model. The login request prediction model then predicts login requests within a preset time period based on the received input data, thereby obtaining the predicted login request volume of the service node within a preset time period after the current moment.

[0034] S130. Based on hardware status data, operating status data, login association data, and business scenario identifier, determine the load assessment result corresponding to the service node at the current moment.

[0035] The load assessment result can be a quantitative assessment result calculated based on the hardware status data, operation status data, login association data and business scenario identifier of the service node. It is used to intuitively and accurately reflect the actual load pressure, busyness and operation load level of the service node at the current moment, and provide the current load basis for subsequent determination of login control strategy.

[0036] In this embodiment, the method for determining the load assessment result may include at least one of the following, and the specific determination method will be described below.

[0037] Optionally, based on hardware status data, operating status data, login association data, and business scenario identifier, the load assessment result corresponding to the service node at the current moment is determined, including: inputting hardware status data, operating status data, login association data, and business scenario identifier into the assessment result determination unit to obtain the output load assessment result corresponding to the service node at the current moment.

[0038] The evaluation result determination unit can be understood as a pre-trained or configured evaluation unit used to comprehensively calculate the input hardware status data, operating status data, business scenario identifiers, and login-related data, and output the corresponding load evaluation result. The evaluation result determination unit can be at least one of the following: a deep learning model, an algorithm module integrating an evaluation result determination algorithm, a conversational language model, and an intelligent agent.

[0039] In one implementation, after obtaining the hardware status data, operational status data, login association data, and business scenario identifier of the service node at the current moment, these data can be input into the evaluation result determination model. Subsequently, the load evaluation result of the service node at the current moment can be obtained.

[0040] Optionally, based on hardware status data, operational status data, login association data, and business scenario identifiers, the load assessment result corresponding to the service node at the current moment is determined, including: based on hardware status data and operational status data, determining the server status assessment result corresponding to the service node at the current moment; based on login association data and business scenario identifiers, determining the login status assessment result corresponding to the service node at the current moment; and based on the server status assessment result and login status assessment result, determining the load assessment result corresponding to the service node at the current moment.

[0041] S140. Based on the predicted login request volume and load assessment results, determine the login control policy corresponding to the service node at the current moment, and perform login concurrency control on the service node according to the login control policy.

[0042] The login control policy can be understood as a set of rules used to schedule and manage user login requests, determined based on the current load assessment results of the service node and the predicted volume of future login requests. The login control policy ensures the stability of the service node, avoids overload, and achieves smooth processing of login traffic. Optionally, the login control policy may include at least one of the following: login rate limiting policy, login queuing policy, login traffic splitting policy, login rate limiting degradation policy, and login rejection policy. Login rate limiting can be understood as limiting the number of login requests allowed per unit time. Login queuing can be used to add login requests exceeding processing capacity to a queue for sequential processing. Login traffic splitting can be used to redirect some login requests to other idle service nodes. Login rate limiting degradation can be used to reduce non-core login logic under high load, prioritizing core login processes. Login rejection can be used to reject login requests exceeding the carrying capacity when a node is overloaded. Login concurrency control can be understood as the process of managing and scheduling simultaneously initiated user login requests according to the login control policy to ensure the stable operation of the service node.

[0043] Optionally, based on the predicted login request volume and load assessment results, the login control policy corresponding to the service node at the current moment is determined, including: based on the login request volume and assessment score mapping rules, determining the predicted assessment score of the service node within a preset time period after the current moment; and based on the predicted assessment score and load assessment results, determining the login control policy corresponding to the service node at the current moment.

[0044] The assessment score mapping rule can be a pre-defined mapping relationship or calculation rule used to convert the predicted login request volume into a corresponding predicted assessment score. The predicted assessment score can be a quantitative score obtained based on the predicted login request volume and the assessment score mapping rule, used to characterize the login service pressure level of the service node within a preset future time period.

[0045] Optionally, based on the predicted evaluation score and load evaluation result, the login control policy corresponding to the service node at the current moment is determined, including: if both the predicted evaluation score and the load evaluation result are within the corresponding low score range, the login control policy corresponding to the service node at the current moment is determined to be a normal login processing policy; if at least one of the predicted evaluation score and the load evaluation result is within the corresponding medium score range, the login control policy corresponding to the service node at the current moment is determined to be a login rate limiting policy or a login queuing policy; if both the predicted evaluation score and the load evaluation result are within the corresponding high score range, the login control policy corresponding to the service node at the current moment is determined to be a login traffic splitting policy or a login denial policy.

[0046] The low score range corresponding to the predicted evaluation score can be a pre-defined numerical range. Scores within this range indicate that the service node experiences low login request traffic, low login service pressure, and sufficient service resources within a pre-defined timeframe. For example, assuming the predicted evaluation score ranges from 0 to 100, the corresponding low score range is 0-40. The low score range corresponding to the load evaluation result can also be a pre-defined numerical range. Scores within this range indicate that the service node currently experiences low overall load, low hardware and service pressure, and sufficient processing resources. For example, assuming the load evaluation result ranges from 0 to 100, the corresponding low score range is 0-40. The medium score range corresponding to the predicted evaluation score can also be a pre-defined numerical range, indicating that the service node experiences moderate login request pressure within a pre-defined timeframe, with some traffic growth but not yet reaching overload levels. For example, assuming the predicted evaluation score ranges from 0 to 100, the corresponding medium score range is 41-70. The medium score range corresponding to the load assessment result can be a pre-defined numerical range, indicating that the overall load of the service node is currently at a medium level, with some pressure, but not yet overloaded. For example, assuming the load assessment result ranges from 0 to 100, the corresponding medium score range is 41-70. The high score range corresponding to the predicted assessment score can also be a pre-defined numerical range, indicating that the login request pressure on the service node is high within a preset future time period, approaching or exceeding the service node's processing capacity. For example, assuming the predicted assessment score ranges from 0 to 100, the corresponding high score range is 71-100. The high score range corresponding to the load assessment result can also be a pre-defined numerical range, indicating that the overall load of the service node is currently high, approaching or exceeding its processing capacity. For example, assuming the load assessment result ranges from 0 to 100, the corresponding high score range is 71-100.

[0047] In one implementation, based on the mapping rules between the predicted login request volume and the evaluation score, the predicted evaluation score of the service node within a preset time period after the current moment is determined. Further, the predicted evaluation score and the load evaluation result are compared with corresponding low, medium, and high score ranges, respectively. When both the predicted evaluation score and the load evaluation result are in their respective low score ranges, the login control policy for the current moment is determined to be a normal login processing policy. When at least one of the predicted evaluation score and the load evaluation result is in its corresponding medium score range, the login control policy is determined to be a login rate limiting policy or a login queuing policy. When both the predicted evaluation score and the load evaluation result are in their respective high score ranges, the login control policy is determined to be a login traffic diversion policy or a login rejection policy, thus completing the process of determining the login control policy. Furthermore, login concurrency control of the service node can be implemented based on the determined login control policy.

[0048] In this embodiment, to further improve the accuracy and adaptability of login concurrency control, after performing login concurrency control on the service node according to the login control strategy, the method further includes: collecting hardware status data and operating status data corresponding to the service node, and correcting the login control strategy based on the hardware status data and operating status data. The advantage of this setup is that it achieves closed-loop feedback and dynamic adaptive adjustment of login concurrency control, enabling real-time correction of the strategy according to the actual operating status of the service node, further improving control stability and rationality.

[0049] In one implementation, after performing login concurrency control on the service node according to the login control policy, hardware status data and operating status data corresponding to the service node can be collected in real time. Based on the collected data, the current load and operating status of the node can be analyzed and judged, and the executed login control policy can be adjusted and optimized according to the analysis results to achieve dynamic correction of the login control policy, so as to adapt it to the actual operating status of the service node, thereby improving the stability and rationality of login concurrency control.

[0050] The technical solution of this disclosure, by acquiring the hardware status data, operational status data, login association data, and business scenario identifier of the service node at the current moment, can comprehensively collect the node's current operating environment, business status, and scenario information, providing a complete data foundation for subsequent prediction and evaluation. Furthermore, by determining the predicted login request volume of the service node within a preset time period after the current moment based on the login association data, business scenario identifier, and login request prediction model, the login traffic trend within the preset time period can be accurately predicted, providing a basis for advance management. Furthermore, by determining the load assessment result corresponding to the service node at the current moment based on the hardware status data, operational status data, login association data, and business scenario identifier, the overall carrying capacity and operational pressure of the service node can be accurately reflected, ensuring the authenticity and reliability of the load assessment. Furthermore, by determining the login control strategy corresponding to the service node at the current moment based on the predicted login request volume and load assessment result, and performing login concurrency control on the service node according to the login control strategy, refined concurrency control based on future predictions and current status can be achieved, ensuring stable operation of the service node and improving user experience. The technical solution of this disclosure solves the technical problems of lagging control response, insufficient prediction, certain limitations, and easy false triggering or missed triggering in related technologies. It realizes accurate prediction of login requests and adaptive login concurrency control based on multi-dimensional data and game business scenarios. By predicting the login request volume, it can sense load changes in advance and dynamically match control strategies, thereby effectively improving the control response speed and accuracy, avoiding false triggering or missed triggering, and significantly improving the operational stability and business carrying capacity of service nodes.

[0051] Figure 2 This is a flowchart illustrating another game login concurrency control method provided in this embodiment. The technical solution of this embodiment can be combined with other embodiments; for the same or related parts, they can be described in conjunction with the descriptions of other embodiments, and will not be repeated here. Figure 2 As shown, the method in this embodiment may specifically include: S210. Obtain the hardware status data, running status data, login association data, and business scenario identifier of the service node at the current moment.

[0052] S220. Based on login association data, business scenario identifiers, and login request prediction models, determine the predicted number of login requests for the service node within a preset time period after the current moment.

[0053] S230. Based on the hardware status data and operating status data, determine the server status assessment result corresponding to the service node at the current moment.

[0054] The server status assessment result can be understood as a comprehensive result derived from hardware status data and operational status data, used to characterize the operational status, carrying capacity, and health level of the service node at the current moment. Optionally, the server status assessment result may include a server status assessment level and / or a server status assessment score.

[0055] Optionally, based on hardware status data and operational status data, the server status assessment result corresponding to the service node at the current moment is determined, including: inputting hardware status data and operational status data into the server status assessment unit to obtain the output server status assessment result corresponding to the service node at the current moment.

[0056] The server status assessment unit can be a pre-set or trained assessment module or model used to comprehensively assess the operational health of the service node based on hardware status data and operational status data, and output the results. Optionally, the server status assessment unit can be at least one of the following: a deep learning model, an algorithm module integrating a server status assessment algorithm, a conversational language model, and an intelligent agent.

[0057] In one implementation, after obtaining the hardware status data and operating status data of the service node at the current moment, the hardware status data and operating status data can be input to the server status evaluation unit. The server status evaluation unit performs a status evaluation on the service node based on the hardware status data and operating status data to obtain the server status evaluation result of the service node at the current moment.

[0058] S240. Based on the login association data and business scenario identifier, determine the login status assessment result of the service node at the current moment.

[0059] The login status assessment result can be understood as a quantitative assessment score or level obtained by comprehensively analyzing the current login-related data and business scenario identifiers of the service node. It reflects the busyness, access pressure, and business load level of the service node's login business at the current moment, and serves as an important basis for subsequently determining the overall load assessment result and login control strategy. Optionally, the login status assessment result may include a login status assessment level and / or a login status assessment score.

[0060] In this embodiment, the method for determining the login status assessment result may include at least one, and the specific methods for determining this at least one will be described below.

[0061] Optionally, based on the login association data and business scenario identifier, the login status assessment result of the service node at the current moment is determined, including: inputting the login association data and business scenario identifier into the login status assessment unit to obtain the output login status assessment result of the service node at the current moment.

[0062] The login status assessment unit can be a pre-set or trained functional processing model used to analyze, process, and comprehensively evaluate the input login-related data and business scenario identifiers, and output a login status assessment result that characterizes the current login service busyness and pressure level of the service node. Optionally, the login status assessment unit can be at least one of the following: a deep learning model, an algorithm module integrating a login status assessment algorithm, a conversational language model, and an intelligent agent.

[0063] In one implementation, after obtaining the login association data and business scenario identifier corresponding to the service node at the current time, the login association data and business scenario identifier can be input into the login status evaluation unit. The login status evaluation unit evaluates the login status of the service node at the current time based on the login association data and business scenario identifier to obtain the output login status evaluation result corresponding to the service node at the current time.

[0064] Optionally, based on login association data and business scenario identifiers, the login status assessment result of the service node at the current moment is determined. This includes: determining the initial assessment result of the service node at the current moment based on login association data and a preset login status assessment algorithm; determining a first coefficient corresponding to the business scenario identifier; and updating the initial assessment result based on the first coefficient to obtain the login status assessment result of the service node at the current moment. The advantage of this setting is that by adaptively adjusting the initial assessment result in conjunction with the coefficient corresponding to the business scenario identifier, the login status assessment result can be adapted to different game business scenarios, improving the accuracy and scenario adaptability of the assessment.

[0065] The login status assessment algorithm can be a pre-defined calculation rule or logic used to calculate a preliminary login pressure assessment value based on the input login-related data. Optionally, the login status assessment algorithm may include at least one of the following: a weighted summation algorithm, a threshold grading algorithm, a normalized scoring algorithm, and a load rate-based algorithm. For example, assuming the login status assessment algorithm is a normalized scoring algorithm, the number of login requests, the login request rate, the login queue length, and the average login response time are obtained from the login-related data. Further, weight values ​​corresponding to the number of login requests, the login request rate, the login queue length, and the average login response time are determined respectively. A weighted summation algorithm is then used to perform a weighted summation operation on the number of login requests, the login request rate, the login queue length, and the average login response time, along with their corresponding weight values. The result obtained is determined as the initial assessment result.

[0066] The first coefficient can be a pre-set weight or adjustment coefficient based on different business scenarios. It is used to adjust the initial evaluation result obtained solely based on login-related data, making the final login status evaluation result more closely reflect the actual login pressure of the current business scenario. For example, the first coefficient is 1 for regular operation scenarios; 1.5 for new server launch scenarios; 2 for large-scale event launch scenarios; 1.2 for version update scenarios; and 0.8 for off-peak maintenance scenarios.

[0067] In one implementation, after obtaining the login association data corresponding to the service node at the current time, a preset login status evaluation algorithm can be used to process the obtained login association data to obtain the initial evaluation result corresponding to the service node at the current time. Further, a first coefficient corresponding to the business scenario identifier can be determined based on a pre-determined scenario coefficient mapping relationship and the business scenario identifier corresponding to the service node at the current time. Further, the product between the first coefficient and the initial evaluation result is determined, and the resulting product is used as the login status evaluation result corresponding to the service node at the current time.

[0068] S250. Based on the server status assessment results and login status assessment results, determine the load assessment result corresponding to the service node at the current moment.

[0069] In this embodiment, the load assessment result may include the server status assessment result and the login status assessment result, or it may be a comprehensive assessment result after processing the server status assessment result and the login status assessment result.

[0070] Optionally, based on the server status assessment result and the login status assessment result, determine the load assessment result corresponding to the service node at the current moment, including: determining the first weight corresponding to the server status assessment result, and determining the second weight corresponding to the login status assessment result; weightedly combining the server status assessment result, the first weight, the login status assessment result, and the second weight to obtain the load assessment result corresponding to the service node at the current moment. The advantage of this setting is that by setting weights for server status and login status separately and then weighted and combining them, the overall load situation of the service node can be reflected more reasonably and accurately, improving the reliability and rationality of the load assessment result, and providing a more accurate basis for subsequent login control.

[0071] The first weight can be a pre-set weighting coefficient corresponding to the server status assessment result, used to adjust the proportion of server hardware status when calculating the overall load. The second weight can be a pre-set weighting coefficient corresponding to the login status assessment result, used to adjust the proportion of login service pressure when calculating the overall load.

[0072] In one implementation, a first weight corresponding to the server status assessment result and a second weight corresponding to the login status assessment result are determined. Further, the product between the server status assessment result and the first weight is determined to obtain a first value, and the product between the login status assessment result and the second weight is determined to obtain a second value. Further, the first value and the second value are added together, and the sum is determined as the load assessment result of the service node at the current time.

[0073] S260. Based on the predicted login request volume and load assessment results, determine the login control policy corresponding to the service node at the current moment, and perform login concurrency control on the service node according to the login control policy.

[0074] The technical solution of this disclosure determines the server status assessment result of the service node at the current moment based on hardware status data and operational status data; further, it determines the login status assessment result of the service node at the current moment based on login association data and business scenario identifier; further, it determines the load assessment result of the service node at the current moment based on the server status assessment result and the login status assessment result. By assessing the server hardware operational status and login business status separately from two dimensions, and then comprehensively integrating them to obtain the load assessment result, the judgment of the overall load of the service node is more comprehensive and accurate, which can truly reflect the actual load pressure of the node and provide a reliable basis for subsequent login control strategies.

[0075] Figure 3This is a schematic diagram of a game login concurrency control device provided in an embodiment of this disclosure. Figure 3 As shown, the game login concurrency control device includes: a data acquisition module 310, a login request prediction module 320, an evaluation result determination module 330, and a control strategy determination module 340. The data acquisition module 310 acquires the hardware status data, operating status data, login-related data, and business scenario identifier of the service node at the current moment; the business scenario identifier indicates the game business operating scenario in which the service node is located at the current moment. The login request prediction module 320 determines the predicted login request volume of the service node within a preset time period after the current moment based on the login-related data, the business scenario identifier, and the login request prediction model. The evaluation result determination module 330 determines the load evaluation result corresponding to the service node at the current moment based on the hardware status data, the operating status data, the login-related data, and the business scenario identifier. The control strategy determination module 340 determines the login control strategy corresponding to the service node at the current moment based on the predicted login request volume and the load evaluation result, so as to perform login concurrency control on the service node according to the login control strategy.

[0076] The technical solution of this disclosure, by acquiring the hardware status data, operational status data, login association data, and business scenario identifier of the service node at the current moment, can comprehensively collect the node's current operating environment, business status, and scenario information, providing a complete data foundation for subsequent prediction and evaluation. Furthermore, by determining the predicted login request volume of the service node within a preset time period after the current moment based on the login association data, business scenario identifier, and login request prediction model, the login traffic trend within the preset time period can be accurately predicted, providing a basis for advance management. Furthermore, by determining the load assessment result corresponding to the service node at the current moment based on the hardware status data, operational status data, login association data, and business scenario identifier, the overall carrying capacity and operational pressure of the service node can be accurately reflected, ensuring the authenticity and reliability of the load assessment. Furthermore, by determining the login control strategy corresponding to the service node at the current moment based on the predicted login request volume and load assessment result, and performing login concurrency control on the service node according to the login control strategy, refined concurrency control based on future predictions and current status can be achieved, ensuring stable operation of the service node and improving user experience. The technical solution of this disclosure solves the technical problems of lagging control response, insufficient prediction, certain limitations, and easy false triggering or missed triggering in related technologies. It realizes accurate prediction of login requests and adaptive login concurrency control based on multi-dimensional data and game business scenarios. By predicting the login request volume, it can sense load changes in advance and dynamically match control strategies, thereby effectively improving the control response speed and accuracy, avoiding false triggering or missed triggering, and significantly improving the operational stability and business carrying capacity of service nodes.

[0077] In some embodiments of this disclosure, optionally, the login request prediction module 320 includes: a data acquisition unit and a login request volume prediction unit. The data acquisition unit is used to acquire historical year-on-year login data and historical month-on-month login data corresponding to the service node at the current time; the login request volume prediction unit is used to input login-related data, business scenario identifiers, historical year-on-year login data, and historical month-on-month login data into the login request prediction model to obtain the predicted login request volume of the service node within a preset time period after the current time.

[0078] In some embodiments of this disclosure, optionally, the evaluation result determination module 330 includes: a server status evaluation unit, a login status evaluation unit, and an evaluation result determination unit. The server status evaluation unit is used to determine the server status evaluation result of the service node at the current time based on the hardware status data and the operating status data; the login status evaluation unit is used to determine the login status evaluation result of the service node at the current time based on the login association data and the business scenario identifier; and the evaluation result determination unit is used to determine the load evaluation result of the service node at the current time based on the server status evaluation result and the login status evaluation result.

[0079] In some embodiments of this disclosure, optionally, the login status assessment unit is specifically configured to determine the initial assessment result corresponding to the service node at the current time based on the login association data and a preset login status assessment algorithm; determine a first coefficient corresponding to the business scenario identifier; and update the initial assessment result based on the first coefficient to obtain the login status assessment result corresponding to the service node at the current time.

[0080] In some embodiments of this disclosure, optionally, the evaluation result determination unit is specifically used to determine a first weight corresponding to the server status evaluation result, and to determine a second weight corresponding to the login status evaluation result; and to perform weighted fusion of the server status evaluation result, the first weight, the login status evaluation result and the second weight to obtain the load evaluation result corresponding to the service node at the current time.

[0081] In some embodiments of this disclosure, optionally, the control strategy determination module 340 includes: an evaluation score determination unit and a control strategy determination unit. The evaluation score determination unit is used to determine the predicted evaluation score of the service node within a preset time period after the current time, based on the predicted login request volume and the evaluation score mapping rule; the control strategy determination unit is used to determine the login control strategy corresponding to the service node at the current time, based on the predicted evaluation score and the load evaluation result.

[0082] Optionally, in some embodiments of this disclosure, the apparatus further includes: a policy correction module, configured to, after performing login concurrency control on the service node according to the login control policy, collect hardware status data and operating status data corresponding to the service node, and correct the login control policy based on the hardware status data and the operating status data.

[0083] The game login concurrency control device provided in this disclosure can execute the game login concurrency control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0084] It is worth noting that the various units and modules included in the above-mentioned game login concurrency control device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this disclosure.

[0085] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0086] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0087] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0088] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as game login concurrency control methods.

[0089] In some embodiments, the game login concurrency control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via read-only memory (ROM) 12 and / or communication unit 19. When the computer program is loaded into random access memory (RAM) 13 and executed by processor 11, one or more steps of the game login concurrency control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the game login concurrency control method by any other suitable means (e.g., by means of firmware).

[0090] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] Computer programs used to implement the game login concurrency control method disclosed herein can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on the machine, partially on the machine, or as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] This disclosure provides a computer-readable storage medium storing computer instructions for causing a processor to execute a game login concurrency control method, comprising: acquiring hardware status data, operating status data, login association data, and a business scenario identifier of a service node at a current moment; the business scenario identifier indicating the game business operating scenario in which the service node is located at the current moment; determining the predicted login request volume of the service node within a preset time period after the current moment based on the login association data, the business scenario identifier, and a login request prediction model; determining the load assessment result corresponding to the service node at the current moment based on the hardware status data, the operating status data, the login association data, and the business scenario identifier; and determining the login control strategy corresponding to the service node at the current moment based on the predicted login request volume and the load assessment result, so as to perform login concurrency control on the service node according to the login control strategy.

[0093] In the context of this disclosure, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0094] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0095] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0096] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0097] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of embodiments of this disclosure.

[0098] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements a game login concurrency control method according to any embodiment of this disclosure.

[0099] In implementing a computer program product, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0100] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0101] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for controlling concurrent login in a game, characterized in that, include: Obtain the hardware status data, running status data, login association data, and business scenario identifier of the service node at the current moment; The business scenario identifier is used to indicate the game business operation scenario in which the service node is at the current moment. Based on the login association data, the business scenario identifier, and the login request prediction model, the predicted number of login requests for the service node within a preset time period after the current time is determined. Based on the hardware status data, the operating status data, the login association data, and the business scenario identifier, determine the load assessment result of the service node at the current moment; Based on the predicted number of login requests and the load assessment results, the login control policy corresponding to the service node at the current time is determined, so as to perform login concurrency control on the service node according to the login control policy.

2. The game login concurrency control method according to claim 1, characterized in that, The step of determining the predicted number of login requests for the service node within a preset time period after the current time, based on the login association data, the business scenario identifier, and the login request prediction model, includes: Obtain the historical year-on-year login data and historical month-on-month login data of the service node at the current time; Input login-related data, business scenario identifiers, historical year-on-year login data, and historical month-on-month login data into the login request prediction model to obtain the predicted number of login requests for the service node within a preset time period after the current moment.

3. The game login concurrency control method according to claim 1, characterized in that, The step of determining the load assessment result of the service node at the current time based on the hardware status data, the operating status data, the login association data, and the business scenario identifier includes: Based on the hardware status data and the operating status data, determine the server status evaluation result of the service node at the current moment; Based on the login association data and the business scenario identifier, determine the login status evaluation result of the service node at the current time; Based on the server status assessment result and the login status assessment result, the load assessment result of the service node at the current time is determined.

4. The game login concurrency control method according to claim 3, characterized in that, The step of determining the login status assessment result of the service node at the current time based on the login association data and the business scenario identifier includes: Based on the login association data and the preset login status evaluation algorithm, determine the initial evaluation result of the service node at the current time; A first coefficient corresponding to the business scenario identifier is determined, and the initial evaluation result is updated based on the first coefficient to obtain the login status evaluation result of the service node at the current time.

5. The game login concurrency control method according to claim 3, characterized in that, The step of determining the load assessment result of the service node at the current time based on the server status assessment result and the login status assessment result includes: Determine a first weight corresponding to the server status assessment result, and determine a second weight corresponding to the login status assessment result; The server status assessment result, the first weight, the login status assessment result, and the second weight are weighted and fused to obtain the load assessment result of the service node at the current time.

6. The game login concurrency control method according to claim 1, characterized in that, The step of determining the login control policy for the service node at the current time based on the predicted login request volume and the load assessment result includes: Based on the mapping rule between the predicted login request volume and the evaluation score, the predicted evaluation score of the service node within a preset time period after the current moment is determined; Based on the predicted evaluation score and the load evaluation result, the login control policy corresponding to the service node at the current time is determined.

7. The game login concurrency control method according to claim 1, characterized in that, After performing login concurrency control on the service node according to the login control policy, the method further includes: Collect hardware status data and operational status data corresponding to the service node, and correct the login control policy based on the hardware status data and operational status data.

8. A game login concurrency control device, characterized in that, include: The data acquisition module is used to acquire the hardware status data, running status data, login association data, and business scenario identifier of the service node at the current moment; The business scenario identifier is used to indicate the game business operation scenario in which the service node is at the current moment. The login request prediction module is used to determine the predicted number of login requests for the service node within a preset time period after the current time based on the login association data, the business scenario identifier, and the login request prediction model. The evaluation result determination module is used to determine the load evaluation result of the service node at the current time based on the hardware status data, the operation status data, the login association data, and the business scenario identifier. The control strategy determination module is used to determine the login control strategy corresponding to the service node at the current time based on the predicted login request volume and the load assessment result, so as to perform login concurrency control on the service node according to the login control strategy.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the game login concurrency control method as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the game login concurrency control method according to any one of claims 1-7.

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