Data intermediate station scheduling method and system adaptive to multiple service scenes

Abstract

The invention is suitable for the field of business data scheduling, and provides a data-in-channel scheduling method and system adaptive to multiple business scenarios, and the method comprises the steps: collecting business feature data in real time, abstracting business departments participating in scheduling as game roles, and distributing resource demand attributes and game chip attributes for the game roles. And based on the task urgency degree coefficient, the resource utilization efficiency and the scene adaptation weight, constructing a bid calculation model to generate automatic bids, allocating exclusive resources to high-priority roles in combination with a resource allocation thermodynamic diagram, and scheduling low-priority roles as required through an elastic resource pool. The system dynamically monitors resource utilization rate and task indexes, evaluates bidding rationality and adjusts chip attributes. According to the method, roles are stimulated to compete efficiently through a game mechanism, resource allocation is optimized in combination with dynamic feedback, the problems that a traditional method is poor in adaptability and not public in allocation are solved, and the resource utilization efficiency and service continuity in a multi-service scene are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the field of business data scheduling, and in particular, relates to a data middle-end scheduling method and system adapted to multiple business scenarios. Background Art

[0002] As enterprises' digital transformation deepens, the resource scheduling efficiency of the data center, as the core infrastructure supporting multiple business scenarios, directly affects business continuity and responsiveness. The current mainstream data center scheduling technologies mostly use static rules or fixed priority mechanisms, such as a first-in-first-out (FIFO) strategy based on task queues or allocating resources according to preset weights. Although such methods can cope with some simple scenarios, they have significant shortcomings in complex and changing business environments:

[0003] Traditional methods lack a scientific multi-dimensional evaluation mechanism, resource allocation is easily affected by subjective judgment, inefficient tasks may occupy key resources, and the urgent needs of high-value tasks cannot be met in time, resulting in resource waste and business delays. In addition, no competition and collaboration mechanism between roles is introduced, departmental resource use lacks self-discipline, and resource allocation strategies cannot be optimized through historical performance feedback, resulting in long-term low resource utilization efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a data middle-station scheduling method and system adapted to multiple business scenarios, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is implemented as follows: a data middle-end scheduling method adapted to multiple business scenarios, which realizes efficient and flexible scheduling of resources by real-time collection and analysis of various business feature data. The method first abstracts multiple business departments as game roles, dynamically identifies the current business scenario type based on real-time data, and accurately allocates resources according to the resource sensitivity of different scenarios.

[0006] The method collects multi-source heterogeneous data in real time, including business behavior data, system resource data and business metadata, and performs standardization processing to generate structured feature vectors. Then, a lightweight classification model is used to identify the current business scenario and dynamically calibrate the resource sensitivity level of each scenario. On this basis, the scheme defines resource demand attributes and game chip attributes for each game role to ensure that each role can reflect its actual needs and influence during the scheduling process.

[0007] To effectively manage resource allocation, the solution introduces virtual resource quotas as the "budget" of each department to ensure that when resources are limited, each department can apply for and use resources reasonably according to immediate business needs. Based on the current business scenario type and task deadline, the urgency coefficient of the task is calculated, and combined with the adaptation weight extracted from the matching matrix, an automatic bid is calculated to ensure the rationality of resource scheduling.

[0008] During the resource allocation process, the solution generates a resource allocation heat map, prioritizes the allocation of exclusive resources to roles with high resource utilization efficiency and high match with the scenario, and triggers elastic resource pool scheduling to meet the needs of low-priority roles. At the same time, the solution dynamically adjusts the budget and credit points of the role by evaluating the match between the automatic bid and the actual performance, ensuring that roles with efficient bids can obtain more resource support, while roles with inefficient bids are limited by the resource application quota. In addition, when the resource demand of multiple roles exceeds the system capacity, the solution can automatically downgrade the priority of inefficient tasks based on the scenario adaptation weight and resource utilization efficiency, and generate alternative scheduling path recommendations for tasks that fail due to insufficient resources.

[0009] The beneficial effects of the present invention are:

[0010] This data middle-office scheduling method adapted to multiple business scenarios brings significant advantages and beneficial effects by abstracting multiple business departments into game roles and assigning resource demand attributes and game chip attributes to each role. First, the abstract definition of game roles enables the system to use the mechanism of game theory to promote the rational allocation and utilization of resources. Under this framework, various departments form a dynamic relationship of competition and cooperation in the competition for resources, prompting them to pay more attention to the contribution of efficiency and value when applying for resources. Compared with traditional resource scheduling methods, this strategy not only enhances the self-management awareness of various departments, but also encourages collaboration between departments, thereby improving the overall resource utilization efficiency and reducing unnecessary resource waste.

[0011] Secondly, a virtual resource quota is allocated to each gaming role as the upper limit of resource use, which clarifies the boundaries of resource use for each department. This design effectively reduces uncertainty and conflict in the resource application and use process, ensuring that each department can make reasonable use of available resources when resources are tight. Compared with existing technologies, many traditional methods often lack clear resource use standards, which can easily lead to unfair or inefficient resource allocation. The setting of virtual resource quotas provides a clear benchmark, making the resource allocation process more transparent and fair, and helping to enhance the trust of various departments in resource allocation.

[0012] By collecting and analyzing business feature data in real time, the current business scenario type and resource sensitivity can be dynamically identified, allowing the system to adapt to different business environments in a timely manner. This dynamic adaptability is unmatched by traditional static scheduling methods. By introducing scheduling decisions based on real-time data, the system can quickly adjust resource scheduling strategies when faced with emergencies or changes in business needs, thereby ensuring the timely completion of key tasks and business continuity. This flexibility not only improves business response speed, but also optimizes resource allocation, thereby further improving overall business efficiency.

[0013] In addition, the system's feedback mechanism is enhanced by combining the evaluation of the performance of each gaming role, dynamically adjusting the budget and reputation points. This feedback mechanism not only makes resource scheduling decisions more scientific, but also promotes healthy competition between departments, and ultimately drives the improvement of the company's overall performance. Compared with traditional methods, this mechanism can quickly identify and reward efficient performance and punish inefficient performance, thereby effectively motivating departments to be more active in resource use. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flowchart of a data middle station scheduling method adapted to multiple business scenarios provided by an embodiment of the present invention;

[0015] Figure 2 A flowchart of an embodiment of the present invention that abstractly defines multiple business departments involved in data scheduling as gaming roles, and allocates resource demand attributes and gaming chip attributes to each role;

[0016] Figure 3 A flowchart for generating automatic bids based on task urgency coefficient, resource utilization efficiency and scenario adaptation weight provided in an embodiment of the present invention;

[0017] Figure 4 A flowchart of an embodiment of the present invention for allocating exclusive resources to gaming characters ranked higher than the bottom line, and automatically triggering elastic resource pool scheduling for the remaining gaming characters to allocate the remaining resources on demand;

[0018] Figure 5 A flowchart of calculating the bid rationality index and input-output ratio and dynamically adjusting the chip attributes of the next scheduling cycle provided by an embodiment of the present invention;

[0019] Figure 6 A structural block diagram of a data middle-end scheduling system adapted to multiple business scenarios provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] A data middle-end scheduling method adapted to multiple business scenarios realizes efficient scheduling of resources in multiple business scenarios through real-time collection and intelligent analysis of business feature data. The main idea is to abstract multiple business departments as game roles, dynamically identify the current business scenario type based on real-time data, and realize accurate resource allocation according to the resource sensitivity of the scenario. In this process, the method first generates structured feature vectors by standardizing multi-source heterogeneous data (such as business behavior data, system resource data, and business metadata), and uses a lightweight classification model to identify business scenarios, and dynamically calibrate the rigidity or elasticity of the scenario's resource requirements, so as to quickly adapt to different business environments.

[0022] In the specific implementation of resource allocation, the method allocates a virtual resource quota to each gaming role as the "budget" or resource usage limit of each department. The setting of this virtual resource quota is intended to provide a clear boundary for resource use, ensuring that each department can reasonably allocate and utilize available resources when resources are tight. By calculating the task urgency coefficient of each gaming role and combining it with the adaptation weight extracted from the matching matrix, an automatic bid is generated to ensure the rationality and efficiency of resource allocation.

[0023] In the resource scheduling mechanism, the method generates a resource allocation heat map and preferentially allocates exclusive resources to roles with high resource utilization efficiency and high match with the scenario. At the same time, for low-priority roles, the system automatically triggers elastic resource pool scheduling to allocate remaining resources on demand. By evaluating the match between automatic bidding and actual performance, the method dynamically adjusts the budget and reputation points of the role. For gaming roles with efficient bidding, the budget will be increased, while roles with inefficient bidding face the risk of budget cuts. In addition, when resource demand exceeds system capacity, the priority of inefficient tasks will be automatically downgraded based on scenario adaptation weights and resource utilization efficiency. The system also generates alternative scheduling path recommendations for tasks that fail due to insufficient resources, thereby ensuring the continuity and stability of various businesses.

[0024] Specific:

[0025] Figure 1 A flowchart of a data center scheduling method adapted to multiple business scenarios provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0026] S100. According to the real-time collected business characteristic data, identify the current business scenario type, abstractly define multiple business departments participating in data scheduling as game roles, and assign resource demand attributes and game chip attributes to each role.

[0027] By regarding business departments as game roles in this step, the concept of game theory can be introduced, prompting each department to form a relationship of competition and cooperation in resource allocation. This role setting can not only clarify the division of labor and responsibilities of each department in resource use, but also encourage mutual coordination and optimization among departments. The resource demand attributes of each role, such as the computing resource baseline and data flow dependency relationship, can help the system accurately identify the specific resource requirements of each department, avoiding resource waste and ineffective competition. At the same time, the game chip attributes, such as dynamic budget, reputation points, and business weights, endow each role with certain competitive advantages and flexibility, enabling each department to reasonably strive for resources according to its historical performance and current needs when resources are scarce.

[0028] Secondly, this abstraction and classification method can help the system more clearly establish the priority of resource allocation. By dynamically calculating the adaptation weights of each role, the system can reflect the relative importance and urgency of each department in the current business scenario in real time. This mechanism not only improves the scientific nature and transparency of decision-making, but also effectively reduces the unfairness of resource allocation caused by subjective judgment, ensuring that resources can be preferentially allocated to departments that contribute more to business value.

[0029] The framework established in this step can significantly improve the flexibility and response speed of resource scheduling. In the face of a complex and changeable business environment, the system can capture the changes in business characteristics in real time and quickly adjust the resource allocation strategy to ensure the smooth progress of various tasks. This dynamic scheduling ability not only improves resource utilization efficiency, but also enhances the continuity and stability of the business, enabling the enterprise to maintain the ability to respond flexibly in competition.

[0030] Such as Figure 2 As shown, the identification of the current business scenario type, the abstract definition of multiple business departments participating in data scheduling as game roles, and the assignment of resource demand attributes and game chip attributes to each role specifically include:

[0031] S110. Real-time collect multi-source heterogeneous data, including business behavior data, system resource data, and business metadata, and perform standardization processing to generate structured feature vectors.

[0032] S120. Based on the structured feature vectors, establish a lightweight classification model to identify the current business scenario type and dynamically calibrate the resource sensitivity level of the business scenario, including:

[0033] High-sensitivity scenario: Resource requirements are rigid, and over-quota preemption is allowed;

[0034] Low-sensitivity scenario, resource requirements are elastic, and delayed scheduling is supported;

[0035] S130, Map business departments to game roles, and define resource requirement attributes and game chip attributes. Among them, the game chip attributes include: dynamic budget, reputation points, and business weights;

[0036] S140, Generate a matching degree matrix between business scenarios and game roles, and identify the adaptation weights of each game role in the current business scenario , where:

[0037] ;

[0038] Among them, is the adaptation degree score, indicating the game role in the business scenario under the specific adaptation degree, is the resource demand sensitivity coefficient of the business scenario , used to reflect the resource demand rigidity of this business scenario , is the total resource demand of the business scenario .

[0039] S200, Collect the historical scheduling data of each role, including task completion timeliness, resource utilization rate, and business value contribution degree, establish a bid calculation model, and generate an automatic bid according to the task urgency coefficient, resource utilization efficiency, and scenario adaptation weight;

[0040] This step will collect the historical scheduling data of each game role. The purpose of collecting historical scheduling data is to establish a performance profile of each game role in past tasks. The integration of this historical data enables the system to evaluate the efficiency and contribution degree of each role in resource use, thus providing a scientific basis for subsequent decision-making. The analysis of this historical performance means that resource allocation is no longer based on subjective judgment or static rules, but on a comprehensive understanding of the real experience and operation of each department. In this way, the system can identify which roles have performed excellently in past resource utilization, which roles need improvement, and make dynamic adjustments based on this.

[0041] The introduction of the calculation task urgency coefficient further emphasizes the time sensitivity of tasks. This coefficient not only considers the remaining time but also combines the scenario time tolerance factor to ensure that urgent and important tasks can be prioritized in resource allocation. This dynamic evaluation mechanism ensures that changes in business requirements can be responded to in a timely manner, enabling resources to be preferentially allocated where they are most needed, thus maximizing the efficiency of business execution.

[0042] By setting the efficiency stability index, the system can motivate the performance of game roles in resource utilization. Additional reward factors are given to roles with high efficiency stability to encourage them to maintain high efficiency in future scheduling. This incentive mechanism not only improves the enthusiasm of each department but also enhances the overall resource utilization efficiency, reducing resource waste and unnecessary competition. This performance-based resource allocation strategy helps to cultivate a healthy competition relationship among departments, enabling the entire system to develop in a more efficient and collaborative direction.

[0043] In addition, the system also evaluates the resource utilization efficiency of each game role in historical scheduling through the efficiency stability index, specifically expressed as the ratio of the standard deviation to the mean of the historical resource utilization rate. This index not only reflects the stability of the role in resource use but also enables the system to give additional rewards to outstanding roles to stimulate their enthusiasm and efficiency in future scheduling. The introduction of this mechanism effectively improves the competition awareness of each role, making each department more cautious when applying for resources, and thus improving the overall resource utilization efficiency.

[0044] In the process of calculating the automatic bid, the system combines the computing resource baseline, task urgency coefficient, and adaptation weight to generate the bid. Such a bid model is very flexible and can timely reflect the real needs and values of each game role in the current scenario. In addition, to ensure the fair allocation of resources, the system also sets a threshold for the credit score, imposing a penalty term weight reduction coefficient on game roles with a credit score lower than this threshold in the bid, so as to promote self-discipline and improvement of each role in resource utilization.

[0045] As Figure 2 shown, the establishment of the bid calculation model generates an automatic bid according to the task urgency coefficient, resource utilization efficiency, and scenario adaptation weight, specifically including:

[0046] S210, based on the current business scenario type and task deadline , calculate the task urgency coefficient :

[0047] ;

[0048] Among them, represents the current time;

[0049] S220. Calculate the ratio of the standard deviation to the mean of the historical resource utilization rate of each game role, and define it as the efficiency stability index. At the same time, set an index threshold, and attach an efficiency reward factor to the game role whose efficiency stability index is higher than the index threshold;

[0050] S230. Extract the adaptation weights of each game role in the current business scenario from the matching degree matrix, and calculate the automatic bid :

[0051] ;

[0052] where, represents the resource demand attribute, represents the adaptation weight, represents the dynamic budget, represents the weight coefficient.

[0053] In this step, after the automatic bid is calculated:

[0054] For the game role in the high-sensitivity scenario, superimpose the emergency scenario bonus coefficient on the calculated automatic bid;

[0055] Set an integral threshold. For the game role whose reputation integral is lower than the integral threshold, superimpose the penalty term weight reduction coefficient on the calculated automatic bid;

[0056] Then the updated automatic bid formula is:

[0057] ;

[0058] where, represents the emergency scenario bonus coefficient, represents the penalty weight reduction coefficient.

[0059] S300. Generate a resource allocation heat map according to the automatic bid result, set a ranking bottom line, allocate exclusive resources to the game role whose ranking is higher than the ranking bottom line, and automatically trigger the elastic resource pool scheduling for the remaining game roles, and allocate the remaining resources as needed;

[0060] The resource priority matrix generated in this step is constructed by comprehensively considering the bid scenario adaptation weight, the efficiency reward factor, and the game role reputation integral. This multi-dimensional evaluation criterion can comprehensively reflect the relative importance of each role in the current business scenario, ensuring that resource allocation not only depends on the bid amount, but also takes into account the historical performance and adaptability of the role. This scientific priority setting mechanism enables those roles that contribute more to the business value to be guaranteed first in the case of resource shortage, thus effectively improving the overall efficiency of the business.

[0061] Implement resource hard isolation by allocating exclusive resources to game roles with high resource utilization efficiency and high scene matching degree. The implementation of this strategy ensures that important tasks can obtain the required resource support at critical moments and avoids preemption by other low-priority tasks. Such measures of resource hard isolation can effectively reduce conflicts caused by resource contention, especially in highly sensitive scenarios, and improve the stability and reliability of the system. In addition, for low-priority roles, the system can automatically trigger the scheduling of the elastic resource pool. This flexible resource allocation mechanism enables the system to maintain a certain degree of resilience and quickly respond to changes in business requirements when facing uncertainties.

[0062] Furthermore, this step can make the resource allocation process transparent. The generation of the resource allocation heat map can not only intuitively display the resource occupancy of each game role but also help the management quickly identify bottlenecks and potential problems in resource use. The introduction of such a visualization tool not only improves the effectiveness of decision-making but also provides important data basis for subsequent resource scheduling.

[0063] Through scientific priority setting, resource hard isolation, and elastic scheduling mechanisms, efficient allocation and utilization of resources are achieved in a complex and ever-changing business environment. It not only ensures the resource guarantee for critical tasks, improves the execution efficiency of the overall business, but also provides a transparent and intuitive perspective on resource use for management. Such a highly flexible and dynamic resource scheduling ability enables enterprises to quickly respond and maintain a competitive advantage when facing uncertainties.

[0064] As Figure 4 shown, generating a resource allocation heat map according to the automatic bidding result, setting a ranking bottom line, allocating exclusive resources to game roles with a ranking higher than the bottom line, and automatically triggering the scheduling of the elastic resource pool for the remaining game roles to allocate the remaining resources as needed specifically includes:

[0065] S310, generate a resource priority matrix according to the automatic bidding result, and the generation rule is:

[0066] First priority: bid scene adaptation weight;

[0067] Second priority: efficiency reward factor;

[0068] Third priority: game role reputation score;

[0069] S320, set a ranking bottom line, and for game roles with a ranking higher than the bottom line, perform resource hard isolation:

[0070] Allocate exclusive computing nodes and prohibit other tasks from preemption;

[0071] Set a resource occupancy threshold and automatically expand when the limit is exceeded;

[0072] After the S330 allocates resources to the game players whose rankings are higher than the ranking bottom line, it divides the remaining resources into a flexible resource pool and sets a flexible allocation rule for distribution.

[0073] S400, collect the actual resource utilization rate and task completion indicators of each player, calculate the bid rationality indicator and input-output ratio, and dynamically adjust the chip attributes in the next scheduling cycle;

[0074] Through the hierarchical determination of the efficiency, rationality, and inefficiency of bids, the system can quickly identify which game players perform excellently in resource utilization and which need improvement. This evaluation of the matching degree not only enables the system to make effective self-adjustments but also provides a basis for subsequent resource scheduling. Game players with efficient bids will receive a budget increase, and this incentive mechanism encourages each department to continue to optimize resource utilization efficiency, forming a virtuous cycle. For game players with inefficient bids, the strategy of budget cut is a kind of restraint, prompting them to be more cautious in future resource applications and enhancing their self-management awareness in resource utilization.

[0075] Set that players with a resource utilization rate ≥ 80% and tasks completed on time can increase their credit scores and enhance their priority in subsequent auctions. This is not only a reward for well-performing players but also sets an example for other players, forming a strong competitive atmosphere. This mechanism not only promotes reasonable competition among players but also enhances the overall business execution efficiency. On the contrary, for players with a resource utilization rate ≤ 50% and task failures, by deducting credit scores and restricting the resource application limit, the rigor of resource use is further enhanced, and the degree of attention of each department to resource utilization is improved.

[0076] Furthermore, the dynamic adjustment ability of this step ensures the flexibility of the system in resource scheduling. By providing real-time feedback and adjusting chip attributes and auction rule parameters, the system can maintain a high sensitivity to changes in the business scenario and quickly respond to changes in the external environment. For example, if the demand for a certain business scenario increases sharply, the system can rely on historical data and real-time feedback to adjust the resource allocation of relevant players to ensure that important tasks are supported in a timely manner.

[0077] By constructing a feedback mechanism and dynamic adjustment strategies, the system can not only optimize the allocation of resources but also promote the enthusiasm and rationality of each game player in business execution, improving the overall resource utilization efficiency of the organization. Such a strategy ensures that the enterprise can flexibly respond and efficiently manage in the face of a rapidly changing and complex market environment, maintaining a competitive advantage.

[0078] This step optimizes the resource scheduling process and enhances the system's responsiveness to business requirement changes by dynamically evaluating and adjusting the performance of game roles. This mechanism makes resource utilization more efficient and reasonable, ensuring the continuous and stable operation of the enterprise's various businesses and thus achieving long-term business goals.

[0079] As Figure 5 shown, calculate the rationality index of the bid and the input-output ratio, and dynamically adjust the chip attributes in the next scheduling cycle, specifically:

[0080] S410, evaluate the matching degree between the automatic bid and the actual efficiency:

[0081] ;

[0082] Among them, represents the matching degree between the automatic bid and the actual efficiency of the game role , represents the automatic bid of the game role , represents the actual efficiency of the game role ;

[0083] S420, conduct a hierarchical determination based on the matching degree:

[0084] Efficient bid: > 1.2, indicating that the resource input-output is higher than the industry average level;

[0085] Rational bid: 0.8 < < 1.2, indicating that the resource allocation is reasonable;

[0086] Inefficient bid: < 0.8, indicating that there is resource waste;

[0087] S430, for the game role with an efficient bid, increase the budget in the next scheduling cycle; for the game role with an inefficient bid, cut the scheduling budget in the next cycle, and the minimum is not less than 50% of the original budget;

[0088] S440, for the role with a resource utilization rate ≥ 80% and the task completed on time, increase the credit score and enhance its priority in subsequent auctions;

[0089] S450, for the role with a resource utilization rate ≤ 50% and the task failed, deduct the credit score and limit the resource application quota.

[0090] S500, identify the tasks that failed due to insufficient resources and generate alternative scheduling path suggestions.

[0091] Figure 6The block diagram of a data middle - platform scheduling system adapted to multiple business scenarios provided by an embodiment of the present invention is as follows Figure 6 As shown, the system includes:

[0092] A game role definition module 100, which is used to identify the current business scenario type according to the real - time collected business feature data, abstractly define multiple business departments participating in data scheduling as game roles, and assign resource requirement attributes and game chip attributes to each role;

[0093] A role bid calculation module 200, which is used to collect the historical scheduling data of each role, including task completion timeliness, resource utilization rate, and business value contribution degree, establish a bid calculation model, and generate an automatic bid according to the task urgency coefficient, resource utilization efficiency, and scenario adaptation weight;

[0094] A resource allocation module 300, which is used to generate a resource allocation heat map according to the automatic bid result, set a ranking bottom line, allocate exclusive resources to game roles with a ranking higher than the ranking bottom line, and automatically trigger the scheduling of the elastic resource pool for the remaining game roles to allocate the remaining resources as needed;

[0095] A resource utilization monitoring module 400, which is used to collect the actual resource utilization rate and task completion indicators of each role, calculate the bid rationality index and input - output ratio, and dynamically adjust the chip attributes in the next scheduling cycle;

[0096] A task secondary allocation module 500, which is used to identify tasks that fail due to insufficient resources and generate alternative scheduling path suggestions.

[0097] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0098] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0099] The above embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0100] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data middle station scheduling method adapted to multiple business scenarios, characterized in that: The method includes: Identifying the current business scenario type according to the real-time collected business characteristic data, abstractly defining multiple business departments participating in data scheduling as game roles, and assigning resource demand attributes and game chip attributes to each role; Collecting the historical scheduling data of each role, including task completion timeliness, resource utilization rate, and business value contribution degree, establishing a bidding calculation model, and generating an automatic bid according to the task urgency coefficient, resource utilization efficiency, and scenario adaptation weight; Generating a resource allocation heat map according to the automatic bid result, setting a ranking bottom line, allocating exclusive resources to the game roles ranked higher than the ranking bottom line, and automatically triggering the scheduling of the elastic resource pool for the remaining game roles to allocate the remaining resources as needed; Collecting the actual resource utilization rate and task completion indicators of each role, calculating the bid rationality indicator and input-output ratio, and dynamically adjusting the chip attributes in the next scheduling cycle; Identifying tasks that fail due to insufficient resources and generating alternative scheduling path suggestions.

2. The method according to claim 1, wherein The identification of the current business scenario type, the abstract definition of multiple business departments participating in data scheduling as game roles, and the assignment of resource demand attributes and game chip attributes to each role specifically include: Real-time collecting multi-source heterogeneous data, including business behavior data, system resource data, and business metadata, and performing standardized processing to generate a structured feature vector; Based on the structured feature vector, establishing a lightweight classification model to identify the current business scenario type and dynamically calibrating the resource sensitivity level of the business scenario, including: High-sensitivity scenario: Rigid resource demand, allowing over-quota preemption; Low-sensitivity scenario, elastic resource demand, supporting delayed scheduling; Mapping business departments to game roles and defining resource demand attributes and game chip attributes, where the game chip attributes include: dynamic budget, reputation points, and business weight; Generate a matching degree matrix between business scenarios and game roles, and identify the adaptation weights of each game role in the current business scenario , where: ； Among them, is the adaptation degree score, indicating the game character in the business scenario under the specific adaptation degree, is the resource demand sensitivity coefficient of the business scenario, used to reflect the business scenario the demand rigidity for resources, is the total resource demand of the business scenario for the business scenario of the total resource demand.

3. The method according to claim 2, characterized in that, The establishment of the bidding calculation model and the generation of an automatic bid according to the task urgency coefficient, resource utilization efficiency, and scenario adaptation weight specifically include: Based on the current business scenario type and task deadline , calculate the task urgency coefficient : ； Among them, represents the current time; Calculating the ratio of the standard deviation to the mean of the historical resource utilization rate of each game role and defining it as the efficiency stability indicator, and at the same time setting an indicator threshold, and adding an efficiency reward factor to the game roles with an efficiency stability indicator higher than the indicator threshold; Extract the adaptation weights of each game role in the current business scenario from the matching degree matrix and calculate the automatic bid : ； Among them, represents the resource requirement attribute, represents the adaptation weight, represents the dynamic budget, represents the weight coefficient.

4. The method according to claim 3, wherein After the automatic bid calculation: For the game roles in the high-sensitivity scenario, adding an emergency scenario bonus coefficient to the calculated automatic bid; Setting an integral threshold, and for the game roles with a reputation score lower than the integral threshold, adding a penalty item weight reduction coefficient to the calculated automatic bid; Then the updated automatic bid formula is: ； Among them, represents the emergency scenario bonus coefficient, represents the penalty downgrading coefficient.

5. The method according to claim 4, wherein The generation of a resource allocation heat map according to the automatic bid result, setting a ranking bottom line, allocating exclusive resources to the game roles ranked higher than the ranking bottom line, and automatically triggering the scheduling of the elastic resource pool for the remaining game roles to allocate the remaining resources as needed specifically includes: Generating a resource priority matrix according to the automatic bid result, and the generation rule is: First priority: Bid scenario adaptation weight; Second priority: Efficiency reward factor; Third priority: Game role reputation score; Setting a ranking bottom line, and for the game roles ranked higher than the ranking bottom line, implementing resource hard isolation: Allocate dedicated computing nodes and prevent other tasks from preempting them; Set the resource occupancy rate threshold and automatically scale out when exceeded; After resource allocation for game roles ranked higher than the ranking bottom line, divide the remaining resources into an elastic resource pool and set elastic allocation rules for distribution.

6. The method according to claim 5, characterized in that Calculate the rationality index of the bid and the input-output ratio, and dynamically adjust the chip attributes in the next scheduling cycle. Specifically: Evaluate the matching degree between the automatic bid and the actual effectiveness: ； Among them, represents the matching degree between the automatic bid and the actual effectiveness of the game role , represents the automatic bid of the game role , represents the actual effectiveness of the game role ; Conduct hierarchical determination based on the matching degree: High-efficiency bidding: > 1.2 indicates that the input-output ratio of resources is higher than the industry average; Reasonable bid: 0.8 < < 1.2, indicating reasonable resource allocation; Inefficient bid: <0.8, indicating resource waste; For game roles with efficient bids, increase the budget in the next scheduling cycle; for game roles with inefficient bids, cut the scheduling budget in the next cycle, with the minimum not less than 50% of the original budget; For roles with a resource utilization rate ≥ 80% and tasks completed on time, increase the reputation score and enhance their priority in subsequent auctions; For roles with a resource utilization rate ≤ 50% and task failures, deduct the reputation score and limit the resource application quota.

7. A data middle station scheduling system adapted to multiple business scenarios, characterized in that: The system includes: A game role definition module, which is used to identify the current business scenario type according to the real-time collected business feature data, abstractly define multiple business departments participating in data scheduling as game roles, and assign resource demand attributes and game chip attributes to each role; A role bid calculation module, which is used to collect the historical scheduling data of each role, including task completion timeliness, resource utilization rate, and business value contribution degree, establish a bid calculation model, and generate an automatic bid according to the task urgency coefficient, resource utilization efficiency, and scenario adaptation weight; A resource allocation module, which is used to generate a resource allocation heat map according to the automatic bid result, set the ranking bottom line, allocate exclusive resources to game roles ranked higher than the ranking bottom line, and automatically trigger the elastic resource pool scheduling for the remaining game roles to allocate the remaining resources as needed; A resource utilization monitoring module, which is used to collect the actual resource utilization rate and task completion indicators of each role, calculate the rationality index of the bid and the input-output ratio, and dynamically adjust the chip attributes in the next scheduling cycle; A task secondary allocation module, which is used to identify tasks that fail due to insufficient resources and generate alternative scheduling path suggestions.

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