Smart library electronic resource cloud management method and system

Abstract

The present invention provides a smart library electronic resource cloud management method and system, which relates to the field of computer technology. The method includes: obtaining processing resources and access records of each partition; determining the number of accesses, average access time, download times and new search times of each partition; obtaining the response time of each access record; obtaining the processing resource occupancy ratio; determining the target partition according to the response time, processing resource occupancy ratio, number of accesses, average access time, download times and new search times of each partition; determining the target partition processing resource adjustment amount according to the response time, processing resource occupancy ratio, number of accesses, average access time, download times and new search times; using the adjusted processing resources to process the access requests of each partition. According to the present invention, it is possible to quickly adapt to the access needs of each partition, maximize the use of processing resources, and improve the access efficiency of each partition.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a smart library electronic resource cloud management method and system. Background Art

[0002] Smart libraries can include a variety of electronic resources, including electronic versions of literature, such as books, papers, etc. Smart libraries can classify and store literature similar to ordinary libraries, for example, they can be classified into science literature, engineering literature, medical literature, economics literature, etc. and stored in categories. For example, they can be stored in multiple partitions of cloud devices, and corresponding processing resources are set for each partition to process the visitor's access operations. In related technologies, the processing resources allocated to each partition are usually evenly distributed, and the processing resources cannot usually be changed after allocation. However, the number of visits to each partition in each time period is different, and the number of people facing each partition is also different. For example, the number of users of engineering literature may be more than that of users of science literature, and the number of visits to the partition storing engineering literature may be greater than the number of visits to the partition storing science literature. For example, during the university graduation season, the time for checking for duplicate papers in various majors may be different. When checking for duplicate papers in engineering majors, the number of visits to the partition storing engineering literature may increase sharply, while the number of visits to the partition storing other literature is lower. When checking for duplicate papers in other majors, the number of visits to the partition storing other literature may increase sharply, while the number of visits to the partition storing engineering literature is lower. Therefore, if the same processing resources are set for each partition, and the amount of processing resources is constant, it may be difficult to cope with the access needs of visitors with different usage needs in different time periods.

[0003] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0004] The embodiment of the present invention provides a smart library electronic resource cloud management method and system, which can quickly adapt to the access requirements of each partition, maximize the use of processing resources, and improve the access efficiency of each partition.

[0005] According to a first aspect of an embodiment of the present invention, a smart library electronic resource cloud management method is provided, the method being used in a cloud device and comprising:

[0006] In the i-th processing cycle, the processing resources corresponding to each partition in the smart library database are obtained, wherein the various types of electronic resources in the smart library are classified and stored in each partition, and i is a positive integer;

[0007] At the end of the i-th processing cycle, obtaining access records of each partition;

[0008] Determine the number of accesses, average access duration, number of downloads, and number of novelty searches for each partition based on the access records;

[0009] Get the response time for each access record;

[0010] Obtain the processing resource occupancy ratio of each partition at each moment in the i-th processing cycle;

[0011] Determine the target partition for adjusting the processing resources according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads and the number of novelty searches of each partition;

[0012] Determining an adjustment amount of processing resources for the target partition according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads, and the number of novelty searches;

[0013] In the (i+1)th processing cycle, the access requests of the partitions are processed using the adjusted processing resources of the partitions.

[0014] According to an embodiment of the present invention, determining a target partition for adjusting processing resources according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads, and the number of novelty searches of each partition includes:

[0015] Determine the average number of visits per unit time according to the number of visits, the average visit duration, and the duration of the i-th processing cycle;

[0016] Determining a first ratio of the number of downloads to the number of visits;

[0017] Determining a second ratio of the number of novelty searches to the number of accesses;

[0018] Determine a high resource usage score according to the first ratio, the second ratio and the average number of visits per unit time;

[0019] Determine a response time score according to the response time and the average access time;

[0020] Determining a task congestion score according to the response time and the processing resource occupancy ratio;

[0021] Determining an adjustment demand score according to the high resource occupancy score, the response time score, and the task congestion score;

[0022] According to the adjustment requirement scores of each partition, the target partition of the processing resources to be adjusted is determined.

[0023] According to an embodiment of the present invention, determining the average number of visits per unit time according to the number of visits, the average visit duration, and the duration of the i-th processing cycle includes:

[0024] According to the formula

[0025]

[0026] Get the average number of visits per unit time of the kth partition in the i-th processing cycle Among them, N k，i is the number of accesses to the kth partition in the i-th processing cycle, is the average access time of the kth partition in the i-th processing cycle, ΔT i is the duration of the i-th processing cycle.

[0027] According to an embodiment of the present invention, determining a high resource occupation score according to the first ratio, the second ratio and the average access volume per unit time includes:

[0028] According to the formula

[0029]

[0030] Determine the high resource usage score G of the kth partition in the i-th processing cycle k，i , where r 1，k，i is the first ratio of the kth partition in the i-th processing cycle, r 2，k，i is the second ratio of the kth partition in the i-th processing cycle, α1 and α2 are preset weights, and K is the total number of partitions.

[0031] According to an embodiment of the present invention, determining a task congestion score according to the response time and the processing resource occupancy ratio includes:

[0032] According to the formula

[0033]

[0034] Determine the task congestion score Y of the kth partition in the i-th processing cycle k，i , where T j，k，i is the response time of the jth access record of the kth partition in the i-th processing cycle, P t , k , i is the processing resource occupancy ratio of the kth partition at the tth moment of the i-th processing cycle, M k ,i is the number of time moments included in the i-th processing cycle.

[0035] According to an embodiment of the present invention, the target partition includes a first target partition whose processing resources are to be increased, and a second target partition whose processing resources are to be decreased;

[0036] Determining the processing resource adjustment amount for the target partition according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads, and the number of novelty searches, including:

[0037] Determining, according to the adjustment demand score, a ratio of resources to be adjusted in the first target partition and a ratio of resources to be reduced in the second target partition;

[0038] Determine the total amount of resources to be increased of the first target partition according to the current amount of processing resources of the first target partition and the proportion of resources to be increased;

[0039] Determining the total resource supply of the second target partition according to the current processing resource amount of the second target partition and the proportion of resources to be reduced;

[0040] Determining an adjustment amount of processing resources for the second target partition according to the current amount of processing resources for the second target partition and the ratio of resources to be reduced;

[0041] In the case where the total amount of resource supply is less than the total amount of resource demand to be increased, determining the processing resource adjustment amount of the first target partition according to the total amount of resource supply, the total amount of resource demand to be increased, the total amount of processing resources in the idle resource pool, and the current amount of processing resources of the first target partition and the ratio of resources to be increased;

[0042] When the total resource supply is greater than or equal to the total resource demand to be increased, the processing resource adjustment amount of the first target partition is obtained according to the current processing resource amount of the first target partition and the proportion of the resources to be increased, and the part of the total resource supply that is greater than the total resource demand to be increased is added to the idle resource library.

[0043] According to an embodiment of the present invention, when the total amount of resource supply is less than the total amount of resource demand to be increased, determining the processing resource adjustment amount of the first target partition according to the total amount of resource supply, the total amount of resource demand to be increased, the total amount of processing resources in the idle resource pool, and the current total amount of processing resources of the first target partition and the proportion of resources to be increased includes:

[0044] According to the formula

[0045]

[0046] Determine the processing resource adjustment amount R of the xth first target partition 1，x , where R 2，y，c is the current processing resource of the yth second target partition, r 2，y，d is the ratio of resources to be reduced in the yth second target partition, R 1，x，c is the current processing resource of the xth first target partition, r 1，x，u is the ratio of resources to be increased in the xth first target partition, R l is the processing resource adjustment amount in the idle resource pool, and R l Less than or equal to the total amount of processing resources in the idle resource pool.

[0047] According to an embodiment of the present invention, determining the proportion of resources to be adjusted in the first target partition and the proportion of resources to be reduced in the second target partition according to the adjustment demand score includes:

[0048] According to the formula

[0049]

[0050] Determine the resource ratio r to be adjusted for the xth first target partition 1，x，u , where A 1，x is the adjustment demand score of the xth first target partition, P T is the target resource occupancy ratio;

[0051] According to the formula

[0052]

[0053] Determine the resource ratio r to be adjusted for the yth second target partition 2，x，d , where A 1，y Score the adjustment need for the y-th second target partition.

[0054] According to a second aspect of an embodiment of the present invention, a smart library electronic resource cloud management system is provided, the system being arranged in a cloud device, the system comprising:

[0055] A processing resource module, used for obtaining processing resources corresponding to each partition in the smart library database in the i-th processing cycle, wherein multiple types of electronic resources in the smart library are classified and stored in each partition, and i is a positive integer;

[0056] An access record module, used for obtaining access records of each partition at the end of the i-th processing cycle;

[0057] A determination module, used to determine the number of accesses, average access duration, number of downloads and number of novelty retrievals of each partition based on the access records;

[0058] A response time module is used to obtain the response time for each access record;

[0059] The occupancy ratio module is used to obtain the processing resource occupancy ratio of each partition at each moment in the i-th processing cycle;

[0060] A target partition module, used to determine the target partition for adjusting the processing resources according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads and the number of novelty searches of each partition;

[0061] An adjustment amount module, used to determine the adjustment amount of the processing resources for the target partition according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads and the number of novelty searches;

[0062] The processing module is used to process the access requests of each partition using the adjusted processing resources of each partition in the (i+1)th processing cycle.

[0063] According to one embodiment of the present invention, the target partition module is further used for:

[0064] Determine the average number of visits per unit time according to the number of visits, the average visit duration, and the duration of the i-th processing cycle;

[0065] Determining a first ratio of the number of downloads to the number of visits;

[0066] Determining a second ratio of the number of novelty searches to the number of accesses;

[0067] Determine a high resource usage score according to the first ratio, the second ratio and the average number of visits per unit time;

[0068] Determine a response time score according to the response time and the average access time;

[0069] Determining a task congestion score according to the response time and the processing resource occupancy ratio;

[0070] Determining an adjustment demand score according to the high resource occupancy score, the response time score, and the task congestion score;

[0071] According to the adjustment requirement scores of each partition, the target partition of the processing resources to be adjusted is determined.

[0072] According to one embodiment of the present invention, the target partition module is further used for:

[0073] According to the formula

[0074]

[0075] Get the average number of visits per unit time of the kth partition in the i-th processing cycle Among them, N k , i is the number of accesses to the kth partition in the i-th processing cycle, is the average access time of the kth partition in the i-th processing cycle, ΔT i is the duration of the i-th processing cycle.

[0076] According to one embodiment of the present invention, the target partition module is further used for:

[0077] According to the formula

[0078]

[0079] Determine the high resource usage score G of the kth partition in the i-th processing cycle k，i , where r 1，k，i is the first ratio of the kth partition in the i-th processing cycle, r 2，k，i is the second ratio of the kth partition in the i-th processing cycle, α1 and α2 are preset weights, and K is the total number of partitions.

[0080] According to one embodiment of the present invention, the target partition module is further used for:

[0081] According to the formula

[0082]

[0083] Determine the task congestion score Y of the kth partition in the i-th processing cycle k，i , where T j，k，i is the response time of the jth access record of the kth partition in the i-th processing cycle, P t，k，i is the processing resource occupancy ratio of the kth partition at the tth moment of the i-th processing cycle, M k，i is the number of time moments included in the i-th processing cycle.

[0084] According to an embodiment of the present invention, the target partition includes a first target partition whose processing resources are to be increased and a second target partition whose processing resources are to be decreased; and the adjustment amount module is further used to:

[0085] Determining, according to the adjustment demand score, a ratio of resources to be adjusted in the first target partition and a ratio of resources to be reduced in the second target partition;

[0086] Determine the total amount of resources to be increased of the first target partition according to the current amount of processing resources of the first target partition and the proportion of resources to be increased;

[0087] Determining the total resource supply of the second target partition according to the current processing resource amount of the second target partition and the proportion of resources to be reduced;

[0088] Determining an adjustment amount of processing resources for the second target partition according to the current amount of processing resources for the second target partition and the ratio of resources to be reduced;

[0089] In the case where the total amount of resource supply is less than the total amount of resource demand to be increased, determining the processing resource adjustment amount of the first target partition according to the total amount of resource supply, the total amount of resource demand to be increased, the total amount of processing resources in the idle resource pool, and the current amount of processing resources of the first target partition and the ratio of resources to be increased;

[0090] When the total resource supply is greater than or equal to the total resource demand to be increased, the processing resource adjustment amount of the first target partition is obtained according to the current processing resource amount of the first target partition and the proportion of the resources to be increased, and the part of the total resource supply that is greater than the total resource demand to be increased is added to the idle resource library.

[0091] According to one embodiment of the present invention, the adjustment amount module is further used for:

[0092] According to the formula

[0093]

[0094] Determine the processing resource adjustment amount R of the xth first target partition 1，x , where R 2，y，c is the current processing resource of the yth second target partition, r 2，y，d is the ratio of resources to be reduced in the yth second target partition, R 1，x，c is the current processing resource of the xth first target partition, r 1，x，u is the ratio of resources to be increased in the xth first target partition, R l is the processing resource adjustment amount in the idle resource pool, and R l Less than or equal to the total amount of processing resources in the idle resource pool.

[0095] According to one embodiment of the present invention, the adjustment amount module is further used for:

[0096] According to the formula

[0097]

[0098] Determine the resource ratio r to be adjusted for the xth first target partition 1，x，u , where A 1，x is the adjustment demand score of the xth first target partition, P T is the target resource occupancy ratio;

[0099] According to the formula

[0100]

[0101] Determine the resource ratio r to be adjusted for the yth second target partition 2，x，d , where A 1，y Score the adjustment need for the y-th second target partition.

[0102] According to a third aspect of an embodiment of the present invention, a smart library electronic resource cloud management device is provided, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the smart library electronic resource cloud management method.

[0103] According to a fourth aspect of an embodiment of the present invention, there is provided a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the smart library electronic resource cloud management method when executed by a processor.

[0104] According to the smart library electronic resource cloud management method of the embodiment of the present invention, when the processing resource allocation of the current processing cycle is difficult to cope with the access needs of each partition, the target partition that needs to adjust the processing resources can be found in time, and when finding the target partition, the three aspects of task processing pressure with high resource occupancy, response speed and task congestion are used to evaluate whether each partition needs to adjust the processing resources, thereby improving the accuracy and objectivity of the evaluation. The total amount of resource supply reduced by the second target partition and the spare processing resources in the idle resource library can be proportionally allocated to each first target partition with a processing resource increase demand to improve the overall processing efficiency and resource utilization. The processing resources corresponding to each partition can be adjusted in time, so as to quickly adapt to the access needs of each partition, maximize the use of processing resources, and improve the access efficiency of each partition, thereby improving the user experience.

[0105] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can be obtained based on these drawings without creative work.

[0107] Figure 1 A flowchart of a method for managing electronic resources cloud in a smart library according to an embodiment of the present invention is exemplarily shown;

[0108] Figure 2 A block diagram of a smart library electronic resource cloud management system according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0109] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0110] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0111] Figure 1 The flowchart of the electronic resource cloud management method of the smart library according to the embodiment of the present invention is shown as follows: Figure 1 As shown, the method includes:

[0112] Step S101, in the i-th processing cycle, obtaining processing resources corresponding to each partition in the smart library database, wherein multiple types of electronic resources in the smart library are classified and stored in each partition, and i is a positive integer;

[0113] Step S102, when the i-th processing cycle ends, obtaining access records of each partition;

[0114] Step S103, determining the number of accesses, average access duration, number of downloads and number of novelty searches of each partition according to the access records;

[0115] Step S104, obtaining the response time for each access record;

[0116] Step S105, obtaining the processing resource occupancy ratio of each partition at each moment in the i-th processing cycle;

[0117] Step S106, determining the target partition for adjusting the processing resources according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads and the number of novelty searches of each partition;

[0118] Step S107, determining an adjustment amount of processing resources for the target partition according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads, and the number of novelty searches;

[0119] Step S108: In the (i+1)th processing cycle, the access requests of the partitions are processed using the adjusted processing resources of the partitions.

[0120] According to the smart library electronic resource cloud management method of the embodiment of the present invention, when the processing resource allocation of the current processing cycle is difficult to cope with the access needs of each partition, the processing resources corresponding to each partition can be adjusted in time, so as to quickly adapt to the access needs of each partition, maximize the use of processing resources, and improve the access efficiency of each partition, thereby improving the user experience.

[0121] According to an embodiment of the present invention, the duration of the processing cycle may be 1 hour, 6 hours, 12 hours, 1 day, etc., and the present invention does not limit the duration of the processing cycle. In step S101, at the beginning of the current processing cycle (i.e., the i-th processing cycle), the amount of processing resources corresponding to each partition may be obtained, and the processing resources may include memory resources, network bandwidth resources, computing resources, etc. allocated by the processor to each partition, and the present invention does not limit the meaning of processing resources.

[0122] According to one embodiment of the present invention, in step S102, at the end of the current processing cycle, the access records of each partition may be obtained. And in step S103, the number of accesses, average access duration, number of downloads, and number of novelty searches of each partition may be determined. In the example, in the access records, the access time and the departure time of each access may be recorded, based on which the duration of each access may be determined, and then the average access duration may be determined. In another example, in the access records, the purpose of each access may be recorded, such as browsing, downloading, novelty searches, etc., and the downloaded content may also be recorded, based on which the number of downloads and novelty searches may be determined.

[0123] According to one embodiment of the present invention, in step S104, the response time of each access may also be recorded in the access record. For example, from the time a user sends a request to a cloud device to access a certain document to the time the user is able to access the content of the document, the timestamp of sending the request and the timestamp of opening the document may be recorded, and the response time of the access may be obtained by subtracting the two.

[0124] According to an embodiment of the present invention, in step S105, the i-th processing cycle can be divided into multiple moments, for example, one moment every 1 second, one moment every 10 seconds, one moment every 30 seconds, one moment every 1 minute, etc. The present invention does not limit the division method. The processing resource occupancy ratio at each moment can be recorded. For example, the processing resource occupancy ratio of partition A at the first moment is 80%, and the processing resource occupancy ratio at the second moment is 85%... The present invention does not limit the specific value of the processing resource occupancy ratio.

[0125] According to an embodiment of the present invention, in step S106, the target partition for adjusting the processing resources can be determined. For example, for a partition with a high processing resource occupancy ratio, which leads to a shortage of processing resources and even requires queuing for processing access requests, the processing resources can be increased to make the processing resources of the partition more abundant and alleviate the shortage of processing resources. For a partition with a low processing resource occupancy ratio and excess processing resources, the processing resources can be reduced so that the partition with shortage of processing resources can use the reduced processing resources, thereby improving the utilization rate of processing resources and improving the overall access efficiency.

[0126] According to one embodiment of the present invention, step S106 may include: determining the average number of visits per unit time based on the number of visits, the average access time, and the duration of the i-th processing cycle; determining a first ratio of the number of downloads to the number of visits; determining a second ratio of the number of novelty searches to the number of visits; determining a high resource occupancy score based on the first ratio, the second ratio, and the average number of visits per unit time; determining a response time score based on the response time and the average access time; determining a task congestion score based on the response time and the processing resource occupancy ratio; determining an adjustment requirement score based on the high resource occupancy score, the response time score, and the task congestion score; and determining a target partition for the processing resources to be adjusted based on the adjustment requirement scores of each partition.

[0127] According to an embodiment of the present invention, the average number of visits per unit time can be used to represent the access pressure borne by a partition at each moment in the i-th processing cycle. According to the number of visits, the average access duration, and the duration of the i-th processing cycle, the average number of visits per unit time is determined, including: according to formula (1), obtaining the average number of visits per unit time of the k-th partition in the i-th processing cycle

[0128]

[0129] Among them, N k , i is the number of accesses to the kth partition in the i-th processing cycle, is the average access time of the kth partition in the i-th processing cycle, ΔT i is the duration of the i-th processing cycle.

[0130] According to one embodiment of the present invention, the average number of visits per unit time of the kth partition in the ith processing cycle is the number of visits to the kth partition in the ith processing cycle multiplied by the ratio between the average visit duration of the kth partition in the ith processing cycle and the duration of the ith processing cycle, that is, the average duration of each visit is the average visit duration, and within this duration, the partition can also accept other visits, and the number of visits accepted within this time period is the average number of visits per unit time. It can represent the average value of the access pressure borne by the kth partition in the ith processing cycle.

[0131] According to one embodiment of the present invention, a first ratio of the number of downloads to the number of accesses can be obtained. Downloading occupies more processing resources and network bandwidth resources. Therefore, the larger the first ratio is, the greater the access pressure on the kth partition in the i-th processing cycle.

[0132] According to one embodiment of the present invention, a second ratio of the number of novelty searches to the number of accesses can be obtained. Since the novelty search requires a sentence-by-sentence comparison of the document to be searched with the document stored in the kth partition, it occupies a large amount of processing resources and network bandwidth resources. Therefore, the larger the second ratio is, the greater the access pressure on the kth partition in the i-th processing cycle.

[0133] According to an embodiment of the present invention, based on the first ratio, the second ratio and the average number of visits per unit time obtained above, a high resource occupancy score can be determined. The high resource occupancy score can be used to reflect whether the kth partition is a partition with a high processing pressure for tasks with high resource occupancy (e.g., downloading and / or novelty search) among multiple partitions of the entire smart library in the i-th processing cycle.

[0134] According to an embodiment of the present invention, determining a high resource occupancy score according to the first ratio, the second ratio and the average access volume per unit time includes: determining a high resource occupancy score G of the kth partition in the i-th processing cycle according to formula (2): k，i ,

[0135]

[0136] Among them, r 1，k，i is the first ratio of the kth partition in the i-th processing cycle, r 2，k，i is the second ratio of the kth partition in the i-th processing cycle, α1 and α2 are preset weights, and K is the total number of partitions.

[0137] According to an embodiment of the present invention, in formula (2), the preset weight α1 can be set as the ratio of the total resource occupation of the download tasks undertaken by the multiple partitions of the smart library in the i-th processing cycle to the total resource occupation of all access tasks, and the preset weight α2 can be set as the ratio of the total resource occupation of the novelty search tasks undertaken by the multiple partitions of the smart library in the i-th processing cycle to the total resource occupation of all access tasks.

[0138] According to one embodiment of the present invention, in formula (2), the numerator may represent the processing pressure of high resource occupancy tasks undertaken by the kth partition at every moment in the i-th processing cycle, and the denominator of formula (2) may represent the average value of the processing pressure of high resource occupancy tasks undertaken by each partition at every moment in the i-th processing cycle. If the kth partition has a higher high resource occupancy score in the i-th processing cycle, it means that the kth partition is a partition with a higher processing pressure of undertaking high resource occupancy tasks among multiple partitions. Conversely, if the kth partition has a lower high resource occupancy score in the i-th processing cycle, it means that the kth partition is a partition with a lower processing pressure of undertaking high resource occupancy tasks among multiple partitions. If the kth partition is one of the multiple partitions that is under a high processing pressure for tasks that require high resource usage, some processing resources can be transferred from other partitions that are under a low processing pressure for tasks that require high resource usage, for use by the kth partition. Conversely, if the kth partition is one of the multiple partitions that is under a low processing pressure for tasks that require high resource usage, some processing resources can be transferred from the kth partition for use by other partitions that are under a high processing pressure for tasks that require high resource usage. In this way, the utilization of processing resources can be improved, and the overall processing efficiency of tasks that require high resource usage can be improved.

[0139] According to one embodiment of the present invention, the response time score can be determined based on the response time and the average access time. For example, the average response time of the kth partition in the i-th processing cycle can be calculated, and the ratio of the average response time to the average access time can be determined. If the ratio is high, it can be considered that the response time is long relative to the access time, which may reduce the user experience. For example, if the average access time is 10 seconds, and the average access time for accessing each page of documents is 20 seconds, then the user needs to wait for 10 seconds each time he accesses a page of documents. This waiting time is long and may reduce the user experience. On the contrary, if the ratio is low, it can be considered that the response time is short relative to the access time, and the user experience is better. For example, if the average access time is 1 second, and the average access time for accessing each page of documents is 20 seconds, then the user only needs to wait for 1 second of response time each time he accesses a page of documents. The waiting time is short and the user experience is better. Therefore, partitions with higher response time scores may lack processing resources, resulting in slower response speeds. Some processing resources can be transferred from other partitions with faster response speeds to partitions with higher response time scores to shorten response time and improve user experience. Conversely, partitions with lower response time scores may have excess processing resources. Some processing resources can be transferred to other partitions with higher response time scores to improve overall response efficiency and user experience.

[0140] According to one embodiment of the present invention, a task congestion score can be determined based on the response time and the processing resource occupancy ratio. The score can indicate whether the access tasks processed by the kth partition in the i-th processing cycle are congested, whether the resource occupancy is too high, or even whether queuing is required to process each access task.

[0141] According to an embodiment of the present invention, determining a task congestion score according to the response time and the processing resource occupancy ratio includes:

[0142] According to formula (3), determine the task congestion score Y of the kth partition in the i-th processing cycle k，i ,

[0143]

[0144] Among them, T j，k，i is the response time of the jth access record of the kth partition in the i-th processing cycle, P t，k，i is the processing resource occupancy ratio of the kth partition at the tth moment of the i-th processing cycle, M k，i is the number of time moments included in the i-th processing cycle.

[0145] According to one embodiment of the present invention, in formula (3), It represents the deviation between the average response time when the average resource occupancy ratio reaches 100% in the kth partition in the ith processing cycle and the actual average response time. The smaller the deviation is, the closer the processing resources of the kth partition are to being fully occupied, and the closer the response time is to the response time under full load (that is, if the response time exceeds the full load state, it is impossible to start processing each access task immediately, and each access task needs to be queued, which makes the response time longer). It can represent the average response time of each partition of the smart library in the i-th processing cycle. is the proportion of the above deviation in the overall average response time. By subtracting this item from 1, we can get the task congestion score of the kth partition in the i-th processing cycle. The lower the task congestion score, the more processing resources remain in the kth partition, and some processing resources can be transferred to other partitions with less processing resources. On the contrary, the higher the task congestion score, the less processing resources remain in the kth partition, and some processing resources can be transferred from other partitions with more processing resources to the kth partition. In this way, the task congestion status and the remaining processing resources in the partition are described by the processing resource proportion and the response time, which improves the accuracy and objectivity of the description of the remaining processing resources.

[0146] According to one embodiment of the present invention, after obtaining the high resource occupancy score, response time score and task congestion score of each partition in the i-th processing cycle, the three scores can be weighted and summed to obtain the adjustment demand score of each partition in the i-th processing cycle. The adjustment demand score can describe the load status of the processing resources of each partition in the i-th processing cycle. If the adjustment demand score is high (higher than the first set threshold, for example, 0.8), it can be considered that the partition has a high processing resource load in the i-th processing cycle, resulting in task congestion and slow response speed. Therefore, the demand for adjusting the processing resources of the partition is high, that is, transferring part of the processing resources from other partitions for use by the partition. If the adjustment demand score is low (lower than the second set threshold, for example, 0.3), it can be considered that the partition has a low processing resource load in the i-th processing cycle and has excess processing resources. Therefore, the processing resources of the partition can be adjusted, that is, transferring part of the processing resources for use by other partitions whose adjustment demand scores are higher than the first set threshold. Therefore, the partitions whose adjustment demand scores are greater than or equal to the first set threshold and less than or equal to the second set threshold can be determined as the target partitions for the processing resources to be adjusted. For a partition whose adjustment demand score is between the first set threshold and the second set threshold, no processing resource adjustment is required.

[0147] In this way, whether each partition needs to adjust its processing resources can be evaluated from three aspects: task processing pressure with high resource consumption, response speed, and task congestion. This improves the accuracy and objectivity of the evaluation, and improves the overall utilization efficiency and response speed of processing resources, thereby improving the user experience.

[0148] According to an embodiment of the present invention, in step S107, after determining the target partition that needs to adjust the processing resources, the processing resources of the target partition can be adjusted. The target partition includes a first target partition whose processing resources are to be increased, and a second target partition whose processing resources are to be decreased. Therefore, part of the processing resources can be transferred from the second target partition for use by the first target partition, that is, after the adjustment, the transferred processing resources become the processing resources of the first target partition, so as to relieve the processing pressure of the first target partition and improve the utilization rate of the processing resources while ensuring that the processing pressure of the second target partition is still at a reasonable level.

[0149] According to one embodiment of the present invention, step S107 includes: determining the proportion of resources to be adjusted of the first target partition and the proportion of resources to be reduced of the second target partition according to the adjustment demand score; determining the total amount of resource demand to be increased of the first target partition according to the current processing resource amount of the first target partition and the proportion of resources to be increased; determining the total amount of resource supply of the second target partition according to the current processing resource amount of the second target partition and the proportion of resources to be reduced; determining the processing resource adjustment amount of the second target partition according to the current processing resource amount of the second target partition and the proportion of resources to be reduced; when the total amount of resource supply is less than the total amount of resource demand to be increased, determining the processing resource adjustment amount of the first target partition according to the total amount of resource supply, the total amount of resource demand to be increased, the total amount of processing resources in the idle resource library, and the current amount of processing resources of the first target partition and the proportion of resources to be increased; when the total amount of resource supply is greater than or equal to the total amount of resource demand to be increased, obtaining the processing resource adjustment amount of the first target partition according to the current amount of processing resources of the first target partition and the proportion of resources to be increased, and adding the part of the total amount of resource supply greater than the total amount of resource demand to be increased to the idle resource library.

[0150] According to an embodiment of the present invention, the ratio of resources to be increased is the ratio between the amount of processing resources to be increased for the first target partition and the existing amount of processing resources for the first target partition, and after the adjustment of the amount of processing resources, the resource occupancy of the first target partition reaches a preset reasonable ratio (for example, 50%) when the access volume remains unchanged. The ratio of resources to be reduced is the ratio between the amount of processing resources to be reduced for the second target partition and the existing amount of processing resources for the second target partition, and after the adjustment of the amount of processing resources, the resource occupancy of the second target partition reaches a preset reasonable ratio (for example, 50%) when the access volume remains unchanged.

[0151] According to an embodiment of the present invention, determining the proportion of resources to be adjusted in the first target partition and the proportion of resources to be reduced in the second target partition according to the adjustment demand score includes:

[0152] According to formula (4), determine the resource ratio r of the xth first target partition to be adjusted 1，x，u ,

[0153]

[0154] Among them, A 1，x is the adjustment demand score of the xth first target partition, P T is the target resource occupancy ratio;

[0155] According to formula (5), determine the resource ratio r to be adjusted for the yth second target partition 2，x，d ,

[0156]

[0157] Among them, A 1，y Score the adjustment need for the y-th second target partition.

[0158] According to one embodiment of the present invention, the adjustment demand score is a value greater than or equal to 0 and less than or equal to 1, which can be used as the current resource load status of the partition, and the target resource occupancy ratio can be used as a reasonable resource load status. After the adjustment of the resource ratio to be adjusted calculated by formulas (4) and (5), the partition can reach a reasonable resource load status when the number of visits remains unchanged. However, the adjustment range of the first target partition may not reach the resource ratio to be adjusted of the first target partition. For example, when the number of visits to the entire smart library is large, the number of second target partitions is small, and the total amount of processing resources that can be reduced is small, the range of the increase that can be made in the first target partition may not reach the resource ratio to be adjusted of the first target partition.

[0159] According to one embodiment of the present invention, the current processing resource amount of each first target partition can be multiplied by the ratio of resources to be increased to obtain the demand for resources to be increased of each first target partition. After summing the demand for resources to be increased of each first target partition, the total demand for resources to be increased can be obtained.

[0160] According to one embodiment of the present invention, the current processing resource amount of each second target partition can be multiplied by the ratio of resources to be reduced to obtain the resource supply amount of each second target partition, and after summing the resource supply amount of each second target partition, the total resource supply amount can be obtained. The resource supply amount of each second target partition is the processing resource adjustment amount of the second target partition, that is, the amount of processing resources to be reduced for each second target partition.

[0161] According to one embodiment of the present invention, if the total amount of resource supply is greater than or equal to the total amount of resource demand to be increased, the current processing resources of the smart library are not tight, the overall access volume is low, and the demand for increased processing resources of some partitions can be met. In this case, the current amount of processing resources of each first target partition can be directly multiplied by the proportion of resources to be adjusted to obtain the processing resource adjustment amount of each first target partition, that is, the amount of adjusted processing resources. If the total amount of resource supply is still remaining after being transferred to the first target partition, it can be added to the idle resource library for standby in subsequent processing cycles.

[0162] According to one embodiment of the present invention, if the total supply of resources is less than the total demand for resources to be increased, the current processing resources of the smart library are relatively tight, and the demand for increased processing resources is greater than the supply of reduced processing resources. In this case, the total supply of resources can be allocated according to the proportion of the demand for increased resources of each first target partition, and if there are spare processing resources in the idle resource library, the idle processing resources can be called for use.

[0163] According to an embodiment of the present invention, when the total amount of resource supply is less than the total amount of resource demand to be increased, the processing resource adjustment amount of the first target partition is determined according to the total amount of resource supply, the total amount of resource demand to be increased, the total amount of processing resources in the idle resource pool, and the current total amount of processing resources of the first target partition and the proportion of resources to be increased, including: determining the processing resource adjustment amount R of the xth first target partition according to formula (6): 1，x ,

[0164]

[0165] Among them, R 2，y，c is the current processing resource of the yth second target partition, r 2，y，d is the ratio of resources to be reduced in the yth second target partition, R1，x，c is the current processing resource of the xth first target partition, r 1，x，u is the ratio of resources to be increased in the xth first target partition, R l is the processing resource adjustment amount in the idle resource pool, and R l Less than or equal to the total amount of processing resources in the idle resource pool.

[0166] According to one embodiment of the present invention, in formula (6), if the total amount of processing resources in the idle resource pool is large, the processing resource adjustment amount R in the idle resource pool to be called is l The upper limit of satisfies formula (7):

[0167]

[0168] That is, the sum of the processing resource adjustment amount and the total resource supply in the idle resource pool is at most equal to the total resource demand to be increased. However, if the total processing resources in the idle resource pool are small, after all the processing resources in the idle resource pool are called, the sum of the processing resource adjustment amount and the total resource supply in the idle resource pool is still less than the total resource demand to be increased, then R l Can be equal to the total amount of processing resources in the idle resource pool.

[0169] According to one embodiment of the present invention, formula (6) is to multiply the current processing resource amount of each first target partition by the ratio of resources to be increased, so as to obtain the target resource adjustment amount of each first target partition. However, if after all the processing resources in the idle resource library are called, the sum of the processing resource adjustment amount and the total resource supply in the idle resource library called is still less than the total resource demand to be increased, then the target resource adjustment amount of each first target partition can be multiplied by the ratio between the sum of the processing resource adjustment amount and the total resource supply in the idle resource library called and the total resource demand to be increased, so as to obtain the processing resource adjustment amount of each first target partition. That is, the sum of the processing resource adjustment amount and the total resource supply in the idle resource library is allocated proportionally, so as to increase the processing resource amount of each first target partition in proportion, so as to improve the processing efficiency of each first target partition and improve the overall utilization rate of processing resources.

[0170] According to an embodiment of the present invention, in step S108, in the i+1th processing cycle, the adjusted processing resources of each partition can be used to process the access requests of each partition. That is, in the i+1th processing cycle adjacent to the current i-th processing cycle, the processing resources of each partition can be adjusted to quickly alleviate the shortage of processing resources of some partitions, thereby improving the overall processing efficiency of access requests and the utilization rate of processing resources.

[0171] According to the smart library electronic resource cloud management method of the embodiment of the present invention, when the processing resource allocation of the current processing cycle is difficult to cope with the access needs of each partition, the target partition that needs to adjust the processing resources can be found in time, and when finding the target partition, the three aspects of task processing pressure with high resource occupancy, response speed and task congestion are used to evaluate whether each partition needs to adjust the processing resources, thereby improving the accuracy and objectivity of the evaluation. The total amount of resource supply reduced by the second target partition and the spare processing resources in the idle resource library can be proportionally allocated to each first target partition with a processing resource increase demand to improve the overall processing efficiency and resource utilization. The processing resources corresponding to each partition can be adjusted in time, so as to quickly adapt to the access needs of each partition, maximize the use of processing resources, and improve the access efficiency of each partition, thereby improving the user experience.

[0172] Figure 2 A block diagram of a smart library electronic resource cloud management system according to an embodiment of the present invention is exemplarily shown, wherein the system is arranged in a cloud device, such as Figure 2 As shown, the system comprises:

[0173] The processing resource module 101 is used to obtain the processing resources corresponding to each partition in the smart library database in the i-th processing cycle, wherein the various types of electronic resources in the smart library are classified and stored in each partition, and i is a positive integer;

[0174] An access record module 102, configured to obtain access records of each partition at the end of the i-th processing cycle;

[0175] A determination module 103, for determining the number of accesses, average access duration, number of downloads and number of novelty searches of each partition according to the access records;

[0176] A response duration module 104, used to obtain a response duration for each access record;

[0177] The occupancy ratio module 105 is used to obtain the processing resource occupancy ratio of each partition at each moment in the i-th processing cycle;

[0178] The target partition module 106 is used to determine the target partition for adjusting the processing resources according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads and the number of novelty searches of each partition;

[0179] An adjustment amount module 107, configured to determine an adjustment amount of processing resources for the target partition according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads, and the number of novelty searches;

[0180] The processing module 108 is used to process the access requests of the partitions using the adjusted processing resources of the partitions in the (i+1)th processing cycle.

[0181] According to one embodiment of the present invention, the target partition module is further used for:

[0182] Determine the average number of visits per unit time according to the number of visits, the average visit duration, and the duration of the i-th processing cycle;

[0183] Determining a first ratio of the number of downloads to the number of visits;

[0184] Determining a second ratio of the number of novelty searches to the number of accesses;

[0185] Determine a high resource usage score according to the first ratio, the second ratio and the average number of visits per unit time;

[0186] Determine a response time score according to the response time and the average access time;

[0187] Determining a task congestion score according to the response time and the processing resource occupancy ratio;

[0188] Determining an adjustment demand score according to the high resource occupancy score, the response time score, and the task congestion score;

[0189] According to the adjustment requirement scores of each partition, the target partition of the processing resources to be adjusted is determined.

[0190] According to one embodiment of the present invention, the target partition module is further used for:

[0191] According to the formula

[0192]

[0193] Get the average number of visits per unit time of the kth partition in the i-th processing cycle Among them, N k，i is the number of accesses to the kth partition in the i-th processing cycle, is the average access time of the kth partition in the i-th processing cycle, ΔT i is the duration of the i-th processing cycle.

[0194] According to one embodiment of the present invention, the target partition module is further used for:

[0195] According to the formula

[0196]

[0197] Determine the high resource usage score G of the kth partition in the i-th processing cycle k，i , where r 1，k，i is the first ratio of the kth partition in the i-th processing cycle, r 2，k，i is the second ratio of the kth partition in the i-th processing cycle, α1 and α2 are preset weights, and K is the total number of partitions.

[0198] According to one embodiment of the present invention, the target partition module is further used for:

[0199] According to the formula

[0200]

[0201] Determine the task congestion score Y of the kth partition in the i-th processing cycle k，i , where T j，k，i is the response time of the jth access record of the kth partition in the i-th processing cycle, P t，k，i is the processing resource occupancy ratio of the kth partition at the tth moment of the i-th processing cycle, M k，i is the number of time moments included in the i-th processing cycle.

[0202] According to an embodiment of the present invention, the target partition includes a first target partition whose processing resources are to be increased and a second target partition whose processing resources are to be decreased; and the adjustment amount module is further used to:

[0203] Determining, according to the adjustment demand score, a ratio of resources to be adjusted in the first target partition and a ratio of resources to be reduced in the second target partition;

[0204] Determine the total amount of resources to be increased of the first target partition according to the current amount of processing resources of the first target partition and the proportion of resources to be increased;

[0205] Determining the total resource supply of the second target partition according to the current processing resource amount of the second target partition and the proportion of resources to be reduced;

[0206] Determining an adjustment amount of processing resources for the second target partition according to the current amount of processing resources for the second target partition and the ratio of resources to be reduced;

[0207] In the case where the total amount of resource supply is less than the total amount of resource demand to be increased, determining the processing resource adjustment amount of the first target partition according to the total amount of resource supply, the total amount of resource demand to be increased, the total amount of processing resources in the idle resource pool, and the current amount of processing resources of the first target partition and the ratio of resources to be increased;

[0208] When the total resource supply is greater than or equal to the total resource demand to be increased, the processing resource adjustment amount of the first target partition is obtained according to the current processing resource amount of the first target partition and the proportion of the resources to be increased, and the part of the total resource supply that is greater than the total resource demand to be increased is added to the idle resource library.

[0209] According to one embodiment of the present invention, the adjustment amount module is further used for:

[0210] According to the formula

[0211]

[0212] Determine the processing resource adjustment amount R of the xth first target partition 1，x , where R 2，y，c is the current processing resource of the yth second target partition, r 2，y，d is the ratio of resources to be reduced in the yth second target partition, R 1，x，c is the current processing resource of the xth first target partition, r 1，x，u is the ratio of resources to be increased in the xth first target partition, R l is the processing resource adjustment amount in the idle resource pool, and R l Less than or equal to the total amount of processing resources in the idle resource pool.

[0213] According to one embodiment of the present invention, the adjustment amount module is further used for:

[0214] According to the formula

[0215]

[0216] Determine the resource ratio r to be adjusted for the xth first target partition 1，x，u , where A 1，x is the adjustment demand score of the xth first target partition, P T is the target resource occupancy ratio;

[0217] According to the formula

[0218]

[0219] Determine the resource ratio r to be adjusted for the yth second target partition 2，x，d , where A 1，y Score the adjustment need for the y-th second target partition.

[0220] According to one embodiment of the present invention, a smart library electronic resource cloud management device is provided, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the smart library electronic resource cloud management method.

[0221] According to one embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the smart library electronic resource cloud management method is implemented.

[0222] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0223] It should be understood by those skilled in the art that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments, and the embodiments of the present invention may be deformed or modified in any way without departing from the principles.

[0224] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart library electronic resource cloud management method, characterized in that: The method is used in a cloud device, and includes: In the i-th processing cycle, the processing resources corresponding to each partition in the smart library database are obtained, wherein the various types of electronic resources in the smart library are classified and stored in each partition, and i is a positive integer; At the end of the i-th processing cycle, obtaining access records of each partition; Determine the number of accesses, average access duration, number of downloads, and number of novelty searches for each partition based on the access records; Get the response time for each access record; Obtain the processing resource occupancy ratio of each partition at each moment in the i-th processing cycle; Determine the target partition for adjusting the processing resources according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads and the number of novelty searches of each partition; Determining an adjustment amount of processing resources for the target partition according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads, and the number of novelty searches; In the (i+1)th processing cycle, the access requests of the partitions are processed using the adjusted processing resources of the partitions; Determining a target partition for adjusting processing resources according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads, and the number of novelty searches of each partition includes: Determine the average number of visits per unit time according to the number of visits, the average visit duration, and the duration of the i-th processing cycle; Determining a first ratio of the number of downloads to the number of visits; Determining a second ratio of the number of novelty searches to the number of accesses; Determine a high resource usage score according to the first ratio, the second ratio and the average number of visits per unit time; Determine a response time score according to the response time and the average access time; Determining a task congestion score according to the response time and the processing resource occupancy ratio; Determining an adjustment demand score according to the high resource occupancy score, the response time score, and the task congestion score; Determine the target partition for the processing resources to be adjusted based on the adjustment demand score of each partition; The target partitions include a first target partition whose processing resources are to be increased, and a second target partition whose processing resources are to be decreased; Determining the processing resource adjustment amount for the target partition according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads, and the number of novelty searches, including: Determining, according to the adjustment demand score, a ratio of resources to be adjusted in the first target partition and a ratio of resources to be reduced in the second target partition; Determine the total amount of resources to be increased of the first target partition according to the current amount of processing resources of the first target partition and the proportion of resources to be increased; Determining the total resource supply of the second target partition according to the current processing resource amount of the second target partition and the proportion of resources to be reduced; Determining an adjustment amount of processing resources for the second target partition according to the current amount of processing resources for the second target partition and the ratio of resources to be reduced; In the case where the total amount of resource supply is less than the total amount of resource demand to be increased, determining the processing resource adjustment amount of the first target partition according to the total amount of resource supply, the total amount of resource demand to be increased, the total amount of processing resources in the idle resource pool, and the current amount of processing resources of the first target partition and the ratio of resources to be increased; When the total resource supply is greater than or equal to the total resource demand to be increased, the processing resource adjustment amount of the first target partition is obtained according to the current processing resource amount of the first target partition and the proportion of the resources to be increased, and the part of the total resource supply that is greater than the total resource demand to be increased is added to the idle resource library.

2. The smart library electronic resource cloud management method according to claim 1 is characterized in that: Determining the average number of visits per unit time according to the number of visits, the average visit duration, and the duration of the i-th processing cycle includes: According to the formula Get the average number of visits per unit time of the kth partition in the i-th processing cycle Among them, N k , i is the number of accesses to the kth partition in the i-th processing cycle, is the average access time of the kth partition in the i-th processing cycle, ΔT i is the duration of the i-th processing cycle.

3. The smart library electronic resource cloud management method according to claim 2 is characterized in that: Determining a high resource usage score according to the first ratio, the second ratio, and the average number of visits per unit time includes: According to the formula Determine the high resource usage score G of the kth partition in the i-th processing cycle k , i , where r1, k , i is the first ratio of the kth partition in the i-th processing cycle, r2, k , i is the second ratio of the kth partition in the i-th processing cycle, α1 and α2 are preset weights, and K is the total number of partitions.

4. The smart library electronic resource cloud management method according to claim 2 is characterized in that: Determining a task congestion score according to the response time and the processing resource occupancy ratio includes: According to the formula Determine the task congestion score Y of the kth partition in the i-th processing cycle k , i , where T j , k , i is the response time of the jth access record of the kth partition in the i-th processing cycle, P t , k , i is the processing resource occupancy ratio of the kth partition at the tth moment of the i-th processing cycle, M k , i is the number of time moments included in the i-th processing cycle.

5. The smart library electronic resource cloud management method according to claim 1, characterized in that: In the case where the total amount of resource supply is less than the total amount of resource demand to be increased, determining the processing resource adjustment amount of the first target partition according to the total amount of resource supply, the total amount of resource demand to be increased, the total amount of processing resources in the idle resource pool, and the current total amount of processing resources of the first target partition and the proportion of resources to be increased, including: According to the formula Determine the processing resource adjustment amount R1 of the x-th first target partition, x , where R2, y , c is the current processing resource amount of the yth second target partition, r2, y , d is the ratio of resources to be reduced in the yth second target partition, R1, x , c is the current processing resource amount of the xth first target partition, r1, x , u is the ratio of resources to be increased in the xth first target partition, R l is the processing resource adjustment amount in the idle resource pool, and R l Less than or equal to the total amount of processing resources in the idle resource pool.

6. The smart library electronic resource cloud management method according to claim 1, characterized in that: Determining, according to the adjustment demand score, a ratio of resources to be adjusted in the first target partition and a ratio of resources to be reduced in the second target partition, including: According to the formula Determine the resource ratio r1 to be adjusted for the xth first target partition, x , u , where A1, x is the adjustment demand score of the xth first target partition, P T is the target resource occupancy ratio; According to the formula Determine the resource ratio r2 to be adjusted for the yth second target partition, x , d , where A1, y Score the adjustment need for the y-th second target partition.

7. A smart library electronic resource cloud management system for executing the method according to any one of claims 1 to 6, characterized in that: The system is arranged in a cloud device, and includes: A processing resource module, used for obtaining processing resources corresponding to each partition in the smart library database in the i-th processing cycle, wherein multiple types of electronic resources in the smart library are classified and stored in each partition, and i is a positive integer; An access record module, used for obtaining access records of each partition at the end of the i-th processing cycle; A determination module, used to determine the number of accesses, average access duration, number of downloads and number of novelty retrievals of each partition based on the access records; A response time module is used to obtain the response time for each access record; The occupancy ratio module is used to obtain the processing resource occupancy ratio of each partition at each moment in the i-th processing cycle; A target partition module, used to determine the target partition for adjusting the processing resources according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads and the number of novelty searches of each partition; An adjustment amount module, used to determine the adjustment amount of the processing resources for the target partition according to the response time, the processing resource occupancy ratio, the number of accesses, the average access time, the number of downloads and the number of novelty searches; The processing module is used to process the access requests of each partition using the adjusted processing resources of each partition in the (i+1)th processing cycle.

8. A smart library electronic resource cloud management device, characterized in that: include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

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