Data compression storage method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a data compression storage method and device, computer equipment, a readable storage medium and a program product, and relates to the technical field of big data. The method comprises the following steps: in response to a compression storage instruction for target data, obtaining performance parameters of a data storage service for compressing and storing the target data; performing fuzzification processing on the performance parameter to obtain a target fuzzy quantity of the performance parameter; performing fuzzy reasoning according to the target fuzzy quantity and a fuzzy reasoning rule to obtain a first fuzzy set; the fuzzy inference rule is used for describing a corresponding relationship between the target fuzzy quantity and each compression algorithm; performing defuzzification processing on the first fuzzy set to obtain a target compression ratio; and based on a compression algorithm matched with the target compression ratio, performing compression storage on the target data through the data storage service. By adopting the method, the data storage efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a data compression storage method, device, computer equipment, readable storage medium and program product. Background Art

[0002] With the rapid development of big data, how to store massive amounts of big data conveniently and economically has become a key concern for all industries. In this context, data storage services have emerged, especially object storage services. In object storage, everything is an object, and it is managed flatly with buckets as the granularity. It supports unlimited expansion and is the only choice for storing massive data.

[0003] At present, faced with the exponentially explosive growth of massive data, data storage services usually choose to compress the data before storing it, thereby saving backend storage capacity and reducing storage costs. However, as the amount of data increases, the compression rate will undoubtedly be affected, thereby affecting the overall data storage efficiency. Summary of the invention

[0004] Based on this, it is necessary to provide a data compression storage method, device, computer equipment, computer-readable storage medium and computer program product that can improve data storage efficiency in response to the above technical problems.

[0005] In a first aspect, the present application provides a data compression and storage method, comprising: in response to a compression and storage instruction for target data, obtaining performance parameters of a data storage service for compressing and storing target data; fuzzifying the performance parameters to obtain a target fuzzy quantity of the performance parameters; performing fuzzy reasoning based on the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the correspondence between the target fuzzy quantity and each compression algorithm; defuzzifying the first fuzzy set to obtain a target compression ratio; and compressing and storing the target data through the data storage service based on a compression algorithm that matches the target compression ratio.

[0006] In one of the embodiments, a performance parameter is fuzzified to obtain a target fuzzy value of the performance parameter, including: obtaining a second fuzzy set of the performance parameter and a membership function for describing the second fuzzy set; the second fuzzy set includes at least one fuzzy element for describing different states of the performance parameter; the parameter value of the performance parameter is input into the membership function and fuzzified to obtain the membership between the parameter value and each fuzzy element; the membership is used to describe the degree of membership between the parameter value and each fuzzy element; and the fuzzy element with the largest membership is used as the target fuzzy value of the performance parameter.

[0007] In one of the embodiments, fuzzy reasoning is performed according to the target fuzzy quantity and the fuzzy reasoning rule to obtain a first fuzzy set, including: comparing the target fuzzy quantity with the premise of the fuzzy reasoning rule to obtain a comparison result between the fuzzy quantity and the fuzzy reasoning rule; determining the selection probability of each compression algorithm based on the comparison result; and combining each selection probability to obtain the first fuzzy set.

[0008] In one of the embodiments, the first fuzzy set is defuzzified to obtain a target compression ratio, including: for each selection probability in the first fuzzy set, determining the coordinate point of the selection probability in the membership function diagram for each compression algorithm; performing a centroid analysis on a target graph composed of each coordinate point to obtain the centroid point of the target graph, and using the compression ratio of the centroid point as the target compression ratio.

[0009] In one of the embodiments, in response to a compression storage instruction for target data, performance parameters of a data storage service for compressing and storing the target data are obtained, including: in response to the compression storage instruction for the target data, obtaining a compression storage requirement indicated by the compression storage instruction and a plurality of candidate parameters of the data storage service for compressing and storing the target data; and screening out performance parameters that match the compression storage requirement from the plurality of candidate parameters.

[0010] In one embodiment, the performance parameters include the available capacity and read / write rate of the data storage service, and the performance parameters are fuzzified to obtain a target fuzzy value of the performance parameters, including: fuzzifying the available capacity of the data storage service to obtain a first fuzzy value of the available capacity; fuzzifying the read / write rate of the data storage service to obtain a second fuzzy value of the read / write rate; and combining the first fuzzy value and the second fuzzy value to obtain a target fuzzy value.

[0011] In the second aspect, the present application also provides a data compression storage device, including: a parameter acquisition module, used to respond to a compression storage instruction for the target data, and obtain the performance parameters of the data storage service used to compress and store the target data; a fuzzification processing module, used to fuzzify the performance parameters to obtain the target fuzzy quantity of the performance parameters; a fuzzy reasoning module, used to perform fuzzy reasoning based on the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the correspondence between the target fuzzy quantity and each compression algorithm; a defuzzification module, used to defuzzify the first fuzzy set to obtain a target compression ratio; a compression storage module, used to compress and store the target data through the data storage service based on a compression algorithm that matches the target compression ratio.

[0012] In a third aspect, the present application also provides a computer device, including a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program: in response to a compression and storage instruction for target data, obtaining performance parameters of a data storage service for compressing and storing target data; fuzzifying the performance parameters to obtain a target fuzzy quantity of the performance parameters; performing fuzzy reasoning based on the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the correspondence between the target fuzzy quantity and each compression algorithm; defuzzifying the first fuzzy set to obtain a target compression ratio; based on a compression algorithm that matches the target compression ratio, compressing and storing the target data through the data storage service.

[0013] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the following steps when the computer program is executed by a processor: in response to a compression storage instruction for target data, obtain performance parameters of a data storage service for compressing and storing target data; fuzzify the performance parameters to obtain a target fuzzy quantity of the performance parameters; perform fuzzy reasoning based on the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the correspondence between the target fuzzy quantity and each compression algorithm; defuzzify the first fuzzy set to obtain a target compression ratio; based on a compression algorithm that matches the target compression ratio, compress and store the target data through the data storage service.

[0014] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the following steps when executed by a processor: in response to a compression storage instruction for target data, obtain performance parameters of a data storage service for compressing and storing target data; fuzzify the performance parameters to obtain a target fuzzy quantity of the performance parameters; perform fuzzy reasoning based on the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the correspondence between the target fuzzy quantity and each compression algorithm; defuzzify the first fuzzy set to obtain a target compression ratio; based on a compression algorithm that matches the target compression ratio, compress and store the target data through the data storage service.

[0015] The above-mentioned data compression storage method, device, computer equipment, computer-readable storage medium and computer program product, in response to the compression storage instruction for the target data, first obtain the performance parameters of the data storage service for compressing and storing the target data, and perform fuzzification processing on the performance parameters to convert the determined value of the performance parameters into the corresponding fuzzy language variable value, i.e., the target fuzzy quantity. Then, according to the target fuzzy quantity and the pre-defined fuzzy reasoning rules, fuzzy reasoning is performed to obtain the first fuzzy set, and the fuzzy reasoning rules describe the corresponding relationship between the target fuzzy quantity and each compression algorithm. Then, the first fuzzy set is defuzzified to obtain a clear target compression ratio. Finally, based on the compression algorithm matching the target compression ratio, the target data is compressed and stored through the data storage service. Therefore, on the one hand, the present application effectively improves the efficiency of data compression storage by using the compression algorithm to compress data storage. On the other hand, the present application introduces fuzzy reasoning technology, which can more effectively process various uncertain information such as performance parameters, thereby providing a more accurate compression ratio, and then matching a more suitable compression algorithm to improve the efficiency and accuracy of data compression storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] FIG1 is an application environment diagram of a data compression storage method in one embodiment;

[0018] FIG2 is a schematic diagram of a flow chart of a data compression storage method in one embodiment;

[0019] FIG3 is a schematic diagram of a fuzzy processing flow in one embodiment;

[0020] FIG4 is a schematic diagram of a membership function of available capacity in one embodiment;

[0021] FIG5 is a schematic diagram of a membership function of a read / write rate in one embodiment;

[0022] FIG6 is a schematic diagram of a fuzzy reasoning process in one embodiment;

[0023] FIG7 is a schematic diagram of a defuzzification process in one embodiment;

[0024] FIG8 is a schematic diagram of a curve of a compression algorithm membership function in one embodiment;

[0025] FIG9 is a schematic diagram of a centroid of a graphic in one embodiment;

[0026] FIG10 is a schematic diagram of a process for determining performance parameters in one embodiment;

[0027] FIG11 is a schematic diagram of a flow chart of fuzzification processing in another embodiment;

[0028] FIG12 is a schematic diagram of a flow chart of a data compression storage method in a specific embodiment;

[0029] FIG13 is a schematic diagram of a fuzzy reasoning process in a specific embodiment;

[0030] FIG14 is a block diagram of a data compression storage device in one embodiment;

[0031] FIG. 15 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application 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 application and are not used to limit the present application.

[0033] The data compression storage method provided in the embodiment of the present application can be applied to the data storage system as shown in FIG1 , wherein the data storage system includes a server 102 and a data storage service 104, and the server 102 communicates with a terminal 106 via a network. The terminal 106 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and Internet of Things devices. The server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0034] Specifically, in response to the compression storage instruction for the target data initiated by the terminal 106, the server 102 first obtains the performance parameters of the data storage service 104 for compressing and storing the target data. Secondly, the performance parameters are fuzzified to obtain the target fuzzy quantity of the performance parameters. Then, fuzzy reasoning is performed based on the target fuzzy quantity and the fuzzy reasoning rules to obtain the first fuzzy set, and the fuzzy reasoning rules are used to describe the corresponding relationship between the target fuzzy quantity and each compression algorithm. Then, the first fuzzy set is defuzzified to obtain the target compression ratio. Finally, based on the compression algorithm matching the target compression ratio, the target data is compressed and stored through the data storage service 104.

[0035] In an exemplary embodiment, as shown in FIG. 2 , a data compression storage method is provided, which is described by taking the method applied to the server 102 in FIG. 1 as an example, and includes:

[0036] Step S202: In response to a compression storage instruction for target data, obtain performance parameters of a data storage service for compressing and storing the target data.

[0037] The target data may refer to data that needs to be compressed and stored. The compression storage instruction may refer to an instruction to compress and store the target data, and compression storage refers to the process of compressing the data before storing it. The data storage service can be understood as a service that provides data storage and management, which is generally provided by cloud storage vendors. The performance parameter may refer to a parameter that characterizes the performance of the data storage service, which may include but is not limited to the available capacity, read and write rate, power consumption, reliability, etc. of the data storage service.

[0038] For example, when the server receives the compression storage instruction for the target data initiated by the terminal, it can first start the data storage service pre-deployed in the data storage system, and obtain relevant parameters characterizing the performance of the data storage service, such as the available capacity, read and write rate, power consumption, reliability, etc. of the data storage service.

[0039] In one example, the server can obtain the performance parameters in real time or at a preset time interval. This allows the server to dynamically match the appropriate compression algorithm based on the performance parameters, further improve the accuracy of the compression algorithm, and thus improve the accuracy and efficiency of data storage compression, achieving the best data storage effect.

[0040] Step S204, fuzzifying the performance parameters to obtain target fuzzy values ​​of the performance parameters.

[0041] Among them, before understanding fuzzy processing, you need to understand fuzzy logic first. Fuzzy logic is a logical reasoning modeling method. In fuzzy logic, the truth or falsity of a statement is not like the classic Boolean logic, that is, the binary true and false, but a continuous degree of truth from 0 (absolute false) to 1 (absolute true). The core idea of ​​fuzzy logic is to imitate the uncertainty concept judgment and reasoning thinking mode of the human brain. It allows data to have fuzziness, uncertainty and partial truth. This idea enables fuzzy logic to handle problems that are difficult to describe with classical binary logic, such as the performance parameters of the data storage service in this embodiment. In practical applications, the performance parameters of data storage services such as available capacity, read and write rates, power consumption, and reliability are often affected by multiple factors, which may be difficult to accurately quantify or have uncertainty. Therefore, fuzzy logic can be used to describe the fuzziness of performance parameters, so that the data storage system can reason and make decisions in an uncertain environment. Fuzzy logic includes fuzzification processing, fuzzy reasoning, and defuzzification processing. Fuzzification processing refers to the process of fuzzifying a certain input value and obtaining a set of fuzzy quantities that can be recognized by a computer. Fuzzy quantity refers to a quantity without clearly defined boundaries or precise values, such as "large available capacity", "small available capacity", "moderate available capacity", "low read / write speed", "moderate read / write speed", "fast read / write speed", etc. These words do not specify the quantity or degree, but provide a more flexible and realistic way of expression. The target fuzzy quantity refers to the fuzzy quantity to which the performance parameter currently belongs.

[0042] For example, the server may pre-define a set of fuzzy quantities corresponding to the performance parameters, and after acquiring the performance parameters of the data storage server, the server may perform fuzzy processing on the performance parameters to determine the fuzzy quantity to which the performance parameter currently belongs in the set of fuzzy quantities.

[0043] In one example, the server may determine the fuzzy quantity to which the performance parameter currently belongs by combining the membership function corresponding to the group of fuzzy quantities.

[0044] Step S206, performing fuzzy reasoning according to the target fuzzy quantity and the fuzzy reasoning rule to obtain a first fuzzy set.

[0045] Among them, the fuzzy reasoning module in fuzzy logic can be considered as an imprecise reasoning, which is the process of converting a given input into an output through fuzzy reasoning rules. It can also be understood as a calculation process that corresponds the input fuzzy quantity to a specific output fuzzy quantity through certain operations. Fuzzy reasoning rules are rules relied upon when performing fuzzy reasoning, and can usually be expressed in natural language. In this embodiment, fuzzy reasoning rules can be used to describe the corresponding relationship between the target fuzzy quantity and each compression algorithm, for example: if the available capacity is large and the read-write rate is low, a compression algorithm with a large compression ratio is used; if the available capacity is large and the read-write rate is moderate, a compression algorithm with a large or moderate compression ratio is used; if the available capacity is large and the read-write rate is fast, a compression algorithm with a moderate or small compression ratio is used; if the available capacity is moderate and the read-write rate is low, a compression algorithm with a large or moderate compression ratio is used; if the available capacity is moderate and the read-write rate is moderate, a compression algorithm with a moderate or small compression ratio is used; if the available capacity is moderate and the read-write rate is moderate, an algorithm with a moderate or small compression ratio is used; if the available capacity is moderate and the read-write rate is fast, an algorithm with a small compression ratio is used; if the available capacity is small and the read-write rate is low, an algorithm with a moderate compression ratio is used; if the available capacity is small and the read-write rate is moderate, an algorithm with a moderate or small compression ratio is used; if the available capacity is small and the read-write rate is fast, an algorithm with a small compression ratio is used. These fuzzy inference rules can be obtained based on the experience of R&D personnel. In practical applications, other content and quantity of fuzzy inference rules can be flexibly defined. In this embodiment, there is no restriction on the specific content and quantity of fuzzy inference rules. The first fuzzy set can include a fuzzy value corresponding to each compression algorithm. This fuzzy value can be used to describe the intensity of the rules corresponding to each compression algorithm being triggered, or the probability of each compression algorithm being selected.

[0046] A compression algorithm may refer to a mathematical algorithm used by a data storage service to compress target data, and may include but is not limited to SNAPPY, ZLIB, ZSTD, BROTLI, and LZ4. Among them, SNAPPY is an open source fast compression library that can be used to quickly compress files in the field of cloud computing. ZLIB is a function library that provides data compression functions. ZSTD is a real-time encoding algorithm that can effectively compress different data and can compress data faster and more efficiently when processing big data. Compared with other encoding algorithms, ZSTD can effectively improve data compression rate while taking into account compression speed. BROTLI is a general lossless compression algorithm with a high compression ratio. LZ4 is a compression algorithm known for its speed and is suitable for scenarios that require fast compression and decompression.

[0047] Exemplarily, after determining the target fuzzy quantity to which the performance parameter currently belongs, the server can perform fuzzy reasoning in fuzzy logic, that is, perform reasoning based on the target fuzzy quantity and predefined fuzzy reasoning rules, thereby obtaining a fuzzy set containing fuzzy values ​​corresponding to each compression algorithm.

[0048] Step S208: Defuzzify the first fuzzy set to obtain a target compression ratio.

[0049] Among them, the compression ratio refers to the ratio of data volume reduction by encoding data through a compression algorithm during data storage. The higher the compression ratio, the smaller the amount of compressed data is relative to the original data, thereby saving storage space and storage time. Conversely, the larger the amount of data, the more storage space and storage time it will take up. Generally speaking, the compression ratio is usually between 1 and 3. The defuzzification process in fuzzy logic is the process of converting the fuzzy set obtained by fuzzy reasoning into a specific and accurate output. The target compression ratio refers to a specific compression ratio obtained after defuzzification of the first fuzzy set, which is used to match the compression algorithm.

[0050] Exemplarily, after obtaining the first fuzzy set through fuzzy reasoning, the server may use a suitable defuzzification method to perform defuzzification processing on the first fuzzy set to obtain a specific compression ratio.

[0051] In one example, the defuzzification method may include but is not limited to the maximum membership method, the centroid method, the weighted average method, etc. Among them, the maximum membership method refers to selecting the element with the largest membership in the first fuzzy set as the output, and the membership is used to indicate the degree to which an element belongs to the fuzzy set. The membership is usually represented by a value between 0 and 1, where 0 indicates that the element does not belong to the set at all, 1 indicates that the element completely belongs to the set, and the value between 0 and 1 indicates a certain degree to which the element belongs to the set. The centroid method is to take the centroid of the area enclosed by the membership function curve and the horizontal axis as the final output value. The weighted average method means that the output value is determined by the weighted average of the coefficients, and the selection of the coefficients should be based on the actual situation.

[0052] Step S210: compress and store the target data through a data storage service based on a compression algorithm that matches the target compression ratio.

[0053] Exemplarily, after the server obtains a target compression ratio through defuzzification processing, it can determine the fuzzy amount corresponding to the target compression ratio, and the fuzzy amount is a set of fuzzy amounts of the compression ratio, such as large compression ratio, small compression ratio, and moderate compression ratio. When the target compression ratio is large, a compression algorithm with a large compression ratio can be selected; when the target compression ratio is moderate, a compression algorithm with a moderate compression ratio can be selected; when the target compression ratio is small, a compression algorithm with a small compression ratio can be selected. Thus, the data storage service calls the determined compression algorithm to compress and store the target data.

[0054] In this embodiment, in response to the compression storage instruction for the target data initiated by the terminal, the server first obtains the performance parameters of the data storage service for compressing and storing the target data, and performs fuzzification processing on the performance parameters to convert the determined value of the performance parameters into the corresponding fuzzy language variable value, i.e., the target fuzzy quantity. Then, according to the target fuzzy quantity and the predefined fuzzy reasoning rules, fuzzy reasoning is performed to obtain the first fuzzy set, and the fuzzy reasoning rules describe the corresponding relationship between the target fuzzy quantity and each compression algorithm. Then, the first fuzzy set is defuzzified to obtain a clear target compression ratio. Finally, based on the compression algorithm matching the target compression ratio, the target data is compressed and stored through the data storage service. Therefore, on the one hand, this embodiment effectively improves the efficiency of data compression storage by using the compression algorithm to compress data storage. On the other hand, this embodiment introduces fuzzy reasoning technology, which can more effectively process various uncertain information such as performance parameters, thereby providing a more accurate compression ratio, and then matching a more suitable compression algorithm to improve the efficiency and accuracy of data compression storage.

[0055] In an exemplary embodiment, as shown in FIG3 , the performance parameter is fuzzified to obtain a target fuzzy value of the performance parameter, including:

[0056] Step S302: Acquire a second fuzzy set of performance parameters and a membership function used to describe the second fuzzy set.

[0057] Among them, the second fuzzy set includes at least one fuzzy element for describing different states of performance parameters, such as "large available capacity", "small available capacity", "moderate available capacity", "low read / write rate", "moderate read / write rate", "fast read / write rate", etc. The membership function is a tool used to process the fuzzification process, and the membership function defines the degree to which the input value belongs to different fuzzy elements. Please refer to Figures 4 and 5. Figure 4 shows a schematic diagram of the membership function of the available capacity, and Figure 5 shows a schematic diagram of the membership function expression of the read / write rate. It can be seen that for the available capacity, its membership function can be when the available capacity is in the range of 80%-100%, it means that the available capacity is large, and the membership is 1 at this time; when the available capacity is in the range of 40%-60%, it means that the available capacity is moderate, and the membership is 1 at this time; when the available capacity is below 20%, it means that the available capacity is small, and the membership is 1 at this time, and the remaining intervals belong to transition intervals. Taking the read / write rate as an example, its membership function can be: when the real-time read / write rate is between 0-3GB / s (gigabytes per second), it means that the read / write rate is low, which further indicates that the load of the data storage service is small, and the membership is 1. When the real-time read / write rate is between 5GB / s and 8GB / s, it means that the read / write rate is moderate, which further indicates that the load of the data storage service is moderate, and the membership is 1. When the real-time read / write rate is greater than 10GB / s, it means that the read / write rate is fast, which further indicates that the load of the data storage service is heavy, and the membership is 1. The rest of the intervals are transition intervals.

[0058] For example, the server may define and store the corresponding fuzzy set and the membership function describing the fuzzy set for each performance parameter in advance, so that when the server receives the compression storage instruction, it can quickly obtain the fuzzy set and the membership function of the performance parameter after obtaining the performance parameter of the data storage service.

[0059] Step S304: input the parameter value of the performance parameter into the membership function, perform fuzzification processing, and obtain the membership between the parameter value and each fuzzy element.

[0060] The parameter value refers to the current value of the performance parameter. The membership degree is used to describe the degree of membership between the parameter value and each fuzzy element.

[0061] Exemplarily, after obtaining the membership function of the performance parameter, the server may input the parameter value of the current performance parameter into the membership function to perform fuzzification processing, thereby obtaining the membership between the parameter value and each fuzzy element. For example, assuming that the current parameter value of the available capacity is 100%, its membership with the fuzzy element of "large available capacity" is 1, while its membership with the two fuzzy elements of "small available capacity" and "moderate available capacity" is 0.

[0062] Step S306: taking the fuzzy element with the largest membership degree as the target fuzzy value of the performance parameter.

[0063] Exemplarily, the server uses the fuzzy element with the largest membership as the target fuzzy value of the performance parameter. Taking the above example, the target fuzzy value of the available capacity may be more available capacity.

[0064] In this embodiment, the fuzzy quantity to which the real-time performance parameter belongs is calculated based on the fuzzy set of the performance parameter and the corresponding membership function, which improves the accuracy of the target fuzzy quantity of the performance parameter, thereby improving the accuracy of subsequent fuzzy reasoning and further improving the accuracy of data storage.

[0065] In an exemplary embodiment, as shown in FIG6 , fuzzy reasoning is performed according to the target fuzzy quantity and the fuzzy reasoning rule to obtain a first fuzzy set, including:

[0066] Step S602, comparing the target fuzzy quantity with the premise of the fuzzy inference rule to obtain a comparison result between the fuzzy quantity and the fuzzy inference rule.

[0067] The premise refers to the condition defined for the performance parameter in the fuzzy reasoning rule. For example, if the available capacity is large and the read / write rate is low, a compression algorithm with a large compression ratio is used. Its premise is that the available capacity is large and the read / write rate is low. The comparison result can be used to characterize whether the fuzzy quantity is consistent with the premise.

[0068] Exemplarily, when performing fuzzy reasoning, the server may compare the target fuzzy quantity of the performance parameter with the precondition in the fuzzy reasoning rule for consistency, thereby obtaining a consistency comparison result between the fuzzy quantity and the precondition.

[0069] Step S604: Determine the selection probability of each compression algorithm based on the comparison result.

[0070] Among them, the selection probability can be understood as the probability of different compression ratios being selected, which reflects the probability of the compression algorithm corresponding to the compression ratio being selected. It can also be understood as the probability of the fuzzy inference rules corresponding to different compression ratios being triggered. It can be understood that when the fuzzy quantity of the performance parameter is consistent with the premise of a fuzzy inference rule, it is considered that this fuzzy inference rule can be triggered, the compression ratio corresponding to this fuzzy inference rule can be selected, that is, the compression algorithm corresponding to the compression ratio can be selected. Of course, each compression algorithm can have multiple fuzzy inference rules, and the selection probability can be the average value of the triggering probabilities of these fuzzy inference rules.

[0071] Exemplarily, after obtaining the consistency comparison result between the fuzzy quantity and the premise condition, the server can screen out the fuzzy inference rules whose comparison results are consistent for each degree of compression ratio, and then count the proportion of these fuzzy inference rules in the total fuzzy inference rules, which is the selection probability of the compression algorithm corresponding to the compression ratio of this degree.

[0072] Step S606: Combine the selection probabilities to obtain a first fuzzy set.

[0073] For example, the server will combine the selection probabilities of compression algorithms corresponding to different compression ratios to obtain a fuzzy set. For example, assuming that the first fuzzy set is [0.3, 0.5, 0], it means that the probability of finally using an algorithm with a small compression ratio is 0.3, the probability of finally using an algorithm with a medium compression ratio is 0.5, and the probability of finally using an algorithm with a large compression ratio is 0.

[0074] In this embodiment, the probability of compression algorithms with different compression ratios being selected is calculated, and then a fuzzy set is formed by combining them. The fuzzy set reflects the probability or strength of different compression algorithms being selected, thereby improving the accuracy of the fuzzy set, thereby improving the accuracy of the subsequent target compression ratio, and further improving the accuracy of data storage.

[0075] In an exemplary embodiment, as shown in FIG7 , defuzzification is performed on the first fuzzy set to obtain a target compression ratio, including:

[0076] Step S702: for each selection probability in the first fuzzy set, determine the coordinate point of the selection probability in the membership function graph for each compression algorithm.

[0077] Among them, the membership function diagram can be understood as a curve diagram of the membership function for the compression algorithm, as shown in Figure 8. Figure 8 uses trigonometric functions to reflect the membership function of the compression algorithm or the compression ratio. It can be seen from Figure 8 that when the compression ratio is equal to 1.5, it means that the compression ratio is small, when the compression ratio is equal to 2.5, it means that the compression ratio is moderate, and when the compression ratio is equal to 2.5, it means that the compression ratio is large. It should be noted that Figure 8 is a membership function defined with a compression ratio within the range of 1-3. In practical applications, the membership function can be defined according to actual conditions. The coordinate point can refer to the coordinate of the selection probability in the membership function diagram. It can be understood that the selection probability can also be understood as the membership.

[0078] Exemplarily, the server may draw a trigonometric function graph corresponding to the compression ratio in advance, obtain a membership function graph and store it. After the server obtains the first fuzzy set, it can locate the coordinate point corresponding to each element in the first fuzzy set, that is, the selection probability, on the membership function graph.

[0079] Step S704, performing centroid analysis on the target graph composed of the coordinate points to obtain the centroid point of the target graph, and taking the compression ratio of the centroid point as the target compression ratio.

[0080] Among them, the centroid can be understood as the balance point or center of gravity of a graph. As shown in Figure 9, Figure 9 takes the first fuzzy set as [0.3, 0.5, 0] as an example. On the basis of Figure 8, the closed graph composed of the coordinate points of each selection probability and the horizontal axis is used as the target graph. It should be noted that since the selection probability corresponding to a large compression ratio is 0, it can be ignored. The target compression ratio is the compression ratio corresponding to the centroid.

[0081] Exemplarily, after locating the coordinate points corresponding to each selection probability in the first fuzzy set on the membership function graph, the server may draw a graph consisting of these coordinate points and the horizontal axis, thereby calculating the centroid of the graph. The specific calculation process may be that the server first multiplies the membership corresponding to each selection probability by its corresponding output compression ratio, then calculates the sum of the integrals of these products, and then divides it by the sum of the integrals of the membership function, to obtain the position of the centroid, that is, the final target compression ratio.

[0082] In this embodiment, a clear compression ratio is calculated using the centroid method, which improves the accuracy of the target compression ratio, thereby improving the accuracy of the compression algorithm and further improving the accuracy of data compression.

[0083] In an exemplary embodiment, as shown in FIG10 , in response to a compression storage instruction for target data, obtaining a performance parameter of a data storage service for compressing and storing the target data includes:

[0084] Step S1002: In response to a compression storage instruction for target data, obtaining a compression storage requirement indicated by the compression storage instruction and a plurality of candidate parameters of a data storage service for compressing and storing the target data.

[0085] The compression storage requirement can be understood as the requirement for compressing and storing the target data, including but not limited to storage rate, reliability, accuracy, security, etc. The data storage service may have multiple parameters that characterize the performance of the data storage service, and these parameters are candidate parameters.

[0086] For example, when the server receives a compression storage instruction for target data initiated by the terminal, it can first parse the compression storage instruction to obtain the compression storage requirement, and obtain multiple candidate parameters of the data storage service for compressing and storing the target data, so as to subsequently select the performance parameters for fuzzy reasoning from these candidate parameters.

[0087] Step S1004: Filter out performance parameters that match the compression storage requirement from multiple candidate parameters.

[0088] Exemplarily, after obtaining multiple candidate parameters, the server can determine whether each candidate parameter can meet the compression storage requirements. If so, the candidate parameter can be used as a performance parameter to participate in subsequent fuzzy reasoning. Candidate parameters that do not meet the compression storage requirements do not need to be used as performance parameters.

[0089] In this embodiment, by obtaining the compression storage requirement, the performance parameters matching the compression storage requirement are screened out, thereby improving the reliability and accuracy of the performance parameters, thereby improving the reliability and accuracy of fuzzy reasoning, and further improving the accuracy of data compression efficiency.

[0090] In an exemplary embodiment, as shown in FIG11 , the performance parameter is fuzzified to obtain a target fuzzy value of the performance parameter, including:

[0091] Step S1102: Fuzzy processing is performed on the available capacity of the data storage service to obtain a first fuzzy amount of the available capacity.

[0092] Step S1104, fuzzy processing is performed on the read and write rates of the data storage service to obtain a second fuzzy value of the read and write rate.

[0093] Among them, the available capacity may refer to the current remaining available capacity of the data storage service, and the read / write rate may refer to the real-time rate at which the data storage server reads and writes the target data. The first fuzzy quantity may refer to the fuzzy quantity of the available capacity, such as "large available capacity", "small available capacity", or "moderate available capacity". The second fuzzy quantity may refer to the fuzzy quantity of the read / write rate, such as "low read / write rate", "moderate read / write rate", or "fast read / write rate". The read / write speed may reflect the load of the data storage service. When the read / write rate is low, it indicates a small load. When the read / write rate is moderate, it indicates a moderate load. When the read / write rate is fast, it indicates a heavy load.

[0094] Exemplarily, the server selects available capacity and read / write rate as performance parameters, and performs fuzzy processing on the available capacity and the read / write rate, respectively, so as to obtain fuzzy quantities to which the available capacity and the read / write rate belong.

[0095] Step S1106: Combine the first blur amount and the second blur amount to obtain a target blur amount.

[0096] The target fuzzy quantity may refer to a fuzzy quantity obtained by combining the first fuzzy quantity and the second fuzzy quantity, such as: large available capacity and moderate read / write speed; small available capacity and low read / write speed, etc.

[0097] Exemplarily, after the server obtains the fuzzy quantities to which the available capacity and the read / write rate respectively belong, the server can combine the two fuzzy quantities to obtain a combined target fuzzy quantity for subsequent fuzzy reasoning.

[0098] In this embodiment, by selecting available capacity and read / write speed as performance parameters for fuzzy processing, the reliability and accuracy of the performance parameters are ensured, thereby improving the accuracy of the fuzzy processing and further improving the accuracy of data storage.

[0099] In a specific embodiment, as shown in FIG12 , the process of the data compression storage method is as follows:

[0100] S1: Upload data. The terminal uploads the target data to the data storage system and requests the data storage system to compress and store the target data.

[0101] S2: Obtain the available capacity and read / write rate of the data storage service. The server of the data storage system obtains the compression storage requirement and selects performance parameters matching the compression storage requirement from multiple candidate parameters of the data storage service, including the available capacity and read / write rate of the data storage service.

[0102] S3: Fuzzy reasoning calculates the target compression ratio. Figure 13 shows a schematic diagram of the fuzzy reasoning process. The server first fuzzifies the available capacity and the read-write rate respectively to obtain a first fuzzy quantity of the available capacity and a second fuzzy quantity of the read-write rate, and then combines the two fuzzy quantities to obtain a target fuzzy quantity. The target fuzzy quantity is then compared with the premise of the fuzzy reasoning rule, and the selection probability of each compression algorithm is determined based on the comparison result. The selection probabilities are combined to obtain a fuzzy set. Finally, for each selection probability in the fuzzy set, the coordinate points of each selection probability in the membership function diagram for each compression algorithm are determined. The centroid method is used to calculate the centroid point of the target graph composed of each coordinate point, and the compression ratio of the centroid point is the target compression ratio.

[0103] S4: Select a compression algorithm for compression storage according to the target compression ratio. The server compresses and stores the target data through the data storage service based on the compression algorithm that matches the target compression ratio. After the data storage server completes the compression and storage of the target data, the server ends the entire data compression process.

[0104] In this embodiment, in response to the compression storage instruction for the target data initiated by the terminal, the server first obtains the performance parameters of the data storage service for compressing and storing the target data, and performs fuzzification processing on the performance parameters to convert the determined value of the performance parameters into the corresponding fuzzy language variable value, i.e., the target fuzzy quantity. Then, according to the target fuzzy quantity and the predefined fuzzy reasoning rules, fuzzy reasoning is performed to obtain the first fuzzy set, and the fuzzy reasoning rules describe the corresponding relationship between the target fuzzy quantity and each compression algorithm. Then, the first fuzzy set is defuzzified to obtain a clear target compression ratio. Finally, based on the compression algorithm matching the target compression ratio, the target data is compressed and stored through the data storage service. Therefore, on the one hand, this embodiment effectively improves the efficiency of data compression storage by using the compression algorithm to compress data storage. On the other hand, this embodiment introduces fuzzy reasoning technology, which can more effectively process various uncertain information such as performance parameters, thereby providing a more accurate compression ratio, and then matching a more suitable compression algorithm to improve the efficiency and accuracy of data compression storage.

[0105] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0106] Based on the same inventive concept, the embodiment of the present application also provides a data compression storage device for implementing the data compression storage method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more data compression storage device embodiments provided below can refer to the limitations on the data compression storage method above, and will not be repeated here.

[0107] In an exemplary embodiment, as shown in FIG14 , a data compression storage device is provided, including: a parameter acquisition module 1402, for acquiring, in response to a compression storage instruction for target data, a performance parameter of a data storage service for compressing and storing target data; a fuzzification processing module 1404, for fuzzifying the performance parameter to obtain a target fuzzy quantity of the performance parameter; a fuzzy reasoning module 1406, for performing fuzzy reasoning based on the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the correspondence between the target fuzzy quantity and each compression algorithm; a defuzzification module 1408, for defuzzifying the first fuzzy set to obtain a target compression ratio; and a compression storage module 1410, for compressing and storing the target data through the data storage service based on a compression algorithm that matches the target compression ratio.

[0108] In one embodiment, the fuzzification processing module 1404 is also used to: obtain a second fuzzy set of performance parameters, and a membership function for describing the second fuzzy set; the second fuzzy set includes at least one fuzzy element for describing different states of the performance parameters; the parameter value of the performance parameter is input into the membership function, and fuzzification processing is performed to obtain the membership between the parameter value and each fuzzy element; the membership is used to describe the degree of membership between the parameter value and each fuzzy element; and the fuzzy element with the largest membership is used as the target fuzzy value of the performance parameter.

[0109] In one embodiment, the fuzzy reasoning module 1406 is also used to: compare the target fuzzy quantity with the premise of the fuzzy reasoning rule to obtain a comparison result between the fuzzy quantity and the fuzzy reasoning rule; determine the selection probability of each compression algorithm based on the comparison result; and combine each selection probability to obtain a first fuzzy set.

[0110] In one embodiment, the defuzzification module 1408 is also used to: for each selection probability in the first fuzzy set, determine the coordinate point of the selection probability in the membership function diagram for each compression algorithm; perform a centroid analysis on the target graph composed of each coordinate point to obtain the centroid point of the target graph, and use the compression ratio of the centroid point as the target compression ratio.

[0111] In one embodiment, the parameter acquisition module 1402 is also used to: in response to a compression storage instruction for target data, obtain the compression storage requirement indicated by the compression storage instruction and multiple candidate parameters of the data storage service for compressing and storing the target data; and filter out performance parameters that match the compression storage requirement from the multiple candidate parameters.

[0112] In one embodiment, the performance parameters include the available capacity and read / write rate of the data storage service, and the fuzzy processing module 1404 is further used to: perform fuzzy processing on the available capacity of the data storage service to obtain a first fuzzy quantity of the available capacity; perform fuzzy processing on the read / write rate of the data storage service to obtain a second fuzzy quantity of the read / write rate; and combine the first fuzzy quantity and the second fuzzy quantity to obtain a target fuzzy quantity.

[0113] Each module in the above data compression storage device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0114] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG15. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data compression storage data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a data compression storage method is implemented.

[0115] Those skilled in the art will understand that the structure shown in FIG. 15 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0116] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: in response to a compression storage instruction for target data, obtaining performance parameters of a data storage service for compressing and storing the target data; fuzzifying the performance parameters to obtain a target fuzzy quantity of the performance parameters; performing fuzzy reasoning based on the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the correspondence between the target fuzzy quantity and each compression algorithm; defuzzifying the first fuzzy set to obtain a target compression ratio; and compressing and storing the target data through the data storage service based on a compression algorithm that matches the target compression ratio.

[0117] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining a second fuzzy set of performance parameters and a membership function for describing the second fuzzy set; the second fuzzy set includes at least one fuzzy element for describing different states of the performance parameters; inputting the parameter value of the performance parameter into the membership function, performing fuzzification processing, and obtaining the membership between the parameter value and each fuzzy element; the membership is used to describe the degree of membership between the parameter value and each fuzzy element; and taking the fuzzy element with the largest membership as the target fuzzy value of the performance parameter.

[0118] In one embodiment, when the processor executes the computer program, the following steps are also implemented: comparing the target fuzzy quantity with the premise of the fuzzy reasoning rule to obtain a comparison result between the fuzzy quantity and the fuzzy reasoning rule; based on the comparison result, determining the selection probability of each compression algorithm; combining each selection probability to obtain a first fuzzy set.

[0119] In one embodiment, when the processor executes the computer program, the following steps are also implemented: for each selection probability in the first fuzzy set, the coordinate point of the selection probability in the membership function diagram for each compression algorithm is determined; a centroid analysis is performed on the target graph composed of each coordinate point to obtain the centroid point of the target graph, and the compression ratio of the centroid point is used as the target compression ratio.

[0120] In one embodiment, when the processor executes the computer program, the processor also implements the following steps: in response to a compression storage instruction for target data, obtaining a compression storage requirement indicated by the compression storage instruction and multiple candidate parameters of a data storage service for compressing and storing the target data; and screening out performance parameters that match the compression storage requirement from the multiple candidate parameters.

[0121] In one embodiment, when the processor executes the computer program, the following steps are also implemented: fuzzifying the available capacity of the data storage service to obtain a first fuzzy quantity of the available capacity; fuzzifying the read and write rates of the data storage service to obtain a second fuzzy quantity of the read and write rate; and combining the first fuzzy quantity and the second fuzzy quantity to obtain a target fuzzy quantity.

[0122] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: in response to a compression storage instruction for target data, performance parameters of a data storage service for compressing and storing the target data are obtained; the performance parameters are fuzzified to obtain a target fuzzy quantity of the performance parameters; fuzzy reasoning is performed based on the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the correspondence between the target fuzzy quantity and each compression algorithm; the first fuzzy set is defuzzified to obtain a target compression ratio; based on a compression algorithm that matches the target compression ratio, the target data is compressed and stored through the data storage service.

[0123] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining a second fuzzy set of performance parameters and a membership function for describing the second fuzzy set; the second fuzzy set includes at least one fuzzy element for describing different states of the performance parameters; inputting the parameter value of the performance parameter into the membership function, performing fuzzification processing, and obtaining the membership between the parameter value and each fuzzy element; the membership is used to describe the degree of membership between the parameter value and each fuzzy element; and taking the fuzzy element with the largest membership as the target fuzzy value of the performance parameter.

[0124] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: comparing the target fuzzy quantity with the premise of the fuzzy reasoning rule to obtain a comparison result between the fuzzy quantity and the fuzzy reasoning rule; based on the comparison result, determining the selection probability of each compression algorithm; combining each selection probability to obtain a first fuzzy set.

[0125] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: for each selection probability in the first fuzzy set, the coordinate point of the selection probability in the membership function diagram for each compression algorithm is determined; a centroid analysis is performed on the target graph composed of each coordinate point to obtain the centroid point of the target graph, and the compression ratio of the centroid point is used as the target compression ratio.

[0126] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: in response to a compression storage instruction for target data, a compression storage requirement indicated by the compression storage instruction and multiple candidate parameters of a data storage service for compressing and storing the target data are obtained; and from the multiple candidate parameters, performance parameters that match the compression storage requirement are screened out.

[0127] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: fuzzifying the available capacity of the data storage service to obtain a first fuzzy quantity of the available capacity; fuzzifying the read and write rates of the data storage service to obtain a second fuzzy quantity of the read and write rate; and combining the first fuzzy quantity and the second fuzzy quantity to obtain a target fuzzy quantity.

[0128] In one embodiment, a computer program product is provided, including a computer program, which implements the following steps when executed by a processor: in response to a compression storage instruction for target data, obtaining performance parameters of a data storage service for compressing and storing the target data; fuzzifying the performance parameters to obtain a target fuzzy quantity of the performance parameters; performing fuzzy reasoning based on the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the correspondence between the target fuzzy quantity and each compression algorithm; defuzzifying the first fuzzy set to obtain a target compression ratio; and compressing and storing the target data through the data storage service based on a compression algorithm that matches the target compression ratio.

[0129] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining a second fuzzy set of performance parameters and a membership function for describing the second fuzzy set; the second fuzzy set includes at least one fuzzy element for describing different states of the performance parameters; inputting the parameter value of the performance parameter into the membership function, performing fuzzification processing, and obtaining the membership between the parameter value and each fuzzy element; the membership is used to describe the degree of membership between the parameter value and each fuzzy element; and taking the fuzzy element with the largest membership as the target fuzzy value of the performance parameter.

[0130] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: comparing the target fuzzy quantity with the premise of the fuzzy reasoning rule to obtain a comparison result between the fuzzy quantity and the fuzzy reasoning rule; based on the comparison result, determining the selection probability of each compression algorithm; combining each selection probability to obtain a first fuzzy set.

[0131] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: for each selection probability in the first fuzzy set, the coordinate point of the selection probability in the membership function diagram for each compression algorithm is determined; a centroid analysis is performed on the target graph composed of each coordinate point to obtain the centroid point of the target graph, and the compression ratio of the centroid point is used as the target compression ratio.

[0132] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: in response to a compression storage instruction for target data, a compression storage requirement indicated by the compression storage instruction and multiple candidate parameters of a data storage service for compressing and storing the target data are obtained; and from the multiple candidate parameters, performance parameters that match the compression storage requirement are screened out.

[0133] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: fuzzifying the available capacity of the data storage service to obtain a first fuzzy quantity of the available capacity; fuzzifying the read and write rates of the data storage service to obtain a second fuzzy quantity of the read and write rate; and combining the first fuzzy quantity and the second fuzzy quantity to obtain a target fuzzy quantity.

[0134] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0135] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0136] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this application.

[0137] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A data compression storage method, characterized in that: The method comprises: In response to a compression storage instruction for target data, obtaining a performance parameter of a data storage service for compressing and storing the target data; Performing fuzzification processing on the performance parameter to obtain a target fuzzy value of the performance parameter; Performing fuzzy reasoning according to the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the corresponding relationship between the target fuzzy quantity and each compression algorithm; Defuzzifying the first fuzzy set to obtain a target compression ratio; Based on a compression algorithm that matches the target compression ratio, the target data is compressed and stored through the data storage service.

2. The method according to claim 1, characterized in that The fuzzy processing of the performance parameter to obtain a target fuzzy value of the performance parameter includes: Acquire a second fuzzy set of the performance parameter and a membership function for describing the second fuzzy set; the second fuzzy set includes at least one fuzzy element for describing different states of the performance parameter; Inputting the parameter value of the performance parameter into the membership function, performing fuzzification processing, and obtaining the membership between the parameter value and each fuzzy element; the membership is used to describe the degree of membership between the parameter value and each fuzzy element; The fuzzy element with the largest membership degree is used as the target fuzzy value of the performance parameter.

3. The method according to claim 1, characterized in that The step of performing fuzzy reasoning according to the target fuzzy quantity and the fuzzy reasoning rule to obtain a first fuzzy set includes: Comparing the target fuzzy quantity with the premise of the fuzzy inference rule to obtain a comparison result between the fuzzy quantity and the fuzzy inference rule; Based on the comparison result, determining the selection probability of each compression algorithm; The selection probabilities are combined to obtain a first fuzzy set.

4. The method according to claim 3, characterized in that The defuzzifying the first fuzzy set to obtain a target compression ratio includes: For each of the selection probabilities in the first fuzzy set, determining a coordinate point of the selection probability in a membership function graph for each of the compression algorithms; A centroid analysis is performed on the target graph composed of the coordinate points to obtain the centroid point of the target graph, and the compression ratio of the centroid point is used as the target compression ratio.

5. The method according to claim 1, characterized in that: The step of obtaining, in response to a compression storage instruction for target data, a performance parameter of a data storage service for compressing and storing the target data comprises: In response to a compression storage instruction for target data, obtaining a compression storage requirement indicated by the compression storage instruction and a plurality of candidate parameters of a data storage service for compressing and storing the target data; From the multiple candidate parameters, performance parameters matching the compression storage requirement are screened out.

6. The method according to claim 1, characterized in that The performance parameters include the available capacity and the read / write rate of the data storage service, and the fuzzification processing of the performance parameters to obtain the target fuzzy value of the performance parameters includes: Performing fuzzy processing on the available capacity of the data storage service to obtain a first fuzzy amount of the available capacity; Performing fuzzy processing on the read and write rate of the data storage service to obtain a second fuzzy value of the read and write rate; The first blur amount and the second blur amount are combined to obtain the target blur amount.

7. A data compression storage device, characterized in that: The device comprises: A parameter acquisition module, configured to acquire, in response to a compression storage instruction for target data, a performance parameter of a data storage service for compressing and storing the target data; A fuzzy processing module, used for performing fuzzy processing on the performance parameter to obtain a target fuzzy value of the performance parameter; A fuzzy reasoning module, used for performing fuzzy reasoning according to the target fuzzy quantity and fuzzy reasoning rules to obtain a first fuzzy set; the fuzzy reasoning rules are used to describe the corresponding relationship between the target fuzzy quantity and each compression algorithm; A defuzzification module, used for performing defuzzification processing on the first fuzzy set to obtain a target compression ratio; A compression storage module is used to compress and store the target data through the data storage service based on a compression algorithm that matches the target compression ratio.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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