A microservice combination configuration method, system and storage medium for rapid response to demand

Abstract

The present application relates to a microservice combination configuration method, system and storage medium that can quickly respond to needs, and relates to the field of microservice combination applications. It solves the problem that users are prone to make mistakes due to their own judgment or negligence during the selection process, resulting in the selected microservice needs not being what they want, which is relatively troublesome. It includes: based on the user's historical settings of the microservice demand information for this time, predicting the user's related demand information for the corresponding microservice and displaying it; obtaining the user's demand information for microservices; establishing multi-color set enclosure matrices for functional requirements and performance requirements respectively, solving the constructed multi-layer multi-color set enclosure matrix layer by layer, forming a microservice combination solution model, solving the microservice combination configuration; obtaining diverse configuration results that meet the needs. The present application has the following effects: effectively improving the probability of users selecting the right microservice needs, thereby improving the efficiency of obtaining microservice configuration solutions that meet the needs.

Description

Technical Field

[0001] The present application relates to the field of microservice combination applications, and in particular to a microservice combination configuration method, system, and storage medium for quickly responding to needs. Background Art

[0002] Microservices is an emerging software architecture that breaks down a large, single application or service into dozens of supporting microservices. A microservices strategy can make it easier to scale individual components rather than the entire application stack to meet service-level agreements.

[0003] Since microservices need to consider functional requirements and performance requirements when providing services, their configurations are diverse. In response to the above situation, a microservice configuration method based on polychromatic set theory is currently used to solve this problem. The specific method is as follows: first, the requirements are divided into functional requirements and performance requirements; based on the relationship between functional requirements and microservices, and the relationship between performance requirements and microservices, a multi-layer polychromatic set contour matrix is ​​established; the multi-layer polychromatic set contour matrix is ​​solved layer by layer to form a microservice combination solution model, so as to quickly respond to requirements and perform combination configuration of microservices.

[0004] Regarding the above-mentioned related technologies, the inventors believe that there are the following defects: the currently used microservice configuration method requires users to select microservice requirements, and there are often many microservice requirements. Users are prone to make mistakes due to their own judgment or negligence during the selection process, resulting in the selected microservice requirements not being what they want, which is quite troublesome. Summary of the Invention

[0005] In order to effectively increase the probability of users selecting the correct microservice requirements, thereby improving the efficiency of obtaining microservice configuration solutions that meet the requirements, the present application provides a microservice combination configuration method, system and storage medium that can quickly respond to requirements.

[0006] First, this application provides a microservice combination configuration method that quickly responds to needs, using the following technical solutions:

[0007] A microservice combination configuration method that quickly responds to needs, including:

[0008] Based on the user's historical settings for the current microservice demand information, predict the user's demand information for the corresponding microservice and display it;

[0009] Obtain user demand information for microservices;

[0010] Divide user demand information into functional demand information and performance demand information;

[0011] Based on the preset correspondence between microservice function categories and microservice resource libraries, select the microservice resource library corresponding to the functional requirement information, and select the microservice resource library corresponding to the performance requirement information;

[0012] Map the functional requirement information to the uniform color of the multi-color set, map the screened microservices to the elements of the multi-color set, and establish the multi-color set contour matrix;

[0013] Solve the established multi-color set contour matrix and select microservices that meet the functional requirements;

[0014] Map the performance requirement information to the uniform color of a multi-color set, map the selected microservices to the elements of the multi-color set, and establish a multi-color set contour matrix;

[0015] Solve the constructed polychromatic set contour matrix to select microservices that meet performance requirements;

[0016] The microservices obtained through multi-layer collection contour matrix screening are microservices that meet the functional and performance requirements. Combining them is a diverse configuration result that meets the requirements.

[0017] By adopting the above technical solutions, it is possible to obtain users' demands for microservices in a more timely and effective manner, and to complete microservice configuration based on users' demands for microservices by applying polychromatic set theory, so as to obtain microservice solutions or solution sets that meet the needs within a reasonable time, thereby improving the efficiency of users in obtaining microservice configuration solutions that meet their needs.

[0018] Optionally, obtaining user demand information for microservices includes:

[0019] Get the demand information of whether the current user has set the corresponding microservice in the history;

[0020] If the current user has set demand information for the corresponding microservice in the past, then based on the demand information of the current user for the corresponding microservice in the past, the current user's demand information for the corresponding microservice is predicted and analyzed, and displayed on the display interface;

[0021] If the current user has not set the demand information for the corresponding microservice in the past, based on the demand information of the current user for similar microservices in the past, the current user's demand information for the corresponding microservice is predicted and analyzed, and displayed on the display interface.

[0022] By adopting the above technical solution, it is possible to effectively adopt appropriate prediction methods to analyze the current user's demand information for the corresponding microservices based on the user's settings for the microservice demand information, and let the user know in a timely manner.

[0023] Optionally, if the current user has previously set demand information for the corresponding microservice, based on the current user's historical demand information for the corresponding microservice, the current user's demand information for the corresponding microservice is predicted and analyzed, and displayed on the display interface, including:

[0024] Get the demand information of the corresponding microservices set by the current user in history, including the specific demand and the number of demands;

[0025] The average of the number of demands set by the user three times in the past is used as the predicted number of demands the user will have on the corresponding microservice this time.

[0026] Based on the specific requirements that the user has set for the corresponding microservice each time in history, the overall frequency of occurrence of each specific requirement is analyzed, and the specific requirements that meet the predicted number of requirements are filtered out according to the frequency. These are used as the predicted specific requirements of the user for the corresponding microservice this time and are displayed on the display interface.

[0027] By adopting the above technical solution, taking into full consideration the user's historical demand information for the corresponding microservices, we first predict and analyze the number of users' demands for the corresponding microservices this time, and then based on the number of demands, we filter out the specific demands that meet the number of demands and have the highest frequency of occurrence, so as to ensure that the selected specific demands are in line with the user's wishes as much as possible.

[0028] Optionally, based on the average of the number of demands set by the user three times in the past, the predicted number of demands for the corresponding microservice by the user this time includes:

[0029] Get the average number of requirements set by the user for the past three times;

[0030] Analyze whether the average number of requirements set by the user three times is an integer;

[0031] If it is an integer, the average of the number of demands set by the user three times in the past is used as the predicted number of demands for the corresponding microservice by the user this time;

[0032] Otherwise, the number of requirements set by the user in the last three times is compared with the average number of requirements set by the user in each time.

[0033] If the number of demands set by the user in the past three times exceeds the average number of demands set by the user in each history, the decimal part of the average is removed and 1 is added. The integer part obtained is used as the predicted number of demands for the corresponding microservice by the user this time;

[0034] If the number of demands actually set by the user in the past three times is lower than the historical average number of demands set by the user each time, the decimal part of the average is removed, and the integer part obtained is used as the predicted number of demands of the user for the corresponding microservice this time.

[0035] By adopting the above technical solution, full consideration is given to the fact that when the average of the number of demands set by the user three times is used as the number of demands for the corresponding microservice this time, there is a situation where the average is not an integer. In this case, based on the comparison between the actual number of demands set by the user three times and the historical average number of demands set each time, it is possible to effectively analyze whether to round up to 1 or just round up. The above setting can better realize the prediction of the number of demands of the user for the corresponding microservice this time.

[0036] Optionally, based on the current user's historical demand information for similar microservices, the current user's demand information for the corresponding microservice is predicted and analyzed, and displayed on the display interface, including:

[0037] Obtain the user's historical demand information for similar microservices, including specific demands and the number of demands;

[0038] Analyze and obtain the specific demand quantity information of similar microservices set by the user in the past, and analyze and obtain the specific demand quantity with the highest frequency as a prediction analysis of the current user's demand quantity for the corresponding microservice;

[0039] Analyze and obtain the overall probability of occurrence of each specific demand of the same microservice set by the user in the past, and combine the probability of specific demands set by the user in the past under the predicted number of demands to predict the probability of occurrence of each specific demand;

[0040] According to the predicted probability of occurrence of each specific demand, at least the specific demands that meet the predicted number of demands are screened out.

[0041] By adopting the above technical solution, the situation where the user has not set the demand information for the same microservice is further taken into consideration. At this time, the user's specific demand for the corresponding microservice is effectively predicted based on the user's historical demand information for the same microservice.

[0042] Optionally, the predicted probability of occurrence of each specific requirement includes:

[0043] Obtain the overall probability of occurrence of each specific requirement of the same microservice set by the user in the past, as well as the probability of the specific requirement set by the user in the past under the predicted number of requirements;

[0044] Apply the preset prediction formula to calculate the probability of occurrence of each specific demand. The specific prediction formula is as follows:

[0045] Z i =A i *q1+B i *q2,0 <i<=N,q1+q2=1;

[0046] N is the total number of specific requirements;

[0047] Z i is the predicted probability of the i-th demand;

[0048] A i The overall occurrence probability of the i-th requirement of the same microservice;

[0049] q1 is A i The weight ratio of

[0050] B i is the probability of the i-th demand set by the user history under the predicted number of demands;

[0051] Q2 is B i The weight ratio of .

[0052] By adopting the above technical solution, it is specifically disclosed how to combine the overall occurrence probability of each specific demand of the same microservice set by the user history and the probability of the specific demand set by the user history under the predicted number of demands to predict the occurrence probability of each specific demand.

[0053] Optionally, the following steps are also included after obtaining the diversity configuration results that meet the requirements:

[0054] Obtaining data on the proportion of the display mode of the diversity configuration results subsequently set by the user when the demand information selected by the user on the display interface is the demand information predicted based on historical demand information for the same microservice or the demand information predicted based on demand information of similar microservices;

[0055] The mode with the highest data percentage is selected as the default mode for displaying the diversity configuration results and displayed.

[0056] By adopting the above technical solution, the method of obtaining user demand information for microservices is associated with the subsequent display of diverse supporting results, so that the subsequent display mode can better meet the needs of users.

[0057] Optionally, analyze whether there are multiple patterns with the highest data percentage;

[0058] If there is only one, the mode with the highest data percentage will be selected as the default mode for displaying the diversity configuration results.

[0059] If there are multiple requirements, further analyze whether the requirement information selected by the user on the display interface is the demand information predicted based on the historical demand information for the same microservice;

[0060] If so, then from among the several modes with the highest proportion data, analyze and obtain the proportion data of the above several modes when the demand information selected by the user in the display interface is the demand information predicted based on the demand information of similar microservices, and select the mode with the highest proportion data as the default mode for displaying the diversity configuration results and display it.

[0061] If not, then analyze the number of times the user sets the above modes near the preset time period from the modes with the highest proportion data, select the mode with the highest number of times as the default mode for displaying the diversity configuration results and display it.

[0062] By adopting the above technical solution, it is specifically disclosed how to further subdivide the patterns in combination with the method of obtaining user demand information for microservices when there are multiple patterns with the same proportion data, so that the displayed patterns can better meet the needs of users.

[0063] Secondly, this application provides a microservice combination configuration system that quickly responds to needs, using the following technical solutions:

[0064] A microservice combination configuration system for rapid response to demand includes a memory, a processor, and a program stored on the memory and executable on the processor. The program can, when loaded and executed by the processor, implement a microservice combination configuration method for rapid response to demand as described in the first aspect.

[0065] By adopting the above technical solution and calling relevant programs, it is possible to obtain users' needs for microservices in a more timely and effective manner, and apply polychromatic set theory based on users' needs for microservices to complete microservice configuration, so as to obtain microservice solutions or solution sets that meet the needs within a reasonable time, thereby improving the efficiency of users in obtaining microservice configuration solutions that meet their needs.

[0066] In a third aspect, the present application provides a computer storage medium, which adopts the following technical solution:

[0067] A computer storage medium includes a program that can be loaded and executed by a processor to implement a microservice combination configuration method for quickly responding to needs as described in the first aspect.

[0068] By adopting the above technical solution and calling relevant programs, it is possible to obtain users' needs for microservices in a more timely and effective manner, and apply polychromatic set theory based on users' needs for microservices to complete microservice configuration, so as to obtain microservice solutions or solution sets that meet the needs within a reasonable time, thereby improving the efficiency of users in obtaining microservice configuration solutions that meet their needs.

[0069] In summary, the beneficial technical effects of this application are:

[0070] 1. Use the polychromatic set method to establish a microservice composition solution model. The solution model uses Boolean matrices to represent information and logical relationships, which helps to solve the problem of improving demand response efficiency caused by the rapid and changing configuration of microservice combinations.

[0071] 2. Before obtaining a microservice solution that meets the needs through polychromatic set theory, timely and effectively predict the user's needs for this microservice and present them, thereby improving the efficiency of users obtaining the microservice solution they need.

[0072] 3. When outputting microservice solutions, it can better meet the user's needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a flow chart of a microservice combination configuration method for quickly responding to needs in an embodiment of the present application.

[0074] Figure 2 yes Figure 1 Specific flow chart of step S100 in FIG.

[0075] Figure 3 yes Figure 2 Specific flow diagram of step S1A0 in FIG.

[0076] Figure 4 yes Figure 3 Specific flow diagram of step S1A2 in FIG.

[0077] Figure 5 yes Figure 2 Specific flow chart of step S1B0 in FIG.

[0078] Figure 6 yes Figure 5 Specific flow chart of step S1B3 in FIG.

[0079] Figure 7 is located in Figure 1 Detailed flowchart after step S700.

[0080] Figure 8 yes Figure 7 Specific flow chart of step S900 in FIG. DETAILED DESCRIPTION

[0081] The present application is further described in detail below with reference to the accompanying drawings.

[0082] Reference Figure 1 , a microservice combination configuration method for quickly responding to needs disclosed in this application, including:

[0083] Step S100 : Based on the user's historical settings of the microservice demand information, predict the user's demand information for the corresponding microservice and display it.

[0084] Step S200: Obtain user demand information for microservices.

[0085] In step S300, the user's demand information is divided and classified into functional demand information and performance demand information.

[0086] Step S400: Based on the preset correspondence between microservice function categories and microservice resource libraries, select the microservice resource library corresponding to the functional requirement information, and select the microservice resource library corresponding to the performance requirement information.

[0087] Step S500: Map the functional requirement information to a uniform color of a multi-color set, map the screened microservices to elements of the multi-color set, and establish a multi-color set contour matrix.

[0088] Step S600: Solve the established polychromatic set contour matrix to screen microservices that meet the functional requirements.

[0089] Step S700 : Map the performance requirement information to a uniform color of a multi-color set, map the screened microservices to elements of the multi-color set, and establish a multi-color set contour matrix.

[0090] Step S800: Solve the constructed polychromatic set contour matrix to select microservices that meet performance requirements.

[0091] It should be noted that the multi-color sets mentioned in steps S500 and S700, i.e., the elements and the entire set, are colored differently to represent the research object and its elemental properties. The multi-color set solution process can be divided into two steps: target microservice family assignment and functional block configuration. Due to different solution conditions and goals, the unified colors and elements of each step are also different.

[0092] In addition, the polychromatic set contour matrix mentioned in step S500 and step S700 is used to reflect the composition of elements in the polychromatic set and the relationship between elements and colors.

[0093] In step S900, the microservices obtained through multi-layer set contour matrix screening are microservices that meet the functional requirements and performance requirements, and they are combined to form a diversified configuration result that meets the requirements.

[0094] Reference Figure 2 , wherein, the step S100 mentioned in the step of predicting the user's demand information for the corresponding microservice based on the user's historical setting of the microservice demand information and displaying it includes steps S110, S1A0, and S1B0, which are specifically as follows:

[0095] Step S110: Obtain demand information of whether the current user has set the corresponding microservice in the history.

[0096] Specifically, how to obtain whether the current user has set the demand information of the corresponding microservice in the history can be analyzed and judged by querying the log of whether the user has set the demand information of the corresponding microservice in the system.

[0097] Step S1A0: If the current user has set demand information for the corresponding microservice in the past, based on the demand information for the corresponding microservice set in the past, the current user's demand information for the corresponding microservice is predicted and analyzed, and displayed on the display interface.

[0098] Specifically, the current user mentioned in step S1A0 has set the demand information of the corresponding microservice in the history, that is, the log information of the user setting the demand information of the corresponding microservice can be queried in the system. The display interface mentioned in step S1A0 is an operation interface that can be controlled by the user, which can be the operation interface of a mobile phone, the operation interface of a computer, or the operation interface of other terminals.

[0099] Reference Figure 3 , wherein step S1A0 includes:

[0100] Step S1A1: Obtain the demand information of the corresponding microservices set by the current user each time in history. The demand information includes specific demands and the number of demands.

[0101] The acquisition of the demand information of each time the current user has set up the corresponding microservice in the past can be obtained by querying a preset storage that stores the demand information of each time the current user has set up the corresponding microservice in the past.

[0102] Step S1A2: The average of the number of demands set by the user three times is used as the predicted number of demands for the corresponding microservice by the user this time.

[0103] For example, assuming that the number of demands set by the user three times is a, b, and c respectively, then the predicted number of demands of the user for the corresponding microservices this time is (a+b+c) / 3.

[0104] Reference Figure 4 , wherein step S1A2 includes:

[0105] Step S1A2.1, obtaining the average number of requirements set by the user three times.

[0106] Step S1A2.2: Analyze whether the average number of requirements set by the user three times is an integer.

[0107] Step S1A2.A: If it is an integer, the average of the number of demands set by the user three times in the past is used as the predicted number of demands of the user for the corresponding microservice this time.

[0108] For example, assuming that (a+b+c) / 3 is 4, which is an exact integer, then the predicted number of user demands for the corresponding microservice is 4.

[0109] Step S1A2.B, on the contrary, compares the number of requirements actually set by the user in the past three times with the average number of requirements set by the user in each historical period.

[0110] Step S1A2.C: If the number of demands actually set by the user in the past three times exceeds the average number of demands set by the user in each history, the decimal part of the average is removed and 1 is added. The integer part obtained is used as the predicted number of demands of the user for the corresponding microservice this time.

[0111] Step S1A2.D: If the number of demands actually set by the user in the past three times is lower than the average number of demands set by the user each time in history, the decimal part of the average is removed and the integer part is obtained as the predicted number of demands of the user for the corresponding microservice this time.

[0112] For example, suppose the number of requirements set by the user in the past three times is 5, and the average number of requirements set by the user each time is 3. Then (a+b+c) / 3=4.2. Then, in this application scenario, remove the decimal part of 4.2 and add 1 to obtain the integer 5, which is the number of requirements set by the user for the corresponding microservice this time.

[0113] In step S1A3, based on the specific needs of the user for the corresponding microservice each time in history, the overall frequency of occurrence of each specific need is analyzed, and the specific needs that meet the predicted number of needs are selected from the most to least according to the frequency, as the predicted specific needs of the user for the corresponding microservice this time, and are displayed on the display interface.

[0114] For example, the predicted number of demands is 4, and the overall frequency of occurrence of specific demands is sorted from most to least as demand 1, demand 3, demand 5, demand 7, demand 9, and demand 10. Then the demands that appear are demand 1, demand 3, demand 5, and demand 7.

[0115] Step S1B0: If the current user has not set demand information for the corresponding microservice in the past, based on the demand information for similar microservices set in the past by the current user, the current user's demand information for the corresponding microservice is predicted and analyzed, and displayed on the display interface.

[0116] Reference Figure 5 , wherein step S1B0 includes:

[0117] Step S1B1: Obtain the user's historical demand information for similar microservices, including specific demands and the number of demands.

[0118] Specifically, the demand information of similar microservices that the user has set in the past can be obtained by querying from a preset database that stores the demand information of similar microservices that the user has set in the past.

[0119] Step S1B2: Analyze and obtain the specific demand quantity information of the same type of microservices set by the user in the past, and analyze and obtain the specific demand quantity with the highest selection frequency as the predicted demand quantity of the current user for the corresponding microservice.

[0120] Among them, the process of analyzing and obtaining the specific demand number with the highest selection frequency as the prediction analysis of the current user's demand number for the corresponding microservice is as follows: first, the setting of the specific demand number each time is screened out, and then the situation with the most identical specific demand numbers is screened out, and the corresponding specific demand number is used as the current user's demand number for the corresponding microservice.

[0121] Step S1B3: Analyze and obtain the overall occurrence probability of each specific demand of the same type of microservice set by the user in the history, and combine the probability of the specific demand set by the user in the history under the predicted number of demands to predict the occurrence probability of each specific demand.

[0122] For example, the predicted number of demands is 5, and the overall frequency of specific demands is sorted from most to least as demand 1, demand 3, demand 5, demand 7, demand 9, and demand 10. Then the demands that appear are demand 1, demand 3, demand 5, demand 7, demand 9, and so on.

[0123] Reference Figure 6 , wherein the predicted probability of occurrence of each specific demand mentioned in step S1B3 includes:

[0124] Step S1B3.1, obtaining the overall occurrence probability of each specific demand of the same type of microservice set by the user in the past, and the probability of the specific demand set by the user in the past under the predicted number of demands.

[0125] Among them, the overall occurrence probability of each specific requirement of the same type of microservices set by the user history can be queried and obtained from a preset database storing the overall occurrence probability of each specific requirement of the same type of microservices set by the user history. In addition, the probability of the specific requirements set by the user history under the predicted number of requirements can also be queried and obtained from a preset database storing the probability of the specific requirements set by the user history under the number of requirements.

[0126] Step S1B3.2, calculate the occurrence probability of each specific requirement by applying a preset prediction formula. The specific prediction formula is as follows: Z i =A i *q1 + B i *q2, 0 < i <= N, q1 + q2 = 1; N is the total number of specific requirements, and Z i is the predicted probability of the i-th requirement; A i is the overall occurrence probability of the i-th requirement of the same type of microservices; q1 is the weight ratio of A i ; B i is the probability of the i-th requirement set by the user history under the predicted number of requirements; Q2 is the weight ratio of B i .

[0127] For example, if q1 is 0.5, q2 is 0.5, the occurrence probability of A1 is 0.3, and the occurrence probability of B1 is 0.2, then Z1 is 0.25.

[0128] Step S1B4, screen out the specific requirements that meet the predicted number of requirements according to the occurrence probability of each predicted specific requirement from more to less.

[0129] Refer to Figure 7 , after step S900, the following steps are further included:

[0130] Step SA00, when the requirement information selected by the user on the display interface is the requirement information predicted based on the same requirement information of the microservices in history or the requirement information predicted based on the requirement information of the same type of microservices, obtain the proportion data of the display mode of the subsequent diversity configuration result set by the user.

[0131] Specifically, when the requirement information selected by the user on the display interface is the requirement information predicted based on the same requirement information of the microservices in history or the requirement information predicted based on the requirement information of the same type of microservices, the acquisition of the proportion data of the display mode of the subsequent diversity configuration result set by the user can be obtained by querying a preset database storing the proportion data of the display mode of the subsequent diversity configuration result set by the user when the requirement information selected by the user on the display interface is the requirement information predicted based on the same requirement information of the microservices in history or the requirement information predicted based on the requirement information of the same type of microservices.

[0132] In addition, the mode of displaying the diverse configuration results mentioned in step SA00 can be a table mode, a document mode, or a PPT mode.

[0133] In step SB00 , the mode with the highest data percentage is selected as the default mode for displaying the diversity configuration results and displayed.

[0134] For example, there are three modes: Mode 1, Mode 2, and Mode 3. Mode 1 accounts for 20%, Mode 2 accounts for 30%, and Mode 3 accounts for 50%. In this case, the default diversity configuration result displays Mode 3.

[0135] Reference Figure 8 , wherein step SB00 includes:

[0136] Step SB10: Analyze whether there are multiple patterns with the highest data percentage.

[0137] In step SBA0, if the number is 1, the mode with the highest data percentage is selected as the default mode for displaying the diversity configuration results and displayed.

[0138] Step SBB0: If there are multiple requirements, further analyze whether the requirement information selected by the user on the display interface is the demand information predicted based on historical requirements for the same microservice.

[0139] Step SBC0, if yes, then from several modes with the highest proportion data, analyze and obtain the proportion data of the above several modes when the demand information selected by the user on the display interface is the demand information predicted based on the demand information of similar microservices, and select the mode with the highest proportion data as the default mode for displaying the diversity configuration results and display it.

[0140] Step SBD0, if the answer is no, then analyze the number of times the user sets the above-mentioned modes near the preset time period from the modes with the highest proportion data, select the mode with the highest number of times as the default mode for displaying the diversity configuration results and display it.

[0141] For example, assume that there are two modes with the highest data share, namely Mode 1 and Mode 2, and the demand information selected by the user on the display interface is not based on the demand information predicted based on the historical demand information for the same microservice. Under this condition, the number of times Mode 1 and Mode 2 are used in the nearby time period will be analyzed. Assuming that the number of times Mode 1 is used is 2 and the number of times Mode 2 is 3, then Mode 3 is actually selected as the default display mode.

[0142] The implementation principle of this embodiment is:

[0143] First, based on the user's historical settings for the microservice demand information, the user's demand information for the corresponding microservice is predicted and displayed, so that the user can be more satisfied when choosing the relevant microservice demand.

[0144] After confirming the microservice requirements selected by the user, we will analyze and calculate the solutions that meet the user's microservice requirements based on polychromatic set theory, and display the results in the way the user prefers.

[0145] An embodiment of the present invention provides a computer-readable storage medium, comprising a computer-readable storage medium capable of being loaded and executed by a processor to implement the following Figures 1-8 Procedure for either method.

[0146] The computer-readable storage medium includes, for example, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0147] Based on the same inventive concept, an embodiment of the present invention provides a safety warning system for deep foundation pit excavation and construction, including a memory and a processor, wherein the memory stores data that can be executed on the processor to implement the following Figures 1 to 8 Procedure for either method.

[0148] The embodiments of this specific implementation method are all preferred embodiments of the present application and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A microservice combination configuration method for rapid response to demand, characterized in that: include: Based on the user's historical settings for the microservice demand information, predict the user's demand information for the corresponding microservice and display it; Obtain user demand information for microservices; Divide user demand information into functional demand information and performance demand information; Based on the correspondence between the preset microservice function categories and the microservice resource libraries, select the microservice resource library corresponding to the functional requirement information, and select the microservice resource library corresponding to the performance requirement information; Map the functional requirement information to the uniform color of the multi-color set, map the screened microservices to the elements of the multi-color set, and establish the multi-color set contour matrix; Solve the established multi-color set contour matrix and select microservices that meet the functional requirements; Map the performance requirement information to the uniform color of the multi-color set, map the screened microservices to the elements of the multi-color set, and establish the multi-color set contour matrix; Solve the constructed multi-color set contour matrix to select microservices that meet performance requirements; The microservices obtained through multi-layer collection contour matrix screening are microservices that meet the functional requirements and performance requirements. Combining them is a diverse configuration result that meets the requirements.

2. According to the method for configuring a microservice combination for rapid response to demand in claim 1, it is characterized in that: Predict the user's demand information for the corresponding microservice and display it, including: Get the demand information of the corresponding microservice whether the current user has set it in the history; If the current user has set demand information for the corresponding microservice in the past, based on the demand information for the corresponding microservice set in the past, the current user's demand information for the corresponding microservice is predicted and analyzed, and displayed on the display interface; If the current user has not set demand information for the corresponding microservice in the past, based on the demand information for similar microservices that the current user has set in the past, the current user's relevant demand information for the corresponding microservice is predicted and analyzed, and displayed on the display interface.

3. According to a microservice combination configuration method for rapid response to demand according to claim 2, it is characterized in that: If the current user has set the demand information of the corresponding microservice in the past, based on the demand information of the current user, the demand information of the current user for the corresponding microservice is predicted and analyzed, and displayed on the display interface, including: Get the demand information of the corresponding microservices that the current user has set each time in history. The demand information includes specific demands and the number of demands. The average number of demands set by the user three times is used as the predicted number of demands for the corresponding microservice by the user this time; Based on the specific needs of the corresponding microservices that the user has set in history, the overall frequency of occurrence of each specific need is analyzed, and the specific needs that meet the predicted number of needs are screened out according to the frequency, as the predicted specific needs of the user for the corresponding microservices this time, and displayed on the display interface.

4. According to the method for configuring a microservice combination for rapid response to demand in claim 3, it is characterized in that: Based on the average number of requirements set by the user three times, the predicted number of requirements for the corresponding microservices by the user this time includes: Get the average number of requirements set by the user three times; Analyze whether the average number of requirements set by the user three times is an integer; If it is an integer, the average of the number of requirements set by the user three times is taken as the predicted number of requirements for the corresponding microservice by the user this time; Otherwise, the number of requirements actually set by the user in the last three times is compared with the average number of requirements set by the user in each time in history; If the number of requirements set by the user in the last three times exceeds the average number of requirements set by the user in history, the decimal part of the average is removed and 1 is added. The integer part obtained is used as the predicted number of requirements for the corresponding microservice by the user this time. If the number of demands actually set by the user in the last three times is lower than the average number of demands set by the user in history, the decimal part of the average is removed, and the integer part is taken as the predicted number of demands of the user for the corresponding microservice this time.

5. A microservice combination configuration method for quickly responding to demand according to any one of claim 2, characterized in that: Based on the demand information of the current user for similar microservices that have been set in the past, the current user's demand information for the corresponding microservice is predicted and analyzed, and displayed on the display interface, including: Get the demand information of similar microservices that users have set in the past, including specific demands and the number of demands; Analyze and obtain the specific demand quantity information of the same type of microservices set by the user in the past, and analyze and obtain the specific demand quantity with the highest frequency as the prediction analysis of the current user's demand quantity for the corresponding microservice; Analyze and obtain the overall occurrence probability of each specific demand of the same microservice set by the user in the past, and combine the probability of the specific demand set by the user in the past under the predicted number of demands to predict the occurrence probability of each specific demand; According to the predicted probability of occurrence of each specific demand, at least the specific demands that meet the predicted number of demands are screened out.

6. A microservice combination configuration method for rapid response to demand according to claim 5, characterized in that: Predicting the probability of occurrence of each specific demand includes: Get the overall occurrence probability of each specific demand of the same microservice set by the user in the past, as well as the probability of the specific demand set by the user in the past under the predicted number of demands; The preset prediction formula is applied to calculate the probability of occurrence of each specific demand. The specific prediction formula is as follows: Z i =A i *q1+B i *q2,0<i<=N,q1+q2=1; N is the total number of specific requirements, Z i is the predicted probability of the i-th demand; A i The overall occurrence probability of the i-th requirement of the same microservice; q1 is A i The weight ratio of B i is the probability of the i-th demand set by the user history under the predicted number of demands; Q2 is B i The weight ratio of .

7. A microservice combination configuration method for quickly responding to demand according to any one of claims 2 to 5, characterized in that: It also includes the following steps after obtaining the diversity configuration results that meet the requirements: Obtaining data on the proportion of the mode of displaying the diversity configuration results subsequently set by the user when the demand information selected by the user on the display interface is the demand information predicted based on the historical demand information of the same microservice or the demand information predicted based on the demand information of the same microservice; The mode with the highest data share is selected as the default mode for displaying the diversity configuration results and displayed.

8. A microservice combination configuration method for rapid response to demand according to claim 7, characterized in that: Select the mode with the highest data share as the default mode for displaying the diversity configuration results and display them as follows: Analyze whether there are multiple patterns with the highest data share; If there is only one, the mode with the highest proportion of data is selected as the default mode for displaying the diversity configuration results and displayed; If there are multiple ones, further analyzing whether the demand information selected by the user on the display interface is the demand information predicted based on the historical demand information for the same microservice; If yes, then from among the several modes with the highest proportion data, analyze and obtain the proportion data of the several modes when the demand information selected by the user in the display interface is the demand information predicted based on the demand information of the same type of microservices, and select the mode with the highest proportion data as the default mode for displaying the diversity configuration results and display it; If not, then from among the modes with the highest percentage, analyze the number of times the user sets the above modes near the preset time period, select the mode with the largest number of times as the default mode for displaying the diversity configuration results and display it.

9. A microservice combination configuration system that quickly responds to needs, characterized in that: It comprises a memory, a processor and a program stored in the memory and executable on the processor, wherein the program can be loaded and executed by the processor to implement a microservice combination configuration method for quickly responding to demand as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that , including a program that can be loaded and executed by a processor to implement a microservice combination configuration method for quickly responding to needs as described in any one of claims 1 to 8.

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