Code pre-configuration method, system, device and storage medium combined with demand-oriented

Abstract

The present application relates to the technical field of code pre-configuration, and particularly relates to a code pre-configuration method, system, device and storage medium combined with demand orientation. According to the functional requirements of the target scene, a plurality of functional function number sequences are matched from a code function library; the functional requirement input parameters and the functional requirement output identifier set are obtained by performing frequent mining on the functional requirement sample of the target scene; the functional requirement input parameters are input into the plurality of functional function number sequences to obtain a plurality of functional requirement prediction outputs; the plurality of functional requirement prediction outputs are iterated to obtain a plurality of output prediction outlier factors; the plurality of functional function number sequences are iterated to obtain a plurality of expected computing power; the plurality of functional function number sequences are sorted based on the plurality of expected computing power and the plurality of output prediction outlier factors to obtain a target functional function number sequence, and code pre-configuration is performed. The code development efficiency is improved, and the code practicability is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a code pre-configuration method, system, device and storage medium combined with demand orientation. BACKGROUND

[0002] The current low-code development platform mainly operates in the mode of pre-configuring separate function functions, and then combining these function functions on a visual interface according to the needs to complete the development task. This scheme only considers the applicability of the function, but does not pay attention to the scene adaptability of the separate function functions and the demand field, resulting in that the function functions may not be able to well meet the needs of specific scenes, and the combination efficiency of the function modules is low, so that the management granularity of the low-code development platform is coarse, and there are technical problems of low development efficiency and poor practicability. SUMMARY

[0003] In view of the technical problems of low code development efficiency and poor practicability in the prior art, the present application provides a code pre-configuration method, system, device and storage medium combined with demand orientation to solve the problems.

[0004] The technical solution of the present application to solve the above technical problems is as follows:

[0005] In a first aspect, the present application provides a code pre-configuration method combined with demand orientation, comprising: matching a plurality of function function number sequences from a code function library according to the function requirements of a target scene; performing function requirement sample frequency mining on the target scene to obtain a function requirement input parameter and a function requirement output identifier set; inputting the function requirement input parameter into the plurality of function function number sequences to obtain a plurality of function requirement prediction outputs; performing outlier analysis on the plurality of function requirement prediction outputs according to the function requirement output identifier set to obtain a plurality of output prediction outlier factors; performing computing power requirement prediction on the plurality of function function number sequences to obtain a plurality of expected computing powers; sorting the plurality of function function number sequences based on the plurality of expected computing powers and the plurality of output prediction outlier factors to obtain a target function function number sequence, and performing code pre-configuration.

[0006] Optionally, the code function library construction step comprises: uploading a function function set from a management end, performing numbering, and obtaining a function function number set, wherein any one of the function function set comprises a function identifier; clustering the function function number set according to the function identifier to obtain a plurality of function identifier function function number sets; and storing the plurality of function identifier function function number sets in association with a plurality of function identifiers to generate the code function library.

[0007] Optionally, the target scene is executed to perform the function requirement sample frequency mining, and the function requirement input parameter and the function requirement output identification set are obtained, including: collecting the function requirement sample set of the target scene, wherein any one function requirement sample of the function requirement sample set includes function requirement input monitoring data and function requirement output monitoring data; performing similarity analysis on the function requirement input monitoring data of the function requirement sample set to obtain an input monitoring data similarity set; based on the input monitoring data similarity set, combining the similarity consistency threshold, clustering the function requirement sample set to obtain a multi-cluster function requirement sample set; respectively extracting one function requirement input monitoring data from the multi-cluster function requirement sample set to obtain a plurality of function requirement input parameters and adding them to the function requirement input parameter; respectively extracting multi-cluster function requirement output monitoring data from the multi-cluster function requirement sample set to perform centralized trend analysis and obtain a plurality of function requirement output identification sets and add them to the function requirement output identification set, wherein the plurality of function requirement input parameters and the plurality of function requirement output identification sets correspond one by one.

[0008] Optionally, according to the function requirement output identification set, the plurality of function requirement prediction outputs are iterated to perform outlier analysis to obtain a plurality of output prediction outlier factors, including: extracting a first function requirement output identification set from the function requirement output identification set until a Qth function requirement output identification set, Q≥1, Q is an integer; extracting a first function requirement prediction output from the plurality of function requirement prediction outputs, wherein the first function requirement prediction output includes a first function requirement sub-prediction output until a Qth function requirement sub-prediction output; analyzing a first output prediction outlier factor of the first function requirement sub-prediction output in the first function requirement output identification set; until analyzing a Qth output prediction outlier factor of the Qth function requirement sub-prediction output in the Qth function requirement output identification set; calculating the mean value of the first output prediction outlier factor and the Qth output prediction outlier factor, and setting it as a first function function number sequence outlier factor and adding it to the plurality of output prediction outlier factors.

[0009] Wherein, analyzing the first output prediction outlier factor of the first function requirement sub-prediction output in the first function requirement output identification set, including: based on the first function requirement sub-prediction output, respectively performing similarity analysis with the first function requirement output identification set to obtain an output data similarity set; based on the output data similarity set, analyzing the LOF outlier factor of the first function requirement sub-prediction output in the first function requirement output identification set, and setting it as the first output prediction outlier factor.

[0010] Optionally, the power requirement prediction is performed on the plurality of function function number sequences to obtain a plurality of expected powers, including: extracting a first function function number sequence from the plurality of function function number sequences, wherein the first function function number sequence includes a first sequence function function to a Yth sequence function function, Y≥1, Y is an integer; traversing the first sequence function function to the Yth sequence function function to extract a first sequence historical individual call log to a Yth sequence historical individual call log, respectively performing a power consumption record value central value evaluation to obtain a first sequence expected power to a Yth sequence expected power, and adding the first sequence expected power to the Yth sequence expected power into a first function function expected power; and adding the first function function expected power into the plurality of expected powers.

[0011] Optionally, the plurality of function function number sequences are sorted based on the plurality of expected powers and the plurality of output prediction outlier factors to obtain a target function function number sequence, including: configuring a first weight for the expected power and a second weight for the output prediction outlier factor, wherein the first weight and the second weight are pre-configured by a management end, and the sum of the first weight and the second weight is equal to 1; performing a dimensionless treatment on the plurality of expected powers and the plurality of output prediction outlier factors to obtain a plurality of expected power dimensionless values and a plurality of output prediction outlier factor dimensionless values; weighting the plurality of expected power dimensionless values and the plurality of output prediction outlier factor dimensionless values based on the first weight and the second weight to obtain a plurality of function function number adaptation coefficients; and performing a minimum value sorting on the plurality of function function number sequences based on the plurality of function function number adaptation coefficients to obtain the target function function number sequence.

[0012] In a second aspect, the present application provides a code pre-configuration system combined with demand orientation, including:

[0013] A function function matching module is configured to match a plurality of function function number sequences from a code function library according to the function demand of the target scene.

[0014] A function demand sample mining module is configured to perform a function demand sample frequency mining on the target scene to obtain a function demand input parameter and a function demand output identifier set.

[0015] A function demand prediction module is configured to input the function demand input parameter into the plurality of function function number sequences to obtain a plurality of function demand prediction outputs.

[0016] A prediction result outlier analysis module is configured to perform an outlier analysis on the plurality of function demand prediction outputs based on the function demand output identifier set to obtain a plurality of output prediction outlier factors.

[0017] The computing power demand prediction module is configured to traverse the plurality of function function number sequences to predict computing power demand and obtain a plurality of expected computing power.

[0018] The function function sorting module is also configured to sort the plurality of function function number sequences based on the plurality of expected computing power and the plurality of output prediction outlier factors to obtain a target function function number sequence and perform code pre-configuration.

[0019] In a third aspect, the present application provides an electronic device, comprising:

[0020] A memory is configured to store a first computer software program.

[0021] A processor is configured to read and execute the first computer software program, thereby realizing the demand-oriented code pre-configuration method in the first aspect.

[0022] In a fourth aspect, the present application provides a storage medium, wherein the storage medium stores a second computer software program, and the second computer software program is executed by a processor to realize the demand-oriented code pre-configuration method in the first aspect.

[0023] By implementing the present application, a plurality of function function number sequences can be matched from a code function library according to the functional demand of a target scene, and the function functions related to the functional demand of the target scene can be quickly located by virtue of the characteristics of the code function library clustered according to function identifiers, thereby reducing the time for blindly searching for function functions, improving the starting efficiency of development, and ensuring that the matched function functions are basically relevant to the demand in terms of functions.

[0024] By implementing the present application, the functional demand sample frequency of the target scene can be mined to obtain a function demand input parameter and a function demand output identifier set, the functional demand characteristics of the target scene can be deeply understood, the key information of the input and output is clarified, accurate basis is provided for the subsequent verification and screening of function functions, the subsequent steps are more targeted, and the problem of unsuitable function functions caused by insufficient understanding of the demand is avoided.

[0025] By implementing the present application, the function demand input parameter can be input into the plurality of function function number sequences to obtain a plurality of function demand prediction outputs, the running situation of the function function number sequences in the target scene is simulated through the actual input and output process, specific data support is provided for subsequent outlier analysis, and whether the function function number sequences can meet the output demand of the target scene is facilitated to be judged.

[0026] By implementing the present application, the output identification set can be output according to the functional requirements, the output prediction outliers of the plurality of functional requirements are obtained by traversing the plurality of functional requirement prediction outputs, the difference between the output results of each functional function number sequence and the expected output of the target scene can be quantitatively evaluated, the smaller the outlier factor is, the more the output of the sequence conforms to the requirements, which provides an important index for subsequent sorting, and helps to select a functional function combination with more accurate output;

[0027] By implementing the present application, the expected computing power of the plurality of functional function number sequences can be obtained by traversing the plurality of functional function number sequences, the computing power consumption of each functional function number sequence can be known in advance, the running problems caused by insufficient computing power in actual application can be avoided, and an index about resource consumption is provided for subsequent sorting, which helps to select a functional function combination with reasonable computing power demand;

[0028] By implementing the present application, the target functional function number sequence can be obtained by sorting the plurality of functional function number sequences based on the plurality of expected computing powers and the plurality of output prediction outliers, the code pre-configuration is executed, the adaptive coefficient is calculated by weighting, and the sorting is performed, so that a functional function combination that meets the functional output demand and has reasonable computing power can be selected, the combination efficiency and adaptability of the functional module are improved, more refined management is realized, and a good foundation is laid for subsequent development work.

[0029] In summary, by implementing the present application, a functional function combination with high adaptability and reasonable computing power can be accurately selected according to the specific requirements of the target scene, the combination efficiency of the functional module is improved, the adaptability of the functional function to the demand field scene is enhanced, the management of the low-code development platform is more refined, and thus the overall performance and practicality of the low-code development platform are improved. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 A flowchart of a code pre-configuration method provided by the present application combined with demand-oriented is provided.

[0031] Figure 2 A structure diagram of a code pre-configuration system provided by the present application combined with demand-oriented is provided.

[0032] Figure 3 A structure diagram of an electronic device provided by the present application is provided.

[0033] Figure 4 A diagram of a storage medium provided by the present application is provided.

[0034] Figure 5 A diagram of a box plot provided by the present application is provided.

[0035] In the drawings, the components represented by the respective reference numerals are as follows:

[0036] The function function matching module 11, the function demand sample mining module 12, the function demand prediction module 13, the prediction result outlier analysis module 14, the computing power demand prediction module 15, the function function sorting module 16, the electronic device 300, the memory 310, the processor 320, the first computer program 311, the storage medium 400, and the second computer program 410. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0038] In the description of the present application, the terms "first" and "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" can explicitly or implicitly include one or more features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0039] In the description of the present application, the term "for example" is used to indicate "as an example, illustration or explanation". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. The following description is given in order to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that those skilled in the art can realize the present application without using these specific details. In other examples, well-known structures and processes will not be described in detail to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the shown embodiments, but is consistent with the broadest scope consistent with the principles and characteristics disclosed.

[0040] Embodiment one, as shown in the present application, provides a code pre-configuration method combined with demand-oriented, which comprises: Figure 1

[0041] S100: According to the function demand of the target scene, a plurality of function function number sequences are matched from the code function library;

[0042] S200: The function demand sample frequency mining is performed on the target scene to obtain the function demand input parameter and the function demand output identification set.​

[0043] S300: inputting the function requirement input parameter into the plurality of function function number sequences to obtain a plurality of function requirement prediction outputs;

[0044] S400: performing outlier analysis on the plurality of function requirement prediction outputs according to the function requirement output identification set to obtain a plurality of output prediction outlier factors;

[0045] S500: performing computing power requirement prediction on the plurality of function function number sequences to obtain a plurality of expected computing powers;

[0046] S600: sorting the plurality of function function number sequences based on the plurality of expected computing powers and the plurality of output prediction outlier factors to obtain a target function function number sequence, and performing code pre-configuration.

[0047] In step S100 of the embodiment of the present application, the code function library construction step comprises:

[0048] uploading a function function set from a management end, performing numbering to obtain a function function number set, wherein any one function function of the function function set comprises a function identification;

[0049] performing clustering on the function function number set according to the function identification to obtain a plurality of function identification function function number sets;

[0050] storing the plurality of function identification function function number sets in association with a plurality of function identifications to generate the code function library.

[0051] In the embodiment of the present application, the core purpose of constructing the code function library is to provide efficient and accurate basic data support in step S100. By standardizing the management and classification of function functions, it is ensured that the function functions related to the target scene function requirement can be quickly located in the subsequent matching process, solving the problems of insufficient function function and scene adaptability and low combination efficiency in traditional low-code platforms, and laying an orderly and searchable function resource foundation for the entire requirement-oriented code pre-configuration process.

[0052] First, a function function set containing a plurality of function functions needs to be uploaded from a management end, and each function function is provided with a unique function identification for describing the core function of the function. Then, each uploaded function function is numbered to form a function function number set, and the unique identification of each function function is realized through numbering, which is convenient for subsequent management and calling.

[0053] For example, the management end uploads two sets of function functions, which are power grid state data preprocessing function set and circuit fault prediction function set. The power grid state data preprocessing function set includes: data cleaning function, function identification: power grid-data cleaning; data standardization function, function identification: power grid-data standardization; outlier filtering function, function identification: power grid-anomaly value filtering.

[0054] The circuit fault prediction function set includes: short circuit fault prediction function, function identification: circuit-short circuit prediction; overload fault prediction function, function identification: circuit-overload prediction; ground fault prediction function, function identification: circuit-ground prediction.

[0055] Then, each function function is uniquely numbered, for example: the data cleaning function is numbered F001, the data standardization function is numbered F002, and the outlier filtering function is numbered F003; the short circuit fault prediction function is numbered F004, the overload fault prediction function is numbered F005, and the ground fault prediction function is numbered F006. Finally, the function function number set is formed: {F001, F002, F003, F004, F005, F006}.

[0056] Then, according to the function identification of the function function, the function function number set is clustered, that is, according to the correlation of the function identification, the function numbers of the same type of function are grouped together. For example, the function identification "power grid-data cleaning", "power grid-data standardization", and "power grid-anomaly value filtering" all belong to the "power grid state data preprocessing" category, so the numbers F001, F002, and F003 are clustered into the "power grid state data preprocessing function function number set". The function identification "circuit-short circuit prediction", "circuit-overload prediction", and "circuit-ground prediction" all belong to the "circuit fault prediction" category, so the numbers F004, F005, and F006 are clustered into the "circuit fault prediction function function number set".

[0057] Then, the two function identification function function number sets after clustering are respectively associated with the corresponding function identification and stored: such as "power grid state data preprocessing" identification association {F001, F002, F003}, "circuit fault prediction" identification association {F004, F005, F006}.

[0058] After the above association relationship is stored, the code function library is formed. When the function requirement of the target scene is "power grid state data preprocessing" in the future, the number set {F001, F002, F003} can be directly matched from the function library, providing accurate resources for the function function number sequence matching of step S100.

[0059] In step S200 of the embodiment of the present application, the function requirement sample frequency mining is performed on the target scene to obtain a function requirement input parameter and a function requirement output identifier set, including:

[0060] The function requirement sample set of the target scene is collected, wherein any function requirement sample of the function requirement sample set includes function requirement input monitoring data and function requirement output monitoring data.

[0061] The similarity analysis is performed on the function requirement input monitoring data of the function requirement sample set to obtain an input monitoring data similarity set.

[0062] Based on the input monitoring data similarity set, the function requirement sample set is clustered in combination with a similarity consistency threshold to obtain a multi-cluster function requirement sample set.

[0063] One function requirement input monitoring data is randomly extracted from the multi-cluster function requirement sample set to obtain a plurality of function requirement input parameters, which are added to the function requirement input parameter.

[0064] The multi-cluster function requirement output monitoring data is extracted from the multi-cluster function requirement sample set to perform centralized trend analysis to obtain a plurality of function requirement output identifier sets, which are added to the function requirement output identifier set, wherein the plurality of function requirement input parameters and the plurality of function requirement output identifier sets correspond to each other.

[0065] In step S200 of the embodiment of the present application, the function requirement sample frequency mining is performed on the target scene to obtain a function requirement input parameter and a function requirement output identifier set, including:

[0066] In the implementation process, first, the function requirement sample set of the target scene needs to be collected. Assuming that the target scene is "power grid state data preprocessing", the function requirement sample set under the scene needs to be collected first, and each sample needs to include "function requirement input monitoring data", such as real-time monitoring values of power grid voltage, current and frequency, and "function requirement output monitoring data", such as standardized voltage values after preprocessing and abnormal current marking results.

[0067] For example, sample 1 can be, input monitoring data: voltage 220V, current 10A; output monitoring data: normalized voltage 1.0, no abnormal mark; sample 2 can be, input monitoring data: voltage 218V, current 12A; output monitoring data: normalized voltage 0.99, no abnormal mark, etc.

[0068] Next, similarity analysis needs to be performed on the function requirement input monitoring data of the function requirement sample set to obtain an input monitoring data similarity set.

[0069] For example, similarity analysis is performed on all input monitoring data in the function requirement sample set in the above example, such as the voltage and current values of different samples, the similarity of any two input monitoring data is calculated, and finally an input monitoring data similarity set is formed.

[0070] Specifically, the calculation method of monitoring data similarity can be monitoring data similarity . In the formula, A1 and A2 are the same attribute values of two groups of input monitoring data, such as the voltage value of sample 1 and the voltage value of sample 2, and ε is a small constant (such as 0.001) to avoid a zero denominator.

[0071] Suppose that the input monitoring data of sample 1 is voltage 220V and current 10A, and the input monitoring data of sample 2 is voltage 218V and current 12A. Then the similarity of the voltage attribute is calculated , and the similarity of the current attribute can be calculated in the same way, such as 0.984. The similarities of multiple attributes are averaged to obtain the monitoring data similarity, such as 0.992. The monitoring data similarity of multiple groups of input monitoring data is calculated to obtain a monitoring data similarity set.

[0072] Then, based on the input monitoring data similarity set, the function requirement sample set is clustered in combination with a similarity consistency threshold to obtain a multi-cluster function requirement sample set. The similarity consistency threshold can be dynamically configured according to the task type of the target scene, for example, the threshold range can be 0.7-0.9. If the standard for similarity judgment is high, a higher threshold can be selected within the range, and vice versa. Suppose the similarity consistency threshold is selected as 0.8, that is, samples with input monitoring data similarity ≥ similarity consistency threshold are classified into the same cluster to form a multi-cluster function requirement sample set. For example, samples 1 and 2 are classified into cluster 1 because their similarity is 0.992 ≥ 0.8; samples 3 and 4 are classified into cluster 2, and so on.

[0073] Next, randomly extract a piece of functional requirement input monitoring data from each of the multiple clusters of functional requirement sample sets. This data serves as the representative of the cluster, i.e., the functional requirement input parameter. Multiple functional requirement input parameters are obtained and added to the functional requirement input parameter. For example, extract the input monitoring data of sample 1 (220V, 10A) from cluster 1, and extract the monitoring data of sample 3 (180V, 5A) from cluster 2 to form multiple functional requirement input parameters.

[0074] Furthermore, it is necessary to extract multi-cluster functional requirement output monitoring data from the multi-cluster functional requirement sample set respectively, perform central trend analysis on the numerical values ​​in the output monitoring data, such as the above-mentioned standardized voltage value, to obtain multiple functional requirement output identification sets, and add them into the functional requirement output identification set, wherein the multiple functional requirement input parameters correspond one-to-one to the multiple functional requirement output identification sets.

[0075] Optionally, the above analysis steps can be presented using a box plot. Taking the output monitoring data of a cluster of functional requirement sample sets in the above-mentioned "grid state data preprocessing" scenario and the preprocessed standardized voltage value as an example, the specific analysis is as follows:

[0076] First, the data range needs to be determined. Assume that the cluster function requirement output monitoring data sample set contains 10 output monitoring data, namely the aforementioned standardized voltage values: 0.98, 1.02, 0.99, 1.01, 1.00, 0.97, 1.03, 0.99, 1.00, and 1.01.

[0077] like Figure 5 As shown, we then need to draw a box plot and calculate its quartiles: lower quartile (Q1) 0.99, median (Q2) 1.00, upper quartile (Q3) 1.01; the box of the box plot extends from Q1 to Q3, and the middle line is the median; and there are no obvious outliers, that is, no data exceeds 1.5 times the interquartile range.

[0078] It can be seen from the box plot that the cluster functional requirement output monitoring data set is concentrated between 0.99-1.01, with a median of 1.00. Therefore, the cluster functional requirement output identification set can be defined as "standardized voltage value 1.00±0.01" as the expected output benchmark corresponding to this type of input parameters.

[0079] The box plot can intuitively reflect the concentrated range and typical values ​​of the output data, and quickly determine the representative output identification set.

[0080] In step S300 of the embodiment of the present application, it is necessary to input the functional requirements as parameters and input the plurality of functional function number sequences to obtain a plurality of functional requirements prediction outputs.

[0081] The core purpose of step S300 in the embodiment of the present application is to simulate the running effect of the function function number sequence in the target scene. By inputting the representative function demand input parameters extracted in S200 into the function function number sequences matched in S100, the corresponding function demand prediction output is generated, which provides actual running data support for subsequent evaluation of the adaptability of these function function number sequences to the target scene. Through this process, the abstract function function combination can be converted into specific output results, so as to be compared and analyzed with the function demand output identification set obtained in S200, thereby laying a foundation for screening function function combinations with high adaptability.

[0082] For example, in the aforementioned target scene of "power grid state data preprocessing", first, the function demand input parameters are obtained from step S200, for example, the input parameters of cluster 1 are voltage 220V and current 10A, and the input parameters of cluster 2 are voltage 180V and current 5A. Then, a plurality of function function number sequences related to the scene are matched from step S100, for example, sequence 1: {F001 (data cleaning), F002 (data standardization)}, and sequence 2: {F001 (data cleaning), F003 (abnormal value filtering)}.

[0083] For each function function number sequence, the function functions in the sequence are called in turn, and the corresponding function demand input parameters are input. For example, the input parameters 220V and 10A of cluster 1 are input into sequence 1, that is, the original data is first processed by F001 (data cleaning), and then the format is converted by F002 (data standardization); similarly, the input parameters are input into sequence 2 to obtain another set of processing results.

[0084] Each function function number sequence outputs corresponding processing results, i.e., function demand prediction outputs, after receiving the input parameters.

[0085] For example, the prediction output of sequence 1 for the input parameters of cluster 1 is standardized voltage 1.0 and no abnormality mark; and the prediction output of sequence 2 is cleaned voltage 220V and no abnormality mark.

[0086] Finally, a plurality of function demand prediction outputs are obtained, each output corresponding to a function function number sequence and a function demand input parameter.

[0087] In step S400 of the embodiment of the present application, according to the function demand output identification set, the plurality of function demand prediction outputs are traversed for outlier analysis, and a plurality of output prediction outlier factors are obtained, including:

[0088] From the function demand output identification set, a first function demand output identification set is extracted until a Qth function demand output identification set, Q≥1, Q is an integer;

[0089] extracting a first functional requirement prediction output from the several functional requirement prediction outputs, wherein the first functional requirement prediction output comprises a first functional requirement sub-prediction output to a Qth functional requirement sub-prediction output;

[0090] analyzing the first functional requirement sub-prediction output in a first output prediction outlier factor of the first functional requirement output identification set;

[0091] until analyzing the Qth functional requirement sub-prediction output in a Qth output prediction outlier factor of the Qth functional requirement output identification set;

[0092] calculating a mean value of the first output prediction outlier factor and the Qth output prediction outlier factor as a first functional function number sequence outlier factor, and adding the first functional function number sequence outlier factor into the several output prediction outlier factors.

[0093] In step S400 of the embodiment of the present application, this step is the initial stage of outlier analysis, and the core purpose is to establish a one-to-one correspondence between the functional requirement prediction output and the expected output, i.e., the functional requirement output identification set, to provide a structured data basis for subsequent calculation of the output prediction outlier factor. Moreover, by decomposing the prediction output into sub-items corresponding to the output identification set, it is ensured that the outlier analysis can accurately locate the deviation in each dimension, avoiding analysis errors caused by mismatching of data structures, thereby providing a fine comparison basis for evaluating the output adaptability of the functional function number sequence.

[0094] Taking the above-mentioned "power grid state data preprocessing" scenario as an example, first, the first functional requirement output identification set to the Qth functional requirement output identification set needs to be extracted, i.e., from the functional requirement output identification set obtained in S200, the first to Qth group of output identification sets are extracted according to the cluster, wherein Q is the number of clusters. For example:

[0095] The first functional requirement output identification set corresponds to the cluster 1 input parameter: {standardized voltage 1.00±0.01, no abnormality mark}; the second functional requirement output identification set corresponds to the cluster 2 input parameter: {standardized voltage 0.82±0.02, no abnormality mark}; for example, Q=2 here, i.e., there are 2 groups of output identification sets, corresponding to 2 input parameter clusters.

[0096] Then, the corresponding functional requirement prediction output needs to be extracted and decomposed into sub-items, i.e., from the several functional requirement prediction outputs obtained in S300, the prediction output of a certain functional function number sequence, i.e., the first functional requirement prediction output, is extracted, and is decomposed into sub-prediction outputs according to the input parameter cluster. For example:

[0097] The predicted output of the first function function number sequence, such as {F001, F002}, respectively includes: the first function demand sub-predicted output corresponding to the processing result of the input parameter of cluster 1, that is, {standardized voltage 1.02, no abnormal mark}; the second function demand sub-predicted output corresponding to the processing result of the input parameter of cluster 2, that is, {standardized voltage 0.85, no abnormal mark}. Wherein the number of sub-predicted outputs is consistent with the number of output identifier sets, both are Q=2, which ensures one-to-one correspondence.

[0098] Through the above steps, the dimension alignment of the output identifier set and the predicted output sub-item is realized, which lays a data matching foundation for subsequent calculation of the outlier factor of each sub-predicted output, and ensures that the outlier analysis can accurately reflect the output adaptability of each function function number sequence under different input scenarios.

[0099] In step S400 of the embodiment of the present application, a first output prediction outlier factor of the first function demand sub-predicted output in the first function demand output identifier set is analyzed, including:

[0100] Based on the first function demand sub-predicted output, similarity analysis is respectively performed with the first function demand output identifier set to obtain an output data similarity set;

[0101] Based on the output data similarity set, a LOF outlier factor of the first function demand sub-predicted output in the first function demand output identifier set is analyzed, which is set as the first output prediction outlier factor.

[0102] In the embodiment of the present application, the core purpose of this step is to quantitatively evaluate the deviation degree of the first function demand sub-predicted output and the first function demand output identifier set, and to judge whether the sub-predicted output conforms to the expected output mode of the target scene by calculating the first output prediction outlier factor. This process provides a key indicator for subsequent screening of adaptive function function number sequences, ensures the output accuracy of the finally selected function function combination in a specific scene, and solves the problem of low matching degree between function output and scene demand in traditional low-code platforms.

[0103] Taking the "power grid state data preprocessing" scenario in the above steps as an example, first, the analysis object needs to be determined, for example, the first function demand sub-predicted output, that is, the processing result of the input parameter of cluster 1 by a certain function function sequence can be: {standardized voltage 1.02, no abnormal mark}; the first function demand output identifier set, that is, the expected output corresponding to cluster 1 can be: {standardized voltage 1.00±0.01, no abnormal mark}

[0104] Then, the output data similarity analysis needs to be performed to obtain an output data similarity set. For example, for each attribute in the output, such as the normalized voltage value and the abnormality label, the output data similarity is calculated respectively. The output data similarity of the normalized voltage attribute can be calculated using the aforementioned monitoring data similarity formula. At this time, the first functional requirement sub-prediction output is A1=1.02, the first functional requirement output label set typical value is A2=1.00, and e=0.001. Then, the output data similarity of the normalized voltage attribute is 0.9998, and for the abnormality label attribute, the first functional requirement sub-prediction output is “no abnormality label”, which is consistent with the label set, and the similarity is 1.0. Therefore, the output data similarity set is {0.9998, 1.0}

[0105] Next, based on the output data similarity set, the LOF outlier factor of the first functional requirement sub-prediction output in the first functional requirement output label set is analyzed, which is set as the first output prediction outlier factor.

[0106] Among them, LOF (Local Outlier Factor) is an algorithm for judging the degree of outlying by comparing the density of samples and neighborhood samples. The value closer to 1 indicates closer to the normal mode, and the larger the value, the higher the outlying degree.

[0107] Suppose there are multiple historical normal output samples in the data set corresponding to the first functional requirement output label set, such as {1.00, no abnormality}, {0.99, no abnormality}, etc. The first functional requirement sub-prediction output (1.02, no abnormality) is put into this data set:

[0108] The density ratio of the first functional requirement sub-prediction output and the neighborhood sample is calculated to obtain the LOF value. Since 1.02 is within the range of 1.00±0.01 of the label set and the abnormality label matches, the LOF value is close to 1, such as 1.05, indicating a low deviation degree. This LOF value can be set as the first output prediction outlier factor, which is used to measure the outlying degree of the first functional requirement sub-prediction output.

[0109] Through the above steps, the matching degree of the sub-prediction output and the expected output is converted into a quantifiable outlier factor, which provides a precise basis for subsequent comprehensive evaluation of the adaptability of the function function number sequence, ensuring that the function combination selected in the specific scene is more in line with the actual demand.

[0110] Further, the output prediction outlier factor needs to be continuously analyzed until the Qth output prediction outlier factor of the Qth functional requirement sub-prediction output in the Qth functional requirement output label set is analyzed.

[0111] For example, the outlier factor of the first sub-prediction output calculated by the above steps is 1.05, the outlier factor of the second sub-prediction output is 1.12, and so on.

[0112] Then, the mean of the first output prediction outlier factor and the Qth output prediction outlier factor needs to be calculated, set as the first function number sequence outlier factor, added to the plurality of output prediction outlier factors.

[0113] In the above example, the first function number sequence outlier factor = (first outlier factor + second outlier factor) / Q = (1.05 + 1.12) / 2 = 1.085. Then 1.085 is taken as the overall outlier factor of the sequence and added to the output prediction outlier factor set.

[0114] In step S500 of the embodiment of the present application, the plurality of function number sequences are traversed to perform computing power demand prediction to obtain a plurality of expected computing powers, including:

[0115] From the plurality of function number sequences, a first function number sequence is extracted, wherein the first function number sequence includes a first sequence number function function to a Yth sequence number function function, Y≥1, Y is an integer;

[0116] The first sequence number historical individual call log to the Yth sequence number historical individual call log are extracted by traversing the first sequence number function function to the Yth sequence number function function, and the central value evaluation of the computing power loss record value set is performed respectively to obtain the first sequence number expected computing power to the Yth sequence number expected computing power, which are added to the first function expected computing power;

[0117] The first function expected computing power is added to the plurality of expected computing powers.

[0118] In the embodiment of the present application, the purpose of step S500 is to quantitatively evaluate the computing power consumption demand of each function number sequence, to predict the expected computing power of each function in the target scene by analyzing the historical call log of each function in the function number sequence, and to provide a key indicator of resource consumption dimension for subsequent step S600 to comprehensively screen the function combination with high adaptability and reasonable computing power. This process solves the problem of low combination efficiency and resource waste caused by only focusing on function adaptability and ignoring computing power cost in traditional low-code platforms. The historical call log records the calling situation of each function in various scenes, including all records of individual calling and combined calling with other functions. The historical individual calling log only records the calling situation of a function when it runs independently, i.e., the log when it is executed alone without being combined with other functions.

[0119] The content of the historical call log includes function unique identifier, call timestamp, input parameter feature, computing power consumption record value, etc. For example, a certain group of historical individual call logs can include a function unique identifier F001, input parameter features including voltage data field, computing power consumption record value 0.4 GB of memory consumption, and execution result identifier success.

[0120] Continuing with the two function number sequences in the "power grid state data preprocessing" scenario in the above steps, i.e. sequence 1 {F001, F002} and sequence 2 {F001, F003}, first extract the function number sequence and composition, select the first function number sequence, such as sequence 1: {F001, F002}, which contains Y=2 functions, i.e. the first function F001 (data cleaning function) and the second function F002 (data standardization function).

[0121] Then, the historical call log needs to be obtained and the expected computing power of the individual function is evaluated, each function in the function number sequence is traversed, the historical individual call log of each function is extracted, and the computing power consumption value at each call is recorded, such as CPU occupancy rate, memory consumption, etc. For example, if the memory space occupied by the function during running is taken as the mark to measure the computing power consumption, the historical computing power consumption record value of F001 can be [0.5GB, 0.6GB, 0.55GB, 0.58GB], and the historical computing power consumption record value of F002 can be [0.8GB, 0.75GB, 0.82GB, 0.78GB].

[0122] Then, the record value of each function is evaluated for central tendency, such as calculating the median or mean, and here the median is taken as an example: the median of the first expected computing power (F001) is 0.565GB, and the median of the second expected computing power (F002) is 0.79GB.

[0123] Next, the overall expected computing power of the first function number sequence needs to be calculated, i.e. the expected computing power of each function in the first function number sequence is added to obtain the first function expected computing power:

[0124] For example, the expected computing power of sequence 1 = 0.565GB + 0.79GB = 1.355GB, and then 1.355GB is added to the expected computing power.

[0125] Then, repeat the above steps for sequence 2 {F001, F003}, assuming that the expected computing power of F003 (abnormal value filtering) is 1.2GB, then the expected computing power of sequence 2 is 0.565GB + 1.2GB = 1.765GB, which is also added to the expected computing power.

[0126] In step S600 of the embodiment of the present application, based on the plurality of expected computing powers and the plurality of output prediction outlier factors, the plurality of function function number sequences are sorted to obtain a target function function number sequence, including:

[0127] The first weight is configured for the expected computing power, and the second weight is configured for the output prediction outlier factor, wherein the first weight and the second weight are pre-configured by the management end, and the sum of the first weight and the second weight is equal to 1;

[0128] The plurality of expected computing powers and the plurality of output prediction outlier factors are de-dimensioned to obtain a plurality of expected computing power de-dimensioned values and a plurality of output prediction outlier factor de-dimensioned values;

[0129] Based on the first weight and the second weight, the plurality of expected computing power de-dimensioned values and the plurality of output prediction outlier factor de-dimensioned values are weighted to obtain a plurality of function function number adaptation coefficients;

[0130] Based on the plurality of function function number adaptation coefficients, the plurality of function function number sequences are sorted by minimum value to obtain the target function function number sequence.

[0131] The purpose of step S600 of the embodiment of the present application is to seek the optimal balance between function adaptability and computing power cost, and to filter out the combination most suitable for the target scene from the candidate function function number sequences through multi-dimensional weighted evaluation. By quantitatively calculating the function function number adaptation coefficient, the problem of resource waste or insufficient adaptability caused by only considering a single index in the selection of function combination in the traditional low-code platform is solved.

[0132] Taking the two function function number sequences in the "power grid state data preprocessing" scenario in the above steps, i.e., sequence 1 and sequence 2, as examples, at this time, after the calculation of the foregoing steps, the outlier factor of sequence 1: {F001, F002} is 1.085, and the expected computing power is 1.355 GB; the outlier factor of sequence 2: {F001, F003} is 1.35, and the expected computing power is 1.765 GB.

[0133] Then, the first weight needs to be configured for the expected computing power, and the second weight needs to be configured for the output prediction outlier factor. The first weight is 0.3-0.5, and the second weight is 0.5-0.7. The sum of the weights is equal to 1. The specific weight value can be configured according to the actual demand in the implementation process. If the energy consumption of the code pre-configuration is more important, the first weight can be appropriately increased. When the accuracy of the code pre-configuration is more important, the second weight can be appropriately increased. For example, the first weight is 0.4, and the second weight is 0.6.

[0134] Then, the several expected computing powers and the several output prediction outlier factors need to be de-dimensioned to obtain several expected computing power de-dimensioned values and several expected computing power de-dimensioned values,

[0135] For the output prediction outlier factor, since it is a dimensionless value, and the smaller the value is, the more the output meets the expectation, therefore, a reverse normalization process is adopted, that is, the smaller the original value is, the closer to 1 the normalized value is. For example, the output prediction outlier factor de-dimensioned value = 1 - (output prediction outlier factor value - minimum value) / (maximum value - minimum value). Wherein the minimum value is the minimum value of the output prediction outlier factor, such as 1.085, and the maximum value is the maximum value of the output prediction outlier factor, such as 1.35. Then, the output prediction outlier factor de-dimensioned value of sequence 1 = 1 - (1.085 - 1.085) / (1.35 - 1.085) = 1 - 0 = 1. The output prediction outlier factor de-dimensioned value of sequence 2 is calculated by the same method, which is 0. The above steps are only used to show the method of obtaining the output prediction outlier factor de-dimensioned value.

[0136] For the expected computing power, the smaller the value is, the less the resource consumption is, therefore, the reverse normalization process is also adopted to obtain the expected computing power de-dimensioned value, and the calculation formula of the expected computing power de-dimensioned value is the same as that of the output prediction outlier factor de-dimensioned value, which will not be described here.

[0137] Then, based on the first weight and the second weight, the several expected computing power de-dimensioned values and the several output prediction outlier factor de-dimensioned values need to be weighted to obtain several function function number adaptation coefficients;

[0138] Suppose that the output prediction outlier factor de-dimensioned value obtained by the above steps is 0.6, and the expected computing power de-dimensioned value is 0.57, then the function function number adaptation coefficient = first weight x expected computing power de-dimensioned value + second weight x output prediction outlier factor de-dimensioned value = 0.4 x 0.57 + 0.6 x 0.6 = 0.228 + 0.36 = 0.588, following the first weight and the second weight described above. The greater the adaptation coefficient is, the better it is, and the several function function number adaptation coefficients are calculated by the above method.

[0139] Finally, among the several function function number adaptation coefficients, the function function number sequence with the largest function function number adaptation coefficient is selected as the target function function number sequence. Based on the target function function number sequence, code pre-configuration is performed.

[0140] Embodiment two, as Figure 2 shown, based on the same inventive concept of the code pre-configuration method provided in embodiment one, the present embodiment also provides a code pre-configuration system combined with demand orientation, comprising:

[0141] The function function matching module 11 is configured to match a plurality of function function number sequences from a code function library according to a function requirement of a target scene.

[0142] The function requirement sample mining module 12 is configured to perform function requirement sample frequency mining on the target scene to obtain a function requirement input parameter and a function requirement output identifier set.

[0143] The function requirement prediction module 13 is configured to input the function requirement input parameter into the plurality of function function number sequences to obtain a plurality of function requirement prediction outputs.

[0144] The prediction result outlier analysis module 14 is configured to perform outlier analysis on the plurality of function requirement prediction outputs according to the function requirement output identifier set to obtain a plurality of output prediction outlier factors.

[0145] The computing power requirement prediction module 15 is configured to perform computing power requirement prediction on the plurality of function function number sequences to obtain a plurality of expected computing powers.

[0146] The function function sorting module 16 is also configured to sort the plurality of function function number sequences based on the plurality of expected computing powers and the plurality of output prediction outlier factors to obtain a target function function number sequence and perform code pre-configuration.

[0147] Further, the function function matching module 11 includes the following execution steps:

[0148] The function function set is uploaded from the management end, the execution number is obtained, and the function function number set is obtained, wherein any one function function of the function function set includes a function identifier.

[0149] The function function number set is clustered according to the function identifier to obtain a plurality of function identifier function function number sets.

[0150] The plurality of function identifier function function number sets are stored in association with a plurality of function identifiers to generate the code function library.

[0151] Further, the function requirement sample mining module 12 includes the following execution steps:

[0152] The function requirement sample set of the target scene is collected, wherein any one function requirement sample of the function requirement sample set includes function requirement input monitoring data and function requirement output monitoring data.

[0153] The function requirement input monitoring data of the function requirement sample set is subjected to similarity analysis to obtain an input monitoring data similarity set.

[0154] Clustering the function requirement sample set based on the input monitoring data similarity set in combination with a similarity consistency threshold, to obtain a multi-cluster function requirement sample set;

[0155] Respectively extracting a function requirement input monitoring data from the multi-cluster function requirement sample set, to obtain a plurality of function requirement input parameters, and adding the plurality of function requirement input parameters into the function requirement input parameter set;

[0156] Respectively extracting a plurality of function requirement output monitoring data from the multi-cluster function requirement sample set, performing centralized trend analysis, to obtain a plurality of function requirement output identifier sets, and adding the plurality of function requirement output identifier sets into the function requirement output identifier set, wherein the plurality of function requirement input parameters and the plurality of function requirement output identifier sets are one-to-one corresponding.

[0157] Further, the prediction result outlier analysis module 14 includes the following execution steps:

[0158] Extracting a first function requirement output identifier set to a Qth function requirement output identifier set from the function requirement output identifier set, Q≥1, Q is an integer;

[0159] Extracting a first function requirement prediction output from the plurality of function requirement prediction outputs, wherein the first function requirement prediction output includes a first function requirement sub-prediction output to a Qth function requirement sub-prediction output;

[0160] Analyzing a first output prediction outlier factor of the first function requirement sub-prediction output in the first function requirement output identifier set;

[0161] Until analyzing a Qth output prediction outlier factor of the Qth function requirement sub-prediction output in the Qth function requirement output identifier set;

[0162] Calculating a mean value of the first output prediction outlier factor and the Qth output prediction outlier factor, and setting it as a first function function number sequence outlier factor, and adding it into the plurality of output prediction outlier factors.

[0163] Wherein, analyzing the first output prediction outlier factor of the first function requirement sub-prediction output in the first function requirement output identifier set includes:

[0164] Based on the first function requirement sub-prediction output, respectively performing similarity analysis with the first function requirement output identifier set, to obtain an output data similarity set;

[0165] Based on the output data similarity set, analyzing the LOF outlier factor of the first function requirement sub-prediction output in the first function requirement output identifier set, and setting it as the first output prediction outlier factor.

[0166] Further, the computing power demand prediction module 15 comprises the following execution steps:

[0167] From the plurality of function function number sequences, a first function function number sequence is extracted, wherein the first function function number sequence comprises a first sequence number function function to a Yth sequence number function function, Y≥1, Y is an integer;

[0168] The first sequence number history individual call log to the Yth sequence number history individual call log is extracted by traversing the first sequence number function function to the Yth sequence number function function, and the first sequence number expected computing power to the Yth sequence number expected computing power is obtained by respectively executing the computing power loss record value central value evaluation, and the first function function expected computing power is added;

[0169] The first function function expected computing power is added to the plurality of expected computing powers.

[0170] Further, the function function sorting module 16 comprises the following execution steps:

[0171] A first weight is configured for the expected computing power, and a second weight is configured for the output prediction outlier factor, wherein the first weight and the second weight are pre-configured by the management end, and the sum of the first weight and the second weight is equal to 1;

[0172] The plurality of expected computing powers and the plurality of output prediction outlier factors are dimensionless processed to obtain a plurality of expected computing power dimensionless values and a plurality of output prediction outlier factor dimensionless values;

[0173] Based on the first weight and the second weight, the plurality of expected computing power dimensionless values and the plurality of output prediction outlier factor dimensionless values are weighted to obtain a plurality of function function number adaptation coefficients;

[0174] Based on the plurality of function function number adaptation coefficients, the plurality of function function number sequences are minimum value sorted to obtain the target function function number sequence.

[0175] Embodiment three, as Figure 3 shown, based on the same inventive concept provided by the code pre-configuration method combined with demand orientation of embodiment one, the present embodiment also provides an electronic device 300, comprising:

[0176] The memory 310 is used to store the first computer software program 311;

[0177] The processor 320 is used to read and execute the first computer software program 311, thereby realizing the code pre-configuration method combined with demand orientation of embodiment one.

[0178] Memory refers to a device in a computer for temporarily storing programs and data being run, including but not limited to random access memory (RAM), read-only memory (ROM), virtual memory, etc.

[0179] Processor is a component in a computer responsible for executing program instructions, processing data and controlling the operation of the computer, including but not limited to general-purpose processor, graphics processing unit, embedded processor, etc.

[0180] Embodiment four, as shown in the same inventive concept based on the code pre-configuration method provided in embodiment one, the present embodiment also provides a storage medium 400, exemplary, the storage medium can be a non-transitory computer readable storage medium, the storage medium has a second computer software program 410, the second computer software program 410 is executed by the processor to realize the method of a combination of demand-oriented code pre-configuration as in embodiment one. Figure 4 Non-transitory storage medium refers to a storage carrier that can persistently store data after power off, including but not limited to SSD (solid state disk), HDD (mechanical hard disk) and flash memory device (U disk, memory card), etc.

[0181] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0182] Those skilled in the art should understand that the embodiments of the present application can provide a method, a system or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0183]

[0184] ​The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts.

[0185] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts.

[0186] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or blocks of the flowcharts.

[0187] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments described and shown, and it is therefore intended that the application cover any and all modifications and forms derived from the embodiments set forth above.

[0188] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application and its equivalents.

Claims

1. Combined with the demand-oriented code preconfiguration method, it is characterized by: include: According to the functional requirements of the target scenario, match several function number sequences from the code function library; Performing frequency mining of functional requirement samples on the target scenario to obtain functional requirement input parameters and functional requirement output identification sets; Input the functional requirement parameters and the functional function number sequences to obtain a plurality of functional requirement prediction outputs; According to the functional requirement output identification set, traversing the plurality of functional requirement prediction outputs to perform outlier analysis and obtain a plurality of output prediction outlier factors; Traversing the plurality of function number sequences to perform computing power demand prediction and obtain a plurality of expected computing powers; sorting the plurality of function number sequences based on the plurality of expected computing powers and the plurality of output prediction outlier factors, obtaining a target function number sequence, and performing code preconfiguration; According to the functional requirement output identifier set, the plurality of functional requirement prediction outputs are traversed to perform outlier analysis to obtain a plurality of output prediction outlier factors, including: Extracting the first functional requirement output identification set to the Qth functional requirement output identification set from the functional requirement output identification set, where Q≥1 and Q is an integer; Extracting a first functional requirement prediction output from the plurality of functional requirement prediction outputs, wherein the first functional requirement prediction output includes a first functional requirement sub-prediction output to a Qth functional requirement sub-prediction output; Analyze the first output prediction outlier factor of the first functional requirement sub-prediction output in the first functional requirement output identification set; Until analyzing the Qth output prediction outlier factor of the Qth functional requirement sub-prediction output in the Qth functional requirement output identification set; Calculate the mean of the first output prediction outlier factor and the Qth output prediction outlier factor, set it as the first functional number sequence outlier factor, and add it to the plurality of output prediction outlier factors.

2. The demand-oriented code preconfiguration method according to claim 1, wherein: The code function library construction steps include: Upload a function set from the management terminal, execute the numbering, and obtain a function number set, wherein any function in the function set includes a function identifier; Clustering the function function number sets according to the function identifiers to obtain a plurality of function identifier function function number sets; The plurality of function identification function number sets are associated with the plurality of function identifications and stored to generate the code function library.

3. The demand-oriented code preconfiguration method according to claim 1, wherein: Performing frequency mining of functional requirement samples on the target scenario to obtain functional requirement input parameters and functional requirement output identifier sets includes: Collecting a functional requirement sample set of the target scenario, wherein any functional requirement sample in the functional requirement sample set includes functional requirement input monitoring data and functional requirement output monitoring data; Performing similarity analysis on the functional requirement input monitoring data of the functional requirement sample set to obtain an input monitoring data similarity set; Based on the similarity set of the input monitoring data and in combination with a similarity consistency threshold, clustering the functional requirement sample set to obtain a multi-cluster functional requirement sample set; Randomly extracting a piece of functional requirement input monitoring data from each of the plurality of functional requirement sample sets, obtaining a plurality of functional requirement input parameters, and adding the obtained data into the functional requirement input parameters; Extract multi-cluster functional requirement output monitoring data from the multi-cluster functional requirement sample set respectively, perform central trend analysis, obtain multiple functional requirement output identification sets, and add them into the functional requirement output identification set, wherein the multiple functional requirement input parameters correspond one-to-one to the multiple functional requirement output identification sets.

4. The demand-oriented code preconfiguration method according to claim 1, wherein: Analyzing the first output prediction outlier factor of the first functional requirement sub-prediction output in the first functional requirement output identification set includes: Based on the first functional requirement sub-prediction output, similarity analysis is performed with the first functional requirement output identification set to obtain an output data similarity set; Based on the output data similarity set, the LOF outlier factor of the first functional requirement sub-prediction output in the first functional requirement output identification set is analyzed and set as the first output prediction outlier factor.

5. The demand-oriented code preconfiguration method according to claim 1, wherein: Traversing the plurality of function number sequences to perform computing power demand prediction, and obtaining a plurality of expected computing powers, including: Extracting a first function function number sequence from the plurality of function function number sequences, wherein the first function function number sequence includes a first sequence number function function to a Yth sequence number function function, where Y≥1 and Y is an integer; Traverse the first sequence number function to the Yth sequence number function, extract the historical individual call logs of the first sequence number to the Yth sequence number, perform centralized value evaluation of the computing power loss record values, obtain the expected computing power of the first sequence number to the Yth sequence number, and add it to the expected computing power of the first function function; The expected computing power of the first functional function is added to the plurality of expected computing powers.

6. The demand-oriented code preconfiguration method according to claim 1, wherein: Sorting the plurality of functional function number sequences based on the plurality of expected computing powers and the plurality of output prediction outlier factors to obtain a target functional function number sequence includes: Configuring a first weight for the expected computing power and configuring a second weight for the output predicted outlier factor, wherein the first weight and the second weight are pre-configured by the management end, and the sum of the first weight and the second weight is equal to 1; De-dimensionalizing the plurality of expected computing powers and the plurality of output prediction outlier factors to obtain a plurality of de-dimensionalized values ​​of the expected computing powers and a plurality of de-dimensionalized values ​​of the output prediction outlier factors; weighting the plurality of expected computing power dimensionless values ​​and the plurality of output prediction outlier factor dimensionless values ​​based on the first weight and the second weight to obtain a plurality of function number adaptation coefficients; Based on the adaptation coefficients of the plurality of functional function numbers, the plurality of functional function number sequences are sorted for minimum values ​​to obtain the target functional function number sequence.

7. Combined with the demand-oriented code pre-configuration system, it is characterized by: include: Function matching module, used to match several function number sequences from the code function library according to the functional requirements of the target scenario; A function requirement sample mining module is used to perform function requirement sample frequency mining on the target scenario to obtain function requirement input parameters and function requirement output identification sets; A function requirement prediction module, configured to input the function requirement input parameters and the plurality of function function number sequences to obtain a plurality of function requirement prediction outputs; A prediction result outlier analysis module is used to traverse the plurality of function requirement prediction outputs according to the function requirement output identifier set to perform outlier analysis and obtain a plurality of output prediction outlier factors; A computing power demand prediction module is used to traverse the plurality of function number sequences to perform computing power demand prediction and obtain a plurality of expected computing powers; The function sorting module also sorts the function number sequences based on the expected computing power and the output prediction outlier factors, obtains a target function number sequence, and executes code preconfiguration; According to the functional requirement output identifier set, the plurality of functional requirement prediction outputs are traversed to perform outlier analysis to obtain a plurality of output prediction outlier factors, including: Extracting the first functional requirement output identification set to the Qth functional requirement output identification set from the functional requirement output identification set, where Q≥1 and Q is an integer; Extracting a first functional requirement prediction output from the plurality of functional requirement prediction outputs, wherein the first functional requirement prediction output includes a first functional requirement sub-prediction output to a Qth functional requirement sub-prediction output; Analyze the first output prediction outlier factor of the first functional requirement sub-prediction output in the first functional requirement output identification set; Until analyzing the Qth output prediction outlier factor of the Qth functional requirement sub-prediction output in the Qth functional requirement output identification set; Calculate the mean of the first output prediction outlier factor and the Qth output prediction outlier factor, set it as the first functional number sequence outlier factor, and add it to the plurality of output prediction outlier factors.

8. An electronic device, characterized in that: include: a memory for storing a first computer software program; A processor is configured to read and execute the first computer software program, thereby implementing the demand-oriented code preconfiguration method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a second computer software program, which, when executed by the processor, implements the demand-oriented code preconfiguration method according to any one of claims 1 to 6.

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