Method, device and equipment for processing data required for development of power model

Abstract

The present disclosure provides a method, device and equipment for processing development requirement data of an electric power model, which relates to the technical field of electric power development requirement. The first development requirement data to be processed is obtained; the development requirement content corresponding to each development requirement text is determined; based on the development requirement content of each development requirement text, a first data set of functional requirement types and a second data set of non-functional requirement types are determined; based on the first text quantity of the text associated with each first sub-requirement type, multiple first sub-requirement types are sorted, and based on the second text quantity of the text associated with each second sub-requirement type, multiple second sub-requirement types are sorted to obtain second development requirement data; based on the second development requirement data, a project book is generated. In this way, the management and statistics of the development requirements of the electric power model are realized, the cost of electric power model development is reduced, the efficiency of electric power model development is improved, and the reliability of development requirement data processing is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power development demand, and in particular to a method, device and equipment for processing development demand data of a power model. Background Art

[0002] Currently, in the development of artificial intelligence (AI) power models, to ensure ease of use and operation, and to meet user needs, it is necessary to collect a wide range of development requirements data to continuously improve the models. However, due to the large volume and wide range of sources for this data, it is difficult to accurately capture power development requirements, which increases the workload for R&D personnel and results in low power model development efficiency. Providing a method for processing power model development requirements data to reduce costs and improve the efficiency of power model development is a pressing issue. Summary of the Invention

[0003] The present disclosure provides a method, apparatus and device for processing development demand data of an electric power model.

[0004] According to one aspect of the present disclosure, a method for processing development demand data of an electric power model is provided, comprising:

[0005] Acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts;

[0006] Determining the development requirement content corresponding to each of the development requirement texts;

[0007] Determining, based on the development requirement content of each development requirement text, a first data set of a functional requirement type and a second data set of a non-functional requirement type, wherein the first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type;

[0008] sorting the plurality of first sub-requirement types based on a first text quantity of the text associated with each of the first sub-requirement types, and sorting the plurality of second sub-requirement types based on a second text quantity of the text associated with each of the second sub-requirement types, to obtain second development requirement data;

[0009] A project document is generated based on the second development requirement data.

[0010] According to another aspect of the present disclosure, there is provided a device for processing development demand data of an electric power model, comprising:

[0011] An acquisition module, configured to acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts;

[0012] A first determining module is used to determine the development requirement content corresponding to each development requirement text;

[0013] a second determining module, configured to determine, based on the development requirement content of each development requirement text, a first data set of a functional requirement type and a second data set of a non-functional requirement type, wherein the first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type;

[0014] a sorting module, configured to sort the texts in the first data set and the texts in the second data set based on a first text quantity of each text associated with the first sub-requirement type and a second text quantity of each text associated with the second sub-requirement type, respectively, to obtain second development requirement data;

[0015] A generation module is used to generate a project book based on the second development requirement data.

[0016] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0017] at least one processor;

[0018] and, a memory communicatively coupled to the at least one processor;

[0019] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute the method of the above embodiment.

[0020] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method according to the above embodiment.

[0021] The present disclosure provides a method, device, and apparatus for processing development requirement data of an electric power model. First, first development requirement data to be processed is obtained, and the development requirement content corresponding to each development requirement text is determined. Then, based on the development requirement content of each development requirement text, a first data set of functional requirement types and a second data set of non-functional requirement types are determined. Then, based on the number of first texts associated with each first sub-requirement type, multiple first sub-requirement types are sorted, and based on the number of second texts associated with each second sub-requirement type, multiple second sub-requirement types are sorted to obtain second development requirement data. Finally, a project document is generated based on the second development requirement data. Thus, the development requirement content of each text in the obtained development requirement data is determined, and the development requirement data is classified based on the development requirement content of each text. Each development requirement text of the functional requirement type and its corresponding sub-requirement type, as well as each development requirement text of the non-functional requirement type and its corresponding sub-requirement type, are obtained and sorted. A project document is generated based on the classified and sorted development requirement data, thereby achieving management and statistics of electric power model development requirements, reducing the cost of electric power model development, improving the efficiency of electric power model development, and improving the reliability of electric power model development requirement data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0023] Figure 1 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure;

[0024] Figure 2 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure;

[0025] Figure 3 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure;

[0026] Figure 4 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure;

[0027] Figure 5 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure;

[0028] Figure 6 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure;

[0029] Figure 7 A schematic diagram of the structure of a device for processing development demand data of an electric power model provided in an embodiment of the present disclosure;

[0030] Figure 8 A schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.

[0031] The above drawings illustrate specific embodiments of the present disclosure, which will be described in more detail below. These drawings and textual descriptions are not intended to limit the scope of the present disclosure in any way, but rather to illustrate the concepts of the present disclosure to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0033] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0034] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution disclosed herein are in compliance with the relevant provisions of national laws and regulations.

[0035] The following describes in detail the method for processing development demand data of the power model according to an embodiment of the present disclosure with reference to the accompanying drawings.

[0036] Figure 1 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure.

[0037] like Figure 1 As shown, the method includes:

[0038] Step 101: Acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts.

[0039] The first development demand data may be demand data for power model development.

[0040] It should be noted that the power model can be a power artificial intelligence model of any type and structure, and this disclosure does not limit this.

[0041] It should be noted that the multiple development requirement texts in the first development requirement data may include content of various types of requirements. For example, the first development requirement data may include development requirement texts related to power model functionality, safety, or reliability, etc., and this disclosure does not limit this.

[0042] In the present disclosure, the first development requirement data may be obtained through various channels and methods, such as user feedback, market research, and team discussions, which are not limited in the present disclosure.

[0043] It should be noted that after obtaining the first development requirement data to be processed, the first development requirement data may be stored in a development requirement pool, which is not limited in the present disclosure.

[0044] It should be noted that after obtaining the first development requirement data, all development requirement texts in the first development requirement data can be centrally recorded using a preset method. For example, all development requirement texts can be recorded using a project tracking and recording application or tool, which is not limited in this disclosure.

[0045] It should be noted that when the acquired first development requirement data contains non-text content such as pictures, audio and other data, the non-text data can be first converted into corresponding text data, and this disclosure does not limit this.

[0046] Step 102: Determine the development requirement content corresponding to each development requirement text.

[0047] It should be noted that different development requirement texts may correspond to different development requirement contents, and this disclosure does not limit this.

[0048] In the present disclosure, after obtaining the first development requirement data, the development requirement content corresponding to each development requirement text contained therein may be determined first, thereby providing a data basis for development requirement data processing.

[0049] Step 103 : determining a first data set of a functional requirement type and a second data set of a non-functional requirement type based on the development requirement content of each development requirement text.

[0050] The first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type.

[0051] The functional requirements may be any functional development requirements in the power model development. For example, the functional requirements may include development requirements for power generation, transmission, transformation, distribution, and consumption, etc., which are not limited in this disclosure.

[0052] Among them, the first sub-requirement type can be a specific functional requirement type under the functional requirement type.

[0053] The non-functional requirements may be any non-functional development requirements in the power model development, such as safety, usability, reliability, etc., which are not limited in this disclosure.

[0054] The second sub-requirement type may be a specific non-functional requirement type under the non-functional requirement type.

[0055] In the present disclosure, after determining the development requirement content corresponding to each development requirement text, the development requirement texts can be classified according to the development requirement content corresponding to each development requirement text, and a first data set belonging to a functional requirement type and a second data set belonging to a non-functional requirement type can be determined, thereby improving the efficiency of development requirement data processing.

[0056] Step 104 , sorting multiple first sub-requirement types based on the first text quantity of the text associated with each first sub-requirement type, and sorting multiple second sub-requirement types based on the second text quantity of the text associated with each second sub-requirement type, to obtain second development requirement data.

[0057] The second development requirement data is data obtained by processing the first development requirement data.

[0058] In the present disclosure, after determining a first data set of functional requirement types and a second data set of non-functional requirement types, the first text quantity of the text associated with each first sub-requirement type in the first data set and the second text quantity of the text associated with each second sub-requirement type in the second data set can be first determined, and then based on the first text quantity corresponding to each first sub-requirement type, multiple first sub-requirement types are sorted, and based on the second text quantity corresponding to each second sub-requirement type, multiple second sub-requirement types are sorted to obtain second development requirement data, thereby realizing the management planning of power model development data and improving the efficiency and reliability of development requirement data processing.

[0059] It should be noted that when sorting multiple first sub-requirement types based on the number of first texts associated with each first sub-requirement type, and when sorting multiple second sub-requirement types based on the number of second texts associated with each second sub-requirement type, the sorting can be performed in any manner. For example, the sub-requirement types can be sorted from most to least by the number of associated texts, but this disclosure does not limit this.

[0060] Step 105: Generate a project document based on the second development requirement data.

[0061] In the present disclosure, after obtaining the second development requirement data, a project book can be generated based on the second development requirement data, so that the power model can be developed based on the project book, which effectively improves the efficiency of determining the power model development requirements, reduces costs, and improves the reliability of power model development.

[0062] It should be noted that after the project book is generated, the format of the project book can be converted into a preset format, such as XML format, which is not limited in this disclosure.

[0063] XML is the abbreviation of Extensible Markup Language (XML).

[0064] In the embodiment of the present disclosure, first, the first development requirement data to be processed is obtained, and the development requirement content corresponding to each development requirement text is determined. Then, based on the development requirement content of each development requirement text, a first data set of functional requirement types and a second data set of non-functional requirement types are determined. Then, based on the number of first texts associated with each first sub-requirement type, multiple first sub-requirement types are sorted, and based on the number of second texts associated with each second sub-requirement type, multiple second sub-requirement types are sorted to obtain second development requirement data. Finally, based on the second development requirement data, a project book is generated. Thus, the development requirement content of each text in the obtained development requirement data is determined, and the development requirement data is classified according to the development requirement content of each text, each development requirement text of the functional requirement type and the corresponding sub-requirement type, as well as each development requirement text of the non-functional requirement type and the corresponding sub-requirement type are obtained and sorted. Based on the classified and sorted development requirement data, a project book is generated, thereby realizing the management and statistics of the power model development requirement data, reducing the cost of power model development, improving the efficiency of power model development, and improving the reliability of power model development requirement data processing.

[0065] Figure 2 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure.

[0066] like Figure 2 As shown, the method includes:

[0067] Step 201: Acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts.

[0068] The specific implementation of step 201 can refer to the detailed description in other embodiments of the present disclosure and will not be described in detail here.

[0069] Step 202: pre-process each development requirement text.

[0070] In the present disclosure, after obtaining the first development requirement data, in order to reduce the noise data in the first development requirement data and improve the efficiency and accuracy of development requirement data processing, before processing the first development requirement data, each development requirement text in the first development requirement data can be preprocessed first, so as to remove abnormal data in the development requirement data, replace erroneous data, and convert data formats, etc.

[0071] It should be noted that each development requirement text may be preprocessed according to a preset preprocessing method, or a preprocessing method determined according to actual conditions, and this disclosure does not limit this.

[0072] In some possible implementation forms, when preprocessing each development requirement text, punctuation marks, special characters such as spaces, tabs, and line breaks in the text (i.e., the development requirement text) can be first removed to retain the main content of the text and reduce the noise data in the text. Then, all the content (such as time, date, and content form) and language in the text are converted into a unified format, standardized, repeated words are reduced, and stop words are removed. Then, the vocabulary in the text is simplified to stem form, and the vocabulary is restored to its basic form using part-of-speech tagging. Then, spelling errors in the text are checked and corrected, including grammatical, vocabulary, and text errors, and replaced through a dictionary. Finally, according to the analysis target, content in the text that is not related to the task is removed. For example, if the analysis target is the development requirements of the power model, then content in the text that is not related to the development requirements of the power model can be removed, etc. The present disclosure does not limit this.

[0073] Step 203 : segment each pre-processed development requirement text to obtain a plurality of words contained in the corresponding development requirement text.

[0074] In the present disclosure, after preprocessing each development requirement text, each preprocessed development requirement text is obtained. In order to improve the accuracy of development data processing, each preprocessed development requirement text can be segmented to divide the text into separate words (words or phrases) to obtain multiple words contained in the corresponding development requirement text.

[0075] Step 204 : Determine the features corresponding to each word contained in each pre-processed development requirement text.

[0076] In the present disclosure, after obtaining multiple words contained in each development requirement text, each word in each text can be firstly subjected to feature extraction and analysis to determine the features corresponding to each word, thereby providing conditions for determining the development requirement content of the text.

[0077] Step 205 : determining the development requirement content of the corresponding development requirement text based on the features corresponding to each word.

[0078] In the present disclosure, after determining the features corresponding to each word contained in each development requirement text, the development requirement content of the corresponding development requirement text can be determined based on the features corresponding to each word, thereby improving the efficiency and accuracy of determining the text content.

[0079] Step 206 : Determine a first data set of a functional requirement type and a second data set of a non-functional requirement type based on the development requirement content of each development requirement text.

[0080] The first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type.

[0081] Step 207 , sorting the multiple first sub-requirement types based on the first text quantity of the text associated with each first sub-requirement type, and sorting the multiple second sub-requirement types based on the second text quantity of the text associated with each second sub-requirement type, to obtain second development requirement data.

[0082] Step 208: Generate a project document based on the second development requirement data.

[0083] The specific implementation of steps 206 to 208 can refer to the detailed descriptions in other embodiments of the present disclosure and will not be described in detail here.

[0084] In an embodiment of the present disclosure, first, the first development requirement data to be processed is obtained, and each development requirement text is preprocessed. Then, each preprocessed development requirement text is segmented to obtain a plurality of words contained in the corresponding development requirement text, and the features corresponding to each word contained in each preprocessed development requirement text are determined. Then, based on the features corresponding to each word, the development requirement content of the corresponding development requirement text is determined, and based on the development requirement content of each development requirement text, a first data set of functional requirement types and a second data set of non-functional requirement types are determined. Finally, based on the number of first texts associated with each first sub-requirement type, multiple first sub-requirement types are sorted, and based on the number of second texts associated with each second sub-requirement type, multiple second sub-requirement types are sorted to obtain second development requirement data, and based on the second development requirement data, a project book is generated. Therefore, after obtaining the development requirement data, the development requirement data is preprocessed, and the text in the preprocessed development requirement data is segmented to obtain multiple words contained in each text, determine the characteristics of each word, and based on the characteristics of the word, determine the development requirement content of the corresponding text. Based on the development requirement content of each text, the development requirement data is classified and sorted, and a project book is generated, thereby improving the efficiency and accuracy of development requirement data processing.

[0085] Figure 3 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure.

[0086] like Figure 3 As shown, the method includes:

[0087] Step 301: Acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts.

[0088] Step 302: pre-process each development requirement text.

[0089] Step 303: segment each pre-processed development requirement text to obtain a plurality of words contained in the corresponding development requirement text.

[0090] The specific implementation of steps 301 to 303 can be referred to the detailed descriptions in other embodiments of the present disclosure, and will not be described in detail here.

[0091] Step 304 : determining the first occurrence number of each word in the development requirement text to which it belongs, and the third number of texts containing the word in the preset corpus.

[0092] The corpus may be a database consisting of development requirement texts of the power model, which may be pre-set or may be a database iteratively updated based on historical development requirement data, and this disclosure does not limit this.

[0093] In the present disclosure, after obtaining multiple words contained in each development requirement text, a vocabulary table can be constructed to determine the first occurrence number of each word in the development requirement text to which it belongs, as well as the number of third texts containing the corresponding word in the corpus, thereby providing a data basis for determining the characteristics of the word.

[0094] Step 305 : Determine the importance of each word based on the first occurrence count, the number of third texts, and the total number of first texts in the corpus.

[0095] The importance may be the importance of a word in the development requirement text to which it belongs.

[0096] In the present disclosure, after determining the first occurrence number and the third text number corresponding to each word, the total number of first texts of the texts contained in the corpus can be obtained first. Based on the first occurrence number, the third text number and the total number of first texts, the importance of each word in the development requirement text to which it belongs is determined by formula (1) of the TF-IDF method. Formula (1) is as follows, wherein formula (1) is only used as an example and is not limited here:

[0097]

[0098] Among them, TF(x i ,d), reflects the importance of the word in the text to which it belongs. The more frequently it appears, the higher the TF value, x i , represents the vocabulary of calculation, d, represents the vocabulary x i the first occurrence in the text to which it belongs; Used to measure the prevalence of vocabulary in the corpus, The higher the value, the less common the word is in the corpus and the stronger the ability to distinguish texts. N is the total number of first texts in the corpus. DF(x i ), which means that the corpus contains vocabulary x i The third text quantity.

[0099] It should be noted that when a word appears more frequently in the development requirement text to which it belongs, but the number of other texts in the corpus that contain the word is small, TF IDF (x i ,d) The higher the value, the more important the word is to the development requirement text to which it belongs, and the higher the corresponding importance.

[0100] On the contrary, when a word appears less frequently in the development requirement text to which it belongs, or when there are more other texts in the corpus containing the word, then TF IDF (x i , the lower the value of d), it can be determined that the word is not important to the development requirement text to which it belongs, and the corresponding importance is low. This disclosure does not limit this.

[0101] Among them, TF-IDF is the abbreviation of Term Frequency–Inverse Document Frequency (TF-IDF).

[0102] Step 306: Determine the semantics of each word contained in each pre-processed development requirement text.

[0103] In the present disclosure, after determining the importance of each word in the development requirement text to which it belongs, the semantics of each word in each development requirement text may be determined, thereby providing a data basis for determining the characteristics of each word.

[0104] It should be noted that the method for determining the semantics of each word in each text can be any pre-set method or a method determined according to actual needs. For example, the semantics of each word can be determined by singular value decomposition of LSA, which is not limited in this disclosure.

[0105] Among them, LSA is the abbreviation of Latent Semantic Analysis (LSA).

[0106] It should be noted that the LSA method can determine the semantics of each word by capturing the potential relationship between words.

[0107] In the present disclosure, when determining the semantics of a word through the singular value decomposition of LSA, a word-text matrix can be first constructed, wherein the rows of the matrix represent words and the text is tabulated, and each element in the matrix is ​​usually the number of occurrences of the word in the text, that is, TF(x i ,d), the constructed matrix is ​​shown in formula (2), where the matrix shown in formula (2) is used as an example and is not limited here:

[0108] A=U∑V T (2)

[0109] Among them, A, represents the word-text matrix, U, is the feature matrix of the word, which can represent the position of each word in the latent semantic space, ∑, is the singular value matrix, the values ​​on its diagonal can represent the importance of each latent semantic dimension, V R, is the feature matrix of the text, which can represent the position of each text in the latent semantic space.

[0110] In step 307 , the importance and semantics of each word are determined as features of the corresponding word.

[0111] In the present disclosure, after determining the importance and semantics of each word in each development requirement text, the importance and semantics of each word can be determined as the characteristics of the corresponding word, thereby providing conditions for determining the development requirement content of the development requirement text to which the word belongs.

[0112] Step 308 : Determine the development requirement content of the corresponding development requirement text according to the semantics corresponding to the words whose importance is greater than the importance threshold.

[0113] The importance threshold may be a critical importance value used to judge the importance of a word to the development requirement text to which it belongs. It may be preset or determined according to actual needs, and this disclosure does not limit this.

[0114] In the present disclosure, after determining the characteristics of each word, in order to improve the efficiency and accuracy of determining the content of the development requirement text, we can first determine the words whose corresponding importance is greater than the importance threshold, and determine that these words are more important to the development requirement text to which they belong. At this time, based on the semantics of these words, we can determine the development requirement content of the development requirement text to which they belong.

[0115] Optionally, when determining the development requirement content of the development requirement text, the development requirement content of the text may also be determined by using an LDA model based on the vocabulary distribution in the text, which is not limited in the present disclosure.

[0116] Among them, LDA is the abbreviation of Linear Discriminant Analysis (LDA).

[0117] Step 309 : Determine a first data set of functional requirement type and a second data set of non-functional requirement type based on the development requirement content of each development requirement text.

[0118] The first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type.

[0119] Step 310 , sorting multiple first sub-requirement types based on the first text quantity of the text associated with each first sub-requirement type, and sorting multiple second sub-requirement types based on the second text quantity of the text associated with each second sub-requirement type, to obtain second development requirement data.

[0120] Step 311: Generate a project document based on the second development requirement data.

[0121] The specific implementation of steps 309 to 311 can refer to the detailed descriptions in other embodiments of the present disclosure and will not be described in detail here.

[0122] In an embodiment of the present disclosure, first development requirement data to be processed is obtained, and each development requirement text is preprocessed. Then, each preprocessed development requirement text is segmented to obtain multiple words contained in the corresponding development requirement text. The first occurrence number of each word in the development requirement text to which it belongs and the number of third texts containing the word in a preset corpus are determined. The importance of each word is determined based on the first occurrence number, the number of third texts, and the total number of first texts in the corpus. Then, the semantics of each word contained in each preprocessed development requirement text are determined, and the importance and semantics of each word are determined as features of the corresponding word. Then, based on the semantics corresponding to the words whose importance is greater than an importance threshold, the development requirement content of the corresponding development requirement text is determined. Based on the development requirement content of each development requirement text, a first data set of functional requirement types and a second data set of non-functional requirement types are determined. Finally, based on the first number of texts associated with each first sub-requirement type, multiple first sub-requirement types are sorted, and based on the second number of texts associated with each second sub-requirement type, multiple second sub-requirement types are sorted. Second development requirement data is obtained, and a project document is generated based on the second development requirement data. Therefore, the acquired development requirement data is preprocessed and segmented to obtain multiple words contained in each development requirement text. The importance of each word is determined based on the number of occurrences of each word in the text to which it belongs, the number of texts containing the word in the corpus, and the total number of texts in the corpus. Then, the semantics of each word is determined. Based on the importance and semantics of the words, the content of the text to which it belongs is determined according to the semantics of the words whose importance is greater than the importance threshold. Based on the content of each text, the development requirement data is classified and sorted, and a project book is generated, thereby improving the accuracy and reliability of the development requirement data processing of the power model.

[0123] Figure 4 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure.

[0124] like Figure 4 As shown, the method includes:

[0125] Step 401: Acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts.

[0126] Step 402: Determine the development requirement content corresponding to each development requirement text.

[0127] The specific implementation of steps 401 to 402 can refer to the detailed descriptions in other embodiments of the present disclosure and will not be described in detail here.

[0128] Step 403: Determine the first demand classification feature.

[0129] The first requirement classification feature may be a feature used to classify each development requirement data in the first development requirement data, and may be determined based on actual needs. For example, when classifying the first development requirement data, the first development requirement data may be first divided into two categories: functional requirement data and non-functional requirement data. In this case, the first requirement classification feature may be whether the development requirement content of the development requirement text to be classified is functional requirement content, which is not limited in this disclosure.

[0130] In the present disclosure, after determining the development requirement content corresponding to each development requirement text and before classifying the first development requirement data, the first requirement classification feature may be determined first, thereby improving the accuracy of development requirement data processing.

[0131] Step 404 : Based on the first requirement classification feature and the development requirement content of each development requirement text, determine each development requirement text belonging to a functional requirement type and each development requirement text belonging to a non-functional requirement type.

[0132] In the present disclosure, after determining the first requirement classification feature, each development requirement text belonging to the functional requirement type and each development requirement text belonging to the non-functional requirement type can be determined based on the first requirement classification feature and the development requirement content of each development requirement text, thereby dividing the first development requirement data into two major categories of data, thereby improving the efficiency of data processing.

[0133] Step 405, return to the operation of determining the first requirement classification feature, determine the first sub-requirement type associated with each development requirement text belonging to the functional requirement type, and the second sub-requirement type associated with each development requirement text belonging to the non-functional requirement type, until the preset conditions are met, and obtain the first data set and the second data set.

[0134] It should be noted that when returning to the operation of determining the first requirement classification feature, the first requirement classification feature at this time may be determined based on the actual classification requirements. For example, when determining the second sub-requirement type associated with each development requirement text of a non-functional requirement type, the first requirement classification feature determined at this time may be a specific sub-non-functional requirement classification feature under the non-functional requirement type, such as whether it is a security development requirement or a reliability development requirement, etc. This disclosure does not limit this.

[0135] The preset condition may be a condition for stopping classification of the development requirement data, which may be preset. For example, the preset condition may be that each development requirement text in the first development requirement data has a corresponding sub-requirement classification determined, which is not limited in this disclosure.

[0136] In the present disclosure, after determining each development requirement text belonging to the functional requirement type and each development requirement text belonging to the non-functional requirement type, in order to determine the specific sub-requirement type of each development requirement text, the operation of determining the first requirement classification feature can be returned to determine the first sub-requirement type associated with each development requirement text belonging to the functional requirement type, and the second sub-requirement type associated with each development requirement text belonging to the non-functional requirement type, until the sub-requirement types of all development requirement texts are determined, and a first data set of functional requirement types and a second data set of non-functional requirement types are obtained.

[0137] In some possible implementation forms, after obtaining the first data set and the second data set, a decision tree of the functional requirement type and a decision tree of the non-functional requirement type can be generated based on the first data set. After the decision tree is constructed, the decision tree can be pruned to reduce overfitting and improve generalization ability. This disclosure does not limit this.

[0138] In the present disclosure, when classifying the first development requirement data, a decision tree algorithm may be used for classification. The information gain in the decision tree algorithm can be used to measure the amount of information increase brought by the feature when dividing the data set. The information gain is shown in formula (3), wherein formula (3) is only an example and is not limited here:

[0139] IG(D,A)=H(D)-H(D|A)(3)

[0140] Wherein, H(D) is the entropy of the dataset D, which is used to measure the uncertainty of the dataset. Its calculation formula is shown in formula (4). H(D|A) is the conditional entropy given by the classification feature A, which is the vocabulary feature in this disclosure. Its calculation formula is shown in formula (5). Among them, (4) and formula (5) are only examples and are not limited here:

[0141]

[0142]

[0143] Among them, p i , is the probability of demand type i, c, is the total number of demand types; D v , is the dataset when the classification feature A takes the value v, |D v |, is the number of samples.

[0144] It should be noted that after obtaining the first data set and the second data set, corresponding visual images such as tree structure diagrams can be generated based on each first sub-requirement type and the associated development requirement text in the first data set, and each second sub-requirement type and the associated development requirement text in the second data set. This disclosure does not limit this.

[0145] Step 406 , sorting the plurality of first sub-requirement types based on the first text quantity of the text associated with each first sub-requirement type, and sorting the plurality of second sub-requirement types based on the second text quantity of the text associated with each second sub-requirement type, to obtain second development requirement data.

[0146] Step 407: Generate a project document based on the second development requirement data.

[0147] The specific implementation of steps 406 to 407 can refer to the detailed descriptions in other embodiments of the present disclosure and will not be described in detail here.

[0148] In the embodiment of the present disclosure, first, the first development requirement data to be processed is obtained, and the development requirement content corresponding to each development requirement text is determined. Then, the first requirement classification feature is determined, and based on the first requirement classification feature and the development requirement content of each development requirement text, each development requirement text belonging to the functional requirement type and each development requirement text belonging to the non-functional requirement type are determined. Then, the operation of determining the first requirement classification feature is returned to perform, and the first sub-requirement type associated with each development requirement text belonging to the functional requirement type and the second sub-requirement type associated with each development requirement text belonging to the non-functional requirement type are determined until the preset conditions are met. The first data set and the second data set are obtained, and based on the first text quantity of the text associated with each first sub-requirement type, multiple first sub-requirement types are sorted, and based on the second text quantity of the text associated with each second sub-requirement type, multiple second sub-requirement types are sorted to obtain second development requirement data. Finally, based on the second development requirement data, a project book is generated. Therefore, by dividing the development requirement data into two categories, functional requirement data and non-functional requirement data, according to the determined classification characteristics, returning to execute the operation of determining the classification characteristics, determining the first sub-requirement type of each text in the functional requirement data, and the second sub-requirement type of each text in the non-functional requirement data, sorting the first sub-requirement types based on the number of texts of each first sub-requirement type, and sorting the second sub-requirement types based on the number of texts of each second sub-requirement type, obtaining the classified and sorted development requirement data, and generating a project book, thereby realizing the management planning of the development requirement data and improving the reliability and efficiency of the development requirement data processing of the power model.

[0149] Figure 5 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure.

[0150] like Figure 5 As shown, the method includes:

[0151] Step 501: Acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts.

[0152] Step 502: Determine the development requirement content corresponding to each development requirement text.

[0153] Step 503 : Determine a first data set of functional requirement type and a second data set of non-functional requirement type based on the development requirement content of each development requirement text.

[0154] The first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type.

[0155] The specific implementation of steps 501 to 503 can be referred to the detailed descriptions in other embodiments of the present disclosure, and will not be described in detail here.

[0156] Step 504 : Obtain the total number of the second texts in the first data set and the total number of the third texts in the second data set.

[0157] In the present disclosure, after determining the first data set of functional requirement types and the second data set of non-functional requirement types, in order to sort the first sub-requirement types under the functional requirement types and the second sub-requirement types under the non-functional requirement types, the total number of the second texts of the texts contained in the first data set and the total number of the third texts of the texts contained in the second data set can be first obtained, thereby providing a data basis for sorting the sub-requirement types.

[0158] Step 505: Determine the first requirement ratio corresponding to each first sub-requirement type based on the first text quantity and the total second text quantity of the text associated with each first sub-requirement type, and determine the second requirement ratio corresponding to each second sub-requirement type based on the second text quantity and the total third text quantity of the text associated with each second sub-requirement type.

[0159] The first demand proportion may be the proportion of each first sub-demand type to all first sub-demand types in the first data set, and the second demand proportion may be the proportion of each second sub-demand type to all second sub-demand types in the second data set.

[0160] In the present disclosure, after obtaining the total number of second texts corresponding to the first data set and the total number of third texts corresponding to the second data set, the first requirement proportion corresponding to each first-word requirement type can be determined based on the ratio between the first text quantity and the total number of second texts of the texts associated with each first sub-requirement type, and the second requirement proportion corresponding to each second sub-requirement type can be determined based on the ratio between the second text quantity and the total number of third texts of the texts associated with each second sub-requirement type, thereby improving the accuracy and reliability of the development requirement data sorting.

[0161] Step 506 : sorting the plurality of first sub-demand types based on the first demand proportion, and sorting the plurality of second sub-demand types based on the second demand proportion.

[0162] In the present disclosure, after determining the first demand ratio of each first sub-demand type and the second demand ratio of each second sub-demand type, multiple first sub-demand types can be sorted from large to small based on the first demand ratio, and multiple second sub-demand types can be sorted from large to small based on the second demand ratio, so that the more important sub-demand types in the development demand data and their associated texts can be ranked in a front position, reducing the cost of determining the power model development demand.

[0163] Step 507: Arrange the sorted first data set before the sorted second data set to obtain second development requirement data.

[0164] In the present disclosure, after sorting the first sub-requirement types under the functional requirement type and the second sub-requirement types under the non-functional requirement type, since the functional requirement development of the power model is more important and urgent than the non-functional requirement development, the sorted first data set can be placed before the sorted second data set, so that the more important and urgent development requirements can be prioritized and the reliability of the power model development can be improved.

[0165] Step 508: Generate a project document based on the second development requirement data.

[0166] The specific implementation of step 508 can be referred to the detailed description in other embodiments of the present disclosure, and will not be described in detail here.

[0167] In an embodiment of the present disclosure, first, the first development requirement data to be processed is obtained, and the development requirement content corresponding to each development requirement text is determined. Then, based on the development requirement content of each development requirement text, a first data set of functional requirement type and a second data set of non-functional requirement type are determined. The total number of second texts of the texts contained in the first data set and the total number of third texts of the texts contained in the second data set are obtained. Then, based on the first text number and the total number of second texts of the texts associated with each first sub-requirement type, the first requirement proportion corresponding to each first sub-requirement type is determined, and based on the second text number and the total number of third texts of the texts associated with each second sub-requirement type, the second requirement proportion corresponding to each second sub-requirement type is determined. Based on the first requirement proportion, multiple first sub-requirement types are sorted, and based on the second requirement proportion, multiple second sub-requirement types are sorted. Finally, the sorted first data set is arranged before the sorted second data set to obtain the second development requirement data, and a project book is generated based on the second development requirement data. Therefore, by dividing the obtained development requirement data into two categories: the first data set of functional requirement type and the second data set of non-functional requirement type, and based on the total number of texts in the first data set and the second data set, and the number of texts of each sub-requirement type respectively contained, the demand proportion of the corresponding sub-requirement type is determined, and the sub-requirement types in the first data set and the second data set are sorted. Based on the classified and sorted development requirement data, a project book is generated, thereby realizing the statistics and management of development requirement data and improving the reliability of development requirement data processing.

[0168] Figure 6 A flowchart of a method for processing development demand data of an electric power model provided in an embodiment of the present disclosure.

[0169] like Figure 6 As shown, the method includes:

[0170] Step 601: Acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts.

[0171] Step 602: Determine the development requirement content corresponding to each development requirement text.

[0172] Step 603 : Determine a first data set of functional requirement type and a second data set of non-functional requirement type based on the development requirement content of each development requirement text.

[0173] The first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type.

[0174] Step 604 : Obtain the total number of the second texts in the first data set and the total number of the third texts in the second data set.

[0175] Step 605: Determine the first requirement ratio corresponding to each first sub-requirement type based on the first text quantity and the total second text quantity of the text associated with each first sub-requirement type, and determine the second requirement ratio corresponding to each second sub-requirement type based on the second text quantity and the total third text quantity of the text associated with each second sub-requirement type.

[0176] Step 606 : sorting the plurality of first sub-demand types based on the first demand proportion, and sorting the plurality of second sub-demand types based on the second demand proportion.

[0177] Step 607: Arrange the sorted first data set before the sorted second data set to obtain second development requirement data.

[0178] The specific implementation of steps 601 to 607 can refer to the detailed descriptions in other embodiments of the present disclosure and will not be described in detail here.

[0179] Step 608 : Determine each target first sub-demand type whose corresponding first demand proportion is greater than the first demand proportion threshold, and each target second sub-demand type whose corresponding second demand proportion is greater than the second demand proportion threshold.

[0180] Among them, the first demand proportion threshold value can be the demand proportion critical value of the first sub-demand type when determining the priority development demand type. It can be pre-set and the present disclosure does not limit this.

[0181] Among them, the second demand proportion threshold can be the critical value of the demand proportion of the second sub-demand type when determining the priority development demand type. It can be the same as the first demand proportion threshold, or it can be different from the first demand proportion threshold. This disclosure does not limit this.

[0182] Among them, the target first sub-demand type and the target second sub-demand type are both priority development demands for power model development.

[0183] In the present disclosure, after obtaining the second development demand data, we can first determine each target first sub-demand type whose corresponding first demand ratio is greater than the first demand ratio threshold, and each target second sub-demand type whose corresponding second demand ratio is greater than the second demand ratio threshold, that is, determine the target first sub-demand type and the target second sub-demand type that are within a certain ranking position in the first data set and the second data set respectively, so as to improve the accuracy of determining the power model development demand.

[0184] It should be noted that after determining the target second sub-requirement type, if there is a prerequisite for the determined target second sub-requirement type, that is, when the target second sub-requirement type has an undeveloped prerequisite functional requirement type, and the target first sub-requirement type does not include the functional requirement type, the functional requirement type can also be determined as the target first sub-requirement type. This disclosure does not limit this.

[0185] Step 609 : Determine a first development solution corresponding to each target first sub-requirement type, and a second development solution corresponding to each target second sub-requirement type.

[0186] The first development plan may be a development plan of a functional requirement type, and the second development plan may be a development plan of a non-functional requirement type.

[0187] In the present disclosure, after determining the target first sub-demand type and the target second sub-demand type, the target first sub-demand type and the target second sub-demand type can be determined as priority development requirements for power model development. At this time, the first development plan corresponding to each target first sub-demand type and the second development plan corresponding to each target second sub-demand type can be determined.

[0188] In step 610 , each first development plan is compared with each other to obtain a first positive score and a first negative score corresponding to each first development plan, and each second development plan is compared with each other to obtain a second positive score and a second negative score corresponding to each second development plan.

[0189] Among them, the positive score can be used to score the positive preference of each development plan.

[0190] Among them, the negative score can be used as a negative preference score for each development plan.

[0191] In this disclosure, after determining the first development plan and the second development plan, the development plans can be evaluated and predicted by the PROMETHEE method. When comparing the development plans pairwise, each pair of plans is taken as an example of plan m and plan n. First, the preference function P is determined for each pair of plans m and n. mn , where the preference function P mn The calculation formula is shown in formula (6), where formula (6) is only an example and is not limited here:

[0192] P mn =max(0,x nn -x mn +q n ) (6)

[0193] Among them, q n, is the threshold value, which can represent the minimum difference accepted between the solutions, P mn , which can express the preference of plan m over plan n, and can measure the degree to which plan m is better than plan n in a certain standard, x nn , can represent the performance value of solution n on a certain standard, x mn , which can represent the performance value of solution m on the same standard, x nn -x mn , which can represent the performance difference between scheme n and Hull scheme m on a certain standard.

[0194] It should be noted that standards can be decision-making criteria for evaluating and forecasting solutions. They can be pre-set or determined based on actual needs. For example, standards can include the urgency of development needs in terms of time, feasibility in terms of performance, etc., which are not limited in this disclosure.

[0195] Then, based on the preference functions of option m and option n, the preference strength between each pair of options is calculated, as shown in formula (7) and formula (8). In particular, formula (7) and formula (8) are only examples and are not limited here:

[0196]

[0197]

[0198] Among them, P nm , can express the preference of plan n over plan m, and can measure the degree to which plan n is better than plan m in a certain standard. is the positive score of option m, i.e. the positive preference score, is the negative score of option m, that is, the negative preference score.

[0199] Among them, PROMETHEE is the abbreviation of Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE).

[0200] Step 611, based on the first positive score and the first negative score of each first development plan, determine the first target score corresponding to the first development plan, and based on the second positive score and the second negative score of each second development plan, determine the second target score corresponding to the second development plan.

[0201] The first target score may be the final score of the first development plan, namely the net flow score, which may indicate the feasibility of the first development plan.

[0202] The second target score may be the final score of the second development plan, namely the net flow score, which may indicate the feasibility of the second development plan.

[0203] It should be noted that the higher the first target score and the second target score, the higher the feasibility of the corresponding first development plan and the second development plan, and this disclosure does not limit this.

[0204] In the present disclosure, after determining the first positive score and the first negative score of each first development plan, and the second positive score and the second negative score of each second development plan, in order to determine the priority development plan therein, the corresponding first target score can be determined based on the first positive score and the first negative score of each first development plan, and the corresponding second target score can be determined based on the second positive score and the second negative score of each second development plan, thereby improving the accuracy and reliability of the determined priority development plan.

[0205] Step 612 : Generate a project book based on the first development plan whose corresponding first target score is greater than the first score threshold, and the second development plan whose corresponding second target score is greater than the second score threshold.

[0206] Among them, the first score threshold can be the first target score critical value used to determine whether the first development plan is the priority development plan. It can be pre-set or determined according to actual needs. This disclosure does not limit this.

[0207] Among them, the second score threshold can be the first target score critical value used to determine whether the second development plan is the priority development plan. It can be the same as the first score threshold, or it can be different from the first score threshold. This disclosure does not limit this.

[0208] In the present disclosure, after determining the first target score of each first development plan and the second target score of each second development plan, in order to improve the reliability and feasibility of the generated project book, a first development plan whose corresponding first target score is greater than the first score threshold and a second development plan whose corresponding second target score is greater than the second score threshold can be selected to generate a project book.

[0209] It should be noted that when the second development plan corresponding to the second target score is greater than the second score threshold and there is an undeveloped prerequisite functional requirement, the development plan corresponding to the prerequisite functional requirement can be first determined and added to the project book. This disclosure does not limit this.

[0210] In the embodiment of the present disclosure, firstly, the first development requirement data to be processed is obtained, the development requirement content corresponding to each development requirement text is determined, and based on the development requirement content of each development requirement text, a first data set of functional requirement type and a second data set of non-functional requirement type are determined, then the second text total number of the text contained in the first data set and the third text total number of the text contained in the second data set are obtained, and based on the first text number and the second text total number of the text associated with each first sub-requirement type, the first requirement proportion corresponding to each first sub-requirement type is determined, and based on the second text number and the third text total number of the text associated with each second sub-requirement type, the second requirement proportion corresponding to each second sub-requirement type is determined, then based on the first requirement proportion, multiple first sub-requirement types are sorted, and based on the second requirement proportion, multiple second sub-requirement types are sorted, the sorted first data set is placed before the sorted second data set, and the second development requirement data is obtained, and then the corresponding second development requirement data is determined. For each target first sub-demand type whose first demand proportion is greater than the first demand proportion threshold, and for each target second sub-demand type whose corresponding second demand proportion is greater than the second demand proportion threshold, determine the first development plan corresponding to each target first sub-demand type, and the second development plan corresponding to each target second sub-demand type, then perform pairwise comparisons between each first development plan to obtain the first positive score and the first negative score corresponding to each first development plan, and perform pairwise comparisons between each second development plan to obtain the second positive score and the second negative score corresponding to each second development plan, finally determine the first target score corresponding to the first development plan based on the first positive score and the first negative score of each first development plan, and determine the second target score corresponding to the second development plan based on the second positive score and the second negative score of each second development plan, and generate a project book based on the first development plan whose corresponding first target score is greater than the first score threshold, and the second development plan whose corresponding second target score is greater than the second score threshold. Therefore, after classifying and sorting the development requirement data and obtaining the sub-requirement type of each development requirement text under the functional requirement type and the sub-requirement type of each development requirement text under the non-functional requirement type, the solutions of functional and non-functional sub-requirement types with a higher proportion of requirements are determined, and the solutions corresponding to the functional requirements are compared pairwise, and the solutions corresponding to the non-functional requirements are compared pairwise to obtain the positive and negative scores of the solutions. Based on the positive and negative scores of the solutions, the target scores of the solutions are determined, and project books are generated based on the solutions with higher target scores, thereby improving the feasibility and reliability of the generated project books and improving the accuracy and efficiency of the development requirement data processing of the power model.

[0211] To implement the above embodiment, the present disclosure further provides a device for processing development demand data of an electric power model.

[0212] Figure 7 A schematic diagram of the structure of a power model development demand data processing device provided in an embodiment of the present disclosure.

[0213] like Figure 7 As shown, the power model development demand data processing device 700 may include:

[0214] An acquisition module 701 is configured to acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts;

[0215] The first determining module 702 is used to determine the development requirement content corresponding to each development requirement text;

[0216] A second determining module 703 is configured to determine, based on the development requirement content of each development requirement text, a first data set of a functional requirement type and a second data set of a non-functional requirement type, wherein the first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type;

[0217] a sorting module 704, configured to sort the plurality of first sub-requirement types based on a first text quantity of text associated with each first sub-requirement type, and sort the plurality of second sub-requirement types based on a second text quantity of text associated with each second sub-requirement type, to obtain second development requirement data;

[0218] The generating module 705 is used to generate a project book based on the second development requirement data.

[0219] Optionally, the first determining module 702 is specifically configured to:

[0220] Preprocess each development requirement text;

[0221] Perform word segmentation on each pre-processed development requirement text to obtain multiple words contained in the corresponding development requirement text;

[0222] Determine the features corresponding to each word contained in each pre-processed development requirement text;

[0223] Based on the features corresponding to each word, the development requirement content of the corresponding development requirement text is determined.

[0224] Optionally, the first determining module 702 is further configured to:

[0225] Determine the number of first occurrences of each word in the development requirement text to which it belongs, and the number of third texts containing the word in the preset corpus;

[0226] determining the importance of each word based on the first occurrence count, the third text quantity, and the total number of first texts in the corpus;

[0227] Determine the semantics of each word contained in each pre-processed development requirement text;

[0228] The importance and semantics of each word are determined as the features of the corresponding word.

[0229] Optionally, the first determining module 702 is further configured to:

[0230] The development requirement content of the corresponding development requirement text is determined according to the semantics corresponding to the words whose importance is greater than the importance threshold.

[0231] Optionally, the second determining module 703 is specifically configured to:

[0232] Determine the first demand classification characteristics;

[0233] Based on the first requirement classification feature and the development requirement content of each development requirement text, determining each development requirement text belonging to a functional requirement type and each development requirement text belonging to a non-functional requirement type;

[0234] Return to the operation of determining the first requirement classification feature, determine the first sub-requirement type associated with each development requirement text belonging to the functional requirement type, and the second sub-requirement type associated with each development requirement text belonging to the non-functional requirement type, until the preset conditions are met, and obtain the first data set and the second data set.

[0235] Optionally, the sorting module 704 is specifically configured to:

[0236] Obtaining a total number of second texts in the first data set and a total number of third texts in the second data set;

[0237] Determine the first demand ratio corresponding to each first sub-demand type based on the first text quantity and the total second text quantity of the text associated with each first sub-demand type, and determine the second demand ratio corresponding to each second sub-demand type based on the second text quantity and the total third text quantity of the text associated with each second sub-demand type;

[0238] sorting the plurality of first sub-demand types based on the proportion of the first demand, and sorting the plurality of second sub-demand types based on the proportion of the second demand;

[0239] The sorted first data set is arranged before the sorted second data set to obtain second development requirement data.

[0240] Optionally, the sorting module 704 is specifically configured to:

[0241] Determine each target first sub-demand type whose corresponding first demand proportion is greater than a first demand proportion threshold, and each target second sub-demand type whose corresponding second demand proportion is greater than a second demand proportion threshold;

[0242] Determine a first development plan corresponding to the first sub-requirement type of each target, and a second development plan corresponding to the second sub-requirement type of each target;

[0243] Performing pairwise comparisons between each first development plan to obtain a first positive score and a first negative score corresponding to each first development plan, and performing pairwise comparisons between each second development plan to obtain a second positive score and a second negative score corresponding to each second development plan;

[0244] Determining a first target score for each first development solution based on the first positive score and the first negative score of each first development solution, and determining a second target score for each second development solution based on the second positive score and the second negative score of each second development solution;

[0245] A project book is generated based on a first development plan whose corresponding first target score is greater than a first score threshold and a second development plan whose corresponding second target score is greater than a second score threshold.

[0246] The functions and specific implementation principles of the above modules in the embodiments of the present disclosure can be referred to the above method embodiments and will not be repeated here.

[0247] In the present disclosure, first, the first development requirement data to be processed is obtained, and the development requirement content corresponding to each development requirement text is determined. Then, based on the development requirement content of each development requirement text, a first data set of functional requirement types and a second data set of non-functional requirement types are determined. Then, based on the number of first texts associated with each first sub-requirement type, multiple first sub-requirement types are sorted, and based on the number of second texts associated with each second sub-requirement type, multiple second sub-requirement types are sorted to obtain second development requirement data. Finally, based on the second development requirement data, a project book is generated. Thus, the development requirement content of each text in the obtained development requirement data is determined, and the development requirement data is classified according to the development requirement content of each text, each development requirement text of the functional requirement type and the corresponding sub-requirement type, as well as each development requirement text of the non-functional requirement type and the corresponding sub-requirement type are obtained and sorted. Based on the classified and sorted development requirement data, a project book is generated, thereby realizing the management and statistics of the power model development requirement data, reducing the cost of power model development, improving the efficiency of power model development, and improving the reliability of power model development requirement data processing.

[0248] Figure 8 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown.

[0249] Figure 8 The electronic device 12 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0250] like Figure 8As shown, electronic device 12 is implemented as a general-purpose computing device. Components of electronic device 12 may include, but are not limited to, one or more processors or processing units 16, memory 28, and a bus 18 that connects various system components (including memory 28 and processing unit 16). Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MCA) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnection (PCI) bus.

[0251] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0252] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 8 Not shown, often called a "hard drive").

[0253] although Figure 8Although not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a floppy disk) and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a Compact Disc Read Only Memory (hereinafter referred to as CD-ROM), a Digital Video Disc Read Only Memory (hereinafter referred to as DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present disclosure.

[0254] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally implement the functions and / or methods of the embodiments described herein.

[0255] The electronic device 12 can also communicate with one or more external devices 14 (e.g., a keyboard, pointing device, display 24, etc.), one or more devices that enable human interaction with the electronic device 12, and / or any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). This communication can occur via an input / output (I / O) interface 22. Furthermore, the electronic device 12 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via a bus 18. It should be understood that, although not shown, other hardware and / or software modules can be used in conjunction with the electronic device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0256] The processing unit 16 executes various functional applications and parameter information determination by running the programs stored in the memory 28 , for example, implementing the power model development requirement data processing method mentioned in the above embodiment.

[0257] In order to implement the above embodiments, the present disclosure further proposes a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for processing development demand data of the power model proposed in the above embodiments of the present disclosure.

[0258] In order to implement the above embodiments, the present disclosure further proposes a computer program product. When an instruction processor in the computer program product is executed, the method for processing development demand data of the power model proposed in the above embodiments of the present disclosure is executed.

[0259] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0260] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0261] It should be noted that, in the description of this disclosure, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of this disclosure, unless otherwise specified, the meaning of "plurality" is two or more.

[0262] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0263] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0264] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0265] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0266] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0267] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0268] Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are illustrative and are not to be construed as limitations on the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A method for processing development demand data of an electric power model, characterized in that: include: Acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts; Determining the development requirement content corresponding to each of the development requirement texts; Determining, based on the development requirement content of each development requirement text, a first data set of a functional requirement type and a second data set of a non-functional requirement type, wherein the first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type; sorting the plurality of first sub-requirement types based on a first text quantity of the text associated with each of the first sub-requirement types, and sorting the plurality of second sub-requirement types based on a second text quantity of the text associated with each of the second sub-requirement types, to obtain second development requirement data; Determining a first demand ratio corresponding to each of the first sub-demand types, and a second demand ratio corresponding to each of the second sub-demand types; Determine each target first sub-demand type whose corresponding first demand proportion is greater than a first demand proportion threshold, and each target second sub-demand type whose corresponding second demand proportion is greater than a second demand proportion threshold; Determine a first development plan corresponding to the first sub-requirement type of each target, and a second development plan corresponding to the second sub-requirement type of each target; Performing pairwise comparisons between each of the first development plans to obtain a first positive score and a first negative score corresponding to each of the first development plans, and performing pairwise comparisons between each of the second development plans to obtain a second positive score and a second negative score corresponding to each of the second development plans; determining a first target score corresponding to the first development solution based on the first positive score and the first negative score of each of the first development solutions, and determining a second target score corresponding to the second development solution based on the second positive score and the second negative score of each of the second development solutions; A project book is generated based on a first development plan whose corresponding first target score is greater than a first score threshold and a second development plan whose corresponding second target score is greater than a second score threshold.

2. The method according to claim 1, wherein Determining the development requirement content corresponding to each development requirement text includes: Preprocessing each of the development requirement texts; Perform word segmentation on each pre-processed development requirement text to obtain multiple words contained in the corresponding development requirement text; Determining features corresponding to each vocabulary contained in each of the preprocessed development requirement texts; Based on the features corresponding to each word, the development requirement content of the corresponding development requirement text is determined.

3. The method according to claim 2, wherein Determining the features corresponding to each word contained in each pre-processed development requirement text includes: Determine the number of first occurrences of each word in the development requirement text to which it belongs, and the number of third texts containing the word in the preset corpus; determining the importance of each word based on the first number of occurrences, the third number of texts, and the total number of first texts in the corpus; Determining the semantics of each word contained in each of the pre-processed development requirement texts; The importance and semantics of each word are determined as features of the corresponding word.

4. The method according to claim 3, wherein The determining of the development requirement content of the corresponding development requirement text based on the features corresponding to each word includes: The development requirement content of the corresponding development requirement text is determined according to the semantics corresponding to the words whose importance is greater than the importance threshold.

5. The method according to claim 1, wherein The determining of the first data set of functional requirement type and the second data set of non-functional requirement type based on the development requirement content of each development requirement text includes: Determine the first demand classification characteristics; Based on the first requirement classification feature and the development requirement content of each development requirement text, determining each development requirement text belonging to a functional requirement type and each development requirement text belonging to a non-functional requirement type; Return to execute the operation of determining the first requirement classification feature, determine the first sub-requirement type associated with each development requirement text belonging to the functional requirement type, and the second sub-requirement type associated with each development requirement text belonging to the non-functional requirement type, until the preset conditions are met, and obtain the first data set and the second data set.

6. The method according to claim 1, wherein The step of sorting the plurality of first sub-requirement types based on a first text quantity of text associated with each of the first sub-requirement types, and sorting the plurality of second sub-requirement types based on a second text quantity of text associated with each of the second sub-requirement types, to obtain second development requirement data, includes: Obtaining a total number of second texts in the texts included in the first data set, and a total number of third texts in the texts included in the second data set; Determining a first demand ratio corresponding to each first sub-demand type based on the first text quantity of each text associated with the first sub-demand type and the total quantity of the second text, and determining a second demand ratio corresponding to each second sub-demand type based on the second text quantity of each text associated with the second sub-demand type and the total quantity of the third text; sorting a plurality of first sub-demand types based on the first demand proportion, and sorting a plurality of second sub-demand types based on the second demand proportion; The sorted first data set is arranged before the sorted second data set to obtain second development requirement data.

7. A method and apparatus for processing development demand data of an electric power model, the apparatus being used to implement the method for processing development demand data of an electric power model as claimed in claim 1, wherein: The device comprises: An acquisition module, configured to acquire first development requirement data to be processed, wherein the first development requirement data includes a plurality of development requirement texts; A first determining module is used to determine the development requirement content corresponding to each development requirement text; a second determining module, configured to determine, based on the development requirement content of each development requirement text, a first data set of a functional requirement type and a second data set of a non-functional requirement type, wherein the first data set includes each development requirement text belonging to the functional requirement type and its associated first sub-requirement type, and the second data set includes each development requirement text belonging to the non-functional requirement type and its associated second sub-requirement type; a sorting module, configured to sort a plurality of first sub-requirement types based on a first text quantity of text associated with each of the first sub-requirement types, and to sort a plurality of second sub-requirement types based on a second text quantity of text associated with each of the second sub-requirement types, to obtain second development requirement data; A generation module is used to generate a project book based on the second development requirement data.

8. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that may be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

Images