Project workload assessment method and device, electronic equipment and readable storage medium

Abstract

The invention provides a project workload assessment method and device, electronic equipment and a readable storage medium, and relates to the technical field of data analysis, and the method comprises the steps: recognizing each business function of each to-be-assessed demand item in a to-be-assessed project, judging the function features of the same business function in different function dimensions, and determining the function features of each to-be-assessed demand item in the to-be-assessed project; identifying function features of the function dimension which can be uniquely judged, and combining the identified function features into a judgment result; performing round-by-round enumeration on the non-uniquely judged function dimensions according to respective candidate function features, and performing multi-round combination with judgment results to obtain all service function operation conditions of the service functions; performing workload evaluation on the operation conditions of all the business functions of the same business function to obtain the final workload of the corresponding business function; and integrating the final workload of each business function to obtain a workload assessment result of the to-be-assessed project. The purpose of automatically evaluating the workload is achieved, personnel burden is reduced, evaluation efficiency is improved, and project workload evaluation accuracy is guaranteed.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and in particular to a project workload assessment method, a project workload assessment device, an electronic device, and a computer-readable storage medium. Background Technology

[0002] Current methods for assessing project workload typically use a benchmark approach, which breaks down the project into multiple requirements and assesses the workload of each requirement based on its functionalities. For example, a "credit card experience optimization project" can be broken down into thousands of requirements, such as "adding event tracking points to the credit card activity zone" and "adapting and modifying the credit card activity zone to a WeChat mini-program." Each requirement can be further broken down into multiple business functions, and the workload of each requirement is then assessed based on these business functions. Depending on the difficulty of implementing the functionalities of each requirement, the functional complexity, upgrade type, and adjustment factor for each business function will vary. Ultimately, the workload of each business function is calculated as f(functional complexity, upgrade type, adjustment factor), where f represents the functional relationship between functional complexity, upgrade type, adjustment factor, and workload. Therefore, workload assessment is equivalent to assessing the functional complexity, upgrade type, and adjustment factor of each business function. Currently, however, the functional complexity, upgrade type, and adjustment factor for each business function are manually maintained based on subjective judgment. Thus, assessing the workload of a project is a time-consuming and labor-intensive task for both the submitter and the reviewer. Furthermore, the assessment process for project workload is heavily influenced by subjective judgment and lacks accuracy. Summary of the Invention

[0003] This application provides a project workload assessment method, a project workload assessment device, an electronic device, and a computer-readable storage medium to address the problem that existing project workload assessment processes are heavily influenced by subjective judgment, are time-consuming and labor-intensive, and lack accuracy.

[0004] A first aspect of this application provides a method for evaluating project workload, the method comprising: Identify the business functions of each requirement item in the project to be evaluated, judge the functional characteristics of the same business function in different functional dimensions, and identify the functional characteristics of the functional dimensions that can be uniquely determined, so as to combine the identified functional characteristics into the judgment result of the corresponding business function. For functional dimensions that are not uniquely determined, enumerate each candidate functional feature in round by round, and perform multiple rounds of combination with the judgment result to obtain all possible business function operation states of the corresponding business function. The workload evaluation is performed on all service function running conditions of the same service function, and the workload evaluation result screening rule is matched based on the number of service function running conditions of the same service function, so as to perform workload evaluation result screening based on the matched workload evaluation result screening rule, and obtain the final workload of the corresponding service function; The final workloads of the service functions of the to-be-evaluated requirements items in the to-be-evaluated project are integrated to obtain the workload evaluation result of the to-be-evaluated project.

[0005] Optionally, the service functions of the to-be-evaluated requirements items in the to-be-evaluated project are identified, including: The corresponding reading rule is called based on the project document format of the to-be-evaluated project, and project text information of the to-be-evaluated project is obtained; The project text information of the to-be-evaluated project is subjected to semantic analysis, and the non-repetitive semantic clustering is performed on the project text information based on the semantic analysis result, so as to obtain a plurality of text information sets; wherein the text information set corresponds to the to-be-evaluated requirement item one by one; The keywords of each text information set are extracted, and the extracted keywords are matched through a preset service function feature word library, so as to screen target keywords related to the service function; Based on the context association relationship of the target keywords in the corresponding text information set, each service function contained in the corresponding to-be-evaluated requirement item is identified.

[0006] Optionally, the obtaining rule of the text information set includes: Based on the project text information after semantic clustering, the text information set with clear semantic attribution and the text information set with unclear semantic attribution in the project text information are determined; The text information set with clear semantic attribution is directly marked as the text information set of the corresponding to-be-evaluated requirement item; The similarity between the text information set with unclear semantic attribution and each marked text information set is determined, and the corresponding set division rule is matched based on the size relationship between the determined similarity and the preset threshold, so as to divide the text information set with unclear semantic attribution into the corresponding marked text information set.

[0007] Optionally, the rule of dividing the text information set with unclear semantic attribution into the corresponding marked text information set includes: In combination with the context logical relationship of the project document of the to-be-evaluated project, a representative semantic segment in the text information set with unclear semantic attribution is extracted, and the extracted semantic segment is subjected to similarity calculation with the marked text information set; Based on the similarity calculation result, the semantic attribution unclear text information set is divided into the text information set with similarity higher than the preset threshold and the highest similarity, and if the similarity between the semantic attribution unclear text information set and each labeled text information set is lower than the preset threshold, the semantic attribution unclear text information set is labeled as a new text information set of the to-be-evaluated demand item, and a unique identifier is generated for the new to-be-evaluated demand item.

[0008] Optionally, keyword extraction is performed on each text information set, including: Based on the semantic analysis result of each text information set, the item text information in each text information set is processed by word segmentation in combination with the preset standard words in the predefined dictionary, and the word segmentation of each text information set is tagged by part of speech; The word segmentation tagged by part of speech in the same text information set is organized according to the structure of compound words to obtain the compound words in the corresponding text information set; A plurality of keyword extraction rules are used to determine the candidate keywords of the compound words in the same text information set, based on the position of the determined candidate keywords and the keyword extraction rules, the composite score weighted determination of the candidate keywords is performed according to the preset position weight and the preset weight of each keyword extraction rule, and according to the order of the composite score from large to small, the front preset number of candidate keywords are selected as the keywords of the corresponding text information set.

[0009] Optionally, the function dimension includes function complexity, function upgrade type and adjustment factor; The function features of the same business function in different function dimensions are judged, including: Based on the function feature judgment rule of each function dimension, the function feature judgment result of whether the same business function has a unique effective function feature under each function dimension is determined, and the function feature of the function dimension that can be uniquely determined is determined; wherein, the effective function feature judgment result is used to represent that the business function has only one function feature judgment result under the function feature judgment rule of the corresponding function dimension.

[0010] Optionally, the function feature judgment rule of the function upgrade type includes: The type of the physical subsystem associated with the to-be-evaluated demand item corresponding to the business function and the number of each type of physical subsystem are obtained; wherein, the physical subsystem is used to represent a set of physical devices of the same type; Based on the conversion rule of converting the type of the physical subsystem into the number of the physical subsystem, the number conversion value matching of each type of physical subsystem is performed, and the matching number conversion value is multiplied by the number of the physical subsystem of the corresponding type to obtain the product value of the physical subsystem of the corresponding type; Summing up the product values of each type of physical subsystem to obtain a total number of physical subsystems corresponding to the business function; Based on the matching relationship between the total number of physical subsystems corresponding to the business function and the function upgrade type, determine the function upgrade type matched with the total number of physical subsystems corresponding to the business function.

[0011] Optionally, the rule for obtaining all possible business function operation conditions corresponding to the business function comprises: Performing arbitrary combination of randomly selected function features in the non-unique determined function dimension in the corresponding business function to obtain a plurality of combination results; Taking the judgment result as a quantitative, performing multi-round combination with each combination result to obtain all possible business function operation conditions corresponding to the business function.

[0012] Optionally, the function dimension includes function complexity, function upgrade type and adjustment factor; The workload assessment of all business function operation conditions of the same business function includes: Matching the feature values of the function features of each function dimension corresponding to the business function operation condition in the preset function feature database; Multiplying the feature value of the function complexity and the feature value of the function upgrade type to obtain a basic workload, adjusting the obtained basic workload by using the feature value of the adjustment factor, and then adjusting the adjusted basic workload by a preset proportion to obtain the workload assessment result corresponding to the business function operation condition.

[0013] Optionally, the workload assessment result screening rule based on the number of business function operation conditions of the same business function includes: When there is only one business function operation condition of the same business function, it is determined that the workload assessment result screening rule is to directly take the workload assessment result as the final workload of the corresponding business function; Otherwise, it is determined that the workload assessment result screening rule is to take the workload assessment result closest to the reference workload of the same business function as the final workload of the corresponding business function.

[0014] Optionally, the determination rule of the reference workload includes: For each business function of each to-be-assessed requirement item, respectively taking the name of the to-be-assessed requirement item and the name of the business function as a search target, searching for requirement item data corresponding to different search targets from a pre-established vector knowledge base to form a requirement item list corresponding to the search target; wherein, the requirement item data includes the name and workload of the requirement item, and the vector knowledge base is established based on historical requirement item data; An intersection of the requirement item list of the same business function of the same to-be-evaluated requirement item is taken as a work breakdown structure of the corresponding business function, an extreme value and an average value of work in the work breakdown structure are obtained, and the obtained extreme value and average value of work are weighted to obtain a benchmark work of the corresponding business function.

[0015] Optionally, the requirement item data corresponding to different search targets is searched from the pre-established vector knowledge base, including: The name of the to-be-evaluated requirement item is taken as a search target, first requirement item data with a similarity to the name of the to-be-evaluated requirement item reaching a preset similarity is searched from the pre-established vector knowledge base, and a first requirement item list is formed by using the searched first requirement item data; The name of the business function is taken as a search target, second requirement item data containing the business function is searched from the pre-established vector knowledge base, and a second requirement item list is formed by using the searched second requirement item data.

[0016] In a second aspect, the application provides a project work evaluation device applying the project work evaluation method, and the device includes: A function feature judgment module is configured to identify each business function of each to-be-evaluated requirement item in a to-be-evaluated project, judge the function features of the same business function in different function dimensions, and identify the function features of the function dimensions that can be uniquely determined, so as to combine the identified function features into a judgment result of the corresponding business function; A business function operation condition obtaining module is configured to perform round-by-round enumeration on the function dimensions that are not uniquely determined according to the respective candidate function features, and perform multi-round combination with the judgment result, so as to obtain all possible business function operation conditions of the corresponding business function; A work evaluation module is configured to perform work evaluation on all business function operation conditions of the same business function, and perform work evaluation result screening based on the number of the business function operation conditions of the same business function and a work evaluation result screening rule, so as to obtain a final work of the corresponding business function based on the matched work evaluation result screening rule; A work comprehensive module is configured to comprehensively integrate the final works of each business function of each to-be-evaluated requirement item in the to-be-evaluated project, so as to obtain a work evaluation result of the to-be-evaluated project.

[0017] In a third aspect, the application provides an electronic device including a processor and a memory connected with the processor in communication; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method.

[0018] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are used to implement the method described above when executed by a processor.

[0019] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and the computer program is used to implement the method described above when executed by a processor.

[0020] The present application judges the functional characteristics of the same business function in different functional dimensions for each business function of each to-be-evaluated requirement item in the to-be-evaluated project, identifies the functional characteristics of the functional dimension that can be uniquely determined, and combines the identified functional characteristics into a judgment result corresponding to the business function. The judgment result obtained by combination is conducive to subsequent combination with the functional characteristics of the functional dimension that cannot be uniquely determined. Then, the functional dimension that cannot be uniquely determined is enumerated round by round according to the respective candidate functional characteristics, and multi-round combination is performed with the judgment result to obtain all possible business function running conditions of the corresponding business function. The workload evaluation result is filtered based on the number of business function running conditions of the same business function and the matching workload evaluation result filtering rule, and the final workload of the corresponding business function is obtained based on the matching workload evaluation result filtering rule. Finally, the final workload of each business function of each to-be-evaluated requirement item in the to-be-evaluated project is integrated to obtain the workload evaluation result of the to-be-evaluated project. The purpose of workload automatic evaluation is achieved, which not only reduces the personnel burden, but also improves the project workload evaluation efficiency, and avoids subjective judgment willingness in the project workload evaluation process, thereby ensuring the accuracy of the project workload evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application together with the specification.

[0022] Figure 1 A method flowchart of a project workload evaluation method provided by an embodiment of the present application; Figure 2 A method flowchart of another project workload evaluation method provided by an embodiment of the present application; Figure 3 A device schematic diagram of a project workload evaluation device provided by an embodiment of the present application; Figure 4 A structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0023] The specific embodiments of the present application have been shown and described in the above drawings and text, and will be described in more detail below. These drawings and text are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments.

[0024] Explanation of Reference Signs 121 - transceiver, 122 - processor, 123 - memory. DETAILED DESCRIPTION

[0025] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below in conjunction with the drawings of the embodiments of the present application. It should be understood that the specific embodiments described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0026] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative positional relationship, movement condition, etc. between components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directional indications also change accordingly.

[0027] In addition, if the embodiments of the present application involve descriptions such as “first”, “second”, etc., the descriptions of “first”, “second”, etc. are only for description purposes, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by “first”, “second” can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can implement it, and when the combination of technical solutions contradicts each other or cannot be implemented, it should be considered that the combination of technical solutions does not exist and is not within the scope of protection claimed by the present application. It can be understood that in the technical solutions of the present application, the acquisition, collection, storage, use, processing, transmission, provision, disclosure and application of data comply with relevant laws and regulations. It should be noted that in the embodiments of the present application, some industry existing solutions, components, models, etc. may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solutions of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0028] Currently, the functional complexity, the functional upgrade type and the adjustment factor of each business function are maintained manually according to subjective will. For a project, the assessment of the workload of the project is a time-consuming and laborious work for both the submitter and the reviewer. Moreover, the assessment process of the workload of the project has strong subjective judgment and lacks accuracy. In order to solve the above problems, as shown in Figure 1 , Figure 1 a method flowchart of a project workload assessment method provided by an embodiment of the present application. In a first aspect, the present application provides a project workload assessment method, which comprises: S110: identifying each business function of each to-be-assessed requirement item in a to-be-assessed project, judging the functional features of the same business function in different functional dimensions, and identifying the functional features of the functional dimensions that can be uniquely determined, so as to combine the identified functional features into the judgment result of the corresponding business function; In some embodiments of the present embodiment, the functional dimensions include the functional complexity, the functional upgrade type and the adjustment factor; the judgment of the functional features of the same business function in different functional dimensions comprises: determining whether the same business function has a unique valid functional feature judgment result in each functional dimension based on the functional feature judgment rule of each functional dimension, and determining the functional features of the functional dimensions that can be uniquely determined; wherein the valid functional feature judgment result is used to represent that the business function has only one functional feature judgment result under the functional feature judgment rule of the corresponding functional dimension.

[0029] Specifically, each business function of each to-be-assessed requirement item in a to-be-assessed project is identified, and multi-functional dimension judgments of the functional complexity, the functional upgrade type and the adjustment factor are performed on each identified business function. Only one functional feature judgment result of the business function under the functional feature judgment rule of the corresponding functional dimension is taken as the functional feature of the functional dimension that can be uniquely determined, and all the functional features of the functional dimensions that can be uniquely determined of the same business function are combined into the judgment result of the corresponding business function. Not only is the assessment of the business function ensured to be comprehensive, accurate and logically rigorous, but also the judgment result obtained by combination is conducive to subsequent combination with the functional features of the functional dimensions that are not uniquely determined.

[0030] In some embodiments of the present embodiment, the function upgrade type function feature judgment rule comprises: obtaining the type of physical subsystem associated with the to-be-evaluated demand item corresponding to the business function and the number of each type of physical subsystem; wherein the physical subsystem is used to represent a set of physical devices of the same type; based on a conversion rule of converting the type of physical subsystem into the number of physical subsystems, performing number conversion value matching of each type of physical subsystem, and multiplying the matched number conversion value by the number of physical subsystems of the corresponding type to obtain the product value of the physical subsystems of the corresponding type; wherein the conversion rule can be that an A-type subsystem is converted into 1.5 subsystems, a B-type subsystem is converted into 1 subsystem, and a C-type subsystem is converted into 0.8 subsystems; summing the product values of each type of physical subsystem to obtain the total number of physical subsystems corresponding to the business function; based on the matching relationship between the total number of physical subsystems corresponding to the business function and the function upgrade type, determining the function upgrade type matched with the total number of physical subsystems corresponding to the business function.

[0031] Wherein, the physical subsystem refers to a specific hardware device, infrastructure or hardware-based computing unit that needs to be modified, upgraded or invoked to meet a certain business or technical requirement. For example, servers and computing resources refer to computers that actually run applications and databases, including: mini computers (such as IBM Power Systems, usually used to run core databases (such as Oracle DB)), PC servers / blades servers (such as servers based on x86 architecture, used to run various application services), virtualization platforms (such as VMware vSphere clusters, which pool physical server resources to create multiple virtual machines), container platforms (such as Kubernetes clusters, which are more modern application deployment methods).

[0032] Specifically, the function upgrade type includes five types of "reconstruction, complex addition, addition, complex modification and simple modification". The judgment process of the function upgrade type is as follows: according to the number and type of associated physical subsystems of the demand item to judge the function upgrade type, the number of associated physical subsystems and the weight of each physical subsystem are different (A class is equal to 1.5 physical subsystems, B class is equal to 1 physical subsystem, C class is equal to 0.8 physical subsystem); by converting the type of physical subsystem into the number of physical subsystems, and then multiplying the converted number by the number of physical subsystems of the corresponding type, and finally adding the product values of the physical subsystems of each type, the total number of physical subsystems of the business function can be obtained. Specifically, when the total number of physical subsystems is greater than or equal to 21, the value is "reconstruction"; when the total number of physical subsystems is greater than or equal to 11 and less than or equal to 20, the value is "complex addition"; when the total number of physical subsystems is greater than or equal to 3 and less than or equal to 11, the value is "complex modification"; when the total number of physical subsystems is less than or equal to 3, the value is "simple modification"; if the business function has no associated workload, it is set to "addition". Thus, the purpose of matching the function upgrade type of the corresponding business function according to the total number of physical subsystems of the business function is achieved.

[0033] In some embodiments of the present embodiment, the function complexity of the business function can be set to be equal to the complexity of the fourth-level task, and if the complexity of the fourth-level task is empty, the business function is considered to be unable to judge the function complexity. It should be noted that the business model of an enterprise can be divided into value chain (first level), business field (second level), third-level activity, fourth-level task, and fifth-level step according to levels, wherein the business field can be a customer management field and an information collection field, the third-level activity refers to an end-to-end process, for example, the third-level activity can be establishing or modifying a customer view, the fourth-level task refers to a task for implementing the third-level activity, and the fifth-level step refers to a step for implementing the fourth-level task. The function complexity includes three types of "high, medium and low".

[0034] In some embodiments of the present embodiment, the corresponding adjustment factor can be matched according to the processing category of the business function itself and the processing category of the demand item corresponding to the business function to be evaluated. The adjustment factor includes four types of "transaction, process management, data analysis and channel".

[0035] In some embodiments of the present embodiment, identifying each business function of each to-be-evaluated requirement item in the to-be-evaluated project comprises: calling a corresponding reading rule based on a project document format of the to-be-evaluated project to obtain project text information of the to-be-evaluated project; performing semantic analysis on the project text information of the to-be-evaluated project, and based on a semantic analysis result, performing non-repetitive semantic clustering on the project text information to obtain a plurality of text information sets; wherein the text information sets correspond one-to-one to the to-be-evaluated requirement items; extracting keywords from each text information set, and matching the extracted keywords with a preset business function feature word library to screen target keywords related to business functions; and based on a context association relationship of the target keywords in the corresponding text information set, identifying each business function contained in the corresponding to-be-evaluated requirement item.

[0036] Specifically, considering that the project document formats of different to-be-evaluated projects are different, a suitable reading rule is selected according to the project document format of the to-be-evaluated project to read the project text information. For example, a document parsing library (such as ApacheTika, python-docx and pdfplumber of Python) can be used to automatically extract original text content (i.e., project text information) from project documents of different formats (such as.docx,.pdf,.pptx). This ensures adaptability to different source documents and achieves the purpose of text extraction of multi-format documents. Then, natural language processing technology can be used to perform semantic analysis on the project text information of the to-be-evaluated project, based on the semantic analysis result, non-repetitive semantic clustering is performed on the project text information to obtain a plurality of text information sets; wherein the text information sets correspond one-to-one to the to-be-evaluated requirement items; keywords are extracted from each text information set, and the extracted keywords are matched with a preset business function feature word library to screen target keywords related to business functions; and based on a context association relationship of the target keywords in the corresponding text information set, each business function contained in the corresponding to-be-evaluated requirement item is identified.

[0037] In some embodiments of the present embodiment, the obtaining rule of the text information set comprises: based on the project text information after semantic clustering, determining a text information set with clear semantic attribution and a text information set with unclear semantic attribution in the project text information; directly marking the text information set with clear semantic attribution as the text information set of the corresponding to-be-evaluated requirement item; determining the similarity between the text information set with unclear semantic attribution and each marked text information set, and based on the size relationship between the determined similarity and a preset threshold, matching a corresponding set division rule to divide the text information set with unclear semantic attribution into the corresponding marked text information set.

[0038] The text information set with clear semantic attribution refers to a text information set that can be explicitly known to belong to which to-be-evaluated requirement item according to the semantic analysis result.

[0039] In some embodiments of the present embodiment, the rule of dividing the set of text information with unclear semantic attribution into the corresponding set of labeled text information comprises: extracting a representative semantic segment from the set of text information with unclear semantic attribution in combination with the contextual logical relationship of the project document of the to-be-evaluated project, and performing similarity calculation on the extracted semantic segment and the set of labeled text information. For example, the process of extracting a representative semantic segment from the set of text information with unclear semantic attribution is as follows: first, the hierarchical structure of the project document is parsed to identify chapter titles, paragraph topic sentences, and key logical conjunctions, thereby constructing a contextual semantic association network. Subsequently, for the set of text information with unclear semantic attribution, the named entity recognition and key phrase extraction algorithms in natural language processing technology are used to filter out noun phrases, verb phrases, and restrictive modifiers that can reflect the core meaning, thereby forming a preliminary candidate set of semantic segments. Then, in combination with the contextual semantic association network, the correlation degree of each candidate semantic segment with the topic of the paragraph in which it is located is calculated, redundant segments with a correlation degree lower than a set threshold are removed, and the top N segments with the highest correlation degree are retained as representative semantic segments. In the extraction process, the expression style and professional term usage habits of the set of text information with clear attribution in the project document also need to be referred to for standardized adjustment of the expression mode of the semantic segments, so as to ensure consistency in the semantic expression mode between the extracted results and the labeled set, thereby improving the accuracy of subsequent similarity calculation.

[0040] Specifically, the cosine similarity algorithm can be used to calculate the vector distance between the set of text information with unclear semantic attribution and each set of labeled text information. The keyword weight, contextual semantic association strength, and part-of-speech features can be considered comprehensively when constructing the vector, thereby ensuring the accuracy of the similarity calculation.

[0041] Based on the similarity calculation result, the set of text information with unclear semantic attribution is divided into the set of text information with the highest similarity that is higher than a preset threshold. If the similarity between the set of text information with unclear semantic attribution and each set of labeled text information is lower than the preset threshold, the set of text information with unclear semantic attribution is labeled as a new set of text information of a to-be-evaluated requirement item, and a unique identifier is generated for the new to-be-evaluated requirement item. This ensures comprehensive classification of the project text information of the to-be-evaluated project, thereby avoiding omission in subsequent workload calculation.

[0042] In some embodiments of the present embodiment, keyword extraction is performed on each set of text information, which comprises: based on the semantic analysis result of each set of text information, in combination with the preset standard words in the predefined dictionary, performing word segmentation on the project text information in each set of text information, and performing part-of-speech tagging on the word segmentation of each set of text information. Specifically, a Chinese word segmentation tool (such as Jieba, HanLP) can be used, and a pre-defined dictionary applicable to the same or similar field as the field of the to-be-evaluated project is loaded. The pre-defined dictionary contains business terms (i.e., preset standard words) corresponding to the field. Thus, through the guidance of the pre-defined dictionary, it can be ensured that the project text information in each text information set can be accurately segmented. Subsequently, part-of-speech tagging is performed to identify nouns, verbs, etc., to prepare for subsequent extraction of "noun-verb" compound word structures.

[0043] The segmented words in the same text information set after part-of-speech tagging are organized according to the structure of compound words to obtain the compound words in the corresponding text information set; candidate keywords are determined for the compound words in the same text information set using a plurality of keyword extraction rules, based on the positions of the determined candidate keywords and the keyword extraction rules, the composite score of the candidate keywords is determined by weighting according to the preset position weight and the preset weight of each keyword extraction rule, and according to the order of the composite scores from large to small, the first preset number of candidate keywords are selected as the keywords of the corresponding text information set.

[0044] Specifically, after obtaining the compound words in each text information set, candidate keywords are first extracted in parallel using a plurality of keyword extraction rules. Among them, the plurality of keyword extraction rules can include: 1, TF-IDF (Term Frequency-Inverse Document Frequency), i.e., calculating the weight of each compound word in the project document (high TF) and in a general project document library (low IDF). Thus, the words specific to the project and with high frequency are found. 2, TextRank: based on a graph model, the compound words are regarded as nodes and the co-occurrence relationship is regarded as edges, and the importance of each compound word is calculated. This method can find those keywords that are not high in frequency but are at the center of the semantic network. 3, rule filtering based on part-of-speech: preferentially retaining phrases composed of specific part-of-speech sequences, such as "adjective + noun" or "noun + noun". The weights of the candidate keywords determined by different keyword extraction rules are different, for example, the weight of the candidate keyword determined by TF-IDF is a, the weight of the candidate keyword determined by TextRank is β, and the weight of the candidate keyword obtained by rule filtering based on part-of-speech is γ. At the same time, the position of the candidate keyword is considered, for example, the keyword appearing in a specific position of the document (such as the title, the first sentence of the chapter, the conclusion section) is given a higher position weight, because these positions usually contain summary information. Thus, the composite score of the candidate keywords is determined by weighting according to the position of the candidate keywords and the keyword extraction rules. The candidate keywords are sorted according to the composite score, and the first N candidate keywords are taken as the final keywords of the corresponding text information set.

[0045] S120: Enumerate each candidate function feature of the non-unique judgment function dimension round by round, and perform multi-round combination with the judgment result to obtain all possible business function operation conditions of the corresponding business function; In some embodiments of the present embodiment, the rule for obtaining all possible business function operation conditions of the corresponding business function includes: performing arbitrary combination on the function features randomly selected under the non-unique judgment function dimension in the corresponding business function to obtain a plurality of combination results; and performing multi-round combination on the judgment result and each combination result to obtain all possible business function operation conditions of the corresponding business function.

[0046] Among them, the function complexity contains three cases of "high, medium and low", the function upgrade type includes five cases of "reconstruction, complex addition, addition, complex modification and simple modification", and the adjustment factor includes four cases of "transaction, process management, data analysis and channel", that is, there are at most 60 cases. If there are function features of the function dimension that can be uniquely determined (for example, the function complexity, the function upgrade type and / or the adjustment factor can be determined), the number of business function operation conditions is less.

[0047] S130: Perform workload assessment on all business function operation conditions of the same business function, and perform workload assessment result screening based on the number of business function operation conditions of the same business function and the matching workload assessment result screening rule to obtain the final workload of the corresponding business function based on the matching workload assessment result screening rule. In some embodiments of the present embodiment, the function dimension includes function complexity, function upgrade type and adjustment factor; the workload assessment on all business function operation conditions of the same business function includes: performing feature value matching on the function features of each function dimension corresponding to the business function operation condition in the preset function feature database; multiplying the feature value of the function complexity and the feature value of the function upgrade type to obtain a basic workload, and adjusting the obtained basic workload by using the feature value of the adjustment factor, and then adjusting the adjusted basic workload by a preset proportion to obtain the workload assessment result of the corresponding business function operation condition.

[0048] Specifically, the calculation formula of the workload assessment result is: workload= (function complexity*function upgrade type*adjustment factor)*preset proportion. Through the readjustment of the adjusted basic workload by the preset proportion, it can be ensured that the obtained workload assessment result is within a reasonable numerical range, neither overestimating the workload to cause resource waste, nor underestimating the workload to delay the project progress. This adjustment method can flexibly determine the final workload assessment result according to the characteristics and needs of different projects, improving the accuracy and reliability of the assessment.

[0049] In some embodiments of the present embodiment, the workload evaluation result screening rule based on the number of service function running conditions of the same service function comprises: when there is only one service function running condition of the same service function, determining that the workload evaluation result screening rule is to directly take the workload evaluation result as the final workload of the corresponding service function; otherwise, determining that the workload evaluation result screening rule is to take the workload evaluation result closest to the benchmark workload of the same service function as the final workload of the corresponding service function.

[0050] In some embodiments of the present embodiment, the determination rule of the benchmark workload comprises: for each service function of each to-be-evaluated requirement item, taking the name of the to-be-evaluated requirement item and the name of the service function as search targets respectively, searching for requirement item data corresponding to different search targets from a pre-established vector knowledge base to form a requirement item list of the corresponding search target; wherein the requirement item data comprises the name and workload of the requirement item, and the vector knowledge base is established based on historical requirement item data; taking the intersection of the requirement item lists of the same service function of the same to-be-evaluated requirement item as the workload breakdown table of the corresponding service function, obtaining the workload extreme value and workload average value in the workload breakdown table, and weighting the obtained workload extreme value and workload average value to obtain the benchmark workload of the corresponding service function.

[0051] In some embodiments of the present embodiment, searching for requirement item data corresponding to different search targets from the pre-established vector knowledge base comprises: taking the name of the to-be-evaluated requirement item as a search target, searching for first requirement item data with a similarity to the name of the to-be-evaluated requirement item reaching a preset similarity from the pre-established vector knowledge base, and forming a first requirement item list using the searched first requirement item data; taking the name of the service function as a search target, searching for second requirement item data containing the service function from the pre-established vector knowledge base, and forming a second requirement item list using the searched second requirement item data.

[0052] Specifically, the method first pushes the historical demand item data and the work amount data corresponding to the demand item to the vector knowledge base for summarization, adopts the vector knowledge base to aggregate the historical work amount details (including: demand item content, business function name, function complexity, function upgrade type, adjustment factor and other information), and maintains the historical work amount data through the vector knowledge base. After determining the to-be-evaluated demand item r and the business function f thereof for which the work amount needs to be calculated, the formal automatic work amount calculation link is entered. First, the historical benchmark work amount needs to be calculated, and the specific process is as follows: first, according to the to-be-evaluated demand item r name, the vector knowledge base is queried to search for a demand item list L1 (i.e., a first demand item list) with a similarity matching degree above a certain threshold value. According to the business function f name, the historical work amount and the corresponding demand item data are searched to obtain a demand item list L2 (i.e., a second demand item list). The intersection demand item list L3 is obtained by taking the intersection of L1 and L2, and the work amount detail list corresponding to L3 is obtained. The maximum value (Max), the minimum value (Min) and the average value (Cm) of the work amount are taken out, and the weighted benchmark work amount C = (Max + Min + K * Cm) / P is calculated, wherein K and P are two integers.

[0053] Please refer to Figure 2 , Figure 2A method flowchart of another project work load evaluation method provided by the embodiments of the present application. First, a user selects a business function for which work load needs to be calculated, and the historical work load associated with the business function is queried to obtain the maximum value (Max), the minimum value (Min) and the average value (Cm) of the historical business function, so as to calculate the reference work load C by using the formula C = (Max + Min + 4 * Cm) / 6. At the same time, the business function is judged in multiple function dimensions of function complexity, function upgrade type and adjustment factor according to the function feature judgment rules of different function dimensions, and the function feature judgment results with unique effective function features in the function complexity, the function upgrade type and the adjustment factor are determined. The function feature of the function dimension with the unique effective function feature judgment result is taken as a constant, and the function features of the remaining function dimensions are taken as variables, and any combination is performed to obtain all possible business function running conditions corresponding to the business function. When the function dimension with the unique effective function feature judgment result has zero, one or two items, all possible business function running conditions corresponding to the business function are traversed, the work load C' of all possible business function running conditions is calculated by using the calculation formula of the work load evaluation result (i.e. work load = (function complexity * function upgrade type * adjustment factor) * preset proportion), and the work load C' of all possible business function running conditions is compared with the reference work load C, and the work load evaluation result closest to the reference work load of the business function is selected as the final work load of the corresponding business function. And the function complexity, the function upgrade type and the adjustment factor of the business function are the business function running conditions corresponding to the final work load. When the function dimension with the unique effective function feature judgment result has three items, the work load evaluation result of the unique business function running condition is directly taken as the final work load of the corresponding business function.

[0054] S140: The final work loads of the business functions of each to-be-evaluated requirement item in the to-be-evaluated project are integrated to obtain the work load evaluation result of the to-be-evaluated project.

[0055] Specifically, the method is for each business function of each to-be-evaluated requirement item in the to-be-evaluated project, judges the functional features of the same business function in different functional dimensions, identifies the functional features of the functional dimension that can be uniquely judged, and combines the identified functional features into the judgment result of the corresponding business function. The judgment result obtained by combination is conducive to subsequent combination with the functional features of the functional dimension that cannot be uniquely judged. Then, the functional dimension that cannot be uniquely judged is enumerated round by round according to the respective candidate functional features, and multi-round combination is performed with the judgment result, so as to obtain all possible business function operation conditions of the corresponding business function. The workload evaluation is performed on all business function operation conditions of the same business function, and the number of the business function operation conditions of the same business function is matched with the workload evaluation result screening rule, so as to perform workload evaluation result screening based on the matched workload evaluation result screening rule, and obtain the final workload of the corresponding business function. Finally, the final workloads of the business functions of each to-be-evaluated requirement item in the to-be-evaluated project are integrated, and the workload evaluation result of the to-be-evaluated project is obtained. The purpose of workload automatic evaluation is achieved, which not only reduces the personnel burden, but also improves the project workload evaluation efficiency, avoids subjective judgment willingness in the project workload evaluation process, and ensures the project workload evaluation accuracy.

[0056] As Figure 3 shown, Figure 3 A device schematic diagram of a project workload evaluation device provided by an embodiment of the present application. The second aspect of the present application provides a project workload evaluation device, which applies the project workload evaluation method described above. The device comprises: A functional feature judgment module configured to identify each business function of each to-be-evaluated requirement item in the to-be-evaluated project, judge the functional features of the same business function in different functional dimensions, and identify the functional features of the functional dimension that can be uniquely judged, so as to combine the identified functional features into the judgment result of the corresponding business function; A business function operation condition obtaining module configured to perform round-by-round enumeration on the functional dimension that cannot be uniquely judged according to the respective candidate functional features, perform multi-round combination with the judgment result, so as to obtain all possible business function operation conditions of the corresponding business function; A workload evaluation module configured to perform workload evaluation on all business function operation conditions of the same business function, and match the number of the business function operation conditions of the same business function with the workload evaluation result screening rule, so as to perform workload evaluation result screening based on the matched workload evaluation result screening rule, and obtain the final workload of the corresponding business function; A workload integration module configured to integrate the final workloads of the business functions of each to-be-evaluated requirement item in the to-be-evaluated project, and obtain the workload evaluation result of the to-be-evaluated project.

[0057] Therefore, the device realizes the purpose of automatic workload evaluation, reduces the personnel burden, improves the project workload evaluation efficiency, avoids subjective judgment in the project workload evaluation process, and ensures the project workload evaluation accuracy.

[0058] It should be noted that the division of each module of the above device is only a logical functional division, and all or part of the actual implementation can be integrated into a physical entity, or can be physically separated. And these modules can all be realized in the form of software called by a processing element; all can be realized in the form of hardware; some modules can be realized in the form of software called by a processing element, and some modules can be realized in the form of hardware. For example, the function feature judgment module can be a separate processing element, or can be integrated into a chip of the above device, in addition, it can also be stored in the form of program code in the memory of the above device, and the function of the above function feature judgment module is called and executed by a processing element of the above device. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or can be independently realized. The processing element here can be an integrated circuit with signal processing capability. In the implementation process, each step of the above method or each module can be completed by the integrated logic circuit of the hardware in the processor element or the instruction in the form of software.

[0059] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory connected with the processor in communication; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to realize the method described above.

[0060] Figure 4 The electronic device provided by the embodiment of the present application is shown in the structural schematic diagram. As shown in the figure, the electronic device can include: a transceiver 121, a processor 122, and a memory 123. Figure 4

[0061] The processor 122 executes the computer execution instructions stored in the memory, so that the processor 122 executes the scheme in the above embodiment. The processor 122 can be a general-purpose processor, including a central processing unit CPU, a network processor NP, etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0062] The memory 123 is connected with the processor 122 through a system bus and completes mutual communication, and the memory 123 is used for storing computer program instructions. ​

[0063] The transceiver 121 can be configured to obtain the to-be-executed task and configuration information of the to-be-executed task.

[0064] The system bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus. The transceiver is configured to realize communication between the database access device and other computers (for example, a client, a read-write library, and a read-only library). The memory can include random access memory (RAM) and can also include non-volatile memory.

[0065] The electronic device provided in the embodiments of the present application can be the terminal device in the above embodiments.

[0066] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores computer execution instructions. When the computer execution instructions are executed by a processor, the computer execution instructions are configured to implement the method described above.

[0067] In a fifth aspect, a computer program product is provided, and the computer program product includes a computer program. When the computer program is executed by a processor, the computer program is configured to implement the method described above.

[0068] In summary, the present application judges the functional characteristics of the same business function in different functional dimensions for each business function of each to-be-evaluated requirement item in the to-be-evaluated project, and identifies the functional characteristics of the functional dimension that can be uniquely judged, to combine the identified functional characteristics into the judgment result of the corresponding business function. The judgment result obtained by combination is conducive to subsequent combination with the functional characteristics of the functional dimension that cannot be uniquely judged. Then, the functional dimension that cannot be uniquely judged is enumerated round by round according to the respective candidate functional characteristics, and multi-round combination is performed with the judgment result, to obtain all possible business function operation conditions of the corresponding business function. The workload evaluation of all business function operation conditions of the same business function is performed, and the number of business function operation conditions of the same business function is matched with the workload evaluation result screening rule, to perform workload evaluation result screening based on the matched workload evaluation result screening rule, and obtain the final workload of the corresponding business function. Finally, the final workload of each business function of each to-be-evaluated requirement item in the to-be-evaluated project is integrated, to obtain the workload evaluation result of the to-be-evaluated project. The purpose of workload automatic evaluation is achieved, not only reducing personnel burden, but also improving project workload evaluation efficiency, and avoiding subjective judgment willingness in the project workload evaluation process, ensuring project workload evaluation accuracy.

[0069] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0070] It is to be understood that the application is not limited to the precise construction described herein and as shown in the attached drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application is to be defined by the claims appended hereto.

Claims

1. A method for evaluating project workload, characterized in that, The method includes: Identify the business functions of each requirement item in the project to be evaluated, judge the functional characteristics of the same business function in different functional dimensions, and identify the functional characteristics of the functional dimensions that can be uniquely determined, so as to combine the identified functional characteristics into the judgment result of the corresponding business function. For functional dimensions that are not uniquely determined, enumerate each candidate functional feature in round by round, and perform multiple rounds of combination with the determination result to obtain all possible business function operation states of the corresponding business function. Workload assessment is performed on the operation status of all business functions for the same business function, and the workload assessment result filtering rules are matched based on the number of business function operation statuses for the same business function. The workload assessment result filtering is then performed based on the matched workload assessment result filtering rules to obtain the final workload of the corresponding business function. The workload assessment result of the project is obtained by comprehensively considering the final workload of each business function of each requirement item in the project to be assessed.

2. The project workload assessment method according to claim 1, characterized in that, The identification of each business function of each requirement item to be evaluated in the project to be evaluated includes: Based on the project document format of the project to be evaluated, call the corresponding reading rules to obtain the project text information of the project to be evaluated; Semantic analysis is performed on the textual information of the project to be evaluated. Based on the semantic analysis results, non-repeating semantic clustering is performed on the textual information to obtain multiple sets of textual information; wherein, each set of textual information corresponds one-to-one with the requirement item to be evaluated. Keyword extraction is performed on each set of text information, and the extracted keywords are matched with a preset business function feature word library to filter target keywords related to business functions; Based on the contextual relationships of target keywords in the corresponding text information set, the business functions included in the corresponding requirements to be evaluated are identified.

3. The project workload assessment method according to claim 2, characterized in that, The rules for obtaining the text information set include: Based on the project text information after semantic clustering, determine the set of text information with clear semantic attribution and the set of text information with unclear semantic attribution. The text information set with clear semantic attribution is directly marked as the text information set corresponding to the requirement item to be evaluated; The similarity between the set of text information with unclear semantic attribution and each of the labeled text information sets is determined. Based on the relationship between the determined similarity and the preset threshold, the corresponding set partitioning rules are matched to classify the set of text information with unclear semantic attribution into the corresponding labeled text information set.

4. The project workload assessment method according to claim 3, characterized in that, The rules for assigning text information sets with unclear semantic attribution to corresponding labeled text information sets include: Based on the contextual logic of the project documents of the project to be evaluated, representative semantic fragments are extracted from the set of text information with unclear semantic attribution. The similarity between the extracted semantic fragments and the set of labeled text information is then calculated. Based on the similarity calculation results, text information sets with unclear semantic attribution are divided into text information sets with similarity higher than a preset threshold and the highest similarity. If the similarity between the text information set with unclear semantic attribution and each of the labeled text information sets is lower than the preset threshold, then the text information set with unclear semantic attribution is marked as a new text information set of the requirement item to be evaluated, and a unique identifier is generated for the new requirement item to be evaluated.

5. The project workload assessment method according to claim 2, characterized in that, The keyword extraction process for each set of textual information includes: Based on the semantic analysis results of each text information set, and combined with the preset standard words in the predefined dictionary, the text information of the items in each text information set is segmented, and the segmented words of each text information set are tagged with part of speech. By organizing the part-of-speech-tagged words in the same set of text information according to the structure of compound words, we can obtain the compound words in the corresponding set of text information. Multiple keyword extraction rules are used to determine candidate keywords for compound words in the same text information set. Based on the position of the determined candidate keywords and the keyword extraction rules, the composite scores of the candidate keywords are weighted according to the preset position weight and the preset weight of each keyword extraction rule. Then, according to the order of composite scores from largest to smallest, a preset number of candidate keywords are selected as the keywords of the corresponding text information set.

6. The project workload assessment method according to claim 1, characterized in that, The functional dimensions include functional complexity, functional upgrade type, and adjustment factor; The judgment of the functional characteristics of the same identified business function in different functional dimensions includes: Based on the functional feature judgment rules of each functional dimension, it is determined whether the same business function has a unique and valid functional feature judgment result under each functional dimension, and the functional features of the functional dimension that can be uniquely judged are determined; wherein, the valid functional feature judgment result is used to characterize that the business function has one and only one functional feature judgment result under the functional feature judgment rule of the corresponding functional dimension.

7. The project workload assessment method according to claim 6, characterized in that, The functional feature judgment rules for the functional upgrade type include: Obtain the type of physical subsystem associated with the requirement item to be evaluated corresponding to the business function and the number of each type of physical subsystem; wherein, the physical subsystem is used to represent a set of physical devices of the same type; Based on the conversion rule of converting the type of physical subsystem into the number of physical subsystems, the conversion value of the number of physical subsystems of each type is matched, and the matched conversion value is multiplied by the number of physical subsystems of the corresponding type to obtain the product value of the physical subsystems of the corresponding type. The summation of the product values ​​of each type of physical subsystem yields the total number of physical subsystems for the corresponding business function. Based on the matching relationship between the total number of physical subsystems converted for business functions and the function upgrade type, determine the function upgrade type that matches the total number of physical subsystems converted for the corresponding business functions.

8. The project workload assessment method according to claim 1, characterized in that, The rules for obtaining the operational status of all possible business functions corresponding to a business function include: Randomly select one functional feature from the non-unique functional dimension in the corresponding business function and perform arbitrary combination to obtain multiple combination results; The judgment result is used as a quantitative measure and combined with each combination result in multiple rounds to obtain all possible business function operation states of the corresponding business function.

9. The project workload assessment method according to claim 1, characterized in that, The functional dimensions include functional complexity, functional upgrade type, and adjustment factor; The workload assessment of the operation of all business functions for the same business function includes: In the preset functional feature database, feature values ​​are matched for the functional features of each functional dimension corresponding to the operation status of the business functions. The basic workload is obtained by multiplying the feature value of functional complexity and the feature value of functional upgrade type. The basic workload is then adjusted using the feature value of adjustment factor. Finally, the adjusted basic workload is adjusted by a preset ratio to obtain the workload assessment result of the corresponding business function operation.

10. The project workload assessment method according to claim 1, characterized in that, The number of business function operation statuses based on the same business function is matched with the workload assessment result filtering rules, including: When there is only one way to run the same business function, the rule for selecting the workload assessment result is to directly use the workload assessment result as the final workload of the corresponding business function. Otherwise, the selection rule for workload assessment results is to use the workload assessment result that is closest to the baseline workload of the same business function as the final workload of the corresponding business function.

11. The project workload assessment method according to claim 10, characterized in that, The rules for determining the baseline workload include: For each business function of each requirement item to be evaluated, the name of the requirement item to be evaluated and the name of the business function are used as search targets to search for requirement item data corresponding to different search targets from a pre-established vector knowledge base, so as to form a requirement item list corresponding to the search target; wherein, the requirement item data includes the name and workload of the requirement item, and the vector knowledge base is established based on historical requirement item data; The intersection of the requirement item lists of the same business function for the same requirement item to be evaluated is used as the workload detail table of the corresponding business function. The workload extreme value and workload average value in the workload detail table are obtained, and the obtained workload extreme value and workload average value are weighted to obtain the baseline workload of the corresponding business function.

12. The project workload assessment method according to claim 11, characterized in that, The step of searching for demand item data corresponding to different search targets from a pre-established vector knowledge base includes: Using the name of the requirement to be evaluated as the search target, search the pre-established vector knowledge base for first requirement data that have a similarity of up to a preset similarity with the name of the requirement to be evaluated, and use the searched first requirement data to form a first requirement list. Using the name of the business function as the search target, search for second requirement item data containing the business function from a pre-established vector knowledge base, and use the searched second requirement item data to form a second requirement item list.

13. A project workload assessment device, employing the project workload assessment method according to any one of claims 1-12, characterized in that, The device includes: The functional feature judgment module is configured to identify the business functions of each requirement item to be evaluated in the project to be evaluated, judge the functional features of the same business function in different functional dimensions, and identify the functional features of the functional dimensions that can be uniquely determined, so as to combine the identified functional features into the judgment result of the corresponding business function. The business function operation status acquisition module is configured to enumerate the non-unique judgment function dimensions according to their respective candidate function features in round by round, and perform multiple rounds of combination with the judgment result to obtain all possible business function operation statuses of the corresponding business function. The workload assessment module is configured to assess the workload of all business functions operating under the same business function, and match the workload assessment result filtering rules based on the number of business function operating under the same business function, and perform workload assessment result filtering based on the matched workload assessment result filtering rules to obtain the final workload of the corresponding business function. The workload assessment module is configured to comprehensively assess the final workload of each business function of each requirement item in the project to be assessed, and obtain the workload assessment result of the project to be assessed.

14. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the project workload assessment method as described in any one of claims 1-12.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the project workload assessment method as described in any one of claims 1-12.

16. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the project workload assessment method as described in any one of claims 1-12.

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