Demand management and control method and device with strategy as guide, storage medium and equipment

Abstract

The invention relates to the technical field of project management and financial science and technology, and discloses a strategic-oriented demand management and control method and device, a storage medium and equipment, and the method comprises the steps: receiving project demand information of a target project, carrying out the work estimation of the project demand information through a workload estimation model, and obtaining the project demand information of the target project; determining an estimated workload of the target project; if the estimated workload reaches a preset access threshold, determining a demand resource related party according to the project demand information, and performing strategic matching degree evaluation on the project demand information; according to the estimated workload of the target project and a strategic matching degree evaluation result, initiating a joint review process to a demand resource related party; and if the target project passes the joint review, dynamically tracking and controlling the production condition of the target project.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of project management and financial technology, and in particular to a strategy-oriented demand control method and device, a storage medium and equipment. BACKGROUND

[0002] Under the dual driving of national economic high-quality development and digital transformation, the financial technology field is experiencing an unprecedented demand explosion. Financial institutions have significantly increased their dependence on technology investment, including infrastructure needs such as core system upgrades and distributed architecture transformations, as well as innovative business needs such as intelligent risk control, open banking, and blockchain applications.

[0003] However, the current industry lacks technical means in the demand access management link. Traditional management models mainly rely on manual experience and offline review, and lack standardized evaluation frameworks and data-driven decision support. Although there are some resource estimation models on the market, they mainly focus on budget allocation in marketing scenarios, and their evaluation dimensions are highly dependent on industry characteristics, making it difficult to directly migrate to the complex technical demand scenarios in the financial technology field. Currently, there is a lack of pre-control of resource allocation, and financial institutions often face the contradiction between explosive demand growth and limited technical resources. Moreover, cross-departmental collaboration is inefficient, and there are information silos in the demand submission, technical evaluation, and resource allocation links. SUMMARY

[0004] Therefore, the embodiments of the present application provide a strategy-oriented demand control method and device, a storage medium, and equipment.

[0005] According to one aspect of the present application, a strategy-oriented demand control method is provided, which comprises:

[0006] receiving project demand information of a target project, estimating the work of the project demand information using a work estimation model, and determining the estimated work of the target project;

[0007] If the estimated work reaches a preset access threshold, determining demand resource related parties according to the project demand information, and evaluating the strategic matching degree of the project demand information;

[0008] According to the estimated work of the target project and the strategic matching degree evaluation result, initiating a joint review process to the demand resource related parties;

[0009] If the target project passes the joint review, dynamically tracking and controlling the production situation of the target project.

[0010] According to another aspect of the present application, a strategy-oriented demand control device is provided, which comprises:

[0011] A workload estimation module is configured to receive project requirement information of a target project, perform workload estimation on the project requirement information using a workload estimation model, and determine an estimated workload of the target project;

[0012] A matching evaluation module is used to determine resource-related parties based on the project demand information if the estimated workload reaches a preset admission threshold, and to perform a strategic matching evaluation on the project demand information;

[0013] An approval process initiation module is used to initiate a joint review process with resource-demanding stakeholders based on the estimated workload and strategic fit assessment results of the target project;

[0014] The project tracking module is used to dynamically track and control the production status of the target project if the target project passes the joint review.

[0015] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the above-mentioned strategy-oriented demand management and control method is implemented.

[0016] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned strategy-oriented demand management method when executing the program.

[0017] Through the above technical solution, the embodiment of the present application provides a strategy-oriented demand management method and device, storage medium, and equipment, which solves the subjective bias of traditional manual estimation through quantitative workload estimation, ensuring the scientific nature of resource allocation; through strategic matching evaluation and joint review by multiple departments, the project is transformed from passive response to demand to active alignment of strategy, avoiding the disconnection between technology and business; the dynamic tracking mechanism responds to the characteristics of rapid iteration of project requirements and dynamic changes in the technical environment, and realizes the full life cycle optimization of resources.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1A flowchart of a strategy-oriented demand management method provided by an embodiment of the present application is shown.

[0021] Figure 2 A flowchart of another strategy-oriented demand management method provided by an embodiment of the present application is shown.

[0022] Figure 3 A structure diagram of a strategy-oriented demand management device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0024] In the present embodiment, a strategy-oriented demand management method is provided, as shown in the figure, the method comprises: Figure 1

[0025] Step 101, receiving project demand information of a target project, estimating work by using a work estimation model, and determining an estimated work amount of the target project.

[0026] In the present embodiment, in traditional project demand management, work estimation often relies on manual experience (such as subjective judgment of a project manager according to historical projects), but with the development of science and technology and technological innovation, current projects have high complexity, and manual estimation is prone to deviation. In the present embodiment, the work estimation model is used to quantitatively analyze target project demand information (including functional demand documents, technical architecture schemes, etc.), and output an estimated work amount (such as man-days or man-months). The following takes the financial technology field as an example to explain the technical solution of the present embodiment, and it should be understood that project demand management in other fields is also within the protection scope of the present application.

[0027] Step 102, if the estimated work amount reaches a preset admission threshold, determining a demand resource related party according to the project demand information, and evaluating a strategy matching degree of the project demand information.

[0028] ​In the embodiments of the present application, when the estimated workload exceeds the preset threshold (for example, a project that needs cross-department cooperation and exceeds 80 person-months needs to be controlled), the demand resource related party (that is, the core interest party involved in the project, such as the technology department responsible for technical implementation, the business department defining demand priority, and the risk compliance department controlling regulatory requirements) needs to be determined, and the consistency of the project with the overall strategy of the enterprise is verified through strategic matching degree evaluation. In the field of financial technology, strategic matching may involve whether it supports the goal of digital transformation (for example, an open bank project needs to match the “ecological service” strategy), whether it meets the regulatory technology requirements (for example, a blockchain cross-border settlement project needs to meet the regulation on transaction traceability), and other dimensions.

[0029] Step 103, according to the estimated workload of the target project and the strategic matching degree evaluation result, a joint review process is initiated to the demand resource related party.

[0030] In the embodiments of the present application, for high workload and strategic matching projects, joint review process can be used to integrate opinions from multiple departments, breaking the information silos in the traditional mode of “demand department submitting demand-technology department developing in a blind way”. Financial technology projects often involve technical feasibility (such as the disaster recovery capability of distributed architecture), business value (such as the expected customer conversion rate improvement of intelligent investment advisor system), resource constraints (such as whether the current cloud server computing power can support it), and other multi-dimensional contradictions. Joint review can systematically solve these problems. For example, when planning to deploy a blockchain supply chain finance platform, joint review needs the technology department to verify whether the technical solution of deploying a consortium chain node is mature, the business department to assess the willingness of core enterprises and upstream and downstream customers to access and the transaction size, the finance department to calculate the annual cost of cloud service rental and node maintenance, and the risk department to investigate the risk of financial loss caused by smart contract vulnerabilities, and finally form a conclusion on whether to admit.

[0031] Step 104, if the target project passes the joint review, the production situation of the target project is dynamically tracked and controlled.

[0032] In the embodiments of the present application, after the project goes online, business demand may be rapidly iterated with market changes, or technical architecture may expose bottlenecks due to traffic surges. This step ensures that resource deployment and actual demand are continuously matched through dynamic tracking (such as real-time monitoring of system throughput, resource utilization, failure rate, and other indicators) and control mechanisms (such as elastic scaling strategy, demand priority reordering).

[0033] By applying the technical solutions of the embodiment, the subjectivity deviation of traditional manual estimation is solved by quantifying workload estimation, ensuring the scientificity of resource deployment; through strategic matching degree evaluation and multi-department joint review, the project is changed from passive response to demand to active alignment with strategy, avoiding the disconnection between technology and business; the dynamic tracking mechanism responds to the characteristics of rapid iteration of project demand and dynamic changes of technical environment, realizing the whole life cycle optimization of resources.

[0034] In the embodiment of the application, optionally, the workload estimation model is used to estimate the project demand information to determine the estimated workload of the target project, comprising: according to the project demand information, obtaining historical project demand information and historical workload of a plurality of historical similar projects matched with the target project in a project sample library, sorting the historical similar projects in descending order according to project implementation time, and determining the weights corresponding to the historical similar projects according to the sorting result, wherein the weights corresponding to the historical similar projects decrease with the decrease of the sorting order; based on the weights corresponding to the historical similar projects, the historical workload of the historical similar projects is weighted and summed to determine the historical similar project workload; the similarity of the historical project demand information of each historical similar project and the project demand information is calculated respectively, and the similarity corresponding to the historical similar projects is weighted and summed based on the weights corresponding to the historical similar projects to determine the historical similar project similarity; the function point number of the target project is determined according to the business complexity corresponding to the project demand information, the complexity coefficient of the target project is determined according to the technical implementation difficulty corresponding to the project demand information, and the product of the function point number and the complexity coefficient is calculated as the demand metadata characteristic value of the target project; the historical similar project workload, the historical similar project similarity and the demand metadata characteristic value are input into the workload estimation model to obtain the estimated workload of the target project.

[0035] In this embodiment, first, projects of the same type as the target project (such as developing an open bank platform for intelligent risk control systems) are screened from the project sample library (which stores data such as the demand documents, development records, and actual workloads of historical projects), and are sorted in descending order of implementation time (with the most recent first and the earliest last), and are assigned decreasing weights (such as 0.6 for projects in the last year, 0.3 for projects in the last 3 years, and 0.1 for projects more than 3 years ago). For example, if a “federated learning-based bank-enterprise data cooperation platform” is to be developed, the historical similar projects in the sample library include: “city commercial bank data sharing platform” launched in 2023 (weight 0.6), “insurance agency joint risk control system” launched in 2021 (weight 0.3), and “cross-bank credit inquiry system” launched in 2019 (weight 0.1). Since the federated learning technology is relatively mature in 2023, the implementation experience of recent projects (such as privacy computing framework selection and multi-party data alignment process) is more relevant to the current project, so the weight of recent projects is higher. Based on the determined weights, the actual completed workloads (such as man-months) of each historical similar project are weighted and summed to obtain the historical similar project workload, thereby dynamically adjusting the contribution of historical data to avoid interference from early low-relevance projects and to better reflect the actual workload level under the current technical environment. For example, assuming that the actual workload of the city commercial bank data sharing platform in 2023 is 80 man-months (weight 0.6), the actual workload of the insurance agency joint risk control system in 2021 is 120 man-months (weight 0.3), and the actual workload of the cross-bank credit inquiry system in 2019 is 150 man-months (weight 0.1), then the historical similar project workload = 80x0.6 + 120x0.3 + 150x0.1 = 48 + 36 + 15 = 99 man-months, which reflects the average workload level of recent similar projects. To further capture the impact of demand differences on workload, the model can also calculate the demand similarity of each historical similar project with the target project (such as analyzing the functional point matching degree of the demand document through a text similarity algorithm), and weight and sum the similarities to obtain the historical similar project similarity. The higher the similarity, the more directly reusable the experience of the historical project, and the smaller the workload deviation. Next, to quantify the complexity of the target project itself, the model needs to extract the feature values of the demand metadata. Among them, the business complexity reflects the depth of project modification to the business process (such as the number of departments involved, the number of external system integrations, and the data interaction frequency). For example, the federated learning-based bank-enterprise data cooperation platform needs to integrate with the bank core system, enterprise ERP, and third-party data service providers (3 external systems), with a business complexity score of 8 out of 10. The technical implementation difficulty reflects the advancement of the technology stack, the team's familiarity, and the performance requirements (such as high concurrency, low latency, and disaster recovery capability).Since federated learning is still in the pilot stage in the financial field, the team has low proficiency in its distributed training framework (such as TensorFlow Federated) and needs to meet financial-level security requirements (such as national encryption algorithms), so the technical implementation difficulty score is 9 (full score 10). Finally, the requirement metadata characteristic value = business complexity x technical implementation difficulty = 8 x 9 = 72 (the larger the numerical value, the higher the project complexity). Finally, the model is trained through historical data to learn the mapping relationship between the historical workload of similar projects, the similarity of similar projects, the requirement metadata characteristic value, and the actual workload, and finally outputs the estimated workload of the target project.

[0036] Further, as a refinement and expansion of the specific implementation of the above embodiment, in order to fully describe the specific implementation process of the present embodiment, another strategy-oriented demand control method is provided, as shown in Figure 2 The method comprises the following steps:

[0037] Step 201, receiving project demand information of a target project, estimating the work of the project demand information by using a work estimation model, and determining the estimated work of the target project.

[0038] Step 202, if the estimated work reaches a preset admission threshold, determining a demand resource related party according to the project demand information, and scoring the target project in multiple strategic dimensions according to the project demand information, and determining a strategic matching degree evaluation result of the target project according to the scoring results of the multiple strategic dimensions, wherein the strategic dimensions include business strategy fit degree, technical strategy fit degree, and compliance strategy fit degree, and the strategic matching degree evaluation result includes a strategic comprehensive score and scores of the multiple strategic dimensions.

[0039] In the embodiment of the present application, if the estimated work reaches the admission threshold (such as the bank sets "projects exceeding 150 man-months need strategic control"), the strategic matching evaluation link is entered, and the demand resource related party is determined, for example, the intelligent anti-fraud system involves the technology department (technical development), the risk control department (business demand), the compliance department (regulatory requirements), and the business department (customer experience). Further, the fit degree of the project and the overall goal of the institution is quantitatively evaluated from the three core strategic dimensions of business strategy fit degree, technical strategy fit degree, and compliance strategy fit degree.

[0040] In an optional implementation, before the scoring of the target project in multiple strategic dimensions according to the project demand information, the method further comprises: obtaining enterprise strategic deployment information in an enterprise business cycle, industry regulatory requirement information, and industry benchmark information, and extracting a plurality of strategic scoring keywords for the business strategic fit degree, the technical strategic fit degree, and the compliance strategic fit degree from the enterprise strategic deployment information, the industry regulatory requirement information, and the industry benchmark information respectively by using a large model; obtaining an expert judgment matrix obtained by experts after comparing the screened strategic scoring keywords two by two, wherein the expert judgment matrix comprises an importance scale of each strategic scoring keyword with respect to each strategic scoring keyword; calculating the sum of all importance scales corresponding to each strategic scoring keyword as the importance of each strategic scoring keyword according to the expert judgment matrix, and performing ratio calculation on the importance of each strategic scoring keyword and the sum of the importance of each strategic scoring keyword to obtain the weight of each strategic scoring keyword, and determining the weight of each strategic dimension according to the sum of the weights of the strategic scoring keywords corresponding to each strategic dimension;

[0041] The determining of the strategic matching degree evaluation result of the target project according to the scoring results of the multiple strategic dimensions comprises: calculating a strategic comprehensive score of the target project according to the weight of each strategic dimension and the scoring result of the strategic dimension.

[0042] In this embodiment, before evaluating the strategic matching degree of the target project, the strategic orientation of the institution is first determined. Specifically, three types of key information can be collected first, including enterprise strategic deployment information, industry regulatory requirement information, and industry benchmark information. Using a vertical field large model (such as an industry large model based on BERT fine-tuning), key words related to business strategy fit, technical strategy fit, and compliance strategy fit are extracted from the above three types of unstructured information. For example: business strategy fit extracts key words such as "retail online", "customer touch efficiency", and "third-party institution access number"; technical strategy fit extracts key words such as "API interface response speed", "data encryption transmission", and "distributed architecture supporting high concurrency"; and compliance strategy fit extracts key words such as "data traceability" and "encryption transmission compliance". These words correspond to the technical capabilities that the open platform needs to meet. Thus, through the natural language processing capabilities of the large model, scattered unstructured information is converted into quantifiable evaluation elements, avoiding omissions or biases in manual sorting. Secondly, to ensure the objectivity of the key word weights, domain experts can be introduced to rank the importance of the extracted key words. Specifically, experts are organized to compare key words pairwise and give importance scales (1-9 points, 1 indicating equal importance and 9 indicating absolute importance). For example, in the key word set of "business strategy fit", experts may consider "customer touch efficiency" more important than "third-party institution access number", and therefore give an importance scale of 3 for "customer touch efficiency" relative to "third-party institution access number". Then the importance score of each key word is calculated and normalized as a weight. The total weight of each strategic dimension (business, technology, compliance) is obtained by summing the weights of all key words under each strategic dimension. For example, if the business dimension contains 5 key words (total weight 0.6), the technology dimension contains 4 key words (total weight 0.3), and the compliance dimension contains 3 key words (total weight 0.1), then the strategic dimension weights of business, technology, and compliance are 0.6, 0.3, and 0.1, respectively. This step aggregates the dispersed key word weights into overall weights for strategic dimensions, reflecting the contribution differences of different dimensions to strategic matching. Based on the above weights, the weighted sum of the scores of the target project in each strategic dimension (e.g., business dimension score 9, technology dimension score 8, and compliance dimension score 7) is calculated to obtain the strategic comprehensive score: strategic comprehensive score = business dimension weight x business score + technology dimension weight x technology score + compliance dimension weight x compliance score = 0.6 x 9 + 0.3 x 8 + 0.1 x 7 = 5.4 + 2.4 + 0.7 = 8.5. If the preset score threshold is 7.5, the strategic matching degree of the project meets the requirements and enters the joint review process.

[0043] In an optional implementation, the scoring of the target project in the plurality of strategic dimensions according to the project requirement information comprises: performing word segmentation on the project requirement information to obtain requirement segmented words, and determining a word frequency of a strategic scoring keyword in each strategic dimension appearing in the project requirement information according to the requirement segmented words; taking each piece of enterprise strategic deployment information, industry regulatory requirement information, and industry benchmarking information as a document respectively, counting a number of documents containing the strategic scoring keyword in each strategic dimension in all documents respectively, and calculating a ratio of the number of documents containing the strategic scoring keyword in each strategic dimension to a total number of all documents as an importance evaluation index of each strategic dimension; and determining the score of the target project in each strategic dimension according to the word frequency of the strategic scoring keyword in each strategic dimension appearing in the project requirement information and the importance evaluation index of each strategic dimension.

[0044] In this embodiment, to evaluate the matching degree of the target project in the strategic dimensions of business, technology, compliance, etc., first, the keywords related to each dimension are extracted from the project requirement information, and the coverage of the requirement is quantified. Specifically, the project requirement information (such as requirement specification, business goal document) is segmented by natural language processing technology, and the keywords related to the strategic dimensions are extracted. The strategic scoring keyword library under each strategic dimension is defined in advance (such as business dimension keywords: "approval efficiency", "online rate", "customer experience"; technical dimension keywords: "model accuracy", "system response time", "distributed architecture"; compliance dimension keywords: "data privacy", "anti-fraud rules", "regulatory reporting"). The number of occurrences (word frequency) of each strategic dimension keyword in the demand segmentation is counted. For example, in the above demand document: the business dimension keyword "approval efficiency" appears twice ("improve online approval efficiency" "reduce manual intervention" indirectly related to efficiency improvement); the technical dimension keyword "model accuracy" appears once ("optimize credit evaluation model accuracy"); the compliance dimension keyword "data privacy" appears once ("ensure that customer data privacy complies with the Personal Information Protection Law"). The word frequency reflects the direct attention of the project requirement to a certain strategic dimension - the higher the word frequency, the more the project focuses on the strategic goals of that dimension. Further, to judge the overall importance of each strategic dimension to the enterprise, quantitative analysis needs to be conducted in combination with enterprise strategic deployment, regulatory requirements, and industry benchmarking. The specific method is to consider the three types of information as "documents", and count the coverage of strategic keywords in these documents to calculate their importance indicators. For each strategic dimension keyword, count the number of documents in which it appears. Thus, the importance evaluation index is calculated: importance index = number of documents containing the keyword / total number of documents. Finally, combining the attention of the project requirement to the keyword (word frequency) and the external importance of the keyword (importance index), the score of the target project in each strategic dimension is calculated. The scoring formula is: strategic dimension score = total word frequency of keywords in that dimension x importance index of that dimension.

[0045] Step 203, if the strategic comprehensive score is higher than the preset score threshold, knowledge recall is performed in the knowledge base according to the project requirement information of the target project, and based on the knowledge recall result and the strategic matching degree evaluation result, an auxiliary decision prompt word is constructed; the auxiliary decision prompt word is input into the large model to obtain auxiliary decision information; according to the auxiliary decision information, the estimated workload and the strategic matching degree evaluation result of the target project, a joint review process is initiated to the demand resource related party; otherwise, the target project is returned.

[0046] In the embodiments of the present application, after the strategic comprehensive score meets the standard, relevant information (such as "2022 similar anti-fraud system compliance acceptance failure case" and "federated learning application guide in financial risk control") is recalled from the knowledge base (storing historical project documents, compliance case library, technical white paper, regulatory policy documents, etc.), and auxiliary decision prompt words (such as "analyze the GDPR compliance risk points of the intelligent anti-fraud system, and recommend three data desensitization technical solutions") are generated in combination with the strategic matching results (such as the need to strengthen data desensitization). The prompt words are input into the large model, and the large model outputs auxiliary decision information (such as recommending differential privacy technology to process customer transaction data, and supplementing the compliance agreement template with the third-party data service provider in the requirement document) based on the knowledge base content and industry knowledge. Finally, combined with the auxiliary decision information, the estimated workload (200 man-months), and the strategic matching results (comprehensive score 8.1), joint review is initiated to the technology, risk control, compliance, and business departments (such as discussing "whether to accept the additional development cost of differential privacy technology" and "whether the compliance agreement template needs to be audited by the legal department"), and the decision-making is promoted to be scientific.

[0047] Step 204, if the target project passes the joint review, the production situation of the target project is dynamically tracked and controlled.

[0048] In the embodiments of the present application, after the project passes the joint review and goes online (such as the intelligent anti-fraud system formally accessing the mobile bank), the production situation is continuously and dynamically tracked, and the production board is displayed.

[0049] Step 205, obtaining the actual workload of the target project, constructing a training sample according to the actual workload corresponding to the target project, the estimated workload, the workload of the historical similar projects, the similarity of the historical similar projects, and the demand metadata characteristic value; a plurality of projects corresponding to the training sample in a preset period are combined to form a training sample set, and the workload estimation model is optimized and trained with the minimum loss between the actual workload and the estimated workload as the target.

[0050] Step 206, using the workload estimation model optimized and trained to estimate the workload of a new project, and obtaining the actual workload of the new project, if the deviation between the estimated workload and the actual workload of the new project is greater than a preset threshold, the workload estimation model is rolled back.

[0051] In the embodiments of the present application, in order to cope with the rapid iteration of technology in the field of financial technology (such as the migration from traditional machine learning to large models), changes in business requirements (such as the introduction of new data security standards by regulators), the accuracy of the work estimation model can also be maintained in the following ways: collecting actual work (such as the actual work of a certain project being 220 man-months), estimating work (200 man-months), historical work of similar projects (180 man-months), historical similarity (0.7), and demand metadata feature values (business complexity 8 x technical difficulty 9 = 72) to form training samples; a plurality of training samples in a preset period (such as every quarter) are combined to form a training set, and the model parameters are retrained with the goal of minimizing the loss between actual work and estimated work. If the estimation deviation of the optimized model on the new project (such as estimating 150 man-months, actual 200 man-months, deviation 33%) exceeds the preset threshold (such as 20%), then roll back to the previous version of the model to avoid resource mismatch due to model failure. Otherwise, keep the model parameters unchanged and wait for the next optimization.

[0052] By applying the technical solutions of the embodiments, through quantitative evaluation of strategic dimensions, it is ensured that the project is deeply bound to the long-term goals of the enterprise (such as avoiding the blind development of "technology for technology" projects); through the auxiliary decision-making driven by large models, fragmented information scattered in the knowledge base is converted into actionable suggestions (such as compliance risk prompts, technical solution recommendations), reducing the cost of cross-department communication; through the continuous optimization and rollback mechanism of the model, the work estimation always adapts to changes in the technical and business environment, avoiding resource waste or project delays due to model lag. The efficiency of resource allocation and the success rate of projects are improved.

[0053] Further, as Figure 1 A specific implementation of the method, the embodiments of the present application provide a demand control device oriented to strategy, as shown in Figure 3 The device comprises:

[0054] A work estimation module is configured to receive project demand information of a target project, estimate work based on the project demand information by using a work estimation model, and determine an estimated work amount of the target project.

[0055] A matching degree evaluation module is configured to determine demand resource related parties based on the project demand information and evaluate the strategic matching degree of the project demand information if the estimated work amount reaches a preset admission threshold.

[0056] An approval process initiation module is configured to initiate a joint review process to the demand resource related parties based on the estimated work amount of the target project and the strategic matching degree evaluation result.

[0057] A project tracking module is configured to dynamically track and control the production of the target project if the target project passes the joint review.

[0058] In the embodiments of the present application, the workload estimation module is configured to:

[0059] According to the project requirement information, historical project requirement information and historical workload of a plurality of historical same-type projects matching the target project are obtained from a project sample library, the historical same-type projects are sorted in descending order according to project implementation time, and the weights corresponding to the historical same-type projects are determined according to the sorting result, wherein the weight corresponding to the historical same-type project decreases with the decrease of the sorting order.

[0060] The historical same-type project workload is determined by weighted summation of the historical workloads of the historical same-type projects based on the weights corresponding to the historical same-type projects.

[0061] The similarity of each historical same-type project and the project requirement information is calculated respectively, and the similarity corresponding to the historical same-type project is determined by weighted summation of the similarities corresponding to the historical same-type projects based on the weights corresponding to the historical same-type projects.

[0062] The function point number of the target project is determined according to the business complexity corresponding to the project requirement information, the complexity coefficient of the target project is determined according to the technical implementation difficulty corresponding to the project requirement information, and the product of the function point number and the complexity coefficient is calculated as the requirement metadata characteristic value of the target project.

[0063] The historical same-type project workload, the historical same-type project similarity and the requirement metadata characteristic value are input into the workload estimation model to obtain the estimated workload of the target project.

[0064] In the embodiments of the present application, the workload estimation module is configured to:

[0065] The actual workload of the target project is obtained, and a training sample is constructed according to the actual workload, the estimated workload, the historical same-type project workload, the historical same-type project similarity and the requirement metadata characteristic value corresponding to the target project.

[0066] A plurality of projects corresponding to the training sample set are obtained within a preset period, and the workload estimation model is optimized and trained to minimize the loss between the actual workload and the estimated workload.

[0067] The trained workload estimation model is used to estimate the workload of a new project, and the actual workload of the new project is obtained. If the deviation between the estimated workload and the actual workload of the new project is greater than a preset threshold, the workload estimation model is rolled back.

[0068] In the embodiments of the present application, the matching degree evaluation module is configured to:

[0069] According to the project requirement information, the target project is scored in multiple strategic dimensions, and a strategic matching degree evaluation result of the target project is determined according to the scoring results of the multiple strategic dimensions, wherein the strategic dimensions include business strategy fit degree, technical strategy fit degree, and compliance strategy fit degree, and the strategic matching degree evaluation result includes a strategic comprehensive score and scores of the multiple strategic dimensions.

[0070] The joint review process is initiated to the demand resource related party according to the estimated workload and the strategic matching degree evaluation result of the target project, including:

[0071] If the strategic comprehensive score is higher than a preset score threshold, the joint review process is initiated to the demand resource related party according to the estimated workload and the strategic matching degree evaluation result of the target project.

[0072] Otherwise, the target project is returned.

[0073] In the embodiments of the present application, the matching degree evaluation module is configured to:

[0074] The enterprise strategy deployment information, the industry regulatory requirement information, and the industry benchmarking information in the enterprise business cycle are obtained, and a large model is used to extract multiple strategic scoring keywords for the business strategy fit degree, the technical strategy fit degree, and the compliance strategy fit degree in the enterprise strategy deployment information, the industry regulatory requirement information, and the industry benchmarking information, respectively.

[0075] An expert judgment matrix obtained by experts comparing the screened strategic scoring keywords two by two is obtained, wherein the expert judgment matrix includes the importance scale of each strategic scoring keyword with respect to each strategic scoring keyword.

[0076] According to the expert judgment matrix, the sum of all importance scales corresponding to each strategic scoring keyword is calculated as the importance of each strategic scoring keyword, and the importance of each strategic scoring keyword is respectively compared with the sum of the importance of each strategic scoring keyword to obtain the weight of each strategic scoring keyword. The weight of each strategic dimension is determined according to the sum of the weights of the strategic scoring keywords corresponding to each strategic dimension.

[0077] The strategy matching degree evaluation result of the target project is determined according to the score result of the plurality of strategic dimensions, and the strategy matching degree evaluation result comprises:

[0078] The strategy comprehensive score of the target project is calculated according to the weight of each strategic dimension and the score result of the strategic dimension.

[0079] In the embodiment of the application, optionally, the matching degree evaluation module is configured to:

[0080] The project demand information is subjected to word segmentation processing to obtain demand words, and the word frequency of the strategic score keyword under each strategic dimension appearing in the project demand information is determined according to the demand words;

[0081] Each piece of enterprise strategic deployment information, industry regulatory requirement information and industry benchmarking information is taken as a document, the number of documents containing the strategic score keyword under each strategic dimension in all documents is counted, and the ratio of the number of documents containing the strategic score keyword under each strategic dimension to the total number of documents is taken as the importance evaluation index of each strategic dimension.

[0082] The score of the target project on each strategic dimension is determined according to the word frequency of the strategic score keyword under each strategic dimension appearing in the project demand information and the importance evaluation index of each strategic dimension.

[0083] In the embodiment of the application, optionally, the workload estimation module is configured to:

[0084] Knowledge recall is performed on the project demand information of the target project in the knowledge base, and an auxiliary decision prompt word is constructed based on the knowledge recall result and the strategy matching degree evaluation result.

[0085] The auxiliary decision prompt word is input into a large model to obtain auxiliary decision information.

[0086] The joint review process is initiated to the demand resource related party according to the estimated workload of the target project and the strategy matching degree evaluation result, and the joint review process comprises:

[0087] The joint review process is initiated to the demand resource related party according to the auxiliary decision information, the estimated workload of the target project and the strategy matching degree evaluation result.

[0088] It should be noted that other corresponding descriptions of the various functional units involved in the demand control device provided in the embodiments of the application are described with reference to Figures 1 to 2 the corresponding description in the method, which will not be described here.

[0089] The embodiments of the present application also provide a computer device, which can be a personal computer, a server, a network device, etc. The computer device comprises a bus, a processor, a memory and a communication interface, and can further comprise an input / output interface and a display device. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store location information. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the steps in the method embodiments.

[0090] Those skilled in the art can understand that the structure of the computer device described above is only part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can comprise more or fewer components, or combine certain components, or have a different component arrangement.

[0091] In one embodiment, a computer readable storage medium is provided, which can be non-volatile or volatile, and has a computer program stored thereon. The computer program is executed by the processor to implement the steps in the method embodiments described above.

[0092] In one embodiment, a computer program product is provided, which comprises a computer program. The computer program is executed by the processor to implement the steps in the method embodiments described above.

[0093] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0094] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0095] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0096] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A strategy-oriented demand management method, characterized by: The method comprises: Receiving project requirement information of a target project, performing work estimation on the project requirement information using a workload estimation model, and determining an estimated workload of the target project; If the estimated workload reaches the preset admission threshold, the resource-required parties are determined based on the project demand information, and a strategic fit assessment is performed on the project demand information; Initiate a joint review process with stakeholders in need of resources based on the estimated workload and strategic fit assessment results of the target project; If the target project passes the joint review, the production status of the target project will be dynamically tracked and controlled.

2. The method according to claim 1, characterized in that The using the workload estimation model to perform workload estimation on the project demand information to determine the estimated workload of the target project includes: Based on the project demand information, historical project demand information and historical workload of multiple historical similar projects matching the target project are obtained from a project sample library, the historical similar projects are sorted in reverse order by project implementation time, and weights corresponding to the historical similar projects are determined according to the sorting results, wherein the weights corresponding to the historical similar projects decrease as the sorting order decreases; Determine the workload of historical similar projects by weighted summing the historical workload of the historical similar projects based on the weights corresponding to the historical similar projects; Calculating the similarity between the historical project demand information of each historical similar project and the project demand information respectively, and performing weighted summation of the similarities corresponding to the historical similar projects based on the weights corresponding to the historical similar projects to determine the similarity of the historical similar projects; Determining the function points of the target project according to the business complexity corresponding to the project requirement information, determining the complexity coefficient of the target project according to the technical implementation difficulty corresponding to the project requirement information, and calculating the product of the function points and the complexity coefficient as the requirement metadata feature value of the target project; The workload of similar historical projects, the similarity of similar historical projects, and the characteristic value of the demand metadata are input into a workload estimation model to obtain the estimated workload of the target project.

3. The method according to claim 2, characterized in that If the target project passes the joint review, after dynamically tracking and controlling the commissioning of the target project, the method further includes: Obtaining the actual workload of the target project, and constructing a training sample based on the actual workload, estimated workload, workload of historical similar projects, similarity of historical similar projects, and demand metadata feature values ​​corresponding to the target project; The training samples corresponding to multiple projects within a preset period are combined into a training sample set, and the workload estimation model is optimized and trained with the goal of minimizing the loss between the actual workload and the estimated workload; The workload estimation model after optimized training is used to estimate the workload of the new project, and the actual workload of the new project is obtained. If the deviation between the estimated workload and the actual workload of the new project is greater than a preset threshold, the workload estimation model is rolled back.

4. The method according to claim 1, wherein The strategic matching evaluation of the project demand information includes: Scoring the target project in multiple strategic dimensions based on the project requirement information, and determining a strategic fit assessment result for the target project based on the scoring results of the multiple strategic dimensions, wherein the strategic dimensions include business strategy fit, technology strategy fit, and compliance strategy fit, and the strategic fit assessment result includes a comprehensive strategic score and scores of the multiple strategic dimensions; Based on the estimated workload and strategic fit assessment results of the target project, a joint review process is initiated with the resource-required stakeholders, including: If the comprehensive strategic score is higher than the preset score threshold, a joint review process will be initiated with the resource-demanding stakeholders based on the estimated workload of the target project and the strategic fit assessment results; Otherwise, the target item is returned.

5. The method according to claim 4, characterized in that Before scoring the target project on multiple strategic dimensions based on the project demand information, the method further includes: Obtaining enterprise strategic deployment information, industry regulatory requirements information, and industry benchmarking information within the enterprise's business cycle, and using a large model to extract multiple strategic scoring keywords for the business strategy fit, the technology strategy fit, and the compliance strategy fit from the enterprise strategic deployment information, the industry regulatory requirements information, and the industry benchmarking information; Obtain an expert judgment matrix obtained by comparing the selected strategic scoring keywords pairwise by experts, wherein the expert judgment matrix includes the importance scale of each strategic scoring keyword relative to each strategic scoring keyword; According to the expert judgment matrix, the sum of all importance scales corresponding to each strategic scoring keyword is calculated as the importance of each strategic scoring keyword, and the importance of each strategic scoring keyword is respectively calculated as the ratio of the importance of each strategic scoring keyword to the sum of the importance of all strategic scoring keywords to obtain the weight of each strategic scoring keyword. The weight corresponding to each strategic dimension is determined according to the sum of the weights of the strategic scoring keywords corresponding to each strategic dimension; Determining the strategic matching evaluation result of the target project based on the scoring results of multiple strategic dimensions includes: The strategic comprehensive score of the target project is calculated based on the weight of each strategic dimension and the scoring results of the strategic dimension.

6. The method according to claim 5, characterized in that According to the project demand information, the target project is scored on multiple strategic dimensions, including: Performing word segmentation processing on the project demand information to obtain demand segmentation, and determining the word frequency of the strategic scoring keywords under each strategic dimension appearing in the project demand information based on the demand segmentation; Each piece of corporate strategic deployment information, industry regulatory requirements information, and industry benchmarking information is treated as a document. The number of documents containing the strategic scoring keywords under each strategic dimension is counted. The ratio of the number of documents containing the strategic scoring keywords under each strategic dimension to the total number of documents is calculated as the importance assessment indicator for each strategic dimension. The score of the target project in each strategic dimension is determined based on the frequency of the strategic scoring keywords under each strategic dimension appearing in the project demand information and the importance evaluation index of each strategic dimension.

7. The method according to any one of claims 1 to 6, characterized in that Before initiating a joint review process with resource-demanding stakeholders based on the estimated workload and strategic fit assessment results of the target project, the method further includes: Recalling knowledge from a knowledge base according to project requirement information of the target project, and constructing auxiliary decision prompt words based on the knowledge recall results and the strategic matching evaluation results; Inputting the auxiliary decision prompt words into the large model to obtain auxiliary decision information; Based on the estimated workload and strategic fit assessment results of the target project, a joint review process is initiated with the resource-required stakeholders, including: Based on the auxiliary decision-making information, the estimated workload of the target project and the strategic matching assessment results, a joint review process is initiated with the stakeholders of the resource demand.

8. A strategy-oriented demand management and control device, characterized in that: The device comprises: A workload estimation module is configured to receive project requirement information of a target project, perform workload estimation on the project requirement information using a workload estimation model, and determine an estimated workload of the target project; A matching evaluation module is used to determine resource-related parties based on the project demand information if the estimated workload reaches a preset admission threshold, and to perform a strategic matching evaluation on the project demand information; An approval process initiation module is used to initiate a joint review process with resource-demanding stakeholders based on the estimated workload and strategic fit assessment results of the target project; The project tracking module is used to dynamically track and control the production status of the target project if the target project passes the joint review.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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