Product research and development process and management method based on market demand

Abstract

The invention discloses a market demand-based product research and development process and management method. The method comprises the following steps of 1, market demand acquisition and standardized modeling; step 2, demand decomposition and association degree quantization; step 3, project information integration and plan generation; 4, performing dynamic plan management and conflict detection; step 5, task execution and state tracking; and step 6, demand closed-loop verification and feedback. According to the method, the requirements are integrated through standardized modeling, quantifiable research and development input is realized, and the problem of analysis fragmentation is solved; by means of automatic tracing and dynamic management and control, the demand change influence is accurately evaluated, and resource waste is avoided; through whole-process standardized collaborative operation, human intervention is reduced, a PDCA cycle optimization mechanism is formed, human error risks are reduced, market demand full-life-cycle closed-loop management is achieved, and research and development efficiency and quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of product R&D and management optimization, and in particular to product R&D processes and management methods based on market demand. Background Art

[0002] Traditional product development processes suffer from delayed response to market demand, distorted demand transmission, and poor controllability during the development process. Existing technologies often focus on prioritizing demand or simple task tracking, lacking closed-loop management of the entire market demand lifecycle. Existing management methods rely solely on manual demand entry and prioritization, failing to establish a dynamic mapping between market demand and product development.

[0003] The defects of the existing technology include: 1. Fragmented market demand analysis makes it difficult to form quantifiable R&D inputs; 2. Lack of automated traceability mechanism when requirements change, resulting in waste of development resources; 3. The process has a low degree of standardization and relies on manual experience, which is prone to human errors. Therefore, a product development process and management method based on market demand is proposed. Summary of the Invention

[0004] In view of the deficiencies of the existing technology, the present invention provides a product development process and management method based on market demand to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a product development process and management method based on market demand, comprising the following steps: Step 1: Market demand collection and standardized modeling: Establish a market demand basic table through multi-source data fusion. The content of the market demand basic table includes demand source, priority and related product feature fields; Define a demand template and force the input of the mapping relationship between market demand and product features; Step 2: Decomposition of requirements and quantification of related factors: Automatically decompose demand into R&D demand items based on demand templates, and establish parent-child relationships between market demand and R&D demand; Calculate the coverage weight of each R&D demand item to the original market demand through the demand correlation algorithm; Step 3: Project information integration and plan generation: Import R&D requirements into the project management platform to automatically generate an initial R&D plan; Define key milestones, including requirements confirmation, prototype design, and test verification stages; Step 4: Dynamic plan management and conflict detection: Monitor plan execution deviations in real time and trigger an alert when the deviation exceeds the threshold; Introducing a demand change impact analysis model to automatically assess the impact of changes on related R&D tasks; Step 5: Task execution and status tracking: Use the Kanban mode to visually display the task status and support manual marking of abnormal status; Establish a demand tracking matrix to record the current status and historical changes of each R&D requirement item; Step 6: Demand closed-loop verification and feedback: Automatically generate demand realization reports during the product release phase to compare the differences between initial requirements and actual delivered functions; Feedback the verification results to the market demand base table to form a PDCA cycle optimization mechanism; Automatically capture user research data, competitive product analysis data, and sales feedback data through API interfaces, use NLP technology to perform semantic analysis, extract structured demand fields such as demand type, specific description, priority, etc., and automatically fill them into the market demand basic table. At the same time, the demand template must clearly define the mapping relationship between market demand and product features, and force input through drop-down menus or predefined options to ensure the accuracy and consistency of the mapping; Through multi-source data fusion and standardized modeling, the comprehensiveness and accuracy of market demand are ensured, providing a solid foundation for subsequent R&D. Secondly, the demand decomposition and correlation quantification mechanism closely links the R&D demand items with the original market demand, effectively avoiding deviations and duplication of work in the R&D process. In the project information integration and plan generation link, the initial R&D plan is quickly generated through automated tools, and key milestone nodes are identified to ensure the orderly progress of the project. The dynamic plan management and conflict detection mechanism can monitor the progress of the project in real time, discover and solve potential problems in a timely manner, and ensure that the project is executed as planned. The task execution and status tracking function makes the project status clear at a glance through the visual Kanban mode, facilitating the collaborative work of team members. Finally, the demand closed-loop verification and feedback mechanism ensures a high degree of fit between product delivery and market demand, while forming a PDCA cycle optimization mechanism, providing valuable experience for subsequent projects and promoting the continuous improvement and optimization of the R&D process.

[0006] Preferably, the multi-source data in step 1 includes user survey data, competitive product analysis data, and sales feedback data, and structured demand fields are extracted using NLP technology; The demand template includes a demand description field, which uses standardized terms to describe market demand and sets mandatory checksums to ensure the consistency and comprehensibility of the description. If the description does not conform to the standardized terms or is not filled in, the system will automatically prompt and prohibit submission to the next stage; Use NLP word segmentation technology to pre-process text data and identify key entities and verb phrases. Next, use pre-defined semantic rules or machine learning models to map this unstructured data into pre-defined requirement field structures, such as requirement type, priority, and associated product features. Finally, perform data cleaning and verification to ensure the accuracy of the extracted structured requirement fields. This product development process and management method based on market demand significantly improves the comprehensiveness and accuracy of demand collection by integrating multi-source data (user surveys, competitive product analysis, sales feedback) and using NLP technology to extract structured demand fields. At the same time, the standardized terminology and mandatory verification mechanism in the demand template ensure the consistency and comprehensibility of the demand description, effectively avoiding subsequent R&D deviations caused by unclear descriptions or omissions of key information. This method not only improves the efficiency of demand management, but also promotes effective communication across departments, ensuring the close connection between R&D direction and market demand, thereby enhancing the market competitiveness of the product. In addition, through dynamic planning management and conflict detection mechanisms, it can monitor project progress in real time, promptly discover and resolve potential problems, ensure that projects are completed on time and with quality, and provide strong support for the company's continuous innovation and development.

[0007] Preferably, the requirement template includes mandatory field verification rules, and submission to the next stage is prohibited when key fields are missing; The mandatory field verification rules include verification logic. When a mandatory field is empty or the format does not meet the requirements, the system will automatically pop up an error prompt box, indicating the incorrect field and the specific error reason, and prevent submission until all mandatory fields meet the requirements; The system has built-in validation logic that performs real-time checks on each required field in the requirement template. When a required field is detected to be empty or in an incorrect format, the system automatically triggers a prompt mechanism, popping up an error prompt box, clearly indicating the incorrect field and the specific error reason, such as "the requirement description field cannot be empty" or "the date format must be YYYY-MM-DD". The user needs to correct the error, and the system will re-validate until all required fields meet the preset rules before submission to the next stage. The verification rules for required items in the requirement template significantly improve the standardization and accuracy of requirement management through an automated verification mechanism. This rule ensures that each requirement item is strictly reviewed before submission, avoiding subsequent problems caused by missing key fields or format errors. This not only reduces the workload of manual review and improves work efficiency, but also ensures the integrity and consistency of requirement data, laying a solid foundation for subsequent product development processes. By providing instant feedback on error information, users can quickly locate and correct problems, thereby accelerating the process of requirement collection and organization, helping the team enter the R&D stage faster and improving the overall project progress.

[0008] Preferably, the demand relevance algorithm in step 2 adopts a weighted scoring model, and the weight parameters include demand urgency, technical implementation difficulty and commercial value; When calculating demand relevance, we take demand stability into account. The stability index is comprehensively evaluated based on the historical change frequency and demand confirmation cycle. Demands with high stability are given higher weights. The specific weights are determined based on historical data analysis. Demand urgency is graded and scored based on the urgency of market demand; technical implementation difficulty is assessed and quantified by the technical team; commercial value is measured through indicators such as market forecast revenue and cost payback period; and stability is determined by statistically analyzing the number of historical demand changes and confirmation cycles, combined with a data fitting algorithm to determine specific weights, and ultimately summarizing the overall relevance score. The design of the demand correlation algorithm has significantly improved R&D efficiency and accuracy. By comprehensively considering the urgency of demand, technical implementation difficulty, commercial value and stability, the algorithm can scientifically quantify the contribution of each R&D demand item to market demand, ensuring that resources are allocated first to high-value and high-stability demands. This not only reduces resource waste, but also accelerates the product launch cycle. At the same time, the dynamically adjusted weight parameters enable the algorithm to flexibly adapt to market changes, continuously optimize R&D strategies, and create greater commercial value for enterprises.

[0009] Preferably, the conflict detection in step 4 includes three types of rule engines: resource occupation conflict, time window conflict and demand dependency conflict; The resource usage conflict includes a real-time monitoring mechanism for resource usage. When resource usage exceeds a preset threshold, the system automatically triggers an early warning and lists in detail in the conflict detection report the tasks that occupy the resources, the duration of occupation, and the expected release time. The system establishes a resource usage monitoring module to collect in real time the resource usage data of each R&D task, including key resources such as CPU, memory, and storage, and sets thresholds for each resource type. When the monitoring data exceeds the threshold, the early warning mechanism is triggered and a conflict detection report is automatically generated. The report lists in detail the name of the task that occupies the resources, the duration of occupation, the expected release time, and other information, so that project managers can adjust resource allocation in a timely manner. The conflict detection mechanism, especially the real-time monitoring and early warning of resource usage conflicts, has brought significant benefits to the product development process based on market demand. First, it ensures the rational allocation and efficient utilization of project resources, avoiding project delays caused by excessive resource usage. Through real-time monitoring, project managers can promptly identify and resolve resource conflicts to ensure that projects proceed as planned. Second, the mechanism improves the transparency and controllability of project management. Detailed conflict detection reports provide strong support for decision-making, help optimize resource allocation, and improve overall R&D efficiency. Finally, through the early warning and reporting mechanism, the team can identify potential risks earlier and take preventive measures, thereby enhancing the stability and success rate of the project.

[0010] Preferably, the kanban mode in step 5 supports multi-dimensional screening of tasks by R&D role, product module and urgency; The kanban mode includes a task progress display function, which intuitively displays the task completion status in the form of a progress bar. The color of the progress bar changes dynamically according to the task progress, such as green for completed, yellow for in progress, and red for delayed; In the Kanban mode, multi-dimensional filtering is achieved by building a task tag system. Each task is bound to the R&D role, product module, and urgency tag. The front-end interface provides filtering controls, and the system filters and displays in real time after the user selects the conditions. The task progress is displayed through an integrated progress data interface, which automatically calculates the completion ratio and maps it to the progress bar. The dynamic color change logic is based on preset thresholds (e.g., 100% completion rate is green, 50%-99% is yellow, and 0%-49% is red). Through multi-dimensional filtering functions, this Kanban mode enables team members to quickly locate tasks based on their roles, responsible modules or task urgency, greatly improving work efficiency and collaboration transparency. Task progress is displayed in the form of an intuitive progress bar with dynamic color changes, which not only makes the task status clear at a glance, but also can promptly identify potential delay risks and promote the team to adjust its strategy in a timely manner. This visual tool enhances the flexibility and responsiveness of project management, helps keep the project on track, and at the same time improves team members' sense of control over the overall progress of the project, promoting communication and collaboration within the team.

[0011] Preferably, when manually marking the abnormal state in step 5, the cause classification label needs to be entered, including three categories: technical bottleneck, demand change and external dependency; The cause classification label includes a custom label function, which allows users to add new abnormal cause labels according to actual conditions and set label usage frequency statistics for subsequent analysis and optimization; When manually marking an exception, the system will pop up an input box. Users need to select from the three preset labels of "Technical Bottleneck", "Requirement Change" and "External Dependency", or enter a new exception reason through the custom label function. After entering, the system automatically records the label and its usage time, and counts the usage frequency of each label in the background to facilitate subsequent analysis and optimization of the exception handling process. This function makes the marking of abnormal status more standardized and systematic by pre-setting cause classification labels, which helps the team quickly understand the root cause of the problem. At the same time, the introduction of the custom label function gives users the ability to flexibly add new labels according to actual conditions, ensuring the comprehensiveness and accuracy of abnormal cause records. The statistics of label usage frequency further provide data support for the team, helping to identify common problems, optimize processing processes, and formulate targeted improvement measures, thereby improving the efficiency and quality of project management.

[0012] Preferably, the demand realization report in step 6 includes three quantitative indicators: demand coverage, defect density and delivery cycle; The demand realization report includes statistics on the number of demand changes, which records in detail the time, content, scope of impact and handling results of each demand change to evaluate the stability of the demand and the impact of the change on the project; The system automatically captures project data and calculates requirement coverage (i.e., number of implemented requirements divided by total requirements), defect density (i.e., number of defects divided by number of lines of code or function points), and lead time (i.e., the time from requirement submission to product release). The system also records detailed information about each requirement change, including time, content, scope of impact, and handling results, creating a change log for subsequent analysis. These quantitative indicators provide companies with objective and measurable evaluation criteria for R&D results, helping management to quickly understand project progress and results. Secondly, by recording detailed information on demand changes, companies can deeply analyze demand stability and the impact of changes on projects, thereby optimizing the demand management process and reducing the risks and costs brought about by unnecessary changes. Ultimately, these measures jointly promote the improvement of R&D efficiency and product quality, and enhance the company's market competitiveness.

[0013] Preferably, a cross-project requirement reuse mechanism is also included, whereby when a new project requirement exceeds a threshold of similarity with a historical requirement, the associated R&D plan template is automatically inherited; When a new project requirement is input into the system, the system first extracts its features to form a feature vector. The system then traverses the historical requirement database and calculates the similarity between the new project requirement feature vector and the feature vectors of each historical requirement. If the similarity exceeds a preset threshold, the system automatically retrieves and inherits the R&D plan template associated with the historical requirement. It also provides a template adjustment interface, allowing users to fine-tune the template based on the actual situation of the new project. By automatically inheriting associated R&D plan templates, new projects can quickly obtain verified R&D paths and resource allocation plans, avoiding the process of groping from scratch. This not only shortens the project startup cycle, but also reduces the waste of resources caused by duplication of work. At the same time, the mechanism ensures the accuracy and adaptability of reuse by dynamically adjusting the similarity threshold, so that projects of different types and different technical architectures can benefit from it. In addition, the introduction of the demand reuse effect evaluation module provides data support for the continuous optimization of reuse strategies, further improving the scientific nature and effectiveness of R&D management.

[0014] Preferably, the similarity threshold supports dynamic adjustment, and the adjustment basis includes product type, technical architecture and market demand change frequency; The cross-project requirement reuse mechanism includes a requirement reuse effect evaluation module, which compares the differences in R&D efficiency (such as task completion time and resource utilization) and quality (such as defect rate and user satisfaction) before and after reuse, and generates a reuse effect analysis report to provide a basis for subsequent reuse decisions; Dynamic adjustment of similarity thresholds can be achieved by establishing an adjustment rule base. This rule base presets the threshold adjustment range based on product type (such as software, hardware), technical architecture (such as microservices, monolithic architecture), and market demand change frequency (such as quarterly, annual). When a new project is launched, the system automatically matches the rule base and combines current market demand data to dynamically calculate and set the similarity threshold to ensure the accuracy and adaptability of demand reuse. The cross-project demand reuse mechanism can accurately match historical demands with current project demands by dynamically adjusting the similarity threshold, effectively reducing duplication of work and improving R&D efficiency. This mechanism is not only based on the similarity of product types and technical architectures, but also fully considers the frequency of changes in market demand to ensure that the reused demands are both technically feasible and close to actual market needs. At the same time, the demand reuse effect evaluation module forms a detailed data analysis report by comparing the R&D efficiency and quality differences before and after reuse, providing management with a scientific basis for reuse decision-making, which helps to continuously optimize the R&D process and improve overall project quality and customer satisfaction.

[0015] In summary, compared with the prior art, the present invention provides a product development process and management method based on market demand, which has the following beneficial effects: This invention achieves the systematic integration and standardized expression of market demand information through market demand collection and standardized modeling steps, which has the benefit of significantly improving the quality of R&D input. This step uses multi-source data fusion to establish a market demand basic table containing demand source, priority and related product feature fields, and defines a demand template that forces the input of the mapping relationship between market demand and product function, thereby converting fragmented market demand analysis into quantifiable R&D input. This not only avoids the transmission distortion problem caused by traditional manual input, but also ensures the integrity and traceability of demand information through standardized modeling, providing a clear and quantifiable input basis for subsequent R&D activities, and effectively solving the defects of fragmented market demand analysis and difficulty in forming quantifiable R&D input. Based on technical means such as requirement decomposition and correlation quantification, dynamic plan management, and conflict detection, this system achieves automated traceability of requirement changes and precise control of the development process, which has the benefit of improving the efficiency of development resource utilization. It automatically decomposes R&D requirement items through requirement templates and establishes parent-child relationships. It uses a requirement correlation algorithm to calculate coverage weights. Combined with dynamic plan management and conflict detection mechanisms, it monitors execution deviations in real time and triggers early warnings. The system can automatically assess the impact of requirement changes on related R&D tasks. This automated traceability mechanism effectively avoids the waste of resources caused by manual processing when requirements change. At the same time, it improves overall efficiency by precisely controlling the development process, overcoming the problems of traditional methods that lack automated traceability mechanisms when requirements change and have poor controllability of the development process. Through the coordinated operation of project information integration and plan generation, task execution and status tracking, demand closed-loop verification and feedback, etc., standardized closed-loop management of the entire product R&D process is achieved, which has the benefits of reducing the risk of human error and forming a continuous optimization mechanism. The system imports R&D demand items into the project management platform to automatically generate an initial plan, define key milestone nodes, use the Kanban mode to visually display the task status and establish a demand tracking matrix, and finally generate a demand realization report and feedback the verification results during the product release phase. This standardized management of the entire process not only reduces dependence on manual experience, but also forms a closed-loop system of continuous improvement through the PDCA cycle optimization mechanism, effectively solving the defects of low process standardization and reliance on manual experience that are prone to human errors, providing closed-loop management guarantees for product R&D throughout its life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a step-by-step diagram of the product development process and management method of the invention based on market demand. DETAILED DESCRIPTION

[0017] Example 1: This invention provides a technical solution, product development process and management method based on market demand, please refer to Figure 1 , including the following steps: Step 1: Market demand collection and standardized modeling: Establish a market demand basic table through multi-source data fusion. The content of the market demand basic table includes demand source, priority and related product feature fields; Define a demand template and force the input of the mapping relationship between market demand and product features; Step 2: Decomposition of requirements and quantification of related factors: Automatically decompose demand into R&D demand items based on demand templates, and establish parent-child relationships between market demand and R&D demand; Calculate the coverage weight of each R&D demand item to the original market demand through the demand correlation algorithm; Step 3: Project information integration and plan generation: Import R&D requirements into the project management platform to automatically generate an initial R&D plan; Define key milestones, including requirements confirmation, prototype design, and test verification stages; Step 4: Dynamic plan management and conflict detection: Monitor plan execution deviations in real time and trigger an alert when the deviation exceeds the threshold; Introducing a demand change impact analysis model to automatically assess the impact of changes on related R&D tasks; Step 5: Task execution and status tracking: Use the Kanban mode to visually display the task status and support manual marking of abnormal status; Establish a demand tracking matrix to record the current status and historical changes of each R&D requirement item; Step 6: Demand closed-loop verification and feedback: Automatically generate demand realization reports during the product release phase to compare the differences between initial requirements and actual delivered functions; Feedback the verification results to the market demand base table to form a PDCA cycle optimization mechanism; Automatically capture user research data, competitive product analysis data, and sales feedback data through API interfaces, use NLP technology to perform semantic analysis, extract structured demand fields such as demand type, specific description, priority, etc., and automatically fill them into the market demand basic table. At the same time, the demand template must clearly define the mapping relationship between market demand and product features, and force input through drop-down menus or predefined options to ensure the accuracy and consistency of the mapping; Multi-source data fusion and standardized modeling ensure the comprehensiveness and accuracy of market demand, providing a solid foundation for subsequent R&D. Secondly, the requirements decomposition and correlation quantification mechanism closely links R&D requirements with original market needs, effectively avoiding deviations and duplication of work during the R&D process. Project information integration and plan generation utilize automated tools to quickly generate initial R&D plans and clearly define key milestones, ensuring the orderly progress of projects. Dynamic plan management and conflict detection mechanisms monitor project progress in real time, promptly identifying and resolving potential issues and ensuring that projects are executed as planned. Task execution and status tracking, through a visual dashboard, provides a clear overview of project status, facilitating collaboration among team members. Finally, the requirements closed-loop verification and feedback mechanism ensures a high degree of alignment between product delivery and market demand, while forming a PDCA cycle optimization mechanism. This provides valuable experience for subsequent projects and promotes the continuous improvement and optimization of the R&D process. Basic tables for market demand, requirements decomposition, project details, calculation management, task execution and status tracking, and requirements closed-loop verification and feedback are defined. Specific rules are set for each field in these basic tables, such as mandatory fields, data types, and character length limits, to ensure data integrity and accuracy. Clarify the six states in the form and the logic that can be selected: Based on the configuration function of the system's underlying business data, the basic table data model and field rules defined in DealsDevelopmentForm and the market demand R&D process model defined in DealsDevelopmentStage are referenced to configure the relationship between the business and basic tables. The constructed market demand R&D process model and flow program number are stored in the relational database. The system logically configures market demand, demand decomposition, project details, calculation management, task execution and status tracking, and demand closed-loop verification and feedback. The sequence serves as the basis for the system's automated operation: Construct a spark listener to enable the system to automatically detect whether the required basic tables are complete. Once a single basic table is fully completed, the system will automatically mark the status of this form. Construct a front-end page and use visual presentation to help users clearly understand the market demand, product development business nodes, demand decomposition, and demand tracking. After the user creates a requirement decomposition, the system automatically obtains and calculates the requirement coverage rate indicator and the requirement realization progress indicator. The system automatically detects the requirement coverage status (DealsFormState) based on the rules injected by the Spark listener, and marks each requirement as 1 (associated) after association. The system also automatically detects the requirement tracking status (DealsStageState) based on the rules injected by the Spark listener, and marks each associated requirement as 1 (tracked) after each task is completed. The system automatically calculates the comprehensive market demand score as follows: Comprehensive score = (demand coverage index * 0.5 + demand realization progress index * 0.5) * 100; According to the comprehensive score: 85-100 meets market demand and is evaluated as A, 60-84 meets market demand and is evaluated as B, and 0-59 meets market demand and is evaluated as C.

[0018] See also Figure 1 The multi-source data in step 1 includes user survey data, competitive product analysis data, and sales feedback data, and structured demand fields are extracted through NLP technology; The demand template includes a demand description field, which uses standardized terminology to describe market demand and sets mandatory checksums to ensure consistency and comprehensibility. If the description does not conform to standardized terminology or is left blank, the system will automatically prompt and prohibit submission to the next stage. Use NLP word segmentation technology to pre-process text data and identify key entities and verb phrases. Next, use pre-defined semantic rules or machine learning models to map this unstructured data into pre-defined requirement field structures, such as requirement type, priority, and associated product features. Finally, perform data cleaning and verification to ensure the accuracy of the extracted structured requirement fields. This product development process and management method based on market demand significantly improves the comprehensiveness and accuracy of demand collection by integrating multi-source data (user surveys, competitive product analysis, sales feedback) and using NLP technology to extract structured demand fields. At the same time, the standardized terminology and mandatory verification mechanism in the demand template ensure the consistency and comprehensibility of the demand description, effectively avoiding subsequent R&D deviations caused by unclear descriptions or omissions of key information. This method not only improves the efficiency of demand management, but also promotes effective communication across departments, ensuring the close connection between R&D direction and market demand, thereby enhancing the market competitiveness of the product. In addition, through dynamic planning management and conflict detection mechanisms, it can monitor project progress in real time, promptly discover and resolve potential problems, ensure that projects are completed on time and with quality, and provide strong support for the company's continuous innovation and development.

[0019] See also Figure 1,Requirement templates include mandatory field verification rules. When key fields are missing, submission to the next stage is prohibited. Required field verification rules include verification logic. When a mandatory field is empty or the format does not meet the requirements, the system automatically pops up an error prompt box, indicating the incorrect field and the specific cause of the error, and prevents submission until all mandatory fields meet the requirements. The system has built-in validation logic that performs real-time checks on each required field in the requirement template. When a required field is detected to be empty or in an incorrect format, the system automatically triggers a prompt mechanism, popping up an error prompt box, clearly indicating the incorrect field and the specific error reason, such as "the requirement description field cannot be empty" or "the date format must be YYYY-MM-DD". The user needs to correct the error, and the system will re-validate until all required fields meet the preset rules before submission to the next stage. The verification rules for required items in the requirement template significantly improve the standardization and accuracy of requirement management through an automated verification mechanism. This rule ensures that each requirement item is strictly reviewed before submission, avoiding subsequent problems caused by missing key fields or format errors. This not only reduces the workload of manual review and improves work efficiency, but also ensures the integrity and consistency of requirement data, laying a solid foundation for subsequent product development processes. By providing instant feedback on error information, users can quickly locate and correct problems, thereby accelerating the process of requirement collection and organization, helping the team enter the R&D stage faster and improving the overall project progress.

[0020] See also Figure 1 ,The demand relevance algorithm in step 2 adopts a weighted scoring model, and the weight parameters include demand urgency, technical implementation difficulty, and commercial value; When calculating demand relevance, we take demand stability into account. The stability index is comprehensively evaluated based on the historical change frequency and demand confirmation cycle. Demands with high stability are given higher weights. The specific weights are determined based on historical data analysis. Demand urgency is graded and scored based on the urgency of market demand; technical implementation difficulty is assessed and quantified by the technical team; commercial value is measured through indicators such as market forecast revenue and cost payback period; and stability is determined by statistically analyzing the number of historical demand changes and confirmation cycles, combined with a data fitting algorithm to determine specific weights, and ultimately summarizing the overall relevance score. The design of the demand correlation algorithm has significantly improved R&D efficiency and accuracy. By comprehensively considering the urgency of demand, technical implementation difficulty, commercial value and stability, the algorithm can scientifically quantify the contribution of each R&D demand item to market demand, ensuring that resources are allocated first to high-value and high-stability demands. This not only reduces resource waste, but also accelerates the product launch cycle. At the same time, the dynamically adjusted weight parameters enable the algorithm to flexibly adapt to market changes, continuously optimize R&D strategies, and create greater commercial value for enterprises.

[0021] See also Figure 1 ,The conflict detection in step four includes three types of rule engines: resource occupancy conflict, time window conflict, and demand dependency conflict; Resource usage conflicts include a real-time monitoring mechanism for resource usage. When resource usage exceeds a preset threshold, the system automatically triggers an alert and lists in detail the tasks occupying the resources, the duration of usage, and the expected release time in the conflict detection report. The system establishes a resource utilization monitoring module to collect real-time data on resource usage by each R&D task, including key resources such as CPU, memory, and storage. Thresholds are set for each resource type. When the monitoring data exceeds the threshold, an early warning mechanism is triggered and a conflict detection report is automatically generated. The report details the task name, duration of resource usage, and expected release time, allowing project managers to adjust resource allocation in a timely manner. The conflict detection mechanism, especially the real-time monitoring and early warning of resource usage conflicts, has brought significant benefits to the product development process based on market demand. First, it ensures the rational allocation and efficient utilization of project resources, avoiding project delays caused by excessive resource usage. Through real-time monitoring, project managers can promptly identify and resolve resource conflicts to ensure that projects proceed as planned. Second, the mechanism improves the transparency and controllability of project management. Detailed conflict detection reports provide strong support for decision-making, help optimize resource allocation, and improve overall R&D efficiency. Finally, through the early warning and reporting mechanism, the team can identify potential risks earlier and take preventive measures, thereby enhancing the stability and success rate of the project.

[0022] See also Figure 1 The Kanban model in step five supports multi-dimensional task filtering by R&D role, product module, and urgency; The Kanban mode includes a task progress display function, which intuitively displays the task completion status in the form of a progress bar. The color of the progress bar changes dynamically according to the task progress, such as green for completed, yellow for in progress, and red for delayed. In the Kanban mode, multi-dimensional filtering is achieved by building a task tag system. Each task is bound to an R&D role, product module, and urgency tag. The front-end interface provides filtering controls, and the system filters and displays in real time after the user selects the conditions. The task progress display automatically calculates the completion ratio and maps it to the progress bar through the integrated progress data interface. The dynamic color change logic is based on preset thresholds (such as green for a completion rate of 100%, yellow for 50%-99%, and red for 0%-49%). Through multi-dimensional filtering functions, this Kanban mode enables team members to quickly locate tasks based on their roles, responsible modules or task urgency, greatly improving work efficiency and collaboration transparency. Task progress is displayed in the form of an intuitive progress bar with dynamic color changes, which not only makes the task status clear at a glance, but also can promptly identify potential delay risks and promote the team to adjust its strategy in a timely manner. This visual tool enhances the flexibility and responsiveness of project management, helps keep the project on track, and at the same time improves team members' sense of control over the overall progress of the project, promoting communication and collaboration within the team.

[0023] See also Figure 1 ,When manually marking abnormal states in step five, you need to enter the cause classification labels, including three categories: technical bottlenecks, demand changes, and external dependencies; The cause classification label includes a custom label function, which allows users to add new exception cause labels based on actual conditions and set label usage frequency statistics for subsequent analysis and optimization; When manually marking an exception, the system will pop up an input box. Users need to select from the three preset labels of "Technical Bottleneck", "Requirement Change" and "External Dependency", or enter a new exception reason through the custom label function. After entering, the system automatically records the label and its usage time, and counts the usage frequency of each label in the background to facilitate subsequent analysis and optimization of the exception handling process. This function makes the marking of abnormal status more standardized and systematic by pre-setting cause classification labels, which helps the team quickly understand the root cause of the problem. At the same time, the introduction of the custom label function gives users the ability to flexibly add new labels according to actual conditions, ensuring the comprehensiveness and accuracy of abnormal cause records. The statistics of label usage frequency further provide data support for the team, helping to identify common problems, optimize processing processes, and formulate targeted improvement measures, thereby improving the efficiency and quality of project management.

[0024] See also Figure 1 ,The demand realization report in step six includes three quantitative indicators : demand ,coverage, defect density and delivery cycle; The demand realization report includes statistics on the number of demand changes. The statistics record the time, content, scope of impact and handling results of each demand change in detail to evaluate the stability of the demand and the impact of the change on the project. The system automatically captures project data and calculates requirement coverage (i.e., number of implemented requirements divided by total requirements), defect density (i.e., number of defects divided by number of lines of code or function points), and lead time (i.e., the time from requirement submission to product release). The system also records detailed information about each requirement change, including time, content, scope of impact, and handling results, creating a change log for subsequent analysis. These quantitative indicators provide companies with objective and measurable evaluation criteria for R&D results, helping management to quickly understand project progress and results. Secondly, by recording detailed information on demand changes, companies can deeply analyze demand stability and the impact of changes on projects, thereby optimizing the demand management process and reducing the risks and costs brought about by unnecessary changes. Ultimately, these measures jointly promote the improvement of R&D efficiency and product quality, and enhance the company's market competitiveness.

[0025] See also Figure 1 ,It also includes a cross-project requirement reuse mechanism. When the similarity threshold between new project requirements and historical requirements is exceeded, the associated R&D plan template is automatically inherited; When a new project requirement is input into the system, the system first extracts its features to form a feature vector. The system then traverses the historical requirement database and calculates the similarity between the new project requirement feature vector and the feature vectors of each historical requirement. If the similarity exceeds a preset threshold, the system automatically retrieves and inherits the R&D plan template associated with the historical requirement. It also provides a template adjustment interface, allowing users to fine-tune the template based on the actual situation of the new project. By automatically inheriting associated R&D plan templates, new projects can quickly obtain verified R&D paths and resource allocation plans, avoiding the process of groping from scratch. This not only shortens the project startup cycle, but also reduces the waste of resources caused by duplication of work. At the same time, the mechanism ensures the accuracy and adaptability of reuse by dynamically adjusting the similarity threshold, so that projects of different types and different technical architectures can benefit from it. In addition, the introduction of the demand reuse effect evaluation module provides data support for the continuous optimization of reuse strategies, further improving the scientific nature and effectiveness of R&D management.

[0026] See also Figure 1 ,The similarity threshold supports dynamic adjustment, and the adjustment basis includes product type, technical architecture and market demand change frequency; The cross-project requirements reuse mechanism includes a requirements reuse effect evaluation module. This module compares the differences in R&D efficiency (such as task completion time and resource utilization) and quality (such as defect rate and user satisfaction) before and after reuse, and generates a reuse effect analysis report to provide a basis for subsequent reuse decisions. Dynamic adjustment of similarity thresholds can be achieved by establishing an adjustment rule base. This rule base presets the threshold adjustment range based on product type (such as software, hardware), technical architecture (such as microservices, monolithic architecture), and market demand change frequency (such as quarterly, annual). When a new project is launched, the system automatically matches the rule base and combines current market demand data to dynamically calculate and set the similarity threshold to ensure the accuracy and adaptability of demand reuse. The cross-project demand reuse mechanism can accurately match historical demands with current project demands by dynamically adjusting the similarity threshold, effectively reducing duplication of work and improving R&D efficiency. This mechanism is not only based on the similarity of product types and technical architectures, but also fully considers the frequency of changes in market demand to ensure that the reused demands are both technically feasible and close to actual market needs. At the same time, the demand reuse effect evaluation module forms a detailed data analysis report by comparing the R&D efficiency and quality differences before and after reuse, providing management with a scientific basis for reuse decision-making, which helps to continuously optimize the R&D process and improve overall project quality and customer satisfaction.

[0027] Example 2: The difference between this embodiment and the first embodiment is that: during the task tracking process in step five, when the automatic calculation finds that the "power consumption management algorithm" task has not made any progress for three consecutive days, in addition to the system automatically marking it as "risk status", the project manager is also allowed to manually change the status to "under technical review" and associate it with the technical review meeting minutes. This operation triggers the conflict detection rule in step four and suspends the start of the downstream "system integration test" task until the status is updated to "resolved". This alternative solution uses a manual intervention mechanism to maintain process standardization while providing flexible handling capabilities for sudden technical problems, ensuring that the R&D process is not interrupted due to abnormal situations.

[0028] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0029] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Product development process and management method based on market demand, characterized by: The steps include: Step 1: Market demand collection and standardized modeling: Establish a market demand basic table through multi-source data fusion. The content of the market demand basic table includes demand source, priority and related product feature fields; Define a demand template and force the input of the mapping relationship between market demand and product features; Step 2: Decomposition of requirements and quantification of related factors: Automatically decompose demand into R&D demand items based on demand templates, and establish parent-child relationships between market demand and R&D demand; Calculate the coverage weight of each R&D demand item to the original market demand through the demand correlation algorithm; Step 3: Project information integration and plan generation: Import R&D requirements into the project management platform to automatically generate an initial R&D plan; Define key milestones, including requirements confirmation, prototype design, and test verification stages; Step 4: Dynamic plan management and conflict detection: Monitor plan execution deviations in real time and trigger an alert when the deviation exceeds the threshold; Introducing a demand change impact analysis model to automatically assess the impact of changes on related R&D tasks; Step 5: Task execution and status tracking: Use the Kanban mode to visually display the task status and support manual marking of abnormal status; Establish a demand tracking matrix to record the current status and historical changes of each R&D requirement item; Step 6: Demand closed-loop verification and feedback: Automatically generate demand realization reports during the product release phase to compare the differences between initial requirements and actual delivered functions; Feedback the verification results to the market demand base table to form a PDCA cycle optimization mechanism.

2. The product development process and management method based on market demand according to claim 1, characterized in that: The multi-source data in step 1 includes user survey data, competitive product analysis data, and sales feedback data, and structured demand fields are extracted using NLP technology; The demand template includes a demand description field, which uses standardized terms to describe market demand and sets a required check. When the description does not conform to the standardized terms or is not filled in, the system automatically prompts and prohibits submission to the next stage.

3. The product development process and management method based on market demand according to claim 1, characterized in that: The requirement template includes mandatory field verification rules, and submission to the next stage is prohibited when key fields are missing; The required field verification rules include verification logic. When a required field is empty or the format does not meet the requirements, the system automatically pops up an error prompt box, points out the error field and the specific error reason, and prevents submission until all required fields meet the requirements.

4. The product development process and management method based on market demand according to claim 1, characterized in that: The demand relevance algorithm in step 2 adopts a weighted scoring model, and the weight parameters include demand urgency, technical implementation difficulty and commercial value; When calculating demand correlation, consideration is added to demand stability. The stability index is comprehensively evaluated through the historical change frequency and demand confirmation cycle. Demands with high stability are given higher weights. The specific weights are determined based on historical data analysis.

5. The product development process and management method based on market demand according to claim 1, characterized in that: The conflict detection in step 4 includes three types of rule engines: resource occupation conflict, time window conflict and demand dependency conflict; The resource occupation conflict includes a real-time monitoring mechanism for resource usage. When the resource usage exceeds a preset threshold, the system automatically triggers an early warning and lists in detail the tasks occupying the resources, the duration of occupation, and the expected release time in the conflict detection report.

6. The product development process and management method based on market demand according to claim 1, characterized in that: The Kanban model in step 5 supports multi-dimensional task filtering by R&D role, product module, and urgency; The kanban mode includes a task progress display function, which intuitively displays the task completion status in the form of a progress bar, and the color of the progress bar changes dynamically according to the task progress.

7. The product development process and management method based on market demand according to claim 1, characterized in that: When manually marking abnormal status in step 5, you need to enter the cause classification label, including technical bottlenecks, demand changes, and external dependencies; The cause classification label includes a custom label function, which allows users to add new abnormal cause labels according to actual conditions and set label usage frequency statistics.

8. The product development process and management method based on market demand according to claim 1, characterized in that: The demand realization report in step 6 includes three quantitative indicators: demand coverage, defect density and delivery cycle; The demand realization report includes statistics on the number of demand changes, and the statistics on the number of demand changes record in detail the time, content, impact scope and processing results of each demand change.

9. The product development process and management method based on market demand according to claim 1, characterized in that: It also includes a cross-project requirement reuse mechanism. When the similarity threshold between new project requirements and historical requirements is exceeded, the associated R&D plan template is automatically inherited.

10. The product development process and management method based on market demand according to claim 9, characterized in that: The similarity threshold supports dynamic adjustment based on product type, technical architecture, and frequency of market demand changes; The cross-project requirement reuse mechanism includes a requirement reuse effect evaluation module, which compares the differences in R&D efficiency and quality before and after reuse, forms a reuse effect analysis report, and provides a basis for subsequent reuse decisions.

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