Design research and development data centralized management system and method based on cloud platform

Abstract

The invention discloses a design research and development data centralized management system and method based on a cloud platform, and relates to the technical field of cloud platforms and data management, and the method comprises the following steps: S100, building a design research and development data management platform at a cloud end, integrating dispersed design research and development data, and carrying out the classification and tag generation; s200, based on design research and development task requirements, formulating resource allocation rules, allocation calculation, memory and storage resources, monitoring resource use conditions, and adjusting a resource allocation scheme; s300, recording resource use data to generate resource efficiency parameters, combining historical data and current resource state analysis, judging the efficiency state of resource allocation, and generating an efficiency state mark; and S400, analyzing the condition that the resource allocation mark is insufficient in efficiency, checking the length change of the task queue, judging whether the problem is a sudden problem or not according to the data, adjusting the resource allocation and recording the state of the newly allocated resource. According to the method, the problem of design, research and development data management is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud platform and data management, and in particular to a centralized management system and method for design and development data based on a cloud platform. Background Art

[0002] With the continuous expansion of the scale of modern enterprises and the increasing complexity of R&D projects, the management of design and development data has become more and more complex and important. Traditional R&D data management methods have problems such as information dispersion, low collaboration efficiency and high security risks, which are difficult to meet the needs of modern R&D scenarios. Cloud platform technology, as an advanced digital tool, can provide powerful storage and computing capabilities, and provide new technical means for the centralized management and intelligent application of enterprise R&D data.

[0003] However, the current management of design and R&D data faces the following problems: First, data storage is scattered among local devices or independent servers of various departments, lacking unified storage and management, resulting in serious data silos and inefficient cross-departmental and cross-regional collaboration; second, traditional R&D data management methods are unable to quickly integrate and process a variety of data types including high-performance computing results, design models, and analysis reports, making it difficult to support complex R&D scenario requirements; finally, data security is insufficient, especially in remote collaboration and mobile office scenarios, where R&D data is more vulnerable to leakage or loss. Summary of the invention

[0004] The purpose of the present invention is to provide a cloud platform-based centralized management system and method for design and development data to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for centralized management of design and development data based on a cloud platform, characterized in that the method comprises the following steps:

[0006] S100. Build a design and development data management platform in the cloud to integrate scattered design and development data, classify the design and development data according to the design stage, functional module or project type, and generate corresponding labels for the classified data;

[0007] S200. Based on the design and development task requirements, formulate resource allocation rules, allocate computing, memory and storage resources, monitor resource usage, adjust resource allocation plans, and record resource usage data;

[0008] S300, recording resource usage data to generate resource efficiency parameters, combining historical data with current resource status analysis, determining the efficiency status of resource allocation, and generating an efficiency status mark;

[0009] S400, analyzing the situation where resource allocation is marked as inefficient, checking the change in the task queue length, judging whether it is a sudden problem based on the data, adjusting resource allocation and recording the status of the newly allocated resources, and updating the resource usage record and marking the task queue status after completion.

[0010] In step S100, the specific steps include:

[0011] S101, building a design and development data management platform in the cloud to centrally manage data resources of the design and development process, wherein the design and development data management platform includes a virtual desktop management module, a template management module, a NAT management module, a script management module, a data classification module and a label generation module;

[0012] S102, setting up a data communication interface on the cloud platform, capturing uploaded and downloaded data packets, monitoring the data transmission process and recording logs, and classifying the design and development data connected to the cloud platform by design stage, functional module or project type, and storing it in the corresponding storage area;

[0013] S103. Generate design phase, function module and project type labels and complete the allocation based on the data classified in step S102 through the label generation module.

[0014] In step S200, the specific steps include:

[0015] S201. Based on the data processing requirements of the design and development tasks, formulate the allocation rules of computing and storage resources, wherein the allocation rules include the computing resources, memory resources, storage capacity and task priority required for the computing tasks, and determine the adapted computing and storage resource pools according to the allocation rules;

[0016] S202, allocating computing resources, memory resources, and storage resources from the resource pool according to task requirements through a scheduling algorithm, and storing the binding relationship between the task and the allocated resources in a scheduling log;

[0017] S203, collecting the usage of allocated resources, including the following parameters: computing resource occupancy, memory resource utilization, storage resource utilization, and task queue length;

[0018] S204, analyzing resource utilization according to monitoring data, and calculating average resource utilization by the following formula: avg =(U c +U m +U s ) / 3; where U avg represents the average resource utilization, U c Indicates the computing resource utilization, U m Indicates memory resource utilization, U sIndicates storage resource utilization. Resource allocation is adjusted using the following logic: When the average resource utilization is higher than the set upper threshold U max , computing or storage resources are added from the resource pool. When the average resource utilization is lower than the set lower limit threshold U min , then the excess resources are released and put back into the resource pool, and the resource adjustment amount is calculated according to the formula ΔR x =α*(D x -U x *R x ) / D x *R x Adjust the resource allocation plan, where ΔR x represents the resource adjustment amount, α represents the adjustment factor, and the value range of α is between 0 and 1. x represents the demand, U x represents the current utilization, R x represents the amount of allocated resources, x∈{c,m,s}, c, m and s represent computing, memory and storage resources respectively, and reallocates storage and computing resources to adapt to current demand.

[0019] In step S300, the specific steps include:

[0020] S301. After each resource allocation and adjustment, record the current storage occupancy rate, computing task load, task completion rate and task queue length, generate a resource usage log, and convert these data into resource efficiency parameters. The resource efficiency parameter calculation formula is as follows:

[0021]

[0022] Where E represents the resource efficiency parameter, W i represents the weight of the i-th task, L q Indicates the length of the task queue, L q,max Indicates the maximum task queue length, T t represents the total task execution time, U ideal represents the ideal resource utilization;

[0023] S302. Calculate the resource efficiency parameter threshold based on historical data. The formula is as follows: threshold =median(E)+γ*(Q3(E)-Q1(E)); where, E threshold represents the threshold of resource efficiency parameter, median(E) represents the median of resource efficiency parameter, Q3(E) and Q1(E) represent the third quartile and first quartile of resource efficiency parameter, and γ represents the adjustment coefficient;

[0024] S303, comparing the current resource efficiency parameter with the resource efficiency parameter threshold. When the resource efficiency parameter is less than the resource efficiency parameter threshold, the allocation efficiency is judged to be insufficient and marked. When the resource efficiency parameter is greater than or equal to the resource efficiency parameter threshold, the allocation efficiency is judged to be reasonable.

[0025] In step S400, the specific steps include:

[0026] S401. When resource allocation is marked as inefficient, record the resource number, task queue information, and historical resource efficiency parameter change trends, check the task queue length changes within a fixed time window, and calculate the resource efficiency parameters based on the L q >L q,avg +β*σ Lq Determine that insufficient allocation efficiency is a sudden problem, temporarily allocate additional computing resources, storage resources, or memory resources from the resource pool, record the newly allocated resource number, and recycle the excess resources after the sudden problem is resolved. q,avg represents the historical average of the task queue length, σ Lq represents the standard deviation of the task queue length, and β represents the adjustment factor;

[0027] S402: Recalculate the resource efficiency parameter E′ according to the state of the newly allocated resource pool after resource adjustment, and compare it with the threshold E threshold By comparison, when the recalculated resource efficiency parameter is greater than or equal to the resource efficiency parameter threshold, the task queue is marked as restored to normal.

[0028] A centralized management system for design and development data based on a cloud platform, characterized in that the system includes: a data acquisition module, a performance evaluation module, a resource allocation module and an adaptive adjustment module, wherein the data acquisition module is used to collect and manage design and development data, monitor access records and log information during data upload and download, and classify data according to design stage, functional module or project type, assign a label to each data item through a label generation function, and record resource usage, including computing resource occupancy rate, memory resource utilization rate, storage resource utilization rate and task queue length, and generate a resource usage log; the performance evaluation module is used to evaluate resource utilization efficiency, formulate resource allocation rules based on design and development task requirements, allocate computing, memory and storage resources, monitor resource usage, and generate resource usage logs; The adaptive adjustment module is used to monitor the task queue status in real time, judge the cause of insufficient resource efficiency, identify the problem of sudden task growth and adjust resource allocation, reallocate or recycle resources according to current and historical resource usage data, and update resource usage records. The output end of the data acquisition module is connected to the input end of the performance evaluation module, the output end of the performance evaluation module is connected to the input end of the resource allocation module, and the output end of the resource allocation module is connected to the input end of the adaptive adjustment module.

[0029] The data acquisition module includes a data monitoring unit, a data classification unit, a label generation unit and a resource usage recording unit. The data monitoring unit is used to monitor the log information during data upload, download and data transmission, and record each access and data operation behavior; the data classification unit is used to classify the data according to the design stage, functional module or project type; the label generation unit is used to generate corresponding labels for the classified data; the resource usage recording unit is used to record the computing task load, storage occupancy, task completion rate and task queue length, and generate a resource usage log.

[0030] The performance evaluation module includes a resource efficiency calculation unit and a resource adjustment amount calculation unit. The resource efficiency calculation unit calculates the average resource utilization rate based on the log information provided by the data acquisition module; the resource adjustment amount calculation unit is used to calculate the resource adjustment amount according to the resource adjustment amount calculation formula to adjust the resource allocation plan; the resource allocation module includes a resource efficiency analysis unit and an abnormal marking unit. The resource efficiency analysis unit is used to calculate the resource efficiency parameters according to the recorded current storage occupancy rate, calculation task load, task completion rate and task queue length data; the abnormal marking unit is used to judge the state of insufficient or reasonable allocation efficiency, and mark the insufficiently efficient resource allocation, and record the relevant resource number and task queue information.

[0031] The adaptive adjustment module includes a queue monitoring unit, a sudden problem processing unit and an adjustment verification unit. The queue monitoring unit is used to monitor the length of the task queue and its changing trend in real time to determine whether there is a sudden problem. The sudden problem processing unit is used to temporarily allocate additional resources from the resource pool to relieve pressure when the task queue surges, and release excess resources after the problem is solved. The adjustment verification unit is used to recalculate resource efficiency parameters according to the adjusted resource status, verify the rationality of the adjustment, and mark the task queue that has returned to normal.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. Build a design and R&D data management platform on the cloud to centrally integrate scattered R&D data, classify and manage based on design stage, functional module or project type, and generate labels to achieve unified storage, rapid retrieval and efficient sharing of R&D data, overcoming the problems of "data islands" and inefficient cross-departmental collaboration in traditional methods.

[0034] 2. Through the collaborative work of the resource efficiency analysis unit and the exception marking unit, real-time monitoring of resource utilization is achieved to realize intelligent allocation and adjustment of computing, storage and memory resources, avoiding resource waste or shortage caused by traditional static allocation methods.

[0035] 3. The cloud platform-based data management method combines data classification and labeling processing to provide log records and reduce the risk of data leakage or loss in remote collaboration and mobile office scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of a process of a centralized management method of design and development data based on a cloud platform according to the present invention;

[0037] Figure 2 This is a schematic diagram of the structure of a cloud platform-based centralized management system for design and development data in the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] like Figure 1-Figure 2 As shown, the present invention provides a technical solution, a method for centralized management of design and development data based on a cloud platform, characterized in that the method comprises the following steps:

[0040] S100. Build a design and development data management platform in the cloud to integrate scattered design and development data, classify the design and development data according to the design stage, functional module or project type, and generate corresponding labels for the classified data;

[0041] S200. Based on the design and development task requirements, formulate resource allocation rules, allocate computing, memory and storage resources, monitor resource usage, adjust resource allocation plans, and record resource usage data;

[0042] S300, recording resource usage data to generate resource efficiency parameters, combining historical data with current resource status analysis, determining the efficiency status of resource allocation, and generating an efficiency status mark;

[0043] S400, analyzing the situation where resource allocation is marked as inefficient, checking the change in the task queue length, judging whether it is a sudden problem based on the data, adjusting resource allocation and recording the status of the newly allocated resources, and updating the resource usage record and marking the task queue status after completion.

[0044] In step S100, the specific steps include:

[0045] S101, building a design and development data management platform in the cloud to centrally manage data resources of the design and development process, wherein the design and development data management platform includes a virtual desktop management module, a template management module, a NAT management module, a script management module, a data classification module and a label generation module;

[0046] S102, setting up a data communication interface on the cloud platform, capturing uploaded and downloaded data packets, monitoring the data transmission process and recording logs, and classifying the design and development data connected to the cloud platform by design stage, functional module or project type, and storing it in the corresponding storage area;

[0047] S103. Generate design phase, function module and project type labels and complete the allocation based on the data classified in step S102 through the label generation module.

[0048] In step S200, the specific steps include:

[0049] S201. Based on the data processing requirements of the design and development tasks, formulate the allocation rules of computing and storage resources, wherein the allocation rules include the computing resources, memory resources, storage capacity and task priority required for the computing tasks, and determine the adapted computing and storage resource pools according to the allocation rules;

[0050] S202, allocating computing resources, memory resources, and storage resources from the resource pool according to task requirements through a scheduling algorithm, and storing the binding relationship between the task and the allocated resources in a scheduling log;

[0051] S203, collecting the usage of allocated resources, including the following parameters: computing resource occupancy, memory resource utilization, storage resource utilization, and task queue length;

[0052] S204, analyzing resource utilization according to monitoring data, and calculating average resource utilization by the following formula: avg =(U c +U m +U s ) / 3; where U avg represents the average resource utilization, U c Indicates the computing resource utilization, U m Indicates memory resource utilization, U s Indicates storage resource utilization. Resource allocation is adjusted using the following logic: When the average resource utilization is higher than the set upper threshold U max , computing or storage resources are added from the resource pool. When the average resource utilization is lower than the set lower limit threshold U min , then the excess resources are released and put back into the resource pool, and the resource adjustment amount is calculated according to the formula ΔR x =α*(D x -U x *R x ) / D x *R x Adjust the resource allocation plan, where ΔR x represents the resource adjustment amount, α represents the adjustment factor, and the value range of α is between 0 and 1. x represents the demand, U x represents the current utilization, R x represents the amount of allocated resources, x∈{c,m,s}, c, m and s represent computing, memory and storage resources respectively, and reallocates storage and computing resources to adapt to current demand.

[0053] In step S300, the specific steps include:

[0054] S301. After each resource allocation and adjustment, record the current storage occupancy rate, computing task load, task completion rate and task queue length, generate a resource usage log, and convert these data into resource efficiency parameters. The resource efficiency parameter calculation formula is as follows:

[0055]

[0056] Where E represents the resource efficiency parameter, W i represents the weight of the i-th task, L q Indicates the length of the task queue, L q,max Indicates the maximum task queue length, T t represents the total task execution time, U ideal represents the ideal resource utilization;

[0057] S302. Calculate the resource efficiency parameter threshold based on historical data. The formula is as follows: threshold =median(E)+γ*(Q3(E)-Q1(E)); where, E threshold represents the threshold of resource efficiency parameter, median(E) represents the median of resource efficiency parameter, Q3(E) and Q1(E) represent the third quartile and first quartile of resource efficiency parameter, and γ represents the adjustment coefficient;

[0058] S303, comparing the current resource efficiency parameter with the resource efficiency parameter threshold. When the resource efficiency parameter is less than the resource efficiency parameter threshold, the allocation efficiency is judged to be insufficient and marked. When the resource efficiency parameter is greater than or equal to the resource efficiency parameter threshold, the allocation efficiency is judged to be reasonable.

[0059] In step S400, the specific steps include:

[0060] S401. When resource allocation is marked as inefficient, record the resource number, task queue information, and historical resource efficiency parameter change trends, check the task queue length changes within a fixed time window, and calculate the resource efficiency parameters based on the L q >L q,avg +β*σ Lq Determine that insufficient allocation efficiency is a sudden problem, temporarily allocate additional computing resources, storage resources, or memory resources from the resource pool, record the newly allocated resource number, and recycle the excess resources after the sudden problem is resolved. q,avg represents the historical average of the task queue length, σ Lq represents the standard deviation of the task queue length, and β represents the adjustment factor;

[0061] S402: Recalculate the resource efficiency parameter E′ according to the state of the newly allocated resource pool after resource adjustment, and compare it with the threshold E threshold By comparison, when the recalculated resource efficiency parameter is greater than or equal to the resource efficiency parameter threshold, the task queue is marked as restored to normal.

[0062] A centralized management system for design and development data based on a cloud platform, characterized in that the system includes: a data acquisition module, a performance evaluation module, a resource allocation module and an adaptive adjustment module, wherein the data acquisition module is used to collect and manage design and development data, monitor access records and log information during data upload and download, and classify data according to design stage, functional module or project type, assign a label to each data item through a label generation function, and record resource usage, including computing resource occupancy rate, memory resource utilization rate, storage resource utilization rate and task queue length, and generate a resource usage log; the performance evaluation module is used to evaluate resource utilization efficiency, formulate resource allocation rules based on design and development task requirements, allocate computing, memory and storage resources, monitor resource usage, and generate resource usage logs; The adaptive adjustment module is used to monitor the task queue status in real time, judge the cause of insufficient resource efficiency, identify the problem of sudden task growth and adjust resource allocation, reallocate or recycle resources according to current and historical resource usage data, and update resource usage records. The output end of the data acquisition module is connected to the input end of the performance evaluation module, the output end of the performance evaluation module is connected to the input end of the resource allocation module, and the output end of the resource allocation module is connected to the input end of the adaptive adjustment module.

[0063] The data acquisition module includes a data monitoring unit, a data classification unit, a label generation unit and a resource usage recording unit. The data monitoring unit is used to monitor the log information during data upload, download and data transmission, and record each access and data operation behavior; the data classification unit is used to classify the data according to the design stage, functional module or project type; the label generation unit is used to generate corresponding labels for the classified data; the resource usage recording unit is used to record the computing task load, storage occupancy, task completion rate and task queue length, and generate a resource usage log.

[0064] The performance evaluation module includes a resource efficiency calculation unit and a resource adjustment amount calculation unit. The resource efficiency calculation unit calculates the average resource utilization rate based on the log information provided by the data acquisition module; the resource adjustment amount calculation unit is used to calculate the resource adjustment amount according to the resource adjustment amount calculation formula to adjust the resource allocation plan; the resource allocation module includes a resource efficiency analysis unit and an abnormal marking unit. The resource efficiency analysis unit is used to calculate the resource efficiency parameters according to the recorded current storage occupancy rate, calculation task load, task completion rate and task queue length data; the abnormal marking unit is used to judge the state of insufficient or reasonable allocation efficiency, and mark the insufficiently efficient resource allocation, and record the relevant resource number and task queue information.

[0065] The adaptive adjustment module includes a queue monitoring unit, a sudden problem processing unit and an adjustment verification unit. The queue monitoring unit is used to monitor the length of the task queue and its changing trend in real time to determine whether there is a sudden problem. The sudden problem processing unit is used to temporarily allocate additional resources from the resource pool to relieve pressure when the task queue surges, and release excess resources after the problem is solved. The adjustment verification unit is used to recalculate resource efficiency parameters according to the adjusted resource status, verify the rationality of the adjustment, and mark the task queue that has returned to normal.

[0066] In the embodiment: the centralized management of design and development data based on the cloud platform includes 5 tasks, each of which has different data volume and priority. The task information in the task queue is as follows: Task 1, data volume 100GB, priority 3; Task 2, data volume 200GB, priority 1; Task 3, data volume 150GB, priority 2; Task 4, data volume 250GB, priority 2; Task 5, data volume 300GB, priority 1; The computing resources of the initial resource pool are 40 CPU cores, the memory resources are 500GB, and the storage resources are 2TB. The data collection module is used to collect and classify the R&D task data and generate them according to the task priority. Labels T1, T2, T3, and labels T1, T2, T3 represent priorities 1, 2, and 3 respectively. Assign priority 1 tasks to high-speed storage areas, assign priority 2 tasks to ordinary storage areas, and assign priority 3 tasks to low-speed storage areas to generate classified data labels; Task 1: {T3, module A, stage 1}, Task 2: {T1, module B, stage 2}, Task 1: {T2, module C, stage 3}, Task 1: {T2, module D, stage 4}, Task 1: {T1, module E, stage 5}, Initial allocation strategy: Allocate computing resources of 8 cores and memory of 100GB to each task, according to the computing resource occupancy rate U c =70%, memory utilization U m =60%, storage resource utilization U s=50%, and combined with the average resource utilization calculation formula to obtain the average resource utilization U avg = 60%, resource utilization is higher than the threshold U max = 80%, computing or storage resources are added from the resource pool; resource utilization is lower than U min =40%, the excess resources are released and put back into the resource pool according to the ideal resource utilization U ideal =90%, maximum task queue length L q,max =20, task execution time T t =10h, using the resource efficiency parameter calculation formula, the resource efficiency parameter of task 1 is 0.067, the resource efficiency parameter of task 2 is 0.045, the resource efficiency parameter of task 3 is 0.1, the resource efficiency parameter of task 4 is 0.056, and the resource efficiency parameter of task 5 is 0.051. Set γ = 1.5, quartiles Q1 = 0.051, Q3 = 0.167, median median = 0.056, and according to the resource efficiency parameter threshold calculation formula, the resource efficiency parameter threshold is 0.222. According to L q >L q,avg +β*σ Lq Determine that the allocation efficiency is insufficient as a sudden problem, according to L q,avg =8,σ Lq =2, β=1.5, substitute into L q,avg +β*σ Lq Calculate 11, then the queue length of task 5 is L q =12 is greater than 11, which is considered an emergency problem. At this time, we temporarily increase the computing resources of task 5 by 2 cores and storage by 20 GB, and recycle the resources of task 1. We then recalculate the resource efficiency parameter E′ and compare it with the threshold E threshold By comparison, when the recalculated resource efficiency parameter is greater than or equal to the resource efficiency parameter threshold, the task queue is marked as restored to normal.

[0067] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for centralized management of design and development data based on a cloud platform, characterized in that: The method comprises the following steps: S100. Build a design and development data management platform in the cloud to integrate scattered design and development data, classify the design and development data according to the design stage, functional module or project type, and generate corresponding labels for the classified data; S200. Based on the design and development task requirements, formulate resource allocation rules, allocate computing, memory and storage resources, monitor resource usage, adjust resource allocation plans, and record resource usage data; S300, recording resource usage data to generate resource efficiency parameters, combining historical data with current resource status analysis, determining the efficiency status of resource allocation, and generating an efficiency status mark; S400, analyzing the situation where resource allocation is marked as inefficient, checking the change in the task queue length, judging whether it is a sudden problem based on the data, adjusting resource allocation and recording the status of the newly allocated resources, and updating the resource usage record and marking the task queue status after completion.

2. According to claim 1, a method for centralized management of design and development data based on a cloud platform is characterized in that: In step S100, the specific steps include: S101, building a design and development data management platform in the cloud to centrally manage data resources of the design and development process, wherein the design and development data management platform includes a virtual desktop management module, a template management module, a NAT management module, a script management module, a data classification module and a label generation module; S102, setting up a data communication interface on the cloud platform, capturing uploaded and downloaded data packets, monitoring the data transmission process and recording logs, and classifying the design and development data connected to the cloud platform by design stage, functional module or project type, and storing it in the corresponding storage area; S103. Generate design phase, function module and project type labels and complete the allocation based on the data classified in step S102 through the label generation module.

3. The method for centralized management of design and development data based on a cloud platform according to claim 1, characterized in that: In step S200, the specific steps include: S201. Based on the data processing requirements of the design and development tasks, formulate the allocation rules of computing and storage resources, wherein the allocation rules include the computing resources, memory resources, storage capacity and task priority required for the computing tasks, and determine the adapted computing and storage resource pools according to the allocation rules; S202, allocating computing resources, memory resources, and storage resources from the resource pool according to task requirements through a scheduling algorithm, and storing the binding relationship between the task and the allocated resources in a scheduling log; S203, collecting the usage of allocated resources, including the following parameters: computing resource occupancy, memory resource utilization, storage resource utilization, and task queue length; S204, analyzing resource utilization according to monitoring data, and calculating average resource utilization by the following formula: avg =(U c +U m +U s ) / 3; where U avg represents the average resource utilization, U c Indicates the computing resource utilization, U m Indicates memory resource utilization, U s Indicates storage resource utilization. Resource allocation is adjusted using the following logic: When the average resource utilization is higher than the set upper threshold U max , computing or storage resources are added from the resource pool. When the average resource utilization is lower than the set lower limit threshold U min , then the excess resources are released and put back into the resource pool, and the resource adjustment amount is calculated according to the formula ΔR x =α*(D x -U x *R x ) / D x *R x Adjust the resource allocation plan, where ΔR x represents the resource adjustment amount, α represents the adjustment factor, and the value range of α is between 0 and 1. x represents the demand, U x represents the current utilization, R x represents the amount of allocated resources, x∈{c,m,s}, c, m and s represent computing, memory and storage resources respectively, and reallocates storage and computing resources to adapt to current demand.

4. The method for centralized management of design and development data based on a cloud platform according to claim 1, characterized in that: In step S300, the specific steps include: S301. After each resource allocation and adjustment, record the current storage occupancy rate, computing task load, task completion rate and task queue length, generate a resource usage log, and convert these data into resource efficiency parameters. The resource efficiency parameter calculation formula is as follows: Where E represents the resource efficiency parameter, W i represents the weight of the i-th task, L q Indicates the length of the task queue, L q,max Indicates the maximum task queue length, T t represents the total task execution time, U ideal represents the ideal resource utilization; S302. Calculate the resource efficiency parameter threshold based on historical data. The formula is as follows: threshold =median(E)+γ*(Q3(E)-Q1(E)); where, E threshold represents the threshold of resource efficiency parameter, median(E) represents the median of resource efficiency parameter, Q3(E) and Q1(E) represent the third quartile and first quartile of resource efficiency parameter, and γ represents the adjustment coefficient; S303, comparing the current resource efficiency parameter with the resource efficiency parameter threshold. When the resource efficiency parameter is less than the resource efficiency parameter threshold, the allocation efficiency is judged to be insufficient and marked. When the resource efficiency parameter is greater than or equal to the resource efficiency parameter threshold, the allocation efficiency is judged to be reasonable.

5. The method for centralized management of design and development data based on a cloud platform according to claim 1, characterized in that: In step S400, the specific steps include: S401. When resource allocation is marked as inefficient, record the resource number, task queue information, and historical resource efficiency parameter change trends, check the task queue length changes within a fixed time window, and calculate the resource efficiency parameters based on the L q >L q,avg +β*σ Lq Determine that insufficient allocation efficiency is a sudden problem, temporarily allocate additional computing resources, storage resources, or memory resources from the resource pool, record the newly allocated resource number, and recycle the excess resources after the sudden problem is resolved. q,avg represents the historical average of the task queue length, σ Lq represents the standard deviation of the task queue length, and β represents the adjustment factor; S402: Recalculate the resource efficiency parameter E′ according to the state of the newly allocated resource pool after resource adjustment, and compare it with the threshold E threshold By comparison, when the recalculated resource efficiency parameter is greater than or equal to the resource efficiency parameter threshold, the task queue is marked as restored to normal.

6. A centralized management system for design and development data based on a cloud platform, characterized by: The system includes: a data acquisition module, a performance evaluation module, a resource allocation module and an adaptive adjustment module. The data acquisition module is used to collect and manage design and development data, monitor access records and log information during data upload and download, and classify data according to design stage, functional module or project type. A label is assigned to each data through a label generation function, and resource usage is recorded at the same time, including computing resource occupancy rate, memory resource utilization rate, storage resource utilization rate and task queue length, and a resource usage log is generated; the performance evaluation module is used to evaluate resource utilization efficiency, formulate resource allocation rules based on design and development task requirements, allocate computing, memory and storage resources, monitor resource usage, and adjust resource allocation plans. , and record resource usage data; the resource allocation module is used to record resource usage data to generate resource efficiency parameters, combine historical data and current resource status analysis, judge the efficiency status of resource allocation, and generate an efficiency status mark; the adaptive adjustment module is used to monitor the task queue status in real time, judge the cause of insufficient resource efficiency, identify the problem of sudden task growth and adjust resource allocation, reallocate or recycle resources according to current and historical resource usage data, and update resource usage records. The output end of the data acquisition module is connected to the input end of the performance evaluation module, the output end of the performance evaluation module is connected to the input end of the resource allocation module, and the output end of the resource allocation module is connected to the input end of the adaptive adjustment module.

7. The cloud platform-based centralized management system for design and development data according to claim 6, characterized in that: The data acquisition module includes a data monitoring unit, a data classification unit, a label generation unit and a resource usage recording unit. The data monitoring unit is used to monitor the log information during data upload, download and data transmission, and record each access and data operation behavior; The data classification unit is used to classify data according to design stage, functional module or project type; the label generation unit is used to generate corresponding labels for the classified data; the resource usage recording unit is used to record computing task load, storage occupancy, task completion rate and task queue length, and generate a resource usage log.

8. The cloud platform-based centralized management system for design and development data according to claim 6, characterized in that: The performance evaluation module includes a resource efficiency calculation unit and a resource adjustment amount calculation unit, and the resource efficiency calculation unit calculates the average resource utilization rate based on the log information provided by the data acquisition module; The resource adjustment amount calculation unit is used to calculate the resource adjustment amount according to the resource adjustment amount calculation formula to adjust the resource allocation plan; the resource allocation module includes a resource efficiency analysis unit and an exception marking unit, the resource efficiency analysis unit is used to calculate the resource efficiency parameters according to the recorded current storage occupancy, calculation task load, task completion rate and task queue length data; the exception marking unit is used to judge the state of insufficient or reasonable allocation efficiency, and mark the resource allocation with insufficient efficiency, and record the relevant resource number and task queue information.

9. The cloud platform-based centralized management system for design and development data according to claim 6, characterized in that: The adaptive adjustment module includes a queue monitoring unit, a sudden problem processing unit and an adjustment verification unit. The queue monitoring unit is used to monitor the length of the task queue and its changing trend in real time to determine whether there is a sudden problem. The sudden problem processing unit is used to temporarily allocate additional resources from the resource pool to relieve pressure when the task queue surges, and release excess resources after the problem is solved. The adjustment verification unit is used to recalculate resource efficiency parameters according to the adjusted resource status, verify the rationality of the adjustment, and mark the task queue that has returned to normal.

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