Data processing method and device, equipment, medium and product

Abstract

The invention discloses a data processing method and device, equipment, a medium and a product. The method comprises the following steps: acquiring project data of a first project; the project data at least comprises one of task execution time consumption data, progress data and resource consumption data; the project data are input into a pre-trained resource consumption over-limit prediction model, a resource consumption over-limit prediction result corresponding to the first project is obtained, the resource consumption over-limit prediction result is the probability of resource consumption over-limit, and the pre-trained resource consumption over-limit prediction model is a decision tree model. According to the technical scheme, the problem that the resource consumption overrun condition cannot be determined in time at present is solved, the resource consumption overrun condition of the project can be intelligently predicted through the machine learning model, and data support is provided for project management.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a data processing method, apparatus, device, medium and product. Background Technology

[0002] Currently, during project operation, it is often impossible to effectively predict the extent to which resources are consumed beyond the limit, leading to situations where resources are consumed excessively. Summary of the Invention

[0003] This invention provides a data processing method, apparatus, device, medium, and product to solve the problem of the inability to determine resource consumption exceeding limits in a timely manner. It can intelligently predict the resource consumption exceeding limits of a project through machine learning models, providing data support for project management.

[0004] In a first aspect, embodiments of the present invention provide a data processing method, the method comprising:

[0005] Obtain the project data for the first project; the project data should include at least one of the following: task execution time data, progress data, and resource consumption data;

[0006] The project data is input into a pre-trained resource consumption over-limit prediction model to obtain the resource consumption over-limit prediction result for the first project. The resource consumption over-limit prediction result is the probability of resource consumption over-limit occurring, and the pre-trained resource consumption over-limit prediction model is a decision tree model.

[0007] Secondly, embodiments of the present invention also provide a data processing apparatus, the apparatus comprising:

[0008] The data acquisition module is used to acquire project data for the first project; the project data includes at least one of the following: task execution time data, progress data, and resource consumption data.

[0009] The model prediction module is used to input project data into a pre-trained resource consumption overrun prediction model to obtain the resource consumption overrun prediction result for the first project. The resource consumption overrun prediction result is the probability of resource consumption overrun occurring. The pre-trained resource consumption overrun prediction model is a decision tree model.

[0010] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0011] At least one processor; and

[0012] A memory communicatively connected to the at least one processor; wherein,

[0013] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data processing method according to any embodiment of the present invention.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions that are used to cause a processor to execute the data processing method described in any embodiment of the present invention.

[0015] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the data processing method as described in any of the embodiments of the present invention.

[0016] In this embodiment of the invention, project data for a first project is acquired. This project data includes at least one of the following: task execution time data, progress data, and resource consumption data. The project data is then input into a pre-trained resource consumption exceedance prediction model to obtain a resource consumption exceedance prediction result for the first project. The resource consumption exceedance prediction result represents the probability of resource consumption exceeding limits, and the pre-trained resource consumption exceedance prediction model is a decision tree model. This invention solves the problem of the inability to promptly determine resource consumption exceedance status. It can intelligently predict resource consumption exceedance status for projects through machine learning models, providing data support for project management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a data processing method provided in an embodiment of the present invention;

[0019] Figure 2 A flowchart of a data processing method provided in an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of the structure of a data processing device provided in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0023] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. The acquisition, storage, use, and processing of data in the technical solutions of this application all comply with the relevant provisions of national laws and regulations.

[0024] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the relevant content of the solution.

[0025] Figure 1 This is a flowchart illustrating a data processing method provided in an embodiment of the present invention. This embodiment is applicable to data processing scenarios. The method can be executed by a data processing device, which can be implemented in software and / or hardware and integrated into an electronic device.

[0026] like Figure 1 As shown, the data processing method includes the following steps:

[0027] S110. Obtain the project data for the first project.

[0028] Project data must include at least one of the following: task execution time, progress, and resource consumption.

[0029] For example, the first project could be a software development project, such as application development, which requires recording the time spent on module development, version iteration progress, and manpower / server resource consumption; an engineering construction project, such as building construction, which requires recording the time spent on each process, the progress of construction nodes, and the consumption of building materials / equipment resources; an event planning project, such as exhibition preparation, which requires tracking the time spent on each stage, the preparation progress, and the consumption of materials / personnel resources; or a scientific research experimental project, such as a laboratory project, which requires recording the time spent on experimental steps, the progress of stage tasks, and the consumption of reagents / instruments resources.

[0030] Project data can be obtained from various modules of the project management system, such as the financial management submodule and the progress management submodule. Data is collected and integrated from each submodule through pre-defined data interaction interfaces. Project data must include at least the source department identifier, data type, data content, and timestamp.

[0031] S120. Input the project data into the pre-trained resource consumption over-limit prediction model to obtain the resource consumption over-limit prediction result for the first project.

[0032] Among them, the resource consumption over-limit prediction result is the probability of resource consumption over-limit occurring, and the pre-trained resource consumption over-limit prediction model is a decision tree model.

[0033] Before inputting project data into the model, the project data can be preprocessed. For example, mean imputation and interpolation can be used to clean missing values ​​in task execution time, progress, and resource consumption data. Outliers can be removed or corrected using box plots and Z-scores. Non-numerical data can be encoded. Numerical features such as time consumption and resource consumption can be normalized or standardized. Derived features can be extracted and multi-dimensional data can be integrated into a structured format that the model can recognize, thereby improving data quality and model training efficiency.

[0034] The preprocessed project data of the first project is input into the pre-trained resource consumption overrun prediction model. Based on the historical project data patterns learned during the training phase, such as the correlation between abnormal task time and resource overrun, and the impact of schedule deviation on resource consumption, the model performs feature matching and logical reasoning on the input data, and finally outputs the probability value of resource consumption overrun for the project, which serves as the resource consumption overrun prediction result for the first project, providing data support for project resource management and risk warning.

[0035] This embodiment uses a decision tree to predict resource consumption exceeding limits. The decision tree improves the total purity of features during feature splits and automatically sorts features by contribution. Overfitting is prevented by structural constraints and pruning strategies to avoid the model learning too much sample noise and ensure generalization ability.

[0036] The technical solution of this embodiment involves acquiring project data for a first project. This project data includes at least one of the following: task execution time data, progress data, and resource consumption data. The project data is then input into a pre-trained resource consumption exceedance prediction model to obtain a resource consumption exceedance prediction result for the first project. The resource consumption exceedance prediction result represents the probability of resource consumption exceeding limits, and the pre-trained resource consumption exceedance prediction model is a decision tree model. This embodiment of the invention solves the problem of the inability to promptly determine resource consumption exceedance status. It utilizes a machine learning model to intelligently predict resource consumption exceedance status for projects, providing data support for project management.

[0037] Figure 2 This is a flowchart illustrating a data processing method provided in an embodiment of the present invention. This embodiment belongs to the same inventive concept as the data processing methods in the above embodiments, and further describes the model prediction process. This method can be executed by a data processing device, which can be implemented in software and / or hardware and integrated into an electronic device with application development capabilities.

[0038] like Figure 2 As shown, the data processing method in this embodiment includes the following steps:

[0039] S210, Obtain the project data for the first project.

[0040] Project data must include at least one of the following: task execution time, progress, and resource consumption.

[0041] S220. Extract features from the project data to obtain project feature data.

[0042] Feature extraction is performed on multiple types of project data to obtain multi-source project feature data for predicting resource consumption exceeding limits.

[0043] In one optional implementation, feature extraction is performed on the project data to obtain project feature data. This can be achieved by extracting features from the resource consumption data within the project data to obtain resource consumption feature data. The resource consumption feature data includes at least one of cumulative resource consumption, average hourly resource consumption, and target total resource consumption. The target total resource consumption is the planned resource consumption. Resource consumption over-limit prediction is the prediction of the probability that cumulative resource consumption will exceed the target total resource consumption.

[0044] In one optional implementation, feature extraction is performed on the project data to obtain project feature data. This can be done by extracting features from the task execution time data in the project data to obtain task execution time feature data. The task execution time feature data includes at least one of the target total task execution time and the cumulative task execution time.

[0045] For example, project feature data may include basic features, time features, schedule features, and risk features.

[0046] Basic characteristics include a unique project identifier and project type, such as software development, hardware manufacturing, and consulting services. Data includes the total contract amount, planned total cost, current cumulative actual cost, and the cost deviation rate calculated by dividing the difference between the current cumulative actual cost and the planned total cost by the planned total cost. This data can be sourced from the project management system, contract system, budget management system, and financial system.

[0047] The time-related features include total planned working hours, current cumulative actual working hours, time deviation rate obtained by dividing the difference between current cumulative actual working hours and total planned working hours by total planned working hours, labor cost rate as a percentage of current cumulative actual cost, and average hourly cost obtained by dividing current cumulative actual cost by current cumulative actual working hours.

[0048] Dynamic schedule features include the current percentage of progress, remaining time, and the cost consumption rate per unit of progress. The cost consumption rate is the ratio of the current cumulative actual cost to the current percentage of progress, as well as the predicted total cost calculated based on this rate.

[0049] Risk-related characteristics include the number of change orders reflecting the frequency of requirement changes, the number of recorded defects or delays, and the team experience value corresponding to the average number of years of project experience of team members.

[0050] S230. Input the project feature data into the pre-trained resource consumption over-limit prediction model to obtain the resource consumption over-limit prediction result for the first project.

[0051] Among them, the resource consumption over-limit prediction result is the probability of resource consumption over-limit occurring, and the pre-trained resource consumption over-limit prediction model is a decision tree model.

[0052] S240. Compare the predicted results of resource consumption exceeding the limit with the preset early warning threshold, and obtain the comparison results.

[0053] There can be multiple preset warning thresholds, such as 30% and 70%. The preset warning thresholds are compared with 30% and 70% to obtain the comparison results.

[0054] S250. Determine the warning level based on the comparison results, and issue a warning according to the warning strategy corresponding to the warning level.

[0055] For example, the warning levels include Warning Level Safe, Warning, and Exceeding Limits. If the predicted probability is <30%, it is at the Safe level; if the probability is 30% ≤ Probability < 70%, it is at the Warning level; if the probability is ≥ 70%, it is at the Exceeding Limits level.

[0056] Regarding the security level, resource consumption is controllable. The early warning strategy involves tracking core data on a regular weekly basis, generating analysis reports, and synchronizing them with the project team to maintain existing control processes without requiring additional intervention.

[0057] For the early warning level, the early warning strategy is to initiate a risk response, check the reasons for abnormalities such as cost and working hour deviation rates and the number of change orders, organize a cross-team assessment meeting to formulate prevention and control measures, shorten the monitoring cycle to daily, and track the implementation effect in real time.

[0058] For cases exceeding the limit, the early warning strategy is to send a red alert to management, establish an emergency team to review the problem, formulate emergency measures such as resource reduction and budget adjustment, and provide feedback on the progress of the handling every half day until the risk is downgraded.

[0059] In one optional implementation, the training process of the resource consumption over-limit prediction model may include: determining sample data, which includes historical project data of at least one historical project and the actual resource consumption over-limit status of the historical project data; determining the model parameters of the decision tree model; and inputting the sample data and the corresponding actual resource consumption over-limit status into the decision tree model to iteratively train the decision tree model to obtain a pre-trained resource consumption over-limit prediction model.

[0060] The decision tree model addresses a binary classification problem: predicting whether a project's resource consumption will exceed limits. A label of 0 indicates no limit exceedance, while 1 indicates limit exceedance. The actual resource consumption exceeding status in historical project data serves as the binary classification label.

[0061] Determine the core parameters of the decision tree model, such as feature selection criteria, maximum depth, minimum number of split samples per node, and class weights. Input preprocessed historical project data along with corresponding binary labels into the decision tree model. Through iterative learning, the model learns the mapping between features and resource consumption exceeding limits. After training, a pre-trained model that can accurately predict whether new projects will experience resource consumption exceeding limits is obtained. The model can be encapsulated as a RESTful API, deployed in a Docker container, and load-balanced using Nginx.

[0062] For example, in a specific model training instance, the sample data is first determined, which includes historical project data of at least one historical project, and the actual resource consumption exceeding the limit status of each historical project, i.e., binary label labeling, where label 0 represents not exceeding the limit and label 1 represents exceeding the limit.

[0063] The model parameters were then configured as follows: the Gini coefficient was used as the feature selection criterion, the maximum depth of the tree was limited to 5 layers, and the minimum number of split samples for a node was set to 20, meaning that splitting was only allowed when the number of samples in a node was greater than or equal to 20. In order to balance the class distribution, a weight of 1 was assigned to samples that did not exceed the limit, and a weight of 3 was assigned to samples that exceeded the limit, thereby strengthening the model's learning of samples that exceeded the limit.

[0064] Finally, the preprocessed historical project feature data and corresponding binary classification labels are input into the decision tree model. The model completes training by iteratively splitting nodes, learning the mapping rules between features and the over-limit state, and obtains a pre-trained resource consumption over-limit prediction model.

[0065] This embodiment's decision tree prediction model can integrate multi-source heterogeneous data such as sales contracts and supply chain logs, breaking through the dependence on single internal structured data. Through dynamic iterative training, it adapts to market changes and historical trends, and can adjust the model logic without manual intervention, solving the problem of insufficient adaptability of static models. It integrates multi-dimensional features such as progress and working hours to achieve comprehensive analysis, making up for the defects of functional fragmentation. At the same time, its native non-linear relationship mining capability and automated training characteristics greatly improve prediction accuracy and response efficiency, avoiding the subjective bias and lag of manual intervention.

[0066] The technical solution of this embodiment involves acquiring project data for a first project; the project data includes at least one of the following: task execution time data, progress data, and resource consumption data; extracting features from the project data to obtain project feature data; inputting the project feature data into a pre-trained resource consumption exceedance prediction model to obtain a resource consumption exceedance prediction result for the first project; wherein, the resource consumption exceedance prediction result represents the probability of resource consumption exceeding the limit, and the pre-trained resource consumption exceedance prediction model is a decision tree model; comparing the resource consumption exceedance prediction result with a preset warning threshold, obtaining a comparison result; determining the warning level based on the comparison result, and issuing a warning according to the warning strategy corresponding to the warning level. This embodiment of the invention solves the problem of the inability to promptly determine resource consumption exceedance status. It can intelligently predict resource consumption exceedance status of projects through machine learning models and issue timely warnings based on the exceedance status, providing data support for project management.

[0067] Figure 3 This is a schematic diagram of the structure of a data processing device provided in an embodiment of the present invention. This embodiment is applicable to data processing scenarios. The device can be implemented by software and / or hardware and integrated into an electronic device.

[0068] like Figure 3 As shown, the data processing device includes a data acquisition module 310 and a model prediction module 320.

[0069] The data acquisition module 310 is used to acquire project data for the first project. The project data includes at least one of the following: task execution time data, progress data, and resource consumption data. The model prediction module 320 is used to input the project data into a pre-trained resource consumption over-limit prediction model to obtain the resource consumption over-limit prediction result corresponding to the first project. The resource consumption over-limit prediction result is the probability of resource consumption over-limit occurring, and the pre-trained resource consumption over-limit prediction model is a decision tree model.

[0070] The technical solution of this invention involves acquiring project data for a first project. This project data includes at least one of the following: task execution time data, progress data, and resource consumption data. The project data is then input into a pre-trained resource consumption exceedance prediction model to obtain a resource consumption exceedance prediction result for the first project. The resource consumption exceedance prediction result represents the probability of resource consumption exceeding limits, and the pre-trained resource consumption exceedance prediction model is a decision tree model. This invention solves the problem of the inability to promptly determine resource consumption exceedance status. It utilizes a machine learning model to intelligently predict resource consumption exceedance status for projects, providing data support for project management.

[0071] In one alternative embodiment, the apparatus further includes:

[0072] The early warning module is used to compare the predicted results of resource consumption exceeding the limit with the preset early warning threshold. The preset early warning threshold is used to obtain the comparison result. The early warning level is determined based on the comparison result, and an early warning is issued according to the early warning strategy corresponding to the early warning level.

[0073] In one alternative implementation, the model prediction module 320 is specifically used for:

[0074] Feature extraction is performed on the project data to obtain project feature data; the project feature data is then input into a pre-trained resource consumption over-limit prediction model to obtain the resource consumption over-limit prediction result for the first project.

[0075] In an optional implementation, the model prediction module 320 is further configured to:

[0076] Feature extraction is performed on the resource consumption data in the project data to obtain resource consumption feature data; the resource consumption feature data includes at least one of the following: cumulative resource consumption, average hourly resource consumption, and target total resource consumption.

[0077] In an optional implementation, the model prediction module 320 is further configured to:

[0078] Feature extraction is performed on the task execution time data in the project data to obtain task execution time feature data; wherein, the task execution time feature data includes at least one of the target total task execution time and the cumulative task execution time.

[0079] In one alternative embodiment, the apparatus further includes:

[0080] The model training module is used to determine sample data, which includes historical project data of at least one historical project and the actual resource consumption exceeding the limit status of the historical project data; determine the model parameters of the decision tree model, and input the sample data and the corresponding actual resource consumption exceeding the limit status into the decision tree model to iteratively train the decision tree model to obtain a pre-trained resource consumption exceeding the limit prediction model.

[0081] The data processing apparatus provided in the embodiments of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0082] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0083] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0084] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0085] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as data processing methods.

[0086] In some embodiments, the data processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the data processing method by any other suitable means (e.g., by means of firmware).

[0087] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0088] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0089] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0090] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0091] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0092] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0093] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0094] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the data processing method provided in any embodiment of this application.

[0095] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0096] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0097] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data processing method, characterized in that, include: Obtain project data for the first project; the project data includes at least one of the following: task execution time data, progress data, and resource consumption data. The project data is input into a pre-trained resource consumption overrun prediction model to obtain the resource consumption overrun prediction result corresponding to the first project. The resource consumption overrun prediction result is the probability of resource consumption overrun occurring, and the pre-trained resource consumption overrun prediction model is a decision tree model.

2. The method according to claim 1, characterized in that, The method further includes: The comparison result is obtained by comparing the predicted resource consumption exceedance result with a preset warning threshold. The warning level is determined based on the comparison results, and a warning is issued according to the warning strategy corresponding to the warning level.

3. The method according to claim 1, characterized in that, The step of inputting the project data into a pre-trained resource consumption exceedance prediction model to obtain the resource consumption exceedance prediction result corresponding to the first project includes: Feature extraction is performed on the project data to obtain project feature data; The project feature data is input into a pre-trained resource consumption overrun prediction model to obtain the resource consumption overrun prediction result for the first project.

4. The method according to claim 3, characterized in that, The step of extracting features from the project data to obtain project feature data includes: Feature extraction is performed on the resource consumption data in the project data to obtain resource consumption feature data; The resource consumption characteristic data includes at least one of the following: cumulative resource consumption, average hourly resource consumption, and target total resource consumption.

5. The method according to claim 3, characterized in that, The step of extracting features from the project data to obtain project feature data further includes: Feature extraction is performed on the task execution time data in the project data to obtain task execution time feature data; The task execution time characteristic data includes at least one of the target total task execution time and the cumulative task execution time.

6. The method according to claim 1, characterized in that, The training process of the resource consumption over-limit prediction model includes: Determine sample data, which includes historical project data of at least one historical project, and the actual resource consumption exceeding the limit status of the historical project data; The model parameters of the decision tree model are determined, and the sample data and the corresponding actual resource consumption exceeding the limit are input into the decision tree model to iteratively train the decision tree model, so as to obtain a pre-trained resource consumption exceeding the limit prediction model.

7. A data processing apparatus, characterized in that, include: The data acquisition module is used to acquire project data for the first project; the project data includes at least one of the following: task execution time data, progress data, and resource consumption data. The model prediction module is used to input the project data into a pre-trained resource consumption overrun prediction model to obtain the resource consumption overrun prediction result corresponding to the first project. The resource consumption overrun prediction result is the probability of resource consumption overrun occurring, and the pre-trained resource consumption overrun prediction model is a decision tree model.

8. An electronic device, characterized in that, The device includes at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data processing method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the data processing method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the data processing method as described in any one of claims 1-6.

Images