CN116187588A - Project task information extraction and cost optimization method and device and electronic equipment - Google Patents
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Abstract
The invention belongs to the technical field of engineering cost information processing, and relates to a project task information extraction and cost optimization method, a project task information extraction and cost optimization device and electronic equipment, wherein the project task information extraction and cost optimization method comprises the following steps of: establishing a history project task information base; extracting configuration data and cost data; classifying and labeling; extracting characteristic parameters of configuration data; establishing a neural network model; training to obtain a cost optimization model; inputting characteristic parameters of configuration data of each current project task into a cost optimization model; according to the cost data of the current project task, adjusting the configuration data of the current project task; and outputting the adjusted configuration data and the manufacturing cost data. According to the invention, the cost optimization model is obtained through the neural network model, after configuration data of project tasks are input, a group of cost data which is subjected to optimization processing of the cost optimization model is obtained, and the configuration data is adjusted, so that the optimal configuration scheme of each project task is obtained.
Description
Technical Field
The invention relates to the technical field of engineering cost information processing, in particular to a project task information extraction and cost optimization method and device and electronic equipment.
Background
The construction cost refers to the construction price of the project, and refers to the sum of all the costs expected or actually required for construction of one project period. A plurality of project tasks exist in one engineering project period, and technicians distribute material resources, manpower resources and the like of each project task in the project period in a manner of artificial design, so that labor cost, material cost, management cost and the like generated in the construction process are in a reasonable range.
The project tasks of one project period are often associated with each other, because the number of personnel and the total amount of materials which can be input are constant, the input distribution of one project task can influence the input of other projects and the cost of the whole project period, but in the prior art, the phenomenon of high manufacturing cost easily occurs by manually distributing the design modes of the material resource, the manpower resource and the like of each project task, in the design process, the designer can only consider the configuration scheme of one resource distribution design, the workload is large when considering various configuration schemes, the design result of the resource configuration of each project task is easily influenced by the experience of the designer, and the problem of unreasonable manufacturing cost of the project is easily caused by the imperfect configuration scheme of the resource distribution design, so that the benefit of construction enterprises cannot be ensured.
Disclosure of Invention
In order to solve the technical problems, the invention provides a project task information extraction and cost optimization method and device and electronic equipment.
In a first aspect, the present invention provides a method for extracting project task information and optimizing cost, including:
establishing a historical project task information base, and acquiring historical project task information;
decomposing the historical project task information, and extracting configuration data and cost data of each project task contained in the historical project task information;
classifying and labeling the manufacturing cost data of each project task according to the size of a set range;
extracting characteristic parameters of configuration data of each project task;
processing characteristic parameters of configuration data of each project task, and establishing a neural network model;
training the neural network model by utilizing characteristic parameters of configuration data of each project task and classification labeling results to obtain a cost optimization model;
acquiring current project task information, decomposing to obtain configuration data of each current project task, and extracting characteristic parameters of the configuration data of each current project task;
inputting characteristic parameters of configuration data of each current project task into the cost optimization model;
loading the cost optimization model, and processing characteristic parameters of configuration data of each current project task by using the cost optimization model to obtain cost data of each current project task;
adjusting the configuration data of the current project task according to the manufacturing cost data of the current project task;
and outputting the configuration data of each current project task after adjustment and the manufacturing cost data of each current project task.
The second aspect provides a project task information extraction and cost optimization device, which comprises an acquisition unit, an extraction unit, a classification labeling unit, a first characteristic parameter extraction unit, a neural network model establishment unit, a neural network model training unit, a second characteristic parameter extraction unit, an input unit, a model loading unit, an adjustment unit and an output unit;
the acquisition unit is used for establishing a history project task information base and acquiring history project task information;
the extraction unit is used for decomposing the historical project task information and extracting configuration data and manufacturing cost data of each project task contained in the historical project task information;
the classifying and labeling unit is used for classifying and labeling the manufacturing cost data of each project task according to the size of a set range;
the first characteristic parameter extraction unit is used for extracting characteristic parameters of configuration data of each project task;
the neural network model building unit is used for processing characteristic parameters of the configuration data of each project task and building a neural network model;
the neural network model training unit is used for training the neural network model by utilizing the characteristic parameters of the configuration data of each project task and the classification labeling result to obtain a cost optimization model;
the second characteristic parameter extraction unit is used for acquiring the current project task information, decomposing to obtain configuration data of each current project task, and extracting characteristic parameters of the configuration data of each current project task;
the input unit is used for inputting characteristic parameters of configuration data of each current project task into the cost optimization model;
the model loading unit is used for loading the cost optimization model, and processing characteristic parameters of configuration data of each current project task by utilizing the cost optimization model to obtain cost data of each current project task;
the adjusting unit is used for adjusting the configuration data of the current project task according to the manufacturing cost data of the current project task;
the output unit is used for outputting the configuration data of each current project task after adjustment and the manufacturing cost data of each current project task.
In a third aspect, the present invention discloses an electronic device, comprising:
a processor and a memory;
the memory is used for storing computer operation instructions;
and the processor is used for executing project task information extraction and cost optimization methods by calling the computer operation instructions.
According to the invention, the cost optimization model is obtained through the neural network model, after configuration data of project tasks are input, a group of cost data which is subjected to optimization processing of the cost optimization model is obtained, and the configuration data is adjusted, so that the optimal configuration scheme of each project task is obtained.
On the basis of the technical scheme, the invention can be improved as follows.
Further, extracting configuration data and cost data of each project task contained in the historical project task information includes: and screening the configuration data of the project task in the historical project task information according to a plurality of set interval ranges, and deleting the configuration data and the manufacturing cost data outside the set interval ranges.
Further, the configuration data of the project task comprises the sequence of the project task name and the single project task name in all the project tasks, the material consumption, the use quantity of construction tools, the personnel configuration quantity and the project task period; the order in the project tasks is configured according to the construction order of a single project task in all project tasks; the cost data comprises labor cost data, material cost data, construction tool use cost data, enterprise management cost data and engineering regulation cost data.
Further, extracting characteristic parameters of configuration data of each project task comprises converting sequences of single project tasks in all project tasks, material consumption of the project tasks, using quantity of construction machines, personnel configuration quantity and project task period into characteristic vectors.
Further, processing the characteristic parameters of the configuration data of each project task includes: coding each obtained set interval range; and taking the result of classifying and labeling the characteristic parameters of the configuration data of the project task after encoding and the manufacturing cost data corresponding to the characteristic parameters of the configuration data of the project task as sample data of the manufacturing cost optimization model.
Further, the one-hot coding mode is utilized to code the characteristic parameters of the configuration data of each project task, and the characteristic vector of the characteristic parameters of the configuration data of each project task is determined;
performing two classification on the feature vector by using a softmax function, and determining an output result of the neural network model;
calculating a cross entropy loss value by using the cross entropy loss function;
and determining the accuracy of the output result of the neural network model according to the cross entropy loss value.
Further, after the cost data of each current project task is obtained, the method further comprises the steps of calculating the sum of the cost data of all the current project tasks as an actual value, matching the input historical project task information with the characteristic parameters in the same setting range, calculating the sum of the cost data in all the historical project task information as a reference value, and comparing the actual value with the reference value until the actual value is smaller than the reference value.
Further, adjusting the configuration data of the current project task includes: and adjusting the project task period of the project task name by changing the sequence of a plurality of project task names in all project tasks, or adjusting one or two or three of the material consumption of the project task names or the use quantity of construction machines or the personnel configuration quantity.
Drawings
FIG. 1 is a flow chart of a project task information extraction and cost optimization method provided in embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a project task information extraction and cost optimization apparatus according to embodiment 2 of the present invention;
fig. 3 is a schematic diagram of an electronic device according to embodiment 3 of the present invention.
Icon: 30-an electronic device; 310-a processor; 320-bus; 330-memory; 340-transceiver.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Example 1
As an embodiment, as shown in fig. 1, to solve the above technical problem, the present embodiment provides a method for extracting project task information and optimizing cost, including:
establishing a historical project task information base, and acquiring historical project task information;
decomposing the historical project task information, and extracting configuration data and cost data of each project task contained in the historical project task information;
classifying and marking the manufacturing cost data of each project task according to the size of a set range;
extracting characteristic parameters of configuration data of each project task;
processing characteristic parameters of configuration data of each project task, and establishing a neural network model;
training the neural network model by utilizing the characteristic parameters of the configuration data of each project task and the classification labeling result to obtain a cost optimization model;
acquiring current project task information, decomposing to obtain configuration data of each current project task, and extracting characteristic parameters of the configuration data of each current project task;
inputting characteristic parameters of configuration data of each current project task into a cost optimization model;
loading a cost optimization model, and processing characteristic parameters of configuration data of each current project task by using the cost optimization model to obtain cost data of each current project task;
adjusting the configuration data of the current project task according to the manufacturing cost data of the current project task;
and outputting the configuration data of each current project task after adjustment and the manufacturing cost data of each current project task.
Optionally, extracting configuration data and cost data of each project task included in the historical project task information includes: and screening the configuration data of the project task in the history project task information according to a plurality of set interval ranges, and deleting the configuration data and the manufacturing cost data outside the set interval ranges.
Optionally, the configuration data of the project task includes the sequence of the project task name and the single project task name in all project tasks, the material consumption, the use quantity of construction tools, the personnel configuration quantity and the project task period; the order in the project tasks is configured according to the construction order of a single project task in all project tasks; the cost data includes labor cost data, material cost data, construction tool use cost data, enterprise management cost data and engineering regulation cost data.
In the actual application process, acquiring the sequence of the project task names and single project task names in all project tasks in the history project task information, respectively setting different ranges for the project task names of the same sequence according to the material consumption, the construction tool use quantity, the personnel configuration quantity and the project task period, and deleting the project task names with the material consumption, the construction tool use quantity, the personnel configuration quantity and the project task period exceeding the set ranges.
Optionally, extracting the feature parameters of the configuration data of each project task includes converting the sequence of the single project task in all project tasks, the material consumption of the project task, the use quantity of construction tools, the personnel configuration quantity and the project task period into feature vectors.
In the practical application process, a code is set in the interval of each set range value, and simultaneously, the project task name and the sequence of the project task name in all project tasks are coded. For example: the codes of the project task material consumption are 21, 22 and 23 … …, the codes of the construction machine use quantity are 31, 32 and 33 … …, the codes of the personnel configuration quantity are 41, 42 and 43 … …, the codes of the project task period are 51, 52 and 53 … …, the codes of the project task names are 01, 02 and 03 … …, and the sequences of the project task names in all project tasks are 11, 12 and 13 … ….
Optionally, processing the feature parameters of the configuration data of each project task includes: coding each obtained set interval range; and taking the result of classifying and labeling the characteristic parameters of the configuration data of the project task after the encoding and the manufacturing cost data corresponding to the characteristic parameters of the configuration data of the project task as sample data of a manufacturing cost optimization model.
In the practical application process, each group of project task names and a group of material consumption codes, construction machine use quantity codes, personnel configuration quantity codes and project task period codes corresponding to the sequence of the single project task name in all project tasks are coded. And merging each group of project task names with a group of coded material consumption, the number of used construction tools, the number of personnel configuration and project task periods corresponding to the sequence of the project task names in all project tasks to obtain a group of characteristic parameters. For example: the code of the project task name is 01, the order of the project task names in all project tasks is 12, the project task material consumption is in a second range section, the construction machine use number is in a first range section, the personnel configuration number is in a third range section, the project task period is in a second range section, and the project task period is represented as (01,12,22,31,43,52) and is used as sample input data with a plurality of attributes.
Optionally, the one-hot coding mode is utilized to code the characteristic parameters of the configuration data of each project task, and the characteristic vector of the characteristic parameters of the configuration data of each project task is determined;
performing two classification on the feature vector by using a softmax function to determine an output result of the neural network model;
calculating a cross entropy loss value by using the cross entropy loss function;
and determining the accuracy of the output result of the neural network model according to the cross entropy loss value.
And encoding a group of characteristic parameters by using a one-hot encoding mode to obtain characteristic vectors, and performing two-classification on the characteristic vectors by using a softmax function to determine an output result of the neural network model, wherein the output result is two result labels and the probability corresponding to each result.
The parameters of the neural network model are obtained by calculating the value of the cross entropy loss and minimizing the cross entropy loss, the smaller the cross entropy loss is, the more accurate the parameters of the neural network model are, and finally the manufacturing cost optimization model is obtained, so that the model parameters can be adjusted conveniently, and the accuracy of the output result of the neural network model is improved.
Optionally, adjusting the configuration data of the current project task includes: the project task period of the project task names is adjusted by changing the sequence of the project task names in all project tasks, or one or two or three of the material consumption of the project task names or the use quantity of construction tools or the personnel configuration quantity are changed, and the project task period of the project task names is adjusted, which concretely comprises the following steps: after configuration data of each project task is input, a group of cost data subjected to cost optimization model optimization processing can be obtained, the configuration data of the current project task is adjusted by obtaining an optimal configuration scheme of each project task through cost comparison, namely, the order or material consumption, the number of construction machines and tools, the personnel configuration number and the project task period of a single project task name in the configuration data in all the project tasks are adjusted, and the optimization of the cost within a preset range is realized, for example: the project task period is reduced by increasing the personnel configuration quantity, and an optimization scheme with lower manufacturing cost is obtained; or the personnel configuration quantity of the first project task is reduced, and the personnel configuration quantity of the second project task is increased, so that the project task period of the second project task is shortened, and an optimization scheme with lower manufacturing cost is obtained; or the order of the first project task and the second project task is adjusted, and the first project task and the second project task are input into the neural network model again to obtain an optimization scheme with lower manufacturing cost; or the project task period is shortened by changing the material consumption, the use quantity of construction tools and the personnel configuration quantity of project task names, and an optimization scheme with lower manufacturing cost is obtained.
Example 2
Based on the same principle as the method shown in the embodiment 1 of the present invention, as shown in fig. 2, the embodiment of the present invention further provides a project task information extraction and cost optimization device, which includes an acquisition unit, an extraction unit, a classification labeling unit, a first feature parameter extraction unit, a neural network model establishment unit, a neural network model training unit, a second feature parameter extraction unit, an input unit, a model loading unit, an adjustment unit and an output unit;
the acquisition unit is used for establishing a history project task information base and acquiring history project task information;
the extraction unit is used for decomposing the historical project task information and extracting configuration data and cost data of each project task contained in the historical project task information;
the classification marking unit is used for classifying and marking the manufacturing cost data of each project task according to the size of the set range;
a first characteristic parameter extraction unit for extracting characteristic parameters of configuration data of each project task;
the neural network model building unit is used for processing the characteristic parameters of the configuration data of each project task and building a neural network model;
the neural network model training unit is used for training the neural network model by utilizing the characteristic parameters of the configuration data of each project task and the classification labeling result to obtain a cost optimization model;
the second characteristic parameter extraction unit is used for acquiring the task information of the current project, decomposing to obtain configuration data of each current project task, and extracting characteristic parameters of the configuration data of each current project task;
the input unit is used for inputting characteristic parameters of configuration data of each current project task into the cost optimization model;
the model loading unit is used for loading a cost optimization model, and processing characteristic parameters of configuration data of each current project task by using the cost optimization model to obtain cost data of each current project task;
the adjusting unit is used for adjusting the configuration data of the current project task according to the manufacturing cost data of the current project task;
the output unit is used for outputting the configuration data of each current project task after adjustment and the manufacturing cost data of each current project task.
Optionally, extracting configuration data and cost data of each project task included in the historical project task information includes: and screening the configuration data of the project task in the history project task information according to a plurality of set interval ranges, and deleting the configuration data and the manufacturing cost data outside the set interval ranges.
Optionally, the configuration data of the project task includes the project task name and the sequence, the material consumption, the number of construction tools used, the personnel configuration number and the project task period of the single project task in all project tasks; the order in the project tasks is configured according to the construction order of a single project task in all project tasks; the cost data includes labor cost data, material cost data, construction tool use cost data, enterprise management cost data and engineering regulation cost data.
Optionally, extracting the feature parameters of the configuration data of each project task includes converting the sequence of the single project task in all project tasks, the material consumption of the project task, the use quantity of construction tools, the personnel configuration quantity and the project task period into feature vectors.
Optionally, processing the feature parameters of the configuration data of each project task includes: coding each obtained set interval range; and taking the result of classifying and labeling the characteristic parameters of the configuration data of the project task after the encoding and the manufacturing cost data corresponding to the characteristic parameters of the configuration data of the project task as sample data of a manufacturing cost optimization model.
Optionally, the one-hot coding mode is utilized to code the characteristic parameters of the configuration data of each project task, and the characteristic vector of the characteristic parameters of the configuration data of each project task is determined;
performing two classification on the feature vector by using a softmax function to determine an output result of the neural network model;
calculating a cross entropy loss value by using the cross entropy loss function;
and determining the accuracy of the output result of the neural network model according to the cross entropy loss value.
Optionally, the project task information extracting and cost optimizing device further comprises a comparison and verification unit, wherein the comparison and verification unit is used for obtaining the cost data of each current project task, calculating the sum of the cost data of all the current project tasks as an actual value, matching the input historical project task information with the characteristic parameters in the same setting range, calculating the sum of the cost data in all the historical project task information as a reference value, and comparing the actual value with the reference value until the actual value is smaller than the reference value.
Optionally, adjusting the configuration data of the current project task includes: the project task period of the project task names is adjusted by changing the sequence of the project task names in all project tasks, or one or two or three of the material consumption of the project task names or the use quantity of construction machines or the personnel configuration quantity are changed, so that the project task period of the project task names is adjusted.
Example 3
Based on the same principle as the method shown in the embodiment of the present invention, there is also provided an electronic device in the embodiment of the present invention, as shown in fig. 3, which may include, but is not limited to: a processor and a memory; a memory for storing a computer program; and the processor is used for executing the project task information extraction and cost optimization method shown in the embodiment of the invention by calling the computer program.
In an alternative embodiment, an electronic device is provided, the electronic device 30 shown in fig. 3 comprising: a processor 310 and a memory 330. Wherein the processor 310 is coupled to the memory 330, such as via a bus 320.
Optionally, the electronic device 30 may further comprise a transceiver 340, and the transceiver 340 may be used for data interaction between the electronic device and other electronic devices, such as transmission of data and/or reception of data, etc. It should be noted that, in practical applications, the transceiver 340 is not limited to one, and the structure of the electronic device 30 is not limited to the embodiment of the present invention.
The processor 310 may be a CPU (Central Processing Unit ), general purpose processor, DSP (Digital Signal Processor, data signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. Processor 310 may also be a combination that performs computing functions, e.g., including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
The memory 330 is used for storing application program codes (computer programs) for executing the inventive arrangements and is controlled to be executed by the processor 310. The processor 310 is configured to execute the application code stored in the memory 330 to implement what is shown in the foregoing method embodiments.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The project task information extraction and cost optimization method is characterized by comprising the following steps:
establishing a historical project task information base, and acquiring historical project task information;
decomposing the historical project task information, and extracting configuration data and cost data of each project task contained in the historical project task information;
classifying and labeling the manufacturing cost data of each project task according to the size of a set range;
extracting characteristic parameters of configuration data of each project task;
processing characteristic parameters of configuration data of each project task, and establishing a neural network model;
training the neural network model by utilizing characteristic parameters of configuration data of each project task and classification labeling results to obtain a cost optimization model;
acquiring current project task information, decomposing to obtain configuration data of each current project task, and extracting characteristic parameters of the configuration data of each current project task;
inputting characteristic parameters of configuration data of each current project task into the cost optimization model;
loading the cost optimization model, and processing characteristic parameters of configuration data of each current project task by using the cost optimization model to obtain cost data of each current project task;
adjusting the configuration data of the current project task according to the manufacturing cost data of the current project task;
and outputting the configuration data of each current project task after adjustment and the manufacturing cost data of each current project task.
2. The method for extracting and optimizing project task information according to claim 1, wherein extracting configuration data and cost data of each project task contained in the history project task information comprises: and screening the configuration data of the project task in the historical project task information according to a plurality of set interval ranges, and deleting the configuration data and the manufacturing cost data outside the set interval ranges.
3. The method for extracting project task information and optimizing cost according to any one of claims 1-2, wherein the configuration data of the project task includes the order of project task names and single project task names in all the project tasks, the material consumption, the number of construction machines and tools used, the number of personnel configuration and the project task period; the order in the project tasks is configured according to the construction order of a single project task in all project tasks; the cost data comprises labor cost data, material cost data, construction tool use cost data, enterprise management cost data and engineering regulation cost data.
4. The method for extracting and optimizing construction cost according to claim 3, wherein extracting the characteristic parameters of the configuration data of each project task includes converting the order of the individual project tasks in all the project tasks, the material consumption of the project tasks, the number of the construction machines, the number of the personnel configuration and the project task period into characteristic vectors.
5. The project task information extraction and cost optimization method according to claim 1, wherein the processing of the characteristic parameters of the configuration data of each project task comprises: coding each obtained set interval range; and taking the result of classifying and labeling the characteristic parameters of the configuration data of the project task after encoding and the manufacturing cost data corresponding to the characteristic parameters of the configuration data of the project task as sample data of the manufacturing cost optimization model.
6. The project task information extraction and cost optimization method according to claim 1, wherein the method is characterized in that the characteristic parameters of the configuration data of each project task are encoded by using a one-hot encoding mode, and the characteristic vector of the characteristic parameters of the configuration data of each project task is determined;
performing two classification on the feature vector by using a softmax function, and determining an output result of the neural network model;
calculating a cross entropy loss value by using the cross entropy loss function;
and determining the accuracy of the output result of the neural network model according to the cross entropy loss value.
7. The method for extracting and optimizing project task information according to claim 1, further comprising calculating a sum of cost data of all the current project tasks as an actual value after obtaining the cost data of each current project task, matching input historical project task information of the characteristic parameters in the same setting range, calculating a sum of the cost data in all the historical project task information as a reference value, and comparing the actual value with the reference value until the actual value is smaller than the reference value.
8. The method for extracting and optimizing project task information as defined in claim 1, wherein the adjusting the configuration data of the current project task comprises: and adjusting the project task period of the project task name by changing the sequence of a plurality of project task names in all project tasks, or adjusting one or two or three of the material consumption of the project task names or the use quantity of construction machines or the personnel configuration quantity.
9. The project task information extraction and cost optimization device is characterized by comprising an acquisition unit, an extraction unit, a classification labeling unit, a first characteristic parameter extraction unit, a neural network model establishment unit, a neural network model training unit, a second characteristic parameter extraction unit, an input unit, a model loading unit, an adjustment unit and an output unit;
the acquisition unit is used for establishing a history project task information base and acquiring history project task information;
the extraction unit is used for decomposing the historical project task information and extracting configuration data and manufacturing cost data of each project task contained in the historical project task information;
the classifying and labeling unit is used for classifying and labeling the manufacturing cost data of each project task according to the size of a set range;
the first characteristic parameter extraction unit is used for extracting characteristic parameters of configuration data of each project task;
the neural network model building unit is used for processing characteristic parameters of the configuration data of each project task and building a neural network model;
the neural network model training unit is used for training the neural network model by utilizing the characteristic parameters of the configuration data of each project task and the classification labeling result to obtain a cost optimization model;
the second characteristic parameter extraction unit is used for acquiring the current project task information, decomposing to obtain configuration data of each current project task, and extracting characteristic parameters of the configuration data of each current project task;
the input unit is used for inputting characteristic parameters of configuration data of each current project task into the cost optimization model;
the model loading unit is used for loading the cost optimization model, and processing characteristic parameters of configuration data of each current project task by utilizing the cost optimization model to obtain cost data of each current project task;
the adjusting unit is used for adjusting the configuration data of the current project task according to the manufacturing cost data of the current project task;
the output unit is used for outputting the configuration data of each current project task after adjustment and the manufacturing cost data of each current project task.
10. An electronic device, comprising:
a processor and a memory;
the memory is used for storing computer operation instructions;
the processor is configured to execute the project task information extraction and cost optimization method according to any one of claims 1 to 8 by calling the computer operation instruction.
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