CN113643007B - Visualization-based research and development project work order processing method and system - Google Patents
Visualization-based research and development project work order processing method and system Download PDFInfo
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Abstract
The invention relates to a method and a system for processing a research and development project work order based on visualization, wherein the work order is checked through an AI model obtained by configuring the research and development project work order theme and the multi-dimensional work order content, the association degree between the content of a periodic work order and the content of an annotation work order is further mined by means of a periodic dividing and treating thought by virtue of a matching result between the research and development project work order and the work order content representing the research and development project work order, the limitation on the analysis of the research and development project work order theme is optimized, the accuracy of the work order checking result is improved, and the credibility of the work order checking is further ensured. In addition, different reference research and development project work orders can be used as references of the research and development project work orders with checking requirements, so that adaptability adjustment and optimization of the research and development project work orders are achieved.
Description
Technical Field
The invention relates to the technical field of visualization and research and development project processing, in particular to a method and a system for processing a work order of a research and development project based on visualization.
Background
Visualization (Visualization) is a theory, method and technique that uses computer graphics and image processing techniques to convert data into graphics or images to be displayed on a screen and then perform interactive processing. With the continuous development of the visualization technology, the application field of the visualization technology is more and more extensive, taking research and development project information processing as an example, the combination of the visualization technology and the research and development project work order can reduce the labor cost and improve the processing efficiency of the research and development project work order. However, in the practical application process, when the work order project needs to be checked, the accuracy and the reliability of the work order check are difficult to be guaranteed by the related technology.
Disclosure of Invention
In a first aspect, an embodiment of the present invention provides a visualization-based method for processing a work order of a research and development project, which is applied to a system for processing a work order of a research and development project, and the method at least includes: determining a research and development project work order with a checking requirement and a reference research and development project work order record, mining key project topics of the research and development project work order with the checking requirement and each reference research and development project work order in the reference research and development project work order record by means of an AI model, determining a transition project topic with the checking requirement bound by the research and development project work order with the checking requirement and a reference transition project topic bound by the reference research and development project work order, and configuring the contents of the research and development project work order and a multi-dimensional work order by the AI model; determining a checking condition bound by the research and development project work order with checking requirements from the reference research and development project work order record according to the transition project theme with checking requirements and the reference transition project theme, wherein the checking condition covers at least one reference research and development project work order; the AI model obtained by configuring the research and development project work order theme and the multi-dimensional work order content comprises the following steps: transmitting the example research and development project work order into the AI model to obtain an example research and development project work order theme, wherein the example research and development project work order comprises work order content indications; performing key project theme mining on the work order content indication according to a content analysis model to obtain example work order information; and configuring the AI model according to the example work order information and the example development project work order subject.
Preferably, the determining, from the reference research and development project work order record according to the transition project theme with the verification requirement and the reference transition project theme, the verification condition bound to the research and development project work order with the verification requirement includes: processing the transitional item theme with the verification requirement and the reference transitional item theme based on the integral compression unit and the classification unit to determine the item theme with the verification requirement and the reference item theme; and determining the verification condition bound by the research and development project work order with the verification requirement from the reference research and development project work order record according to the project theme with the verification requirement and the reference project theme.
Preferably, the method further comprises the following steps: performing key project theme mining on the visual content associated with the research and development project work order with the verification requirement according to a content analysis model, and determining work order information; and positioning the checking condition according to the work order information, and determining the optimized checking condition bound by the research and development project work orders with checking requirements, wherein the optimized checking condition covers at least one reference research and development project work order.
Preferably, the positioning the verification condition according to the work order information and determining the optimized verification condition bound to the research and development project work order with the verification requirement includes: positioning according to the quantitative difference between the work order information and at least one reference transition item theme bound to the verification condition; and determining at least one reference transition project theme of which the quantitative difference is not greater than a set judgment value, and taking the reference research and development project work order bound by the determined reference transition project theme as the optimized verification condition.
Preferably, the method further comprises: performing key project theme mining on at least one content label associated with the research and development project work order with the checking requirement according to the content analysis model, and determining visual label expression, wherein each content label corresponds to at least one content set in the research and development project work order with the checking requirement; positioning the checking condition or the optimized checking condition according to the visual label expression, and determining the target checking condition bound by the research and development project work order with checking requirement, wherein the target checking condition covers at least one reference research and development project work order; wherein, the positioning the checking condition or the optimized checking condition according to the visual type label expression and determining the target checking condition bound by the research and development project work order with checking requirement comprises: locating according to the quantitative difference between the visual type label expression and the checking condition or the optimized checking condition, wherein the reference transition item theme is not less than one; and determining at least one reference project theme with the quantization difference not larger than a set judgment value, and taking a reference development project work sheet bound with the determined reference transition project theme as the target verification condition.
Preferably, the configuring the AI model according to the example work order information and the example development project work order topic includes: determining an overall transfer index according to the example work order information and the example research and development project work order theme; determining an integrity model evaluation by means of a relative model evaluation according to the integrity transfer index and the relevance of the sample development project work order and the work order content indication; configuring the AI model according to the evaluation of the integrity model; wherein determining an overall transfer index based on the example work order information and the example development project work order topic comprises: compressing the work order subject characteristics of the example research and development project and then carrying out difference processing on the compressed work order subject characteristics and the example work order information to obtain a distinctive subject array; carrying out upstream and downstream quantization processing on the distinctive subject array to determine an upstream and downstream fusion array; and performing quantitative conversion processing on the upstream and downstream fusion arrays to obtain an overall transfer index for representing the overall association degree.
Preferably, before performing key project topic mining on the work order content indication according to the content analysis model to obtain example work order information, the method further includes: pre-configuring the content analysis model in accordance with an example content segment, the example content segment including instructional work order information; wherein the pre-configuring of the content analysis model in accordance with example content segments comprises: transmitting the example content segments into the content analysis model to obtain a first auxiliary example analysis result; and improving the model variables of the content analysis model according to the first auxiliary example analysis result and the instruction type worksheet information.
Preferably, the method further comprises the following steps: performing key project theme mining on at least one stage event indication in the work order content indications according to the content analysis model to obtain at least one stage project theme, wherein each stage event indication aims to express that at least one work order content set in the example research and development project work order obtains stage model evaluation according to the stage project theme and the example research and development project work order theme; configuring the AI model in accordance with the integrity model evaluation includes: configuring the AI model according to the overall model evaluation and the staged model evaluation;
wherein, before mining key project topics for at least one stage event indication in the work order content indication according to the content analysis model to obtain at least one stage project topic, the method further includes: parsing the work order content indications to obtain not less than one periodic event indication, each of the periodic event indications covering not less than one annotation, the periodic event indication corresponding to an indication likelihood, each quantitative value representing a quantitative likelihood that the periodic event indication corresponds to the example development project work order;
wherein, the disassembling the work order content indication to obtain at least one stage event indication comprises: performing intention identification on each work order requirement data in the work order content indication to obtain the intention bound by each work order requirement data; according to the intention and a set disassembly strategy, disassembling the work order content instruction into at least one stage event instruction;
wherein the obtaining of the staged model evaluation according to the staged project theme and the example development project work order theme comprises: describing and simplifying the work order theme of the example research and development project to obtain the integral visual key data distribution; determining an attention coefficient according to the overall visual key data distribution and the staged project theme; determining a test quantification likelihood to which each episodic event indicator is bound as a function of the attention coefficient and the example development project work order topic; determining the staged model evaluation according to the test quantification possibility and the indication possibility bound by the staged event indication;
wherein the determining an attention coefficient according to the overall visual key data distribution and the episodic project topic comprises: performing difference processing on the item theme evaluation of each distribution label in the overall visual key data distribution and the staged item theme respectively to obtain a staged distinctive theme array; performing numerical processing on each array unit in the staged distinctive subject array to determine a staged upstream and downstream fused array; determining an attention coefficient according to the staged upstream and downstream fusion array;
wherein, the determining the attention coefficient according to the staged upstream and downstream fusion array comprises: processing the staged upstream and downstream fusion array according to a classification submodel to obtain a compatibility index for expressing the compatibility degree of the staged event indication and the sample research and development project work order; performing quantitative conversion on an array formed by compatibility indexes of each distribution label in the overall visual key data distribution bound by each stage event indication to obtain an attention coefficient bound by each stage event indication;
wherein the determining the quantified likelihood of the each phased event indicating the bound test as a function of the attention coefficient and the example development project work order topic comprises: fusing the project theme evaluation of each distribution label in the example research and development project work order theme with the attention coefficient to obtain a fused description array set corresponding to each stage event indication; merging the arrays in the fusion description array set to obtain a stage merging result in the stage event indication corresponding to the example research and development project work order; determining the testing quantification possibility of each work order demand data in the periodic event indication according to the periodic combination result; determining the testing quantification possibility bound by the periodic event indication according to the testing quantification possibility of each work order demand data in the periodic event indication;
wherein the determining the test quantification possibility of each work order demand data in the periodic event indication according to the periodic merging result comprises: converting the periodic event indication into a work order demand data queue, transmitting the periodic merging result into a loop model, and determining that at least one potential feature exists, wherein each work order demand data corresponds to a description array; determining the potential characteristics of the next time node by the action of the cycle model of the potential characteristics of the previous time node and the project theme array bound by the current work order requirement data of each time node; performing characteristic transformation according to the at least one potential characteristic to obtain a test array of the required data of each work order; obtaining the test quantization possibility of each work order demand data in the periodic event indication according to the test array;
determining the testing quantification possibility bound by the periodic event indication according to the testing quantification possibility of each work order demand data in the periodic event indication, wherein the testing quantification possibility comprises the following steps: and taking the weighted result of the test quantification possibility of each work order demand data in the periodic event indication as the test quantification possibility of the periodic event indication.
Preferably, the configuring the AI model according to the overall model evaluation and the staging model evaluation includes: combining the overall model evaluation and the staged model evaluation to obtain a mixed model evaluation; and evaluating model variables for improving the AI model according to the hybrid model.
In a second aspect, an embodiment of the present invention further provides a visualization-based development project work order processing system, which includes a processing engine, a network module, and a memory, where the processing engine and the memory communicate with each other through the network module, and the processing engine is configured to read a computer program from the memory and execute the computer program, so as to implement the foregoing method.
According to the method and the system for processing the research and development project work order based on visualization, the research and development project work order with the verification requirement and the reference research and development project work order record are determined; performing key project theme mining on the research and development project work order with the verification requirement and each reference research and development project work order in the reference research and development project work order record by using an AI model, determining a transition project theme with the verification requirement bound by the research and development project work order with the verification requirement and a reference transition project theme bound by the reference research and development project work order, and configuring the content of the research and development project work order and the multidimensional work order by using the AI model; the method comprises the steps of determining a checking condition bound by a research and development project work order with checking requirements from a reference research and development project work order record according to a transition project theme with checking requirements and a reference transition project theme, wherein the checking condition covers at least one reference research and development project work order, performing work order checking through an AI model obtained through the research and development project work order theme and multi-dimensional work order content configuration, further mining the association degree between the content of a periodic work order and the content of an annotated work order by means of a matching result between the research and development project work order and the work order content representing the research and development project work order, optimizing the analysis limit of the research and development project work order, improving the accuracy of the work order checking result and further ensuring the credibility of the work order checking. In addition, different reference research and development project work orders can be used as references of the research and development project work orders with checking requirements, so that adaptability adjustment and optimization of the research and development project work orders are achieved.
In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which reference numerals represent similar mechanisms throughout the various views of the drawings.
FIG. 1 is a block diagram illustrating an application scenario of an exemplary visualization-based development project work order processing method, according to some embodiments of the invention.
FIG. 2 is a diagram illustrating the hardware and software components of an exemplary development project work order processing system, according to some embodiments of the invention.
FIG. 3 is a flow diagram illustrating an exemplary visualization-based development project work order processing method and/or process, according to some embodiments of the invention.
Detailed Description
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant guidance. It will be apparent, however, to one skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, systems, compositions, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the invention.
These and other features, functions, methods of execution, and combination of functions and elements of related elements in the structure disclosed in the present application, and the economics of production may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form a part of this disclosure. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. It should be understood that the drawings are not to scale. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. It should be understood that the drawings are not to scale.
The present invention uses flow charts to illustrate the execution processes performed by a system according to an embodiment of the present invention. It should be expressly understood that the processes performed by the flowcharts may be performed out of order. Rather, these implementations may be performed in the reverse order or simultaneously. In addition, at least one other implementation may be added to the flowchart. One or more implementations may be deleted from the flowchart.
Fig. 1 is a block diagram illustrating an exemplary visualization-based development project work order processing system 300, which may include a development project work order processing system 100 and a project processing apparatus 200, according to some embodiments of the invention.
In some embodiments, as shown in FIG. 2, the development project work order processing system 100 may include a processing engine 110, a network module 120, and a memory 130, the processing engine 110 and the memory 130 communicating through the network module 120.
Network module 120 may facilitate the exchange of information and/or data. In some embodiments, the network module 120 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 120 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 120 may include at least one network access point. For example, the network module 120 may include wired or wireless network access points, such as base stations and/or network access points.
The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processing engine 110 executes the program after receiving the execution instruction.
It will be appreciated that the configuration shown in FIG. 2 is merely illustrative and that the development project work order processing system 100 may include more or fewer components than shown in FIG. 2 or may have a different configuration than shown in FIG. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.
Fig. 3 is a flowchart illustrating an exemplary visualized development project work order processing method and/or process, which is applied to the development project work order processing system 100 in fig. 1, according to some embodiments of the present invention, and may further include the following technical solutions.
Step S31, determining the research and development project work order with the checking requirement and the reference research and development project work order record, mining the key project theme of each reference research and development project work order in the research and development project work order with the checking requirement and the reference research and development project work order record by means of an AI model, and determining the transition project theme with the checking requirement bound by the research and development project work order with the checking requirement and the reference transition project theme bound by the reference research and development project work order.
In the embodiment of the application, the AI model is obtained by configuring the subject of the research and development project work order and the content of the multidimensional work order, the verification requirement can be flexibly set according to the type of the work order or the progress of the research and development project, the reference research and development project work order record comprises a plurality of reference research and development project work orders, and the products related to the research and development project work orders include, but are not limited to, chips, intelligent devices and the like. Further, key project topic mining can be understood as feature extraction, and correspondingly, a project topic can be understood as work order feature information of different research and development project work orders.
And step S32, determining the verification condition bound by the research and development project work order with the verification requirement from the reference research and development project work order record according to the transition project theme with the verification requirement and the reference transition project theme.
It is to be understood that the verification case covers not less than one of the reference development project work orders. Further, the AI model configured by the developed project work order theme and the multi-dimensional work order content may include the following: transmitting the example research and development project work order into the AI model to obtain an example research and development project work order theme, wherein the example research and development project work order comprises work order content indications; performing key project theme mining on the work order content indication according to a content analysis model to obtain example work order information; and configuring the AI model according to the example work order information and the example development project work order subject.
In the embodiment of the application, the checking condition bound by the research and development project work order corresponds to the reference research and development project work order, so that the adaptability adjustment and optimization of the research and development project work order can be performed according to the items corresponding to the reference research and development project work order.
In some possible embodiments, configuring the AI model based on the example work order information and the example development project work order topic may include: determining an overall transfer index according to the example work order information and the example research and development project work order theme; determining an integrity model evaluation by means of a relative model evaluation according to the integrity transfer index and the relevance of the sample development project work order and the work order content indication; and configuring the AI model according to the overall model evaluation.
For example, the overall transfer index can be understood as a global correlation index, the value range can be 0-1, in addition, the overall model evaluation can be understood as a global loss, and therefore the overall model evaluation can be accurately determined based on the overall transfer index, and the AI model can be accurately configured based on the overall model evaluation.
Based on the above, determining the overall delivery index based on the example work order information and the example development project work order topic may include the following: compressing the work order subject characteristics of the example research and development project and then carrying out difference processing on the compressed work order subject characteristics and the example work order information to obtain a distinctive subject array; carrying out upstream and downstream quantization processing on the distinctive subject array to determine an upstream and downstream fusion array; and performing quantitative conversion processing on the upstream and downstream fusion arrays to obtain an overall transfer index for representing the overall association degree.
For example, the distinctive subject array may be understood as a difference feature vector, further, the feature compression may be understood as a pooling process, and the upstream and downstream quantization processes may be understood as a bitwise numerical operation, such as a square operation, and the like, so that an upstream and downstream fused array (such as an associated feature) may be obtained. Further, the quantization transformation may be understood as a normalization process, and thus, the accuracy of the overall transfer index may be ensured.
In some other embodiments, before performing key project topic mining on the work order content indication according to the content analysis model to obtain example work order information, the following may be further included: the content analysis model is preconfigured with example content segments that include instructional work order information.
Further, the pre-configuring the content analysis model in accordance with example content segments includes: transmitting the example content segments into the content analysis model to obtain a first auxiliary example analysis result; and improving the model variables of the content analysis model according to the first auxiliary example analysis result and the instruction type worksheet information.
In some possible embodiments, the determining, from the reference development project work order record, the verification situation bound by the development project work order with the verification requirement according to the transition project topic with the verification requirement and the reference transition project topic in step S32 may include the following step S321 and step S322.
Step S321, processing the transitional item theme with the verification requirement and the reference transitional item theme based on the overall compression unit and the classification unit to determine the item theme with the verification requirement and the reference item theme.
For example, the global compression unit and the classification unit may be understood as a max pooling layer and a full connection layer.
Step S322, determining the verification condition bound by the research and development project work order with verification requirements from the reference research and development project work order record according to the project subject with verification requirements and the reference project subject.
By means of the design, the project theme and the reference project theme with the verification requirement can be efficiently and accurately obtained by the aid of the integral compression unit and the classification unit, and accordingly integrity of the verification condition can be guaranteed.
On the basis of the above step S321 and step S322, the method may further include the following: performing key project theme mining on the visual content associated with the research and development project work order with the verification requirement according to a content analysis model, and determining work order information; and positioning the checking condition according to the work order information, and determining the optimized checking condition bound by the research and development project work orders with checking requirements, wherein the optimized checking condition covers at least one reference research and development project work order.
In some possible embodiments, the positioning the verification condition according to the work order information, and determining the optimized verification condition bound by the development project work order with the verification requirement may include the following: positioning according to the quantitative difference between the work order information and at least one reference transition item theme bound to the verification condition; and determining at least one reference transition project theme of which the quantitative difference is not greater than a set judgment value, and taking the reference research and development project work order bound by the determined reference transition project theme as the optimized verification condition.
For example, the quantitative difference may be understood as a word vector distance between different project topics.
On the basis of the above, the method may further include: performing key project theme mining on at least one content label associated with the research and development project work order with the checking requirement according to the content analysis model, and determining visual label expression, wherein each content label corresponds to at least one content set in the research and development project work order with the checking requirement; and positioning the checking condition or the optimized checking condition according to the visual label expression, and determining the target checking condition bound by the research and development project work order with the checking requirement, wherein the target checking condition covers at least one reference research and development project work order.
For example, visual-type tag expressions may be understood as keyword features.
In some possible embodiments, the locating the verification condition or the optimized verification condition according to the visual label expression to determine the target verification condition bound by the development project work order with the verification requirement may include the following: locating according to the quantitative difference between the visual type label expression and the checking condition or the optimized checking condition, wherein the reference transition item theme is not less than one; and determining at least one reference project theme with the quantization difference not larger than a set judgment value, and taking a reference development project work sheet bound with the determined reference transition project theme as the target verification condition.
By the design, the integrity of the target verification condition can be ensured.
On the basis of the above, the method may further include: and performing key project theme mining on at least one stage event instruction in the work order content instructions according to the content analysis model to obtain at least one stage project theme, wherein each stage event instruction is used for expressing that at least one work order content set in the example research and development project work order obtains stage model evaluation according to the stage project theme and the example research and development project work order theme. Based on this, the configuring the AI model according to the overall model evaluation may include the following: and configuring the AI model according to the overall model evaluation and the staged model evaluation.
In some other embodiments, before the mining of the key project topic for not less than one stage event indication in the work order content indication according to the content analysis model to obtain not less than one stage project topic, the following may be further included: and disassembling the work order content indication to obtain not less than one stage event indication, wherein each stage event indication covers not less than one annotation, the stage event indication corresponds to an indication possibility, and each quantitative value represents the quantitative possibility of the stage event indication corresponding to the example development project work order.
For example, a periodic event indication may be understood as a local indication or a local identification. In this manner, it is possible to accurately determine the indication possibility.
On the basis of the above contents, the disassembling the work order content indication to obtain at least one stage event indication may include the following contents: performing intention identification on each work order requirement data in the work order content indication to obtain the intention bound by each work order requirement data; and according to the intention and a set disassembly strategy, disassembling the work order content indication into at least one stage event indication.
In some possible embodiments, the obtaining a staging model evaluation according to the staging project topic and the example development project work order topic includes: describing and simplifying the work order theme of the example research and development project to obtain the integral visual key data distribution; determining an attention coefficient according to the overall visual key data distribution and the staged project theme; determining a test quantification likelihood to which each episodic event indicator is bound as a function of the attention coefficient and the example development project work order topic; and determining the evaluation of the periodic model according to the test quantitative possibility and the indication possibility bound by the indication of the periodic events. In this way, the accuracy of the staged model evaluation can be ensured.
Further, the determining an attention coefficient according to the overall visual key data distribution and the staged project topic may include the following: performing difference processing on the item theme evaluation of each distribution label in the overall visual key data distribution and the staged item theme respectively to obtain a staged distinctive theme array; performing numerical processing on each array unit in the staged distinctive subject array to determine a staged upstream and downstream fused array; and determining an attention coefficient according to the staged upstream and downstream fusion array.
For example, the attention coefficient may be understood as a saliency weight.
It is to be understood that, the determining the attention coefficient according to the staged upstream and downstream fusion array may include the following: processing the staged upstream and downstream fusion array according to a classification submodel to obtain a compatibility index for expressing the compatibility degree of the staged event indication and the sample research and development project work order; and carrying out quantitative conversion on an array formed by the compatibility indexes of each distribution label in the overall visual key data distribution bound by each stage event indication to obtain the attention coefficient bound by each stage event indication.
Further, the determining the quantified likelihood of the each phased event indicating the bound test as a function of the attention coefficient and the example development project work order topic may include the following: fusing the project theme evaluation of each distribution label in the example research and development project work order theme with the attention coefficient to obtain a fused description array set corresponding to each stage event indication; merging the arrays in the fusion description array set to obtain a stage merging result in the stage event indication corresponding to the example research and development project work order; determining the testing quantification possibility of each work order demand data in the periodic event indication according to the periodic combination result; and determining the testing quantification possibility bound by the periodic event indication according to the testing quantification possibility of each work order demand data in the periodic event indication. Thus, the test quantization possibility can be accurately and reliably determined.
It is understood that the determination of the test quantification possibility of each work order requirement data in the periodic event indication according to the periodic combination result may include the following: converting the periodic event indication into a work order demand data queue, transmitting the periodic merging result into a loop model, and determining that at least one potential feature exists, wherein each work order demand data corresponds to a description array; determining the potential characteristics of the next time node by the action of the cycle model of the potential characteristics of the previous time node and the project theme array bound by the current work order requirement data of each time node; performing characteristic transformation according to the at least one potential characteristic to obtain a test array of the required data of each work order; and obtaining the test quantization possibility of each work order demand data in the stage event indication according to the test array.
For example, the time node can be understood as a time, the potential feature can be understood as a hidden feature, and further, the loop model can be understood as a long-short term memory network, so that timeliness of testing the quantization possibility can be ensured as much as possible.
In some possible embodiments, determining the test quantification possibility bound by the periodic event indication according to the test quantification possibility of each work order requirement data in the periodic event indication includes: and taking the weighted result of the test quantification possibility of each work order demand data in the periodic event indication as the test quantification possibility of the periodic event indication.
Further, the configuring the AI model according to the overall model evaluation and the staging model evaluation may include: combining the overall model evaluation and the staged model evaluation to obtain a mixed model evaluation; and evaluating model variables for improving the AI model according to the hybrid model.
By the design, the model variables of the AI model can be accurately adjusted and optimized by combining the evaluation of the hybrid model.
In summary, according to the method and system for processing the research and development project work order based on visualization provided by the embodiments of the present application, the research and development project work order with the verification requirement and the reference research and development project work order record are determined; performing key project theme mining on the research and development project work order with the verification requirement and each reference research and development project work order in the reference research and development project work order record by using an AI model, determining a transition project theme with the verification requirement bound by the research and development project work order with the verification requirement and a reference transition project theme bound by the reference research and development project work order, and configuring the content of the research and development project work order and the multidimensional work order by using the AI model; the method comprises the steps of determining a checking condition bound by a research and development project work order with checking requirements from a reference research and development project work order record according to a transition project theme with checking requirements and a reference transition project theme, wherein the checking condition covers at least one reference research and development project work order, performing work order checking through an AI model obtained through the research and development project work order theme and multi-dimensional work order content configuration, further mining the association degree between the content of a periodic work order and the content of an annotated work order by means of a matching result between the research and development project work order and the work order content representing the research and development project work order, optimizing the analysis limit of the research and development project work order, improving the accuracy of the work order checking result and further ensuring the credibility of the work order checking. In addition, different reference research and development project work orders can be used as references of the research and development project work orders with checking requirements, so that adaptability adjustment and optimization of the research and development project work orders are achieved.
The skilled person can unambiguously determine some preset, reference, predetermined, set and target technical features/terms, such as threshold values, threshold intervals, threshold ranges, etc., from the above disclosure. For some technical characteristic terms which are not explained, the technical solution can be clearly and completely implemented by those skilled in the art by reasonably and unambiguously deriving the technical solution based on the logical relations in the previous and following paragraphs. Prefixes of unexplained technical feature terms, such as "first", "second", "previous", "next", "current", "history", "latest", "best", "target", "specified", and "real-time", etc., can be unambiguously derived and determined from the context. Suffixes of technical feature terms not to be explained, such as "list", "feature", "sequence", "set", "matrix", "unit", "element", "track", and "list", etc., can also be derived and determined unambiguously from the foregoing and the following.
The foregoing disclosure of embodiments of the present invention will be apparent to those skilled in the art. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present invention, and thus the above contents are not an inventive judgment of the overall scheme.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting. Various modifications, improvements and adaptations to the present invention may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed within the present invention and are intended to be within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the present invention uses specific terms to describe embodiments of the present invention. Such as "one embodiment," "an embodiment," and/or "some embodiments" means a feature, structure, or characteristic described in connection with at least one embodiment of the invention. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, some of the features, structures, or characteristics of at least one embodiment of the present invention may be combined as suitable.
In addition, those skilled in the art will recognize that the various aspects of the invention may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, articles of manufacture, or materials, or any new and useful modifications thereto. Accordingly, aspects of the present invention may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component", or "system". Furthermore, aspects of the present invention may be embodied as a computer product, located in at least one computer-readable medium, comprising computer-readable program code.
A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the execution of aspects of the present invention may be written in any combination of one or more programming languages, including object oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, or similar conventional programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages, such as Python, Ruby, and Groovy, or other programming languages. The programming code may execute entirely on the user's computer, as a stand-alone software package, partly on the user's computer, partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Furthermore, unless otherwise indicated by the claims, the order of processing elements and sequences, the use of numerical letters or other designations of the invention are not intended to limit the order of the processes and methods described herein. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments of the invention. For example, although the system components described above may be implemented by hardware means, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
It should also be appreciated that in the foregoing description of embodiments of the invention, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the invention. However, this method of disclosure is not intended to suggest that the claimed subject matter requires more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Claims (8)
1. A method for processing a research and development project work order based on visualization is applied to a research and development project work order processing system, and at least comprises the following steps:
determining a research and development project work order with a checking requirement and a reference research and development project work order record, mining key project topics of the research and development project work order with the checking requirement and each reference research and development project work order in the reference research and development project work order record by means of an AI model, determining a transition project topic with the checking requirement bound by the research and development project work order with the checking requirement and a reference transition project topic bound by the reference research and development project work order, and configuring the contents of the research and development project work order and a multi-dimensional work order by the AI model;
determining a checking condition bound by the research and development project work order with checking requirements from the reference research and development project work order record according to the transition project theme with checking requirements and the reference transition project theme, wherein the checking condition covers at least one reference research and development project work order;
the AI model obtained by configuring the research and development project work order theme and the multi-dimensional work order content comprises the following steps: transmitting the example research and development project work order into the AI model to obtain an example research and development project work order theme, wherein the example research and development project work order comprises work order content indications; performing key project theme mining on the work order content indication according to a content analysis model to obtain example work order information; configuring the AI model according to the example work order information and the example development project work order theme;
wherein configuring the AI model based on the example work order information and the example development project work order topic comprises: determining an overall transfer index according to the example work order information and the example research and development project work order theme; determining an integrity model evaluation by means of a relative model evaluation according to the integrity transfer index and the relevance of the sample development project work order and the work order content indication; configuring the AI model according to the evaluation of the integrity model;
wherein determining an overall transfer index based on the example work order information and the example development project work order topic comprises: compressing the work order subject characteristics of the example research and development project and then carrying out difference processing on the compressed work order subject characteristics and the example work order information to obtain a distinctive subject array; carrying out upstream and downstream quantization processing on the distinctive subject array to determine an upstream and downstream fusion array; performing quantitative conversion processing on the upstream and downstream fusion arrays to obtain an overall transfer index for representing overall relevance;
wherein, still include: performing key project theme mining on at least one stage event indication in the work order content indications according to the content analysis model to obtain at least one stage project theme, wherein each stage event indication aims to express that at least one work order content set in the example research and development project work order obtains stage model evaluation according to the stage project theme and the example research and development project work order theme;
configuring the AI model in accordance with the integrity model evaluation includes: configuring the AI model according to the overall model evaluation and the staged model evaluation;
wherein, before mining key project topics for at least one stage event indication in the work order content indication according to the content analysis model to obtain at least one stage project topic, the method further includes: parsing the work order content indications to obtain not less than one periodic event indication, each of the periodic event indications covering not less than one annotation, the periodic event indication corresponding to an indication likelihood, each quantitative value representing a quantitative likelihood that the periodic event indication corresponds to the example development project work order;
wherein, the disassembling the work order content indication to obtain at least one stage event indication comprises: performing intention identification on each work order requirement data in the work order content indication to obtain the intention bound by each work order requirement data; according to the intention and a set disassembly strategy, disassembling the work order content instruction into at least one stage event instruction;
wherein the obtaining of the staged model evaluation according to the staged project theme and the example development project work order theme comprises: describing and simplifying the work order theme of the example research and development project to obtain the integral visual key data distribution; determining an attention coefficient according to the overall visual key data distribution and the staged project theme; determining a test quantification likelihood to which each episodic event indicator is bound as a function of the attention coefficient and the example development project work order topic; determining the staged model evaluation according to the test quantification possibility and the indication possibility bound by the staged event indication;
wherein the determining an attention coefficient according to the overall visual key data distribution and the episodic project topic comprises: performing difference processing on the item theme evaluation of each distribution label in the overall visual key data distribution and the staged item theme respectively to obtain a staged distinctive theme array; performing numerical processing on each array unit in the staged distinctive subject array to determine a staged upstream and downstream fused array; determining an attention coefficient according to the staged upstream and downstream fusion array;
wherein, the determining the attention coefficient according to the staged upstream and downstream fusion array comprises: processing the staged upstream and downstream fusion array according to a classification submodel to obtain a compatibility index for expressing the compatibility degree of the staged event indication and the sample research and development project work order; performing quantitative conversion on an array formed by compatibility indexes of each distribution label in the overall visual key data distribution bound by each stage event indication to obtain an attention coefficient bound by each stage event indication;
wherein the determining the quantified likelihood of the each phased event indicating the bound test as a function of the attention coefficient and the example development project work order topic comprises: fusing the project theme evaluation of each distribution label in the example research and development project work order theme with the attention coefficient to obtain a fused description array set corresponding to each stage event indication; merging the arrays in the fusion description array set to obtain a stage merging result in the stage event indication corresponding to the example research and development project work order; determining the testing quantification possibility of each work order demand data in the periodic event indication according to the periodic combination result; determining the testing quantification possibility bound by the periodic event indication according to the testing quantification possibility of each work order demand data in the periodic event indication;
wherein the determining the test quantification possibility of each work order demand data in the periodic event indication according to the periodic merging result comprises: converting the periodic event indication into a work order demand data queue, transmitting the periodic merging result into a loop model, and determining that at least one potential feature exists, wherein each work order demand data corresponds to a description array; determining the potential characteristics of the next time node by the action of the cycle model of the potential characteristics of the previous time node and the project theme array bound by the current work order requirement data of each time node; performing characteristic transformation according to the at least one potential characteristic to obtain a test array of the required data of each work order; obtaining the test quantization possibility of each work order demand data in the periodic event indication according to the test array;
determining the testing quantification possibility bound by the periodic event indication according to the testing quantification possibility of each work order demand data in the periodic event indication, wherein the testing quantification possibility comprises the following steps: and taking the weighted result of the test quantification possibility of each work order demand data in the periodic event indication as the test quantification possibility of the periodic event indication.
2. The method of claim 1, wherein the determining the verification condition bound by the development project work order with verification requirements from the reference development project work order record according to the transition project topic with verification requirements and the reference transition project topic comprises:
processing the transitional item theme with the verification requirement and the reference transitional item theme based on the integral compression unit and the classification unit to determine the item theme with the verification requirement and the reference item theme;
and determining the verification condition bound by the research and development project work order with the verification requirement from the reference research and development project work order record according to the project theme with the verification requirement and the reference project theme.
3. The method of claim 2, further comprising:
performing key project theme mining on the visual content associated with the research and development project work order with the verification requirement according to a content analysis model, and determining work order information;
and positioning the checking condition according to the work order information, and determining the optimized checking condition bound by the research and development project work orders with checking requirements, wherein the optimized checking condition covers at least one reference research and development project work order.
4. The method of claim 3, wherein the locating the verification condition according to the work order information and determining the optimized verification condition bound by the development project work order with the verification requirement comprises:
positioning according to the quantitative difference between the work order information and at least one reference transition item theme bound to the verification condition;
and determining at least one reference transition project theme of which the quantitative difference is not greater than a set judgment value, and taking the reference research and development project work order bound by the determined reference transition project theme as the optimized verification condition.
5. The method of claim 4, wherein the method further comprises:
performing key project theme mining on at least one content label associated with the research and development project work order with the checking requirement according to the content analysis model, and determining visual label expression, wherein each content label corresponds to at least one content set in the research and development project work order with the checking requirement;
positioning the checking condition or the optimized checking condition according to the visual label expression, and determining the target checking condition bound by the research and development project work order with checking requirement, wherein the target checking condition covers at least one reference research and development project work order;
wherein, the positioning the checking condition or the optimized checking condition according to the visual type label expression and determining the target checking condition bound by the research and development project work order with checking requirement comprises: locating according to the quantitative difference between the visual type label expression and the checking condition or the optimized checking condition, wherein the reference transition item theme is not less than one; and determining at least one reference project theme with the quantization difference not larger than a set judgment value, and taking a reference development project work sheet bound with the determined reference transition project theme as the target verification condition.
6. The method of claim 1, wherein said mining key project topics from said work order content indications according to said content analysis model further comprises, prior to obtaining example work order information: pre-configuring the content analysis model in accordance with an example content segment, the example content segment including instructional work order information;
wherein the pre-configuring of the content analysis model in accordance with example content segments comprises: transmitting the example content segments into the content analysis model to obtain a first auxiliary example analysis result; and improving the model variables of the content analysis model according to the first auxiliary example analysis result and the instruction type worksheet information.
7. The method of claim 1, wherein said configuring the AI model in accordance with the global model evaluation and the phasic model evaluation comprises: combining the overall model evaluation and the staged model evaluation to obtain a mixed model evaluation; and evaluating model variables for improving the AI model according to the hybrid model.
8. A visualization-based development project work order processing system, comprising a processing engine, a network module, and a memory, the processing engine and the memory communicating through the network module, the processing engine being configured to read a computer program from the memory and execute the computer program to implement the method of any one of claims 1-7.
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