CN109615311B - Moon knot information processing method based on new product development, electronic device and readable storage medium - Google Patents
Moon knot information processing method based on new product development, electronic device and readable storage medium Download PDFInfo
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Abstract
The invention relates to development, and provides a moon knot information processing method, an electronic device and a readable storage medium based on new product development, wherein the method comprises the following steps: acquiring product item information submitted by a target user; searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database according to the product item information, wherein the moon knot model information comprises preset product item model information, corresponding running time model information and corresponding demand list model information; when the moon knot model information matched with the product item information exists in the moon knot model database, the running time model information and the demand list model information in the searched moon knot model information are output. The invention can output moon knot information more efficiently, improve working efficiency and save time.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a lunar junction information processing method, an electronic device, and a readable storage medium based on new product development.
Background
Currently, in the new product development process, it is generally required to output month knot information at a specific time. At present, the programmer generally manually generates the moon knot information, for example, the programmer needs to manually select various moon knot parameters corresponding to the developed new products, so that the generating process of the moon knot information is complicated, the manual operation is more, the time is wasted, and the generating efficiency of the moon knot information is lower.
Disclosure of Invention
The invention aims to provide a moon knot information processing method based on new product development, an electronic device and a readable storage medium, aiming to improve moon knot information generation efficiency.
To achieve the above object, the present invention provides an electronic device including a memory, a processor, and a new product development-based moon cake information processing system stored on the memory and operable on the processor, the new product development-based moon cake information processing system implementing the following steps when executed by the processor:
acquiring product item information submitted by a target user;
searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database according to the product item information, wherein the moon knot model information comprises preset product item model information, corresponding running time model information and corresponding demand list model information;
when the moon cake model information matched with the product item information exists in the moon cake model database, running batch time model information and demand list model information in the searched moon cake model information are output, so that a user can input the running batch time data according to the running batch time model information, input the demand list data according to the demand list model information, and the moon cake information is generated by utilizing the input running batch time data and the demand list data.
Preferably, after the step of searching whether the moon node model information matched with the product item information exists in the pre-stored moon node model database according to the product item information is implemented by the processor, the method further includes the following steps:
when the moon knot model information matched with the product item information does not exist in the moon knot model database, sending a moon knot model information request to a preset external server, wherein the moon knot model information request comprises the product item information;
and receiving running time information and demand list information which are sent by the preset external server and correspond to the product item information.
Preferably, when the lunar junction information processing system based on new product development is executed by the processor, the following steps are further implemented:
and updating the corresponding relation between the product item information, the received running batch time information and the received demand list information into the pre-stored month knot model database.
Preferably, the requirement list information includes at least one of problem ring ratio increment information, problem type information, and problem ring ratio change information.
In addition, in order to achieve the above object, the present invention also provides a lunar junction information processing method based on new product development, the method comprising:
acquiring product item information submitted by a target user;
searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database according to the product item information, wherein the moon knot model information comprises preset product item model information, corresponding running time model information and corresponding demand list model information;
when the moon cake model information matched with the product item information exists in the moon cake model database, running batch time model information and demand list model information in the searched moon cake model information are output, so that a user can input the running batch time data according to the running batch time model information, input the demand list data according to the demand list model information, and the moon cake information is generated by utilizing the input running batch time data and the demand list data.
Preferably, after the step of searching for whether there is the moon cake model information matching the product item information in the pre-stored moon cake model database according to the product item information, the method further includes:
and when the moon knot model information matched with the product item information does not exist in the moon knot model database, receiving running time information and demand list information which are input by a user and correspond to the product item information.
Preferably, after the step of searching for whether there is the moon cake model information matching the product item information in the pre-stored moon cake model database according to the product item information, the method further includes:
when the moon knot model information matched with the product item information does not exist in the moon knot model database, sending a moon knot model information request to a preset external server, wherein the moon knot model information request comprises the product item information;
and receiving running time information and demand list information which are sent by the preset external server and correspond to the product item information.
Preferably, the method further comprises:
and updating the corresponding relation between the product item information, the received running batch time information and the received demand list information into the pre-stored month knot model database.
Preferably, the requirement list information includes at least one of problem ring ratio increment information, problem type information, and problem ring ratio change information.
Further, to achieve the above object, the present invention also provides a computer-readable storage medium storing a new product development-based moon cake information processing system executable by at least one processor to cause the at least one processor to perform the steps of the new product development-based moon cake information processing method as described above.
According to the new product development-based moon knot information processing method, the electronic device and the readable storage medium, whether moon knot model information matched with product item information exists or not is searched in a pre-stored moon knot model database through the product item information submitted by a target user, wherein the moon knot model information comprises corresponding relations among the product item model information, the running time model information and the demand list model information; when the moon knot model information matched with the product item information exists in the moon knot model database, outputting the run time model information and the demand list model information in the searched moon knot model information. Because the running time model information and the demand list model information corresponding to the developed product project information can be automatically output, a user does not need to manually select various moon knot parameters corresponding to the developed new product, and the user can generate the moon knot information only by inputting corresponding data according to the running time model information and the demand list model information which are automatically output, so that the moon knot information can be more efficiently output, the working efficiency is improved, and the time is saved.
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FIG. 1 is a schematic diagram of a preferred embodiment of a lunar junction information handling system based on new product development in accordance with the present invention;
FIG. 2 is a flow chart of a method for processing lunar junction information based on new product development according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the description of "first", "second", etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implying an indication of the number of technical features being indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The invention provides a lunar junction information processing system based on new product development. Referring now to FIG. 1, therein is shown a schematic diagram of an operating environment for a preferred embodiment of a lunar junction information handling system 10 based on new product development in accordance with the present invention.
In the present embodiment, the lunar junction information processing system 10 based on new product development is installed and operated in the electronic device 1. The electronic device 1 may include, but is not limited to, a memory 11, a processor 12, and a display 13. Fig. 1 shows only an electronic device 1 with components 11-13, but it is understood that not all shown components are required to be implemented, and that more or fewer components may alternatively be implemented.
The memory 11 is at least one type of readable computer storage medium, and the memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic apparatus 1, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic apparatus 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic apparatus 1. The memory 11 is used for storing application software and various data installed in the electronic device 1, such as program codes of the lunar junction information processing system 10 based on new product development. The memory 11 may also be used for temporarily storing data that has been output or is to be output.
The processor 12 may in some embodiments be a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip for executing program code or processing data stored in the memory 11, such as executing the new product development-based moon-tie information processing system 10, etc.
The display 13 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like in some embodiments. The display 13 is used for displaying information processed in the electronic device 1 and for displaying visual user interfaces, such as product item information, corresponding run time model information and demand list model information, generated month knot information, etc. The components 11-13 of the electronic device 1 communicate with each other via a system bus.
The new product development based month node information processing system 10 includes at least one computer readable instruction stored in the memory 11, which is executable by the processor 12 to implement embodiments of the present application.
Wherein the lunar junction information processing system 10 based on new product development, when executed by the processor 12, performs the following steps:
step S1, obtaining product item information submitted by a target user.
Step S2, searching whether the moon knot model information matched with the product item information exists in a prestored moon knot model database according to the product item information, wherein the moon knot model information comprises the corresponding relation among the product item model information, the running time model information and the demand list model information; the demand list information includes at least one of issue ring ratio delta information, issue type information, and issue ring ratio change information.
In this embodiment, different product item information may be categorized in advance, and similar product item information may be categorized into one category. If the category of the product item information input by the target user is a new category, the new category may be saved.
Specifically, a plurality of clustering categories of the product item information can be pre-stored, then clustering analysis is carried out on the product item information submitted by the target user, and the clustering category to which the product item information submitted by the target user belongs is determined.
The month knot model database comprises a plurality of month knot model information, and each month knot model information comprises a corresponding relation among product project model information, batch time model information and demand list model information. The product item model information of each month model information is a clustering type, and the product item model information of different month model information is a different clustering type.
And when the clustering category of the product item information submitted by the user is the same as the clustering category of the product item model information in the moon model information, the product item information is considered to be matched with the moon model information.
And when the clustering type of the product item information submitted by the user is different from the clustering type of the product item model information in the moon model information, the product item information is not matched with the moon model information.
The clustering algorithm adopted by the clustering analysis is any one of K-Means clustering, mean shift clustering, DBSCAN clustering, GMM clustering, aggregation level clustering and graph group detection clustering.
K-Means (K Means) clustering, algorithm steps:
(1) Arbitrarily selecting k objects from n data objects as initial clustering centers;
(2) Calculating the distance between each object and the center objects according to the average value (center object) of each clustered object; dividing the corresponding objects again according to the minimum distance;
(3) Recalculating the mean (center object) of each (changed) cluster;
(4) Cycling (2) through (3) until each cluster no longer changes.
The k-means algorithm accepts an input k; then dividing n data objects into k clusters so as to enable the obtained clusters to meet the requirement that the object similarity in the same cluster is higher; while objects in different clusters are less similar. Cluster similarity is calculated using a "central object" obtained from the mean of the objects in each cluster.
2. Mean shift clustering:
mean shift clustering is a sliding window based algorithm to find dense areas of data points. This is a centroid-based algorithm that locates the center point of each group/class by updating the candidate points for the center point to the mean of the points within the sliding window. And then removing similar windows from the candidate windows to finally form a center point set and corresponding groups.
The method comprises the following specific steps:
1) The radius r of the sliding window is determined, and the sliding starts with a round sliding window with the radius r of the randomly selected center point C. Mean shift resembles a hill climbing algorithm, moving to a higher density region in each iteration until convergence.
2) Each time a new region is slid to, the mean value within the sliding window is calculated as the center point, the number of points within the sliding window being the density within the window. In each movement, the window would want to move in a higher density area.
3) Moving the window, calculating the center point in the window and the density in the window, knowing that no direction can accommodate more points in the window, i.e., moving until the density in the circle no longer increases.
4) Steps one to three will generate a plurality of sliding windows, when the sliding windows overlap, the window containing the most points is reserved, and then clustering is performed according to the sliding window where the data points are located.
3. A clustering method (DBSCAN) based on density, which comprises the following specific steps:
1) Radius r and minPoints are first determined, starting from an arbitrary data point that has not been accessed, and centering on this point, whether the number of points contained within a circle with radius r is greater than or equal to minPoints, if greater than or equal to minPoints, the point is marked as a central point, and otherwise, as a noise point.
2) Repeating step 1, if a noise point exists within a circle with a radius of a certain central point, this point is marked as an edge point, and vice versa. Repeating step 1) knowing that all points have been accessed.
4. Clustering with maximum Expectation (EM) of Gaussian Mixture Model (GMM), the specific steps are:
1) The number of clusters (similar to K-Means) is selected and the gaussian distribution parameters (mean and variance) for each cluster are randomly initialized. It is also possible to observe the data first to give a relatively accurate mean and variance.
2) Given the gaussian distribution of each cluster, the probability that each data point belongs to each cluster is calculated. The closer a point is to the center of the gaussian distribution, the more likely it is that it belongs to the cluster.
3) Based on these probabilities we calculate gaussian distribution parameters to maximize the probability of the data point, and a weighting of the probability of the data point, i.e. the probability that the data point belongs to the cluster, can be used to calculate these new parameters.
4) Iterations 2) and 3) are repeated until the variation in the iterations is not large.
5. Aggregation hierarchical clustering
Hierarchical clustering algorithms fall into two categories: top-down and bottom-up. Aggregation level clustering (HAC) is a bottom-up clustering algorithm. HAC first treats each data point as a single cluster and then calculates the distance between all clusters to merge clusters until all clusters are clustered into one cluster.
6. Graph group detection (Graph Community Detection), specific steps:
1) Each vertex is initially assigned to its own community first, and then the modularity M of the entire network is calculated.
2) Step 1) requires that each community pair be linked by at least one single edge, and if two communities are fused together, the algorithm calculates the resulting modular change ΔM.
3) Step 2) is to take the community pair where ΔM has the greatest growth and then fuse. A new modularity M is then calculated for this cluster and recorded.
4) Repeating the step 1) and the step 2), fusing the community pairs each time, obtaining the maximum gain of delta M finally, and recording a new clustering mode and the corresponding modularity score M.
5) Repeating the step 1) and the step 2), fusing the community pairs each time, obtaining the maximum gain of delta M finally, and recording a new clustering mode and the corresponding modularity score M.
And step S3, outputting the run time model information and the demand list model information in the searched moon knot model information when the moon knot model information matched with the product item information exists in the moon knot model database.
In the embodiment, product item information submitted by a target user is obtained; searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database according to the product item information, wherein the moon knot model information comprises a corresponding relation among the product item model information, the running time model information and the demand list model information; when the moon knot model information matched with the product item information exists in the moon knot model database, the running time model information and the demand list model information in the searched moon knot model information are output, so that a user can output the moon knot information more efficiently, the working efficiency is improved, and the time is saved.
Further, in an alternative embodiment, based on the above embodiment, after the new product development-based moon cake information processing system 10 is executed by the processor 12 to implement the step S3, the following steps are implemented:
receiving batch time data input by a user according to the batch time model information, receiving demand list data input by the user according to the demand list model information, and generating month junction information by utilizing the input batch time data and the demand list data.
In this embodiment, the user directly generates the running time data and the demand list data by filling corresponding data in the running time model information and the demand list model information according to the output running time model information and the demand list model information, so that the required month knot information can be generated, and the output efficiency of the month knot information is effectively improved.
Further, in an alternative embodiment, after the new product development-based lunar junction information processing system 10 is executed by the processor 12 to implement the step S2, the following steps are implemented:
and when the moon knot model information matched with the product item information does not exist in the moon knot model database, receiving running time information and demand list information which are input by a user and correspond to the product item information. Therefore, the running batch time information and the demand list information corresponding to the product item information are input through the month knot model, and the month knot information is manually input by a user, so that the flexibility of generating the month knot information is further improved.
Further, in an alternative embodiment, after the new product development-based lunar junction information processing system 10 is executed by the processor 12 to implement the step S2, the following steps are implemented:
when the moon knot model information matched with the product item information does not exist in the moon knot model database, sending a moon knot model information request to a preset external server or external network, wherein the moon knot model information request comprises the product item information;
in this embodiment, if the preset external server or external network pre-stores the month node model information matched with the product item information, the running time information and the demand list information corresponding to the product item information sent by the external server or the external network are received. Therefore, the local server can share the moon knot model with the external server or the external network at the same time, and the flexibility of moon knot information generation is further improved.
Further, in an alternative embodiment, the new product development-based lunar junction information processing system 10, when executed by the processor 12, further performs the steps of:
and updating the corresponding relation between the product item information, the received running batch time information and the received demand list information into the pre-stored month knot model database.
Thus, when each time the run time information and the demand list information corresponding to the product item information input by the user are received, or the run time information and the demand list information corresponding to the product item information sent by an external server or an external network are received, the product item information, the run time information and the demand list information corresponding to the product item information are updated to the pre-stored month model database. When the product item information matched with the submitted product item information or belonging to the same clustering category is obtained again later, the user does not need to manually input the moon knot information, and the moon knot information is not required to be acquired from an external server or an external network, so that the efficiency of generating the moon knot information is further improved.
As shown in fig. 2, fig. 2 is a flow chart of an embodiment of a lunar junction information processing method based on new product development according to the present invention, the method includes the following steps:
step S10, obtaining product item information submitted by a target user.
Step S20, searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database according to the product item information, wherein the moon knot model information comprises a corresponding relation among the product item model information, the running time model information and the demand list model information; the demand list information includes at least one of issue ring ratio delta information, issue type information, and issue ring ratio change information.
In this embodiment, different product item information may be categorized in advance, and similar product item information may be categorized into one category. If the category of the product item information input by the target user is a new category, the new category may be saved.
Specifically, a plurality of clustering categories of the product item information can be pre-stored, then clustering analysis is carried out on the product item information submitted by the target user, and the clustering category to which the product item information submitted by the target user belongs is determined.
The month knot model database comprises a plurality of month knot model information, and each month knot model information comprises a corresponding relation among product project model information, batch time model information and demand list model information. The product item model information of each month model information is a clustering type, and the product item model information of different month model information is a different clustering type.
And when the clustering category of the product item information submitted by the user is the same as the clustering category of the product item model information in the moon model information, the product item information is considered to be matched with the moon model information.
And when the clustering type of the product item information submitted by the user is different from the clustering type of the product item model information in the moon model information, the product item information is not matched with the moon model information.
The clustering algorithm adopted by the clustering analysis is any one of K-Means clustering, mean shift clustering, DBSCAN clustering, GMM clustering, aggregation level clustering and graph group detection clustering.
K-Means (K Means) clustering, algorithm steps:
(1) Arbitrarily selecting k objects from n data objects as initial clustering centers;
(2) Calculating the distance between each object and the center objects according to the average value (center object) of each clustered object; dividing the corresponding objects again according to the minimum distance;
(3) Recalculating the mean (center object) of each (changed) cluster;
(4) Cycling (2) through (3) until each cluster no longer changes.
The k-means algorithm accepts an input k; then dividing n data objects into k clusters so as to enable the obtained clusters to meet the requirement that the object similarity in the same cluster is higher; while objects in different clusters are less similar. Cluster similarity is calculated using a "central object" obtained from the mean of the objects in each cluster.
2. Mean shift clustering:
mean shift clustering is a sliding window based algorithm to find dense areas of data points. This is a centroid-based algorithm that locates the center point of each group/class by updating the candidate points for the center point to the mean of the points within the sliding window. And then removing similar windows from the candidate windows to finally form a center point set and corresponding groups.
The method comprises the following specific steps:
1) The radius r of the sliding window is determined, and the sliding starts with a round sliding window with the radius r of the randomly selected center point C. Mean shift resembles a hill climbing algorithm, moving to a higher density region in each iteration until convergence.
2) Each time a new region is slid to, the mean value within the sliding window is calculated as the center point, the number of points within the sliding window being the density within the window. In each movement, the window would want to move in a higher density area.
3) Moving the window, calculating the center point in the window and the density in the window, knowing that no direction can accommodate more points in the window, i.e., moving until the density in the circle no longer increases.
4) Steps one to three will generate a plurality of sliding windows, when the sliding windows overlap, the window containing the most points is reserved, and then clustering is performed according to the sliding window where the data points are located.
3. A clustering method (DBSCAN) based on density, which comprises the following specific steps:
1) Radius r and minPoints are first determined, starting from an arbitrary data point that has not been accessed, and centering on this point, whether the number of points contained within a circle with radius r is greater than or equal to minPoints, if greater than or equal to minPoints, the point is marked as a central point, and otherwise, as a noise point.
2) Repeating step 1, if a noise point exists within a circle with a radius of a certain central point, this point is marked as an edge point, and vice versa. Repeating step 1) knowing that all points have been accessed.
4. Clustering with maximum Expectation (EM) of Gaussian Mixture Model (GMM), the specific steps are:
1) The number of clusters (similar to K-Means) is selected and the gaussian distribution parameters (mean and variance) for each cluster are randomly initialized. It is also possible to observe the data first to give a relatively accurate mean and variance.
2) Given the gaussian distribution of each cluster, the probability that each data point belongs to each cluster is calculated. The closer a point is to the center of the gaussian distribution, the more likely it is that it belongs to the cluster.
3) Based on these probabilities we calculate gaussian distribution parameters to maximize the probability of the data point, and a weighting of the probability of the data point, i.e. the probability that the data point belongs to the cluster, can be used to calculate these new parameters.
4) Iterations 2) and 3) are repeated until the variation in the iterations is not large.
5. Aggregation hierarchical clustering
Hierarchical clustering algorithms fall into two categories: top-down and bottom-up. Aggregation level clustering (HAC) is a bottom-up clustering algorithm. HAC first treats each data point as a single cluster and then calculates the distance between all clusters to merge clusters until all clusters are clustered into one cluster.
6. Graph group detection (Graph Community Detection), specific steps:
1) Each vertex is initially assigned to its own community first, and then the modularity M of the entire network is calculated.
2) Step 1) requires that each community pair be linked by at least one single edge, and if two communities are fused together, the algorithm calculates the resulting modular change ΔM.
3) Step 2) is to take the community pair where ΔM has the greatest growth and then fuse. A new modularity M is then calculated for this cluster and recorded.
4) Repeating the step 1) and the step 2), fusing the community pairs each time, obtaining the maximum gain of delta M finally, and recording a new clustering mode and the corresponding modularity score M.
5) Repeating the step 1) and the step 2), fusing the community pairs each time, obtaining the maximum gain of delta M finally, and recording a new clustering mode and the corresponding modularity score M.
And step S30, when the moon knot model information matched with the product item information exists in the moon knot model database, outputting the run time model information and the demand list model information in the searched moon knot model information.
In the embodiment, product item information submitted by a target user is obtained; searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database according to the product item information, wherein the moon knot model information comprises a corresponding relation among the product item model information, the running time model information and the demand list model information; when the moon knot model information matched with the product item information exists in the moon knot model database, the running time model information and the demand list model information in the searched moon knot model information are output, so that a user can output the moon knot information more efficiently, the working efficiency is improved, and the time is saved.
Further, in an alternative embodiment, based on the above embodiment, after the step S30, the method further includes:
receiving batch time data input by a user according to the batch time model information, receiving demand list data input by the user according to the demand list model information, and generating month junction information by utilizing the input batch time data and the demand list data.
In this embodiment, the user directly generates the running time data and the demand list data by filling corresponding data in the running time model information and the demand list model information according to the output running time model information and the demand list model information, so that the required month knot information can be generated, and the output efficiency of the month knot information is effectively improved.
Further, in an alternative embodiment, after said step S20, the method further comprises:
and when the moon knot model information matched with the product item information does not exist in the moon knot model database, receiving running time information and demand list information which are input by a user and correspond to the product item information. Therefore, the running batch time information and the demand list information corresponding to the product item information are input through the month knot model, and the month knot information is manually input by a user, so that the flexibility of generating the month knot information is further improved.
Further, in an alternative embodiment, after said step S20, the method further comprises:
when the moon knot model information matched with the product item information does not exist in the moon knot model database, sending a moon knot model information request to a preset external server or external network, wherein the moon knot model information request comprises the product item information;
in this embodiment, if the preset external server or external network pre-stores the month node model information matched with the product item information, the running time information and the demand list information corresponding to the product item information sent by the external server or the external network are received. Therefore, the local server can share the moon knot model with the external server or the external network at the same time, and the flexibility of moon knot information generation is further improved.
Further, in an alternative embodiment, the method further comprises:
and updating the corresponding relation between the product item information, the received running batch time information and the received demand list information into the pre-stored month knot model database.
Thus, when each time the run time information and the demand list information corresponding to the product item information input by the user are received, or the run time information and the demand list information corresponding to the product item information sent by an external server or an external network are received, the product item information, the run time information and the demand list information corresponding to the product item information are updated to the pre-stored month model database. When the product item information matched with the submitted product item information or belonging to the same clustering category is obtained again later, the user does not need to manually input the moon knot information, and the moon knot information is not required to be acquired from an external server or an external network, so that the efficiency of generating the moon knot information is further improved.
In addition, the present invention also provides a computer-readable storage medium storing a new product development-based moon cake information processing system executable by at least one processor to cause the at least one processor to perform the steps of the new product development-based moon cake information processing method as in the above embodiments, and specific implementation procedures of the new product development-based moon cake information processing method steps S10, S20, S30, etc. are described above and will not be repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, and thus do not limit the scope of the claims of the present invention. The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments. In addition, while a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in a different order than is shown.
Those skilled in the art will appreciate that many modifications are possible in which the invention is practiced without departing from its scope or spirit, e.g., features of one embodiment can be used with another embodiment to yield yet a further embodiment. Any modification, equivalent replacement and improvement made within the technical idea of the present invention should be within the scope of the claims of the present invention.
Claims (10)
1. An electronic device comprising a memory, a processor, and a new product development based moon cake information processing system stored on the memory and operable on the processor, the new product development based moon cake information processing system when executed by the processor performing the steps of:
acquiring product item information submitted by a target user;
searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database according to the product item information, wherein the moon knot model information comprises a corresponding relation among the product item model information, the running time model information and the demand list model information;
when the moon cake model database is found to have moon cake model information matched with the product item information, running batch time model information and demand list model information in the searched moon cake model information are output, running batch time data are generated according to data input by a user in the running batch time model information, demand list data are generated according to data input by the user in the demand list model information, and moon cake information is generated by utilizing the input running batch time data and the demand list data;
the searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database comprises the following steps:
carrying out cluster analysis on the product item information submitted by the target user, and determining the cluster category to which the product item information submitted by the target user belongs, wherein a clustering algorithm adopted by the cluster analysis is one of K-Means clustering, mean shift clustering, DBSCAN clustering, GMM clustering, aggregation hierarchical clustering and graph group detection clustering;
and when the clustering category to which the product item information submitted by the target user belongs is the same as the clustering category to which the product item information in the moon-knot model information stored in the moon-knot model database belongs, the product item information is considered to be matched with the moon-knot model information.
2. The electronic device of claim 1, wherein after the step of implementing the step of searching a pre-stored moon-pool model database for the presence of moon-pool model information matching the product item information according to the product item information, the new product development-based moon-pool information processing system is further implemented by the processor as follows:
when the moon knot model information matched with the product item information does not exist in the moon knot model database, sending a moon knot model information request to a preset external server, wherein the moon knot model information request comprises the product item information;
and receiving running time information and demand list information which are sent by the preset external server and correspond to the product item information.
3. The electronic device of claim 2, wherein the new product development based lunar junction information processing system, when executed by the processor, further performs the steps of:
and updating the corresponding relation between the product item information, the received running batch time information and the received demand list information into the pre-stored month knot model database.
4. The electronic device of claim 2 or 3, wherein the requirement list information includes at least one of problem ring ratio delta information, problem type information, and problem ring ratio change information.
5. A new product development-based moon knot information processing method, the method comprising:
acquiring product item information submitted by a target user;
searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database according to the product item information, wherein the moon knot model information comprises a corresponding relation among the product item model information, the running time model information and the demand list model information;
when the moon cake model database is found to have moon cake model information matched with the product item information, running batch time model information and demand list model information in the searched moon cake model information are output, running batch time data are generated according to data input by a user in the running batch time model information, demand list data are generated according to data input by the user in the demand list model information, and moon cake information is generated by utilizing the input running batch time data and the demand list data;
the searching whether the moon knot model information matched with the product item information exists in a pre-stored moon knot model database comprises the following steps:
carrying out cluster analysis on the product item information submitted by the target user, and determining the cluster category to which the product item information submitted by the target user belongs, wherein a clustering algorithm adopted by the cluster analysis is one of K-Means clustering, mean shift clustering, DBSCAN clustering, GMM clustering, aggregation hierarchical clustering and graph group detection clustering;
and when the clustering category to which the product item information submitted by the target user belongs is the same as the clustering category to which the product item information in the moon-knot model information stored in the moon-knot model database belongs, the product item information is considered to be matched with the moon-knot model information.
6. The new product development-based moon cake information processing method according to claim 5, further comprising, after the step of searching for whether there is moon cake model information matching the product item information in a pre-stored moon cake model database according to the product item information:
and when the moon knot model information matched with the product item information does not exist in the moon knot model database, receiving running time information and demand list information which are input by a user and correspond to the product item information.
7. The new product development-based moon cake information processing method according to claim 5, further comprising, after the step of searching for whether there is moon cake model information matching the product item information in a pre-stored moon cake model database according to the product item information:
when the moon knot model information matched with the product item information does not exist in the moon knot model database, sending a moon knot model information request to a preset external server, wherein the moon knot model information request comprises the product item information;
and receiving running time information and demand list information which are sent by the preset external server and correspond to the product item information.
8. The new product development-based moon cake information processing method according to claim 6 or 7, further comprising:
and updating the corresponding relation between the product item information, the received running batch time information and the received demand list information into the pre-stored month knot model database.
9. The new product development-based moon information processing method according to claim 6 or 7, wherein the requirement list information includes at least one of problem ring ratio increment information, problem type information, and problem ring ratio change information.
10. A computer-readable storage medium, wherein a new product development-based moon cake information processing system implementing the steps of the new product development-based moon cake information processing method according to any one of claims 6 to 9 when executed by a processor is stored thereon.
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| KR20020014648A (en) * | 2000-08-16 | 2002-02-25 | 이호관 | Knowledge service system through ordered workflow, service method thereof, service method for developing products through network and system thereof |
| KR20070104099A (en) * | 2006-04-21 | 2007-10-25 | (주) 디엠디 | System for network-based development and management of information technology solutions and method thereof |
| CN108509344A (en) * | 2018-04-04 | 2018-09-07 | 深圳前海微众银行股份有限公司 | Cut race batch test method, equipment and readable storage medium storing program for executing day |
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