CN118034619A - Printing information management method and system of time management label printer - Google Patents
Printing information management method and system of time management label printer Download PDFInfo
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Abstract
The invention relates to the technical field of printing information management, in particular to a printing information management method and system of a time management label printer. The method comprises the following steps: acquiring a to-be-printed task of a time management label printer, determining a text vector of each to-be-printed task, obtaining text space points, and further determining initial clustering centers of all the text space points; clustering all text space points to obtain a clustering area, determining an optimal distance according to the distribution characteristics of the clustering area after each clustering, and taking the clustering area under the optimal distance as a target area; acquiring a printing keyword of each target area; according to the method and the device, the printing order of all the tasks to be printed in each target area is determined according to the preset importance degree of each printing keyword, and the printing order arrangement can be carried out on the tasks to be printed in the time management label printer based on text information, so that the rationality of the printing order is enhanced, and the reliability and objectivity of the printing task management and resource utilization are improved.
Description
Technical Field
The invention relates to the technical field of printing information management, in particular to a printing information management method and system of a time management label printer.
Background
The time management label printer is a printer for printing time management labels of corresponding articles, is generally applied to a plurality of fields such as medicine management, warehouse management and the like, and is critical to the ordering of tasks to be printed in the time management label printer due to the diversity and complexity of the labels to be printed.
In the related art, the time sequence of the printing queue in the time management label printer is used for ordering the printing sequence, and in this way, a plurality of different sources of the to-be-printed tasks can be input into the queue at the same time, so that the printing process of the whole time management label printer is disordered in time sequence, the to-be-printed tasks cannot be effectively managed, the printing efficiency is lower, and the printing resource waste is caused.
Disclosure of Invention
In order to solve the technical problems that when a plurality of tasks to be printed with different sources are simultaneously input into a queue in the related art, the printing process of the whole time management label printer generates time sequence disorder, so that the tasks to be printed cannot be effectively managed, the printing efficiency is low, and the printing resource is wasted, the invention provides a printing information management method and a printing information management system of the time management label printer, and the adopted technical scheme is as follows:
the invention provides a printing information management method of a time management label printer, which comprises the following steps:
Acquiring at least two to-be-printed tasks of a time management label printer, wherein each to-be-printed task comprises a to-be-printed text, and determining a text vector of each to-be-printed task according to the to-be-printed text of each to-be-printed task; mapping all the text vectors into a multidimensional vector space to obtain text space points, and determining initial clustering centers of all the text space points according to the distance between any two text space points;
Taking the distance between the initial clustering center and other text space points as a distance to be detected, taking different distances to be detected as density reachable distances respectively, clustering all the text space points to obtain clustering areas, and determining clustering preference degrees respectively corresponding to the different distances to be detected according to the number of clustering areas after each clustering, the number of text space points in each clustering area and the distance between all the text space points in each clustering area and the initial clustering center;
Determining an optimal distance according to clustering preference degrees respectively corresponding to all the distances to be detected, and taking a clustering area under the optimal distance as a target area; extracting keywords from the text to be printed corresponding to the text space points included in each target area, and obtaining the printing keywords of each target area;
and acquiring the preset importance degree of each printing keyword, and determining the printing order of all the tasks to be printed in each target area according to the preset importance degree of each printing keyword.
Further, the determining the initial clustering center of all the text space points according to the distance between any two text space points includes:
Taking any text space point as a reference point, and calculating the sum of Euclidean distances between the reference point and all other text space points except the reference point to be used as a reference distance;
And taking a text space point corresponding to the minimum value in the reference distance as an initial clustering center.
Further, the step of clustering all the text space points to obtain a clustered region, wherein the distance between the initial clustering center and each other text space point is used as a distance to be detected, and different distances to be detected are respectively used as density reachable distances, and the step of clustering comprises the following steps:
based on a DBSCAN density clustering algorithm, density clustering processing is respectively carried out based on different density reachable distances, and a clustering area corresponding to each density reachable distance is obtained.
Further, determining the cluster preference degrees corresponding to different distances to be measured according to the number of the clustered regions after each clustering, the number of the text space points in each clustered region and the distances between all the text space points in each clustered region and the initial clustering center, includes:
Taking the number of clustered areas after density clustering processing corresponding to each distance to be detected as the area number, calculating the average value of the area number under all the distances to be detected as the area average value, and taking the absolute value of the difference value between the area number corresponding to each distance to be detected and the area average value as the average value area difference;
Taking the variance of the number of text space points in all the clustering areas after the density clustering processing corresponding to each distance to be measured as the variance of the number of clusters to be measured;
Taking variances of Euclidean distances of text space points in all clustering areas after density clustering processing corresponding to each distance to be detected as variances of the distance to be detected;
And determining the clustering preference degree corresponding to the distance to be measured according to the mean value region difference, the cluster quantity variance to be measured and the cluster distance variance to be measured corresponding to the same distance to be measured.
Further, the mean value region difference and the clustering preference degree are in a negative correlation relationship, the to-be-detected clustering quantity variance and the to-be-detected clustering distance variance are in a positive correlation relationship with the clustering preference degree, and the value of the clustering preference degree is a normalized numerical value.
Further, the determining the optimal distance according to the clustering preference degrees respectively corresponding to all the distances to be measured includes:
and taking the distance to be measured corresponding to the highest clustering preference degree as the optimal distance.
Further, the extracting keywords from the text to be printed corresponding to the text space points included in each target area to obtain the printing keywords of each target area includes:
And extracting keywords from the text to be printed based on the TF-IDF algorithm to obtain the printing keywords of each target area.
Further, the determining the printing order of all the tasks to be printed in each target area according to the preset importance degree of each printing keyword includes:
Taking a preset number of printing keywords with highest frequency of all tasks to be printed in a target area as representative keywords of the target area, and calculating the sum of preset importance degrees of all representative keywords in each target area as an area importance coefficient;
distributing a corresponding region sequence for each target region according to the sequence from big to small of the region importance coefficients;
And according to the region order, sequencing all the tasks to be printed in each target region according to the time sequence added into the task queue to obtain the printing order of each task to be printed.
Further, the determining the text vector of each to-be-printed task according to the to-be-printed text of each to-be-printed task includes:
and converting the text to be printed into a corresponding text vector based on a Word2vec model.
The invention also provides a printing information management system of the time management label printer, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the printing information management method of the time management label printer when executing the computer program.
The invention has the following beneficial effects:
According to the method and the device, the initial clustering centers of all the tasks to be printed are determined according to the distribution of the text vectors of the tasks to be printed, wherein the initial clustering centers can effectively represent the center positions of all the tasks to be printed, so that the cluster analysis of all the tasks to be printed is facilitated according to the initial clustering centers; by clustering each distance to be detected as a density reachable distance, the clustering optimization degree corresponding to each distance to be detected can be determined according to the characteristics of the clustering areas obtained by clustering under different distances to be detected, the clustering optimization degree is the effective degree of the clustering result under different distances to be detected, the optimal distance is conveniently determined according to the clustering optimization degree, an optimal clustering mode is obtained, the printing keywords are determined through the optimal target areas, and the determination of the printing sequence of the tasks to be printed in different target areas is realized according to the preset importance degree of the printing keywords. The invention can arrange the printing order of all the to-be-printed tasks in the time management label printer based on the text information, so that the printing process of the whole time management label printer can effectively combine the content of the printing tasks to reasonably order, the rationality of the printing order is enhanced, the reliability and objectivity of the management of the printing tasks and the utilization of resources are improved, and the printing working efficiency is further improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a print information management method of a time management label printer according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of a printing information management method and system for a time management label printer according to the present invention, with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of a print information management method of a time management label printer provided by the present invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for managing printing information of a time management label printer according to an embodiment of the present invention is shown, where the method includes:
S101: acquiring at least two to-be-printed tasks of a time management label printer, wherein each to-be-printed task comprises a to-be-printed text, and determining a text vector of each to-be-printed task according to the to-be-printed text of each to-be-printed task; and mapping all the text vectors into a multidimensional vector space to obtain text space points, and determining initial clustering centers of all the text space points according to the distance between any two text space points.
The implementation scenario of the present invention may specifically be, for example, printing a job to be printed by using a time management label printer, and it can be understood that, in the related art, the printing order of the job to be printed is usually based on the time order when the job to be printed is added to the task queue, that is, when the job to be printed is first added to the print queue, the corresponding printing order is front, and when the job to be printed is added to the print queue after the job to be printed, the corresponding printing order is back, in this way, because there are multiple jobs to be printed simultaneously and input into the print queue, the layout of the job to be printed in the print queue is disordered, and there are multiple jobs to be printed simultaneously and the printing is disordered phenomenon caused by the problem of adding time sequence of the same batch of the job to be printed.
The time management label printer generally needs to print corresponding time management labels, the text part is generally text remarks of the time management labels, the time management labels with the same or similar text remarks are generally from the same printing requirements and different sources, and the corresponding text contents can generate larger difference, so that the embodiment of the invention takes the text contents as the dividing basis, and the reliability of the ordering of the tasks to be printed is effectively improved.
Further, in some embodiments of the present invention, determining a text vector for each to-be-printed task from to-be-printed text for each to-be-printed task includes: and converting the text to be printed into a corresponding text vector based on the Word2vec model.
Wherein a Word2vec model group is used to generate a neural network model group of Word vectors. In the embodiment of the invention, the text to be printed can be input into a Word2vec model which is preset, and the corresponding text vector is processed and output through the Word2vec model. It is understood that since the Word2vec model is a neural network model well known in the art, the setting and processing of the Word2vec model will not be further described.
In the embodiment of the invention, all the text vectors are mapped into the multidimensional vector space to obtain the text space points, and the dimension of the multidimensional vector space corresponds to the dimension of all the text vectors, namely, all the text vectors are characterized in the multidimensional vector space in the form of points, thereby being convenient for carrying out specific analysis on the similarity among the texts.
Further, in some embodiments of the present invention, determining an initial cluster center for all text space points based on a distance between any two text space points includes: taking any text space point as a reference point, and calculating the sum of Euclidean distances between the reference point and all other text space points except the reference point to be used as a reference distance; and taking the text space point corresponding to the minimum value in the reference distance as an initial clustering center.
In the embodiment of the present invention, the larger the reference distance is, the larger the distance between the corresponding reference point and all other text space points in the multidimensional vector space is, i.e. the more the reference point is away from other text space points, and the smaller the reference distance is, the smaller the distance between the corresponding reference point and all other text space points in the multidimensional vector space is, i.e. the more the reference point is at the center position of all text space points.
In the embodiment of the invention, since the text represented by different text space points may have larger difference, namely the Euclidean distance difference between the different text space points is larger, the text space point corresponding to the minimum value in the reference distance is selected as the initial clustering center, so that the embodiment of the invention can conveniently analyze all the text space points based on the initial clustering center.
S102: and clustering all the text space points by taking the distance between the initial clustering center and other text space points as the distance to be detected, taking different distances to be detected as density reachable distances respectively, obtaining clustering areas, and determining clustering preference degrees respectively corresponding to the different distances to be detected according to the number of the clustering areas after each clustering, the number of the text space points in each clustering area and the distance between all the text space points in each clustering area and the initial clustering center.
In the embodiment of the invention, the distance between the initial clustering center and each other text space point can be used as the distance to be measured, and then different distances to be measured are respectively used as the density reachable distances for specific analysis. The density reachable distance is a distance index commonly used in density clustering, and in the embodiment of the invention, density clustering processing can be performed on all text space points based on an initial clustering center and the density reachable distance.
Further, in some embodiments of the present invention, a distance between an initial clustering center and each other text space point is used as a distance to be measured, different distances to be measured are respectively used as density reachable distances, and clustering is performed on all the text space points to obtain a clustering region, including: based on a DBSCAN density clustering algorithm, density clustering processing is respectively carried out based on different density reachable distances, and a clustering area corresponding to each density reachable distance is obtained.
The DBSCAN density clustering algorithm is a density clustering algorithm well known in the art, and the distance to be measured is set as a density reachable distance, and density clustering is carried out by combining an initial clustering center, so that a clustering area corresponding to each density reachable distance is determined.
In the embodiment of the invention, in the density clustering process, if one text space point has other text space points in the range corresponding to the density reachable distance, the corresponding text space point can be used as a class, and if one text space point does not have other text space points in the range corresponding to the density reachable distance, the corresponding text space point can be used as an isolated point, and the isolated point can be used as a clustering area, so that all the text space points are divided into a plurality of clustering areas.
Further, in some embodiments of the present invention, determining the cluster preference degrees corresponding to different distances to be measured according to the number of clustering areas after each clustering, the number of text space points in each clustering area, and the distance between all text space points in each clustering area and the initial clustering center includes: taking the number of clustered areas after density clustering processing corresponding to each distance to be detected as the area number, calculating the average value of the area number under all the distances to be detected as the area average value, and taking the absolute value of the difference value between the area number corresponding to each distance to be detected and the area average value as the average value area difference; taking the variance of the number of text space points in all the clustering areas after the density clustering processing corresponding to each distance to be measured as the variance of the number of clusters to be measured; taking variances of Euclidean distances of text space points in all clustering areas after density clustering processing corresponding to each distance to be detected as variances of the distance to be detected; and determining the clustering preference degree corresponding to the distance to be measured according to the mean value region difference, the cluster quantity variance to be measured and the cluster distance variance to be measured corresponding to the same distance to be measured.
In the embodiment of the invention, after clustering is performed by taking each distance to be detected as the density reachable distance, a plurality of corresponding clustering areas can be obtained, and then the number of the clustering areas can be taken as the area number. It can be understood that the more the number of regions, the smaller the corresponding density reachable distance, that is, the finer the division of the clustered regions, and the more the number of regions, the larger the corresponding density reachable distance, therefore, the number of regions in the embodiment of the present invention needs to be maintained at a middle position, in the embodiment of the present invention, the average value of the number of regions under all the distances to be measured is taken as the region average value, the absolute value of the difference between the number of regions corresponding to each distance to be measured and the region average value is taken as the average value region difference, and the larger the region difference, the less the corresponding number of regions meets the actual requirement, so that the embodiment of the present invention can perform specific analysis according to the average value region difference.
Of course, in other embodiments of the present invention, specific analysis may be performed according to the source of the job to be printed, or according to the preset number of areas, which is not limited.
In the embodiment of the invention, the variance of the number of clusters to be tested characterizes the discrete degree of the number distribution of text space points in all the cluster areas, and the larger the discrete degree is, the less areas with the same density are in all the cluster areas, and it can be understood that the data generally does not have uniform density distribution in DBSCAN clustering operation due to different printing source requirements, and the more practical printing conditions are met when the discrete degree of the distribution is larger.
The larger the variance of the clustering distance to be measured is, the larger the corresponding text difference among all the clustering areas is, and therefore, the more practical printing conditions are met.
In the embodiment of the invention, determining the clustering preference degree corresponding to the distance to be measured according to the mean area difference, the variance of the number of clusters to be measured and the variance of the distance to be measured, which are corresponding to the distance to be measured, comprises the following steps: the mean value region difference and the clustering preference degree are in a negative correlation relationship, the to-be-detected clustering quantity variance and the to-be-detected clustering distance variance are in a positive correlation relationship, and the value of the clustering preference degree is a normalized numerical value.
The positive correlation relationship indicates that the dependent variable increases along with the increase of the independent variable, the dependent variable decreases along with the decrease of the independent variable, and the specific relationship can be multiplication relationship, addition relationship, idempotent of an exponential function and is determined by practical application; the negative correlation indicates that the dependent variable decreases with increasing independent variable, and the dependent variable increases with decreasing independent variable, which may be a subtraction relationship, a division relationship, or the like, and is determined by the actual application.
In one embodiment of the present invention, the normalization process may specifically be, for example, maximum and minimum normalization processes, and the normalization in the subsequent steps may be performed by using the maximum and minimum normalization processes, and in other embodiments of the present invention, other normalization methods may be selected according to a specific range of values, which will not be described herein.
That is, in the embodiment of the present invention, a preset constant parameter may be set, a sum of the mean area difference and the preset constant parameter is calculated and inverted, where the preset constant parameter is a safety value set to prevent the denominator from being 0, alternatively, the preset constant parameter may be specifically, for example, 1, which is not limited thereto. And calculating the product of the corresponding reciprocal and three parameters of the variance of the number of clusters to be detected and the variance of the distance between clusters to be detected, and normalizing the product value to obtain the clustering preference degree.
Of course, in other embodiments of the present invention, a variety of other arbitrary possible implementation manners may be used to ensure that the cluster preference degree is calculated under the condition that the mean area difference and the cluster preference degree have a negative correlation, and the variance of the number of clusters to be detected and the variance of the distance between clusters to be detected have a positive correlation, and the value of the cluster preference degree is a normalized value, which is not limited.
In the embodiment of the invention, when the clustering preference degree is higher, the corresponding region quantity can be represented to be more consistent with the actual printing condition, and the distribution of text space points among the clustering regions and the distance distribution among the clustering regions are more consistent with the objective printing condition, each distance to be measured can be analyzed according to the clustering preference degree, and the invention is particularly referred to the subsequent embodiment.
S103: determining an optimal distance according to the clustering preference degrees respectively corresponding to all the distances to be detected, and taking a clustering area under the optimal distance as a target area; and extracting keywords from the text to be printed corresponding to the text space points included in each target area, and obtaining the printing keywords of each target area.
Further, in some embodiments of the present invention, determining the optimal distance according to the cluster preference degrees corresponding to all the distances to be measured, includes: and taking the distance to be measured corresponding to the highest clustering preference degree as the optimal distance.
In the embodiment of the invention, as the clustering preference degree is higher, the distribution of text space points among the indicating areas, the distribution of distance among the clustering areas and the distribution of distance among the clustering areas are more consistent with the actual printing scene, namely the objectivity of the corresponding clustering effect is stronger, therefore, the invention can select the distance to be detected corresponding to the highest clustering preference degree as the optimal distance, and the clustering area under the optimal distance is taken as the target area; the target area is the clustering effect area of the task to be printed under the objective condition.
Further, in some embodiments of the present invention, extracting keywords from text to be printed corresponding to text space points included in each target area, to obtain print keywords of each target area includes: and extracting keywords from the text to be printed based on the TF-IDF algorithm to obtain the printing keywords of each target area.
The TF-IDF algorithm is a keyword extraction algorithm well known in the art, and keyword extraction is performed on the text to be printed through word frequency and inverse text frequency indexes to obtain a printed keyword. Of course, in other embodiments of the present invention, a variety of other arbitrary possible implementations may be used to extract keywords from the text to be printed, which is not described and limited herein. It can be understood that the keyword extraction can acquire the printing keywords in the text to be printed, so that the printing planning can be conveniently performed according to the printing keywords.
S104: and acquiring the preset importance degree of each printing keyword, and determining the printing order of all the tasks to be printed in each target area according to the preset importance degree of each printing keyword.
In the embodiment of the present invention, the number may be used to represent the corresponding preset importance level, for example, the larger the preset importance level is, the larger the corresponding number is, for example, the preset importance level of the print keyword "file" is 5, and the preset importance level of the print keyword "picture" is 1, and the corresponding print keyword "file" is more important than "picture".
Of course, in other embodiments of the present invention, the letter level may be used to represent, for example, that the preset importance level "a" is greater than the preset importance level "B", and the preset importance level only represents the importance of the corresponding print keyword, so as to perform importance analysis on the task to be printed according to the importance.
Further, in some embodiments of the present invention, determining the printing order of all the tasks to be printed in each target area according to the preset importance level of each printing keyword includes: taking the preset number of printing keywords with highest frequency of all the tasks to be printed in the target area as representative keywords of the target area, and calculating the sum of preset importance degrees of all the representative keywords in each target area as an area importance coefficient; distributing corresponding region orders for each target region according to the sequence from large to small of the region importance coefficients; and according to the region order, sequencing all the tasks to be printed in each target region according to the sequence order added into the task queue to obtain the printing order of each task to be printed.
In the embodiment of the present invention, the preset number is the number of print keywords, and in the embodiment of the present invention, the preset number of print keywords with highest frequency in all the tasks to be printed in each target area may be selected as the integral keyword feature of the corresponding target area, that is, the representative keyword of the target area.
In the embodiment of the present invention, the sum of all preset importance levels representing keywords in each target area may be used as the area importance coefficients, for example, when the preset number is 3 and the preset importance levels representing keywords in a certain target area are 1, 5 and 2, the corresponding area importance coefficient is 8, thereby obtaining the area importance coefficient of each target area, and then, the corresponding area order is allocated to each target area according to the order of the area importance coefficients from large to small.
For example, there are 4 target areas, namely a target area X, a target area Y, a target area Z and a target area R; the region importance coefficient of the target region X is 8, the region importance coefficient of the target region Y is 10, the region importance coefficient of the target region Z is 5, and the region importance coefficient of the target region R is 3, and the region order is the target region Y, the target region X, the target region Z and the target region R.
In the embodiment of the invention, all the tasks to be printed in each target area can be printed in a continuous time period, and the printing order of the tasks to be printed in the corresponding target area can be determined for the time management label printer according to the time sequence, namely the time sequence of adding the tasks to be printed into the task queue, namely the combination area order is from small to large, and then the time sequence of the tasks to be printed in each target area is combined.
In other embodiments of the present invention, the printing order of the to-be-printed tasks in the target area may be determined according to the preset importance levels and values of all the printing keywords of each to-be-printed task, which is not limited.
According to the method and the device, the initial clustering centers of all the tasks to be printed are determined according to the distribution of the text vectors of the tasks to be printed, wherein the initial clustering centers can effectively represent the center positions of all the tasks to be printed, so that the cluster analysis of all the tasks to be printed is facilitated according to the initial clustering centers; by clustering each distance to be detected as a density reachable distance, the clustering optimization degree corresponding to each distance to be detected can be determined according to the characteristics of the clustering areas obtained by clustering under different distances to be detected, the clustering optimization degree is the effective degree of the clustering result under different distances to be detected, the optimal distance is conveniently determined according to the clustering optimization degree, an optimal clustering mode is obtained, the printing keywords are determined through the optimal target areas, and the determination of the printing sequence of the tasks to be printed in different target areas is realized according to the preset importance degree of the printing keywords. The invention can arrange the printing order of all the to-be-printed tasks in the time management label printer based on the text information, so that the printing process of the whole time management label printer can effectively combine the content of the printing tasks to reasonably order, the rationality of the printing order is enhanced, the reliability and objectivity of the management of the printing tasks and the utilization of resources are improved, and the printing working efficiency is further improved.
The invention also provides a printing information management system of the time management label printer, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the printing information management method of the time management label printer when executing the computer program.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. A print information management method of a time management label printer, the method comprising:
Acquiring at least two to-be-printed tasks of a time management label printer, wherein each to-be-printed task comprises a to-be-printed text, and determining a text vector of each to-be-printed task according to the to-be-printed text of each to-be-printed task; mapping all the text vectors into a multidimensional vector space to obtain text space points, and determining initial clustering centers of all the text space points according to the distance between any two text space points;
Taking the distance between the initial clustering center and other text space points as a distance to be detected, taking different distances to be detected as density reachable distances respectively, clustering all the text space points to obtain clustering areas, and determining clustering preference degrees respectively corresponding to the different distances to be detected according to the number of clustering areas after each clustering, the number of text space points in each clustering area and the distance between all the text space points in each clustering area and the initial clustering center;
Determining an optimal distance according to clustering preference degrees respectively corresponding to all the distances to be detected, and taking a clustering area under the optimal distance as a target area; extracting keywords from the text to be printed corresponding to the text space points included in each target area, and obtaining the printing keywords of each target area;
and acquiring the preset importance degree of each printing keyword, and determining the printing order of all the tasks to be printed in each target area according to the preset importance degree of each printing keyword.
2. The printing information management method of a time management label printer according to claim 1, wherein said determining an initial cluster center of all text space points based on a distance between any two text space points comprises:
Taking any text space point as a reference point, and calculating the sum of Euclidean distances between the reference point and all other text space points except the reference point to be used as a reference distance;
And taking a text space point corresponding to the minimum value in the reference distance as an initial clustering center.
3. The method for managing printing information of a time management label printer according to claim 1, wherein the clustering process is performed on all the text space points to obtain a clustered region by taking a distance between the initial clustering center and each other text space point as a distance to be measured and taking different distances to be measured as density reachable distances, respectively, and comprises:
based on a DBSCAN density clustering algorithm, density clustering processing is respectively carried out based on different density reachable distances, and a clustering area corresponding to each density reachable distance is obtained.
4. The method for managing printing information of a time management label printer according to claim 1, wherein determining the cluster preference levels corresponding to different distances to be measured according to the number of clustering areas after each clustering, the number of text space points in each clustering area, and the distances between all text space points in each clustering area and an initial clustering center, respectively, comprises:
Taking the number of clustered areas after density clustering processing corresponding to each distance to be detected as the area number, calculating the average value of the area number under all the distances to be detected as the area average value, and taking the absolute value of the difference value between the area number corresponding to each distance to be detected and the area average value as the average value area difference;
Taking the variance of the number of text space points in all the clustering areas after the density clustering processing corresponding to each distance to be measured as the variance of the number of clusters to be measured;
Taking variances of Euclidean distances of text space points in all clustering areas after density clustering processing corresponding to each distance to be detected as variances of the distance to be detected;
And determining the clustering preference degree corresponding to the distance to be measured according to the mean value region difference, the cluster quantity variance to be measured and the cluster distance variance to be measured corresponding to the same distance to be measured.
5. The method for managing printing information of a time management label printer according to claim 4, wherein the mean area difference and the cluster preference degree have a negative correlation, the variance of the number of clusters to be measured and the variance of the distance between clusters to be measured have a positive correlation with the cluster preference degree, and the value of the cluster preference degree is a normalized value.
6. The method for managing printing information of a time management label printer according to claim 1, wherein the determining the optimal distance according to the cluster preference levels respectively corresponding to all the distances to be measured comprises:
and taking the distance to be measured corresponding to the highest clustering preference degree as the optimal distance.
7. The method for managing printing information of a time management label printer according to claim 1, wherein the extracting keywords from the text to be printed corresponding to the text space points included in each target area, to obtain the printing keywords of each target area, comprises:
And extracting keywords from the text to be printed based on the TF-IDF algorithm to obtain the printing keywords of each target area.
8. The printing information management method of a time management label printer according to claim 1, wherein the determining the printing order of all the jobs to be printed in each target area according to the preset importance level of each printing keyword comprises:
Taking a preset number of printing keywords with highest frequency of all tasks to be printed in a target area as representative keywords of the target area, and calculating the sum of preset importance degrees of all representative keywords in each target area as an area importance coefficient;
distributing a corresponding region sequence for each target region according to the sequence from big to small of the region importance coefficients;
And according to the region order, sequencing all the tasks to be printed in each target region according to the time sequence added into the task queue to obtain the printing order of each task to be printed.
9. The printing information management method of a time management label printer according to claim 1, wherein said determining a text vector for each of the to-be-printed tasks based on the to-be-printed text for each of the to-be-printed tasks comprises:
and converting the text to be printed into a corresponding text vector based on a Word2vec model.
10. A print information management system of a time management label printer, the system comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of a print information management method of a time management label printer according to any one of claims 1 to 9.
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