CN117235500A - Material category identification method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a method, a device, electronic equipment and a storage medium for identifying material categories, wherein the method comprises the following steps: determining a to-be-used characteristic value corresponding to the to-be-identified material and a to-be-matched characteristic value corresponding to each to-be-matched material; determining Euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified and the material to be matched based on the Euclidean distance; and determining target matched materials matched with the materials to be identified based on the weights to be used, and taking the material category of the target matched materials as the material category of the materials to be identified. The technical scheme of the embodiment of the invention solves the problems of misjudgment, missed judgment and the like when the material category is determined by the AI technology in the prior art, namely the inaccurate material category identification, and achieves the technical effect of efficiently identifying the material.
Description
Technical Field
The present invention relates to the field of computer processing technologies, and in particular, to a method and apparatus for identifying a material class, an electronic device, and a storage medium.
Background
The method is affected by requirements, supply, early warning and the like, the stock management of the stock rolling of the stock is more and more important, the stock rolling risk can be found in advance according to actual scenes, the starvation of the stock and the loss of manpower can be reduced, but the stock rolling is difficult to identify manually, with the development of computer artificial intelligence, a computer-aided computing system is more and more applied to the stock of the warehouse, the workload of personnel can be lightened, the accuracy of stock rolling identification is improved, and therefore, the automatic identification of the stock rolling by utilizing the deep learning and other technologies is particularly important.
Along with the high-speed development of artificial intelligence, more and more automatic detection algorithms for material rolling and storage gradually enter a warehouse to fall to the ground, but phenomena such as missed judgment, misjudgment and the like can occur through automatic detection of AI, wherein the same material is possibly repeatedly allocated, so that a result is not very accurate, whether the same material is repeatedly allocated or not is accurately identified based on the situation, and the improvement of detection accuracy is a current urgent need to be solved.
Disclosure of Invention
The invention provides a method, a device, electronic equipment and a storage medium for identifying material categories, so as to realize the technical effect of effectively and accurately identifying materials.
According to an aspect of the present invention, there is provided a method of identifying a category of materials, the method comprising:
determining a to-be-used characteristic value corresponding to the to-be-identified material and a to-be-matched characteristic value corresponding to each to-be-matched material;
determining Euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified and the material to be matched based on the Euclidean distance;
and determining target matched materials matched with the materials to be identified based on the weights to be used, and taking the material category of the target matched materials as the material category of the materials to be identified.
Further, determining the feature value to be matched corresponding to each material to be matched includes:
determining a to-be-used influence factor corresponding to each to-be-matched material according to-be-processed historical scheduling data corresponding to each to-be-matched material in each historical period;
and determining the characteristic values to be matched corresponding to the materials to be matched according to the influence factors to be used and the objective functions of the materials to be matched.
Further, determining the to-be-used influence factor corresponding to each to-be-matched material according to the to-be-processed historical scheduling data corresponding to each to-be-matched material in each historical period includes:
for each material to be matched in each history period, determining a to-be-average influence factor corresponding to the current material to be matched according to-be-processed scheduling data of the current material to be matched in the current history period, wherein the to-be-processed scheduling data comprises scheduling times, average scheduling data, maximum scheduling inventory data, average scheduling time intervals and maximum scheduling time intervals;
and determining the influence factors to be used according to the influence factors to be averaged of the current materials to be matched in each history period.
Further, the determining, according to the to-be-processed scheduling data of the current to-be-matched material in the current history period, a to-be-averaged influence factor corresponding to the current to-be-matched material includes:
determining a calling-in influence factor and a calling-out influence factor according to the scheduling times, the calling-out times, the first weight and the second weight;
determining an allocation inventory influence factor according to the average allocation quantity, the maximum allocation inventory quantity and a third weight;
determining an allocation time influence factor according to the average allocation time interval, the maximum allocation time interval and the fourth weight;
and determining the to-be-averaged influence factor based on the call-in influence factor, the call-out influence factor, the call inventory influence factor and the call time influence factor.
Further, the determining the euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified and the material to be matched based on the euclidean distance includes:
for each feature value to be matched, determining a difference value between the feature value to be used and the current feature value to be matched, and determining the Euclidean distance based on the difference value;
and taking the reciprocal of the Euclidean distance as the weight to be used of the material to be identified relative to the material to be matched.
Further, the determining, based on the weights to be used, the target matching material that matches the material to be identified includes:
and taking the material to be matched corresponding to the maximum weight to be used as the target matching material.
Further, the method further comprises:
and dispatching the material category corresponding to the target matched material, taking the material category as the material category of the material to be identified, dispatching the material to be identified based on the material category, and updating material information and dispatching data corresponding to the material to be identified.
According to another aspect of the present invention, there is provided an identification device of a category of materials, the device comprising:
the characteristic value determining module is used for determining characteristic values to be used corresponding to the materials to be identified and characteristic values to be matched corresponding to the materials to be matched;
the weight determining module is used for determining Euclidean distance between the feature value to be used and each feature value to be matched and determining the weight to be used of the material to be identified relative to the material to be matched based on the Euclidean distance;
and the material category identification module is used for determining target matched materials matched with the materials to be identified based on the weights to be used, and taking the material category of the target matched materials as the material category of the materials to be identified.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of identifying a category of asset according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for identifying a category of material according to any one of the embodiments of the present invention when executed.
According to the technical scheme, the characteristic values to be used corresponding to the materials to be identified and the characteristic values to be matched corresponding to the materials to be matched are determined; determining Euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified and the material to be matched based on the Euclidean distance; the method and the device have the advantages that the target matched materials matched with the materials to be identified are determined based on the weights to be used, and the material category of the target matched materials is used as the material category of the materials to be identified, so that the problems of misjudgment, missed judgment and the like when the material category is determined through the AI technology in the prior art, namely the problem of inaccurate material category identification, are solved, and the technical effect of efficiently identifying the materials is achieved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for identifying a material category according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a method for identifying a material category according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a device for identifying a material category according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a data processing method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flow chart of a method for identifying a material category according to an embodiment of the present invention, where the method for identifying a material category may be applied to a situation where a material is identified when the material is put in and put out, and the method for identifying a material category may be implemented by a device for identifying a material category, where the device may be implemented in the form of hardware and/or software, where the hardware may be integrated in a computer device, where the computer device may be a mobile terminal or a PC terminal, etc.
As shown in fig. 1, the method includes:
s110, determining the characteristic value to be used corresponding to the material to be identified and the characteristic value to be matched corresponding to each material to be matched.
The materials to be identified can be materials which come in and go out of the warehouse, and the types of the materials need to be determined. The materials to be matched are some materials stored in the material library. The characteristic value to be used corresponds to the material to be identified, and the characteristic value to be matched corresponds to the material to be matched. The characteristic values may characterize the corresponding supplies.
In this embodiment, determining the feature value to be used corresponding to the material to be identified and the feature value to be matched corresponding to each material to be matched may be: determining a to-be-used influence factor corresponding to each to-be-matched material according to-be-processed historical scheduling data corresponding to each to-be-matched material in each historical period; and determining the characteristic values to be matched corresponding to the materials to be matched according to the influence factors to be used and the objective functions of the materials to be matched.
The feature value to be matched corresponding to each material to be matched can be determined by taking one period as a calculation standard. For each history period, the history scheduling data of the current material to be matched can be obtained, and the characteristic value to be matched is determined according to the history scheduling data. That is, the history scheduling data is regarded as history scheduling data to be processed. The historical schedule data to be processed can be the call-in times, call-out times, call-in average value and the like of the materials to be matched. And determining an influence factor corresponding to the corresponding material to be matched based on the historical scheduling data, and taking the influence factor as the influence factor to be used. An objective function may be understood as a function of which the characteristic values to be matched are determined by further processing the influence factors to be used.
In this embodiment, by using the to-be-processed historical scheduling data corresponding to each to-be-matched material in each historical period, the determining of the corresponding to-be-matched feature value may be: for each material to be matched in each history period, determining a to-be-average influence factor corresponding to the current material to be matched according to-be-processed scheduling data of the current material to be matched in the current history period, wherein the to-be-processed scheduling data comprises scheduling times, average scheduling data, maximum scheduling inventory data, average scheduling time intervals and maximum scheduling time intervals; and determining the influence factors to be used according to the influence factors to be averaged of the current materials to be matched in each history period.
It should be noted that, the determination manner of the influence factor to be used corresponding to each material to be matched is the same, and in this embodiment, the determination of the influence factor to be used of one material to be matched is illustrated as an example.
The calling-in times can be understood as the times of calling the materials to be matched into the warehouse in the current history period, and the calling-out times can be the times of calling the materials to be matched out of the warehouse in the current history period. The average allocation number may be the average allocation number of the materials to be matched, i.e. the average number value determined by combining the allocation and the allocation. The maximum dial inventory quantity may be an inventory quantity system determined in combination with the dial-in quantity and the dial-out quantity. The average commit time interval may be an average time interval determined in combination with the commit time and the rollout time. The maximum allocation time interval may be a maximum allocation time interval obtained by determining an interval duration between two adjacent time points after combining the allocation time and the allocation time, thereby determining the maximum time interval, and taking the time interval as the maximum allocation time interval. According to the historical scheduling data to be processed, the average influence factor to be measured corresponding to the current material to be matched in the current historical period can be determined. After determining the to-be-averaged influence factors corresponding to the current to-be-matched materials in each history period, the to-be-used influence factors of the current to-be-matched materials can be determined.
That is, when determining the influence factor to be used corresponding to each material to be matched, the influence factor to be averaged corresponding to each material to be matched may be determined first, and optionally, the influence factor to be averaged may be determined: determining a calling-in influence factor and a calling-out influence factor according to the scheduling times, the calling-out times, the first weight and the second weight; determining an allocation inventory influence factor according to the average allocation quantity, the maximum allocation inventory quantity and a third weight; determining an allocation time influence factor according to the average allocation time interval, the maximum allocation time interval and the fourth weight; and determining the to-be-averaged influence factor based on the call-in influence factor, the call-out influence factor, the call inventory influence factor and the call time influence factor.
Specifically, the total scheduling times may be determined according to the number of times of scheduling in and the number of times of scheduling out. And calculating the ratio of the number of times of tuning-in to the total number of times of scheduling, calculating the product of the ratio and the first weight, and determining a tuning-in influence factor. And calculating the ratio of the number of times of dispatching and the total number of times of dispatching, calculating the product of the ratio and the second weight, and determining the dispatching influence factor. After calculating the ratio of the average allocation quantity to the maximum allocation inventory quantity, multiplying the ratio by a third weight to determine an allocation inventory influence factor; and calculating the ratio of the average allocation time interval to the maximum allocation time interval, multiplying the ratio by a fourth weight, and determining an allocation time influence factor. And respectively calling in the influence factors, calling out the inventory influence factors and calling out the time influence factors, and carrying out summation treatment on the influence factors to obtain the influence factors to be averaged.
Illustratively, the impact factor to be averaged may be determined based on the following equation:
wherein w is 1 Is of a first weight, w 2 Is of a second weight, w 3 Is of a third weight w 4 For the fourth weight, a 1 For tuning in times a 2 To call out the times a 3 For average number of allocation, a 4 For maximum inventory allocation, a 5 For average allocated time interval, a 6 Is the maximum commit time interval.
It can be understood that the to-be-averaged impact factor corresponding to each to-be-matched material in the corresponding history period can be determined based on the above formula. Based on the average influence factors to be used corresponding to the materials to be matched in each history period, the influence factors to be used can be determined, and optionally, the average value of the influence factors to be used of the same materials to be matched in each history period is processed, and the influence factors to be used are determined.
On the basis of the technical scheme, after the influence factors to be used corresponding to the materials to be matched are determined, the characteristic values to be matched corresponding to the materials to be matched can be determined based on the objective function.
Optionally, the objective function may be a function determined based on a normalization influence factor, and optionally, the normalization influence factor may be determined based on a scheduling number of the corresponding material to be matched. The normalization impact factors may be different or the same for different materials to be matched, and whether the normalization impact factors are the same or not is determined based on the scheduling data.
Normalizing the influence factor value
Wherein y is 2 For normalizing value, a 1 Is the actual value, a 2 At a minimum value of a 3 Is the maximum value.
The normalization impact factor may be determined based on the above formula as an objective function.
After the normalization influence factors are determined, the historical scheduling data of each material to be matched can be processed to obtain corresponding normalization values, and then the feature values to be matched corresponding to each material to be matched are determined based on the normalization values and the objective function. The historical scheduling data is processed by multiplying the normalization impact factor by a corresponding numerical value.
The objective function may be: y is 3 ＝[y 1 ·a 1 ,y 1 ·a 2 ,y 1 ·a 3 ,y 1 ·a 4 ]
Wherein y is 3 For normalizing value, y 1 To influence factor value, a 1 For normalizing the number of times of calling in, a 2 To normalize the call-out times, a 3 To normalize the average allocation quantity, a 4 To normalize the average commit time interval.
The feature value to be used corresponding to the material to be identified can be determined based on the same manner as described above.
S120, determining Euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified and the material to be matched based on the Euclidean distance.
The euclidean distance can represent the similarity between the identification materials and the materials to be matched, and alternatively, the smaller the euclidean distance is, the more similar the description is, and the larger the euclidean distance is, the more dissimilar the description is correspondingly.
Specifically, a target formula may be used to determine the euclidean distance between the feature value to be used and each feature value to be matched. The weight to be used of each material to be identified relative to the material to be matched can be determined based on the euclidean distance.
S130, determining target matched materials matched with the materials to be identified based on the weights to be used, and taking the material category of the target matched materials as the material category of the materials to be identified.
It is understood that the materials to be matched include materials matched with the materials to be identified. And taking the materials consistent with the materials to be identified as target matched materials, wherein the material category of the target matched materials can be taken as the material category of the materials to be identified correspondingly.
Optionally, the material to be matched corresponding to the maximum weight to be used is used as the target matching material.
It can be understood that the material to be matched corresponding to the maximum weight value to be used can be used as the target matching material, and then the material category of the target matching material can be used as the material category of the material to be matched.
According to the technical scheme, the characteristic values to be used corresponding to the materials to be identified and the characteristic values to be matched corresponding to the materials to be matched are determined; determining Euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified and the material to be matched based on the Euclidean distance; the method and the device have the advantages that the target matched materials matched with the materials to be identified are determined based on the weights to be used, and the material category of the target matched materials is used as the material category of the materials to be identified, so that the problems of misjudgment, missed judgment and the like when the material category is determined through the AI technology in the prior art, namely the problem of inaccurate material category identification, are solved, and the technical effect of efficiently identifying the materials is achieved.
Example two
Fig. 2 is a schematic flow chart of a method for identifying a category of a material according to a second embodiment of the present invention, on the basis of the foregoing embodiment, the euclidean distance between a feature value to be used corresponding to the material to be identified and a feature value to be matched of each material to be matched may be determined, and further, the weight to be used of the material to be identified relative to each material to be matched may be determined based on the euclidean distance, and a specific implementation manner of the method may be described in detail in this embodiment, where the technical terms identical to or corresponding to the foregoing embodiment are not repeated herein.
As shown in fig. 2, the method includes:
s210, determining the characteristic value to be used corresponding to the material to be identified and the characteristic value to be matched corresponding to each material to be matched.
S220, for each feature value to be matched, determining a difference value between the feature value to be used and the current feature value to be matched, and determining the Euclidean distance based on the difference value.
In this embodiment, the manner of determining the euclidean distance between the feature value to be used and the feature value to be matched is the same, and one of them may be taken as an example to describe.
And taking the currently introduced characteristic value to be matched as the current characteristic value to be matched. Alternatively, the difference between the current feature value to be matched and the feature value to be used may be calculated. The inverse of the difference may be taken as the euclidean distance between the current feature value to be matched and the feature value to be used.
Illustratively, for the distance between the test data point t and the training data point i, the euclidean distance (Euclidean distance) is used:the weights to be used are: />
S230, taking the reciprocal of the Euclidean distance as the weight to be used of the material to be identified relative to the material to be matched.
S240, determining target matched materials matched with the materials to be identified based on the weights to be used, and taking the material category of the target matched materials as the material category of the materials to be identified.
For example, a weighted voting method may be used for determining the category of the materials, and the determination is performed according to the weight of the neighbor:
t: the test data points, i.e., the material or sample to be classified. It has eigenvalues for comparison and classification with training data points, i: training data points, i.e., historical data of known classifications. It has the same characteristics as the test data points and is used to calculate the distance from the test data points, d (t, i): euclidean distance (Euclidean distance) for measuring test data point t and training data pointSimilarity between i. It is the square root of the sum of squares of eigenvalue differences, w (i): weight, representing the inverse of the distance between the test data point and the training data point. The weight measures the similarity of neighbors, the closer the distance is, the larger the weight is, the smaller the weight is, k: the number of neighbors, representing the number of nearest training data points to consider in classification. c j : the category of the j-th neighbor, i.e., the category to which the training data point i belongs.
Indicating a function, when training class c of data point i i Class c equal to the jth neighbor j And 1 if not, and 0 if not. It is used to determine if the class of neighbors is the same as the class of test data points.
On the basis of the technical scheme, after determining the target matching material, the method further comprises the following steps: and dispatching the material category corresponding to the target matched material, taking the material category as the material category of the material to be identified, dispatching the material to be identified based on the material category, and updating material information and dispatching data corresponding to the material to be identified.
It can be understood that, the material category corresponding to the target matching material is called, and is used as the material category of the material to be identified, and after the identification, the material information and the scheduling data corresponding to the target matching material can be updated according to the calling-in or calling-out data of the material to be matched. The material information may be the number of materials, and the scheduling data may be the number of call-in times, call-out times, and the like mentioned above. The advantage of updating the above data is that after receiving the material to be identified again, the material category of the material to be identified can be determined based on the updated scheduling data.
According to the technical scheme, the characteristic values to be used corresponding to the materials to be identified and the characteristic values to be matched corresponding to the materials to be matched are determined; determining Euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified and the material to be matched based on the Euclidean distance; the method and the device have the advantages that the target matched materials matched with the materials to be identified are determined based on the weights to be used, and the material category of the target matched materials is used as the material category of the materials to be identified, so that the problems of misjudgment, missed judgment and the like when the material category is determined through the AI technology in the prior art, namely the problem of inaccurate material category identification, are solved, and the technical effect of efficiently identifying the materials is achieved.
Example III
Fig. 3 is a schematic structural diagram of a data processing apparatus according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: the feature value determination module 310, the weight determination module 320, and the asset class identification module 330.
The feature value determining module 310 is configured to determine feature values to be used corresponding to the materials to be identified, and feature values to be matched corresponding to the materials to be matched; the weight determining module 320 is configured to determine euclidean distances between the feature values to be used and the feature values to be matched, and determine weights to be used between the materials to be identified and the materials to be matched based on the euclidean distances; and a material category identification module 330, configured to determine, based on each weight to be used, a target matching material that matches the material to be identified, and take the material category of the target matching material as the material category of the material to be identified.
On the basis of the technical scheme, the characteristic value determining module comprises:
the to-be-used influence factor determining unit is used for determining to-be-used influence factors corresponding to each to-be-matched material according to-be-processed historical scheduling data corresponding to each to-be-matched material in each historical period;
the to-be-matched characteristic value determining unit is used for determining to-be-matched characteristic values corresponding to the to-be-matched materials according to-be-used influence factors and objective functions of the to-be-matched materials.
On the basis of the above technical solution, the to-be-used influence factor determining unit includes:
the to-be-averaged influence factor determining subunit is used for determining to-be-averaged influence factors corresponding to current to-be-matched materials according to-be-processed scheduling data of the current to-be-matched materials in the current history period for each to-be-matched material in each history period, wherein the to-be-processed scheduling data comprises scheduling times, average scheduling data, maximum scheduling inventory data, average scheduling time intervals and maximum scheduling time intervals; and the to-be-used influence factor determining subunit is used for determining the to-be-used influence factor according to the to-be-averaged influence factor of the current to-be-matched material in each history period.
On the basis of the above technical solutions, the to-be-averaged influence factor determining subunit includes:
the first influence factor determining subunit is used for determining a calling-in influence factor and a calling-out influence factor according to the scheduling times, the calling-out times, the first weight and the second weight; a second influencing factor determining subunit, configured to determine an allocated inventory influencing factor according to the average allocation data, the maximum allocation inventory data, and a third weight; a third influencing factor determining subunit, configured to determine an allocating time influencing factor according to the average allocating time interval, the maximum allocating time interval and the fourth weight; and the fourth influence factor determining subunit is used for determining the influence factor to be averaged based on the calling-in influence factor, the calling-out influence factor, the inventory allocation influence factor and the allocation time influence factor.
On the basis of the above technical solutions, the weight value determining module includes:
the Euclidean distance determining unit is used for determining the difference value between the feature value to be used and the current feature value to be matched for each feature value to be matched, and determining the Euclidean distance based on the difference value; and the weight determining unit is used for taking the reciprocal of the Euclidean distance as the weight to be used of the material to be identified relative to the material to be matched.
On the basis of the technical schemes, the material category identification module is also used for taking the material to be matched corresponding to the maximum weight to be used as the target matching material.
Based on the above technical solutions, the device further includes a data retrieving module, configured to retrieve a material category corresponding to the target matched material, and take the material category as the material category of the material to be identified, so as to schedule the material to be identified based on the material category, and update material information and scheduling data corresponding to the material to be identified.
According to the technical scheme, the characteristic values to be used corresponding to the materials to be identified and the characteristic values to be matched corresponding to the materials to be matched are determined; determining Euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified and the material to be matched based on the Euclidean distance; the method and the device have the advantages that the target matched materials matched with the materials to be identified are determined based on the weights to be used, and the material category of the target matched materials is used as the material category of the materials to be identified, so that the problems of misjudgment, missed judgment and the like when the material category is determined through the AI technology in the prior art, namely the problem of inaccurate material category identification, are solved, and the technical effect of efficiently identifying the materials is achieved.
The identification device for the material category provided by the embodiment of the invention can execute the identification method for the material category provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the identification method of the category of materials.
In some embodiments, the data processing method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more of the steps of the data processing method described above may be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, the processor 11 may be configured to perform the data processing method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of identifying a category of material, comprising:
determining a to-be-used characteristic value corresponding to the to-be-identified material and a to-be-matched characteristic value corresponding to each to-be-matched material;
determining Euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified and the material to be matched based on the Euclidean distance;
and determining target matched materials matched with the materials to be identified based on the weights to be used, and taking the material category of the target matched materials as the material category of the materials to be identified.
2. The method of claim 1, wherein determining the feature value to be matched for each material to be matched comprises:
determining a to-be-used influence factor corresponding to each to-be-matched material according to-be-processed historical scheduling data corresponding to each to-be-matched material in each historical period;
and determining the characteristic values to be matched corresponding to the materials to be matched according to the influence factors to be used and the objective functions of the materials to be matched.
3. The method of claim 2, wherein determining the impact factor to be used corresponding to each of the materials to be matched according to the historical schedule data to be processed corresponding to each of the materials to be matched in each of the historical periods comprises:
for each material to be matched in each history period, determining a to-be-average influence factor corresponding to the current material to be matched according to-be-processed history scheduling data of the current material to be matched in the current history period, wherein the to-be-processed history scheduling data comprises scheduling times, average scheduling number, maximum scheduling inventory number, average scheduling time interval and maximum scheduling time interval;
and determining the influence factors to be used according to the influence factors to be averaged of the current materials to be matched in each history period.
4. The method of claim 1, wherein the determining the to-be-averaged impact factor corresponding to the current to-be-matched material according to the to-be-processed scheduling data of the current to-be-matched material in the current history period comprises:
determining a calling-in influence factor and a calling-out influence factor according to the calling-in times, the calling-out times, the first weight and the second weight;
determining an allocation inventory influence factor according to the average allocation quantity, the maximum allocation inventory quantity and the third weight;
determining an allocation time influence factor according to the average allocation time interval, the maximum allocation time interval and the fourth weight;
and determining the to-be-averaged influence factor based on the call-in influence factor, the call-out influence factor, the call inventory influence factor and the call time influence factor.
5. The method of claim 1, wherein the determining the euclidean distance between the feature value to be used and each feature value to be matched, and determining the weight to be used between the material to be identified relative to the material to be matched based on the euclidean distance, comprises:
for each feature value to be matched, determining a difference value between the feature value to be used and the current feature value to be matched, and determining the Euclidean distance based on the difference value;
and taking the reciprocal of the Euclidean distance as the weight to be used of the material to be identified relative to the material to be matched.
6. The method of claim 1, wherein the determining, based on the respective weights to be used, a target match asset that matches the asset to be identified comprises:
and taking the material to be matched corresponding to the maximum weight to be used as the target matching material.
7. The method as recited in claim 1, further comprising:
and dispatching the material category corresponding to the target matched material, taking the material category as the material category of the material to be identified, dispatching the material to be identified based on the material category, and updating material information and dispatching data corresponding to the material to be identified.
8. An identification device for a category of material, comprising:
the characteristic value determining module is used for determining characteristic values to be used corresponding to the materials to be identified and characteristic values to be matched corresponding to the materials to be matched;
the weight determining module is used for determining Euclidean distance between the feature value to be used and each feature value to be matched and determining the weight to be used of the material to be identified relative to the material to be matched based on the Euclidean distance;
and the material category identification module is used for determining target matched materials matched with the materials to be identified based on the weights to be used, and taking the material category of the target matched materials as the material category of the materials to be identified.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of identifying a category of asset as claimed in any one of claims 1 to 7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of identifying a category of material as claimed in any one of claims 1 to 7.
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| CN202311206725.1A CN117235500A (en) | 2023-09-19 | 2023-09-19 | Material category identification method and device, electronic equipment and storage medium |
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| CN202311206725.1A CN117235500A (en) | 2023-09-19 | 2023-09-19 | Material category identification method and device, electronic equipment and storage medium |
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