CN115081920B - Attendance check-in scheduling management method, system, equipment and storage medium - Google Patents
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Abstract
The invention discloses a attendance check-in scheduling management method, a system, equipment and a storage medium, wherein the system comprises the following steps: collecting weather data of an attendance place area in real time, and preprocessing the collected weather data to obtain an initial sample set; constructing a predictive model based on the improved generation countermeasure network; calculating a loss function between the predicted target and the real target image; training a prediction model through a loss function, wherein in the training, a target of a discriminator D correctly identifies a real sample and correctly eliminates a generated false sample, and a target of a generator G is to minimize the probability that a generated prediction value is eliminated by the discriminator D until the trained prediction model is output; and inputting the weather data into a trained prediction model to obtain a predicted value, and executing corresponding attendance checking operation according to the predicted value. The invention adopts the improved GAN network to monitor the weather condition of the attendance place in real time, automatically adjusts the attendance mode and the attendance system in time, improves the working efficiency and avoids data errors.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to a attendance check-in scheduling management method, a attendance check-in scheduling management system, attendance check-in scheduling equipment and a storage medium.
Background
In the existing check-in method, setting of holiday rest is mostly carried out by manually modifying an attendance rule or presetting legal holidays, but manual early intervention is often required for rest which is not suitable for attendance due to incapacity reasons such as typhoon days and the like in emergency. And the data such as statistics of attendance data, attendance rate, salary and the like also need to be manually derived and calculated. These operations are dependent on manual work to some extent, and if the manual operation is wrong or operation delay can cause a large amount of error data and dirty data, the workload and difficulty of post statistics data can be increased.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provide a attendance check-in scheduling management method, a attendance check-in scheduling management system, attendance check-in scheduling management equipment and a storage medium, which are used for monitoring weather conditions of an attendance check-in place in real time, and predicting weather conditions of the next day by combining related information issued by a local weather bureau, such as emergency response notification, early warning signal data, typhoon data and the like, so as to judge whether shutdown and lessons stopping are needed. If required, the attendance on the same day is automatically set to have a rest and each attendance person is notified, so that the occurrence of personnel safety accidents is avoided. And the attendance data is automatically counted according to the attendance parameters set by the manager at regular time every month, the attendance rate and the salary and other related data are calculated, the working efficiency of related workers is improved, and errors are avoided.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
The invention provides an attendance check-in scheduling management method, which comprises the following steps:
Collecting weather data of an attendance place area in real time, and preprocessing the collected weather data to obtain an initial sample set;
Constructing a prediction model based on the improved generation countermeasure network, specifically: the method comprises the steps of correcting and superposing input original data by adopting a first convolution layer and a correction linear function, obtaining an initial feature map, then up-sampling the initial feature map, amplifying the extracted initial feature map, and correcting three subsequent convolution layers and the correction linear function with higher resolution; then multiplying the initial feature map with a feature map generated by a fourth convolution layer, and obtaining specific feature information through a spatial attention mechanism and a channel attention mechanism; finally, up-sampling the specific characteristic information again, and then correcting and superposing the specific characteristic information by adopting a fifth convolution layer and a hyperbolic tangent function to obtain a fifth characteristic image, and building a prediction model by stacking the characteristic images extracted from the original image by the five convolution layers, wherein the prediction model is used for outputting a prediction target;
Calculating a loss function between the predicted target and the real target image, the loss function comprising a loss optimization function of a discriminator D generating an countermeasure network and a loss optimization function of a generator G generating the countermeasure network;
Training a prediction model through a loss function, wherein in the training, a target of a discriminator D correctly identifies a real sample and correctly eliminates a generated false sample, a target of a generator G minimizes the probability that a generated predicted value is eliminated by the discriminator D, and the continuous iterative training is carried out, wherein for each iterative process, the characteristic information of a data set is respectively updated by the discriminator D and the generator G until the trained prediction model is output;
And inputting the weather data into a trained prediction model to obtain a predicted value, and executing corresponding attendance checking operation according to the predicted value.
As a preferable technical scheme, the weather data comprise weather live, early warning signals, typhoon data, S-band radar data and hail data;
the preprocessing comprises data cleaning, data conversion and data integration.
As a preferable technical solution, the calculating the loss function between the predicted target and the real target image specifically includes:
the loss function consists of two parts:
Wherein the method comprises the steps of The function is optimized for the loss of the arbiter D,A loss optimization function of generator G;
And (3) with The calculation formula of (2) is as follows:
Wherein L bce is a cross entropy loss function; x, Y are the input image sequence and the real image data of the target time respectively; respectively obtaining a generation result G k for a plurality of scales k of the image; n is the degree of scale; y i is a true value; y i' is the predicted value; n is the number of samples in the cross entropy loss function.
As a preferred embodiment, the objective function of the discriminator D is as follows:
Wherein P data (x) is the distribution of real data; x is a real data; p (z) is the distribution of the predicted data.
As a preferable technical scheme, for the discriminator D, a weather data sample { x 1,x2,...,xm } is obtained from the real dataset, and then a historical weather data sample { z 1,z2,...,zm } is obtained from the real dataset; for each historical image sample z k, a prediction result G (z k) is obtained by the generator G, and the model parameter θ d of the discriminator D is updated according to the following formula:
Wherein the method comprises the steps of A loss function for the arbiter; d (x i) is a discrimination result of the real weather dataset; a discrimination result of the image generated by the generator for the discriminator; Is the gradient partial derivative; θ d is a model parameter variable, the result is corrected once when the model is trained once, and the training result is reversely transmitted to the model for correction; η is the learning rate.
As a preferred solution, for each iteration process of generator G, a historical data sample { z 1,z2,...,zm } is obtained from the real dataset, and the model parameters θ g of the arbiter are updated according to the following formula:
Wherein the method comprises the steps of A generator loss function; g (z i) is a result calculated together with θ g by the real data; and theta g is a model parameter variable, the result is corrected once every time the model is trained, and the training result is reversely transmitted to the model for correction.
As a preferable technical scheme, in the training process, the generator of the first generation generates predicted values, and then the predicted values and the true values are put into the discriminator of the first generation to learn, so that the discriminator of the first generation can truly distinguish the generated data and the true data; then, the second generation generator is provided, the data generated by the second generation generator G can cheat the first generation of the discriminant D, at the moment, the second generation of the discriminant D is trained again, and so on.
The invention further provides an attendance check-in scheduling management system, which comprises a data acquisition module, a model construction module, a loss function calculation module, a model training module and a prediction module;
The data acquisition module is used for acquiring weather data of the attendance place area in real time and preprocessing the acquired weather data to obtain an initial sample set;
The model construction module is used for constructing a prediction model based on the improved generation countermeasure network, and specifically comprises the following steps: the method comprises the steps of correcting and superposing input original data by adopting a first convolution layer and a correction linear function, obtaining an initial feature map, then up-sampling the initial feature map, amplifying the extracted initial feature map, and correcting three subsequent convolution layers and the correction linear function with higher resolution; then multiplying the initial feature map with a feature map generated by a fourth convolution layer, and obtaining specific feature information through a spatial attention mechanism and a channel attention mechanism; finally, up-sampling the specific characteristic information again, and then correcting and superposing the specific characteristic information by adopting a fifth convolution layer and a hyperbolic tangent function to obtain a fifth characteristic image, and building a prediction model by stacking the characteristic images extracted from the original image by the five convolution layers, wherein the prediction model is used for outputting a prediction target;
The loss function calculation module is used for calculating a loss function between a prediction target and a real target image, and the loss function comprises a loss optimization function of a discriminator D for generating an countermeasure network and a loss optimization function of a generator G for generating the countermeasure network;
the model training module is used for training the prediction model through a loss function, in the training, the target of the discriminator D correctly identifies a real sample and correctly eliminates a generated false sample, the target of the generator G aims at minimizing the probability that the generated predicted value is eliminated by the discriminator D, the iterative training is carried out continuously, and for each iterative process, the characteristic information of the data set is respectively updated by the discriminator D and the generator G until the trained prediction model is output;
And the prediction module inputs the weather data into a trained prediction model to obtain a predicted value, and executes corresponding attendance checking operation according to the predicted value.
In yet another aspect, the present invention provides an electronic device, including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the attendance check-in schedule management method.
In still another aspect, the present invention provides a computer readable storage medium storing a program, where the program when executed by a processor implements the attendance check-in scheduling management method.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. according to the invention, the weather condition of the attendance place is monitored in real time by adopting the GAN network, the attendance mode and the attendance system are automatically adjusted in time, the complicated manual adjustment is avoided, the working efficiency of related staff is improved, and the data error is avoided.
2. The invention provides a convenient and quick attendance data statistics mode, an attendance manager can count attendance personnel attendance data on a month by only setting an attendance rule once and then counting the attendance data on a month by one key, and the risk of manual statistics errors is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, 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 application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an attendance check-in scheduling management method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an image prediction model based on an improved GAN method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a weather forecast countermeasure learning model based on GAN according to an embodiment of the present invention;
FIG. 4 is a schematic view of attendance management after a model is deployed to a server in accordance with the present invention;
FIG. 5 is a block diagram of an attendance check-in scheduling management system according to an embodiment of the present invention;
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present application with reference to the accompanying drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the described embodiments of the application may be combined with other embodiments.
As shown in fig. 1, the attendance check-in scheduling management method of the embodiment includes the following steps:
s1, acquiring weather data of an attendance place area in real time, and preprocessing the acquired weather data to obtain an initial sample set.
Further, the weather data includes weather live, warning signals, typhoon data, S-band radar data, and hail data.
Further, the preprocessing comprises data cleaning, data conversion and data integration.
S2, establishing a prediction model based on an improved generation countermeasure network (GENERATIVE ADVERSARIAL Networks, GAN), as shown in FIG. 2, specifically:
S21, firstly, correcting and superposing input original data by adopting a convolution layer and a correction linear function ReLU to obtain a preliminary feature map, then, up-sampling upsampled is carried out on the initial feature map, and the extracted initial feature map is amplified, so that the correction of three subsequent convolution layers and the correction linear function ReLU is carried out with higher resolution;
S22, multiplying the initial feature map with the feature map generated by the fourth convolution layer, and then acquiring more specific target feature information through a spatial attention mechanism and a channel attention mechanism;
S23, up-sampling the obtained specific feature information again, and then correcting and superposing the specific feature information by adopting a convolution layer and a hyperbolic tangent function Sigmoid to obtain a fifth feature map.
S24, building a prediction model by stacking the features extracted from the original image by 5 convolution layers, and outputting a prediction target.
Further, a loss function between the forecast target and the real target image is calculated, and the model is trained through the loss function, so that the model is optimized in the correct direction.
The formula of the ReLU function is as follows:
The Sigmoid function formula is shown below:
S3, calculating a loss function between the prediction target and the real target image, wherein the loss function comprises a loss optimization function of a discriminator D for generating an countermeasure network and a loss optimization function of a generator G for generating the countermeasure network;
It will be appreciated that the GAN network is composed mainly of a generator G and a discriminator D, G being a network that generates a picture, which receives a random noise z, and then generates a picture from this noise, and the generated data is denoted as G (z). D is a discrimination network that discriminates whether a picture is "authentic" (whether or not it is kneaded). Its input parameters are x, which represents a picture, and the output D (x) represents the probability that x is a true picture, if 1, represents a true picture, and if 0, represents a picture that is not possible to be true, as shown in fig. 3.
In combination with the availability of the predictive model, the loss function consists of two parts.
Wherein the method comprises the steps ofThe function is optimized for the loss of the arbiter D,The loss optimization function of generator G.
And (3) withThe detailed formula of (2) is as follows:
Wherein L bce is a cross entropy loss function; x, Y are the input image sequence and the real image data of the target time respectively; respectively obtaining a generation result G k for a plurality of scales k of the image; n is the degree of scale; y i is a true value; y i' is the predicted value; n is the number of samples in the cross entropy loss function.
S4, training a prediction model through a loss function;
in training, the goal of the arbiter D is to identify the true samples as correctly as possible (output as "true", or 1), and to reject the false samples generated as correctly as possible (output as "false", or 0). These two targets correspond to the first and second terms of the objective function formula, respectively. The goal of generator G is to minimize the probability of it being rejected by the arbiter as much as possible, as opposed to the arbiter.
The objective function formula is as follows:
Wherein P data (x) is the distribution of real data; x is a real data; p (z) is the distribution of the predicted data.
For each iteration process, the characteristic information of the data set is updated by the discriminator D and the generator G respectively, and the recognition model is gradually improved.
For the arbiter D, a weather data sample { x 1,x2,...,xm } is obtained from the real dataset, and then a historical weather data sample { z 1,z2,...,zm } is obtained from the real dataset. For each historical image sample z k, a prediction result G (z k) is obtained by the generator G, and the model parameter θ d of the discriminator D is updated according to the following formula:
Wherein the method comprises the steps of A loss function for the arbiter; d (x i) is a discrimination result of the real weather dataset; a discrimination result of the image generated by the generator for the discriminator; Is the gradient partial derivative; θ d is a model parameter variable, the result is corrected once when the model is trained once, and the training result is reversely transmitted to the model for correction; η is a learning rate, and the initial value is set to 4×10 -5 according to experience specification during training.
For each iteration of generator G, a historical data sample { z 1,z2,...,zm } is obtained from the real dataset, and model parameters θ g of the discriminators are updated according to the following formula:
Wherein the method comprises the steps of A generator loss function; g (z i) is a result calculated together with θ g by the real data; and theta g is a model parameter variable, the result is corrected once every time the model is trained, and the training result is reversely transmitted to the model for correction.
The first generation generator G generates predicted values, and then places these predicted values and true values in the first generation arbiter D to learn, so that the first generation arbiter D can truly distinguish between the generated data and the real data. Then there is again a generator G of the second generation. The data generated by the second generation generator G can fool the first generation arbiter D. At this time, the second generation of discriminators D is trained again, and so on.
The generator G and the discriminator D form a min-max game, and both sides continuously optimize themselves in the training process until reaching equilibrium, i.e. both sides cannot become better, i.e. the false sample is completely indistinguishable from the true sample, and the output predicted value is used for making a neighbor forecast.
S5, inputting the weather data into a trained prediction model to obtain a predicted value, and executing corresponding attendance checking operation according to the predicted value.
Further, as shown in fig. 4, the obtained model is deployed in a server and added into a check-in system, the system executes corresponding checking-in operation according to the predicted value, and sends confirmation information to a checking-in manager, and the checking-in manager automatically adjusts the checking-in system and the check-in mode after confirmation. And finally, sending the attendance information to attendance staff.
It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present invention is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present invention.
Based on the same ideas that of the attendance check-in scheduling management method in the embodiment, the invention also provides an attendance check-in scheduling management system which can be used for executing the attendance check-in scheduling management method. For ease of illustration, only those portions of the structural schematic diagram of an embodiment of the attendance check-in schedule management system relevant to the embodiments of the present invention are shown, and those skilled in the art will appreciate that the illustrated structure is not limiting of the apparatus and may include more or fewer components than illustrated, or may combine certain components, or a different arrangement of components.
Referring to fig. 5, in another embodiment of the present application, an attendance check-in scheduling management system 100 is provided, which includes a data acquisition module 101, a model construction module 102, a loss function calculation module 103, a model training module 104, and a prediction module 105;
The data acquisition module 101 is configured to acquire weather data of an attendance location area in real time, and perform preprocessing on the acquired weather data to obtain an initial sample set;
The model building module 102 is configured to build a prediction model based on the improved generation countermeasure network, specifically: the method comprises the steps of correcting and superposing input original data by adopting a first convolution layer and a correction linear function, obtaining an initial feature map, then up-sampling the initial feature map, amplifying the extracted initial feature map, and correcting three subsequent convolution layers and the correction linear function with higher resolution; then multiplying the initial feature map with a feature map generated by a fourth convolution layer, and obtaining specific feature information through a spatial attention mechanism and a channel attention mechanism; finally, up-sampling the specific characteristic information again, and then correcting and superposing the specific characteristic information by adopting a fifth convolution layer and a hyperbolic tangent function to obtain a fifth characteristic image, and building a prediction model by stacking the characteristic images extracted from the original image by the five convolution layers, wherein the prediction model is used for outputting a prediction target;
The loss function calculation module 103 is configured to calculate a loss function between the prediction target and the real target image, where the loss function includes a loss optimization function of a discriminator D that generates an countermeasure network and a loss optimization function of a generator G that generates the countermeasure network;
The model training module 104 is configured to train the prediction model through a loss function, in training, the objective of the discriminator D correctly identifies a real sample and correctly rejects a generated false sample, the objective of the generator G is to minimize the probability that the generated predicted value is rejected by the discriminator D, and continuously iterate training, and for each iteration process, the discriminator D and the generator G respectively update the feature information of the dataset until the trained prediction model is output;
the prediction module 105 inputs the weather data into a trained prediction model to obtain a predicted value, and executes corresponding attendance checking operation according to the predicted value.
It should be noted that, the attendance check-in scheduling management system and the attendance check-in scheduling management method of the present invention are in one-to-one correspondence, and the technical features and the beneficial effects described in the embodiments of the attendance check-in scheduling management method are applicable to the embodiments of the attendance check-in scheduling management, and specific content can be referred to the description in the embodiments of the method of the present invention, which is not repeated here, and thus is stated.
In addition, in the implementation manner of the attendance check-in scheduling management system of the foregoing embodiment, the logic division of each program module is merely illustrative, and in practical application, the above-mentioned function allocation may be performed by different program modules according to needs, for example, in view of configuration requirements of corresponding hardware or convenience of implementation of software, that is, the internal structure of the attendance check-in scheduling management system is divided into different program modules, so as to complete all or part of the functions described above.
Referring to fig. 6, in one embodiment, an electronic device implementing a method for attendance check-in scheduling management is provided, where the electronic device 200 may include a first processor 201, a first memory 202, and a bus, and may further include a computer program, such as an attendance check-in scheduling management program 203, stored in the first memory 202 and executable on the first processor 201.
The first memory 202 includes at least one type of readable storage medium, which includes flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The first memory 202 may in some embodiments be an internal storage unit of the electronic device 200, such as a mobile hard disk of the electronic device 200. The first memory 202 may also be an external storage device of the electronic device 200 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a secure digital (SecureDigital, SD) card, a flash memory card (FLASH CARD), etc. that are provided on the electronic device 200. Further, the first memory 202 may also include both an internal memory unit and an external memory device of the electronic device 200. The first memory 202 may be used to store not only application software installed in the electronic device 200 and various data, such as a code of the attendance check-in schedule management program 203, but also temporarily store data that has been output or is to be output.
The first processor 201 may be comprised of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, and various combinations of control chips, etc. The first processor 201 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device 200 and processes data by running or executing programs or modules stored in the first memory 202 and calling data stored in the first memory 202.
Fig. 6 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 6 is not limiting of the electronic device 200 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
The attendance check-in scheduling manager 203 stored in the first memory 202 of the electronic device 200 is a combination of instructions that, when executed in the first processor 201, may implement:
Collecting weather data of an attendance place area in real time, and preprocessing the collected weather data to obtain an initial sample set;
Constructing a prediction model based on the improved generation countermeasure network, specifically: the method comprises the steps of correcting and superposing input original data by adopting a first convolution layer and a correction linear function, obtaining an initial feature map, then up-sampling the initial feature map, amplifying the extracted initial feature map, and correcting three subsequent convolution layers and the correction linear function with higher resolution; then multiplying the initial feature map with a feature map generated by a fourth convolution layer, and obtaining specific feature information through a spatial attention mechanism and a channel attention mechanism; finally, up-sampling the specific characteristic information again, and then correcting and superposing the specific characteristic information by adopting a fifth convolution layer and a hyperbolic tangent function to obtain a fifth characteristic image, and building a prediction model by stacking the characteristic images extracted from the original image by the five convolution layers, wherein the prediction model is used for outputting a prediction target;
Calculating a loss function between the predicted target and the real target image, the loss function comprising a loss optimization function of a discriminator D generating an countermeasure network and a loss optimization function of a generator G generating the countermeasure network;
Training a prediction model through a loss function, wherein in the training, a target of a discriminator D correctly identifies a real sample and correctly eliminates a generated false sample, a target of a generator G minimizes the probability that a generated predicted value is eliminated by the discriminator D, and the continuous iterative training is carried out, wherein for each iterative process, the characteristic information of a data set is respectively updated by the discriminator D and the generator G until the trained prediction model is output;
And inputting the weather data into a trained prediction model to obtain a predicted value, and executing corresponding attendance checking operation according to the predicted value.
Further, the modules/units integrated with the electronic device 200 may be stored in a non-volatile computer readable storage medium if implemented in the form of software functional units and sold or used as a stand-alone product. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
Claims (6)
1. The attendance check-in scheduling management method is characterized by comprising the following steps of:
Collecting weather data of an attendance place area in real time, and preprocessing the collected weather data to obtain an initial sample set;
Constructing a prediction model based on the improved generation countermeasure network, specifically: the method comprises the steps of correcting and superposing input original data by adopting a first convolution layer and a correction linear function, obtaining an initial feature map, then up-sampling the initial feature map, amplifying the extracted initial feature map, and correcting three subsequent convolution layers and the correction linear function with higher resolution; then multiplying the initial feature map with a feature map generated by a fourth convolution layer, and obtaining specific feature information through a spatial attention mechanism and a channel attention mechanism; finally, up-sampling the specific characteristic information again, and then correcting and superposing the specific characteristic information by adopting a fifth convolution layer and a hyperbolic tangent function to obtain a fifth characteristic image, and building a prediction model by stacking the characteristic images extracted from the original image by the five convolution layers, wherein the prediction model is used for outputting a prediction target;
Calculating a loss function between the predicted target and the real target image, the loss function comprising a loss optimization function of a discriminator D generating an countermeasure network and a loss optimization function of a generator G generating the countermeasure network;
Training a prediction model through a loss function, wherein in the training, a target of a discriminator D correctly identifies a real sample and correctly eliminates a generated false sample, a target of a generator G minimizes the probability that a generated predicted value is eliminated by the discriminator D, and the continuous iterative training is carried out, wherein for each iterative process, the characteristic information of a data set is respectively updated by the discriminator D and the generator G until the trained prediction model is output;
And inputting the weather data into a trained prediction model to obtain a predicted value, and executing corresponding attendance checking operation according to the predicted value.
2. The attendance check-in scheduling management method according to claim 1, wherein the weather data includes weather live, warning signals, typhoon data, S-band radar data and hail data;
the preprocessing comprises data cleaning, data conversion and data integration.
3. The attendance check-in scheduling management method according to claim 1, wherein in the training process, the first generation generator generates predicted values, and then the predicted values and the true values are put in the first generation discriminator to learn, so that the first generation discriminator can truly distinguish the generated data and the true data; then, the second generation generator is provided, the data generated by the second generation generator G can cheat the first generation of the discriminant D, at the moment, the second generation of the discriminant D is trained again, and so on.
4. The attendance check-in scheduling management system is characterized by comprising a data acquisition module, a model construction module, a loss function calculation module, a model training module and a prediction module;
The data acquisition module is used for acquiring weather data of the attendance place area in real time and preprocessing the acquired weather data to obtain an initial sample set;
The model construction module is used for constructing a prediction model based on the improved generation countermeasure network, and specifically comprises the following steps: the method comprises the steps of correcting and superposing input original data by adopting a first convolution layer and a correction linear function, obtaining an initial feature map, then up-sampling the initial feature map, amplifying the extracted initial feature map, and correcting three subsequent convolution layers and the correction linear function with higher resolution; then multiplying the initial feature map with a feature map generated by a fourth convolution layer, and obtaining specific feature information through a spatial attention mechanism and a channel attention mechanism; finally, up-sampling the specific characteristic information again, and then correcting and superposing the specific characteristic information by adopting a fifth convolution layer and a hyperbolic tangent function to obtain a fifth characteristic image, and building a prediction model by stacking the characteristic images extracted from the original image by the five convolution layers, wherein the prediction model is used for outputting a prediction target;
The loss function calculation module is used for calculating a loss function between a prediction target and a real target image, and the loss function comprises a loss optimization function of a discriminator D for generating an countermeasure network and a loss optimization function of a generator G for generating the countermeasure network;
the model training module is used for training the prediction model through a loss function, in the training, the target of the discriminator D correctly identifies a real sample and correctly eliminates a generated false sample, the target of the generator G aims at minimizing the probability that the generated predicted value is eliminated by the discriminator D, the iterative training is carried out continuously, and for each iterative process, the characteristic information of the data set is respectively updated by the discriminator D and the generator G until the trained prediction model is output;
And the prediction module inputs the weather data into a trained prediction model to obtain a predicted value, and executes corresponding attendance checking operation according to the predicted value.
5. 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 computer program instructions executable by the at least one processor to enable the at least one processor to perform the attendance check-in schedule management method as claimed in any one of claims 1 to 3.
6. A computer-readable storage medium storing a program, wherein the program, when executed by a processor, implements the attendance check-in schedule management method of any one of claims 1 to 3.
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