Automatic card punching method, device and equipment based on wireless network and storage medium

Abstract

The invention belongs to the field of artificial intelligence and mobile communication, and relates to an automatic card punching method based on a wireless network, which comprises the following steps: deploying a plurality of wireless network access points according to deployment positions, and when mobile equipment is connected to the wireless network, acquiring real-time signal intensity data of each wireless network access point through the mobile equipment; inputting the real-time signal intensity data into the trained wireless network positioning model to obtain position coordinates of the mobile equipment; judging whether the mobile equipment enters the range of the preset area or not according to the position coordinates; and when the mobile equipment enters the range of the preset area, judging whether the mobile equipment enters the preset area for the first time in a preset time period, and if so, executing a clock-in operation. The invention further provides an automatic card punching device based on the wireless network, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the position coordinates can be stored in a block chain. The positioning precision can be improved, and automatic card punching is realized.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence and mobile communication technology, and in particular to an automatic clocking-in method, device, equipment and storage medium based on a wireless network. Background Art

[0002] In the field of employee attendance management, the traditional clock-in method has long relied on physical cards or manual input for recording. Specifically, employees need to carry specific clock-in cards and swipe them on a designated clock-in machine to mark their attendance time, or manually enter their personal number and password through the keyboard to complete the clock-in process. However, this traditional clock-in method has many shortcomings. For example, employees may forget to clock in due to negligence, resulting in incomplete attendance records; in addition, there is the risk of clocking in on behalf of others, that is, someone else clocks in on behalf of others, thereby undermining the accuracy and credibility of the attendance data. These problems not only bring inconvenience to the company's attendance management, but also affect the fairness and effectiveness of employee attendance statistics.

[0003] In order to overcome the limitations of the traditional clock-in method, an automatic clock-in system has emerged in the prior art. This type of system is mainly based on the global positioning system (GPS) to realize automatic attendance of employees. Through GPS positioning technology, the system can obtain the geographical coordinates of employees in real time, and judge whether the employees arrive or leave the work area within the specified time, thereby automatically completing the clock-in record. However, although GPS positioning technology performs well in outdoor environments, in indoor environments or areas with dense buildings, GPS signals are often blocked by obstacles such as walls and roofs, resulting in unstable signals or even loss. This signal instability seriously affects the accuracy and reliability of the automatic clock-in system, which limits the practical application of the GPS-based automatic clock-in system. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose an automatic clock-in method, device, equipment and storage medium based on a wireless network to solve the technical problem of inaccurate positioning caused by unstable signals in the prior art.

[0005] In order to solve the above technical problems, the embodiment of the present application provides an automatic clock-in method based on a wireless network, which adopts the following technical solution:

[0006] Planning the deployment locations of multiple wireless network access points according to an electronic map of a predetermined area, and deploying the wireless network access points according to the deployment locations;

[0007] When a mobile device is connected to a wireless network, obtaining real-time signal strength data of each wireless network access point through the mobile device;

[0008] Inputting the real-time signal strength data into a trained wireless network positioning model to obtain the location coordinates of the mobile device;

[0009] Determining whether the mobile device has entered the range of the predetermined area according to the position coordinates;

[0010] When the mobile device enters the range of the predetermined area, it is determined whether the mobile device enters the predetermined area for the first time within a preset time period, and if so, a clock-in operation is performed.

[0011] In order to solve the above technical problems, the embodiment of the present application further provides an automatic clock-in device based on a wireless network, which adopts the following technical solution:

[0012] A planning module, used to plan the deployment locations of multiple wireless network access points according to an electronic map of a predetermined area, and deploy the wireless network access points according to the deployment locations;

[0013] An acquisition module, used to acquire real-time signal strength data of each wireless network access point through the mobile device when the mobile device is connected to the wireless network;

[0014] A positioning module, used to input the real-time signal strength data into a trained wireless network positioning model to obtain the location coordinates of the mobile device;

[0015] A position determination module, used to determine whether the mobile device has entered the range of the predetermined area according to the position coordinates;

[0016] The clock-in module is used to determine whether the mobile device enters the predetermined area for the first time within a preset time period when the mobile device enters the predetermined area, and if so, perform a clock-in operation.

[0017] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0018] The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the automatic clock-in method based on a wireless network when executing the computer-readable instructions.

[0019] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0020] The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the automatic clock-in method based on the wireless network as described above are implemented.

[0021] Compared with the prior art, this application has the following beneficial effects:

[0022] The present application provides an automatic clocking-in method based on a wireless network. By planning the deployment positions of multiple wireless network access points according to an electronic map of a predetermined area, and deploying the wireless network access points according to the deployment positions, the signal coverage range can be maximized and the signal blind spots can be minimized, thereby ensuring full signal coverage and enhancing the signal strength at each position. When a mobile device is connected to a wireless network, the signal strength data between each wireless network access point of the mobile device is obtained, and the signal strength data is input into a trained wireless network positioning model to obtain the position coordinates of the mobile device. By obtaining the signal strength between the mobile device and each access point, the accurate position of the mobile device is calculated, thereby significantly improving the accuracy and reliability of wireless network positioning. By judging whether the mobile device has entered the range of a predetermined area through the position coordinates, and when within the range of the predetermined area, judging whether the mobile device has entered the predetermined area for the first time within a preset time period, if so, executing the clocking-in operation, the automatic clocking-in can be realized, thereby improving the accuracy and intelligence level of attendance management. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0025] Figure 2 is a flow chart of an embodiment of an automatic clock-in method based on a wireless network according to the present application;

[0026] Figure 3 It is a structural schematic diagram of an embodiment of an automatic clock-in device based on a wireless network according to the present application;

[0027] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0031] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0032] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0033] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0034] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0035] It should be noted that the automatic clocking-in method based on a wireless network provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the automatic clocking-in device based on a wireless network is generally arranged in the server / terminal device.

[0036] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0037] Continue to refer Figure 2 , shows a flow chart of an embodiment of an automatic clock-in method based on a wireless network according to the present application, comprising the following steps:

[0038] Step S201 : planning deployment locations of a plurality of wireless network access points according to an electronic map of a predetermined area, and deploying the wireless network access points according to the deployment locations.

[0039] The pre-determined area is the work area, which refers to the physical or virtual space where employees carry out their daily work according to the company's regulations and are subject to the company's attendance management system. High-definition electronic map images of the pre-determined area are obtained through drone aerial photography or satellite remote sensing technology, and the map is geometrically corrected and registered using image processing software, and then the electronic map data is extracted, including key information such as regional area and building layout.

[0040] Specifically, based on the area and building layout of the predetermined area, a preliminary estimate is made of the number of wireless network access points required, and the initial access point deployment locations are determined; a wireless network signal propagation model, such as a free space propagation model or a ray tracing model, is used to simulate the signal coverage and strength distribution of each wireless network access point; through an iterative optimization algorithm, such as a genetic algorithm or a particle swarm optimization algorithm, the deployment location and transmission power of the wireless network access point are adjusted to maximize the signal coverage and minimize the signal blind spot; during the optimization process, network quality evaluation indicators, such as signal strength, signal-to-noise ratio, etc., are introduced to ensure that the signal quality of each location in each predetermined area meets the preset service quality requirements; if there is a signal blind spot or a weak coverage area, additional wireless network access points are added at the corresponding location until full coverage is achieved; a final wireless network access point deployment plan is generated, including parameters such as the access point location and transmission power, and the coverage and signal strength distribution of each access point are marked on an electronic map to form an intuitive network planning diagram to guide subsequent equipment installation and network optimization work.

[0041] Step S202: when the mobile device is connected to the wireless network, real-time signal strength data of the corresponding wireless network access point is obtained through the mobile device.

[0042] Among them, mobile devices are various smart devices that can realize attendance functions carried by target personnel, including but not limited to smart phones, tablet computers and smart wearable devices. When the target personnel enter the signal coverage area, the mobile device they carry automatically connects to the wireless network and realizes communication with the corresponding wireless network access point. At this time, the signal strength data of each wireless network access point in the current area is obtained in real time through the wireless network card driver of the mobile device. Specifically, the RSSI (Received Signal Strength Indication) algorithm can be used to collect signal strength data every 1 second in dBm units.

[0043] In this embodiment, the real-time signal strength data is used to calculate the location coordinates of the mobile device to achieve accurate positioning.

[0044] In some optional implementations, before the step of connecting the mobile device to the wireless network, the method further includes:

[0045] Acquire network signal characteristic information of multiple wireless network access points in the surrounding environment in real time through the wireless network card driver on the mobile device;

[0046] Extract and organize the network signal characteristic information to obtain a signal data structure including the network identifier and the corresponding signal strength;

[0047] Sort the multiple wireless network access points in descending order according to signal strength, and remove wireless network access points whose signal strength is lower than a preset strength threshold, to obtain candidate connection networks;

[0048] Obtaining network connection information pre-configured by the mobile device, traversing candidate connection networks in sorted order, and determining whether the candidate connection network is a network authorized for connection by the mobile device;

[0049] When the candidate connection network is an authorized connection network, a network connection request is initiated to establish a wireless network connection between the mobile device and the candidate connection network.

[0050] Specifically, the wireless network card driver scans at regular intervals, such as once every 200 milliseconds, to capture the network signal characteristic information of the wireless network access point, which includes the network identifier (SSID) and signal strength (RSSI). Using the preset data structure hash table, with SSID as the key and RSSI as the value, a mapping relationship including the network identifier and the corresponding signal strength is obtained. Then, the extracted multiple wireless network access points are sorted and screened according to the signal strength value. The RSSI can be arranged in descending order using a quick sorting algorithm, and the wireless network access points with RSSI lower than the preset strength threshold are eliminated to obtain the sorting result, which is stored in a priority queue. Then, according to the sorting result, the wireless network access point with the largest RSSI is selected as the current candidate connection network. According to the user's pre-configured network connection information obtained by the wireless network card driver, including SSID and password, a string matching algorithm is used to determine whether the SSID of the current candidate connection network is in the known network list. If the match is successful, a network connection request is initiated, the network interface provided by the operating system is called, the SSID and password are passed in, and a wireless network connection is established; otherwise, the next wireless network access point with a larger RSSI in the priority queue is selected, and the SSID matching and judgment process is repeated until the connection is successful or all candidate connection networks are traversed.

[0051] Through this method based on RSSI sorting and user-authorized network matching, the mobile device can automatically select the optimal wireless network and initiate a connection request, thereby improving the efficiency and accuracy of wireless network connection.

[0052] Step S203: input the real-time signal strength data into the trained wireless network positioning model to obtain the location coordinates of the mobile device.

[0053] In this embodiment, the real-time signal strength data is input into the trained wireless network positioning model, and the wireless network positioning model performs feature extraction and feature matching on the input real-time signal strength data, and matches the extracted signal features with the location features pre-stored in the model to obtain the location features that best match the real-time signal strength data. Based on the matched location features, the location features are mapped to corresponding location coordinates through the preset location mapping relationship in the wireless network positioning model, thereby obtaining the location coordinates of the mobile device.

[0054] It should be emphasized that in order to further ensure the privacy and security of the location coordinates, the above location coordinates can also be stored in a blockchain node.

[0055] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0056] In some optional implementations of this embodiment, before the step of inputting the signal strength data into the trained wireless network positioning model, the following steps are also included:

[0057] Acquire signal fingerprint raw data collected in a wireless network, pre-process the signal fingerprint raw data, and form pre-processed signal fingerprint data;

[0058] Extract key signal features from preprocessed signal fingerprint data to form a feature set, and vectorize the feature set to form a feature vector;

[0059] Construct a training sample set based on the feature vector, where each training sample consists of a feature vector and a corresponding position label;

[0060] According to the training sample set, support vector machine algorithm and random forest algorithm are used for training to generate a wireless network positioning model.

[0061] Specifically, the original signal fingerprint data includes information such as the network identification, signal strength, and corresponding location coordinates of the wireless network. The original signal fingerprint data is preprocessed to remove outliers and noise data, extract key features such as signal strength mean, variance, kurtosis and other statistical features and frequency domain features, and vectorize the features to form a training sample set. Each training sample in the training sample set consists of a feature vector and a corresponding location label. The training sample set is trained using the support vector machine algorithm and the random forest algorithm to establish a mapping relationship model between signal features and locations, and obtain a trained wireless network positioning model. The wireless network positioning model is used to preprocess and extract features from the real-time signal strength data to obtain a feature vector, and the corresponding location coordinates are predicted based on the acquired feature vector.

[0062] By training the wireless network positioning model for positioning, the accuracy of the positioning algorithm can be improved. At the same time, the parameters of the positioning algorithm are continuously and dynamically adjusted during the training process, thereby improving the positioning accuracy.

[0063] In this embodiment, the steps of training with a support vector machine algorithm and a random forest algorithm based on a training sample set to generate a wireless network positioning model include:

[0064] According to the training sample set, the support vector machine algorithm is used to map the feature vector to a high-dimensional space through a preset kernel function, find the optimal classification hyperplane, and generate a support vector machine positioning model;

[0065] If the positioning accuracy of the support vector machine positioning model does not reach the preset accuracy threshold, the random forest algorithm is used to randomly select feature subsets and sample subsets from the training sample set, build a preset number of decision trees, and perform ensemble learning to generate a random forest positioning model;

[0066] The support vector machine positioning model and the random forest positioning model are integrated to obtain the final wireless network positioning model.

[0067] Specifically, the SVM (support vector machine) algorithm is used to select the Gaussian kernel function, and the feature vector in the training sample set is mapped to a high-dimensional space. Through grid search and cross-validation, the SVM model parameters, such as the penalty factor and the kernel function parameters, are optimized to generate the support vector machine positioning model; if the positioning accuracy of the support vector machine positioning model is lower than the preset accuracy threshold, such as 90%, the random forest algorithm is used to randomly select 80% of the feature subsets and sample subsets from the training sample set, and a preset number of decision trees are constructed. Through ensemble learning, a random forest positioning model is generated. The support vector machine positioning model and the random forest positioning model are weighted and fused, for example, the weight ratio of the support vector machine positioning model and the random forest positioning model is 6:4 for weighted fusion to obtain the final wireless network positioning model. In actual positioning, the signal data of the device to be positioned is obtained, and key features such as signal strength (RSSI) and signal-to-noise ratio (SNR) are extracted to construct a feature vector. The feature vector is input into the wireless network positioning model, and the location coordinates (x, y) of the device to be positioned are calculated through the model mapping relationship, and compared with the actual coordinates. If the error is less than the preset threshold, accurate positioning is completed.

[0068] By comprehensively utilizing the SVM algorithm and the random forest algorithm, through feature engineering and model fusion, the accuracy and robustness of wireless network positioning are improved.

[0069] Step S204: determining whether the mobile device has entered a predetermined area based on the location coordinates.

[0070] Specifically, obtain the pre-stored electronic map information of the predetermined area, determine the area coverage of the predetermined area according to the electronic map information; compare the area coverage with the location coordinates; if the location coordinates are within the area coverage, determine that the mobile device is within the predetermined area; if the location coordinates are outside the area coverage, determine that the mobile device is not within the predetermined area.

[0071] For example, the electronic map of the predetermined area is divided into several grids, and the size of each grid can be set according to the positioning accuracy requirement. For example, the predetermined area can be divided into 100×100 grids, and the size of each grid is 1 square meter. By comparing the location coordinates of the mobile device with the grids, it can be determined whether the mobile device is located in the working area.

[0072] By obtaining the real-time location coordinates, it is possible to accurately determine whether the mobile device is in the predetermined area, thereby improving the accuracy of positioning.

[0073] Step S205, when the mobile device enters the range of the predetermined area, it is determined whether the mobile device enters the predetermined area for the first time in a preset time period, and if so, a clock-in operation is performed.

[0074] When the mobile device enters the predetermined area, that is, the holder of the mobile device also enters the predetermined area, that is, enters the normal clock-in range, in order to avoid repeated clock-ins, after the mobile device enters the predetermined area, the clock-in data record within the preset time period is obtained to determine whether there has been a clock-in operation within the preset time period. If not, it means that it is the first time to enter the predetermined area within the preset time period, and then a clock-in request is sent to the clock-in server to perform an automatic clock-in operation. Among them, the preset time period can be set according to the working time period. For example, if the working time period is 9:00-18:00, the preset time period can be set to 6:00-20:00, or it can be set to 0:00-24:00, and there is no restriction here.

[0075] In this way, missed clocking in and clocking in on behalf of others can be effectively prevented, and there is no need for manual clocking in, saving time and labor costs.

[0076] The present application plans the deployment locations of multiple wireless network access points according to the electronic map of the predetermined area, and deploys the wireless network access points according to the deployment locations, so as to maximize the signal coverage range and minimize the signal blind spots, ensure the full signal coverage, and enhance the signal strength of each location; when the mobile device is connected to the wireless network, the signal strength data between each wireless network access point of the mobile device is obtained, and the signal strength data is input into the trained wireless network positioning model to obtain the location coordinates of the mobile device, and the accurate location of the mobile device is calculated by obtaining the signal strength between the mobile device and each access point, which significantly improves the accuracy and reliability of the wireless network positioning; the location coordinates are used to determine whether the mobile device has entered the range of the predetermined area, and when within the range of the predetermined area, it is determined whether the mobile device has entered the predetermined area for the first time in a preset time period, and if so, a punch-in operation is performed, which can realize automatic punch-in and improve the accuracy and intelligence level of attendance management.

[0077] In some optional implementations, after the above-mentioned step of performing the clock-in operation, the following steps are further included:

[0078] Record the clock-in data and extract the clock-in time from the clock-in data;

[0079] Compare the clock-in time with the preset normal clock-in time period to determine whether the clock-in is abnormal;

[0080] If the clock-in time is not within the normal clock-in time period, it is considered an abnormal clock-in;

[0081] Mark the clock-in data that is determined to be abnormal and generate an abnormal clock-in record.

[0082] Among them, the punch-in data includes the punch-in time, punch-in location and punch-in frequency, etc. After automatic punching in, the corresponding punch-in data is recorded and stored in a pre-set punch-in record database. The extracted punch-in time is compared with the normal punch-in time period to determine whether the punch-in time falls within the normal punch-in time period. If so, the punch-in is normal. Otherwise, the punch-in is determined to be an abnormal punch-in, and the punch-in data is marked as an abnormal punch-in, and an abnormal punch-in record is generated and stored in the database.

[0083] This application can provide data support for subsequent attendance management and abnormal behavior analysis by determining whether the clock-in data is abnormal. It can also realize comprehensive analysis and anomaly detection of clock-in data on mobile devices, thereby improving the accuracy and efficiency of attendance management.

[0084] In some optional implementations, after the above-mentioned step of performing the clock-in operation, the following steps are further included:

[0085] Starting a preset observation time window, monitoring the position coordinates in real time within the preset observation time window, and determining whether the position coordinates are within the predetermined area;

[0086] If the location coordinates are within the predetermined area, the mobile device is recorded as being on duty;

[0087] If the location coordinates are not within the predetermined area, the clock-in operation is performed and the mobile device is recorded as being off-duty.

[0088] In this embodiment, after the mobile device enters the predetermined area, a continuous monitoring program will be triggered. The continuous monitoring program will collect the location coordinates of the mobile device at preset time intervals within the preset observation time window, and check whether the location coordinates remain in the predetermined area throughout the preset observation time window. If so, the mobile device is recorded as on-duty, that is, the employee is on-duty within the preset observation time window; if the mobile device is monitored to leave the predetermined area, that is, the location coordinates are not within the predetermined area, the clock-in operation is performed, and the mobile device is recorded as off-duty.

[0089] Among them, the preset observation time window can be set according to the working time period.

[0090] In this embodiment, by collecting the clock-in and clock-out data of mobile devices, statistics are automatically collected and attendance reports are generated, providing data support for the attendance management and human resource optimization of the enterprise.

[0091] This application effectively ensures the accuracy and real-time nature of the data and the accuracy and reliability of the attendance data by real-time monitoring of the location coordinates of the mobile device in a preset time observation window.

[0092] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0093] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0094] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0095] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0096] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of an automatic card punching device based on a wireless network. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0097] like Figure 3As shown, the automatic clock-in device 300 based on a wireless network described in this embodiment includes: a planning module 301, an acquisition module 302, a positioning module 303, a position determination module 304 and a clock-in module 305. Among them:

[0098] The planning module 301 is used to plan the deployment locations of multiple wireless network access points according to the electronic map of the predetermined area, and deploy the wireless network access points according to the deployment locations;

[0099] The acquisition module 302 is used to acquire the real-time signal strength data of each wireless network access point through the mobile device when the mobile device is connected to the wireless network;

[0100] The positioning module 303 is used to input the real-time signal strength data into the trained wireless network positioning model to obtain the location coordinates of the mobile device;

[0101] The location determination module 304 is used to determine whether the mobile device has entered the range of the predetermined area according to the location coordinates;

[0102] The clock-in module 305 is used to determine whether the mobile device enters the predetermined area for the first time within a preset time period when the mobile device enters the predetermined area, and if so, perform a clock-in operation.

[0103] It should be emphasized that in order to further ensure the privacy and security of the location coordinates, the above location coordinates can also be stored in a blockchain node.

[0104] Based on the above-mentioned wireless network-based automatic clocking-in device 300, by planning the deployment positions of multiple wireless network access points according to the electronic map of the predetermined area, the wireless network access points can be deployed, so as to maximize the signal coverage range and minimize the signal blind spots, ensure the full signal coverage, and enhance the signal strength of each location; when the mobile device is connected to the wireless network, the signal strength data between each wireless network access point of the mobile device is obtained, and the signal strength data is input into the trained wireless network positioning model to obtain the location coordinates of the mobile device, and the accurate location of the mobile device is calculated by obtaining the signal strength between the mobile device and each access point, which significantly improves the accuracy and reliability of the wireless network positioning; the location coordinates are used to determine whether the mobile device has entered the range of the predetermined area, and when it is within the range of the predetermined area, it is determined whether the mobile device has entered the predetermined area for the first time in the preset time period, and if so, the clocking-in operation is performed, which can realize automatic clocking-in and improve the accuracy and intelligence level of attendance management.

[0105] In this embodiment, the automatic clock-in device 300 based on a wireless network further includes a network connection module for:

[0106] Acquiring network signal characteristic information of multiple wireless network access points in the surrounding environment in real time through the wireless network card driver on the mobile device;

[0107] Extracting and sorting the network signal characteristic information to obtain a signal data structure including a network identifier and a corresponding signal strength;

[0108] Sort the plurality of wireless network access points in descending order according to the signal strength, and remove the wireless network access points whose signal strength is lower than a preset strength threshold, to obtain candidate connection networks;

[0109] Obtaining network connection information pre-configured by the mobile device, traversing the candidate connection networks in sorted order, and determining whether the candidate connection network is a network authorized by the mobile device to connect;

[0110] When the candidate connection network is a network authorized for connection, a network connection request is initiated to establish a wireless network connection between the mobile device and the candidate connection network.

[0111] Through this method based on signal strength sorting and user-authorized network matching, the mobile device can automatically select the optimal wireless network and initiate a connection request, thereby improving the efficiency and accuracy of wireless network connection.

[0112] In some optional implementations, the wireless network-based automatic clock-in device 300 further includes a training module, which includes:

[0113] The preprocessing submodule is used to obtain the original signal fingerprint data collected in the wireless network, and preprocess the original signal fingerprint data to form preprocessed signal fingerprint data;

[0114] A feature forming submodule, used for extracting key signal features from the preprocessed signal fingerprint data to form a feature set, and vectorizing the feature set to form a feature vector;

[0115] A construction submodule is used to construct a training sample set according to the feature vector, each training sample consists of a feature vector and a corresponding position label;

[0116] The training submodule is used to perform training based on the training sample set using a support vector machine algorithm and a random forest algorithm to generate a wireless network positioning model.

[0117] By training the wireless network positioning model for positioning, the accuracy of the positioning algorithm can be improved. At the same time, the parameters of the positioning algorithm are continuously and dynamically adjusted during the training process, thereby improving the positioning accuracy.

[0118] In some optional implementations of this embodiment, the training submodule is further used to:

[0119] According to the training sample set, a support vector machine algorithm is used to map the feature vector to a high-dimensional space through a preset kernel function, find the optimal classification hyperplane, and generate a support vector machine positioning model;

[0120] If the positioning accuracy of the support vector machine positioning model does not reach the preset accuracy threshold, a random forest algorithm is used to randomly select a feature subset and a sample subset from the training sample set, construct a preset number of decision trees, and perform ensemble learning to generate a random forest positioning model;

[0121] The support vector machine positioning model and the random forest positioning model are integrated to obtain a final wireless network positioning model.

[0122] By comprehensively utilizing the SVM algorithm and the random forest algorithm, through feature engineering and model fusion, the accuracy and robustness of wireless network positioning are improved.

[0123] In some optional implementations of this embodiment, the location determination module 304 is further configured to:

[0124] Acquire pre-stored electronic map information of the predetermined area, and determine the area coverage of the predetermined area according to the electronic map information;

[0125] Comparing the area coverage with the location coordinates;

[0126] If the location coordinates are within the coverage area of ​​the area, determining that the mobile device is within the predetermined area;

[0127] If the location coordinates are outside the coverage range of the area, it is determined that the mobile device is not within the predetermined area.

[0128] By obtaining the real-time location coordinates, it is possible to accurately determine whether the mobile device is in the predetermined area, thereby improving the accuracy of positioning.

[0129] In some optional implementations, the wireless network-based automatic clock-in device 300 further includes an abnormality determination module, which is used to:

[0130] Recording the clock-in data, and extracting the clock-in time from the clock-in data;

[0131] Compare the clock-in time with the preset normal clock-in time period to determine whether the clock-in is abnormal;

[0132] If the clock-in time is not within the normal clock-in time period, it is determined to be an abnormal clock-in;

[0133] The clock-in data determined to be abnormal clock-in is marked to generate an abnormal clock-in record.

[0134] By judging whether the clock-in data is abnormal, it can provide data support for subsequent attendance management and abnormal behavior analysis. At the same time, it can realize comprehensive analysis and anomaly detection of clock-in data on mobile devices, and improve the accuracy and efficiency of attendance management.

[0135] In some optional implementations, the wireless network-based automatic clock-in device 300 further includes a monitoring module for:

[0136] Starting a preset observation time window, monitoring the position coordinates in real time within the preset observation time window, and determining whether the position coordinates are within a predetermined area;

[0137] If the location coordinates are within the predetermined area, recording the mobile device as being on duty;

[0138] If the location coordinates are not within the predetermined area, a clock-in operation is performed and the mobile device is recorded as being off-duty.

[0139] By real-time monitoring of the location coordinates of mobile devices in the preset time observation window, the accuracy and real-time nature of the data can be effectively ensured, thereby ensuring the accuracy and reliability of attendance data.

[0140] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0141] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 4 with a memory 41, a processor 42, and a network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0142] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0143] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of an automatic clock-in method based on a wireless network, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0144] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the automatic clock-in method based on the wireless network.

[0145] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0146] By planning the deployment locations of multiple wireless network access points according to the electronic map of the predetermined area, the wireless network access points can be deployed, so as to maximize the signal coverage range and minimize the signal blind spots, ensure full signal coverage, and enhance the signal strength at each location; when the mobile device is connected to the wireless network, the signal strength data between each wireless network access point of the mobile device is obtained, and the signal strength data is input into the trained wireless network positioning model to obtain the location coordinates of the mobile device, and the accurate location of the mobile device is calculated by obtaining the signal strength between the mobile device and each access point, which significantly improves the accuracy and reliability of the wireless network positioning; the location coordinates are used to determine whether the mobile device has entered the range of the predetermined area, and when within the range of the predetermined area, it is determined whether the mobile device has entered the predetermined area for the first time within the preset time period, and if so, a punch-in operation is performed, which can realize automatic punch-in and improve the accuracy and intelligence level of attendance management.

[0147] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the automatic clock-in method based on a wireless network as described above.

[0148] By planning the deployment locations of multiple wireless network access points according to the electronic map of the predetermined area, the wireless network access points can be deployed, so as to maximize the signal coverage range and minimize the signal blind spots, ensure full signal coverage, and enhance the signal strength at each location; when the mobile device is connected to the wireless network, the signal strength data between each wireless network access point of the mobile device is obtained, and the signal strength data is input into the trained wireless network positioning model to obtain the location coordinates of the mobile device, and the accurate location of the mobile device is calculated by obtaining the signal strength between the mobile device and each access point, which significantly improves the accuracy and reliability of the wireless network positioning; the location coordinates are used to determine whether the mobile device has entered the range of the predetermined area, and when within the range of the predetermined area, it is determined whether the mobile device has entered the predetermined area for the first time within the preset time period, and if so, a punch-in operation is performed, which can realize automatic punch-in and improve the accuracy and intelligence level of attendance management.

[0149] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0150] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. An automatic clock-in method based on a wireless network, characterized in that: The steps include: Planning the deployment locations of multiple wireless network access points according to an electronic map of a predetermined area, and deploying the wireless network access points according to the deployment locations; When a mobile device is connected to a wireless network, obtaining real-time signal strength data of each wireless network access point through the mobile device; Inputting the real-time signal strength data into a trained wireless network positioning model to obtain the location coordinates of the mobile device; Determining whether the mobile device has entered the range of the predetermined area according to the position coordinates; When the mobile device enters the range of the predetermined area, it is determined whether the mobile device enters the predetermined area for the first time within a preset time period, and if so, a clock-in operation is performed.

2. The automatic clock-in method based on a wireless network according to claim 1, characterized in that: Before the step of connecting the mobile device to a wireless network, the method further includes: Acquiring network signal characteristic information of multiple wireless network access points in the surrounding environment in real time through the wireless network card driver on the mobile device; Extracting and sorting the network signal characteristic information to obtain a signal data structure including a network identifier and a corresponding signal strength; Sort the plurality of wireless network access points in descending order according to the signal strength, and remove the wireless network access points whose signal strength is lower than a preset strength threshold, to obtain candidate connection networks; Obtaining network connection information pre-configured by the mobile device, traversing the candidate connection networks in sorted order, and determining whether the candidate connection network is a network authorized by the mobile device to connect; When the candidate connection network is a network authorized for connection, a network connection request is initiated to establish a wireless network connection between the mobile device and the candidate connection network.

3. The automatic clock-in method based on a wireless network according to claim 1, characterized in that: Before the step of inputting the real-time signal strength data into the trained wireless network positioning model, the method further includes: Acquire signal fingerprint original data collected in a wireless network, and preprocess the signal fingerprint original data to form preprocessed signal fingerprint data; Extracting key signal features from the preprocessed signal fingerprint data to form a feature set, and vectorizing the feature set to form a feature vector; Constructing a training sample set according to the feature vector, each training sample consists of a feature vector and a corresponding position label; According to the training sample set, a support vector machine algorithm and a random forest algorithm are used for training to generate a wireless network positioning model.

4. The automatic clock-in method based on a wireless network according to claim 3, characterized in that: The step of training the wireless network positioning model by using a support vector machine algorithm and a random forest algorithm according to the training sample set comprises: According to the training sample set, a support vector machine algorithm is used to map the feature vector to a high-dimensional space through a preset kernel function, find the optimal classification hyperplane, and generate a support vector machine positioning model; If the positioning accuracy of the support vector machine positioning model does not reach the preset accuracy threshold, a random forest algorithm is used to randomly select a feature subset and a sample subset from the training sample set, construct a preset number of decision trees, and perform ensemble learning to generate a random forest positioning model; The support vector machine positioning model and the random forest positioning model are integrated to obtain a final wireless network positioning model.

5. The automatic clock-in method based on a wireless network according to claim 1, characterized in that: The step of determining whether the mobile device is within the range of the predetermined area according to the position coordinates comprises: Acquire pre-stored electronic map information of the predetermined area, and determine the area coverage of the predetermined area according to the electronic map information; Comparing the area coverage with the location coordinates; If the location coordinates are within the coverage area of ​​the area, determining that the mobile device is within the predetermined area; If the location coordinates are outside the coverage range of the area, it is determined that the mobile device is not within the predetermined area.

6. The automatic clock-in method based on a wireless network according to claim 5, characterized in that: After the step of executing the clock-in operation, the method further includes: Recording the clock-in data, and extracting the clock-in time from the clock-in data; Compare the clock-in time with the preset normal clock-in time period to determine whether the clock-in is abnormal; If the clock-in time is not within the normal clock-in time period, it is determined to be an abnormal clock-in; The clock-in data determined to be abnormal clock-in is marked to generate an abnormal clock-in record.

7. The automatic clock-in method based on a wireless network according to claim 1, characterized in that: After the step of executing the clock-in operation, the method further includes: Starting a preset observation time window, monitoring the position coordinates in real time within the preset observation time window, and determining whether the position coordinates are within a predetermined area; If the location coordinates are within the predetermined area, recording the mobile device as being on duty; If the location coordinates are not within the predetermined area, a clock-in operation is performed and the mobile device is recorded as being off-duty.

8. An automatic clock-in device based on a wireless network, characterized in that: include: A planning module, used to plan the deployment locations of multiple wireless network access points according to an electronic map of a predetermined area, and deploy the wireless network access points according to the deployment locations; An acquisition module, used to acquire real-time signal strength data of each wireless network access point through the mobile device when the mobile device is connected to the wireless network; A positioning module, used to input the real-time signal strength data into a trained wireless network positioning model to obtain the location coordinates of the mobile device; A position determination module, used to determine whether the mobile device has entered the range of the predetermined area according to the position coordinates; The clock-in module is used to determine whether the mobile device enters the predetermined area for the first time within a preset time period when the mobile device enters the predetermined area, and if so, perform a clock-in operation.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the automatic clocking-in method based on a wireless network as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the automatic clock-in method based on a wireless network as described in any one of claims 1 to 7 are implemented.

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