Dynamic attendance checking method, device, equipment and medium

Abstract

The invention discloses a dynamic attendance checking method, device and equipment and a medium. The method comprises the following steps: acquiring personnel post information of attendance checking employees; classifying the staff post information according to post properties, and determining post type information of the attendance staff; according to the post type information, selecting a corresponding verification strategy to carry out post verification on the attendance employee, and judging whether the attendance employee meets a response condition or not; if the attendance employee does not meet the response condition, judging whether the post state of the attendance employee meets a preset alarm condition based on a preset post judgment rule; and if the post state of the attendance employee meets the preset alarm condition, adopting different alarm processing modes based on the alarm level. It can be seen that the corresponding verification strategy is selected according to the post type information to perform post verification on the attendance employees, it can be ensured that the verification strategy is matched with the post type information, accurate dynamic attendance checking of the attendance employees is achieved, and the accuracy of enterprise employee attendance checking is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent attendance technology, and in particular to a dynamic attendance method, device, equipment and medium. Background Art

[0002] The orderly operation of an enterprise is inseparable from the orderly management of its employees. A key aspect of employee management is the effective management of their working hours. Currently, the security industry is labor-intensive and labor-intensive, characterized by low technological content and low levels of knowledge among its personnel. This results in a small number of managers facing the challenge of managing a large number of front-line security personnel.

[0003] However, traditional attendance systems primarily rely on single biometric identification for attendance verification, failing to verify employees' actual attendance. This presents high-risk vulnerabilities such as ghost workers, clocking in and out for others, and identity forgery. Especially in highly mobile positions like security and property management, paper clocking systems are prone to fraud and the fixed-time clocking mechanism is easily circumvented. This makes it difficult to meet the demands of complex enterprise management, resulting in low employee attendance accuracy. Therefore, improving employee attendance accuracy is a pressing technical challenge. Summary of the Invention

[0004] Based on this, it is necessary to address the above technical problems and provide a dynamic attendance method, device, equipment and medium in the embodiments of the present invention to improve the accuracy of enterprise employee attendance.

[0005] A first aspect of an embodiment of the present application provides a dynamic attendance method, the dynamic attendance method comprising: Obtain the personnel position information of the attendance employees; Classifying the personnel position information according to the nature of the position to determine the position type information of the attendance employee, wherein the position type information includes key positions, non-key positions, and night positions; Selecting a corresponding verification strategy according to the position type information to perform position verification on the attendance employee, and determining whether the attendance employee meets the response conditions; If the attendance employee does not meet the response conditions, then based on the preset position judgment rules, determine whether the position status of the attendance employee meets the preset alarm conditions; If the job status of the attendance employee meets the preset alarm condition, different alarm handling methods are adopted based on the alarm level, wherein the alarm handling method is used to indicate the alarm handling of the attendance employee.

[0006] A second aspect of an embodiment of the present application provides a dynamic attendance device, the dynamic attendance device comprising: The acquisition module is used to obtain the personnel position information of the attendance employees; A classification module is used to classify the personnel position information according to the nature of the position and determine the position type information of the attendance employee, wherein the position type information includes key positions, non-key positions and night positions; A selection module, configured to select a corresponding verification strategy according to the position type information to perform position verification on the attendance employee, and determine whether the attendance employee meets the response conditions; A judgment module is used to judge whether the job status of the attendance employee meets the preset alarm condition based on the preset job judgment rule if the attendance employee does not meet the response condition; The taking module is used to take different alarm handling methods based on the alarm level if the job status of the attendance employee meets the preset alarm condition, wherein the alarm handling method is used to indicate the alarm handling of the attendance employee.

[0007] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the advertising content distribution method as described in the first aspect is implemented.

[0008] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the dynamic attendance method as described in the first aspect is implemented.

[0009] In summary, the present invention provides a dynamic attendance method, device, equipment and medium, which obtains the personnel position information of the attendance employee, classifies the personnel position information according to the nature of the position, determines the position type information of the attendance employee, wherein the position type information includes key positions, non-key positions and night positions, selects the corresponding verification strategy according to the position type information to verify the position of the attendance employee, and judges whether the attendance employee meets the response conditions. If the attendance employee does not meet the response conditions, then based on the preset position judgment rules, judges whether the position status of the attendance employee meets the preset alarm conditions. If the position status of the attendance employee meets the preset alarm conditions, then different alarm handling methods are adopted based on the alarm level, wherein the alarm handling method is used to indicate the alarm handling of the attendance employee. It can be seen that the present application can ensure that the verification strategy matches the position type information by selecting the corresponding verification strategy according to the position type information to verify the position of the attendance employee, so as to realize accurate dynamic attendance of the attendance employee, improve the accuracy of the enterprise employee attendance, and accurately identify anomalies based on the matching of the position status and the preset alarm conditions, thereby improving the standardization and effectiveness of attendance management. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0011] Figure 1 This is a flow chart of a dynamic attendance method provided by one embodiment of the present invention; Figure 2 It is a structural diagram of a dynamic attendance device provided by one embodiment of the present invention.

[0012] Figure 3 It is a structural diagram of a computer device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0015] It will also be understood that the term "and / or" used in the present description and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0016] As used in the present specification and the appended claims, the term “if” may be interpreted as “when” or “upon” or “in response to determining”, depending on the context. Similarly, the phrase “if it is determined” or “if compared to [described condition or event]” may be interpreted as meaning “upon determination” or “in response to determination” or “upon comparison to [described condition or event]” or “in response to comparison to [described condition or event]”, depending on the context.

[0017] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0019] It should be understood that the order of execution of the steps in the following embodiments does not necessarily mean the order in which they are executed. The order in which each process is executed should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0020] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0021] See also Figure 1 , is a flow chart of a dynamic attendance method provided by an embodiment of the present invention, such as Figure 1 As shown, the dynamic attendance method can be implemented through the following steps.

[0022] S101: Obtaining the personnel position information of the attendance employee.

[0023] In step S101, the employee's position information can be one or more of the employee's name, position, work shift, or other personal information. The name ensures each employee has a unique identity. The position clearly identifies each employee's responsibilities and department, facilitating subsequent targeted verification and management. The work shift is the employee's work schedule for a day or week. Different verification strategies are implemented for employees on different shifts (e.g., day and night shifts). When the attendance employee arrives at their work station, they enter their basic information, such as their work ID, team, name, and position, into a mobile app. The mobile app then communicates with the post monitoring system responsible for post monitoring, transmitting the aforementioned employee position information to the post monitoring system. The post monitoring system can then issue an identity verification request to the mobile app, such as various types of biometric recognition, including but not limited to facial recognition, fingerprint recognition, iris recognition, and voiceprint recognition. The employee can then perform the corresponding biometric verification operation on the mobile app, which then transmits the identification data to the post monitoring system. This allows the post monitoring system to confirm the employee's identity information.

[0024] In an embodiment of the present application, by obtaining the personnel position information of the attendance employees, the position monitoring system can have a more comprehensive understanding of the personnel position information of the attendance employees, and by analyzing the attendance patterns of different positions, it can subsequently improve the dynamic attendance accuracy and efficiency of the attendance employees.

[0025] S102: Classifying the personnel position information according to the nature of the position, and determining the position type information of the attendance employee, wherein the position type information includes key positions, non-key positions, and night positions.

[0026] In step S102, key positions are defined as core business positions requiring intensive monitoring; non-key positions are defined as auxiliary positions with minimal impact on business continuity; and night shifts are defined as positions requiring night shifts (e.g., security and customer service), with working hours between 10:00 PM and 6:00 AM). By adopting a unified classification standard based on the nature of each position's work, working hours, and impact on company operations, positions are classified according to these standards to determine the position type information for employees' attendance records. This distinguishes key positions, non-key positions, and night shifts. Key positions can be divided into key fixed positions and key mobile positions. For example, key fixed positions include sales and fire control rooms; key mobile positions include security patrols; non-key positions include cleaning and logistics; and night shifts include shift-based positions such as customer service and monitoring.

[0027] In one embodiment of the invention, the personnel position information is classified according to the nature of the position to determine the position type information of the attendance employee, including: Extracting the job keywords of the attendance employee and the behavior sources corresponding to the job keywords from the personnel job information; Determine the score corresponding to the job keyword based on the behavior source corresponding to the job keyword; Sort the job keywords in descending order according to the scores corresponding to the job keywords; According to the sorting results, select the preset number of job keywords that are ranked first as the final keywords; According to the basic information of the attendance employee and the final keyword, the personnel position information is input into a position classification model to classify the nature of the position, and the position type information of the attendance employee is determined.

[0028] Specifically, using text analysis tools or natural language processing (NLP) methods, we extract job keywords and the corresponding behavioral sources from employee job information. These keywords can include "R&D," "patrol," "night shift," "22:00-6:00," and "cleaning." Based on the context of each keyword, we annotate the corresponding behavioral source. Behavior sources can include specific tasks (such as job completion status) and historical records (such as attendance records and work logs). The behavioral source refers to the source of the behavioral data corresponding to the job keywords in the employee's job information. For example, attendance employee A clocks in at 8 o'clock in the internal attendance platform, so the target behavior data of attendance employee A is "clocks in at 8 o'clock". The skill keyword of attendance employee A can be extracted from the target behavior data as "clocks in", and the corresponding behavior source is "clocked in". In order to determine the importance of job keywords, a fixed score can be set in advance for the behavior source corresponding to the job keywords, and then the job keywords can be scored according to the preset fixed score and the behavior source of the job keywords. For each attendance employee, after determining the job keywords of the attendance employee, the job keywords can be sorted in order from large to small according to the scores corresponding to the job keywords. Among them, the preset number refers to the pre-set number of job keywords to be selected, and the final keyword refers to the job keyword with high importance.

[0029] As can be understood, higher scores indicate more frequent occurrences of job keywords, indicating a greater importance of these keywords to the employee. Therefore, for each employee, after determining the ranking order of the target employee's job keywords, a preset number of the top-ranked job keywords can be selected as the final keywords. Based on the employee's basic information and the final keywords, the employee's job information is then input into a job classification model (such as a random forest or support vector machine) to classify the job nature and determine the employee's job type. For example, if the keyword contains "security," the employee is classified as a key fixed position; if the keyword contains "security" or "patrol," the employee is classified as a key mobile position; if the keyword contains "night shift" and the working hours include "10:00 PM - 6:00 AM," the employee is classified as a night shift; otherwise, the employee is classified as a non-key position. By introducing job keywords, their scores, and ranking these keywords, the aforementioned steps enable more accurate determination of the employee's job type, thereby improving attendance efficiency.

[0030] In this embodiment, by classifying personnel position information according to the nature of the position and determining the position type information of the attendance employees, it is possible to accurately prevent and control high-risk positions, ensure business continuity, reduce the management cost of low-risk positions, improve the attendance efficiency of attendance employees, and optimize attendance management.

[0031] S103: Selecting a corresponding verification strategy according to the position type information to perform position verification on the attendance employee, and determining whether the attendance employee meets a response condition.

[0032] In step S103, all possible job types in the system are identified, such as key jobs, non-key jobs, and night shifts. A specific verification strategy is defined for each job type. Key jobs are triggered using near-real-time (30-second cycle) automatic verification plus random triggering (non-uniformly distributed random triggering), with a response time of ≥5 minutes (reserving time for emergency response). Non-key jobs are triggered using random triggering (non-uniformly distributed random triggering), with a response time of ≤1 minute. The night shift trigger frequency is triggered every 15 minutes, with a response time of ≤1 minute. By selecting a basic verification strategy based on job type information, adjusting the trigger frequency and response time based on the shift (e.g., night shift), recording the employee's response time and comparing it to a preset response time threshold, if the response time is ≤ the threshold, the employee meets the response criteria and verification passes. Otherwise, if the response time exceeds the threshold, the employee does not meet the response criteria, verification fails, and an alarm is triggered.

[0033] In one embodiment of the invention, a corresponding verification strategy is selected according to the position type information to perform position verification on the attendance employee, and it is determined whether the attendance employee meets the response conditions, including: If the position type information is a key position, a first verification strategy is selected to perform position verification on the attendance employee to obtain a first verification result, wherein the first verification strategy includes a first daily attendance verification and a first on-the-job status verification; If the position type information is a non-key position, a second verification strategy is selected to perform position verification on the attendance employee to obtain a second verification result, wherein the second verification strategy includes a second daily attendance verification and a second on-the-job status verification; If the job type information is night job, a third verification strategy is selected to perform job verification on the attendance employee to obtain a third verification result, wherein the third verification strategy includes a second daily attendance verification and a third on-the-job status verification; respectively determining whether the first verification result, the second verification result, and the third verification result are responded to within a predetermined response time of the post; If the first verification result, the second verification result and the third verification result respond in sequence within the post scheduled response time, it is determined that the post status corresponding to the attendance employee meets the response condition.

[0034] Specifically, if the position type information is a key position, the first verification strategy is selected to perform position verification on the attendance employee and obtain a first verification result. The first verification strategy includes a first daily attendance verification and a first on-the-job status verification. It is determined whether the first verification result responds within the position's scheduled response time. If the first verification result responds within the position's scheduled response time, it is determined that the position status corresponding to the attendance employee meets the response conditions. That is, it is pre-performed before work and after work according to the get off work shift, using multi-dimensional biometric information verification. Because there is ample time at this time and the requirements for security are higher, it is ensured that employees arrive and leave on time to prevent violations such as lateness and early departure. Then, different on-the-job verification strategies are implemented for different positions, and it is determined whether the attendance employee meets the response conditions to ensure that employees remain vigilant and efficient while on the job. The key posts can be divided into key fixed posts and key mobile posts. The verification strategy for key fixed posts (such as fire control rooms) is automatic verification in quasi-real time (such as 30s cycle) and triggering additional verification at random time (non-uniformly distributed random trigger). If the verification needs to respond within the post's scheduled response time (such as 5 minutes), it is slightly longer than other posts because it may be necessary to handle important events such as fire alarms, so as to ensure that employees at key fixed posts always remain vigilant and can respond to emergencies in a timely manner; the verification strategy for key mobile posts (such as scheduled patrols) is similar to that of key fixed posts, using quasi-real-time automatic verification and random time triggering verification. If the verification also needs to respond within the post's scheduled response time (such as 5 minutes), it is to ensure that employees at key mobile posts patrol according to the established route and promptly discover and deal with safety hazards.

[0035] If the position type information indicates a non-key position, a second verification strategy is selected to verify the employee's position and obtain a second verification result. This second verification strategy includes a second daily attendance verification and a second on-the-job status verification. The second verification result is determined to be a response within the position's scheduled response time. If the second verification result is a response within the scheduled response time, the employee's position status is determined to meet the response conditions. This verification is pre-processed before and after work hours, based on the employee's work shift, using multi-dimensional biometric information. This is because time is available and security requirements are higher during these times. This ensures that employees arrive and leave on time, preventing violations such as lateness and early departure. Different on-the-job verification strategies are then implemented for different positions to determine whether the employee meets the response conditions. This ensures that employees remain vigilant and efficient while on the job. The verification strategy for non-key positions is a random time trigger (non-uniformly distributed random triggering). If the verification is required to respond within the position's scheduled response time (e.g., 1 minute), this ensures that employees in non-key positions also maintain a certain level of vigilance, preventing violations such as absenteeism.

[0036] If the position type information indicates night shift, a third verification strategy is selected to verify the employee's position and obtain a third verification result. This third verification strategy includes a third daily attendance verification and a third on-duty status verification. The third verification result is then determined to be valid within the position's scheduled response time. If the third verification result is valid within the scheduled response time, the employee's corresponding position status is determined to meet the response conditions. This verification is pre-processed before and after work hours, based on the employee's work shift, using multi-dimensional biometric information. This is because time is available and security requirements are higher during these times. This ensures that employees arrive and leave on time, preventing violations such as lateness and early departure. Different on-duty verification strategies are then implemented for different positions to determine whether the employee meets the response conditions. This ensures that employees remain vigilant and efficient while on duty. The night shift verification strategy uses a randomized (non-uniformly distributed) triggering verification frequency to prevent sleepovers. If verifications must be completed within the scheduled response time (e.g., one minute), nighttime is a time of high risk for safety hazards. By increasing the number of verifications and improving response speed, night shift employees remain highly vigilant. It can be seen that through the above steps, adopting different verification strategies for different job types can accurately control risks, improve the security level, continuously optimize the verification strategy, and improve the accuracy and efficiency of on-the-job status monitoring of attendance employees.

[0037] In one embodiment of the invention, a first verification strategy is selected to perform position verification on the attendance employee, and a first verification result is obtained, including: Obtaining a first work environment type and first multi-dimensional biometric information of the attendance employee; Determining, based on the first work environment type and the position type information, a first verification weight of each dimension of biometric feature information in the first multi-dimensional biometric feature information; Performing a first daily attendance verification on the attendance employee according to the first verification weight of each dimension of the biometric information to obtain a first attendance verification result; Performing a first on-the-job status verification on each of the attendance employees using a pre-set fixed time interval and a non-uniformly distributed random triggering algorithm to obtain a first on-the-job status verification result; A first verification result is determined according to the first attendance verification result and the first on-the-job status verification result.

[0038] Specifically, the system obtains the employee's first work environment type and first multi-dimensional biometric information. The first work environment type is determined by the device's environmental sensing sensors, which automatically sense parameters such as ambient light and background noise. When the ambient light level falls below a preset threshold (e.g., 50 Lux), it is considered a low-light environment; when the background noise level exceeds a preset threshold (e.g., 65 dB), it is considered a noisy environment. The first multi-dimensional biometric information includes facial recognition at five angles (front, 30° left, 30° right, 15° upward, 15° downward), voiceprints collected using multiple dynamic passwords (including mixed Chinese, English, and numerical commands) to increase the complexity and security of voiceprint recognition, and fingerprints collected from at least two fingers to improve the reliability and redundancy of fingerprint recognition. During system operation, the biometric collection device and environmental sensors installed on the system device are invoked at preset intervals to collect the employee's biometric information and work environment type. Then, based on the first work environment type and job type information as the key positions, the first verification weight of each dimension of the first multi-dimensional biometric information is determined. For example, in low-light environments (<50Lux), the voiceprint feature verification weight ratio is increased; in noisy environments (>65dB), the facial recognition feature verification weight ratio is increased. For example, the voiceprint feature verification weight ratio for customer service positions should be higher than that for security positions and higher than that for cleaning positions, because cleaning and security positions require lower levels of standard Mandarin, and dialects may cause a decrease in voiceprint recognition accuracy. By dynamically adjusting the weight distribution of biometric features based on the different work environments and job type information automatically perceived by the equipment, the job monitoring system can be more adaptable to various complex actual situations, improve the accuracy and reliability of employee identity verification for attendance, and meet security needs in different scenarios.

[0039] Furthermore, based on the first verification weight of each dimension of biometric information, a first daily attendance verification is performed on the attendance employee to obtain a first attendance verification result. A first on-the-job status verification is performed on the attendance employee using a pre-set fixed time interval and a non-uniformly distributed random triggering algorithm to obtain a first on-the-job status verification result. For example, the fire control room performs automatic verification in quasi-real time (e.g., a 30-second period) + random time (non-uniformly distributed random triggering) triggering verification; scheduled patrols then perform automatic verification in quasi-real time (e.g., a 30-second period) + random time (non-uniformly distributed random triggering) triggering verification. The non-uniformly distributed random triggering refers to developing an algorithm, such as constructing a verification interval prediction model based on a Markov chain, to adapt to different positions while preventing the managed person from achieving regular circumvention (e.g., if triggered every half hour, the relevant personnel may summarize the regularity and complete on-the-job verification in different projects in adjacent areas at the same time, resulting in one person working multiple jobs at the same time, affecting work quality). The first verification result is then determined based on the first attendance verification result and the first on-the-job status verification result. Through the above steps, multiple biometric identification dimensions can be covered, reducing the risk of a single feature being forged or interfered with. The verification weight of each biometric feature can be dynamically adjusted according to the first working environment type (such as indoor / outdoor, noise / light conditions) and job type (such as physical labor / mental labor), ensuring accurate identification in complex environments, improving the verification success rate, and providing data support for human resource management.

[0040] In one embodiment of the invention, a second verification strategy is selected to perform position verification on the attendance employee, and a second verification result is obtained, including: Obtaining a second work environment type and a second multi-dimensional biometric feature information of the attendance employee; Determining, based on the second work environment type and the position type information, a second verification weight of each dimension of biometric feature information in the second multi-dimensional biometric feature information; Performing a second daily attendance verification on the attendance employee according to the second verification weight of each dimension of the biometric information to obtain a second attendance verification result; Performing a second on-the-job status verification on the attendance employee using a non-uniform distribution random triggering algorithm to obtain a second on-the-job status verification result; A second verification result is determined according to the second attendance verification result and the second on-the-job status verification result.

[0041] Specifically, the system acquires the employee's second work environment type and second multi-dimensional biometric information. The second work environment type is determined by the device's environmental sensing sensors, which automatically sense parameters such as ambient light and background noise. When ambient light falls below a preset threshold (e.g., 50 Lux), it is considered a low-light environment; when background noise exceeds a preset threshold (e.g., 65 dB), it is considered a noisy environment. The first multi-dimensional biometric information includes facial recognition at five angles (front, 30° left, 30° right, 15° upward, 15° downward, etc.), voiceprints collected using multiple dynamic passwords (including mixed Chinese, English, and numerical commands) to increase the complexity and security of voiceprint recognition, and fingerprints collected from at least two fingers to improve the reliability and redundancy of fingerprint recognition. During system operation, the biometric collection device and environmental sensors installed on the system device are invoked at preset intervals to collect the employee's biometric information and work environment type. Then, based on the second work environment type and position type information being a non-key position, the second verification weight of each dimension of the second multi-dimensional biometric information is determined. For example, in low-light environments (<50Lux), the voiceprint feature verification weight ratio is increased, and in noisy environments (>65dB), the facial recognition feature verification weight ratio is increased. For example, the voiceprint feature verification weight ratio for customer service positions should be higher than that for security positions and higher than that for cleaning positions, because cleaning and security positions require lower levels of standard Mandarin, and dialects may cause a decrease in voiceprint recognition accuracy. By dynamically adjusting the biometric weight distribution based on the different work environments and position type information automatically perceived by the device, the position monitoring system can be made more adaptable to various complex actual situations, improve the accuracy and reliability of employee identity verification for attendance, and meet security needs in different scenarios.

[0042] Furthermore, based on the second verification weight of the biometric information of each dimension, a second daily attendance verification is performed on the attendance employee to obtain a second attendance verification result, and a second on-the-job status verification is performed on the attendance employee using a pre-set fixed time interval and a non-uniformly distributed random triggering algorithm to obtain a second on-the-job status verification result, for example, random time (non-uniformly distributed random triggering) triggering verification, wherein non-uniformly distributed random triggering refers to developing an algorithm, such as constructing a verification interval prediction model based on a Markov chain, which adapts to different positions while preventing the managed personnel from achieving regularity circumvention (for example, if it is triggered every half hour, the relevant personnel may summarize the rules and complete on-the-job verification in different projects in adjacent areas at the same time, resulting in one person working multiple jobs at the same time, affecting the quality of work), and then determining the second verification result based on the second attendance verification result and the first on-the-job status verification result. Through the above steps, multiple biometric identification dimensions can be covered, reducing the risk of a single feature being forged or interfered with. The verification weight of each biometric feature can be dynamically adjusted according to the second working environment type (such as indoor / outdoor, noise / light conditions) and job type (such as physical labor / mental labor), ensuring accurate identification in complex environments, improving the verification success rate, and providing data support for human resource management.

[0043] In one embodiment of the invention, a third verification strategy is selected to perform position verification on the attendance employee, and a third verification result is obtained, including: Obtaining a third work environment type and a third multi-dimensional biometric feature information of the attendance employee; determining, according to the third work environment type and the position type information, a third verification weight of each dimension of biometric feature information in the third multi-dimensional biometric feature information; Performing a third daily attendance verification on the attendance employee based on the third verification weight of each dimension of biometric information to obtain a third attendance verification result; Performing a third on-the-job status verification on the attendance employee using a non-uniform distribution random triggering algorithm to obtain a third on-the-job status verification result; A third verification result is determined according to the third attendance verification result and the third on-the-job status verification result.

[0044] Specifically, the system acquires the employee's third work environment type and third multi-dimensional biometric information. The third work environment type is determined by the device's environmental sensing sensors, which automatically detect parameters such as ambient light and background noise. When ambient light falls below a preset threshold (e.g., 50 Lux), it is considered a low-light environment; when background noise exceeds a preset threshold (e.g., 65 dB), it is considered a noisy environment. The third multi-dimensional biometric information includes facial recognition at five angles (front, 30° left, 30° right, 15° upward, and 15° downward), voiceprints collected using multiple dynamic passwords (including mixed Chinese, English, and numerical commands) to increase the complexity and security of voiceprint recognition, and fingerprints collected from at least two fingers to improve the reliability and redundancy of fingerprint recognition. During system operation, the biometric collection device and environmental sensors installed on the system device are invoked at preset intervals to collect the employee's biometric information and work environment type. Then, based on the third work environment type and job type information being night shifts, the third verification weights for each dimension of the third multi-dimensional biometric information are determined. For example, in low-light environments (<50 Lux), the voiceprint feature verification weight ratio is increased, and in noisy environments (>65dB), the facial recognition feature verification weight ratio is increased. For example, the voiceprint feature verification weight ratio for customer service positions should be higher than that for security positions and higher than that for cleaning positions, because cleaning and security positions require lower Mandarin proficiency levels, and dialects may reduce voiceprint recognition accuracy. By dynamically adjusting the biometric weight distribution based on the different work environments and job type information automatically perceived by the device, the job monitoring system can be more adaptable to various complex actual situations, improve the accuracy and reliability of employee identity verification during attendance, and meet security needs in different scenarios.

[0045] Furthermore, according to the third verification weight of the biometric information of each dimension, a third daily attendance verification is performed on the attendance employee to obtain a third attendance verification result, and a third on-the-job status verification is performed on the attendance employee using a pre-set fixed time interval and a non-uniform distribution random triggering algorithm to obtain a third on-the-job status verification result, for example, random time (non-uniform distribution random triggering) triggering verification, wherein non-uniform distribution random triggering refers to developing an algorithm, such as constructing a verification interval prediction model based on a Markov chain, which adapts to different positions while preventing the managed personnel from achieving regularity avoidance (for example, if it is triggered every half hour, the relevant personnel may summarize the rules and complete on-the-job verification in different projects in adjacent areas at the same time, resulting in one person working multiple jobs at the same time, affecting the quality of work), and then determining the third verification result based on the third attendance verification result and the third on-the-job status verification result. Through the above steps, multiple biometric identification dimensions can be covered, reducing the risk of a single feature being forged or interfered with. The verification weight of each biometric feature can be dynamically adjusted according to the first working environment type (such as indoor / outdoor, noise / light conditions) and job type (such as physical labor / mental labor), ensuring accurate identification in complex environments, improving the verification success rate, and providing data support for human resource management.

[0046] In this embodiment, by selecting the corresponding verification strategy based on the job type information to verify the position of the attendance employees, more accurate attendance management can be performed for different work environments based on the nature of the position, which can help managers make necessary adjustments to ensure that employees are working under the best conditions and improve work efficiency, thereby reducing the risks caused by insufficient employee capabilities or illegal operations.

[0047] S104: If the attendance employee does not meet the response condition, then based on the preset position judgment rule, it is judged whether the position status of the attendance employee meets the preset alarm condition.

[0048] In step S104, specific criteria for attendance verification failure are identified (e.g., number of attendance failures, biometric match below a threshold, on-duty status verification failure, etc.). If the employee fails to meet the response criteria, the pre-set position judgment rules are used to determine whether the employee's position status meets the pre-set alarm conditions. If the employee meets the pre-set alarm conditions, the subsequent process, step S105, is immediately triggered. Different alarm handling methods are implemented based on the alarm level. The alarm handling method indicates the alarm handling for the employee. If the employee fails to meet the pre-set alarm conditions, the subsequent process is not executed. Different judgment rules are preset based on position type (e.g., key positions, non-key positions, and night positions). For key positions, the spatial dimensions must conform to the patrol route (based on GIS maps), and the position's spatial dimensions must not deviate from the position location by xx meters. For non-key positions, the response time after triggering verification can be longer than that of other positions, allowing customer service staff to prioritize their work. For night positions, the response time after triggering verification should be shorter than that of daytime positions, as less work is required at night. Key posts: If the verification fails and the spatial dimension should deviate from the post position by 100 meters, the post status of the attendance employee will be determined to meet the preset alarm conditions; Non-key posts: If the verification fails and the response time is not longer than that of other posts, the post status of the attendance employee will be determined to meet the preset alarm conditions; Night posts: If the verification fails and the response time is not shorter than that of other posts, the post status of the attendance employee will be determined to meet the preset alarm conditions. In one embodiment of the invention, based on a preset job judgment rule, determining whether the job status of the attendance employee meets a preset alarm condition includes: Acquire multiple historical alarm information, and determine, based on the content of the historical alarm information, a preset alarm type corresponding to the historical alarm information and a preset alarm level corresponding to the historical alarm information; Building an alarm evaluation system for the attendance employee according to each of the plurality of historical alarm information, the preset alarm type corresponding to the historical alarm information, and the preset alarm level; Based on the preset job judgment rules and the alarm evaluation system, it is judged whether the job status of the attendance employee meets the preset alarm conditions.

[0049] Specifically, multiple historical alarm information is extracted from the attendance system, alarm database or log file, including information such as on-the-job status, alarm reasons, historical alarm types and historical alarm levels, and duplicate, invalid or erroneous data is removed to ensure data quality. The alarm information is converted into a structured format (such as JSON, database table), and the new alarm category corresponding to each historical alarm information in the multiple historical alarm information is determined by manual setting, and the new alarm category is set as the preset alarm category. Alarm type classification: Attendance anomalies: such as lateness, early departure, and absence. Equipment failure: such as attendance machine failure, network interruption. Violations: such as punching in for others, falsifying attendance. Advanced alarms: serious violations (such as falsifying attendance, long-term absence). Intermediate alarms: abnormal behavior (such as frequent lateness, equipment failure).

[0050] Low-level alarms: Minor violations (such as occasional lateness or network fluctuations). Trigger conditions: Position deviation > preset deviation threshold; multiple consecutive verification failures; two consecutive failures to respond to verification. Then, based on each historical alarm message in multiple historical alarm messages, the preset alarm type corresponding to the historical alarm message, and the preset alarm level, an alarm evaluation system for attendance employees is established. Based on the preset job judgment rules and alarm evaluation system, it is determined whether the attendance employee's on-the-job status is within the alarm evaluation system. If the attendance employee's on-the-job status is within the alarm evaluation system, it is determined that the attendance employee's job status meets the preset alarm conditions. It can be seen that by processing historical alarm information, alarms can be effectively identified and classified, improving management efficiency and reducing errors in manual processing. Building an evaluation system based on alarm type and level helps to form a standardized processing process, thereby improving the standardization and consistency of attendance management, helping managers to take preventive measures in advance and reduce risks.

[0051] In this embodiment, different judgment rules are set for different job types to accurately manage jobs. This can accurately determine whether the job status of the attendance employees meets the preset alarm conditions, and then set different levels of alarms according to the severity of the alarms. This helps to identify potential problems early, reduce the impact that may result in the future, and improve management accuracy.

[0052] S105: If the job status of the attendance employee meets the preset alarm condition, different alarm handling methods are adopted based on the alarm level, wherein the alarm handling method is used to indicate alarm handling for the attendance employee.

[0053] In step S105, if the employee's job status meets the preset alarm conditions, the alarm is divided into different levels (such as high, medium, and low) based on the severity and scope of the alarm. High-level alarms: serious violations (such as being off-duty for overtime, falsifying attendance records), positioning deviation greater than 100 meters, requiring immediate action; medium-level alarms: abnormal behavior (such as frequent lateness, attendance anomalies), and n consecutive verification failures (such as biometric matching <30%), requiring special attention; low-level alarms: minor violations (such as occasional failure to clock in), and two consecutive failures to respond to verification, can be delayed. Disposition methods are configured for different levels: High-level alarms: Activate the emergency inspection plan and verify the employee's on-duty status on site; medium-level alarms: Immediately freeze the employee's system permissions to prevent further violations and conduct remote manual review; low-level alarms: Send an alarm notification to the employee, requiring rectification within a limited time, and notify the immediate superior via SMS, email, app push, etc. That is, when an employee's job status meets the preset alarm conditions, the system automatically determines the alarm level, and then adopts different alarm handling methods based on the alarm level. After receiving the alarm notification, the employee must feedback the processing results within the specified time. The system records the entire process of alarm handling, including trigger time, handling method, employee feedback, etc.

[0054] In this embodiment, different levels of alarms correspond to different alarm handling methods, which can ensure targeted problem solving, rapid response to emergencies, and improved overall management efficiency. Clear alarm handling methods can shorten decision-making time, allowing managers to respond to alarms more quickly, reduce potential risks, make management processes more efficient, improve response time and decision-making quality, and enhance management accuracy and efficiency.

[0055] In summary, the present invention provides a dynamic attendance method, device, equipment and medium, which obtains the personnel position information of the attendance employee, classifies the personnel position information according to the nature of the position, determines the position type information of the attendance employee, wherein the position type information includes key positions, non-key positions and night positions, selects the corresponding verification strategy according to the position type information to verify the position of the attendance employee, and judges whether the attendance employee meets the response conditions. If the attendance employee does not meet the response conditions, then based on the preset position judgment rules, judges whether the position status of the attendance employee meets the preset alarm conditions. If the position status of the attendance employee meets the preset alarm conditions, then different alarm handling methods are adopted based on the alarm level, wherein the alarm handling method is used to indicate the alarm handling of the attendance employee. It can be seen that the present application can ensure that the verification strategy matches the position type information by selecting the corresponding verification strategy according to the position type information to verify the position of the attendance employee, so as to realize accurate dynamic attendance of the attendance employee, improve the accuracy of the enterprise employee attendance, and accurately identify anomalies based on the matching of the position status and the preset alarm conditions, thereby improving the standardization and effectiveness of attendance management.

[0056] See also Figure 2 , Figure 2 This is a structural diagram of a dynamic attendance device provided by an embodiment of the present invention. The dynamic attendance device corresponds to the dynamic attendance method in the above embodiment. Figure 1 as well as Figure 1 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 2 The dynamic attendance device 20 includes: an acquisition module 21, a classification module 22, a selection module 23, a judgment module 24, and an adoption module 25.

[0057] The acquisition module 21 is used to obtain the personnel position information of the attendance employee; A classification module 22 is used to classify the personnel position information according to the nature of the position and determine the position type information of the attendance employee, wherein the position type information includes key positions, non-key positions and night positions; A selection module 23 is configured to select a corresponding verification strategy according to the position type information to perform position verification on the attendance employee and determine whether the attendance employee meets a response condition; The judgment module 24 is used to judge whether the job status of the attendance employee meets the preset alarm condition based on the preset job judgment rule if the attendance employee does not meet the response condition; The taking module 25 is used to take different alarm handling methods based on the alarm level if the job status of the attendance employee meets the preset alarm condition, wherein the alarm handling method is used to indicate the alarm handling of the attendance employee.

[0058] Optionally, the classification module 22 is specifically configured to: Extracting the job keywords of the attendance employee and the behavior sources corresponding to the job keywords from the personnel job information; Determine the score corresponding to the job keyword based on the behavior source corresponding to the job keyword; Sort the job keywords in descending order according to the scores corresponding to the job keywords; According to the sorting results, select the preset number of job keywords that are ranked first as the final keywords; According to the basic information of the attendance employee and the final keyword, the personnel position information is input into a position classification model to classify the nature of the position, and the position type information of the attendance employee is determined.

[0059] Optionally, the selection module 23 is specifically configured to: If the position type information is a key position, a first verification strategy is selected to perform position verification on the attendance employee to obtain a first verification result, wherein the first verification strategy includes a first daily attendance verification and a first on-the-job status verification; If the position type information is a non-key position, a second verification strategy is selected to perform position verification on the attendance employee to obtain a second verification result, wherein the second verification strategy includes a second daily attendance verification and a second on-the-job status verification; If the job type information is night job, a third verification strategy is selected to perform job verification on the attendance employee to obtain a third verification result, wherein the third verification strategy includes a second daily attendance verification and a third on-the-job status verification; respectively determining whether the first verification result, the second verification result, and the third verification result are responded to within a predetermined response time of the post; If the first verification result, the second verification result and the third verification result respond in sequence within the post scheduled response time, it is determined that the post status corresponding to the attendance employee meets the response condition.

[0060] Optionally, the selection module 23 is further configured to: Obtaining a first work environment type and first multi-dimensional biometric information of the attendance employee; Determining, based on the first work environment type and the position type information, a first verification weight of each dimension of biometric feature information in the first multi-dimensional biometric feature information; Performing a first daily attendance verification on the attendance employee according to the first verification weight of each dimension of the biometric information to obtain a first attendance verification result; Performing a first on-the-job status verification on each of the attendance employees using a pre-set fixed time interval and a non-uniformly distributed random triggering algorithm to obtain a first on-the-job status verification result; A first verification result is determined according to the first attendance verification result and the first on-the-job status verification result.

[0061] Optionally, the selection module 23 is further configured to: Obtaining a second work environment type and a second multi-dimensional biometric feature information of the attendance employee; Determining, based on the second work environment type and the position type information, a second verification weight of each dimension of biometric feature information in the second multi-dimensional biometric feature information; Performing a second daily attendance verification on the attendance employee according to the second verification weight of each dimension of the biometric information to obtain a second attendance verification result; Performing a second on-the-job status verification on the attendance employee using a non-uniform distribution random triggering algorithm to obtain a second on-the-job status verification result; A second verification result is determined according to the second attendance verification result and the second on-the-job status verification result.

[0062] Optionally, the selection module 23 is further configured to: Obtaining a third work environment type and a third multi-dimensional biometric feature information of the attendance employee; determining, according to the third work environment type and the position type information, a third verification weight of each dimension of biometric feature information in the third multi-dimensional biometric feature information; Performing a third daily attendance verification on the attendance employee based on the third verification weight of each dimension of biometric information to obtain a third attendance verification result; Performing a third on-the-job status verification on the attendance employee using a non-uniform distribution random triggering algorithm to obtain a third on-the-job status verification result; A third verification result is determined according to the third attendance verification result and the third on-the-job status verification result.

[0063] Optionally, the judgment module 24 is specifically configured to: Acquire multiple historical alarm information, and determine, based on the content of the historical alarm information, a preset alarm type corresponding to the historical alarm information and a preset alarm level corresponding to the historical alarm information; Building an alarm evaluation system for the attendance employee according to each of the plurality of historical alarm information, the preset alarm type corresponding to the historical alarm information, and the preset alarm level; Based on the preset job judgment rules and the alarm evaluation system, it is judged whether the job status of the attendance employee meets the preset alarm conditions.

[0064] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0065] Figure 3 This is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 3 As shown, the computer device of this embodiment includes: at least one processor ( Figure 3 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor. When the processor executes the computer program, the steps in the above-mentioned dynamic attendance method embodiment are implemented.

[0066] The computer device may include, but is not limited to, a processor and a memory. Figure 3 The above is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input system.

[0067] In one embodiment, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor in an electronic device, the electronic device is enabled to perform the steps of any embodiment of a dynamic attendance timekeeping method disclosed herein, which are not repeated here. The computer-readable storage medium can be either non-volatile or volatile.

[0068] The processor may be a CPU, other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0069] Memory includes readable storage media, internal memory, and the like. Internal memory can be the internal memory of an electronic device, providing an environment for the execution of the operating system and computer-readable instructions stored in the readable storage medium. The readable storage medium can be the electronic device's hard drive. In other embodiments, it can also be an external storage device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a flash memory card. Furthermore, memory can include both the electronic device's internal storage unit and an external storage device. Memory is used to store the operating system, associated applications, a boot loader, data, and other programs, such as computer program code. Memory can also be used to temporarily store data that has been output or is about to be output.

[0070] It is understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0071] Those skilled in the art can clearly understand that for the sake of convenience and brevity in description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0072] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, persons skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A dynamic attendance method, characterized in that: include: Obtain the personnel position information of the attendance employees; Classifying the personnel position information according to the nature of the position to determine the position type information of the attendance employee, wherein the position type information includes key positions, non-key positions, and night positions; Selecting a corresponding verification strategy according to the position type information to perform position verification on the attendance employee, and determining whether the attendance employee meets the response conditions; If the attendance employee does not meet the response conditions, then based on the preset position judgment rules, determine whether the position status of the attendance employee meets the preset alarm conditions; If the job status of the attendance employee meets the preset alarm condition, different alarm handling methods are adopted based on the alarm level, wherein the alarm handling method is used to indicate the alarm handling of the attendance employee.

2. The dynamic attendance method according to claim 1, wherein: The step of selecting a corresponding verification strategy according to the position type information to perform position verification on the attendance employee and determining whether the attendance employee meets a response condition includes: If the position type information is a key position, a first verification strategy is selected to perform position verification on the attendance employee to obtain a first verification result, wherein the first verification strategy includes a first daily attendance verification and a first on-the-job status verification; If the position type information is a non-key position, a second verification strategy is selected to perform position verification on the attendance employee to obtain a second verification result, wherein the second verification strategy includes a second daily attendance verification and a second on-the-job status verification; If the job type information is night job, a third verification strategy is selected to perform job verification on the attendance employee to obtain a third verification result, wherein the third verification strategy includes a second daily attendance verification and a third on-the-job status verification; respectively determining whether the first verification result, the second verification result, and the third verification result are responded to within a predetermined response time of the post; If the first verification result, the second verification result and the third verification result respond in sequence within the post scheduled response time, it is determined that the post status corresponding to the attendance employee meets the response condition.

3. The dynamic attendance method according to claim 2, characterized in that: The selecting of the first verification strategy to perform position verification on the attendance employee to obtain a first verification result includes: Obtaining a first work environment type and first multi-dimensional biometric information of the attendance employee; Determining, based on the first work environment type and the position type information, a first verification weight of each dimension of biometric feature information in the first multi-dimensional biometric feature information; Performing a first daily attendance verification on the attendance employee according to the first verification weight of each dimension of the biometric information to obtain a first attendance verification result; Performing a first on-the-job status verification on each of the attendance employees using a pre-set fixed time interval and a non-uniformly distributed random triggering algorithm to obtain a first on-the-job status verification result; A first verification result is determined according to the first attendance verification result and the first on-the-job status verification result.

4. The dynamic attendance method according to claim 2, wherein: The selecting of the second verification strategy to perform position verification on the attendance employee to obtain a second verification result includes: Obtaining a second work environment type and a second multi-dimensional biometric feature information of the attendance employee; Determining, based on the second work environment type and the position type information, a second verification weight of each dimension of biometric feature information in the second multi-dimensional biometric feature information; Performing a second daily attendance verification on the attendance employee according to the second verification weight of each dimension of the biometric information to obtain a second attendance verification result; Performing a second on-the-job status verification on the attendance employee using a non-uniform distribution random triggering algorithm to obtain a second on-the-job status verification result; A second verification result is determined according to the second attendance verification result and the second on-the-job status verification result.

5. The dynamic attendance method according to claim 2, wherein: The selecting of the third verification strategy to perform position verification on the attendance employee to obtain a third verification result includes: Obtaining a third work environment type and a third multi-dimensional biometric feature information of the attendance employee; determining, according to the third work environment type and the position type information, a third verification weight of each dimension of biometric feature information in the third multi-dimensional biometric feature information; Performing a third daily attendance verification on the attendance employee based on the third verification weight of each dimension of biometric information to obtain a third attendance verification result; Performing a third on-the-job status verification on the attendance employee using a non-uniform distribution random triggering algorithm to obtain a third on-the-job status verification result; A third verification result is determined according to the third attendance verification result and the third on-the-job status verification result.

6. The dynamic attendance method according to claim 1, wherein: The step of judging whether the employee's job status satisfies a preset alarm condition based on a preset job judgment rule includes: Acquire multiple historical alarm information, and determine, based on the content of the historical alarm information, a preset alarm type corresponding to the historical alarm information and a preset alarm level corresponding to the historical alarm information; Building an alarm evaluation system for the attendance employee according to each of the plurality of historical alarm information, the preset alarm type corresponding to the historical alarm information, and the preset alarm level; Based on the preset job judgment rules and the alarm evaluation system, it is judged whether the job status of the attendance employee meets the preset alarm conditions.

7. The dynamic attendance method according to claim 1, wherein: The step of classifying the personnel position information according to the nature of the position and determining the position type information of the attendance employee includes: Extracting the job keywords of the attendance employee and the behavior sources corresponding to the job keywords from the personnel job information; Determine the score corresponding to the job keyword based on the behavior source corresponding to the job keyword; Sort the job keywords in descending order according to the scores corresponding to the job keywords; According to the sorting results, select the preset number of job keywords that are ranked first as the final keywords; According to the basic information of the attendance employee and the final keyword, the personnel position information is input into a position classification model to classify the nature of the position, and the position type information of the attendance employee is determined.

8. A dynamic attendance device, characterized in that: include: The acquisition module is used to obtain the personnel position information of the attendance employees; A classification module is used to classify the personnel position information according to the nature of the position and determine the position type information of the attendance employee, wherein the position type information includes key positions, non-key positions and night positions; A selection module, configured to select a corresponding verification strategy according to the position type information to perform position verification on the attendance employee, and determine whether the attendance employee meets the response conditions; A judgment module is used to judge whether the job status of the attendance employee meets the preset alarm condition based on the preset job judgment rule if the attendance employee does not meet the response condition; The taking module is used to take different alarm handling methods based on the alarm level if the job status of the attendance employee meets the preset alarm condition, wherein the alarm handling method is used to indicate the alarm handling of the attendance employee.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the dynamic attendance method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the dynamic attendance method according to any one of claims 1 to 7 is implemented.

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