Employee dormitory management system, control method thereof and storage medium

Abstract

The invention discloses an employee dormitory management system and a control method thereof, and a storage medium. The method comprises the following steps: obtaining identity information of a to-be-checked-in employee from a first terminal; sending the identity information of the person to a second terminal, and obtaining an approval result about the to-be-checked-in employee from the second terminal; in response to the approval result, inputting the identity information of the person and the current dormitory state into a deep feature matching model to obtain a first score value, a second score value and a third score value between the employee and each bed; according to the three score values, generating a matching degree between the employee and each bed; determining bed number information of the employees according to the matching degree; in response to a first operation instruction from the second terminal, sending the bed number information to the first terminal, or updating the bed information and sending the updated bed information to the first terminal; and in response to a second operation instruction from the first terminal, updating the current dormitory state. According to the invention, the management efficiency and quality of employee dormitory allocation can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an employee dormitory management system and a control method and storage medium thereof. Background Art

[0002] In modern enterprise management, employee dormitory management is an important part of human resource management and logistics support. As the scale of enterprises continues to expand and the number of employees increases, traditional employee dormitory management methods face many challenges. Traditional dormitory allocation often relies on manual operations. For example, managers collect employee information through paper forms, and then allocate beds based on their own experience and limited understanding of dormitory conditions. This method is inefficient. When processing a large amount of employee check-in information, it takes a lot of time and effort to manually organize and allocate beds, which can easily lead to a slow check-in process, affect employees' timely check-in, and reduce employees' satisfaction with the company's logistics services.

[0003] At the same time, manual allocation makes it difficult to fully consider the precise matching of individual differences between employees and dormitory resources. Due to the lack of effective data integration and analysis tools, it is impossible to fully measure the adaptability between employees' special needs and the dormitory environment corresponding to each bed, resulting in unreasonable dormitory allocation from time to time. For example, some employees may have their rest quality affected by the fact that the beds they are assigned do not match their work and rest habits, which in turn affects their work efficiency. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes an employee dormitory management system and a control method and storage medium thereof, which can improve the management efficiency and quality of employee dormitory allocation.

[0005] In a first aspect, the present application provides a control method for an employee dormitory management system, comprising:

[0006] Obtaining the identity information of the employee to be checked in from the first terminal;

[0007] Sending the employee information to a second terminal, and obtaining the approval result of the employee to be admitted from the second terminal;

[0008] In response to the approval result, the personal identity information and the current dormitory status are input into a preset deep feature matching model to obtain a first score value, a second score value and a third score value between the employee and each bed, respectively; wherein the current dormitory status includes bed information corresponding to each bed;

[0009] generating a matching degree between the employee and each bed according to the first score value, the second score value, and the third score value;

[0010] Determining the bed number information of the employee according to the matching degree;

[0011] In response to a first operation instruction from the second terminal, sending the bed number information to the first terminal, or updating the bed information and sending the updated bed information to the first terminal;

[0012] In response to a second operation instruction from the first terminal, the current dormitory status is updated.

[0013] According to the control method of the employee dormitory management system of the first embodiment of the present application, at least the following beneficial effects are achieved: first, the personal identity information of the employee to be accommodated is obtained from the first terminal, and then the personal identity information obtained is sent to the second terminal, so that the manager can approve the accommodation application of the employee to be accommodated in the second terminal and generate the corresponding approval result. If the approval result is passed, the personal identity information and the current dormitory status of the employee to be accommodated are input into the preset deep feature matching model, where the current dormitory status covers the bed information corresponding to each bed, and the first score value, the second score value and the third score value between the employee and each bed are obtained respectively through the deep feature matching model, which can quantitatively evaluate the adaptability of the employee and the bed from multiple different dimensions, and then multiply the three score values ​​with their corresponding weights and add them together to generate the matching degree between the employee and each bed. According to the matching degree generated above, the system analyzes and evaluates the adaptation of each bed to the employee, so as to determine the bed number information that best suits the employee. At the same time, after the bed number information is generated, the second terminal is also required to confirm the bed number information. It can directly confirm the bed number information assigned by the system through the first operation instruction; if the bed number information assigned by the system is considered inappropriate, the bed number information can also be modified through the first operation instruction and then sent. Employees perform specific operations on the first terminal. After the system receives the instruction, it will know that the employee has known and recognized the assigned bed number information, thereby completing the information interaction process of this link. In this application, from obtaining the personal identity information of the employees to be admitted, the approval process to the matching and allocation of beds, the entire process is automatically transferred through the system. For example, information is quickly transmitted between the first terminal (employee end) and the second terminal (management end), without the need for manual sorting and transmission of a large number of paper documents or data tables, which greatly saves time and labor costs. In addition, using the preset deep feature matching model, a large amount of employee information and dormitory bed information can be comprehensively analyzed in a short time, and the matching degree can be quickly generated and the bed number information can be determined. Compared with the traditional manual evaluation and allocation of beds one by one, the allocation speed is significantly improved. Furthermore, after the bed number information is generated, through the first operation instruction of the second terminal, the management personnel can adjust the system allocation results according to the actual situation. Through the combination of manual and system methods, the matching results can be further optimized on the basis of model allocation to adapt to special situations or enterprise-specific management needs, thereby improving the accuracy and flexibility of matching.

[0014] According to some embodiments of the first aspect of the present application, in response to the approval result, the personal identity information and the current dormitory status are input into a preset deep feature matching model to generate a matching degree between the employee and each bed, including:

[0015] In response to the approval result, the personal identity information and the current dormitory status are input into a preset deep feature matching model to obtain a first score value, a second score value, and a third score value between the employee and each bed, respectively;

[0016] A matching degree between the employee and each bed is generated according to the first score value, the second score value, and the third score value.

[0017] According to some embodiments of the first aspect of the present application, the personal identity information includes basic information, project participation information, work performance information and overtime regularity information, and the bed information includes safety assessment data of the area corresponding to each bed;

[0018] The personal identity information and the current dormitory status are input into the preset deep feature matching model to obtain the first score value between the employee and each bed, including:

[0019] Extracting the classified basic information and the numerical basic information from the basic information;

[0020] Performing one-hot encoding and full-connection layer processing on the classification basic information to obtain a first basic feature vector;

[0021] Normalizing and convolving the numerical basic information to obtain a second basic eigenvector;

[0022] Performing data combination and full connection layer processing on the project participation information to obtain a project feature vector;

[0023] Normalizing and convolving the work performance information to obtain a performance feature vector;

[0024] Performing periodic data integration on the overtime regularity information and inputting the information into a preset time recurrent neural network to obtain an overtime feature vector;

[0025] The first basic feature vector and the second basic feature vector are concatenated to obtain a comprehensive basic feature vector, and the project feature vector, the performance feature vector and the overtime feature vector are concatenated to obtain a comprehensive work feature vector;

[0026] Normalizing and convolution processing the safety assessment data to obtain a safety feature vector;

[0027] The comprehensive basic feature vector, the comprehensive work feature vector and the safety feature vector are input into a preset first activation function to obtain a first score value between the employee and each bed.

[0028] According to some embodiments of the first aspect of the present application, the personal identity information includes personal interest and hobby information, personal social data information and personal living habit information, and the bed information includes the roommate identity information of each bed corresponding to other occupied beds in the same dormitory room; wherein the roommate identity information includes roommate interest and hobby information, roommate social data information and roommate living habit information;

[0029] The personal identity information and the current dormitory status are input into the preset deep feature matching model to obtain the second score value between the employee and each bed, including:

[0030] Inputting the personal interest information, personal social data information and personal living habit information into a preset large language model to extract semantic features, and obtaining the personal interest feature vector, the personal social data feature vector and the personal living habit feature vector;

[0031] Inputting the roommate interest and hobby information, roommate social data information and roommate living habit information into the large language model to extract semantic features, and obtaining a roommate interest and hobby feature vector, a roommate social data feature vector and a roommate living habit feature vector;

[0032] Performing similarity calculation processing on the interest feature vector of the person and the interest feature vector of the roommate, the social data feature vector of the person and the social data feature vector of the roommate, and the living habit feature vector of the person and the living habit feature vector of the roommate, respectively, to obtain a first similarity, a second similarity, and a third similarity;

[0033] A second score value between the employee and each bed is obtained according to the first similarity, the second similarity and the third similarity.

[0034] According to some embodiments of the first aspect of the present application, the personal identity information includes bed preference information and demand information, and the bed information includes location information corresponding to each bed and room information of the dormitory room;

[0035] The personal identity information and current dormitory status are input into the preset deep feature matching model to obtain the third score value between the employee and each bed, including:

[0036] Classifying the bed preference information to obtain a preference feature vector;

[0037] Performing one-hot encoding processing on the demand information, the location information, and the room information respectively to obtain a demand feature vector, a location feature vector, and a room feature vector;

[0038] Concatenate the preference feature vector and the demand feature vector to obtain a comprehensive demand feature vector;

[0039] Concatenate the position feature vector and the room feature vector to obtain a comprehensive room feature vector;

[0040] The demand comprehensive feature vector and the room comprehensive feature vector are input into a preset classifier model and decision processing is performed to obtain a third score value between the employee and each bed.

[0041] According to some embodiments of the first aspect of the present application, the present invention further includes:

[0042] Obtain energy consumption in dormitory rooms;

[0043] In response to a first setting instruction from the second terminal, setting an energy consumption cost gradient comparison table;

[0044] Obtaining energy payment items according to the energy consumption and the energy consumption fee gradient comparison table;

[0045] Adding the energy payment item to a preset bill and sending it to the first terminal;

[0046] In response to a payment completion instruction from the first terminal, the energy payment item corresponding to the payment completion instruction is deleted from the bill.

[0047] According to some embodiments of the first aspect of the present application, after the step of obtaining the energy consumption in the dormitory room, the method further includes:

[0048] In response to a second setting instruction from the first terminal or the second terminal, setting an energy consumption threshold;

[0049] When the energy consumption is greater than or equal to the energy consumption threshold, an energy consumption warning notification is sent to the first terminal.

[0050] According to some embodiments of the first aspect of the present application, the present invention further includes:

[0051] Get multiple real-time temperature values, real-time humidity values, and real-time light sensitivity values ​​in dormitory rooms;

[0052] According to the multiple real-time temperature values, the multiple real-time humidity values ​​and the multiple real-time light sensitivity values, an average temperature value, an average humidity value and an average light sensitivity value are obtained;

[0053] In response to a third setting instruction from the first terminal or the second terminal, setting a temperature threshold range, a humidity threshold range, and a light threshold range;

[0054] When the average temperature value is outside the temperature threshold range, a first control instruction is sent to the corresponding electrical equipment in the dormitory room, and when the average temperature value is within the temperature threshold range, a first stop instruction is sent to the corresponding electrical equipment in the dormitory room;

[0055] When the average humidity value is outside the humidity threshold range, a second control instruction is sent to the corresponding electrical equipment in the dormitory room, until the average humidity value is within the humidity threshold range, a second stop instruction is sent to the corresponding electrical equipment in the dormitory room;

[0056] When the average light sensitivity value is outside the light sensitivity threshold range, a third control instruction is sent to the corresponding electrical equipment in the dormitory room, until the average light sensitivity value is within the light sensitivity threshold range, a third stop instruction is sent to the corresponding electrical equipment in the dormitory room.

[0057] In a second aspect, the present application also provides an employee dormitory management system, including:

[0058] at least one memory;

[0059] at least one processor;

[0060] at least one program;

[0061] The program is stored in the memory, and the processor executes at least one of the programs to implement the control method of the employee dormitory management system as described in any one of the embodiments of the first aspect.

[0062] In a third aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer-executable signal, and the computer-executable signal is used to execute the control method of the employee dormitory management system as described in any embodiment of the first aspect.

[0063] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0065] Figure 1 A flowchart of a control method for an employee dormitory management system provided in the first embodiment of the present application;

[0066] Figure 2 For this application Figure 1 A flowchart for calculating a first score value;

[0067] Figure 3 For this application Figure 1 A flowchart for calculating the second score value;

[0068] Figure 4 For this application Figure 1 A flowchart for calculating the third score value;

[0069] Figure 5 A flowchart of a control method for an employee dormitory management system provided in a second embodiment of the present application;

[0070] Figure 6 For this application Figure 5 Regarding the flowchart after step S510;

[0071] Figure 7 This is a flowchart of a control method for an employee dormitory management system provided in the third embodiment of the present application. DETAILED DESCRIPTION

[0072] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0073] In the description of the present application, it should be understood that descriptions involving orientation, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0074] In the description of this application, if there is a description of first or second, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0075] In the description of this application, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in this application based on the specific content of the technical solution.

[0076] First, some nouns involved in this application are analyzed:

[0077] One-Hot Encoding: Also known as one-bit effective encoding. It is a data encoding method mainly used to process categorical data. In categorical data, variables usually have multiple categories. The purpose of one-hot encoding is to convert these categorical variables into numerical forms that can be better understood by machine learning algorithms.

[0078] Fully Connected Layer: It is a basic structural layer in artificial neural networks. In this layer, neurons are fully connected to all neurons in the previous layer, which means that the output of each neuron in the previous layer is input as a weight to each neuron in the current layer. It is mainly used to integrate and classify the extracted features, and is a key part of deep neural networks in making final decisions or generating high-level feature representations.

[0079] Normalization: is a data preprocessing technique used to convert the feature values ​​of data to a specific range, usually [0,1] or [-1,1]. Its purpose is to eliminate the differences in dimensions and value ranges between different features, so that data can be processed at the same scale, improve data quality, and thus improve the performance of machine learning models.

[0080] Recurrent Neural Network (RNN): is a neural network specifically designed to process sequence data. Sequence data refers to data types in which data points are arranged in time or other order, such as time series data (such as stock prices over time, time-ordered waveforms of voice signals), text data (order of words in a sentence), etc. Unlike traditional feedforward neural networks, RNN has recurrent connections and can use information from previously processed elements when processing each element in the sequence.

[0081] From the perspective of extracting semantics, the Large Language Model (LLM) is a tool trained on massive text data. The attention mechanism in its Transformer architecture can dynamically focus on relevant content and effectively capture semantic relationships. It automatically extracts semantic features at all levels, from vocabulary to pragmatics, through a multi-layer structure, and converts text into semantic vectors. It can accurately understand semantics and generate coherent text based on context, and can also mine deep semantics, adapt across domains, and extract semantics to handle multiple tasks.

[0082] 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.

[0083] 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.

[0084] The control method of the employee dormitory management system provided in the embodiment of the present application relates to the field of artificial intelligence technology. The control method of the employee dormitory management system provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application of the control method of the employee dormitory management system, etc., but is not limited to the above forms.

[0085] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0086] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0087] In a first aspect, the present application provides a control method for an employee dormitory management system, which may include but is not limited to the following steps:

[0088] Step S110: Obtaining the identity information of the employee to be checked in from the first terminal;

[0089] Step S120: Sending the personal identity information to the second terminal, and obtaining the approval result of the employee to be admitted from the second terminal;

[0090] Step S130: In response to the approval result, the personal identity information and the current dormitory status are input into a preset deep feature matching model to obtain a first score value, a second score value and a third score value between the employee and each bed, respectively; wherein the current dormitory status includes bed information corresponding to each bed;

[0091] Step S140: generating a matching degree between the employee and each bed according to the first score value, the second score value and the third score value;

[0092] Step S150: Determine the employee's bed number information according to the matching degree;

[0093] Step S160: in response to the first operation instruction from the second terminal, the bed number information is sent to the first terminal, or the bed information is updated and the updated bed information is sent to the first terminal;

[0094] Step S170: In response to the second operation instruction from the first terminal, the current dormitory status is updated.

[0095] It should be noted that the first terminal can be a mobile device for employees to submit check-in applications or an application terminal designated by the company, and the second terminal can be a terminal device used by managers responsible for dormitory management and approval, such as a computer management system, etc. This application does not make specific limitations on this.

[0096] In step S110 to step S160, first, the personal identity information of the employee to be accommodated is obtained from the first terminal, and then the obtained personal identity information is sent to the second terminal, so that the manager can approve the accommodation application of the employee to be accommodated in the second terminal and generate the corresponding approval result. If the approval result is passed, the personal identity information and the current dormitory status of the employee to be accommodated are input into the preset deep feature matching model. Here, the current dormitory status covers the bed information corresponding to each bed. Through the operation of the deep feature matching model, the relevant factors in the employee identity information and the characteristics of the bed information are comprehensively considered to generate the matching degree between the employee and each bed. According to the matching degree generated above, the system analyzes and evaluates the adaptation of each bed to the employee, so as to determine the bed number information that best suits the employee. At the same time, after the bed number information is generated, the second terminal is required to confirm the bed number information, which can directly confirm the bed number information assigned by the system through the first operation instruction; if the bed number information assigned by the system is considered inappropriate, the bed number information can also be modified through the first operation instruction before sending. By performing specific operations on the first terminal, the system will know that the employee has known and recognized the assigned bed number information after receiving the instruction, thereby completing the information interaction process of this link. In this application, from obtaining the personal identity information of the employees to be admitted, the approval process to the bed matching and allocation, the whole process is automatically transferred through the system. For example, information is quickly transmitted between the first terminal (employee end) and the second terminal (management end), without the need to manually organize and transmit a large number of paper documents or data tables, which greatly saves time and labor costs. In addition, using the preset deep feature matching model, a large amount of employee information and dormitory bed information can be comprehensively analyzed in a short time, and the matching degree can be quickly generated and the bed number information can be determined. Compared with the traditional manual evaluation and allocation of beds one by one, the allocation speed is significantly improved. Furthermore, after the bed number information is generated, through the first operation instruction of the second terminal, the management personnel can adjust the system allocation results according to the actual situation. Through the combination of manual and system, the matching results can be further optimized on the basis of model allocation, adapted to special circumstances or enterprise-specific management needs, and the accuracy and flexibility of matching can be improved.

[0097] It should be noted that there can be multiple second terminals, and each second terminal needs to agree to approve before a bed can be allocated to the employee; or the first second terminal needs to agree to approve, and then the identity information of the employee to be admitted will be sent to the second second terminal for review, that is, multiple second terminals need to complete the approval in sequence before a bed can be allocated to the employee. The specific approval method can be determined according to the company's specific management structure. This multi-terminal approval method can give full play to the functional advantages of each department and comprehensively consider the employee's admission application from multiple dimensions, thereby maximizing the rationality, compliance and fairness of dormitory allocation, and avoiding the occurrence of wrong decisions or unreasonable allocations that may be caused by the limitations of a single department's review.

[0098] In step S150, in some cases, the manager may feel that the bed number information assigned by the system is not appropriate based on his comprehensive consideration of the overall layout of the dormitory, personnel arrangement, etc. For example, there may be a situation where the dispersed distribution of employees in the same department is not conducive to team collaboration and communication, or some special beds (such as being close to public facilities may be affected by noise, but the system does not fully consider the sensitivity of specific employees, etc.) are allocated to unsuitable employees. The manager can modify the bed number information using the first operation instruction through the relevant operation function of the second terminal. For example, reselect a bed number that is more suitable for the needs of specific employees or in line with the overall dormitory arrangement plan. After the modification is completed, the first operation instruction is issued through the second terminal to send the modified bed number information to the first terminal to ensure that the employee receives an accurate and appropriate bed allocation result. In this step, bed allocation not only depends on the automatic matching model of the system, but also combines the experience and judgment of the manager based on the actual situation. This not only ensures the efficiency of the system's automatic matching and the scientific nature based on multi-factor considerations, but also can be flexibly adjusted according to specific scenarios when necessary, further improving the rationality of bed allocation and meeting the diverse needs of enterprises in dormitory management.

[0099] In step S160, the system can clearly know whether the employee has received and approved the allocation result, avoiding uncertainty in the information transmission process, ensuring that information in each link can be effectively fed back, and the employee's confirmation of the bed number information is used as the second operation instruction, so that the entire information transmission process forms a complete closed loop. Only when the employee confirms the bed number information can the system regard this allocation as the final effective allocation, and perform subsequent related dormitory status updates and other operations based on this (such as marking the bed as occupied, etc.). This confirmation operation is crucial to ensure that the entire dormitory allocation process proceeds smoothly according to the predetermined plan, and to prevent subsequent management confusion that may be caused by employee failure to confirm, such as the occurrence of repeated allocation of beds.

[0100] In step S130 to step S140, the first score value, the second score value and the third score value between the employee and each bed are obtained respectively through the deep feature matching model, and the compatibility of the employee and the bed can be quantitatively evaluated from multiple different dimensions. Among them, different weights can be assigned to the first score value, the second score value and the third score value respectively, and the size of the weight depends on the relative importance of the factor represented by each score value in the overall matching evaluation, and then the three scores are multiplied by their corresponding weights and added to obtain the final matching degree.

[0101] It is understandable that personal identity information includes basic information, project participation information, work performance information and overtime regularity information, and bed information includes safety assessment data of the area corresponding to each bed. The above basic information can be name, gender, age, position, place of origin, marital status, position code, etc. Project participation information can include project type, project duration, project urgency, etc. Overtime regularity information can include average weekly overtime hours, overtime time distribution, etc. In the basic information, information such as gender, place of origin, and marital status can be classified as type-based basic information, and age and position code can be classified as numerical basic information.

[0102] Reference Figure 2 It can be understood that, in the step of inputting the personal identity information and the current dormitory status into the preset deep feature matching model to obtain the first score value between the employee and each bed, the following steps may be included but not limited to:

[0103] Step S310: extracting the classified basic information and the numerical basic information from the basic information;

[0104] Step S320: Perform one-hot encoding and full-connection layer processing on the classification basic information to obtain a first basic feature vector;

[0105] Step S330: normalizing and convolving the numerical basic information to obtain a second basic feature vector;

[0106] Step S340: Perform data combination and full connection layer processing on the project participation information to obtain a project feature vector;

[0107] Step S350: normalizing and convolution processing the work performance information to obtain a performance feature vector;

[0108] Step S360: performing periodic data integration on the overtime regularity information and inputting the data into a preset time recurrent neural network to obtain an overtime feature vector;

[0109] Step S370: concatenate the first basic feature vector and the second basic feature vector to obtain a comprehensive basic feature vector, and concatenate the project feature vector, the performance feature vector and the overtime feature vector to obtain a comprehensive work feature vector;

[0110] Step S380: normalizing and convolution processing the security assessment data to obtain a security feature vector;

[0111] Step S390: Input the comprehensive basic feature vector, the comprehensive work feature vector and the safety feature vector into a preset first activation function to obtain a first score value between the employee and each bed.

[0112] In the above steps S310 to S390, in one embodiment, the basic information may be input as follows:

[0113] Name Li Ming gender male age 30 years old Position Software Engineer Place of origin Shenzhen, Guangdong Marital status Married

[0114] Project participation information: Currently participating in a large-scale artificial intelligence project, which is expected to last for 1 year and is a key and urgent project of the company. In the past year, I participated in 3 medium-sized software development projects, with project durations of 3 months, 5 months and 4 months respectively.

[0115] The work performance information is that the performance evaluation results for the past three months were 90 points, 88 points and 92 points respectively (out of 100 points). The amount of task completion in the project is at the forefront of the team, and the code quality is high with a low defect rate.

[0116] The overtime pattern information shows that the average overtime hours per week are about 10 hours, and the overtime time period is mainly concentrated from 7pm to 10pm from Tuesday to Thursday.

[0117] During the processing, the basic information of the classification such as "gender (male), native place (Shenzhen, Guangdong), marital status (married)" is one-hot encoded and mapped through a fully connected layer. For example, these encoded vectors are input into a fully connected layer with 32 neurons and the ReLU activation function is used. Assume that after this layer of processing, a 32-dimensional feature vector is obtained, in which the values ​​of some dimensions may represent an abstract representation of factors such as male, married, and from a specific region in the model. In addition, for the numerical basic information of "age (30 years old) and position (software engineer)", since the position can be coded in a certain classification (such as 1 for software engineer, 2 for hardware engineer, etc.), it is normalized together with age and features are extracted through a small CNN. For example, the age and position codes are combined into a time series-like data (such as [30,1]), and a 3x1 convolution kernel is used for convolution operation to extract local features, and then the maximum pooling (pooling kernel 2x1) is used to reduce the dimension and then connected to a 64-neuron fully connected layer and activated with ReLU. This layer may extract the characteristics of dormitory requirements for age and position combinations. For example, young software engineers may have higher requirements for dormitory network facilities. The model reflects this feature association in certain neurons through learning.

[0118] For project participation, the urgency of the current project (assuming that the priority and urgent project is coded as 3), the expected duration (1 year is coded as 12), the number of past projects (3), and the total duration (3+5+4=12 months is coded as 12) are combined into a vector (such as [3,12,3,12]). Feature mapping is performed through a fully connected layer, with 32 neurons set and the ReLU activation function used. This layer can learn the characteristics of dormitory needs due to the complexity and continuity of project participation. For example, employees who participate in multiple projects for a long time may need a more comfortable rest environment to relieve work pressure, and the model will reflect this relationship in some neurons.

[0119] For work performance information, the average performance evaluation results of the past three months are calculated ((90+88+92) / 3=90), and combined with information such as task completion and code quality (assuming that the task completion is at the forefront of the team, coded as 2, and high code quality is coded as 3) to form a vector (such as [90,2,3]). After normalization, a convolutional neural network (CNN) is used for feature extraction. For example, a 2x1 convolution kernel is used to perform convolution operations on the data to extract local features, and then the pooling layer (such as maximum pooling, pooling kernel 2x1) is used to reduce the dimension and connect it to a fully connected layer of 32 neurons, using the ReLU activation function. This step can extract the potential relationship between performance and dormitory needs.

[0120] For overtime patterns, data is organized according to a seven-day cycle, such as [0,10,10,10,0,0,0] (indicating 10 hours of overtime from Tuesday to Thursday and no overtime at other times), and input into a time recurrent neural network. Specifically, the event recurrent network can be an LSTM (Long Short-Term Memory) network. LSTM learns the periodicity and duration characteristics of overtime, and its output is connected to a 32-neuron fully connected layer and a ReLU activation function. This may result in a feature representation of employees' overtime habits regarding dormitory location (such as being close to the company to reduce commuting fatigue) or dormitory facilities (such as the need for better sound insulation facilities so that they can rest well after overtime).

[0121] The overall safety evaluation data of the dormitory area is obtained from the bed information. Assume that the safety evaluation of the dormitory area is 8 points (out of 10 points). After normalizing this data, feature mapping is performed through an additional fully connected layer, such as setting 16 neurons and using the ReLU activation function. This layer will learn the impact characteristics of safety evaluation data on employee matching. For example, for Li Ming who participated in the key project, a higher safety evaluation may reflect a positive matching tendency in some neurons. Then the feature vector is spliced ​​with the previous basic information and work feature processed vector to form a comprehensive feature vector for subsequent matching calculation.

[0122] The feature vectors obtained by different processing are concatenated to form a comprehensive feature vector. Assume that a (32+64+32+32+32+16=208)-dimensional vector is obtained after concatenation. Then the matching degree is calculated through a fully connected layer, which has only one neuron and uses the Sigmoid activation function. This fully connected layer comprehensively calculates the first score value of the employee's basic and work feature branches and a bed based on the various features extracted previously.

[0123] The processing of various types of information, the construction of feature vectors, and the generation of score values ​​throughout the process provide a rich basis for data-driven decision-making for dormitory management. Managers can understand the overall characteristics and demand distribution of employee groups by analyzing these feature vectors and score values. For example, if it is found that employees participating in a certain type of project generally have a higher first score in a certain bed area, it means that the allocation of bed resources in this area is more reasonable in meeting the needs of this type of employees. It can be used as a reference for subsequent resource planning and adjustments, and help managers formulate more scientific and reasonable dormitory management strategies and resource optimization allocation plans.

[0124] It can be understood that personal identity information includes personal interest and hobby information, personal social data information and personal living habit information, and bed information includes the roommate identity information of each bed corresponding to other occupied beds in the same dormitory room; among them, roommate identity information includes roommate interest and hobby information, roommate social data information and roommate living habit information.

[0125] The step of inputting the personal identity information and the current dormitory status into the preset deep feature matching model to obtain the second score value between the employee and each bed may include but is not limited to the following steps:

[0126] Step S410: Inputting the personal interest information, personal social data information and personal living habit information into a preset large language model to extract semantic features, and obtaining the personal interest feature vector, personal social data feature vector and personal living habit feature vector;

[0127] Step S420: Inputting the roommate's hobby information, the roommate's social data information, and the roommate's living habit information into the large language model to extract semantic features, thereby obtaining a roommate's hobby feature vector, a roommate's social data feature vector, and a roommate's living habit feature vector;

[0128] Step S430: performing similarity calculations on the interest feature vector of the individual and the interest feature vector of the roommate, the social data feature vector of the individual and the social data feature vector of the roommate, and the living habit feature vector of the individual and the living habit feature vector of the roommate, respectively, to obtain a first similarity, a second similarity, and a third similarity;

[0129] Step S440: Obtain a second score value between the employee and each bed according to the first similarity, the second similarity, and the third similarity.

[0130] In step S410 to step S440, in one embodiment, Li Ming fills in "I like to read science fiction novels, love outdoor sports, especially mountaineering, and am keen on photography" in the hobby column. These hobby descriptions are input into the pre-trained large language model. The large language model undergoes semantic understanding and analysis. For example, the large language model extracts feature dimensions related to imagination, knowledge exploration, etc. for the hobby of reading science fiction novels, physical requirements, adventurous spirit, etc. for the hobby of mountaineering, and perception of light and composition for the hobby of photography, thereby forming a feature vector of personal interests and hobbies. Similarly, through the semantic understanding and analysis of the large language model, the feature vector of personal social data and the feature vector of personal living habits are obtained respectively.

[0131] Subsequently, the roommate's interest and hobby feature vector, roommate's social data feature vector, and roommate's living habit feature vector are calculated in the same way. By extracting the interest and hobby feature vector of the person and the interest and hobby feature vector of the roommate and calculating the first similarity, employees with similar interests and hobbies can be arranged together. Using the second similarity calculation of the social data feature vector of the person and the social data feature vector of the roommate, beds are allocated considering the social behavior patterns and social circle characteristics of the employees. By extracting the living habit feature vector of the person and the living habit feature vector of the roommate and calculating the third similarity, staying with roommates with the same living habits can maintain a good sanitary condition of the dormitory environment, reduce friction and disputes caused by differences in living habits, and make employees more comfortable in dormitory life.

[0132] The similarity calculation results of various feature vectors are combined to obtain the second score value, which provides a quantitative decision basis for bed allocation. Managers can intuitively judge the degree of match between employees and different beds (based on roommates) based on this score value, so as to make more scientific bed allocation decisions. This data-driven approach reduces the subjectivity and uncertainty of human judgment, makes dormitory management more standardized and intelligent, and improves management efficiency and decision-making quality.

[0133] It is understandable that the personal identity information includes bed preference information and demand information, and the bed information includes the location information corresponding to each bed and the room information of the dormitory room. The location information corresponding to each bed may include the floor where the bed is located, the direction of the bed, the upper and lower bunk conditions, etc., and the room information may include the area of ​​the room, the upper limit of the number of occupants, toilet facilities, electrical appliances, etc.

[0134] The step of inputting the personal identity information and the current dormitory status into the preset deep feature matching model to obtain the third score value between the employee and each bed may include but is not limited to the following steps:

[0135] Step S510: classify the bed preference information to obtain a preference feature vector;

[0136] Step S520: performing unique-hot encoding processing on the demand information, location information, and room information respectively to obtain a demand feature vector, a location feature vector, and a room feature vector;

[0137] Step S530: concatenate the preference feature vector and the demand feature vector to obtain a comprehensive demand feature vector;

[0138] Step S540: concatenate the position feature vector and the room feature vector to obtain a comprehensive room feature vector;

[0139] Step S550: Input the demand comprehensive feature vector and the room comprehensive feature vector into a preset classifier model and perform decision processing to obtain a third score value between the employee and each bed.

[0140] In step S510 to step S550, by classifying the bed preference information to obtain the preference feature vector, the special needs and preferences of employees for beds can be accurately captured. For example, some employees may prefer high-rise beds for better views and ventilation, or prefer lower bunks for convenience. Converting these preference information into feature vectors and involving them in the matching model can greatly increase the probability that bed allocation meets employee expectations, so that employees can feel the company's respect and satisfaction for their personalized needs when they move in, thereby improving employees' satisfaction with dormitory arrangements and their favorability towards the company. In addition to bed preferences, demand information (such as whether it is necessary to be close to the window for study or work, whether a quiet environment is needed, etc.) is uniquely encoded to obtain a demand feature vector, and is spliced ​​with the preference feature vector into a comprehensive demand feature vector. This enables the model to comprehensively consider employees' various needs for accommodation, not only limited to the bed itself, but also including the needs of the surrounding environment and facilities. For example, for employees who need to study online or work remotely in dormitories, being assigned to a bed room with a stable network signal, close to a window, and with ample light will greatly improve their learning and work efficiency and comfort, and further enhance the adaptability of employees to beds and dormitory environments. The classifier model can be a machine learning model such as a support vector machine or a decision tree. The classifier model analyzes and makes decisions on the two input comprehensive feature vectors based on pre-trained parameters and algorithms. After calculation by the model, a third score value representing the degree of match between the employee and each bed is output. The third score value can be a value between 0 and 1. The higher the value, the higher the match between the employee and the bed in terms of demand and room conditions.

[0141] The entire process is based on a preset deep feature matching model and classifier model, which enables intelligent decision-making for dormitory bed allocation. The model automatically generates a third-score value between employees and each bed through in-depth analysis and processing of employee identity information and dormitory status information, reducing the subjectivity and arbitrariness of manual bed allocation. This intelligent decision-making mechanism can quickly process a large amount of employee and dormitory data, improve the efficiency and accuracy of bed allocation, and make dormitory management more scientific, standardized and efficient.

[0142] It is understandable that the control method of the employee dormitory management system provided in the present application may also include but is not limited to the following steps:

[0143] Step S610: Obtaining energy consumption in dormitory rooms;

[0144] Step S620: In response to the first setting instruction from the second terminal, setting an energy consumption cost gradient comparison table;

[0145] Step S630: Obtaining energy payment items according to the energy consumption and energy consumption cost gradient comparison table;

[0146] Step S640: adding the energy payment item to the preset bill and sending it to the first terminal;

[0147] Step S650: In response to the payment completion instruction from the first terminal, the energy payment item corresponding to the payment completion instruction is deleted from the bill.

[0148] In step S610 to step S650, the system obtains the energy consumption data of each dormitory room in real time by connecting or collecting data with relevant energy metering equipment (such as electric meters, water meters, etc.) in the dormitory room. These devices can regularly transmit energy usage information to the dormitory management system. For example, the electric meter sends the electricity consumption data within the time period at regular intervals (such as every hour, every day, etc.), and the water meter uploads the water consumption data in the same way, so as to accurately grasp the energy consumption of each dormitory room. The management personnel issue a first setting instruction to set the energy consumption cost gradient comparison table at the second terminal (management end) according to the relevant regulations of the enterprise or dormitory management, cost accounting, and energy market prices. When setting the comparison table, first determine the basic pricing units of different energy types (such as electricity, water, etc.) (such as how much money per kilowatt-hour, how much money per ton of water). Then, set different cost gradients according to the ladder situation of energy consumption. For example, for electricity consumption, it may be set that the monthly electricity consumption is within the range of 0-50 degrees, and the cost per kilowatt-hour is 0.5 yuan; within the range of 51-100 degrees, the cost per kilowatt-hour is 0.6 yuan; after exceeding 100 degrees, the cost per kilowatt-hour is 0.7 yuan, etc. Similarly, a similar tiered fee standard is set for water consumption to form a complete energy consumption fee gradient comparison table and store it in the system. After the system obtains the energy consumption of each dormitory room, it will calculate the corresponding energy cost of each room according to the set energy consumption fee gradient comparison table, and the system will add the calculated energy payment items of each dormitory room to the preset unified bill. After the employee completes the payment of energy fees at the first terminal, he will send a payment completion instruction to the dormitory management system through terminal operation. After receiving the instruction, the system will search for the energy payment item corresponding to the payment completion instruction in the preset bill and delete it from the bill.

[0149] By setting up a gradient comparison table for energy consumption fees, charging different fees according to different energy consumption, and clearly adding energy payment items to the bill and sending them to the first terminal, employees can clearly understand the energy consumption of their dormitory rooms and the corresponding fee details. The system automatically completes a series of operations such as obtaining energy consumption, calculating fees, generating and updating bills, greatly improving the efficiency of financial management. Managers do not need to manually perform tedious energy cost accounting and bill sorting work, saving a lot of manpower and time costs.

[0150] It is understandable that after step S610, the following steps may also be included but not limited to:

[0151] Step S710: In response to a second setting instruction from the first terminal or the second terminal, setting an energy consumption threshold;

[0152] Step S720: When the energy consumption is greater than or equal to the energy consumption threshold, an energy consumption warning notification is sent to the first terminal.

[0153] In step S710 to step S720, the system can receive a second setting instruction issued from the first terminal (employee terminal) or the second terminal (management terminal). This instruction contains specific setting information about the energy consumption threshold, such as the electricity consumption threshold is 100 degrees per month, the water consumption threshold is 10 tons per month, etc., and is set separately for different energy types (electricity, water, etc.). After receiving the instruction, the system will store these energy consumption thresholds in the database. If a new second setting instruction is received subsequently, the system will promptly update the corresponding energy consumption threshold to ensure that the latest setting value is used.

[0154] When the energy consumption is greater than or equal to the energy consumption threshold, the system will automatically generate an energy consumption warning notification. This notification may include information such as the dormitory room number, the energy type that exceeds the threshold, the current energy consumption, and possible energy-saving suggestions. Employees can receive timely notifications on their terminal devices to understand that the energy consumption of their dormitory rooms has exceeded the set standards. In this method step, the energy consumption warning notification can let employees know in a timely manner that the energy consumption of the dormitory room has reached or exceeded the threshold, thereby causing employees to pay attention to energy usage, reducing unnecessary use of electrical appliances, shortening the use time of electrical appliances, etc., effectively reducing energy waste and reducing energy costs.

[0155] It is understandable that the control method of the employee dormitory management system provided in the present application may also include but is not limited to the following steps:

[0156] Step S810: Acquire multiple real-time temperature values, real-time humidity values, and real-time light sensitivity values ​​in the dormitory room;

[0157] Step S820: obtaining an average temperature value, an average humidity value, and an average light sensitivity value according to the multiple real-time temperature values, the multiple real-time humidity values, and the multiple real-time light sensitivity values;

[0158] Step S830: In response to a third setting instruction from the first terminal or the second terminal, setting a temperature threshold range, a humidity threshold range, and a light threshold range;

[0159] Step S840: When the average temperature value is outside the temperature threshold range, a first control instruction is sent to the electrical equipment in the corresponding dormitory room, and when the average temperature value is within the temperature threshold range, a first stop instruction is sent to the electrical equipment in the corresponding dormitory room;

[0160] Step S850: When the average humidity value is outside the humidity threshold range, a second control instruction is sent to the electrical equipment in the corresponding dormitory room, and when the average humidity value is within the humidity threshold range, a second stop instruction is sent to the electrical equipment in the corresponding dormitory room;

[0161] Step S860: When the average light sensitivity value is outside the light sensitivity threshold range, a third control instruction is sent to the electrical equipment in the corresponding dormitory room, until the average light sensitivity value is within the light sensitivity threshold range, and then a third stop instruction is sent to the electrical equipment in the corresponding dormitory room.

[0162] In step S810 to step S860, the system obtains multiple real-time temperature values, real-time humidity values ​​and real-time light values ​​in each dormitory room in real time by installing multiple temperature sensors, humidity sensors and light sensors in the dormitory room. These sensors will continuously collect data at a certain time interval and transmit the collected data to the dormitory management system. For the multiple real-time temperature values, real-time humidity values ​​and real-time light values ​​in each dormitory room obtained, the system calculates the average value respectively. Specifically, the average temperature value can be obtained by removing the maximum and minimum values ​​of multiple real-time temperature values ​​in the same time, and then averaging the remaining real-time temperature values. Similarly, the calculation method of the above-mentioned average humidity value and average light value is similar to the calculation method of the average temperature value, and will not be repeated here. The system can receive a third setting instruction issued from the first terminal (employee end) or the second terminal (management end), which is used to set the temperature threshold range, humidity threshold range and light threshold range. When the average temperature value of a dormitory room calculated by the system is outside the set temperature threshold range, the first control instruction will be sent to the electrical equipment (such as air conditioner, electric heater, etc.) of the dormitory room according to the specific situation. Similarly, when the average humidity value of a dormitory room is outside the set humidity threshold range, the system will send a second control instruction to the relevant electrical equipment (such as dehumidifiers, humidifiers, etc.) in the dormitory room. When the average light sensitivity value of a dormitory room is outside the set light sensitivity threshold range, the system will send a third control instruction to the lamps, electric curtains and other electrical equipment in the dormitory room. By automatically controlling the temperature, humidity and light sensitivity of the dormitory room, a more comfortable living environment can be provided for employees.

[0163] Maintaining the temperature, humidity and light perception within the appropriate threshold range can prevent employees from feeling uncomfortable due to overheating, overcooling, overhumidity, overdryness or overly dark or bright light, thereby improving the quality of life and satisfaction of employees in the dormitory. In addition, the entire environmental control process is automatically completed by the dormitory management system, without the need for manual frequent visits to each dormitory room to check the environmental conditions and manually adjust electrical equipment. Managers only need to set the appropriate threshold range to achieve intelligent management of the dormitory environment, which greatly reduces the manpower burden of management work and improves the efficiency of dormitory management.

[0164] In a second aspect, the present application also provides an employee dormitory management system, comprising: at least one memory, at least one processor and at least one program, the program is stored in the memory, and the processor executes one or more programs to implement the control method of the above-mentioned employee dormitory management system.

[0165] In the system, first, the personal identity information of the employee to be admitted is obtained from the first terminal, and then the obtained personal identity information is sent to the second terminal, so that the manager can approve the admission application of the employee to be admitted in the second terminal and generate the corresponding approval result. If the approval result is passed, the personal identity information and the current dormitory status of the employee to be admitted are input into the preset deep feature matching model. Here, the current dormitory status covers the bed information corresponding to each bed. Through the operation of the deep feature matching model, the relevant factors in the employee identity information and the characteristics of the bed information are comprehensively considered to generate the matching degree between the employee and each bed. According to the matching degree generated above, the system analyzes and evaluates the adaptation of each bed to the employee, so as to determine the bed number information that best suits the employee. At the same time, after the bed number information is generated, the second terminal is also required to confirm the bed number information, which can directly confirm the bed number information assigned by the system through the first operation instruction; if the bed number information assigned by the system is considered inappropriate, the bed number information can also be modified through the first operation instruction before sending. By performing specific operations on the first terminal, the system will know that the employee has known and recognized the assigned bed number information after receiving the instruction, thereby completing the information interaction process of this link. In this application, from obtaining the personal identity information of the employees to be admitted, the approval process to the bed matching and allocation, the whole process is automatically transferred through the system. For example, information is quickly transmitted between the first terminal (employee end) and the second terminal (management end), without the need to manually organize and transmit a large number of paper documents or data tables, which greatly saves time and labor costs. In addition, using the preset deep feature matching model, a large amount of employee information and dormitory bed information can be comprehensively analyzed in a short time, and the matching degree can be quickly generated and the bed number information can be determined. Compared with the traditional manual evaluation and allocation of beds one by one, the allocation speed is significantly improved. Furthermore, after the bed number information is generated, through the first operation instruction of the second terminal, the management personnel can adjust the system allocation results according to the actual situation. Through the combination of manual and system, the matching results can be further optimized on the basis of model allocation, adapted to special circumstances or enterprise-specific management needs, and the accuracy and flexibility of matching can be improved.

[0166] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs, non-transient computer executable programs and signals, such as program instructions / signals corresponding to the processing module in the embodiment of the present application. The processor executes various functional applications and data processing by running the non-transient software programs, instructions and signals stored in the memory, that is, the control method of the employee dormitory management system of the above method embodiment is realized.

[0167] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store relevant data of the control method of the above-mentioned employee dormitory management system, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processing module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0168] One or more signals are stored in the memory, and when executed by one or more processors, the control method of the employee dormitory management system in any of the above method embodiments is executed.

[0169] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. The computer program is executed by one or more processors, so that the one or more processors can execute the control method of the employee dormitory management system in the above method embodiment.

[0170] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., may be located in one place, or may be distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0171] Through the description of the above embodiments, it will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed methods above can be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as a computer-readable signal, a data structure, a program module or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, disk storage or other magnetic storage device, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media generally embodies computer readable signals, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0172] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0173] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0174] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0175] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0176] 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. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0177] The embodiments of the present application are described in detail above in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present application.

Claims

1. A control method for an employee dormitory management system, characterized in that: include: Obtaining the identity information of the employee to be checked in from the first terminal; Sending the personal identity information to a second terminal, and obtaining the approval result of the employee to be admitted from the second terminal; In response to the approval result, the personal identity information and the current dormitory status are input into a preset deep feature matching model to obtain a first score value, a second score value and a third score value between the employee and each bed, respectively; wherein the current dormitory status includes bed information corresponding to each bed; generating a matching degree between the employee and each bed according to the first score value, the second score value, and the third score value; Determining the bed number information of the employee according to the matching degree; In response to a first operation instruction from the second terminal, sending the bed number information to the first terminal, or updating the bed information and sending the updated bed information to the first terminal; In response to a second operation instruction from the first terminal, the current dormitory status is updated.

2. The control method of the employee dormitory management system according to claim 1 is characterized in that: The personal identity information includes basic information, project participation information, work performance information and overtime regularity information; the bed information includes safety assessment data of the area corresponding to each bed; The personal identity information and the current dormitory status are input into the preset deep feature matching model to obtain the first score value between the employee and each bed, including: Extracting the classified basic information and the numerical basic information from the basic information; Performing one-hot encoding and full-connection layer processing on the classification basic information to obtain a first basic feature vector; Normalizing and convolving the numerical basic information to obtain a second basic eigenvector; Performing data combination and full connection layer processing on the project participation information to obtain a project feature vector; Normalizing and convolving the work performance information to obtain a performance feature vector; Performing periodic data integration on the overtime regularity information and inputting the information into a preset time recurrent neural network to obtain an overtime feature vector; The first basic feature vector and the second basic feature vector are concatenated to obtain a comprehensive basic feature vector, and the project feature vector, the performance feature vector and the overtime feature vector are concatenated to obtain a comprehensive work feature vector; Normalizing and convolution processing the safety assessment data to obtain a safety feature vector; The comprehensive basic feature vector, the comprehensive work feature vector and the safety feature vector are input into a preset first activation function to obtain a first score value between the employee and each bed.

3. The control method of the employee dormitory management system according to claim 1 is characterized in that: The personal identity information includes personal interest information, personal social data information and personal living habit information; the bed information includes the roommate identity information of each bed corresponding to other occupied beds in the same dormitory room; wherein the roommate identity information includes roommate interest information, roommate social data information and roommate living habit information; The personal identity information and the current dormitory status are input into the preset deep feature matching model to obtain the second score value between the employee and each bed, including: Inputting the personal interest information, personal social data information and personal living habit information into a preset large language model to extract semantic features, and obtaining the personal interest feature vector, the personal social data feature vector and the personal living habit feature vector; Inputting the roommate interest and hobby information, roommate social data information and roommate living habit information into the large language model to extract semantic features, and obtaining a roommate interest and hobby feature vector, a roommate social data feature vector and a roommate living habit feature vector; Performing similarity calculation processing on the interest feature vector of the person and the interest feature vector of the roommate, the social data feature vector of the person and the social data feature vector of the roommate, and the living habit feature vector of the person and the living habit feature vector of the roommate, respectively, to obtain a first similarity, a second similarity, and a third similarity; A second score value between the employee and each bed is obtained according to the first similarity, the second similarity and the third similarity.

4. The control method of the employee dormitory management system according to claim 1, characterized in that: The personal identity information includes bed preference information and demand information, and the bed information includes location information corresponding to each bed and room information of the dormitory room; The personal identity information and current dormitory status are input into the preset deep feature matching model to obtain the third score value between the employee and each bed, including: Classifying the bed preference information to obtain a preference feature vector; Performing one-hot encoding processing on the demand information, the location information, and the room information respectively to obtain a demand feature vector, a location feature vector, and a room feature vector; Concatenate the preference feature vector and the demand feature vector to obtain a comprehensive demand feature vector; Concatenate the position feature vector and the room feature vector to obtain a comprehensive room feature vector; The demand comprehensive feature vector and the room comprehensive feature vector are input into a preset classifier model and decision processing is performed to obtain a third score value between the employee and each bed.

5. The control method of the employee dormitory management system according to claim 1, characterized in that: Also includes: Obtain energy consumption in dormitory rooms; In response to a first setting instruction from the second terminal, setting an energy consumption cost gradient comparison table; Obtaining energy payment items according to the energy consumption and the energy consumption fee gradient comparison table; Adding the energy payment item to a preset bill and sending it to the first terminal; In response to a payment completion instruction from the first terminal, the energy payment item corresponding to the payment completion instruction is deleted from the bill.

6. The control method of the employee dormitory management system according to claim 5, characterized in that: After the steps to obtain the energy consumption in the dormitory room, it also includes: In response to a second setting instruction from the first terminal or the second terminal, setting an energy consumption threshold; When the energy consumption is greater than or equal to the energy consumption threshold, an energy consumption warning notification is sent to the first terminal.

7. The control method of the employee dormitory management system according to claim 1, characterized in that: Also includes: Get multiple real-time temperature values, real-time humidity values, and real-time light sensitivity values ​​in dormitory rooms; According to the multiple real-time temperature values, the multiple real-time humidity values ​​and the multiple real-time light sensitivity values, an average temperature value, an average humidity value and an average light sensitivity value are obtained; In response to a third setting instruction from the first terminal or the second terminal, setting a temperature threshold range, a humidity threshold range, and a light threshold range; When the average temperature value is outside the temperature threshold range, a first control instruction is sent to the corresponding electrical equipment in the dormitory room, and when the average temperature value is within the temperature threshold range, a first stop instruction is sent to the corresponding electrical equipment in the dormitory room; When the average humidity value is outside the humidity threshold range, a second control instruction is sent to the corresponding electrical equipment in the dormitory room, until the average humidity value is within the humidity threshold range, a second stop instruction is sent to the corresponding electrical equipment in the dormitory room; When the average light sensitivity value is outside the light sensitivity threshold range, a third control instruction is sent to the corresponding electrical equipment in the dormitory room, until the average light sensitivity value is within the light sensitivity threshold range, a third stop instruction is sent to the corresponding electrical equipment in the dormitory room.

8. A staff dormitory management system, characterized in that: include: at least one memory; at least one processor; at least one program; The programs are stored in the memory, and the processor executes at least one of the programs to implement the control method of the employee dormitory management system according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer-executable signal, and the computer-executable signal is used to execute the control method of the employee dormitory management system according to any one of claims 1 to 7.

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