A management method based on intelligent physical examination queuing call system

Abstract

The present invention relates to the field of medical information technology, and discloses a management method based on an intelligent physical examination queuing and calling system. The method constructs an intelligent call model, models the physical examination queuing and call management process as a Markov decision process, and realizes dynamic adjustment of the queuing strategy and call management plan. The model defines a multidimensional state space including the examinee's information, queuing situation, physical examination equipment and calling equipment, as well as an action space including actions such as adjusting the order of physical examination items, guiding the examinee, and sending call notifications. By designing a reward function that comprehensively considers the examinee's satisfaction, waiting time, equipment utilization and emergency response capabilities, a deep reinforcement learning algorithm is used to train the model, and the strategy is continuously optimized with the goal of maximizing the cumulative reward. The present invention improves the efficiency of physical examinations and service quality, reduces the waiting time of examinees, and enhances the system's ability to respond to emergencies. It has significant technical advantages and application value.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a management method based on an intelligent physical examination queuing call system. Background Art

[0002] With the rapid development of modern healthcare, physical examinations, as a crucial component of preventive medicine, are attracting increasing attention for their efficiency and quality. Traditional physical examination queuing and call systems often rely on simple electronic call systems to coordinate the queuing and call processing of examinees. However, these traditional methods have significant drawbacks when dealing with large crowds during peak periods, the complexity of multi-item examinations, and unexpected situations such as equipment failure.

[0003] The existing system usually follows a fixed queuing order and cannot be flexibly adjusted according to the actual situation of the examinee, resulting in some urgent or important items being delayed, reducing the examinee's satisfaction and experience.

[0004] During a physical examination, if a piece of equipment fails or the examinee fails to respond to the call in time, traditional systems often lack an effective immediate response mechanism, which may lead to increased equipment idle time, reduced equipment utilization, and prolonged waiting time for other examinees.

[0005] The existing system is unable to analyze physical examination site data in real time, such as the number of examinees, project duration, equipment status, etc., and therefore cannot dynamically adjust queuing strategies and call management plans, thereby limiting the system's ability to respond to emergencies and reducing the efficiency of the overall physical examination process. Summary of the Invention

[0006] The purpose of the present invention is to provide a management method based on an intelligent physical examination queuing call system to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a management method based on an intelligent physical examination queuing call system, the method comprising:

[0008] Build an intelligent call model for the medical examination queue call system, model the medical examination queue and call management process as a Markov decision process, and dynamically adjust the medical examination queue call management strategy by analyzing real-time data from the medical examination site;

[0009] The steps to build an intelligent call model include:

[0010] Define the state space, including the examinee information dimension, queuing status dimension, examination equipment dimension, and call device dimension at the examination site;

[0011] Define an action space, including the possible actions that the agent can take when handling medical examination queues and call exceptions, including adjusting the order of medical examination items, guiding the examinee to an idle device, sending call notifications, and activating a backup call device;

[0012] Design a reward function to evaluate the results of actions based on patient satisfaction, patient waiting time, equipment utilization, equipment utilization, and emergency response capabilities, and give corresponding rewards or penalties;

[0013] Formulate strategies, rules for selecting actions in a given state, and continuously optimize them through machine learning algorithms to maximize cumulative rewards;

[0014] Use deep reinforcement learning algorithms to train intelligent call models.

[0015] Preferably, the examinee information dimension includes the examinee's scheduled items, completed items and estimated completion time; the queuing situation dimension includes the real-time queue length, average waiting time and longest waiting time of each examination item; the examination equipment dimension includes the usage status, equipment efficiency and estimated available time of the examination equipment; the calling equipment dimension includes the working status of the pager, the last call time, the call success rate and the response time.

[0016] Preferably, the indicators of the reward function include examinee satisfaction, examinee waiting time, equipment utilization, and emergency response capability. The action results are evaluated using the following formula and corresponding rewards or penalties are given:

[0017] R=α·S satis faction -β·T wa iting +γ·U device +δ·R response

[0018] Among them, S satis faction Indicates the satisfaction of the examinee, obtained through questionnaire survey or feedback system, with a value range of [0,1], where 1 indicates complete satisfaction; T wa iting Indicates the average waiting time of the examinee, in minutes, calculated through real-time data; U device It represents the equipment utilization rate, which is calculated by dividing the actual equipment usage time by the total equipment available time, and its value range is [0,1]. responseIt represents the emergency response capability, which is determined by measuring the system's response speed and processing efficiency under abnormal conditions. Its value range is [0,1], where 1 indicates the fastest response speed and the highest processing efficiency. α, β, γ, and δ are weight coefficients, representing the importance of each indicator in the reward function, and satisfying α+β+γ+δ=1. The specific value is adjusted according to the actual application scenario and needs.

[0019] Preferably, a deep Q network DQN algorithm is used to train the intelligent call model.

[0020] Preferably, the step of training the intelligent call model includes:

[0021] S1: Design a deep Q-network structure to represent the policy. The network input is the current state, and the output is the probability distribution of each action. Use the deep Q-network to parameterize the policy, denoted as Q(s, a; θ), where s represents the state, a represents the action, and θ represents the parameters of the neural network.

[0022] S2: Based on the defined state space and action space, construct representations of the neural network input and output layers. The state space includes dimensions of the examinee information, queue status, examination equipment, and call device at the examination site. This state information will be encoded as input features of the neural network. The action space, as the output of the neural network, represents the possible actions that the model may take.

[0023] S3: By simulating the medical examination queuing and calling process, the four-tuple (s, a, r, s) of state, action, reward and next state is collected as training data;

[0024] S4: Using the collected training data, the parameters of the neural network are updated through the backpropagation algorithm and gradient descent method. During the training process, the model continuously tries different actions and adjusts its strategy based on the feedback obtained from the reward function to maximize the cumulative reward. This further includes:

[0025] S401: For each training iteration, randomly extract a batch of state transition samples (s, a, r, s) from the experience replay memory, where s is the current state, a is the action taken, r is the reward, and s′ is the next state;

[0026] S402: According to the Bellman equation, calculate the loss function L(θ), which is L(θ) = (r + γmax a 'Q(s',a';θ')-Q(s,a;θ)) 2 , where γ is the discount factor, Q(s,a;θ) is the estimate of the state and action value function under the current policy, and θ′ is the parameter of the target network;

[0027] S403: Update the neural network parameters θ using the gradient descent algorithm to minimize the loss function L(θ);

[0028] S5: During the training process, the performance of the intelligent call model is regularly evaluated, including indicators such as patient satisfaction, waiting time, equipment utilization, and emergency response capability. Based on the evaluation results, the weight coefficient of the reward function and the learning rate of the model are adjusted;

[0029] S6: Repeat steps S4 and S5 until a preset number of training rounds is reached.

[0030] Preferably, in step 403, the formula for updating the parameters of the gradient descent algorithm is:

[0031]

[0032] Among them, θ represents the model parameters, including all weights and biases that the model needs to learn; ω represents the learning rate, which is used to control the step size of parameter update; represents the gradient of the loss function J(θ) with respect to the parameter θ, It is a vector pointing to the direction where the loss function grows fastest; := represents the assignment operation, that is, updating the value of the parameter θ.

[0033] Preferably, an ε-greedy strategy is adopted to dynamically adjust the exploration and utilization ratio of the model in the early and late stages of training.

[0034] Preferably, after the training of the intelligent call model is completed, the model is tested and evaluated, and a strategy combining simulation and field application is adopted to compare the performance of the queuing and calling solutions generated by the model with the traditional management methods in terms of examinee waiting time, call accuracy, system response speed and examination process efficiency.

[0035] Preferably, MATLAB / Simulink simulation software is used to build a simulation environment for physical examination queuing and calling.

[0036] Preferably, in the constructed simulation environment, various physical examination scenarios are simulated, including the time difference of different physical examination items, the arrival distribution of physical examinees, and the parallel operation of multiple types of physical examination equipment; by adjusting the simulation parameters, a variety of test cases are generated to cover various situations encountered in actual operations.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The intelligent call model can analyze data from the physical examination site in real time, such as the number of examinees, project duration, equipment status, etc., and dynamically adjust the queuing strategy and call management plan based on this data, avoiding the inefficiency caused by a fixed queuing order, ensuring the smooth progress of the physical examination process, and thus significantly improving the overall physical examination efficiency.

[0039] By defining a rich action space, including adjusting the order of physical examination items, guiding the examinee to an idle device, sending call notifications, and activating backup call devices, the intelligent call model can quickly respond to emergencies such as equipment failure and the examinee's failure to respond to the call in time, effectively reducing equipment idle time, improving equipment utilization, and reducing the waiting time for other examinees.

[0040] The intelligent call model fully considers the examinee's actual situation, such as scheduled items, completed items, and estimated completion time. This allows for more flexible scheduling of examinations, ensuring that urgent or important items are handled promptly. Furthermore, by optimizing call management, call confusion and missed calls are reduced, improving examinee satisfaction and experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a processing step diagram of a management method based on an intelligent physical examination queuing and calling system according to the present invention;

[0042] Figure 2 Schematic diagram of the reward function design;

[0043] Figure 3 This is the training flowchart of the intelligent call model. DETAILED DESCRIPTION

[0044] 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] See also Figure 1-3 The present invention provides a technical solution: a management method based on an intelligent medical examination queue call system. This method models the medical examination queue and call management process as a Markov decision process and trains the intelligent call model through a deep reinforcement learning algorithm to dynamically adjust the medical examination queue call management strategy. The following is a specific implementation method of this method:

[0046] First, the intelligent call model is constructed, which includes the following steps:

[0047] Defining the state space: The state space covers multiple dimensions of the physical examination scene, including:

[0048] Physical examinee information dimension: includes the physical examinee's scheduled items, completed items, and estimated completion time. This information is used to understand the needs and progress of each physical examinee.

[0049] Queuing situation dimension: including the real-time queue length, average waiting time and longest waiting time of each physical examination item. These data reflect the congestion level and service efficiency of the physical examination site.

[0050] Physical examination equipment dimension: includes the usage status, equipment efficiency, and estimated availability time of physical examination equipment, which helps to understand the operating status and availability of the equipment.

[0051] Call device dimensions: including the working status of the pager, the last call time, call success rate and response time. This information is crucial for monitoring the performance and reliability of the call system.

[0052] Define the action space: The action space contains various possible actions that the agent can take when handling medical examination queues and call exceptions, including:

[0053] Adjust the order of physical examination items: Intelligently adjust the order of physical examination items according to real-time conditions to optimize the process.

[0054] Guide examinees to idle equipment: Guide examinees to idle equipment to reduce waiting time.

[0055] Send call notification: notify the examinee to go to the designated examination area or equipment through the calling system.

[0056] Start the backup call device: When the main call device fails, start the backup device to ensure the continuity of the system.

[0057] Design a reward function: The reward function is used to evaluate the results of the actions taken by the agent and give corresponding rewards or penalties based on the examinee's satisfaction, examinee's waiting time, equipment utilization, and emergency response capabilities. Specific indicators include:

[0058] Physical examiner satisfaction: calculated based on the physical examiner's feedback and evaluation.

[0059] Waiting time of the examinee: the shorter the waiting time, the higher the reward.

[0060] Equipment utilization: The more efficiently the equipment is used, the greater the reward.

[0061] Emergency Response Capability: In abnormal situations, the speed and effectiveness of the agent's response will also be taken into account in the reward.

[0062] Strategy Development: A strategy is a set of rules for selecting actions in a given state. Machine learning algorithms (particularly deep reinforcement learning algorithms) are used to continuously optimize the strategy to maximize cumulative rewards. The agent will select the optimal action based on the current state to achieve the long-term goal.

[0063] Using deep reinforcement learning algorithms to train intelligent call models:

[0064] The intelligent call model is trained using a deep reinforcement learning algorithm. By simulating real-world scenarios of medical checkup queues and calls, the model parameters are continuously iterated and optimized, enabling the intelligent agent to make optimal decisions in various situations.

[0065] This attack is further described below in conjunction with Examples 1 to 3:

[0066] Example 1:

[0067] The reward function is used to evaluate the agent's performance in the medical checkup queue and call management process, and to give corresponding rewards or penalties accordingly to optimize the management strategy. This embodiment is used to illustrate the design of the reward function.

[0068] The indicators for designing the reward function include patient satisfaction, patient waiting time, equipment utilization, and emergency response capability. The following formula is used to evaluate the action results and give corresponding rewards or penalties:

[0069] R=α·S satis faction -β·T wa iting +γ·U device +δ·R response

[0070] Among them, S satis faction Indicates the examinee's satisfaction, obtained through questionnaires or feedback systems, with a value range of [0,1], where 1 indicates complete satisfaction. This indicator reflects the examinee's overall evaluation of the queuing and call management services. In practical applications, the examinee's feedback data can be collected regularly and the average value can be calculated as S satis faction The value of .

[0071] T wa iting The average waiting time for medical examinees, in minutes, is calculated using real-time data. This value reflects the waiting time for medical examinees while waiting in line. To reduce patient waiting time, the system should optimize the queuing strategy and improve service efficiency.

[0072] U device Indicates the equipment utilization rate, which is calculated by dividing the actual equipment usage time by the total equipment available time. The value range is [0,1]. This indicator reflects the efficiency of equipment use. Improving equipment utilization helps reduce resource waste and improve overall service efficiency. The calculation formula is:

[0073]

[0074] Rresponse It represents the emergency response capability, which is determined by measuring the system's response speed and processing efficiency under abnormal conditions. The value range is [0,1], where 1 indicates the fastest response speed and the highest processing efficiency. This indicator can be evaluated by simulating the system response time and processing results under abnormal conditions.

[0075] α, β, γ, and δ are weight coefficients, representing the importance of each indicator in the reward function, and satisfying α + β + γ + δ = 1. The specific values ​​are adjusted according to the actual application scenario and needs. For example, if you place more emphasis on patient satisfaction and waiting time, you can increase the values ​​of α and β appropriately; if you focus more on equipment utilization and emergency response capabilities, you can increase the values ​​of γ and δ appropriately.

[0076] Assume that during a physical examination queue and call management process, the system obtains the following data:

[0077] Physical examination patient satisfaction S satis faction =0.85;

[0078] Average waiting time for physical examination patients T wa iting = 15 minutes;

[0079] Equipment utilization rate U device =0.75;

[0080] Emergency Response Capability response =0.90;

[0081] According to the reward function formula, the reward value of this management can be calculated:

[0082] R=0.3·0.85-0.25·15+0.2·0.75+0.25·0.90=0.255-3.75+0.15+0.225=-2.92

[0083] Since the reward value is negative, it indicates that the management strategy needs to be optimized. The system can adjust the strategy based on the evaluation results to increase future reward values.

[0084] Example 2:

[0085] This embodiment is used to describe the training process of the intelligent call model.

[0086] The Deep Q Network (DQN) algorithm is used to train the intelligent call model. The steps for training the intelligent call model include:

[0087] S1: Design a deep Q-network, which represents the policy. Its input is the current state, and its output is the probability distribution of each action. The policy is parameterized using the deep Q-network, denoted as Q(s, a; θ), where s represents the state, a represents the action, and θ represents the parameters of the neural network. The deep Q-network consists of a multi-layer neural network, including an input layer, hidden layers, and an output layer.

[0088] S2: Based on the defined state space and action space, construct the input layer and output layer of the neural network. The state space contains multi-dimensional data on the examinee information at the physical examination site (such as the number of examinees, physical examination item requirements, etc.), queuing conditions (such as the length of each queue, waiting time, etc.), physical examination equipment (such as equipment status, number of equipment, etc.) and calling equipment (such as call frequency, call content, etc.). These data will be encoded as input feature vectors of the neural network. The action space corresponds to the output layer of the neural network, which represents various actions that the intelligent agent may take, such as adjusting the order of physical examination items, guiding the examinee to an idle device, etc. Each action corresponds to a neuron in the output layer, and the output value is the value estimate of the action.

[0089] S3: By simulating the medical checkup queue and call process, the four-tuple (s, a, r, s) of state, action, reward, and next state is collected as training data. In the simulated environment, the agent selects an action based on the current state, and the environment provides feedback on the reward and next state based on the action. This training data is used to update the parameters of the neural network, gradually approximating it to the optimal Q function.

[0090] S4: Using the collected training data, the parameters of the neural network are updated through the backpropagation algorithm and gradient descent method. During the training process, the model continuously tries different actions and adjusts its strategy based on the feedback obtained from the reward function to maximize the cumulative reward. This further includes:

[0091] S401: For each training iteration, a batch of state transition samples (s, a, r, s) are randomly extracted from the experience replay memory, where s is the current state, a is the action taken, r is the reward, and s′ is the next state; the experience replay memory is a buffer of finite size used to store recent state transition samples to break the correlation between samples and improve training stability.

[0092] S402: According to the Bellman equation, calculate the loss function L(θ), which is L(θ) = (r + γmax a ' Q(s',a';θ')-Q(s,a;θ)) 2, where γ is the discount factor, Q(s,a;θ) is the estimate of the state and action value function under the current policy, and θ′ is the parameter of the target network; the target network is a stable network that periodically copies parameters from the main network to calculate the target value to stabilize the training process.

[0093] S403: Update the neural network parameters θ using the gradient descent algorithm to minimize the loss function L(θ). The formula for updating the parameters using the gradient descent algorithm is:

[0094]

[0095] Among them, θ represents the model parameters, including all weights and biases that the model needs to learn; ω represents the learning rate, which is used to control the step size of parameter update; represents the gradient of the loss function J(θ) with respect to the parameter θ, It is a vector pointing to the direction where the loss function grows fastest; := represents the assignment operation, that is, updating the value of the parameter θ.

[0096] S5: During training, regularly evaluate the performance of the intelligent call model, including metrics such as patient satisfaction, wait time, equipment utilization, and emergency response capability. Based on the evaluation results, adjust the weight coefficients of the reward function and the model's learning rate to optimize model performance. The reward function should comprehensively consider multiple objectives, such as patient satisfaction, equipment utilization, and emergency response capability, to guide the model in learning a reasonable call strategy.

[0097] S6: Repeat steps S4 and S5 until the preset number of training rounds is reached. During training, the model will gradually learn the optimal calling strategy to maximize cumulative rewards while meeting multiple requirements, such as patient satisfaction, equipment utilization, and emergency response capabilities. Through continuous iterative training, the performance of the intelligent calling model will gradually improve, ultimately achieving an efficient and intelligent medical examination calling service.

[0098] In the early stages of training, the model needs to explore different actions to discover optimal strategies; in the later stages of training, it should make greater use of learned knowledge to make decisions. To achieve this balance, methods such as the ε-greedy strategy or soft strategy are used to dynamically adjust the ratio of exploration and exploitation to improve training efficiency and model performance.

[0099] The following is a specific example showing the application of the above intelligent call model in a physical examination center:

[0100] Every day, a large number of patients undergo multiple examinations at a health examination center. To optimize the examination process, improve patient satisfaction, and enhance equipment utilization, the center has introduced an intelligent call model based on the Deep Q-Network (DQN) algorithm. The model first constructs the neural network's input feature vector based on multi-dimensional data from the examination site, including the patient's scheduled items, completed items, and estimated completion time, as well as the real-time queue length, average wait time, equipment usage status, and the operating status of the call device for each examination item. The model then learns an optimal call strategy through training.

[0101] In practice, once a patient arrives at the medical examination center and registers, the intelligent call model automatically assigns the optimal examination sequence based on their current status and notifies them via the call device to proceed to the designated examination area. If the queue for a particular examination item is too long, the model promptly adjusts its strategy, directing the patient to proceed with other items first, thereby balancing the load across examination areas. Furthermore, if a device malfunctions or requires maintenance, the model quickly responds and adjusts its call strategy to ensure the examination process is not impacted. By introducing the intelligent call model, the medical examination center has successfully optimized its examination process. Waiting times for patients have been significantly reduced, equipment utilization has been improved, and patient satisfaction has subsequently increased. Furthermore, the model has emergency response capabilities, enabling rapid adjustments in emergencies such as equipment failures, ensuring the smooth progress of the examination.

[0102] Example 3:

[0103] After the intelligent call model is trained, it must be accurately tested and evaluated to ensure its effectiveness and practicality. This testing and evaluation uses a strategy that combines simulation and field application to compare the performance of the model-generated queuing and calling solutions with traditional management methods in terms of patient waiting time, call accuracy, system response speed, and examination process efficiency. The specific implementation methods are as follows:

[0104] Conduct simulation test:

[0105] Build a simulation environment: Use MATLAB / Simulink simulation software to build a simulation environment for physical examination queuing and calling. This environment can simulate various situations at the physical examination site, including the arrival of the examinee, queuing, physical examination process, and call notification. In the constructed simulation environment, various physical examination scenarios are simulated to comprehensively evaluate the performance of the model. These scenarios include but are not limited to: ① Differences in the duration of different physical examination items: Different physical examination durations are set according to the time requirements of the actual physical examination items to simulate the complexity of different items. ② Arrival distribution of physical examinees: Based on historical data or assumptions, simulate the arrival time distribution of physical examinees, such as uniform distribution, Poisson distribution, etc., to reflect the arrival situation in actual physical examinations. ③ Parallel operation of multiple types of physical examination equipment: Simulate the parallel operation of multiple physical examination equipment (such as B-ultrasound, X-ray, blood testing, etc.), including the use status, efficiency, and maintenance time of the equipment.

[0106] Generate test cases: Generate diverse test cases by adjusting simulation parameters, such as the arrival rate of examinees, the duration of examination items, and the number of devices. These cases should cover various scenarios that may be encountered in actual operations, such as peak hour congestion and equipment failure, to ensure the stability and adaptability of the model under different circumstances.

[0107] Performance Evaluation: The intelligent call model was run in a simulation environment, and the resulting queue and call plans were recorded. A traditional management approach was also run for comparison. By collecting and analyzing data, the performance of the two approaches was evaluated in terms of patient wait time, call accuracy, system response speed, and examination process efficiency.

[0108] Conduct field application tests:

[0109] Select test locations: Select representative physical examination centers or medical institutions as test locations to ensure the authenticity and diversity of the test environment.

[0110] Deploy a medical examination queuing and call system based on an intelligent call model at a test site and ensure its stable operation. Collect data from actual operations, including patient wait times, call logs, and system response times. Compare and analyze this data to evaluate the performance and effectiveness of the intelligent call system in real-world applications. Based on the results of the field test, gather user feedback and suggestions to make necessary adjustments and optimizations to the intelligent call model. This can also improve traditional management methods to enhance the efficiency and service quality of the overall medical examination process.

[0111] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0112] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A management method based on an intelligent physical examination queuing call system, characterized in that: The method comprises: Build an intelligent call model for the medical examination queue call system, model the medical examination queue and call management process as a Markov decision process, and dynamically adjust the medical examination queue call management strategy by analyzing real-time data from the medical examination site; The steps to build an intelligent call model include: Define the state space, including the examinee information dimension, queuing status dimension, examination equipment dimension, and call device dimension at the examination site; Define an action space, including the possible actions that the agent can take when handling medical examination queues and call exceptions, including adjusting the order of medical examination items, guiding the examinee to an idle device, sending call notifications, and activating a backup call device; Design a reward function to evaluate the results of actions based on patient satisfaction, patient waiting time, equipment utilization, equipment utilization, and emergency response capabilities, and give corresponding rewards or penalties; Formulate strategies, rules for selecting actions in a given state, and continuously optimize them through machine learning algorithms to maximize cumulative rewards; Use deep reinforcement learning algorithms to train intelligent call models; The examinee information dimension includes the examinee's scheduled items, completed items, and estimated completion time; the queuing status dimension includes the real-time queue length, average waiting time, and maximum waiting time of each examination item; the examination equipment dimension includes the use status, equipment efficiency, and estimated available time of the examination equipment; the calling equipment dimension includes the working status of the pager, the last call time, the call success rate, and the response time; The indicators for designing the reward function include patient satisfaction, patient waiting time, equipment utilization, and emergency response capability. The following formula is used to evaluate the action results and give corresponding rewards or penalties: in, It indicates the satisfaction of the examinee, obtained through questionnaire survey or feedback system, with a value range of [0,1], where 1 indicates complete satisfaction; Indicates the average waiting time for physical examinations, in minutes, calculated based on real-time data; Indicates the equipment utilization rate, which is calculated by dividing the actual equipment usage time by the total equipment available time. The value range is [0,1]. Indicates the emergency response capability, which is determined by measuring the system's response speed and processing efficiency under abnormal conditions. The value range is [0,1], where 1 indicates the fastest response speed and the highest processing efficiency; 、 、 and are weight coefficients, representing the importance of each indicator in the reward function, and satisfying The specific value is adjusted according to the actual application scenario and requirements.

2. A management method based on an intelligent medical examination queuing call system according to claim 1, characterized in that: The deep Q network DQN algorithm is used to train the intelligent calling model.

3. A management method based on an intelligent physical examination queuing call system according to claim 2, characterized in that: The steps to train the intelligent call model include: S1: Design the structure of the deep Q network to represent the strategy. The input of the network is the current state, and the output is the probability distribution of each action. Use the deep Q network to parameterize the strategy, denoted as ,in Indicates status, Indicates action, Represents the parameters of the neural network; S2: Based on the defined state space and action space, construct representations of the neural network input and output layers. The state space includes dimensions of the examinee information, queue status, examination equipment, and call device at the examination site. This state information will be encoded as input features of the neural network. The action space, as the output of the neural network, represents the possible actions that the model may take. S3: By simulating the physical examination queuing and calling process, the four-tuple of state, action, reward and next state is collected As training data; S4: Using the collected training data, the parameters of the neural network are updated through the backpropagation algorithm and gradient descent method. During the training process, the model continuously tries different actions and adjusts its strategy based on the feedback obtained from the reward function to maximize the cumulative reward. This further includes: S401: For each training iteration, randomly extract a batch of state transition samples from the experience replay memory ,in, is the current state, For the actions taken, For reward, For the next state; S402: Calculate the loss function based on the Bellman equation , the loss function is ,in, is the discount factor, is the estimate of the state and action value function under the current policy, are the parameters of the target network; S403: Update neural network parameters using gradient descent algorithm , so that the loss function minimize; S5: During the training process, the performance of the intelligent call model is regularly evaluated, including indicators such as patient satisfaction, waiting time, equipment utilization, and emergency response capability. Based on the evaluation results, the weight coefficient of the reward function and the learning rate of the model are adjusted; S6: Repeat steps S4 and S5 until the preset number of training rounds is reached.

4. A management method based on an intelligent physical examination queuing call system according to claim 3, characterized in that: In step 403, the formula for updating the parameters of the gradient descent algorithm is: in, Represents the model parameters, including all weights and biases that the model needs to learn; Represents the learning rate, which is used to control the step size of parameter update; Represents the loss function About parameters The gradient, is a vector pointing to the direction where the loss function grows fastest; Indicates assignment operation, i.e. updating parameters The value of .

5. A management method based on an intelligent medical examination queuing and calling system according to claim 4, characterized in that: Adopting the ε-greedy strategy, the model's exploration and utilization ratios in the early and late stages of training are dynamically adjusted.

6. The management method based on the intelligent medical examination queuing call system according to claim 1 is characterized by: After the training of the intelligent call model is completed, the model is tested and evaluated. A strategy combining simulation and field application is adopted to compare the performance of the queuing and calling solutions generated by the model with the traditional management methods in terms of the waiting time of the examinees, call accuracy, system response speed and examination process efficiency.

7. A management method based on an intelligent medical examination queuing and calling system according to claim 6, characterized in that: MATLAB / Simulink simulation software is used to build a simulation environment for physical examination queuing and calling.

8. The management method based on the intelligent medical examination queuing call system according to claim 7 is characterized by: In the constructed simulation environment, various physical examination scenarios are simulated, including the time differences of different physical examination items, the arrival distribution of physical examination subjects, and the parallel operation of multiple types of physical examination equipment; By adjusting simulation parameters, diverse test cases are generated to cover various situations encountered in actual operations.

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