Elevator AED intelligent scheduling method and system in complex scene, equipment and medium

Abstract

The invention discloses an elevator AED intelligent scheduling method and system in a complex scene, equipment and a medium. The method comprises the steps of establishing an AED scheduling distribution optimization model under the complex multi-elevator system, rapidly screening out an optimal solution direction of an optimization problem based on the AED scheduling distribution optimization model, carrying out fine processing based on the AED scheduling distribution optimization model to obtain an optimal solution of the optimization problem, and executing AED intelligent scheduling in the complex multi-elevator system. A complex multi-elevator system includes a plurality of elevator hoistways in which various types of elevators are selectively configured, the elevators being selectively configured for different partition services, the elevators being selectively configured with AED devices. By the adoption of the scheme, the emergency response efficiency in a complex scene can be improved, it is ensured that a patient urgently needing cardio-pulmonary resuscitation can be effectively treated in time, defibrillation support can be obtained as early as possible after ventricular fibrillation happens to the patient suffering from sudden cardiac arrest, and therefore the treatment speed and the survival rate of the patient are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of elevators and cardiopulmonary resuscitation, and in particular to an elevator AED intelligent dispatching method, system, equipment and medium in complex scenarios. Background Art

[0002] The statements in this section merely provide background technical information related to the present application and do not necessarily constitute prior art.

[0003] In modern building environments, elevators are used frequently as vertical transportation tools connecting different floors. However, due to various reasons, such as heart disease, accidental injuries, etc., emergency events may occur inside or outside the elevator, especially emergency medical conditions such as cardiac arrest. Cardiac arrest is a sudden and life-threatening condition that requires immediate and effective emergency measures. Among them, the automated external defibrillator (AED) is one of the most effective emergency equipment. AED external defibrillation is an important step in CPR (Cardio Pulmonary Resuscitation). The person who uses the AED is the AED, the implementer of CPR (hereinafter referred to as the implementer), and the person who receives the AED external defibrillation is the AED, the recipient of CPR (hereinafter referred to as the patient).

[0004] In the existing technology, AEDs are installed in or near elevators to allow patients to receive external defibrillation from the AED device in a timely manner when an emergency incident of cardiac arrest occurs inside or outside the building. However, the acquisition and application of AEDs in or near elevators can only be applied to simple elevator systems. For example, in a simple elevator system, there are only one or two elevators serving all floors. When an AED (automated external defibrillator) needs to be delivered to a certain floor, only these one or two elevators need to be dispatched. The scenario is simple and the dispatch difficulty is low.

[0005] However, for high-rise buildings, there are complex multi-elevator systems, which usually include multiple elevator shafts and multiple types of elevators, such as high-speed elevators, low-speed elevators, shuttle elevators, service / freight elevators, etc. High-speed elevators are used to quickly reach high-rise floors and are usually faster. Low-speed elevators serve lower floors and are relatively slow. Shuttle elevators are specially designed to quickly transport from the ground floor or transfer floor to a specific intermediate floor or sky lobby. Service / freight elevators are used to transport goods and service personnel, and are sometimes also used for evacuation. For the above complex multi-elevator systems, the service floors, service categories, and running speeds of various types of elevators are different. Moreover, each elevator does not reach all floors and there are cross-service partitions. Therefore, it is no longer possible to use conventional scheduling methods to apply AED equipment scheduling in complex multi-elevator systems. Therefore, in the complex scenarios of high-rise buildings, elevator AED scheduling will have problems such as uncertainty as whether the AED equipment can be delivered, limited coverage, and difficulty in scheduling emergency treatment. Therefore, it is impossible to use effective AED external defibrillation for patients in the first time. In order to improve the coverage rate, it may be necessary to increase the number of AED equipment, which is costly.

[0006] In view of this, how to solve the problems existing in elevator AED dispatching in complex scenarios of high-rise buildings, such as uncertainty in whether AED equipment can be delivered, limited coverage, and difficulty in dispatching emergency treatment, has become the subject to be studied and solved by the present invention. Summary of the invention

[0007] The purpose of the present invention is to provide an elevator AED intelligent scheduling method, system, equipment and medium in complex scenarios.

[0008] To achieve the above object, the first aspect of the present invention proposes an elevator AED intelligent scheduling method in a complex scenario, the method comprising:

[0009] Establish an AED dispatching and allocation optimization model under a complex multi-elevator system, summarize the allocation plan of AED equipment and emergency personnel who obtain AED equipment under a complex multi-elevator system into an optimization problem, define the prior parameters in the complex multi-elevator system and the shortest path to the required decision variables based on the optimization problem, construct the objective function related to the macro-dispatching of AED equipment and emergency personnel for the optimization problem and the constraints related to the AED equipment resource allocation and floor capacity limit with the prior parameters and decision variables, and establish an AED dispatching and allocation optimization model;

[0010] Based on the AED dispatch allocation optimization model, the optimal solution direction of the optimization problem is quickly screened out. The AED dispatch allocation optimization model randomly generates one or more groups of candidate solutions. Each individual in the multiple candidate solutions represents an allocation plan. After substituting the candidate solution into the macro dispatch objective function, the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system is obtained. According to the model result, the optimal solution direction of the allocation plan is quickly screened out, and the optimization parameters of the current optimal individual are selectively updated according to the optimal solution direction.

[0011] Based on the refined processing of the AED dispatch allocation optimization model, the optimal solution of the optimization problem is obtained, and refined local optimization is performed according to the optimal solution direction of the allocation plan to ensure that the allocation plan in the optimal solution direction meets all constraints related to the AED equipment resource configuration and floor capacity restrictions, and the current optimal individual is used for iterative optimization until the maximum number of iterations is reached or the improvement corresponding to the optimization parameter of the optimal individual is less than the preset amplitude threshold, and the optimal solution allocation plan is output, and at least one suboptimal solution allocation plan is retained;

[0012] AED intelligent scheduling is performed in a complex multi-elevator system, and the optimal solution allocation plan is used to intelligently schedule AED equipment and emergency personnel who obtain AED equipment. If the optimal solution allocation plan is interrupted during execution, the suboptimal solution allocation plan is started.

[0013] The second aspect of the present invention proposes a complex multi-elevator system for use with the method described in the first aspect, wherein the complex multi-elevator system comprises a plurality of elevator shafts, each elevator shaft being selectively configured with various types of elevators, the elevators being selectively configured to serve different partitions, and the elevators being selectively configured with AED equipment.

[0014] The third aspect of the present invention proposes an elevator AED intelligent dispatching system under complex scenarios, wherein the elevator AED intelligent dispatching system under complex scenarios includes the complex multi-elevator system as described in the second aspect of the present invention, and an AED intelligent dispatching system platform for dispatching the complex multi-elevator system and AED equipment and first aid personnel who obtain the AED equipment. An AED dispatching allocation optimization model for the complex multi-elevator system is established in the AED intelligent dispatching system platform. The optimal solution direction of the optimization problem is quickly screened out based on the AED dispatching allocation optimization model, and the optimal solution of the optimization problem is obtained through fine processing based on the AED dispatching allocation optimization model, and AED intelligent dispatching is performed in the complex multi-elevator system to respond to first aid events.

[0015] The fourth aspect of the present invention proposes an intelligent AED lifting platform for use in the method described in the first aspect, wherein the intelligent AED lifting platform device includes an elevator and an AED device configured in the elevator.

[0016] The fifth aspect of the present invention proposes a readable storage medium, on which a control program is stored. When the control program is executed by a processor, the processor executes the steps of the elevator AED intelligent scheduling method under complex scenarios as described in the first aspect.

[0017] The relevant contents of the present invention are explained as follows:

[0018] 1. Through the implementation of the above technical solutions of the present invention, in view of the existing problems in elevator AED dispatching in complex scenes of high-rise buildings, such as uncertainty about whether the AED equipment can be delivered, limited coverage, and difficulty in dispatching emergency treatment, innovative research and development have been designed to design an intelligent dispatching method for elevator AED in complex scenes, a complex multi-elevator system, an intelligent dispatching system for elevator AED in complex scenes, an intelligent AED lifting platform device and a readable storage medium, so that emergency personnel and AED equipment can be macro-controlled to the emergency scene in complex scenes of high-rise buildings within the golden time of emergency treatment, and the elevator AED and the emergency personnel who take the AED can reach the target floor as quickly as possible with the minimum time (path); in this application, by establishing an AED dispatching and allocation optimization model under a complex multi-elevator system, the allocation scheme of AED equipment and emergency personnel who obtain AED equipment in complex scenes under the original complex multi-elevator system is summarized as an optimization problem that can be solved by a mathematical model, thereby establishing basic conditions for the optimization and macro-control of the allocation scheme. When establishing an AED dispatching and allocation optimization model under a complex multi-elevator system, the prior parameters and the shortest path to the required decision variables in the complex multi-elevator system are defined based on the optimization problem, so that the model has scientific, reasonable and highly consistent parameters and variables when it is established, thereby providing a clear mathematical description; the prior parameters and decision variables are used to construct the objective function related to the macro-dispatching of AED equipment and emergency personnel for the optimization problem, as well as the constraints related to the resource allocation of AED equipment and floor capacity limitations, so that the application and representation of the optimization problem under the AED dispatching and allocation optimization model are more accurate, which is consistent with the corresponding goals and constraints based on resource limitations, capacity limitations, etc. in the elevator AED dispatching process in actual complex scenarios, so that the establishment of the final AED dispatching and allocation optimization model fully takes into account the uncertainty of whether the AED equipment can be delivered, limited coverage, and difficulty in dispatching emergency treatment. When performing the step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling and allocation optimization model, unlike the general gradient solution algorithm, the present application randomly generates one or more groups of candidate solutions by the AED scheduling and allocation optimization model at the beginning of responding to the emergency incident. Unlike the exhaustive solution, the present application substitutes the candidate solution into the macro scheduling objective function to obtain the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system, so as to quickly screen out the optimal solution direction of the allocation plan, and simulate the possibility of large-scale adjustment of limited resources in the entire building by substitution, generally quickly find possible excellent configuration plans, avoid falling into local optimality, but when implemented, it will be affected by other factors and fail to achieve the purpose of prime time delivery, and after quickly screening out the optimal solution direction of the allocation plan according to the model results, the optimization parameters of the current optimal individual are selectively updated according to the optimal solution direction, so as to provide a better allocation plan for subsequent fine processing.In the step of obtaining the optimal solution to the optimization problem through fine processing based on the AED scheduling allocation optimization model, fine local optimization is performed according to the optimal solution direction of the allocation plan to ensure that the allocation plan in the optimal solution direction meets all constraints related to the AED equipment resource configuration and floor capacity limitations, so as to further perform more fine local optimization within limited resource constraints, and use the current optimal individual for iterative optimization until the maximum number of iterations is reached or the improvement corresponding to the optimization parameters of the optimal individual is less than the preset amplitude threshold, so as to quickly obtain the optimal solution allocation plan, improve the emergency response efficiency, and give the allocation plan in the shortest possible time to avoid missing the golden time for emergency treatment. Through the combination of the above two steps of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model and obtaining the optimal solution of the optimization problem based on the AED scheduling allocation optimization model, it is possible to quickly screen out the optimal solution direction of the optimization problem in a large range in the entire building with limited resources to avoid falling into local optimality, and to obtain the optimal solution of the optimization problem through fine processing in the screened optimal solution direction, further optimize resource allocation, improve emergency response efficiency, ensure that patients in urgent need of cardiopulmonary resuscitation can receive effective treatment in a timely manner, and provide cardiac arrest patients with defibrillation support as soon as possible after ventricular fibrillation occurs, thereby improving the patient's treatment speed and survival rate.

[0019] 2. In the first aspect of the above technical solution, in the step of establishing an AED dispatch allocation optimization model for a complex multi-elevator system:

[0020] The a priori parameters are parameters set according to the floor partitions, elevator partitions, AED equipment layout in the elevators, and personnel flow in the complex multi-elevator system;

[0021] The decision variables are the variables that enable the emergency personnel and the elevator AED to reach the desired destination by the shortest path within the planning period.

[0022] This will further improve the AED scheduling allocation optimization model when establishing local more scientific, reasonable and highly compatible parameters and variables, thereby providing a more precise mathematical description and providing basic conditions for the optimization of allocation plans in complex scenarios under complex multi-elevator systems.

[0023] 3. In the first aspect of the above technical solution, the priori parameters include:

[0024] I, indicating the configuration or location index of the AED device or first aid personnel;

[0025] J, represents the floor index in the building;

[0026] M, represents the total number of AED equipment configuration points;

[0027] N, represents the total number of floors;

[0028] B i , represents the probability of initialization response of AED equipment and emergency personnel in the configuration area i within the planning period;

[0029] H i , represents the probability of target response of available AED equipment in configuration area i within the planning period;

[0030] E i , represents the probability of target response of available first aid personnel in the configuration area i within the planning period;

[0031] H j , represents the set of AED devices on floor j that arrive at the target floor within the planning period;

[0032] E j , represents the set of emergency personnel arriving at the target floor on floor j within the planning period;

[0033] L j , indicating that floor j reaches the efficiency set within the planning period;

[0034] U j , represents the set of AED equipment and first aid personnel efficiency consumed on floor j;

[0035] M j , represents the set of serviceable elevator AED devices on floor j;

[0036] N j , represents the set of elevators that can serve floor j.

[0037] These more specific and detailed prior parameters are used as parameters set according to floor and elevator partitions, elevator AED layout, and personnel flow conditions, making the application of the AED scheduling and allocation optimization model more efficient and accurate.

[0038] 4. In the first aspect of the above technical solution, the decision variables include:

[0039] C ij , represents the response priority of the elevator set from configuration point i to floor j, which is used as the decision variable in the AED scheduling allocation optimization model;

[0040] W ij , represents the elevator resources allocated from configuration point i to floor j, which serves as the decision variable in the AED scheduling allocation optimization model.

[0041] This more specific and detailed decision variable is used as the variable that enables emergency personnel and elevator AED to reach the desired destination by the shortest path within the cycle, making the application of the AED scheduling and allocation optimization model more efficient and accurate.

[0042] 5. In the first aspect of the above technical solution, in the step of establishing the AED dispatch allocation optimization model under the complex multi-elevator system, the objective function related to the macro-dispatching of AED equipment and emergency personnel corresponds to a multi-objective optimization problem containing three objectives, namely J1, J2 and J3, where:

[0043] J1: Minimize the cumulative response probability of AED and emergency personnel for the target floor;

[0044] J2: Minimize the path of the AED elevator to the target floor in the minimized floor system;

[0045] J3: Minimize the path for emergency personnel's elevator to reach the target floor in the minimized floor system.

[0046] In this way, the macro-dispatch of AEDs (automated external defibrillators) and emergency personnel in high-rise buildings can be regarded as a multi-objective optimization problem with three objectives - J1, J2 and J3. The construction of this optimization problem is scientific and reasonable, which can provide correctness guarantee for the optimization and implementation of subsequent allocation plans.

[0047] 6. In the first aspect of the above technical solution, in the step of establishing the AED dispatch allocation optimization model under a complex multi-elevator system, the objective function is defined as follows:

[0048] J1: During the planning period, maximize the cumulative response probability of AED equipment and emergency personnel for the target floor:

[0049] , which aims to maximize the cumulative response probability of AED and first responders;

[0050] J2, minimize the AED equipment in the minimized floor system. The elevator reaches the target floor path:

[0051] , aims to minimize the total AED response cost or time and ensure that the AED equipment can reach the required floor via the optimal path;

[0052] J3, minimize the path of the emergency personnel elevator reaching the target floor in the minimized floor system:

[0053] , aims to minimize the total emergency personnel response cost or time and ensure that emergency personnel can reach the required floor via the optimal path.

[0054] Through the further refinement and confirmation of the multi-objective optimization problem mentioned above, the process of establishing an AED scheduling allocation optimization model in complex scenarios and complex multi-elevator systems can be realized, and the fastest time and lowest risk can be used as the goals, which is in line with the urgency and necessity of obtaining defibrillation support for patients with cardiac arrest after ventricular fibrillation occurs. In the process of defining the various formulas used in the objective function, the prior parameters and decision variables in the problems and scenarios that need to be solved in each optimization problem are further matched, making the expression of the multi-objective optimization problem and the objective function more accurate.

[0055] 7. In the first aspect of the above technical solution, in the step of establishing the AED dispatch allocation optimization model under the complex multi-elevator system, the constraints related to the AED equipment resource configuration and floor capacity limitation include constraint one, constraint two, and constraint three, respectively, wherein:

[0056] Constraint 1 is the resource limit of the configuration point. The total amount of resources allocated to each floor by configuration point i cannot exceed its total available resources: ;

[0057] Constraint 2 is the minimum demand capacity limit and maximum demand capacity limit of the floor. The minimum demand capacity limit is , the maximum demand capacity limit is ;

[0058] Constraint three is a non-negativity constraint, where the number of allocated resources cannot be negative. , n represents a positive integer set .

[0059] Through the establishment and definition of the constraints above, the resource constraints, capacity constraints, demand constraints and other constraints existing in the prior parameters and decision variables in each constraint condition are further matched, so that more accurate and reasonable constraints are provided for the establishment of the AED scheduling allocation optimization model under the allocation state of limited resources, thereby ensuring that the allocation plan generated when responding to emergency incidents can be smoothly implemented.

[0060] 8. In the first aspect of the above technical solution, in the step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model, the AED scheduling allocation optimization model randomly generates candidate solutions, and the candidate solutions include the response priority C of the elevator set from the configuration point i to the floor j. ij and the elevator resources W allocated from configuration point i to floor j ij ,Right now , where K is the number of candidate solution populations, this group of candidate solutions constitutes the initial population, and each candidate solution Represents a possible position in the search space, and directly substitutes the candidate solution into the formula corresponding to the objective function J1, J2 and J3 to obtain the model result of the optimal decision variable under each corresponding prior parameter. This is further different from the conventional gradient solution algorithm, and can further quickly screen the optimal solution direction of the optimization problem in a large range of the entire building with limited resources.

[0061] 9. In the first aspect of the above technical solution, in the process of switching from the step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model to the step of obtaining the optimal solution of the optimization problem based on the AED scheduling allocation optimization model, the migration factor TF determines whether to switch the flow. , where t is the current iteration number, t max is the maximum number of iterations. When TF≤0.5, continue to quickly screen out the optimal solution direction of the optimization problem; when TF>0.5, enter the fine processing to obtain the optimal solution of the optimization problem. In this way, it is determined whether to switch from quickly screening out the optimal solution direction of the optimization problem to fine processing to obtain the optimal solution of the optimization problem. In the method of this application, the value of the migration factor that determines the flow switching can also be set to 0.4, 0.6, etc.

[0062] 10. In the first aspect of the above technical solution, the step of obtaining the optimal solution of the optimization problem based on the fine processing of the AED scheduling allocation optimization model includes the following steps:

[0063] Determine the number of individuals and the maximum number of iterations t of the allocation scheme max , a group or multiple groups of candidate solutions are randomly generated by the AED dispatch allocation optimization model, each individual in the multiple candidate solutions represents an allocation scheme for an AED device and emergency personnel who obtain the AED device, and the range of each constant parameter and acceleration is set;

[0064] Substituting the candidate solution into the macro scheduling objective function, the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system is obtained, and the optimal solution direction of the allocation plan of AED equipment and emergency personnel who obtain AED equipment is quickly screened out according to the model result;

[0065] Selectively update the optimization parameters of the current best individual according to the direction of the optimal solution, and use the optimization parameters of the current best individual to update the optimization parameters of other individuals in the candidate solution;

[0066] Calculate the migration factor and parameter amplitude factor. The migration factor determines whether to continue to quickly screen out the optimal solution direction of the optimization problem or to enter the fine processing to obtain the optimal solution of the optimization problem. The parameter amplitude factor is used to adjust the amplitude of the optimization parameter update of the optimal individual and other individuals.

[0067] When the migration factor is ≤0.5, continue to quickly screen out the optimal solution direction of the optimization problem.

[0068] The above more detailed step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model further optimizes the implementation of this step, ensuring the accuracy and reliability of quickly screening out the optimal solution direction of the optimization problem.

[0069] 11. In the first aspect of the above technical solution, the step of obtaining the optimal solution of the optimization problem based on the fine processing of the AED scheduling allocation optimization model includes the following steps:

[0070] When the migration factor is greater than 0.5, fine processing is performed to obtain the optimal solution to the optimization problem;

[0071] Perform fine local optimization according to the optimal solution direction of the allocation plan to ensure that the allocation plan in the optimal solution direction meets all constraints related to AED equipment resource configuration and floor capacity restrictions;

[0072] Use the current optimal individual as the starting point and perform local iterative optimization. If a better allocation scheme is found during the iterative optimization process, the better allocation scheme is used as the new current optimal individual and the corresponding optimization parameters are updated. If no better allocation scheme is found, the optimization parameters of the current optimal individual are continuously updated according to the parameter amplitude factor.

[0073] When the maximum number of iterations is reached or the update amplitude of the optimization parameters of the best individual is less than the preset amplitude threshold, stop this step and output the optimal solution allocation plan; otherwise, continue the step of selectively updating the optimization parameters of the current best individual according to the optimal solution direction and subsequent steps.

[0074] The above more detailed step of obtaining the optimal solution to the optimization problem through fine processing based on the AED scheduling allocation optimization model further optimizes the implementation of this step, ensuring the accuracy and reliability of quickly screening out the optimal solution allocation plan for the optimization problem.

[0075] 12. In the first aspect of the above technical solution, in the step of executing the intelligent scheduling of AED in a complex multi-elevator system, the suboptimal solution allocation plan is calculated at the same time. If the optimal solution allocation plan is interrupted during execution, the suboptimal solution allocation plan is started. Finally, in the step of executing the intelligent scheduling of AED in a complex multi-elevator system, in addition to maintaining the execution of the optimal solution allocation plan, the suboptimal solution allocation plan is also calculated at the same time to avoid the situation where the optimal solution allocation plan cannot be executed due to a small probability accident and the suboptimal solution allocation plan is immediately activated to ensure that patients who urgently need cardiopulmonary resuscitation can be effectively treated in a timely manner.

[0076] 13. In the second aspect of the above technical solution, the elevators include high-speed elevators, low-speed elevators, shuttle elevators, and service / freight elevators;

[0077] The high-speed elevator is used to quickly reach high floors;

[0078] The low-speed elevator is used to serve the lower floors;

[0079] The shuttle elevator is used to quickly transport from the ground floor or transfer floor to a specific intermediate floor or sky lobby;

[0080] The service / cargo elevator is used for transporting goods and service personnel, as well as for evacuation;

[0081] The zoned service includes zoned elevators and double-deck elevators; the zoned elevators only serve a specific range of floors; and the double-deck elevators stop at two adjacent floors at the same time.

[0082] The above design makes the establishment of a complex multi-elevator system more perfect. In the complex multi-elevator system of the present application, the elevator types may include a combination of two or more of the listed types.

[0083] Due to the application of the above scheme, the present invention has the following advantages and effects compared with the prior art:

[0084] 1. Through the implementation of the technical solution of the present invention, in view of the existing problems in elevator AED dispatching in complex scenes of high-rise buildings, such as uncertainty about whether AED equipment can be delivered, limited coverage, and difficulty in dispatching emergency treatment, innovative research and development have been designed to design an intelligent dispatching method for elevator AED in complex scenes, a complex multi-elevator system, an intelligent dispatching system for elevator AED in complex scenes, an intelligent AED lifting platform device and a readable storage medium. During the golden time of emergency treatment, in complex scenes of high-rise buildings, emergency personnel and AED equipment are macro-controlled to the emergency site, and the elevator AED and the emergency personnel who take the AED are balanced with the minimum time (path) to reach the target floor as quickly as possible; in this application, by establishing an AED dispatching and allocation optimization model under a complex multi-elevator system, the allocation scheme of AED equipment and emergency personnel who obtain AED equipment in complex scenes under the original complex multi-elevator system is summarized as an optimization problem that can be solved by a mathematical model, thereby establishing basic conditions for the optimization and macro-control of the allocation scheme.

[0085] 2. Through the implementation of the technical solution of the present invention, when establishing an AED scheduling and allocation optimization model under a complex multi-elevator system, the prior parameters in the complex multi-elevator system and the decision variables for the shortest path to reach the desired destination are defined based on the optimization problem, so that the model has scientific, reasonable and highly consistent parameters and variables when it is established, thereby providing a clear mathematical description; and the objective function related to the macro-scheduling of AED equipment and emergency personnel for the optimization problem and the constraints related to the resource allocation of AED equipment and floor capacity restrictions are constructed with the prior parameters and decision variables, so that the application and characterization of the optimization problem under the AED scheduling and allocation optimization model are more accurate, which is consistent with the corresponding goals in the elevator AED scheduling process in actual complex scenarios and the constraints based on resource restrictions, capacity restrictions, etc., so that the establishment of the final AED scheduling and allocation optimization model fully takes into account the uncertainty of whether the AED equipment can be delivered, limited coverage, and difficulty in scheduling emergency treatment.

[0086] 3. Through the implementation of the technical solution of the present invention, when the step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model is performed, unlike the general gradient solution algorithm, the present application randomly generates one or more groups of candidate solutions by the AED scheduling allocation optimization model at the beginning of responding to the emergency incident. Unlike the exhaustive solution, the present application substitutes the candidate solution into the macro scheduling objective function to obtain the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system, so as to quickly screen out the optimal solution direction of the allocation scheme, and simulate the possibility of large-scale adjustment of limited resources in the entire building by substitution, generally quickly find possible excellent configuration schemes, avoid falling into local optimality, but when implemented, it will be affected by other factors and fail to achieve the purpose of prime time delivery, and after quickly screening out the optimal solution direction of the allocation scheme according to the model results, the optimization parameters of the current optimal individual are selectively updated according to the optimal solution direction, so as to provide a better allocation scheme for subsequent fine processing.

[0087] 4. Through the implementation of the technical solution of the present invention, in the step of obtaining the optimal solution of the optimization problem through fine processing based on the AED scheduling allocation optimization model, fine local optimization is performed according to the optimal solution direction of the allocation plan to ensure that the allocation plan in the optimal solution direction meets all constraints related to the AED equipment resource configuration and floor capacity limitations, so as to further perform more fine local optimization within limited resource constraints, and use the current optimal individual for iterative optimization until the maximum number of iterations is reached or the improvement corresponding to the optimization parameters of the optimal individual is less than the preset amplitude threshold, so as to quickly obtain the optimal solution allocation plan, improve the emergency response efficiency, and give the allocation plan in the shortest possible time to avoid missing the golden time for emergency treatment.

[0088] 5. In summary, after adopting the scheme of the present invention, on the basis of establishing a reliable and efficient AED scheduling and allocation optimization model, the optimal solution direction of the optimization problem based on the AED scheduling and allocation optimization model is quickly screened out, and the optimal solution of the optimization problem is obtained through fine processing based on the AED scheduling and allocation optimization model. The combination of these two steps can not only quickly screen out the optimal solution direction of the optimization problem in a large range in the entire building with limited resources to avoid falling into local optimality, but also obtain the optimal solution of the optimization problem through fine processing in the screened optimal solution direction, further optimize resource allocation, improve emergency response efficiency in complex scenarios, ensure that patients in urgent need of cardiopulmonary resuscitation can be effectively treated in time, and provide cardiac arrest patients with defibrillation support as soon as possible after ventricular fibrillation occurs, thereby improving the treatment speed and survival rate of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is a flow chart of the intelligent dispatching method of elevator AED in complex scenarios according to an embodiment of the present invention;

[0090] Figure 2 A schematic diagram of a special dispatching strategy in an emergency scenario according to an embodiment of the present invention;

[0091] Figure 3 A schematic diagram of a complex multi-elevator system according to an embodiment of the present invention;

[0092] Figure 4 Schematic diagram of the flow of the hybrid algorithm in an embodiment of the present invention;

[0093] Figure 5 This is a schematic diagram of the elevator TV floor navigation. DETAILED DESCRIPTION

[0094] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0095] The terms used herein are only for describing specific embodiments and are not intended to be limiting of the present invention. Singular forms such as "a", "this", "here", "this" and "the" as used herein also include plural forms.

[0096] The terms “first”, “second”, etc. used in this document do not specifically refer to an order or sequence, nor are they used to limit the present case. They are only used to distinguish components or operations described with the same technical terms.

[0097] As used herein, “connection” or “positioning” may refer to direct physical contact between two or more components or lifting platforms, or indirect physical contact between two or more components or lifting platforms, or mutual operation or movement between two or more components or lifting platforms.

[0098] The terms “include,” “including,” “have,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0099] The terms used in this document generally have the ordinary meaning of each term used in this field, in the context of the case and in the specific context, unless otherwise noted. Certain terms used to describe the present invention will be discussed below or elsewhere in this specification to provide additional guidance to those skilled in the art in describing the present invention.

[0100] The present invention aims to solve the problem of intelligent macro-dispatching of elevator AEDs in elevators when emergency first aid situations occur, balance the elevator AED and the personnel who take the AED to reach the target floor as quickly as possible, and for complex multi-elevator systems including high-speed elevators, low-speed elevators, shuttle elevators, etc., solve the problem of both the personnel assigned to the elevator AED and the personnel who can carry the AED reaching the target floor in the minimum time (path) within the golden time for first aid. The intelligent dispatching method of elevator AEDs in complex scenarios, complex multi-elevator systems, intelligent dispatching systems of elevator AEDs in complex scenarios, intelligent AED lifting platform equipment and readable storage media are innovatively developed and designed, which can macro-control first aid personnel and AED equipment to the first aid site in complex scenarios of high-rise buildings within the golden time for first aid, and balance the elevator AED and the first aid personnel who take the AED to reach the target floor as quickly as possible in the minimum time (path).

[0101] Embodiment 1, as Figure 1 As shown, Embodiment 1 of the present invention proposes an elevator AED intelligent scheduling method in a complex scenario, and the method includes:

[0102] Establish an AED dispatching and allocation optimization model under a complex multi-elevator system, summarize the allocation plan of AED equipment and emergency personnel who obtain AED equipment under a complex multi-elevator system into an optimization problem, define the prior parameters in the complex multi-elevator system and the shortest path to the required decision variables based on the optimization problem, construct the objective function related to the macro-dispatching of AED equipment and emergency personnel for the optimization problem and the constraints related to the AED equipment resource allocation and floor capacity limit with the prior parameters and decision variables, and establish an AED dispatching and allocation optimization model;

[0103] Based on the AED dispatch allocation optimization model, the optimal solution direction of the optimization problem is quickly screened out. According to the occurrence of the emergency incident, the AED dispatch allocation optimization model randomly generates one or more groups of candidate solutions. Each individual in the multiple candidate solutions represents an allocation plan for an AED device and emergency personnel who obtain the AED device. After substituting the candidate solution into the macro dispatch objective function, the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system is obtained. According to the model result, the optimal solution direction of the allocation plan for the AED device and emergency personnel who obtain the AED device is quickly screened out, and the optimization parameters of the current optimal individual are selectively updated according to the optimal solution direction;

[0104] Based on the AED dispatch allocation optimization model, the optimal solution of the optimization problem is obtained through fine processing. According to the optimal solution direction of the allocation plan of the AED equipment and the emergency personnel who obtain the AED equipment, fine local optimization is performed to ensure that the allocation plan in the optimal solution direction meets all constraints related to the AED equipment resource configuration and floor capacity restrictions. The current optimal individual is used for iterative optimization until the maximum number of iterations is reached or the improvement corresponding to the optimization parameter of the optimal individual is less than the preset threshold, and the final optimal solution allocation plan is output;

[0105] Perform intelligent AED dispatch in complex multi-elevator systems, and use the optimal solution allocation plan to intelligently dispatch AED equipment and emergency personnel who obtain AED equipment.

[0106] The steps of the above method include establishing an AED scheduling and allocation optimization model under a complex multi-elevator system, quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling and allocation optimization model, obtaining the optimal solution of the optimization problem based on the AED scheduling and allocation optimization model, and executing AED intelligent scheduling in a complex multi-elevator system.

[0107] When establishing an AED dispatching and allocation optimization model under a complex multi-elevator system, the prior parameters and the shortest path to the required decision variables in the complex multi-elevator system are defined based on the optimization problem, so that the model has scientific, reasonable and highly consistent parameters and variables when it is established, thereby providing a clear mathematical description; the prior parameters and decision variables are used to construct the objective function related to the macro-dispatching of AED equipment and emergency personnel for the optimization problem, as well as the constraints related to the resource allocation of AED equipment and floor capacity limitations, so that the application and representation of the optimization problem under the AED dispatching and allocation optimization model are more accurate, which is consistent with the corresponding goals and constraints based on resource limitations, capacity limitations, etc. in the elevator AED dispatching process in actual complex scenarios, so that the establishment of the final AED dispatching and allocation optimization model fully takes into account the uncertainty of whether the AED equipment can be delivered, limited coverage, and difficulty in dispatching emergency treatment.

[0108] When performing the step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling and allocation optimization model, unlike the general gradient solution algorithm, the present application randomly generates one or more groups of candidate solutions by the AED scheduling and allocation optimization model at the beginning of responding to the emergency incident. Unlike the exhaustive solution, the present application substitutes the candidate solution into the macro scheduling objective function to obtain the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system, so as to quickly screen out the optimal solution direction of the allocation plan, and simulate the possibility of large-scale adjustment of limited resources in the entire building by substitution, generally quickly find possible excellent configuration plans, avoid falling into local optimality, but when implemented, it will be affected by other factors and fail to achieve the purpose of prime time delivery, and after quickly screening out the optimal solution direction of the allocation plan according to the model results, the optimization parameters of the current optimal individual are selectively updated according to the optimal solution direction, so as to provide a better allocation plan for subsequent fine processing.

[0109] In the step of obtaining the optimal solution to the optimization problem through fine processing based on the AED scheduling allocation optimization model, fine local optimization is performed according to the optimal solution direction of the allocation plan to ensure that the allocation plan in the optimal solution direction meets all constraints related to the AED equipment resource configuration and floor capacity limitations, so as to further perform more fine local optimization within limited resource constraints, and use the current optimal individual for iterative optimization until the maximum number of iterations is reached or the improvement corresponding to the optimization parameters of the optimal individual is less than the preset amplitude threshold, so as to quickly obtain the optimal solution allocation plan, improve the emergency response efficiency, and give the allocation plan in the shortest possible time to avoid missing the golden time for emergency treatment.

[0110] In the first embodiment of the present invention, in the step of establishing an AED dispatching and allocation optimization model in a complex multi-elevator system:

[0111] The a priori parameters are parameters set according to the floor partitions, elevator partitions, AED equipment layout in the elevators, and personnel flow in the complex multi-elevator system;

[0112] The decision variables are the variables that enable the emergency personnel and the elevator AED to reach the desired destination by the shortest path within the planning period.

[0113] This will further improve the AED scheduling allocation optimization model when establishing local more scientific, reasonable and highly compatible parameters and variables, thereby providing a more precise mathematical description and providing basic conditions for the optimization of allocation plans in complex scenarios under complex multi-elevator systems.

[0114] Specifically, the prior parameters include:

[0115] I, indicating the configuration or location index of the AED device or first aid personnel;

[0116] J, represents the floor index in the building;

[0117] M, represents the total number of AED equipment configuration points;

[0118] N, represents the total number of floors;

[0119] B i , represents the probability of initialization response of AED equipment and emergency personnel in the configuration area i within the planning period;

[0120] H i , represents the probability of target response of available AED equipment in configuration area i within the planning period;

[0121] E i , represents the probability of target response of available first aid personnel in the configuration area i within the planning period;

[0122] H j , represents the set of AED devices on floor j that arrive at the target floor within the planning period;

[0123] E j , represents the set of emergency personnel arriving at the target floor on floor j within the planning period;

[0124] L j , represents the arrival efficiency set of floor j within the planning period, taking into account factors such as elevator accessibility, elevator speed, and congestion level;

[0125] U j , represents the set of AED equipment and first aid personnel efficiency consumed on floor j;

[0126] M j , represents the set of serviceable elevator AED devices on floor j;

[0127] N j , represents the set of elevators that can serve floor j.

[0128] These more specific and detailed prior parameters are used as parameters set according to floor and elevator partitions, elevator AED layout, and personnel flow conditions, making the application of the AED scheduling and allocation optimization model more efficient and accurate.

[0129] Specifically, the decision variables include:

[0130] C ij , represents the response priority of the elevator set from configuration point i to floor j, which is used as the decision variable in the AED scheduling allocation optimization model;

[0131] W ij, represents the elevator resources allocated from configuration point i to floor j, which serves as the decision variable in the AED scheduling allocation optimization model.

[0132] This more specific and detailed decision variable is used as the variable that enables emergency personnel and elevator AED to reach the desired destination by the shortest path within the cycle, making the application of the AED scheduling and allocation optimization model more efficient and accurate.

[0133] In the first embodiment of the present invention, in the step of establishing the AED dispatching and allocation optimization model under the complex multi-elevator system, the objective function related to the macroscopic dispatching of AED equipment and emergency personnel corresponds to a multi-objective optimization problem including three objectives, namely J1, J2 and J3, where:

[0134] J1: Minimize the cumulative response probability of AED and emergency personnel for the target floor;

[0135] J2: Minimize the path of the AED elevator to the target floor in the minimized floor system;

[0136] J3: Minimize the path for emergency personnel's elevator to reach the target floor in the minimized floor system.

[0137] In this way, the macro-dispatch of AEDs (automated external defibrillators) and emergency personnel in high-rise buildings can be regarded as a multi-objective optimization problem with three objectives - J1, J2 and J3. The construction of this optimization problem is scientific and reasonable, which can provide correctness guarantee for the optimization and implementation of subsequent allocation plans.

[0138] In the first embodiment of the present invention, in the step of establishing the AED dispatching and allocation optimization model in a complex multi-elevator system, the objective function is defined as follows:

[0139] J1: During the planning period, maximize the cumulative response probability of AED equipment and emergency personnel for the target floor:

[0140] , which aims to maximize the cumulative response probability of AED and first responders;

[0141] J2, minimize the AED equipment in the minimized floor system. The elevator reaches the target floor path:

[0142] , aims to minimize the total AED response cost or time and ensure that the AED equipment can reach the required floor via the optimal path;

[0143] J3, minimize the path of the emergency personnel elevator reaching the target floor in the minimized floor system:

[0144] , aims to minimize the total emergency personnel response cost or time and ensure that emergency personnel can reach the required floor via the optimal path.

[0145] Through the further refinement and confirmation of the multi-objective optimization problem mentioned above, the process of establishing an AED scheduling allocation optimization model in complex scenarios and complex multi-elevator systems can be realized, and the fastest time and lowest risk can be used as the goals, which is in line with the urgency and necessity of obtaining defibrillation support for patients with cardiac arrest after ventricular fibrillation occurs. In the process of defining the various formulas used in the objective function, the prior parameters and decision variables in the problems and scenarios that need to be solved in each optimization problem are further matched, making the expression of the multi-objective optimization problem and the objective function more accurate.

[0146] In the first embodiment of the present invention, in the step of establishing the AED scheduling and allocation optimization model under the complex multi-elevator system, the constraints related to the AED equipment resource configuration and the floor capacity limitation include constraint one, constraint two, and constraint three, respectively, wherein:

[0147] Constraint 1 is the resource limit of the configuration point. The total amount of resources allocated to each floor by configuration point i cannot exceed its total available resources: ;

[0148] Constraint 2 is the minimum demand capacity limit and maximum demand capacity limit of the floor. The minimum demand capacity limit is , the maximum demand capacity limit is ;

[0149] Constraint three is a non-negativity constraint, where the number of allocated resources cannot be negative. , n represents a positive integer set .

[0150] Through the establishment and definition of the constraints above, the resource constraints, capacity constraints, demand constraints and other constraints existing in the prior parameters and decision variables in each constraint condition are further matched, so that more accurate and reasonable constraints are provided for the establishment of the AED scheduling allocation optimization model under the allocation state of limited resources, thereby ensuring that the allocation plan generated when responding to emergency incidents can be smoothly implemented.

[0151] In the first embodiment of the present invention, in the step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model, the AED scheduling allocation optimization model randomly generates candidate solutions, and the candidate solutions include the response priority C of the elevator set from the configuration point i to the floor j. ij and the elevator resources W allocated from configuration point i to floor j ij ,Right now , where K is the number of candidate solution populations, this group of candidate solutions constitutes the initial population, and each candidate solution Represents a possible position in the search space, and directly substitutes the candidate solution into the formula corresponding to the objective function J1, J2 and J3 to obtain the model result of the optimal decision variable under each corresponding prior parameter. This is further different from the conventional gradient solution algorithm, and can further quickly screen the optimal solution direction of the optimization problem in a large range of the entire building with limited resources.

[0152] In the first embodiment of the present invention, in the process of switching from the step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model to the step of obtaining the optimal solution of the optimization problem based on the AED scheduling allocation optimization model, the migration factor TF determines whether to switch the flow. , where t is the current iteration number, t max is the maximum number of iterations. When TF≤0.5, continue to quickly screen out the optimal solution direction of the optimization problem; when TF>0.5, enter the fine processing to obtain the optimal solution of the optimization problem. In this way, it is determined whether to switch from quickly screening out the optimal solution direction of the optimization problem to fine processing to obtain the optimal solution of the optimization problem. In the method of this application, the value of the migration factor that determines the flow switching can also be set to 0.4, 0.6, etc.

[0153] In the first embodiment of the present invention, the step of obtaining the optimal solution of the optimization problem based on the fine processing of the AED scheduling allocation optimization model includes the following steps:

[0154] Determine the number of individuals and the maximum number of iterations t of the allocation scheme max , a group or multiple groups of candidate solutions are randomly generated by the AED dispatch allocation optimization model, each individual in the multiple candidate solutions represents an allocation scheme for an AED device and emergency personnel who obtain the AED device, and the range of each constant parameter and acceleration is set;

[0155] Substituting the candidate solution into the macro scheduling objective function, the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system is obtained, and the optimal solution direction of the allocation plan of AED equipment and emergency personnel who obtain AED equipment is quickly screened out according to the model result;

[0156] Selectively update the optimization parameters of the current best individual according to the direction of the optimal solution, and use the optimization parameters of the current best individual to update the optimization parameters of other individuals in the candidate solution;

[0157] Calculate the migration factor and parameter amplitude factor. The migration factor determines whether to continue to quickly screen out the optimal solution direction of the optimization problem or to enter the fine processing to obtain the optimal solution of the optimization problem. The parameter amplitude factor is used to adjust the amplitude of the optimization parameter update of the optimal individual and other individuals.

[0158] When the migration factor is ≤0.5, continue to quickly screen out the optimal solution direction of the optimization problem.

[0159] The above more detailed step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model further optimizes the implementation of this step, ensuring the accuracy and reliability of quickly screening out the optimal solution direction of the optimization problem.

[0160] In the first embodiment of the present invention, in the step of executing the intelligent scheduling of AED in a complex multi-elevator system, the suboptimal solution allocation plan is calculated at the same time. If the optimal solution allocation plan is interrupted during execution, the suboptimal solution allocation plan is started. Finally, in the step of executing the intelligent scheduling of AED in a complex multi-elevator system, in addition to maintaining the execution of the optimal solution allocation plan, the suboptimal solution allocation plan is also calculated at the same time to avoid the suboptimal solution allocation plan being immediately activated when the optimal solution allocation plan cannot be executed due to a small probability accident, so as to ensure that patients who urgently need cardiopulmonary resuscitation can be effectively treated in time.

[0161] The first embodiment of the present invention is further described below with a more specific detailed embodiment. The elevator AED intelligent dispatching method in a complex scenario of the detailed embodiment includes the following detailed contents.

[0162] The detailed embodiments of the present invention mainly provide an intelligent computing solution for AED equipment scheduling and emergency personnel scheduling in elevator AED scheduling in complex scenarios of high-rise buildings.

[0163] In this intelligent computing solution, the first step is to establish an AED scheduling and allocation optimization model under a complex multi-elevator system, summarize the allocation plan of AED equipment and emergency personnel who obtain AED equipment under the complex multi-elevator system into an optimization problem, and define the prior parameters in the complex multi-elevator system and the decision variables for the shortest path to reach the desired value based on the optimization problem. The prior parameters are parameters set according to the floor divisions, elevator divisions, AED equipment layout in the elevator, and personnel flow in the complex multi-elevator system, and the decision variables are the variables that enable emergency personnel and elevator AED to reach the desired value by the shortest path within the planning period; the objective function related to the macro-scheduling of AED equipment and emergency personnel corresponds to a A multi-objective optimization problem with three objectives, namely J1, J2 and J3, where: J1: Minimize the cumulative response probability of AED and first aid personnel for the target floor; J2: Minimize the path of the AED elevator to the target floor in the floor system; J3: Minimize the path of the first aid personnel elevator to the target floor in the floor system. In the steps of establishing the AED scheduling and allocation optimization model under a complex multi-elevator system, the constraints related to the AED equipment resource configuration and floor capacity restrictions include constraint one, constraint two, and constraint three, where: Constraint one is the resource restriction of the configuration point. The total amount of resources allocated to each floor by the configuration point i cannot exceed its total available resources: ; Constraint 2 is the minimum demand capacity limit and maximum demand capacity limit of the floor. The minimum demand capacity limit is , the maximum demand capacity limit is ; Constraint three is a non-negative constraint, in which the number of allocated resources cannot be negative. , n represents a positive integer set The objective function related to the macro-scheduling of AED equipment and emergency personnel and the constraints related to the resource allocation of AED equipment and floor capacity limitation are constructed for the optimization problem with prior parameters and decision variables, and an AED scheduling allocation optimization model is established.

[0164] When an emergency incident occurs, the elevator AED intelligent dispatching system receives the emergency notification in a complex scenario and uses the AED dispatching allocation optimization model to quickly screen out the optimal solution direction of the optimization problem based on the AED dispatching allocation optimization model, which includes the following steps:

[0165] Determine the number of individuals and the maximum number of iterations t of the allocation scheme max , a group or multiple groups of candidate solutions are randomly generated by the AED dispatch allocation optimization model, each individual in the multiple candidate solutions represents an allocation scheme for an AED device and emergency personnel who obtain the AED device, and the range of each constant parameter and acceleration is set;

[0166] Substituting the candidate solution into the macro scheduling objective function, the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system is obtained, and the optimal solution direction of the allocation plan of AED equipment and emergency personnel who obtain AED equipment is quickly screened out according to the model result;

[0167] Selectively update the optimization parameters of the current best individual according to the direction of the optimal solution, and use the optimization parameters of the current best individual to update the optimization parameters of other individuals in the candidate solution;

[0168] Calculate the migration factor and parameter amplitude factor. The migration factor determines whether to continue to quickly screen out the optimal solution direction of the optimization problem or to enter the fine processing to obtain the optimal solution of the optimization problem. The parameter amplitude factor is used to adjust the amplitude of the optimization parameter update of the optimal individual and other individuals.

[0169] When the migration factor is ≤0.5, continue to quickly screen out the optimal solution direction of the optimization problem.

[0170] Based on the AED dispatch optimization model, the optimal solution of the optimization problem is obtained through fine processing, which includes the following steps:

[0171] When the migration factor is greater than 0.5, fine processing is performed to obtain the optimal solution to the optimization problem;

[0172] Perform fine local optimization according to the optimal solution direction of the allocation plan to ensure that the allocation plan in the optimal solution direction meets all constraints related to AED equipment resource configuration and floor capacity restrictions;

[0173] Use the current optimal individual as the starting point and perform local iterative optimization. If a better allocation scheme is found during the iterative optimization process, the better allocation scheme is used as the new current optimal individual and the corresponding optimization parameters are updated. If no better allocation scheme is found, the optimization parameters of the current optimal individual are continuously updated according to the parameter amplitude factor.

[0174] When the maximum number of iterations is reached or the update amplitude of the optimization parameters of the best individual is less than the preset amplitude threshold, stop this step and output the optimal solution allocation plan; otherwise, continue the step of selectively updating the optimization parameters of the current best individual according to the optimal solution direction and subsequent steps.

[0175] AED intelligent scheduling is performed in a complex multi-elevator system. The optimal solution allocation plan is used to intelligently schedule AED equipment and emergency personnel who obtain AED equipment. At the same time, the suboptimal solution allocation plan is calculated. If the optimal solution allocation plan is interrupted during execution, the suboptimal solution allocation plan is started.

[0176] In the process of implementing this application, the main factors that need to be considered in the scheduling of AED equipment and emergency personnel in complex multi-elevator systems involve balancing the elevator AED and the personnel who take the AED (emergency personnel) to reach the target floor as quickly as possible in the minimum time (path). It solves the problem of both the personnel assigned to the elevator AED and the personnel who can carry the AED reaching the target floor in the minimum time (path) during the golden time for emergency treatment. It is worth noting that in the emergency treatment scenario, even if the emergency personnel and the elevator where the AED equipment is stored are not in the same elevator, the AED equipment elevator and the elevator where the emergency personnel take the equipment can be independently scheduled to reach the target floor. Figure 2 The special dispatch strategy in the emergency scenario shown above is that when an AED (automated external defibrillator) needs to be delivered to a certain floor, it is necessary to design a dispatch method for the elevator system. The main design is to design a complete dispatch logic that adjusts the priority of elevator operation to complete the dispatch of personnel and AED.

[0177] Embodiment 2, as Figure 3 As shown, embodiment 2 of the present invention proposes a complex multi-elevator system, which includes multiple elevator shafts, each elevator shaft is selectively configured with various types of elevators, the elevators are selectively configured to serve different partitions, and AED equipment is selectively configured in the elevators.

[0178] In the complex multi-elevator system, the elevators include high-speed elevators, low-speed elevators, shuttle elevators, and service / freight elevators;

[0179] The high-speed elevator is used to quickly reach high floors;

[0180] The low-speed elevator is used to serve the lower floors;

[0181] The shuttle elevator is used to quickly transport from the ground floor or transfer floor to a specific intermediate floor or sky lobby;

[0182] The service / cargo elevator is used for transporting goods and service personnel, as well as for evacuation;

[0183] The zoned service includes zoned elevators and double-deck elevators; the zoned elevators only serve a specific range of floors; and the double-deck elevators stop at two adjacent floors at the same time.

[0184] The above design makes the establishment of a complex multi-elevator system more perfect. In the complex multi-elevator system of the present application, the elevator types may include a combination of two or more of the listed types.

[0185] For the complex multi-elevator system above, we take a high-rise system that includes high-speed elevators, low-speed elevators, and shuttle elevators as an example. In order to optimize personnel flow and improve efficiency, these elevators are usually divided into zones. The assumed elevator zone service is as follows:

[0186] Low-speed elevators: Mainly serve lower floors (such as 1-10 floors) because they are slower but have a large capacity and are suitable for handling large numbers of passengers traveling short distances.

[0187] High-speed elevators: divided into two or more groups, each group serves a different high-floor area. For example:

[0188] The first set of high-speed elevators serves floors 10-35.

[0189] The second set of high-speed elevators serves floors 25-50.

[0190] Shuttle elevator: specially designed for fast transportation from the ground floor to the transfer floor or sky lobby, such as going directly from the 1st floor to the 30th or 50th floor, and then passengers can transfer to other elevators to continue to higher floors.

[0191] Embodiment 3. Embodiment 3 of the present invention proposes an intelligent AED dispatching system for elevators in complex scenarios, wherein the intelligent AED dispatching system for elevators in complex scenarios includes the complex multi-elevator system as described in Embodiment 2 of the present invention, and an AED intelligent dispatching system platform for dispatching the complex multi-elevator system and AED equipment and first aid personnel who obtain the AED equipment. An AED dispatching allocation optimization model for the complex multi-elevator system is established in the AED intelligent dispatching system platform, which quickly screens out the optimal solution direction of the optimization problem based on the AED dispatching allocation optimization model, obtains the optimal solution of the optimization problem based on the AED dispatching allocation optimization model, and performs AED intelligent dispatching in the complex multi-elevator system to respond to first aid events.

[0192] Embodiment 4: Embodiment 4 of the present invention proposes an intelligent AED lifting platform for use in the method described in the first aspect, wherein the intelligent AED lifting platform device comprises an elevator and an AED device disposed in the elevator.

[0193] Embodiment 5. Embodiment 5 of the present invention proposes a readable storage medium, on which a control program is stored. When the control program is executed by a processor, the processor executes the steps of the elevator AED intelligent scheduling method in a complex scenario as described in Embodiment 1.

[0194] The following is an explanation of the algorithm involved in the elevator AED intelligent scheduling method under complex scenarios in the present invention.

[0195] In the step of establishing the AED dispatching and allocation optimization model under a complex multi-elevator system, as defined in the first embodiment of the present invention, the objective function is defined as follows:

[0196] J1: During the planning period, maximize the cumulative response probability of AED equipment and emergency personnel for the target floor:

[0197] (1);

[0198] J2, minimize the path of the AED equipment elevator to the target floor in the minimized floor system:

[0199] (2);

[0200] J3, minimize the path of the emergency personnel elevator reaching the target floor in the minimized floor system:

[0201] (3);

[0202] Constraint 1 is the resource limit of the configuration point. The total amount of resources allocated to each floor by configuration point i cannot exceed its total available resources:

[0203] (4);

[0204] Constraint 2 is the minimum demand capacity limit and maximum demand capacity limit of the floor. The minimum demand capacity limit is:

[0205] (5);

[0206] The maximum demand capacity is limited to:

[0207] (6);

[0208] Constraint three is a non-negativity constraint, where the number of allocated resources cannot be negative:

[0209] (7);

[0210] n represents a positive integer set , constraint three can be specifically .

[0211] In the step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model, the optimal solution direction of the optimization problem can be quickly screened out based on the AED scheduling allocation optimization model and the AOA algorithm (Archimedes Optimization Algorithm). In the AED scheduling allocation optimization model, it is mainly necessary to solve the optimal decision parameters C of the targets J1, J2 and J3 under the prior parameters. ij and W ij AOA is different from the general optimal value search by gradient solution. At the beginning of the algorithm, a set of candidate solutions is randomly generated. , where K is the number of candidate solution groups, this group of candidate solutions constitutes the initial population, each solution Represents a possible position in the search space. Each individual represents a configuration scheme for AED and emergency personnel, which can be directly substituted into formulas (1), (2) and (3) to obtain the results of J1, J2 and J3 under the prior parameters. However, unlike the exhaustive solution, its solution characteristics are described by volume (vol), density (den) and acceleration (acc), and the direction of the optimal solution is quickly screened out. The AOA algorithm simulates the movement of individuals in the solution space and is divided into a stage of quickly screening out the optimal solution direction of the optimization problem and a stage of fine processing to obtain the optimal solution of the optimization problem, so as to realize the search for the optimal solution allocation scheme.

[0212] The stage of quickly screening out the optimal solution direction for the optimization problem: simulate the large-scale adjustment of resources in the entire building, aiming to find possible excellent configuration solutions and avoid falling into local optimality.

[0213] The optimal solution stage of the optimization problem is obtained through fine processing: detailed adjustments are made within the neighborhood of the current excellent solution to further optimize resource allocation and improve emergency response efficiency.

[0214] The switching between the two phases is determined by the migration factor (TF), which is defined as follows:

[0215] (8);

[0216] Among them, t is the current iteration number, t max is the maximum number of iterations. When TF≤0.5, the algorithm enters the stage of quickly screening out the optimal solution direction of the optimization problem; otherwise, it enters the stage of fine processing to obtain the optimal solution of the optimization problem.

[0217] In the initialization phase, AOA randomly generates a set of resource allocation schemes (i.e., individuals in the population) and initializes the volume (vol i )、density(den i ) and acceleration (acc i). By evaluating the fitness of each solution, the best individual (x best ), and using its parameters (den best 、vol best ,acc best ) to update the density and volume of other individuals as follows:

[0218] (9);

[0219] (10)

[0220] When TF≤0.5, the algorithm enters the stage of quickly screening out the optimal solution direction of the optimization problem and updates the individual acceleration:

[0221] (11).

[0222] When TF>0.5, the optimal solution of the optimization problem is obtained through fine processing. The acceleration of the individual is calculated according to the formula:

[0223] (12).

[0224] According to the following formula, AOA normalizes the acceleration of an individual to update the individual's position, and u and l are used to adjust the normalization range:

[0225] (13).

[0226] In the stage of quickly screening out the optimal solution direction of the optimization problem, the position of the individual is updated according to the following formula, where and x represent the positions of the i-th individual in the t+1th and tth positions respectively, and x rand is the position of a random individual in the tth generation, rand is a random number between 0 and 1, C 1 is a constant, d is a density factor, and its update formula is as shown below:

[0227] (14);

[0228] (15).

[0229] In the stage of obtaining the optimal solution of the optimization problem through fine processing, the position of the individual is updated according to the following formula, where is a fixed constant, ,and ; F is the direction factor, which is used to determine the direction of the position update in the iteration, and its definition is shown in formulas (16) and (17):

[0230] (16);

[0231] (17).

[0232] In the stage of obtaining the optimal solution of the optimization problem through fine processing, the L-BFGS-B algorithm can be used. L-BFGS-B (Limited-memory BFGS with Bounds) is an iterative algorithm for solving constrained or unconstrained nonlinear optimization problems. It updates the variables by approximating the Hessian matrix of the objective function, thereby accelerating convergence while considering the variable boundary constraints. In the optimization of AED and emergency personnel configuration, the L-BFGS-B algorithm can be used to perform fine local optimization of the current optimal solution to further improve the rationality of resource allocation.

[0233] In the steps of quickly screening out the optimal solution direction of the optimization problem based on the AED dispatch allocation optimization model and obtaining the optimal solution of the optimization problem based on the AED dispatch allocation optimization model, the specific hybrid algorithm process can be referred to Figure 4 As shown, the steps are as follows:

[0234] Step 1: Initialize algorithm parameters.

[0235] 1. Determine the population size (number of individuals) and the maximum number of iterations t max .

[0236] 2. Set constant parameter C 1 , C 2 , C 3 , C 4 and the range of acceleration [l, u].

[0237] 3. Randomly generate the initial population, each individual represents a resource allocation plan.

[0238] Step 2: Evaluate fitness and record the best individual.

[0239] 1. Calculate the fitness value of each individual in the population, that is, evaluate the performance of the resource allocation plan in meeting emergency response needs, minimizing response time and resource deviation, etc.

[0240] 2. Update the optimization parameter x of the current optimal individual best 、density best Volume best and acceleration acc best .

[0241] Step 3: Update density and volume.

[0242] 1. According to formulas (9) and (10), use the density and volume of the current optimal individual to update the density and volume of other individuals in the population.

[0243] Step 4: Calculate the migration factor and density factor.

[0244] 1. Calculate the migration factor TF to determine whether the algorithm enters the global exploration or local development stage.

[0245] 2. Calculate the density factor d t+1 , used to adjust the amplitude of individual position updates

[0246] Step 5: Quickly screen out the optimal solution direction for the optimization problem (when TF≤0.5).

[0247] 1. Update acceleration: Update the individual acceleration according to formulas (11) and (12).

[0248] 2. Update position: Update the position of the individual according to formula (13).

[0249] Step 6: Fine processing to obtain the optimal solution stage of the optimization problem (when TF>0.5).

[0250] 1. Use the current optimal individual x best As the initial point, the L-BFGS-B algorithm is executed for local optimization;

[0251] 2. If a better resource allocation solution is found, update x best and its corresponding parameters.

[0252] 3. Otherwise, update acceleration: update the individual's acceleration according to formula (10); update position: update the individual's position according to formula (14).

[0253] Step 7: Check the stop condition.

[0254] 1. If the maximum number of iterations t is reached max Or if the improvement of the optimal solution is less than the preset threshold, the algorithm terminates and outputs the optimal solution allocation plan.

[0255] 2. Otherwise, set t = t + 1 and return to step 2 to continue the next iteration.

[0256] 3. Finally output the optimal solution individual

[0257] Through the implementation of the above embodiments, after an emergency incident actually occurs in a high-rise building, the main dispatching process can be referred to as follows:

[0258] 1. Receive the request:

[0259] When an emergency occurs on a certain floor and an AED is needed, personnel on that floor make a request through an emergency button or communication device.

[0260] After receiving the request, the system confirms the specific floor where the AED is needed and determines j as the target response floor.

[0261] 2. Locate the AED:

[0262] The system queries the current location of the AED. If the AED is placed on a fixed floor, the system will know its location directly; if the AED can be moved, the system needs to determine the current location of the AED through sensors or other means, determine the elevator AED set and the response probability H i and H j .

[0263] The system queries the current emergency personnel response location and determines the elevator emergency personnel set and response probability E i and E j .

[0264] 3. Calculate the optimal path:

[0265] The patented method system calculates the optimal dispatch path from the current location of the AED to the target floor. This includes considering the current state of the elevator (such as whether it is running, the current floor, etc.) and whether there are other passengers' needs.

[0266] If the elevator has no other current mission, it will go directly to the floor where the AED is located.

[0267] If the elevator is otherwise busy, the system evaluates whether it can be immediately rerouted to prioritize the AED request.

[0268] 4. Adjust the elevator task:

[0269] If the elevator is performing other tasks, the system will evaluate whether it can interrupt the current task to prioritize the AED request. For example, if the elevator is going up and approaching the floor where the AED is located, it may choose to pick up the AED first and then continue to complete the previous task.

[0270] If the elevator cannot respond immediately, the system notifies the requester of the estimated time of arrival and may suggest using the stairs or other emergency measures.

[0271] 5. Transporting AED:

[0272] Once the elevator reaches the floor where the AED is located, the system will prompt the operator to put the AED into the elevator.

[0273] After the elevator doors close, go directly to the destination floor that requires an AED.

[0274] 6. Arrive at the target floor:

[0275] When the elevator reaches the target floor, the system will prompt the operator to take out the AED and quickly send it to the location where emergency treatment is needed.

[0276] The elevator may stop briefly at the target floor to allow the operator enough time to retrieve the AED.

[0277] The elevator's luggage can display a floor navigation map to the target location so that operators and emergency personnel can quickly reach the target location. The elevator car TV floor navigation diagram can also provide a navigation map. The navigation map can be referenced Figure 5 shown.

[0278] 7. Resumption of normal services:

[0279] Once the AED is successfully delivered, the elevator system will resume normal service mode and continue to process other passengers' requests.

[0280] 8. Monitoring and Recording:

[0281] During the entire process, the system will continuously monitor the status of the elevator and the delivery of the AED.

[0282] After the incident is over, the system will record the emergency response for subsequent analysis and improvement.

[0283] 9. Alternative plan:

[0284] In addition to maintaining the execution of the most optimized first scheduling plan, the elevator scheduling system also maintains the calculation of the sub-optimal scheduling plan. If there is a situation in which the first optimization plan cannot be executed, the sub-optimal scheduling plan is started.

[0285] If the elevator fails or fails to respond, the system should have a backup plan, such as notifying staff on other floors to manually carry the AED to the target floor, or using other available vertical transportation.

[0286] Through the implementation of the above embodiments of the present invention, on the basis of establishing a reliable and efficient AED scheduling and allocation optimization model, the above two steps of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling and allocation optimization model and obtaining the optimal solution of the optimization problem based on the AED scheduling and allocation optimization model are combined. It is possible to quickly screen out the optimal solution direction of the optimization problem in a large range of the entire building with limited resources to avoid falling into local optimality, and to obtain the optimal solution of the optimization problem through fine processing in the screened optimal solution direction, further optimize resource allocation, improve emergency response efficiency, ensure that patients in urgent need of cardiopulmonary resuscitation can be effectively treated in time, and provide cardiac arrest patients with ventricular fibrillation After the occurrence of ventricular fibrillation, defibrillation support can be obtained as soon as possible, thereby improving the patient's treatment speed and survival rate, and achieving the purpose of the present invention.

[0287] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent dispatching method for elevator AED in complex scenarios, characterized in that: The method comprises: Establish an AED dispatching and allocation optimization model under a complex multi-elevator system, summarize the allocation plan of AED equipment and emergency personnel who obtain AED equipment under a complex multi-elevator system into an optimization problem, define the prior parameters in the complex multi-elevator system and the shortest path to the required decision variables based on the optimization problem, construct the objective function related to the macro-dispatching of AED equipment and emergency personnel for the optimization problem and the constraints related to the AED equipment resource allocation and floor capacity limit with the prior parameters and decision variables, and establish an AED dispatching and allocation optimization model; Based on the AED dispatch allocation optimization model, the optimal solution direction of the optimization problem is quickly screened out. The AED dispatch allocation optimization model randomly generates one or more groups of candidate solutions. Each individual in the multiple candidate solutions represents an allocation plan. After substituting the candidate solution into the macro dispatch objective function, the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system is obtained. According to the model result, the optimal solution direction of the allocation plan is quickly screened out, and the optimization parameters of the current optimal individual are selectively updated according to the optimal solution direction. Based on the refined processing of the AED dispatch allocation optimization model, the optimal solution of the optimization problem is obtained, and refined local optimization is performed according to the optimal solution direction of the allocation plan to ensure that the allocation plan in the optimal solution direction meets all constraints related to the AED equipment resource configuration and floor capacity restrictions, and the current optimal individual is used for iterative optimization until the maximum number of iterations is reached or the improvement corresponding to the optimization parameter of the optimal individual is less than the preset amplitude threshold, and the optimal solution allocation plan is output; Perform intelligent AED dispatch in complex multi-elevator systems, and use the optimal solution allocation plan to intelligently dispatch AED equipment and emergency personnel who obtain AED equipment.

2. The intelligent dispatching method for elevator AED in complex scenarios according to claim 1 is characterized in that: In the steps of establishing the AED dispatch allocation optimization model under a complex multi-elevator system: The a priori parameters are parameters set according to the floor partitions, elevator partitions, AED equipment layout in the elevators, and personnel flow in the complex multi-elevator system; The decision variables are the variables that enable the emergency personnel and the elevator AED to reach the desired destination by the shortest path within the planning period.

3. The intelligent dispatching method of elevator AED in complex scenarios according to claim 2 is characterized in that: The prior parameters include: I, indicating the configuration or location index of the AED device or first aid personnel; J, represents the floor index in the building; M, represents the total number of AED equipment configuration points; N, represents the total number of floors; B i , represents the probability of initialization response of AED equipment and emergency personnel in the configuration area i within the planning period; H i , represents the probability of target response of available AED equipment in configuration area i within the planning period; E i , represents the probability of target response of available first aid personnel in the configuration area i within the planning period; H j , represents the set of AED devices on floor j that arrive at the target floor within the planning period; E j , represents the set of emergency personnel arriving at the target floor on floor j within the planning period; L j , indicating that floor j reaches the efficiency set within the planning period; U j , represents the set of AED equipment and first aid personnel efficiency consumed on floor j; M j , represents the set of serviceable elevator AED devices on floor j; N j , represents the set of elevators that can serve floor j.

4. The intelligent dispatching method for elevator AED in complex scenarios according to claim 3 is characterized in that: The decision variables include: C ij , represents the response priority of the elevator set from configuration point i to floor j, which is used as the decision variable in the AED scheduling allocation optimization model; W ij , represents the elevator resources allocated from configuration point i to floor j, which serves as the decision variable in the AED scheduling allocation optimization model.

5. The intelligent dispatching method for elevator AED in complex scenarios according to claim 4 is characterized in that: In the step of establishing the AED dispatching and allocation optimization model under the complex multi-elevator system, the objective function related to the macro-dispatching of AED equipment and emergency personnel corresponds to a multi-objective optimization problem with three objectives, namely J1, J2 and J3, where: J1: Minimize the cumulative response probability of AED and emergency personnel for the target floor; J2: Minimize the path for the AED elevator to reach the target floor in the minimized floor system; J3: Minimize the path for emergency personnel's elevator to reach the target floor in the minimized floor system.

6. The intelligent dispatching method for elevator AED in complex scenarios according to claim 5 is characterized in that: In the step of establishing the AED dispatching and allocation optimization model under a complex multi-elevator system, the objective function is defined as follows: J1: During the planning period, maximize the cumulative response probability of AED equipment and emergency personnel for the target floor: , which aims to maximize the cumulative response probability of AED and first responders; J2, minimize the path of the AED equipment elevator to the target floor in the minimized floor system: , aims to minimize the total AED response cost or time and ensure that the AED equipment can reach the required floor via the optimal path; J3, minimize the path of the emergency personnel elevator reaching the target floor in the minimized floor system: , aims to minimize the total emergency personnel response cost or time and ensure that emergency personnel can reach the required floor via the optimal path.

7. The intelligent dispatching method for elevator AED in complex scenarios according to claim 6 is characterized in that: In the step of establishing the AED dispatching and allocation optimization model under the complex multi-elevator system, the constraints related to the AED equipment resource configuration and floor capacity limitation include constraint one, constraint two, and constraint three, respectively, where: Constraint 1 is the resource limit of the configuration point. The total amount of resources allocated to each floor by configuration point i cannot exceed its total available resources: ; Constraint 2 is the minimum demand capacity limit and maximum demand capacity limit of the floor. The minimum demand capacity limit is , the maximum demand capacity limit is ; Constraint three is a non-negativity constraint, where the number of allocated resources cannot be negative. , n represents a positive integer set .

8. The intelligent dispatching method for elevator AED in complex scenarios according to claim 7 is characterized in that: In the step of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling allocation optimization model, the AED scheduling allocation optimization model randomly generates candidate solutions, and the candidate solutions include the response priority C of the elevator set from the configuration point i to the floor j. ij and the elevator resources W allocated from configuration point i to floor j ij ,Right now , where K is the number of candidate solution populations, this group of candidate solutions constitutes the initial population, and each candidate solution Represents a possible position in the search space, and the candidate solution is directly substituted into the formula corresponding to the objective functions J1, J2 and J3 to obtain the model results of the optimal decision variables under each corresponding prior parameter.

9. The intelligent dispatching method for elevator AED in complex scenarios according to claim 8 is characterized in that: In the process of switching from the step of quickly screening out the optimal solution direction of the optimization problem based on the AED dispatch allocation optimization model to the step of fine processing based on the AED dispatch allocation optimization model to obtain the optimal solution of the optimization problem, the migration factor TF determines whether to switch. , where t is the current iteration number, t max is the maximum number of iterations. When TF≤0.5, continue to quickly screen out the optimal solution direction of the optimization problem; when TF>0.5, enter the fine processing to obtain the optimal solution of the optimization problem.

10. The intelligent dispatching method for elevator AED in complex scenarios according to claim 9 is characterized in that: The steps of quickly screening out the optimal solution direction of the optimization problem based on the AED scheduling and allocation optimization model include the following steps: Determine the number of individuals and the maximum number of iterations t of the allocation scheme max , a group or multiple groups of candidate solutions are randomly generated by the AED dispatch allocation optimization model, each individual in the multiple candidate solutions represents an allocation scheme for an AED device and emergency personnel who obtain the AED device, and the range of each constant parameter and acceleration is set; Substituting the candidate solution into the macro scheduling objective function, the model result of the optimal decision variable of the corresponding objective function under the prior parameters in the complex multi-elevator system is obtained, and the optimal solution direction of the allocation plan of AED equipment and emergency personnel who obtain AED equipment is quickly screened out according to the model result; Selectively update the optimization parameters of the current best individual according to the direction of the optimal solution, and use the optimization parameters of the current best individual to update the optimization parameters of other individuals in the candidate solution; Calculate the migration factor and parameter amplitude factor. The migration factor determines whether to continue to quickly screen out the optimal solution direction of the optimization problem or to enter the fine processing to obtain the optimal solution of the optimization problem. The parameter amplitude factor is used to adjust the amplitude of the optimization parameter update of the optimal individual and other individuals. When the migration factor is ≤0.5, continue to quickly screen out the optimal solution direction of the optimization problem.

11. The intelligent dispatching method for elevator AED in complex scenarios according to claim 10 is characterized in that: The steps of obtaining the optimal solution of the optimization problem based on the AED scheduling and allocation optimization model include the following steps: When the migration factor is greater than 0.5, fine processing is performed to obtain the optimal solution to the optimization problem; Perform fine local optimization according to the optimal solution direction of the allocation plan to ensure that the allocation plan in the optimal solution direction meets all constraints related to AED equipment resource configuration and floor capacity restrictions; Use the current optimal individual as the starting point and perform local iterative optimization. If a better allocation scheme is found during the iterative optimization process, the better allocation scheme is used as the new current optimal individual and the corresponding optimization parameters are updated. If no better allocation scheme is found, the optimization parameters of the current best individual are continuously updated according to the parameter amplitude factor; When the maximum number of iterations is reached or the update amplitude of the optimization parameters of the best individual is less than the preset amplitude threshold, stop this step and output the optimal solution allocation plan; Otherwise, continue with the step of selectively updating the optimization parameters of the current optimal individual according to the direction of the optimal solution and subsequent steps.

12. The intelligent dispatching method for elevator AED in complex scenarios according to claims 1 to 11, characterized in that: In the step of executing AED intelligent dispatch in a complex multi-elevator system, a suboptimal solution allocation scheme is calculated simultaneously. If the optimal solution allocation scheme is interrupted during execution, the suboptimal solution allocation scheme is started.

13. A complex multi-elevator system, used in the method according to any one of claims 1 to 12, characterized in that: The complex multi-elevator system includes multiple elevator shafts, each elevator shaft is selectively configured with various types of elevators, the elevators are selectively configured to serve different partitions, and the elevators are selectively configured with AED equipment.

14. The complex multi-elevator system according to claim 13, characterized in that: The elevators include high-speed elevators, low-speed elevators, shuttle elevators, and service / freight elevators; The high-speed elevator is used to quickly reach high floors; The low-speed elevator is used to serve the lower floors; The shuttle elevator is used to quickly transport from the ground floor or transfer floor to a specific intermediate floor or sky lobby; The service / cargo elevator is used for transporting goods and service personnel, as well as for evacuation; The zoning service includes zoning elevators and double-decker elevators; The zoned elevator only serves a specific range of floors; the double-deck elevator stops at two adjacent floors at the same time.

15. An elevator AED intelligent dispatching system in complex scenarios, characterized by: The elevator AED intelligent dispatching system in complex scenarios includes a complex multi-elevator system as described in claim 13 or 14, and an AED intelligent dispatching system platform for dispatching the complex multi-elevator system and AED equipment and first aid personnel who obtain AED equipment. An AED dispatching allocation optimization model for the complex multi-elevator system is established in the AED intelligent dispatching system platform. The optimal solution direction of the optimization problem is quickly screened out based on the AED dispatching allocation optimization model, and the optimal solution of the optimization problem is obtained through fine processing based on the AED dispatching allocation optimization model, and AED intelligent dispatching is performed in the complex multi-elevator system to respond to first aid events.

16. An intelligent AED lifting platform device, used in the method according to any one of claims 1 to 12, characterized in that: The intelligent AED lifting platform equipment includes an elevator and an AED device configured in the elevator.

17. A readable storage medium, characterized in that: The readable storage medium stores a control program, and when the control program is executed by a processor, the processor executes the steps of the method according to any one of claims 1 to 12.

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