Emergency rescue system optimization method and device

Abstract

The present application proposes an emergency rescue system optimization method and device, which relates to the field of emergency rescue technology, wherein the method includes: initializing the service rate of each service point to each demand point and determining the state probability in the emergency rescue system; correcting the service rate according to the state probability; updating the state probability of the emergency rescue system according to the updated service rate; correcting the service rate with the goal of reducing the gap between the state probability in the current iteration and the state probability in the previous iteration to obtain the target service rate, and performing comparative analysis to determine the service points to be optimized for optimization. By optimizing the service rate based on the gap in state probabilities between adjacent iteration rounds, an accurate estimation of the target probability corresponding to each state in the system is achieved, and comparative analysis is performed based on the target probability to determine the service points to be optimized for optimization, avoiding the mismatch between the service capacity of the service point and the demand for the service point, and improving the service quality and efficiency of the emergency rescue system.

Description

Technical Field

[0001] The present application relates to the field of emergency rescue technology, and in particular to an emergency rescue system optimization method and device. Background Art

[0002] Road traffic safety is a focus of global public health and development. After a road traffic accident occurs, effectively deploying rescue forces to the scene and shortening the accident response time are the key to reducing accident losses and casualties. The earlier the rescue forces arrive at the scene, the more conducive it is to the treatment of the injured, the restoration of traffic order, and the further reduction of secondary disasters.

[0003] At present, the setting of service points in the emergency rescue system and the classification of rescue forces in the service points are mainly based on the experience of urban planners. This setting method is highly subjective and has no scientific basis. It will lead to a low response capability of the emergency rescue system to traffic accidents, and it is difficult to meet the requirements for service quality and efficiency. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first purpose of this application is to propose an emergency rescue system optimization method.

[0006] The second object of the present application is to provide a device.

[0007] The third objective of the present application is to provide an electronic device.

[0008] A fourth objective of the present application is to provide a computer-readable storage medium.

[0009] A fifth object of the present application is to provide a computer program product.

[0010] To achieve the above objectives, the first embodiment of the present application proposes an emergency rescue system optimization method, including:

[0011] Obtain information on each service point and demand point in the current emergency rescue system;

[0012] Initializing the service rate of each of the service points to each of the demand points;

[0013] Determine the state probability corresponding to each system state in the emergency rescue system according to the service rate, and end this round of iteration;

[0014] Starting the next iteration, and correcting the service rate according to the state probability;

[0015] Update the state probability corresponding to each system state in the emergency rescue system according to the updated service rate;

[0016] The service rate is corrected with the goal of reducing the gap between the state probability in the current iteration and the state probability in the previous iteration to obtain a target service rate;

[0017] The target probability corresponding to each system state in the emergency rescue system is determined according to the target service rate, and the service point to be optimized is determined by comparing and analyzing the target probability with the service capacity of each service point, and the service point to be optimized is optimized.

[0018] Optionally, in the step of initializing the service rate of each service point to each demand point, the service rate of a single service point to each demand point is the same.

[0019] Optionally, determining the state probability corresponding to each system state in the emergency rescue system according to the service rate includes:

[0020] Solving a first equation according to the service rate to determine the state probabilities corresponding to each system state in the emergency rescue system;

[0021] The first equation is formulated as:

[0022]

[0023]

[0024] in, is the current system status, The emergency rescue system is in system state The probability of, j is the demand point, J is the set of demand points, i is the service point, I is the set of service points, λ j is the demand arrival rate of demand point j, System Status The state of service point i, System Status The state of service point i, is the service rate corresponding to i, for The corresponding transfer-in or transfer-out status, for A collection of The emergency rescue system is in system state The probability of

[0025] The system state caused by completing the service for the service point changes from Transfer In The corresponding second state transition indicator, where Transfer In When only one service point ends service otherwise

[0026] The system state caused by demand point j changes from Transfer In The corresponding first state transition indicator, where when the system state changes from Transfer In When it meets the priority list otherwise

[0027] Optionally, the modifying the service rate according to the state probability includes:

[0028] Calculate the service rate corresponding to each service point to find the service times ratio π of service point i to demand point j ij , where π ij It is the ratio of the number of times service point i serves demand point j to the number of times service point i has completed all services;

[0029] According to all π corresponding to the service point j ij The service rates are modified.

[0030] Optionally, the solution is to calculate the service times ratio π of service point i to demand point j ij The formula expression is:

[0031]

[0032] Among them, λ k is the demand arrival rate of the kth demand point.

[0033] Optionally, the service point i corresponds to all π ij The formula for modifying the service rate is expressed as:

[0034]

[0035] in, is the corrected service rate of the service point i, μ ij It is the original service rate of each service point to the demand point.

[0036] Optionally, the modifying the service rate with the goal of reducing the gap between the state probability in the current iteration and the state probability in the previous iteration to obtain a target service rate includes:

[0037] Obtain the difference between the state probability in this iteration and the state probability in the previous iteration;

[0038] In response to the difference being less than a preset threshold, the service rate in the current iteration is determined as the target service rate; or, in response to the difference being greater than or equal to the threshold, the next iteration is started.

[0039] Optionally, performing comparative analysis based on the target probability and the service capabilities of each of the service points to determine the service point to be optimized includes:

[0040] Determine the busy rate of each of the service points according to the target probability of each of the system states, wherein the busy rate is the probability that the service point is in a busy state;

[0041] Obtaining a service capability indicator of the service point;

[0042] Obtaining the matching degree between the busy rate of each of the service points and the service capability indicator;

[0043] The service point whose matching degree is lower than a preset matching degree threshold is determined as the service point to be optimized.

[0044] To achieve the above-mentioned purpose, the second embodiment of the present application proposes an emergency rescue system optimization device, including:

[0045] An initialization module is used to obtain information about each service point and demand point in the current emergency rescue system; and initialize the service rate corresponding to each of the service points;

[0046] A probability determination module is used to initialize the service rate of each service point to each demand point; determine the state probability corresponding to each system state in the emergency rescue system according to the service rate, and end this round of iteration; start the next round of iteration, correct the service rate according to the state probability; update the state probability corresponding to each system state in the emergency rescue system according to the updated service rate;

[0047] An updating module, configured to correct the service rate with the goal of reducing the gap between the state probability in this iteration and the state probability in the previous iteration, so as to obtain a target service rate;

[0048] The optimization module is used to determine the target probability corresponding to each system state in the emergency rescue system according to the target service rate, and to determine the service point to be optimized by comparing and analyzing the target probability with the service capacity of each service point, and to optimize the service point to be optimized.

[0049] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0050] The memory stores computer-executable instructions;

[0051] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0052] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0053] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, which implements any method in the first aspect when executed by a processor.

[0054] The emergency rescue system optimization method, device, electronic device and storage medium provided in the present application optimize the service rate by optimizing the difference in state probabilities between adjacent iteration rounds, achieves accurate estimation of the target probability corresponding to each state in the system, and performs comparative analysis based on the target probability to determine the service points to be optimized, thereby avoiding a mismatch between the service capacity of the service point and the demand for the service point, and improving the service quality and efficiency of the emergency rescue system.

[0055] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0057] Figure 1 A flowchart of an emergency rescue system optimization method provided in an embodiment of the present application;

[0058] Figure 2 A schematic diagram of state transition of an emergency rescue system provided in an embodiment of the present application;

[0059] Figure 3 A flowchart of an emergency rescue system optimization method provided in an embodiment of the present application;

[0060] Figure 4 A schematic diagram of comparison between a busy rate and the service capability index provided in an embodiment of the present application;

[0061] Figure 5 A schematic diagram of the structure of an emergency rescue system optimization device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0063] Road traffic safety is a focus of global public health and development. After a road traffic accident occurs, effectively deploying rescue forces to the scene and shortening the accident response time are the key to reducing accident losses and casualties. The earlier the rescue forces arrive at the scene, the more conducive it is to the treatment of the injured, the restoration of traffic order, and the further reduction of secondary disasters.

[0064] At present, the setting of service points in the emergency rescue system and the classification of rescue forces in the service points are mainly based on the experience of urban planners. This setting method is highly subjective and has no scientific basis. It will lead to a low response capability of the emergency rescue system to traffic accidents, and it is difficult to meet the requirements for service quality and efficiency.

[0065] To address this problem, the present invention provides an optimization method for an emergency rescue system. Figure 1 The following is a flow chart of an emergency rescue system optimization method provided in an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0066] Step 101, obtaining information of each service point and demand point in the current emergency rescue system; Step 102, initializing the service rate of each service point to each demand point; Step 103, determining the state probability corresponding to each system state in the emergency rescue system according to the service rate, and ending this round of iteration;

[0067] Step 104, starting the next iteration, and correcting the service rate according to the state probability;

[0068] Step 105, updating the state probability corresponding to each system state in the emergency rescue system according to the updated service rate;

[0069] Step 106, correcting the service rate with the goal of reducing the gap between the state probability in the current iteration and the state probability in the previous iteration, so as to obtain a target service rate;

[0070] Step 107, determining the target probability corresponding to each system state in the emergency rescue system according to the target service rate, and performing comparative analysis between the target probability and the service capacity of each service point to determine the service point to be optimized, and optimizing the service point to be optimized.

[0071] In this embodiment, when analyzing the emergency rescue system, the emergency rescue station is abstracted as a service point, the area requiring emergency rescue is divided into multiple small areas, and these small areas are abstracted as multiple service points. In this way, a model is constructed that describes the operation and conversion of the emergency rescue system in a region.

[0072] The service rate is the service probability of each service point to the demand point. In the relevant model, the service rate of the rescue service point depends only on the service point itself. When the system is in different states, the proportion of served objects will change, so the average service efficiency of the service point will also change. However, in the emergency road rescue system, the impact of different road rescue demand points on service efficiency must be considered. These impacts mainly come from the differences in the geographical locations of road rescue demand points, which have a significant impact on service efficiency that cannot be ignored. Some simple approximation methods, such as directly averaging the service efficiency, may lead to very large errors.

[0073] In order to solve the above problems, this embodiment approximately assumes that the service rate of the service point will not change with the change of the system state. Therefore, the modified service rate can be used to represent the service capacity of the service point. When initializing the service rate corresponding to each of the service points, the service rate of each service point to each demand point is the same. Optionally, for each service point, the median of the service efficiency of all demand points is selected as its corresponding service rate.

[0074] In a possible embodiment, Table 1 shows the service rate μ of each service point i to the demand point j in a city. ij , the column title is the service point, and the row title is the demand point. These service rates are obtained based on the historical data of the service points. The service rate is initialized based on this table, that is, for each service point, the median of the service efficiency of all demand points is selected as its corresponding service rate.

[0075]

[0076] Table 1

[0077] For the service point of Fire Rescue Station A, the median of the service efficiency of all corresponding demand points (0.600+0.652) / 2=0.626 is used as the service rate of the service point to all demand points, and the same is true for other service points.

[0078] Optionally, determining the state probability corresponding to each system state in the emergency rescue system according to the service rate includes:

[0079] Solving a first equation according to the service rate to determine the state probabilities corresponding to each system state in the emergency rescue system;

[0080] The first equation is formulated as:

[0081]

[0082] in, is the current system status, The emergency rescue system is in system state The probability of, j is the demand point, J is the set of demand points, i is the service point, I is the set of service points, λ j is the demand arrival rate of demand point j, System Status The state of service point i, System Status The state of service point i, is the service rate corresponding to i, for The corresponding transfer-in or transfer-out status, for A collection of The emergency rescue system is in system state The probability of The system state caused by completing the service for the service point changes from Transfer In The corresponding second state transition indicator, where Transfer In When only one service point ends service otherwise

[0083] The system state caused by demand point j changes from Transfer In The corresponding first state transition indicator, where when the system state changes from the demand caused by demand point j to Transfer In When it meets the priority list otherwise

[0084] Formula (1) is the state transition equation of the emergency rescue system. The process of the system evolving to a steady state over time is given by the steady state probability of each state: Indicates that the left side of the equal sign in formula (1) indicates the exit state All types of outgoing transitions occur when: (i) the state There is at least one idle service point in the state to handle the newly arrived service request; or (ii) The busy service point in has completed the service. The right side of the equation represents all the entry states These situations occur when: (iii) an idle service point is assigned, causing the system to move from state Transition to state to process an incoming service request; or (iv) status Busy but in condition The idle service points in the above table complete the service. Optionally, in case (iii), service points can be allocated to demand points according to the preference list. The preference list contains the priority of the service point corresponding to each demand point. The priority reflects whether the service point is convenient for handling the demand point. The higher the priority, the more convenient it is for the service point to handle the demand point. When allocating, the idle service points can be preferentially allocated to the corresponding demand points.

[0085] Take a system with three service points as an example, the value of i is 1, 2 or 3, and the system status is and The description method is and is the state of a single service point i, or The value of 1 indicates that service point i is busy. or The value of 0 indicates that service point i is in idle state.

[0086] Indicates that the system status changes from In the case of transfer out, since the service point will end the service only when it is busy, only Item.

[0087] Indicates that the system status changes from Transfer to In this case, the service point corresponding to the end of the service Other service points

[0088] It should be noted that the premise of using formula (1) is that at least one service point in the system is idle. When all service points are busy, it is necessary to remove the ∑ on the left side of the equal sign in formula (1). J λ j , and the right side of the equal sign

[0089] Let us take a system with 3 service points (S1, S2, S3) and 3 demand points (D1, D2, D3) as an example, where the priority list of the system is shown in Table 2. We will analyze the state The transfer equation. It means that the first and third service points are idle and the second service point is busy.

[0090] Figure 2 A state transition diagram of an emergency rescue system provided in an embodiment of the present application is shown in FIG. Figure 2 As shown. and For example, the three elements represent three service points, and the value of the element represents whether the service point is busy (0 represents idle service point, 1 represents busy service point), [0,1,1] represents that service point S1 is idle, and service points S2 and S3 are busy. The possible transitions between them include: (i) When the system is in state When the service request of the second demand point arrives, since the second service point is busy, demand point 2 is handled by the third service point. (ii) When the system is in state When the service request of the third demand point arrives, since the service point 3 is the first priority for the demand point 3, the demand point 3 is processed by the third service point. (iii) When the system is in state When , the third service point completed the service. In summary, the state The transfer equation is shown in equation (2).

[0091]

[0092]

[0093] Table 2

[0094] As shown in Table 2, for demand point D1, the priority of demand points is from high to low: S1, S2, S3. That is, if demand point D1 generates demand (emergency), and the three service points are all idle, then S1 will be dispatched to handle the demand of D1; if S1 is busy, S2 will be dispatched to handle the demand of D1; if both S1 and S2 are busy, S3 will be dispatched to handle the demand of D1.

[0095] According to Table 2, the corresponding first state transition index in the process of transitioning between two states can be determined and the second state transition indicator

[0096] The state transition in the system needs to follow the priority order in the priority list. Each time a service point is dispatched to serve a demand point, the service point with the highest priority among the idle service points for the demand point needs to be dispatched. For example, if the current system state is [0,1,0], then if D1 generates a demand at this time, according to Table 2, it can be determined that S1 needs to be dispatched to handle D1's demand, and S3 cannot be dispatched to handle D1's demand. Then, from the current state to [1,1,0], the corresponding Set to 1; transfer from the current state to [0,1,1], the corresponding Set to 0.

[0097] The state transition of the service point in the system to complete the service needs to follow the queuing principle, that is, each state transition can only change the state of a single service point from 1 to 0. For example: the current system state is [1,1,0]. If the system state is transferred from the current state to [0,1,0], only the service of service point S1 ends, then it complies with the queuing principle. Set to 1; if the system state is transferred from the current state to [0,0,0], service points S1 and S2 both end their services, which does not comply with the queuing principle. Set to 0.

[0098] For a system with s service points, the model contains 2 s states, which means that 2 s linear equations. For each state, a state transition equation (1) can be listed, so the total number of equations is exactly equal to 2 s However, these equations are not linearly independent, so equation (3) is also required to replace any of them in order to make the model solvable.

[0099]

[0100] Optionally, the modifying the service rate according to the state probability includes:

[0101] Calculate the service rate corresponding to each service point to find the service times ratio π of service point i to demand point j ij , where π ij It is the ratio of the number of times service point i serves demand point j to the number of times service point i has completed all services;

[0102] According to all π corresponding to the service point i ij The service rates are modified.

[0103] Optionally, the solution is to calculate the service times ratio π of service point i to demand point j ij The formula expression is:

[0104]

[0105] Among them, λ k is the demand arrival rate of the kth demand point, The system state caused by the demand point k changes from Transfer In The corresponding first state transition indicator, where when the system state changes from the demand caused by demand point k to Transfer In When it meets the priority list otherwise

[0106] In this embodiment, due to the existence of a preference list, for each service point, each demand point is not evenly distributed to each service point based on the arrival rate. In other words, the proportion of demand points served by each service point may be different. However, if we know the probability of the system in each state, this proportion can be calculated. The calculation formula is shown in equation (4), where the numerator represents the frequency of demand point i arriving at service point j, and the denominator represents the frequency of service point j accepting the demand.

[0107] Optionally, the service point i corresponds to all π ij The formula for modifying the service rate is expressed as:

[0108]

[0109] in, is the second service rate corresponding to the service point i. ij It is the original service rate of each service point to the demand point, which is a pre-configured value.

[0110] In this embodiment, as shown in equation (5), after determining the proportion of demand points served by each service point, we use this proportion as a weight to calculate the weighted harmonic mean of the efficiency of each service point:

[0111]

[0112] Optionally, the modifying the service rate with the goal of reducing the gap between the state probability in the current iteration and the state probability in the previous iteration to obtain a target service rate includes:

[0113] Obtain the difference between the state probability in this iteration and the state probability in the previous iteration;

[0114] In response to the difference being less than a preset threshold, the service rate in the current iteration is determined as the target service rate; or, in response to the difference being greater than or equal to the threshold, the next iteration is started.

[0115] In this embodiment, the calculation formula of the modified service point efficiency requires the service point probability of the system under various states, which is exactly the solution of the hypercube queue equilibrium equation group composed of multiple (1). Therefore, we can continuously iterate and solve through equation (1) and equation (4).

[0116] Figure 3 A flowchart of an emergency rescue system optimization method provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the method includes:

[0117] Step 301, initializing the service rate of the service point, for example, selecting the median of the service rates of all demand points for each service point.

[0118] Step 302, using the service rates of these service points as parameters to solve the equation system of equations (1) and (3), completing one iteration.

[0119] Step 303, after each iteration of solving equation (1) is completed, we compare the state probabilities corresponding to each state calculated in this iteration with the state probabilities corresponding to each state obtained in the previous iteration. If the state probabilities corresponding to all states change less than a preset threshold (such as 0.001), then exit the iteration and output the target service rate. Otherwise, execute step 304 to enter the next iteration.

[0120] In step 304, we use the solution of equation (1) to substitute into equations (4) and (5) to update the service rate of the service point, and then re-execute step 301 and input it into equation (1) to solve it. Finally, an iterative loop is formed.

[0121] Optionally, performing comparative analysis based on the target probability and the service capabilities of each of the service points to determine the service point to be optimized includes:

[0122] Determine the busy rate of each of the service points according to the target probability of each of the system states, wherein the busy rate is the probability that the service point is in a busy state;

[0123] Obtaining a service capability indicator of the service point;

[0124] Obtaining the matching degree between the busy rate of each of the service points and the service capability indicator;

[0125] The service point whose matching degree is lower than a preset matching degree threshold is determined as the service point to be optimized.

[0126] In this embodiment, after the target probability of each system state is obtained, the busy rate and service capacity of each service point in the system need to be analyzed according to the target probability to determine whether they match, so as to avoid the situation where the service capacity of a busier service point is not high enough, resulting in the inability to provide service to the demand point in a timely manner, or the service capacity of a less busy service point is too high, resulting in a waste of rescue resources.

[0127] First, the busy rate of each service point is determined according to the target probability of each system state. The target probability reflects the probability of the system being in each state. For a single demand point, its busy rate can be obtained by adding the target probabilities corresponding to the system states of the demand point being in a busy state.

[0128] The service capability index of the service point reflects the rescue capability of the service point. There are many service capability indicators, such as the travel time or average service time of the service point, the average loss in the historical service process, etc.

[0129] Taking the travel time as an example, when only considering this indicator, the shorter the travel time, the stronger the service capacity of the service point.

[0130] Obtain the matching degree between the busy rate of each service point and the service capability index, and set a certain threshold to screen the service points. If the matching degree is lower than the preset matching degree threshold, it means that the service capability of the service point is not well matched with its busy rate, and it is determined as a service point to be optimized.

[0131] Optionally, the various indicators of the system can be calculated based on the target probability of the system state, and the service capacity of the system can be evaluated based on these indicators to provide reference data for optimizing the system. Optionally, the indicators include: system saturation rate, busy rate of each service point, average rescue travel time of each demand point, average travel time of each service point, proportion of each demand point served by a single service point, proportion of each demand point served by each service point, etc.

[0132] In one possible embodiment, Figure 4 A comparison diagram of a busy rate and the service capability index provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, Figure 4 The figure reflects the busy rate and service capacity indicators of the five service points S1, S2, S3, S4, and S5. The left vertical axis is the service rate, and the right vertical axis is the travel time. It can be clearly seen that S2 has a high busy rate and a long travel time, which makes it difficult for S2 to provide emergency rescue services to the service points efficiently. The service capacity of this service point is not matched with its busy rate, so it is determined as a service point to be optimized.

[0133] There are many optimization methods for the service points to be optimized. For example, for a service point to be optimized with a high busy rate and a long travel time, the rescue resources (such as rescue personnel and rescue equipment) of the service point can be increased, or the location of the service point can be adjusted to improve the rescue speed and efficiency of the service point. For a service point to be optimized with a low busy rate and a short travel time, the rescue resources (such as rescue personnel and rescue equipment) of the service point can be reduced, or the location of the service point can be adjusted to avoid wasting rescue resources.

[0134] In order to implement the above embodiments, the present application also proposes an emergency rescue system optimization device. Figure 5 This is a schematic diagram of the structure of an emergency rescue system optimization device provided in an embodiment of the present application. Figure 5 As shown, the device comprises:

[0135] Initialization module 510 is used to obtain information about each service point and demand point in the current emergency rescue system; initialize the service rate corresponding to each service point;

[0136] The probability determination module 520 is used to initialize the service rate of each service point to each demand point; determine the state probability corresponding to each system state in the emergency rescue system according to the service rate, and end this round of iteration; start the next round of iteration, modify the service rate according to the state probability; update the state probability corresponding to each system state in the emergency rescue system according to the updated service rate;

[0137] An updating module 530 is used to modify the service rate with the goal of reducing the difference between the state probability in this iteration and the state probability in the previous iteration to obtain a target service rate;

[0138] The optimization module 540 is used to determine the target probability corresponding to each system state in the emergency rescue system according to the target service rate, and to determine the service point to be optimized by comparing and analyzing the target probability with the service capacity of each service point, and to optimize the service point to be optimized.

[0139] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0140] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0141] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0142] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0143] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0144] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0145] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0146] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0147] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0148] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0149] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0150] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0151] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0152] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. An emergency rescue system optimization method, characterized in that: The following steps are involved: Obtain information on each service point and demand point in the current emergency rescue system; Initializing the service rate of each of the service points to each of the demand points; Determine the state probability corresponding to each system state in the emergency rescue system according to the service rate, and end this round of iteration; Starting the next iteration, and correcting the service rate according to the state probability; Update the state probability corresponding to each system state in the emergency rescue system according to the updated service rate; The service rate is corrected with the goal of reducing the gap between the state probability in the current iteration and the state probability in the previous iteration to obtain a target service rate; Determine the target probability corresponding to each system state in the emergency rescue system according to the target service rate, and compare and analyze the target probability with the service capacity of each service point to determine the service point to be optimized, and optimize the service point to be optimized. The service capacity index of the service point reflects the rescue capacity of the service point. The service capacity index includes the travel time or average service time of the service point and the average loss in the historical service process; Determining the state probability corresponding to each system state in the emergency rescue system according to the service rate includes: Solving a first equation according to the service rate to determine the state probabilities corresponding to each system state in the emergency rescue system; The first equation is formulated as: in, is the current system status, The emergency rescue system is in system state The probability of, j is the demand point, J is the set of demand points, i is the service point, I is the set of service points, λ j is the demand arrival rate of demand point j, System Status The state of service point i, System Status The state of service point i, is the service rate corresponding to i, for The corresponding transfer-in or transfer-out status, for A collection of The emergency rescue system is in system state probability; The system state caused by completing the service for the service point changes from Transfer In The corresponding second state transition indicator, where when there is only one service point that completes the service, the system changes from Transfer In Only otherwise The system state caused by demand point j changes from Transfer In The corresponding first state transition indicator, where when the system state changes from Transfer In When it meets the priority list otherwise 2. The method according to claim 1, characterized in that In the step of initializing the service rate of each service point to each demand point, the service rate of a single service point to each demand point is the same.

3. The method according to claim 1, characterized in that The modifying the service rate according to the state probability includes: Calculate the service rate corresponding to each service point to find the service times ratio π of service point i to demand point j ij , where π ij It is the ratio of the number of times service point i serves demand point j to the number of times service point i has completed all services; According to all π corresponding to the service point i ij The service rates are modified.

4. The method according to claim 3, characterized in that The service times of service point i to demand point j account for π ij The formula expression is: Among them, λ k is the demand arrival rate of the kth demand point.

5. The method according to claim 4, characterized in that According to all π corresponding to the service point i ij The formula for modifying the service rate is expressed as: in, is the corrected service rate of the service point i, μ ij It is the original service rate of each service point to the demand point.

6. The method according to claim 5, characterized in that The step of correcting the service rate with the goal of reducing the gap between the state probability in the current iteration and the state probability in the previous iteration to obtain a target service rate includes: Obtain the difference between the state probability in this iteration and the state probability in the previous iteration; In response to the difference being less than a preset threshold, the service rate in the current iteration is determined as the target service rate; or, in response to the difference being greater than or equal to the threshold, the next iteration is started.

7. The method according to any one of claims 1 to 6, characterized in that The comparing and analyzing the target probability with the service capabilities of each of the service points to determine the service point to be optimized includes: Determine the busy rate of each of the service points according to the target probability of each of the system states, wherein the busy rate is the probability that the service point is in a busy state; Obtaining a service capability indicator of the service point; Obtaining the matching degree between the busy rate of each of the service points and the service capability indicator; The service point whose matching degree is lower than a preset matching degree threshold is determined as the service point to be optimized.

8. An emergency rescue system optimization device, characterized in that: include: Initialization module, used to obtain information of each service point and demand point in the current emergency rescue system; Initializing the service rate corresponding to each of the service points; A probability determination module is used to initialize the service rate of each service point to each demand point; determine the state probability corresponding to each system state in the emergency rescue system according to the service rate, and end this round of iteration; start the next round of iteration, correct the service rate according to the state probability; update the state probability corresponding to each system state in the emergency rescue system according to the updated service rate; An updating module, configured to correct the service rate with the goal of reducing the gap between the state probability in this iteration and the state probability in the previous iteration, so as to obtain a target service rate; An optimization module, used to determine the target probability corresponding to each system state in the emergency rescue system according to the target service rate, and to determine the service point to be optimized by comparing and analyzing the target probability with the service capacity of each service point, and to optimize the service point to be optimized. The service capacity index of the service point reflects the rescue capacity of the service point. The service capacity index includes the travel time or average service time of the service point and the average loss in the historical service process; Determining the state probability corresponding to each system state in the emergency rescue system according to the service rate includes: Solving a first equation according to the service rate to determine the state probabilities corresponding to each system state in the emergency rescue system; The first equation is formulated as: in, is the current system status, The emergency rescue system is in system state The probability of, j is the demand point, J is the set of demand points, i is the service point, I is the set of service points, λ j is the demand arrival rate of demand point j, System Status The state of service point i, System Status The state of service point i, is the service rate corresponding to i, for The corresponding transfer-in or transfer-out status, for A collection of The emergency rescue system is in system state probability; The system state caused by completing the service for the service point changes from Transfer In The corresponding second state transition indicator, where when there is only one service point that completes the service, the system changes from Transfer In Only otherwise The system state caused by demand point j changes from Transfer In The corresponding first state transition indicator, where when the system state changes from Transfer In When it meets the priority list otherwise 9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

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