Medical examination call number management method and system based on matching of human flow density and space

Abstract

A physical examination call number management method and system based on crowd density and space matching, the method comprising: obtaining an initial data set, inputting historical crowd data and real-time crowd data in the initial data set into a preset time sequence model, outputting target crowd prediction data corresponding to the initial data set, determining a resource scheduling area corresponding to the target crowd prediction data, and determining a regional priority level and a resource allocation demand corresponding to the resource scheduling area, determining a target call number strategy for the resource scheduling area based on the regional priority level and the resource allocation demand, generating a target call number scheduling instruction corresponding to the resource scheduling area based on the target call number strategy, and sending the target call number scheduling instruction to an execution terminal, controlling the execution terminal to parse the target call number scheduling instruction and update call number queue information of an electronic call number screen. Through the above method, the matching of system resources and real-time demand is realized, and the call number management efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of call sign management, and in particular to a physical examination call sign management method and system based on crowd density and space matching. Background Art

[0002] In the field of modern urban management and public services, optimizing crowd distribution and improving service efficiency have become demands that cannot be ignored in crowded places. Traditional management methods rely on manual management when dealing with complex crowd flow scenarios. For example, the ticket gates of popular scenic spots continue to let people in, but they are unaware that the number of people at the popular check-in spots within the scenic area has exceeded the actual capacity, or they are not aware of it with a delay. This will make it impossible for reserved staff to respond to this situation quickly, resulting in delayed resource allocation in the scenic area.

[0003] In order to optimize the distribution of pedestrian flow and improve service efficiency, the historical data of pedestrian flow distribution is currently used to train the prediction model to obtain the trained target prediction model. In actual application, real-time pedestrian flow data is input into the target prediction model, and the pedestrian flow prediction data is output. Then, based on the pedestrian flow prediction data, pedestrian flow management strategies are formulated in advance and available resources are prepared in advance. Due to the complexity and variability of pedestrian flow data in complex scenarios, there is a deviation between historical data and actual data, resulting in a deviation between the pedestrian flow prediction data determined in advance and the actual pedestrian flow data, which in turn leads to deviations in pedestrian flow management strategies and the inability to match resource scheduling with actual needs.

[0004] In summary, how to achieve accurate crowd flow trend prediction and dynamically adjust call sign strategies based on crowd flow data has become the main problem to be solved at present. Summary of the Invention

[0005] The present application provides a physical examination call sign management method and system based on crowd density and space matching, which realizes the dynamic adjustment of call sign strategy, thereby improving the accuracy of crowd trend prediction.

[0006] In a first aspect, the present application provides a medical examination call sign management method based on crowd density and space matching, the method comprising:

[0007] Get the initial dataset;

[0008] Inputting historical crowd flow data and real-time crowd flow data in the initial data set into a preset time series model, and outputting target crowd flow prediction data corresponding to the initial data set;

[0009] Determining a resource scheduling area corresponding to the target passenger flow prediction data, and determining a regional priority level and a resource allocation requirement corresponding to the resource scheduling area, and determining a target call sign strategy for the resource scheduling area based on the regional priority level and the resource allocation requirement;

[0010] A target call sign scheduling instruction corresponding to the resource scheduling area is generated based on the target call sign strategy, and the target call sign scheduling instruction is sent to the execution terminal, and the execution terminal is controlled to parse the target call sign scheduling instruction and update the call sign queue information of the electronic call screen.

[0011] Through the above method, historical crowd flow data is integrated with real-time crowd flow data, making the target crowd flow prediction data more accurate, and the target call sign strategy is dynamically adjusted based on regional priority and resource allocation requirements, achieving a match between system resources and actual needs, thereby improving call sign management efficiency.

[0012] In a possible design, the inputting of historical crowd flow data and real-time crowd flow data in the initial data set into a preset time series model and the outputting of target crowd flow prediction data corresponding to the initial data set includes:

[0013] Inputting the historical crowd flow data and the real-time crowd flow data into the preset time series model, and outputting crowd flow prediction data corresponding to the initial data set;

[0014] Determine actual crowd flow data, compare the actual crowd flow data with the crowd flow prediction data, and obtain a deviation parameter;

[0015] If the deviation parameter is greater than the preset deviation parameter, the model parameters of the preset time series model are adjusted based on the deviation parameter, and the initial data set is re-predicted based on the adjusted preset time series model to obtain target crowd flow prediction data, wherein the target deviation parameter between the target crowd flow prediction data and the actual crowd flow data is less than the preset deviation parameter.

[0016] Through the above method, based on the comparison between the deviation parameter and the preset deviation parameter, the preset time series model is verified to ensure that the target deviation parameter of the target crowd flow prediction data output by the preset time series model is less than the preset deviation parameter, thereby improving the precision of the preset time series model and the accuracy of the target crowd flow prediction data.

[0017] In one possible design, determining the resource scheduling area corresponding to the target passenger flow prediction data includes:

[0018] Obtaining crowd flow distribution characteristics of each area corresponding to the target crowd flow prediction data;

[0019] Determine the congestion parameters corresponding to the crowd flow distribution characteristics of each area;

[0020] If the congestion parameter meets the preset congestion condition, the corresponding area is determined as the resource scheduling area.

[0021] Through the above method, the congestion parameters of each area are determined based on the crowd distribution characteristics, and the resource scheduling area is screened out based on the preset congestion conditions, ensuring that the system can quickly respond to the resource allocation needs of the resource scheduling area, which is conducive to achieving system resource optimization.

[0022] In one possible design, determining the regional priority level corresponding to the resource scheduling area includes:

[0023] Obtaining congestion parameters of the resource scheduling area;

[0024] If the congestion parameter is greater than a preset congestion parameter, the regional priority level corresponding to the resource scheduling area is determined based on a mapping relationship between the preset congestion parameter and a preset regional priority level.

[0025] By using the above method, the regional priority level of the resource scheduling area is determined, which is beneficial for the system to allocate resources to the resource scheduling area based on the regional priority level, thereby ensuring the resource scheduling efficiency of the system.

[0026] In one possible design, determining the target call sign strategy for the resource scheduling area based on the area priority and the resource allocation requirement includes:

[0027] Obtaining the crowd flow distribution characteristics and resource allocation requirements of the resource scheduling area;

[0028] Determining a resource allocation order for the resource scheduling area based on the crowd flow distribution characteristics and the resource allocation requirements;

[0029] A target call sign strategy is generated based on the regional priority levels and the resource allocation order.

[0030] Through the above method, the target call sign strategy is generated based on the regional priority and resource allocation order, ensuring that the target call sign strategy can be dynamically adjusted based on the regional priority and resource allocation order, which is conducive to the dynamic matching of system resources and actual needs.

[0031] In one possible design, generating the target call sign scheduling instruction corresponding to the resource scheduling area based on the target call sign strategy includes:

[0032] Mapping the target call sign strategy and the preset time period constraint condition to obtain the call sign time and call sign sequence of the resource scheduling area;

[0033] generating a call sign scheduling instruction corresponding to the resource scheduling area based on the call sign time and the call sign sequence;

[0034] Replacing the key fields in the call sign dispatch instruction to obtain a call sign desensitization instruction;

[0035] If there is a sensitive field in the call sign desensitization instruction, the sensitive field in the call sign desensitization instruction is encrypted in sections to obtain the target call sign scheduling instruction.

[0036] Through the above method, after obtaining the call sign scheduling instruction, the key fields in the call sign scheduling instruction are replaced, and the sensitive fields in the call sign desensitizing instruction are segmented and encrypted, ensuring the security and privacy of the data in the target call sign scheduling instruction.

[0037] In one possible design, obtaining the call sign time of the resource scheduling area includes:

[0038] Obtaining the actual available amount of resources corresponding to the call sign time;

[0039] If the actual available resources are less than the resource allocation usage of the resource allocation requirement, the call time is readjusted until the target actual available resources of the readjusted call time are greater than the resource allocation usage, thereby obtaining the adjusted call time.

[0040] Through the above method, the call sign time is dynamically adjusted according to the relationship between the actual available resources and the allocated resource usage, which ensures the flexibility of system resource scheduling and is conducive to improving the efficiency of call sign management.

[0041] In a possible design, obtaining the call sign desensitization instruction includes:

[0042] Obtaining real-time congestion data of the current execution state, and obtaining real-time resource usage corresponding to the real-time congestion data;

[0043] determining a real-time congestion parameter corresponding to the real-time congestion data; if the real-time congestion parameter is greater than a preset congestion parameter and the real-time resource usage is greater than the current resource allocation, reordering the regional priorities of the resource scheduling areas in the target passenger flow prediction data to obtain a regional priority order;

[0044] The call sign desensitization instruction is generated based on the regional priority order.

[0045] Through the above method, the regional priority level of the resource scheduling area is dynamically updated based on the current execution state of the system, which ensures the rationality of the system resource scheduling and is conducive to improving the resource allocation efficiency of the system.

[0046] In a second aspect, the present application provides a physical examination call sign management system based on crowd density and space matching, the system comprising:

[0047] Acquisition module, used to obtain the initial data set;

[0048] A prediction module, configured to input historical crowd flow data and real-time crowd flow data in the initial data set into a preset time series model, and output target crowd flow prediction data corresponding to the initial data set;

[0049] a determination module, configured to determine a resource scheduling area corresponding to the target passenger flow prediction data, and determine a regional priority level and a resource allocation requirement corresponding to the resource scheduling area, and determine a target call sign strategy for the resource scheduling area based on the regional priority level and the resource allocation requirement;

[0050] The control module is used to generate a target call sign scheduling instruction corresponding to the resource scheduling area based on the target call sign strategy, and send the target call sign scheduling instruction to the execution terminal, control the execution terminal to parse the target call sign scheduling instruction and update the call sign queue information of the electronic call screen.

[0051] In one possible design, the prediction module is specifically used to input the historical crowd flow data and the real-time crowd flow data into the preset time series model, output the crowd flow prediction data corresponding to the initial data set, determine the actual crowd flow data, compare the actual crowd flow data with the crowd flow prediction data to obtain a deviation parameter, if the deviation parameter is greater than the preset deviation parameter, adjust the model parameters of the preset time series model based on the deviation parameter, and re-predict the initial data set based on the adjusted preset time series model to obtain target crowd flow prediction data, wherein the target deviation parameter between the target crowd flow prediction data and the actual crowd flow data is less than the preset deviation parameter.

[0052] In one possible design, the determination module is specifically used to obtain the crowd flow distribution characteristics of each area corresponding to the target crowd flow prediction data, determine the congestion parameters corresponding to the crowd flow distribution characteristics of each area, and if the congestion parameters meet the preset congestion conditions, the corresponding area is determined as the resource scheduling area.

[0053] In one possible design, the determination module is also used to obtain a congestion parameter of the resource scheduling area. If the congestion parameter is greater than a preset congestion parameter, the regional priority level corresponding to the resource scheduling area is determined based on a mapping relationship between the preset congestion parameter and the preset regional priority level.

[0054] In one possible design, the determination module is also used to obtain the crowd distribution characteristics and resource allocation requirements of the resource scheduling area, determine the resource allocation order of the resource scheduling area based on the crowd distribution characteristics and the resource allocation requirements, and generate a target call sign strategy based on the area priority and the resource allocation order.

[0055] In one possible design, the control module is specifically used to map the target call sign strategy and the preset time period constraint conditions to obtain the call sign time and call sign sequence of the resource scheduling area, generate a call sign scheduling instruction corresponding to the resource scheduling area based on the call sign time and the call sign sequence, replace the key fields in the call sign scheduling instruction to obtain a call sign desensitizing instruction, and if there are sensitive fields in the call sign desensitizing instruction, then the sensitive fields in the call sign desensitizing instruction are segmented and encrypted to obtain the target call sign scheduling instruction.

[0056] In one possible design, the control module is also used to obtain the actual available resources corresponding to the call sign time. If the actual available resources are less than the resource allocation usage of the resource allocation requirement, the call sign time is readjusted until the target actual available resources of the readjusted call sign time are greater than the resource allocation usage, thereby obtaining the adjusted call sign time.

[0057] In one possible design, the control module is also used to obtain real-time congestion data of the current execution state, and obtain the real-time resource usage corresponding to the real-time congestion data, determine the real-time congestion parameter corresponding to the real-time congestion data, if the real-time congestion parameter is greater than the preset congestion parameter, and the real-time resource usage is greater than the current resource allocation, then the regional priority of each resource scheduling area in the target passenger flow prediction data is re-sorted to obtain a regional priority order, and the call sign desensitization instruction is generated based on the regional priority order.

[0058] In a third aspect, the present application provides an electronic device, comprising:

[0059] Memory for storing computer programs;

[0060] The processor is configured to implement the above-mentioned steps of the medical examination call sign management method based on matching of crowd density and space when executing the computer program stored in the memory.

[0061] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the above-mentioned steps of a physical examination call sign management method based on matching of crowd density and space.

[0062] For each of the above-mentioned aspects from the first to the fourth aspects and the technical effects that may be achieved by each of the aspects, please refer to the above-mentioned description of the technical effects that can be achieved by the first aspect or various possible solutions in the first aspect, and no further details will be given here. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1A flowchart of the steps of a medical examination call sign management method based on crowd density and space matching provided by this application;

[0064] Figure 2 This is a structural diagram of a medical examination call sign management system based on crowd density and space matching provided by this application;

[0065] Figure 3 This is a schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail with reference to the accompanying drawings. The specific operating methods in the method embodiments can also be applied to device embodiments or system embodiments. It should be noted that in the description of the present application, "multiple" is understood as "at least two". "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. A is connected to B, which can represent the following two situations: A is directly connected to B and A is connected to B through C. In addition, in the description of the present application, words such as "first" and "second" are only used to distinguish the purpose of description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

[0067] To address the aforementioned issues, the present invention provides a method for managing physical examination call signs based on crowd density and spatial matching, enabling accurate prediction of crowd flow trends and dynamic adjustment of call sign strategies. The method and device described in the present invention are based on the same technical concept. Since the principles underlying the problems addressed by the method and device are similar, the embodiments of the device and method can be referenced in conjunction with each other, and any repetitions will not be repeated.

[0068] The following is a detailed description of the first embodiment of the present application in conjunction with the accompanying drawings.

[0069] Reference Figure 1 This application provides a physical examination call sign management method based on crowd density and space matching. This method can achieve accurate crowd trend prediction and dynamic adjustment of call sign strategy. The implementation process of this method is as follows:

[0070] Step S1: Obtain the initial data set.

[0071] In order to achieve accurate crowd flow trend prediction, the embodiment of the present application needs to obtain historical crowd flow data and real-time crowd flow data from multiple network databases based on sensors or image acquisition devices. In order to better perform data analysis, both historical crowd flow data and real-time crowd flow data need to be marked with timestamps. Historical crowd flow data can be crowd flow data from the past two years, and real-time crowd flow data can be crowd flow data from the past week. Historical crowd flow data and real-time crowd flow data can be collected according to actual conditions, and the original data set can be generated based on the historical crowd flow data and real-time crowd flow data.

[0072] To ensure the accuracy of the original data set, it is necessary to process the historical and real-time crowd flow data in the original data set. The specific process of data processing is as follows:

[0073] Detect whether there are timestamp missing and data anomalies in the historical crowd flow data and the real-time crowd flow data in the original data set. In the embodiment of the present application, data exceeding the threshold is determined as abnormal data. For example, data with an hourly flow of more than 1000 people in the real-time crowd flow data is determined as abnormal data. The threshold can be adjusted according to actual conditions. This is only used as an example.

[0074] If there is a missing timestamp or data anomaly, the corresponding data will be deleted from the original data set to obtain a cleaned data set. In order to classify the cleaned data set, it is necessary to cluster the cleaned data set. The clustering method can be a mean clustering algorithm (full name in English: K-means clustering algorithm, abbreviated as: K-means), to obtain multiple clusters, determine the crowd density of each cluster, and label the clusters with crowd density exceeding the preset crowd density with peak time periods, and label the clusters with crowd density lower than the preset crowd density with regular time periods. The data with peak time period labels and the data with regular time period labels are classified and stored to obtain the initial data set. The initial data set contains fields such as timestamp, number of people, and time period label. The embodiment of the present application can store the initial data set in Parquet format, which will not be explained in detail here.

[0075] Through the above method, the original data set is cleaned and clustered to obtain the initial data set, which improves the efficiency of data analysis and ensures the accuracy of the initial data set.

[0076] Step S2: inputting the historical crowd flow data and the real-time crowd flow data in the initial data set into a preset time series model, and outputting the target crowd flow prediction data corresponding to the initial data set.

[0077] In order to effectively integrate historical crowd flow data with real-time crowd flow data, it is necessary to extract historical crowd flow data and real-time crowd flow data from the initial data set and input the historical crowd flow data and real-time crowd flow data into a preset time series model. The preset time series model can be an autoregressive integrated moving average model (ARIMA). In the initial state of the preset time series model, the integrity of the historical crowd flow data is statistically analyzed. If the integrity is greater than the preset integrity, the initial weight value of the historical crowd flow data is set to a parameter higher than the weight threshold; if the integrity is less than the preset integrity, the initial weight value of the historical crowd flow data is set to a parameter lower than the weight threshold.

[0078] Determine the fluctuation value of the real-time crowd flow data. If the fluctuation value is greater than the preset fluctuation value, set the initial weight value of the real-time crowd flow data to a parameter lower than the weight threshold; if the fluctuation value is less than the preset fluctuation value, set the initial weight value of the real-time crowd flow data to a parameter higher than the weight threshold.

[0079] The above-mentioned weight threshold is 0.5. The embodiment of the present application can adjust the initial weight value of the historical crowd flow data and the initial weight value of the real-time crowd flow data according to actual conditions, and make the total weight value in the preset time series model 1. No further explanation is given here.

[0080] Input historical and real-time crowd flow data into the preset time series model and output target crowd flow prediction data.

[0081] In one possible design, in order to make the target crowd flow prediction data more accurate, historical crowd flow data and real-time crowd flow data are input into a preset time series model, and crowd flow prediction data is output. Since the crowd flow prediction data is data predicted in advance, it is necessary to obtain actual crowd flow data, compare the actual crowd flow data with the crowd flow prediction data, and obtain a deviation parameter. The embodiment of the present application can calculate the daily crowd flow difference between the actual crowd flow data and the crowd flow prediction data, determine the sum of the daily crowd flow differences, and then divide the sum by the number of days to obtain the deviation parameter.

[0082] The above-mentioned deviation parameter represents the deviation between the crowd flow prediction data predicted by the preset time series model and the actual crowd flow data. The larger the deviation, the lower the accuracy of the preset time series model. It will not be explained in detail here.

[0083] When the deviation parameter is greater than the preset deviation parameter, it means that the prediction accuracy of the preset time series model needs to be adjusted, and the first weight value of the current historical crowd flow data and the second weight value of the real-time crowd flow data are obtained. Since there are interference factors that affect the crowd flow prediction data, the embodiment of the present application also needs to determine the interference weight value of the interference factor. For example: when the crowd flow prediction data includes temperature, the interference factor is humidity. The interference factor can be adjusted according to the actual scenario, which will not be explained in detail here.

[0084] The first weight value, the second weight value, and the interference weight value may be model parameters of a preset time series model, and the model parameters may be adjusted.

[0085] Due to the problems of failure to effectively integrate historical crowd flow data with real-time crowd flow data and excessive interference factors, it is necessary to adjust at least one of the first weight value, the second weight value, and the interference weight value. The embodiment of the present application takes the adjustment of the first weight value, the second weight value, and the interference weight value as an example for explanation. The specific adjustment process is as follows:

[0086] The first weight value is modified to the target first weight value, the second weight value is modified to the target second weight value, and the interference weight value is modified to the target interference weight value. The preset time series model is then retrained based on the initial data set, the target first weight value, the target second weight value, and the target interference weight value. The target crowd flow prediction data is output, and the target deviation parameter between the target crowd flow prediction data and the actual crowd flow data is determined. When the target deviation parameter is less than the preset deviation parameter, the model training is stopped, and the current preset time series model is determined as the target time series model.

[0087] In one possible design, after determining the real-time crowd flow data, the embodiment of the present application can use a sliding window to count the hourly crowd flow change rate. When the crowd flow change rate exceeds the preset crowd flow change rate, the crowd flow trend warning information is automatically triggered, and the system can allocate resources based on the crowd flow trend warning information.

[0088] For example: at the Tth moment, T is a positive integer, the passenger flow is 200, at the T+1th moment, the passenger flow is 230, and the passenger flow change rate is (230-200) / 100×100%=30%.

[0089] Through the above method, historical crowd flow data is effectively integrated with real-time crowd flow data, which improves the prediction accuracy of the preset time series model and ensures the timeliness and accuracy of the system's resource scheduling.

[0090] Step S3: Determine the resource scheduling area corresponding to the target passenger flow prediction data, and determine the regional priority and resource allocation requirements corresponding to the resource scheduling area. Based on the regional priority and resource allocation requirements, determine the target call sign strategy of the resource scheduling area.

[0091] Since the target crowd flow prediction data is the prediction result of crowd flow trends in multiple areas, and the congestion conditions in different areas are different, it is necessary to obtain the crowd flow distribution characteristics of each area from the target crowd flow prediction data. The crowd flow distribution characteristics can be unit crowd flow density, crowd flow speed, residence time ratio, etc., and determine the congestion parameters corresponding to the crowd flow distribution characteristics of each area. The congestion parameters can be calculated based on unit crowd flow density, crowd flow speed, and residence time ratio. The calculation formula of the congestion parameters is as follows:

[0092] Congestion parameter = α × D_norm + β × (1 - V_norm) + γ × T_stay, where α + β + γ = 1. α, β, and γ can be adjusted according to the actual scenario. For example, in a hospital, α = 0.5, β = 0.3, and γ = 0.2.

[0093] D_norm (normalized value of pedestrian density) = current density / maximum design density;

[0094] V_norm (speed normalization value) = current speed / free flow speed;

[0095] T_stay (percentage of stay time) directly takes the value [0, 1];

[0096] Before incorporating crowd density, crowd speed, and dwell time ratio into the congestion parameter calculation formula, they all need to be normalized so that each parameter is converted into a dimensionless value in the range of 0 to 1. To prevent data distortion, a robust normalization method can be used to normalize crowd density, crowd speed, and dwell time ratio to prevent the crowd distribution characteristics from being disturbed by outliers and to avoid distortion introduced during the normalization process.

[0097] When the congestion parameter meets the preset congestion condition, the corresponding area is determined as the resource scheduling area. The preset congestion condition is a preset congestion parameter range. The embodiment of the present application stores the association between the preset congestion parameter range and the preset congestion level. The association is shown in Table 1:

[0098]

[0099] Table 1

[0100] The above Table 1 lists the preset congestion levels corresponding to the preset congestion parameter ranges. Different preset congestion levels represent different congestion conditions. The parameters in the above Table 1 are only for example. The preset congestion parameter ranges and the preset congestion levels corresponding to the preset congestion parameter ranges can be adjusted according to actual conditions.

[0101] In one possible design, the preset congestion parameter range can be adjusted according to the time period to which the current time belongs or the area type corresponding to the area. The total number of people flow in each time period can be obtained based on historical crowd flow data. The time period with a total number of people flow greater than the preset total number of people flow is determined as a peak time period, and the time period with a total number of people flow not greater than the preset total number of people flow is determined as a regular time period. The area type is determined based on the scenario, and the area type can be a school, hospital, etc., which will not be elaborated here.

[0102] For example: if the regional type is hospital, the preset congestion parameter range will be increased by 20% during the morning rush hour period of the physical examination center, and the preset congestion parameter range corresponding to the preset congestion level will be increased, so that the preset congestion level can more accurately reflect the actual situation, avoid resource scheduling bottlenecks in the system, and help improve the efficiency of resource scheduling in the system.

[0103] Based on Table 1 above, the preset congestion parameter range to which the congestion parameter belongs can be determined, and the preset congestion level can be determined based on the preset congestion parameter range, so that the preset congestion level is determined as the congestion level corresponding to the congestion parameter. If the congestion level is light congestion or heavy congestion, the area corresponding to the congestion parameter is determined as the resource scheduling area, indicating that the system needs to allocate resources to resolve the congestion in this area.

[0104] Furthermore, in order to prevent insufficient resources and only adjusting the time, the embodiment of the present application adopts the following methods to solve the congestion situation, and the specific methods are as follows:

[0105] Method 1: The embodiment of the present application can pre-set a computing resource pool in standby or low-load state, and determine the computing resource pool as a backup resource pool. When the system has insufficient resources and can only adjust time, the backup resource pool is started, and the system solves the congestion problem by calling the resources in the backup resource pool.

[0106] Method 2: The system determines the resource scheduling order of the congested area and sends the tasks with lower resource scheduling order to other low-load areas, or the system determines non-real-time resource scheduling tasks and sends them to other low-load areas.

[0107] Method 3: The system diverts the flow of people in congested areas and guides the diverted flow of people to areas with lower regional priority.

[0108] Specifically, the system determines the non-congested areas and the actual passenger flow capacity of the non-congested areas, and then guides the passenger flow in the congested areas according to the actual passenger flow capacity, thereby guiding the passenger flow to the non-congested areas.

[0109] When insufficient resources occur and only time adjustment is required, the embodiment of the present application can select at least one of the above methods for processing according to actual conditions, thereby ensuring the availability and stability of the system.

[0110] In order to optimize system resources, it is necessary to determine the congestion parameter of the resource scheduling area. When the congestion parameter is greater than the preset congestion parameter, the regional priority level corresponding to the resource scheduling area is determined based on the mapping relationship between the preset congestion parameter and the preset regional priority level. The mapping relationship between the preset congestion parameter and the preset regional priority level is shown in Table 2:

[0111]

[0112] Table 2

[0113] In Table 2 above, when the congestion parameter is greater than 0.8, the regional priority level of the resource scheduling area of ​​the congestion parameter is determined to be level one; when the congestion parameter is greater than 0.2, the regional priority level of the resource scheduling area of ​​the congestion parameter is determined to be level three. The smaller the level parameter corresponding to the regional priority level, the higher the priority level. The preset congestion parameters in Table 2 above can be adjusted according to actual conditions, and the preset regional priority levels can also be adjusted based on the examples in Table 2 above, which will not be elaborated here.

[0114] Based on the mapping relationship between the preset congestion parameters and the preset regional priority levels, the regional priority level of the resource scheduling area can be determined. In this embodiment of the present application, the resource allocation requirement of the resource scheduling area also needs to be determined. For example, the resource allocation requirement may be to allocate two maintenance personnel at 10:00. In this embodiment of the present application, the target call sign strategy of the resource scheduling area can be determined based on the regional priority level and the resource allocation requirement. The specific determination process is as follows:

[0115] In one possible design, in order to optimize system resources, it is necessary to determine the resource allocation order of the resource scheduling area based on the passenger flow distribution characteristics and resource allocation requirements of each resource allocation area.

[0116] It should be noted that the resource allocation order can be determined based on congestion parameters, business priorities corresponding to business resource allocation needs, historical overflow risks, and resource response rates. In the system, different areas correspond to different businesses, and business priorities can be pre-set according to actual conditions. Resource response rate = number of completed resource scheduling times / unit time, historical overflow risk = number of historical overloads / total days. The obtained business priority, historical overflow risk, and resource response rate are normalized, and the weight values ​​of business priority, historical overflow risk, and resource response rate can be set according to actual conditions so that the total weight value is 1. Thus, the business priority, historical overflow risk, and resource response rate can be weighted and summed to calculate the parameters corresponding to the resource allocation order, so as to quantify the resource allocation order and improve the resource allocation efficiency of the system.

[0117] After determining the regional priority levels and resource allocation order, a target call sign strategy is generated based on the regional priority levels and resource allocation order.

[0118] In a possible design, the target call sign strategy may be a call sign strategy pre-stored in the system, and the target call sign strategy may be an emergency priority strategy, a resource-saving strategy, a balanced scheduling strategy, and the like.

[0119] For example, if the regional priority is the first priority and the resource allocation requirement is that the resource scheduling area needs to prioritize the allocation of two technicians and one set of spare parts, the target call sign strategy is determined to be the emergency priority strategy.

[0120] When the target call sign strategy is a call sign strategy pre-stored in the system, it is necessary to match the resource allocation requirements of the resource scheduling area with the actual requirements in the pre-stored call sign strategy. When the resource allocation requirements are consistent with the actual requirements, the pre-stored call sign strategy corresponding to the actual requirements is determined as the target call sign strategy of the resource scheduling area.

[0121] Through the above method, a target call sign strategy is generated based on regional priority and resource allocation order, so that the target call sign strategy can be dynamically adjusted according to regional priority and resource allocation order, thereby improving the efficiency of the system in call sign management.

[0122] Step S4: Generate a target call sign scheduling instruction corresponding to the resource scheduling area based on the target call sign strategy, and send the target call sign scheduling instruction to the execution terminal, control the execution terminal to parse the target call sign scheduling instruction and update the call sign queue information of the electronic call screen.

[0123] After determining the target call sign strategy, the target call sign strategy and the preset time period constraints are mapped to obtain the call sign sequence and call sign time of the resource scheduling area. The mapping processing tools can be summary code mapping models, predictive outbound call platforms, intelligent scheduling engines, etc., which are not explained in detail here. For example: the preset time period constraint is 9:00 to 11:00.

[0124] Furthermore, in order to ensure that the system's resource scheduling matches the resource requirements, it is necessary to obtain the actual resource availability corresponding to the call sign time. When the actual resource availability is less than the resource allocation usage of the resource allocation requirement, it means that the system's resources are insufficient. Therefore, it is necessary to readjust the call sign time and re-determine the target actual resource availability corresponding to the adjusted call sign time. When the target actual resource availability is greater than the resource allocation usage, it means that the system's resources are sufficient, thereby obtaining the adjusted call sign time.

[0125] When the actual available resources are greater than the resource allocation usage of the resource allocation requirements, it means that the system resources are sufficient for allocation. The embodiment of the present application can also determine the load rate of the system. When the load rate is greater than the preset load rate, the call time is readjusted. When the load rate is greater than the preset load rate, the system allocates resources according to the resource allocation requirements to prevent the system from overloading and ensure the efficiency of system resource allocation.

[0126] The embodiment of the present application can generate a call sign scheduling instruction corresponding to a resource scheduling area based on the call sign time and the call sign sequence. The system can send the call sign scheduling instruction to an execution terminal, which is a terminal controlled by the system.

[0127] In one possible design, in order to ensure the security and privacy of data transmission, it is necessary to replace the key fields in the call sign dispatch instruction to obtain a call sign desensitization instruction. The key fields can be set according to actual conditions, for example: the key fields are name, contact information, identity picture, detailed area name, device identification, etc., which are not elaborated here.

[0128] In one possible design, real-time congestion data of the current execution state of the system and the real-time resource usage corresponding to the real-time congestion data are obtained. The real-time resource usage can be determined based on the real-time resource distribution information corresponding to the real-time congestion data. For example, the real-time resource distribution information may be that device A requires two technicians to repair, but currently only one technician is repairing it, which means that the resource allocation of device A is insufficient. The real-time congestion parameter corresponding to the real-time congestion data is then calculated. When the real-time congestion parameter is greater than the preset congestion parameter and the real-time resource usage is greater than the current resource allocation, the current resource allocation is the amount of resources that can be dispatched by the system under the current execution state. The regional priorities of each resource scheduling area in the target passenger flow prediction data are re-sorted to obtain the regional priority order, and a call sign desensitization instruction is generated based on the regional priority order.

[0129] After receiving the call sign desensitization instruction, it is necessary to detect whether there is a sensitive field in the call sign desensitization instruction. The sensitive field may be a name, an ID number, etc. The sensitive field may be determined according to the actual situation. This is only used as an example.

[0130] When there are sensitive fields in the call sign desensitizing instruction, the sensitive fields in the call sign desensitizing instruction are encrypted in segments to obtain the target call sign scheduling instruction. For example, the name and ID number are encrypted using different encryption methods. When there are no sensitive fields in the call sign desensitizing instruction, in order to prevent excessive encryption from increasing delays, the call sign desensitizing instruction can be encrypted using a lightweight encryption method to obtain the target call sign scheduling instruction. The call sign desensitizing instruction can also be determined as the target call sign scheduling instruction based on the actual application scenario.

[0131] After the system sends the target call sign scheduling instruction to the execution terminal, it controls the execution terminal to parse the target call sign scheduling instruction and obtain the call sign time and call sign sequence of the resource scheduling area. The execution terminal can update the call sign queue information of the electronic call screen according to the call sign time and call sign sequence.

[0132] Through the above method, historical crowd flow data is integrated with real-time crowd flow data, making the preset time series model more accurate. In addition, the target call sign strategy is dynamically adjusted according to the regional priority level of the resource scheduling area and the resource allocation requirements, thereby achieving a precise match between the system's resource scheduling and actual needs, thereby improving the efficiency of call sign management.

[0133] Based on the same inventive concept, the embodiment of the present application also provides a physical examination call sign management system based on crowd density and space matching. The encrypted transmission device is used to implement the function of a physical examination call sign management method based on crowd density and space matching. Figure 2 , the device comprises:

[0134] Acquisition module 201, used to acquire an initial data set;

[0135] The prediction module 202 is configured to input the historical crowd flow data and the real-time crowd flow data in the initial data set into a preset time series model, and output target crowd flow prediction data corresponding to the initial data set;

[0136] Determination module 203, configured to determine a resource scheduling area corresponding to the target passenger flow prediction data, determine a regional priority level and a resource allocation requirement corresponding to the resource scheduling area, and determine a target call sign strategy for the resource scheduling area based on the regional priority level and the resource allocation requirement;

[0137] The control module 204 is used to generate a target call sign scheduling instruction corresponding to the resource scheduling area based on the target call sign strategy, and send the target call sign scheduling instruction to the execution terminal, control the execution terminal to parse the target call sign scheduling instruction and update the call sign queue information of the electronic call screen.

[0138] In one possible design, the prediction module 202 is specifically used to input the historical crowd flow data and the real-time crowd flow data into the preset time series model, output the crowd flow prediction data corresponding to the initial data set, determine the actual crowd flow data, compare the actual crowd flow data with the crowd flow prediction data to obtain a deviation parameter, if the deviation parameter is greater than the preset deviation parameter, adjust the model parameters of the preset time series model based on the deviation parameter, and re-predict the initial data set based on the adjusted preset time series model to obtain target crowd flow prediction data, wherein the target deviation parameter between the target crowd flow prediction data and the actual crowd flow data is less than the preset deviation parameter.

[0139] In one possible design, the determination module 203 is specifically used to obtain the crowd flow distribution characteristics of each area corresponding to the target crowd flow prediction data, determine the congestion parameters corresponding to the crowd flow distribution characteristics of each area, and if the congestion parameters meet the preset congestion conditions, the corresponding area is determined as the resource scheduling area.

[0140] In one possible design, the determination module 203 is also used to obtain a congestion parameter of the resource scheduling area. If the congestion parameter is greater than a preset congestion parameter, the regional priority level corresponding to the resource scheduling area is determined based on a mapping relationship between the preset congestion parameter and the preset regional priority level.

[0141] In one possible design, the determination module 203 is also used to obtain the crowd distribution characteristics and resource allocation requirements of the resource scheduling area, determine the resource allocation order of the resource scheduling area based on the crowd distribution characteristics and the resource allocation requirements, and generate a target call sign strategy based on the area priority and the resource allocation order.

[0142] In one possible design, the control module 204 is specifically used to map the target call sign strategy and the preset time period constraint conditions to obtain the call sign time and call sign sequence of the resource scheduling area, generate a call sign scheduling instruction corresponding to the resource scheduling area based on the call sign time and the call sign sequence, replace the key fields in the call sign scheduling instruction to obtain a call sign desensitizing instruction, and if there are sensitive fields in the call sign desensitizing instruction, then the sensitive fields in the call sign desensitizing instruction are segmented and encrypted to obtain the target call sign scheduling instruction.

[0143] In one possible design, the control module 204 is also used to obtain the actual resource availability corresponding to the call sign time. If the actual resource availability is less than the resource allocation usage of the resource allocation requirement, the call sign time is readjusted until the target actual resource availability of the readjusted call sign time is greater than the resource allocation usage, thereby obtaining the adjusted call sign time.

[0144] In one possible design, the control module 204 is also used to obtain real-time congestion data of the current execution state, and obtain the real-time resource usage corresponding to the real-time congestion data, and determine the real-time congestion parameter corresponding to the real-time congestion data. If the real-time congestion parameter is greater than the preset congestion parameter, and the real-time resource usage is greater than the current resource allocation, the regional priority of each resource scheduling area in the target passenger flow prediction data is re-sorted to obtain a regional priority order, and the call sign desensitization instruction is generated based on the regional priority order.

[0145] Based on the same inventive concept, an electronic device is also provided in the embodiment of the present application. The electronic device can realize the function of the aforementioned physical examination call sign management system based on crowd density and space matching, referring to Figure 3 , the electronic device includes:

[0146] At least one processor 301, and a memory 303 connected to the at least one processor 301. The specific connection medium between the processor 301 and the memory 303 is not limited in the embodiment of the present application. Figure 3 In the example, the processor 301 and the memory 303 are connected via the bus 300. Figure 3The bus 300 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The diagram is represented by only one thick line, but this does not mean that there is only one bus or one type of bus. Alternatively, the processor 301 may also be referred to as a controller, without limitation to the name.

[0147] In the embodiment of the present application, the memory 303 stores instructions that can be executed by at least one processor 301. By executing the instructions stored in the memory 303, the at least one processor 301 can execute the above-mentioned method for managing call signs based on matching of crowd density and space. The processor 301 can implement Figure 2 The functions of each module in the system are shown.

[0148] Among them, the processor 301 is the control center of the device, which can use various interfaces and lines to connect the various parts of the entire control device, and monitor the system as a whole by running or executing instructions stored in the memory 303 and calling data stored in the memory 303, the various functions of the system and processing data.

[0149] In one possible design, processor 301 may include one or more processing units. Processor 301 may integrate an application processor and a modem processor. The application processor primarily processes the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 301. In some embodiments, processor 301 and memory 303 may be implemented on the same chip. In some embodiments, they may also be implemented on separate chips.

[0150] Processor 301 can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the medical examination call sign management method based on crowd density and space matching disclosed in the embodiments of this application can be directly implemented as a hardware processor, or can be implemented using a combination of hardware and software modules in the processor.

[0151] The memory 303 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 303 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 303 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0152] By designing and programming the processor 301, the code corresponding to the medical examination call sign management method based on matching of crowd density and space introduced in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 1 The embodiment shown is a medical examination call sign management step based on crowd density and space matching. How to design and program the processor 301 is a technology well known to those skilled in the art and will not be described in detail here.

[0153] Based on the same inventive concept, an embodiment of the present application also provides a storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes a method for physical examination call sign management based on crowd density and space matching discussed above.

[0154] In some possible embodiments, the present application provides various aspects of a physical examination call sign management method based on crowd density and space matching, which can also be implemented in the form of a program product, which includes program code. When the program product is run on the device, the program code is used to enable the control device to execute the steps of a physical examination call sign management method based on crowd density and space matching according to various exemplary embodiments of the present application described above in this specification.

[0155] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0159] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A physical examination call sign management method based on crowd density and space matching, characterized in that: include: Get the initial dataset; Inputting historical crowd flow data and real-time crowd flow data in the initial data set into a preset time series model, and outputting target crowd flow prediction data corresponding to the initial data set; Determining a resource scheduling area corresponding to the target passenger flow prediction data, and determining a regional priority level and a resource allocation requirement corresponding to the resource scheduling area, and determining a target call sign strategy for the resource scheduling area based on the regional priority level and the resource allocation requirement; Generate a target call sign scheduling instruction corresponding to the resource scheduling area based on the target call sign strategy, send the target call sign scheduling instruction to the execution terminal, and control the execution terminal to parse the target call sign scheduling instruction and update the call sign queue information of the electronic call screen; The step of inputting the historical crowd flow data and the real-time crowd flow data in the initial data set into a preset time series model and outputting the target crowd flow prediction data corresponding to the initial data set includes: Inputting the historical crowd flow data and the real-time crowd flow data into the preset time series model, and outputting crowd flow prediction data corresponding to the initial data set; Determine actual crowd flow data, compare the actual crowd flow data with the crowd flow prediction data, and obtain a deviation parameter; If the deviation parameter is greater than the preset deviation parameter, the model parameters of the preset time series model are adjusted based on the deviation parameter, and the initial data set is re-predicted based on the adjusted preset time series model to obtain target crowd flow prediction data, wherein the target deviation parameter between the target crowd flow prediction data and the actual crowd flow data is less than the preset deviation parameter.

2. The method according to claim 1, wherein Determining the resource scheduling area corresponding to the target passenger flow prediction data includes: Obtaining crowd flow distribution characteristics of each area corresponding to the target crowd flow prediction data; Determine the congestion parameters corresponding to the crowd flow distribution characteristics of each area; If the congestion parameter meets the preset congestion condition, the corresponding area is determined as the resource scheduling area.

3. The method according to claim 1, wherein Determining the regional priority level corresponding to the resource scheduling area includes: Obtaining congestion parameters of the resource scheduling area; If the congestion parameter is greater than a preset congestion parameter, the regional priority level corresponding to the resource scheduling area is determined based on a mapping relationship between the preset congestion parameter and a preset regional priority level.

4. The method according to claim 1, wherein The determining of the target call sign strategy for the resource scheduling area based on the area priority and the resource allocation requirement includes: Obtaining the crowd flow distribution characteristics and resource allocation requirements of the resource scheduling area; Determining a resource allocation order for the resource scheduling area based on the crowd flow distribution characteristics and the resource allocation requirements; A target call sign strategy is generated based on the regional priority levels and the resource allocation order.

5. The method according to claim 1, wherein Generating the target call sign scheduling instruction corresponding to the resource scheduling area based on the target call sign strategy includes: Mapping the target call sign strategy and the preset time period constraint condition to obtain the call sign time and call sign sequence of the resource scheduling area; generating a call sign scheduling instruction corresponding to the resource scheduling area based on the call sign time and the call sign sequence; Replacing the key fields in the call sign dispatch instruction to obtain a call sign desensitization instruction; If there is a sensitive field in the call sign desensitization instruction, the sensitive field in the call sign desensitization instruction is encrypted in sections to obtain the target call sign scheduling instruction.

6. The method according to claim 5, wherein The obtaining of the call sign time of the resource scheduling area includes: Obtaining the actual available amount of resources corresponding to the call sign time; If the actual available resources are less than the resource allocation usage of the resource allocation requirement, the call time is readjusted until the target actual available resources of the readjusted call time are greater than the resource allocation usage, thereby obtaining the adjusted call time.

7. The method according to claim 5, wherein Obtaining the call sign desensitization instruction includes: Obtaining real-time congestion data of the current execution state, and obtaining real-time resource usage corresponding to the real-time congestion data; determining a real-time congestion parameter corresponding to the real-time congestion data; if the real-time congestion parameter is greater than a preset congestion parameter and the real-time resource usage is greater than the current resource allocation, reordering the regional priorities of the resource scheduling areas in the target passenger flow prediction data to obtain a regional priority order; The call sign desensitization instruction is generated based on the regional priority order.

8. A medical examination call sign management system based on crowd density and space matching, implementing the method steps described in any one of claims 1 to 7, characterized in that: include: Acquisition module, used to obtain the initial data set; A prediction module, configured to input historical crowd flow data and real-time crowd flow data in the initial data set into a preset time series model, and output target crowd flow prediction data corresponding to the initial data set; a determination module, configured to determine a resource scheduling area corresponding to the target passenger flow prediction data, and determine a regional priority level and a resource allocation requirement corresponding to the resource scheduling area, and determine a target call sign strategy for the resource scheduling area based on the regional priority level and the resource allocation requirement; The control module is used to generate a target call sign scheduling instruction corresponding to the resource scheduling area based on the target call sign strategy, and send the target call sign scheduling instruction to the execution terminal, control the execution terminal to parse the target call sign scheduling instruction and update the call sign queue information of the electronic call screen.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the method steps described in any one of claims 1 to 7 when executing the computer program stored in the memory.

Images