Smart city service method and system based on cloud computing and big data

Abstract

The application provides a kind of smart city service method and system based on cloud computing and big data, it is related to the technical field of smart city, method includes: according to first traffic flow and first crowd, respectively predict the second traffic flow and second crowd in the second preset time length of target area;According to historical traffic flow data and historical crowd data, respectively determine the third traffic flow and third crowd in the second preset time length of target area;Second traffic flow and third traffic flow are combined to determine target traffic flow, and calculate first congestion index;Second crowd, third crowd and appointment outpatient volume data are combined to determine target crowd, and calculate second congestion index;First congestion index and second congestion index are combined to determine target congestion index, when target congestion index exceeds preset index, generate adjustment scheme.The application has the technical effect that: accurately predict the traffic condition of hospital surrounding area, timely formulate effective preventive adjustment scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart city, in particular to a smart city service method and system based on cloud computing and big data. BACKGROUND

[0002] With the continuous advancement of urbanization, the traffic congestion problem of the surrounding area of large hospitals is increasingly prominent. Especially during the peak of outpatient service, the concentration of a large number of patients and vehicles not only affects the traffic efficiency of the surrounding roads of the hospital, but also causes disorder inside the hospital, seriously affecting the medical experience of patients. Therefore, how to accurately predict and timely prevent the congestion problem of the surrounding area of the hospital has become a technical problem to be solved.

[0003] At present, some smart city systems have predicted the traffic state of the surrounding area of the hospital by obtaining real-time vehicle flow and pedestrian flow data of the surrounding area of the hospital. Although the above method can reflect the regional traffic condition to a certain extent, it is difficult to fully reflect the traffic change trend in a complex dynamic environment by relying on single real-time data for prediction, resulting in low prediction accuracy of the traffic condition of the surrounding area of the hospital, and unable to timely develop effective preventive adjustment scheme. SUMMARY

[0004] The present application provides a smart city service method and system based on cloud computing and big data, which is used for accurately predicting the traffic condition of the surrounding area of the hospital and timely developing effective preventive adjustment scheme.

[0005] In a first aspect, the application provides a smart city service method based on cloud computing and big data, the method comprising: acquiring a first vehicle flow and a first pedestrian flow of a target area of a target hospital within a first preset time period, predicting a second vehicle flow and a second pedestrian flow of the target area within a second preset time period according to the first vehicle flow and the first pedestrian flow, the first preset time period being before the second preset time period; acquiring historical vehicle flow data and historical pedestrian flow data of the target area, determining a third vehicle flow and a third pedestrian flow of the target area within the second preset time period according to the historical vehicle flow data and the historical pedestrian flow data; combining the second vehicle flow and the third vehicle flow to determine a target vehicle flow of the target area within the second preset time period, and calculating a first congestion index of the target area according to the target vehicle flow; acquiring a reserved outpatient amount data of the target hospital within the second time period, combining the second pedestrian flow, the third pedestrian flow and the reserved outpatient amount data to determine a target pedestrian flow of the target area within the second preset time period, and calculating a second congestion index of the target area according to the target pedestrian flow; combining the first congestion index and the second congestion index to determine a target congestion index of the target area, and generating an adjustment scheme when the target congestion index exceeds a preset index.

[0006] By adopting the above technical scheme, real-time data within a first preset time period of a target area of a target hospital is acquired for prediction, historical data is analyzed, and reserved outpatient amount data is included in the prediction model, so as to calculate congestion indexes in two dimensions of vehicle flow and pedestrian flow. By comprehensively considering multi-dimensional data and combining two congestion indexes, the traffic change trend of the target area can be more comprehensively reflected, the traffic condition of the area around the hospital can be accurately predicted, and an effective preventive adjustment scheme can be timely developed.

[0007] Optionally, the predicting a second vehicle flow and a second pedestrian flow of the target area within a second preset time period according to the first vehicle flow and the first pedestrian flow comprises: determining a first change trend of vehicle flow within the first preset time period according to the first vehicle flow, predicting a second vehicle flow of the target area within the second preset time period according to the first change trend of vehicle flow; and determining a first change trend of pedestrian flow within the first preset time period according to the first pedestrian flow, predicting a second pedestrian flow of the target area within the second preset time period according to the first change trend of pedestrian flow.

[0008] By adopting the technical scheme, the first change trend of the vehicle flow and the first change trend of the pedestrian flow are obtained by analyzing the change trends of the first vehicle flow and the first pedestrian flow in the first preset time period, and the second vehicle flow and the second pedestrian flow in the second preset time period are predicted based on the change trends, so that the dynamic change rule of the vehicle flow and the pedestrian flow in the target area can be grasped more accurately, and the prediction accuracy of the traffic condition in the subsequent period is improved.

[0009] Optionally, the combining the second vehicle flow and the third vehicle flow to determine the target vehicle flow of the target area in the second preset time period comprises: obtaining a first weight coefficient corresponding to the second vehicle flow and a second weight coefficient corresponding to the third vehicle flow; performing an arithmetic multiplication of the second vehicle flow and the first weight coefficient to obtain a first target vehicle flow; performing an arithmetic multiplication of the third vehicle flow and the second weight coefficient to obtain a second target vehicle flow; and performing an arithmetic addition of the first target vehicle flow and the second target vehicle flow to obtain the target vehicle flow of the target area in the second preset time period.

[0010] By adopting the technical scheme, different weight coefficients are respectively assigned to the second vehicle flow obtained by real-time prediction and the third vehicle flow obtained based on historical data, and the target vehicle flow is obtained by weighted calculation, so that the instantaneity of the current real-time data is considered, and the regularity of the historical data is combined, so that the finally determined target vehicle flow can more comprehensively and accurately reflect the actual traffic condition of the target area.

[0011] Optionally, the calculating the first congestion index of the target area according to the target vehicle flow comprises: obtaining a maximum vehicle flow that the target area can accommodate in a unit time, a lane number, a vehicle type, and a total length of a road; and substituting the maximum vehicle flow, the lane number, the vehicle type, the total length of the road, and the target vehicle flow into a first preset formula to calculate the first congestion index of the target area; wherein the first preset formula is: In the formula, CI1 is the first congestion index, a is a first adjustment coefficient, V t is the target vehicle flow, C is the maximum vehicle flow that the target area can accommodate in a unit time, β1 is a road grade coefficient of the target area, n is the lane number of the target area, σ v is a vehicle flow fluctuation coefficient, β2 is a vehicle type proportion correction coefficient, and L is the total length of the road of the target area.

[0012] By adopting the technical scheme, the first congestion index is calculated by introducing the first preset formula, the formula comprehensively considers the ratio of the target vehicle flow to the maximum vehicle flow, the road grade coefficient, the number of lanes, the vehicle flow fluctuation, the vehicle type proportion and the road total length and is corrected by the first adjustment coefficient, so that the vehicle congestion condition of the target area can be quantified more scientifically and accurately, and the congestion prediction result is more in line with the actual traffic condition.

[0013] Optionally, the second preset time period is determined according to the second time period, the third time period and the fourth time period, and the target area is determined according to the target hospital.

[0014] By adopting the technical scheme, different weight coefficients are respectively assigned to the second real-time predicted vehicle flow and the third vehicle flow based on historical data, the weight calculation is adopted to obtain the target vehicle flow, and the reservation outpatient quantity data is combined for further adjustment, so that the real-time data instantaneity and the historical data regularity are considered, and the unique reservation medical information of the hospital is also considered, so that the finally determined target vehicle flow can more accurately reflect the actual personnel density of the area around the hospital.

[0015] Optionally, the second preset time period is determined according to the second time period, the third time period and the fourth time period, and the target area is determined according to the target hospital.

[0016] By adopting the technical scheme, the reservation outpatient quantity data is converted into specific number of patients, and a positive correlation between the number of patients and the target vehicle flow is established, the target vehicle flow is adjusted, the final target vehicle flow not only reflects the regular change of the vehicle flow, but also accurately reflects the change of the vehicle flow caused by the hospital, so that the actual vehicle flow characteristics of the area around the hospital are more in line with the actual vehicle flow characteristics, and the accuracy of the vehicle flow prediction is improved.

[0017] Optionally, the calculating the second congestion index of the target area according to the target crowd flow comprises: obtaining a maximum crowd flow that the target area can accommodate in a unit time; and substituting the maximum crowd flow and the target crowd flow into a second preset formula to calculate the second congestion index of the target area; wherein the second preset formula is: wherein CI2 is the second congestion index, γ is a second adjustment coefficient, P t is the target crowd flow, H is the maximum crowd flow that the target area can accommodate in a unit time, β3 is a crowd flow density correction coefficient of the target area, and β4 is a total area of the target area.

[0018] By using the above technical solution, the second congestion index is calculated by introducing the second preset formula, which comprehensively considers key factors such as the ratio of the target crowd flow to the maximum crowd flow, the crowd flow density correction coefficient, and the total area of the target area, and is corrected by the second adjustment coefficient, so that the personnel congestion degree of the target area can be more reasonably quantified, and the people flow congestion prediction result is more in line with the actual crowd gathering characteristics of the hospital surrounding area.

[0019] In a second aspect, the present application provides a smart city service system based on cloud computing and big data, which comprises an acquisition module, a determination module, a first combination module, a second combination module and a generation module. The acquisition module is configured to acquire a first vehicle flow and a first crowd flow of a target area of a target hospital in a first preset time period, and predict a second vehicle flow and a second crowd flow of the target area in a second preset time period according to the first vehicle flow and the first crowd flow, wherein the first preset time period is before the second preset time period. The determination module is configured to acquire historical vehicle flow data and historical crowd flow data of the target area, and determine a third vehicle flow and a third crowd flow of the target area in the second preset time period according to the historical vehicle flow data and the historical crowd flow data. The first combination module is configured to combine the second vehicle flow and the third vehicle flow to determine a target vehicle flow of the target area in the second preset time period, and calculate a first congestion index of the target area according to the target vehicle flow. The second combination module is configured to acquire appointment outpatient quantity data of the target hospital in the second time period, combine the second crowd flow, the third crowd flow and the appointment outpatient quantity data to determine a target crowd flow of the target area in the second preset time period, and calculate a second congestion index of the target area according to the target crowd flow. The generation module is configured to combine the first congestion index and the second congestion index to determine a target congestion index of the target area, and generate an adjustment scheme when the target congestion index exceeds a preset index.

[0020] In a third aspect, the present application provides an electronic device, which adopts the technical scheme as follows: comprising a processor, a memory, a user interface and a network interface, the memory is used for storing instructions, the user interface and the network interface are used for communication with other devices, and the processor is used for executing the instructions stored in the memory to enable the electronic device to execute the computer program of any of the above-mentioned smart city service methods based on cloud computing and big data.

[0021] In a fourth aspect, the present application provides a computer readable storage medium, which adopts the technical scheme as follows: storing the computer program capable of being loaded by a processor and executing any of the above-mentioned smart city service methods based on cloud computing and big data.

[0022] In summary, the present application includes at least one of the following beneficial technical effects:

[0023] By acquiring real-time data in a target region of a target hospital within a first preset time period for prediction, combining historical data for analysis, and including the number of reserved outpatient services in the prediction model, the congestion index is calculated in two dimensions of traffic flow and passenger flow. By comprehensively considering multi-dimensional data and combining two congestion indexes, the traffic change trend of the target region can be more comprehensively reflected, the traffic condition around the hospital can be accurately predicted, and effective preventive adjustment schemes can be timely formulated. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flowchart of a smart city service method based on cloud computing and big data provided by an embodiment of the present application;

[0025] Figure 2 is a structural schematic diagram of a smart city service system based on cloud computing and big data provided by an embodiment of the present application;

[0026] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0027] Explanation of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below in conjunction with the drawings in the embodiments of the present specification. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments.

[0029] In the description of the embodiments of the present application, the words "exemplary", "for example", or "e.g." are used to mean serving as an example, instance, or illustration. Any embodiment or design presented as "exemplary", "for example", or "e.g." in the embodiments of the present application should not be construed as preferred or superior over other embodiments or design solutions. Rather, the use of "exemplary", "for example", or "e.g." is intended to present relevant concepts in a particular manner.

[0030] Figure 1 is a flowchart of a smart city service method based on cloud computing and big data provided by the embodiments of the present application. As shown in Figure 1 , the method comprises S101-S105:

[0031] S101, acquiring first traffic flow and first passenger flow of a target area of a target hospital within a first preset time period, and predicting second traffic flow and second passenger flow of the target area within a second preset time period according to the first traffic flow and the first passenger flow, the first preset time period being before the second preset time period.

[0032] In combination with the embodiments of the present application, the existing smart city service method usually only makes prediction based on historical data, and cannot fully consider the current real-time traffic and passenger flow changes of the target area, resulting in a large deviation between the prediction result and the actual situation. In order to solve the above problems, the present application provides a prediction method based on real-time data.

[0033] Specifically, first, through data acquisition devices such as video monitoring devices, traffic flow detectors, and passenger flow detectors set in the target area of the target hospital, the first traffic flow and the first passenger flow within the first preset time period (for example, 2 hours before the current time) are acquired. The video monitoring device is used to identify and count vehicles and pedestrians; the traffic flow detector records the number of passing vehicles through geomagnetic induction, infrared, or ultrasonic sensors; and the passenger flow detector counts the number of personnel in the area through infrared detection or Wi-Fi probe technology. All collected data is uploaded to the cloud computing platform in real time through the Internet of Things device for processing and storage.

[0034] After acquiring the real-time data within the first preset time period, the present application analyzes the traffic flow change trend according to the first traffic flow data. Specifically, by calculating the traffic flow difference value of adjacent time periods within the first preset time period, the increase and decrease rate of traffic flow is determined. If the traffic flow shows a continuous growth trend, it is predicted that the second traffic flow within the second preset time period (for example, the next 2 hours) will increase accordingly; if the traffic flow shows a downward trend, it is predicted that the second traffic flow will decrease. Similarly, by analyzing the change trend of the first passenger flow within the first preset time period, the second passenger flow within the second preset time period can be predicted.

[0035] On the basis of the above embodiments, as an optional implementation, in S101, according to the first vehicle flow and the first pedestrian flow, the second vehicle flow and the second pedestrian flow of the target area within the second preset time period are respectively predicted, which specifically includes S11-S12: S11, according to the first vehicle flow, a first change trend of the vehicle flow within the first preset time period is determined, and according to the first change trend of the vehicle flow, the second vehicle flow of the target area within the second preset time period is predicted.

[0036] First, the vehicle flow data of the road section in the past two hours (the first preset time period) is collected as the first vehicle flow. The number of passing vehicles is recorded every 5 minutes through the intelligent monitoring system, and combined with vehicle type, vehicle speed and other information, a complete vehicle flow data sequence is formed. The system analyzes these data and identifies the first change trend of the vehicle flow. For example, during the morning peak period on weekdays, the monitoring shows that the vehicle flow presents a change characteristic of rapid rise first and then tends to be stable during 7:00-9:00, and reaches the peak value during 8:15-8:45.

[0037] Based on the analysis result of the first change trend, the system predicts the second vehicle flow within the next 30 minutes (the second preset time period). When predicting, not only the current change trend is considered, but also historical same period data, weather conditions, surrounding activities and other influencing factors are combined to improve the accuracy of prediction. For example, when it is monitored that the vehicle flow reaches 2800 vehicles / hour during 7:00-9:00 and presents a continuous rising trend, the system will predict that the vehicle flow during 9:00-9:30 may reach 3200 vehicles / hour.

[0038] S12, according to the first pedestrian flow, a first change trend of the pedestrian flow within the first preset time period is determined, and according to the first change trend of the pedestrian flow, the second pedestrian flow of the target area within the second preset time period is predicted.

[0039] The system first collects the pedestrian flow data within the first preset time period in the target area as the first pedestrian flow. Through various monitoring devices such as video monitoring and infrared sensors, the number of people passing by is recorded every 5 minutes, and information such as personnel moving speed and density is collected, forming a complete pedestrian flow data sequence. The system analyzes these data and identifies the first change trend of the pedestrian flow. Within the first preset time period, the pedestrian flow may present various change trends such as rapid growth, slow rise, stable fluctuation or gradual decline.

[0040] Based on the analysis result of the first change trend, the system predicts the second pedestrian flow within the second preset time period. In the prediction process, not only the current change trend is considered, but also historical same period data, weather factors, surrounding activities and other multi-dimensional information are combined to improve the accuracy of prediction. When the system detects that the pedestrian flow within the first preset time period presents a continuous growth trend, the possible change of the pedestrian flow within the second preset time period will be predicted accordingly.

[0041] S102, acquire historical vehicle flow data and historical passenger flow data of the target area, and determine third vehicle flow and third passenger flow of the target area in a second preset time period according to the historical vehicle flow data and the historical passenger flow data.

[0042] In one example, the vehicle flow and passenger flow of the target hospital area often have certain periodicity and regularity characteristics, such as flow differences in time periods such as weekdays and weekends, rush hours, etc. Simply relying on real-time data for prediction may ignore these inherent regularities, resulting in prediction results that are not comprehensive and accurate. To solve the above problems, the present application proposes a prediction method based on historical data.

[0043] Specifically, the present application acquires historical vehicle flow data and historical passenger flow data of the target area in the past year from the database of the cloud computing platform. These historical data include vehicle flow and passenger flow records under different time periods (weekdays, weekends and holidays), different weather conditions. The data collection frequency is every 5 minutes, ensuring the continuity and integrity of the data. The system classifies and stores the acquired historical data according to time attributes (such as Monday to Sunday) and time period attributes (such as early morning rush hour from 7:00 to 9:00, morning clinic period from 9:00 to 11:00, afternoon clinic period from 14:00 to 16:00, late afternoon rush hour from 17:00 to 19:00, etc.).

[0044] In determining the third vehicle flow in the second preset time period (such as the next 2 hours), the system first identifies the time attribute of the second preset time period, for example, if the prediction period is Tuesday 10:00-12:00, then all the vehicle flow data in the historical data of Tuesday 10:00-12:00 is filtered out. Then, the system calculates the arithmetic mean of the historical vehicle flow in this period to obtain the third vehicle flow. Similarly, the system also processes the historical passenger flow data in the same way to calculate the third passenger flow.

[0045] S103, determine the target vehicle flow of the target area in the second preset time period in combination with the second vehicle flow and the third vehicle flow, and calculate the first congestion index of the target area according to the target vehicle flow.

[0046] Specifically, the application determines the target traffic volume in the second preset time period by using a weighted average method. The system first calculates the difference between the second traffic volume and the third traffic volume, and when the difference is small (for example, the difference is not more than 20%), the weights of the two are set to 0.5 respectively; when the difference is large, the system dynamically adjusts the weight value according to the current weather condition, whether it is a special period (such as a holiday, a hospital expert clinic day, etc.) and other factors. For example, in rainy and snowy weather, a higher weight (such as 0.7) is given to the second traffic volume predicted in real time, and the weight of the third traffic volume predicted by the historical data is correspondingly reduced (such as 0.3). The target traffic volume obtained by weighted calculation not only considers the historical regularity, but also reflects the real-time change.

[0047] After determining the target traffic volume, the system calculates the first congestion index in combination with the road capacity of the target area. The specific calculation method is as follows: first, the basic parameters of the target area are obtained through the road basic database, including the maximum traffic volume C that can be accommodated per unit time (for example, 1000 vehicles per hour), the number of lanes n (for example, 4 lanes in both directions), the vehicle type (including small, medium and large vehicles, etc.) and the total length of the road L (for example, 2 kilometers). These parameters reflect the basic traffic capacity of the road.

[0048] After obtaining the basic parameters, the application substitutes these parameters and the target traffic volume calculated in the foregoing step into the first preset formula: , wherein a is the first adjustment coefficient, which is used to correct the congestion index according to different time periods (such as morning and evening peak, flat peak, etc.); β1 is the road grade coefficient, which reflects the influence of the design grade of the road on the traffic capacity; σ v is the traffic volume fluctuation coefficient, which represents the instability of the traffic volume; β2 is the vehicle type proportion correction coefficient, which is used to consider the influence of large vehicles on the traffic capacity of the road.

[0049] The first congestion index CI1 calculated by the formula comprehensively considers multiple influencing factors: reflects the basic saturation, (1+σ v ) considers the influence of traffic fluctuation, β2 reflects the influence of vehicle composition, and 1 / L takes the road length factor into consideration. For example, when the target traffic volume is 800 vehicles / hour, the maximum traffic volume is 1000 vehicles / hour, the road grade coefficient is 0.9, the number of lanes is 4, the traffic volume fluctuation coefficient is 0.2, the vehicle type proportion correction coefficient is 1.1, and the road length is 2 kilometers, and the first adjustment coefficient is 1.0, the first congestion index can be calculated.

[0050] The formula consists of five parts, the first part is: This term reflects the basic use of the road. Among them, V tV represents the target traffic volume, i.e. the predicted actual number of vehicles passing through; C represents the maximum traffic volume that can be accommodated per unit of time, which is the theoretical traffic capacity determined during road design; β1 is the road grade coefficient, as the actual traffic efficiency of different grade roads (such as expressway, main road, secondary road) is different, and needs to be corrected through this coefficient, for example, expressway: β1 = 1.0 (base value); main road: β1 = 0.95; secondary road: β1 = 0.9; branch road: β1 = 0.85. n represents the number of lanes, and the traffic capacity is proportional to the number of lanes. The combination of these parameters can accurately reflect the actual utilization rate of the road.

[0051] The second part is α:

[0052] This coefficient is used to adapt to the traffic characteristics of different time periods. For example, during the morning and evening peak hours, even the same traffic volume will cause more serious congestion, and at this time α takes a larger value (such as 1.2); during the flat peak period, the traffic flow is smoother, and α takes a smaller value (such as 0.8). This dynamic adjustment makes the congestion evaluation more in line with the actual situation.

[0053] The third part is (1+σ v ):

[0054] σ v is the traffic volume fluctuation coefficient, reflecting the stability of the traffic flow. When the traffic flow is uniform, σ v is close to 0; when the traffic flow is fast and slow, and stop and go, σ v increases. In the hospital area, due to ambulances, emergencies and other factors, the traffic flow fluctuates greatly, and the introduction of this item can reflect the impact of this instability on congestion.

[0055] The fourth part is β2:

[0056] Different types of vehicles have different effects on road traffic capacity. For example, large vehicles not only occupy more road space, but also reduce the overall traffic efficiency. β2 is calculated according to the proportion of each type of vehicle, and when the proportion of large vehicles increases, β2 increases (such as 1.2), indicating that it is more likely to cause congestion.

[0057] The fifth part is 1 / L:

[0058] L is the total length of the road, and its reciprocal 1 / L is used as a correction term because the same traffic volume is more likely to cause congestion on a shorter road section, while on a longer road section the vehicles can be relatively dispersed, and the congestion degree is relatively lighter. This reflects the influence of road section length on the formation of congestion.

[0059] For example, when the target area appears the following situation during the morning peak period: the target traffic volume V t= 800 vehicles / hour, maximum traffic flow C = 1000 vehicles / hour, road grade coefficient β1 = 0.9 (secondary road), number of lanes n = 4, first adjustment coefficient α = 1.2 (morning peak), traffic flow fluctuation coefficient σ = 0.3 (greater fluctuation), vehicle type ratio correction coefficient β2 = 1.1 (20% large vehicles included), total road length L = 2 kilometers. Substituting into the formula: CI1 = 1.2 x (800 / (1000 x 0.9 x 4)) x (1 + 0.3) x 1.1 x 1 / 2 = 0.87. v = 800 vehicles / hour, maximum traffic flow C = 1000 vehicles / hour, road grade coefficient β1 = 0.9 (secondary road), number of lanes n = 4, first adjustment coefficient α = 1.2 (morning peak), traffic flow fluctuation coefficient σ = 0.3 (greater fluctuation), vehicle type ratio correction coefficient β2 = 1.1 (20% large vehicles included), total road length L = 2 kilometers. Substituting into the formula: CI1 = 1.2 x (800 / (1000 x 0.9 x 4)) x (1 + 0.3) x 1.1 x 1 / 2 = 0.87.

[0060] On the basis of the above embodiments, as an optional implementation, in S103, the target traffic flow of the target region within the second preset time period is determined in combination with the second traffic flow and the third traffic flow, specifically including S31-S33:

[0061] S31, obtaining a first weight coefficient corresponding to the second traffic flow and a second weight coefficient corresponding to the third traffic flow.

[0062] S32, arithmetically multiplying the second traffic flow by the first weight coefficient to obtain a first target traffic flow; arithmetically multiplying the third traffic flow by the second weight coefficient to obtain a second target traffic flow.

[0063] S33, arithmetically adding the first target traffic flow and the second target traffic flow to obtain the target traffic flow of the target region within the second preset time period.

[0064] In determining the target traffic flow of the target region, in order to improve the prediction accuracy, the second traffic flow based on the change trend prediction and the third traffic flow based on the historical data prediction need to be considered at the same time. By reasonably setting the weight coefficients, the results of the two prediction methods are weighted and fused, which can fully utilize the real-time data and historical experience to obtain more accurate prediction results.

[0065] Firstly, the system automatically obtains a first weight coefficient corresponding to the second traffic flow and a second weight coefficient corresponding to the third traffic flow. The setting of the weight coefficients is based on the reliability and applicability of the prediction method, which is determined through a large amount of data analysis and verification. For example, when the traffic flow of the target region changes relatively stably, the reference value of the historical data is higher, at this time the second weight coefficient may be set to be larger; when the traffic flow presents sudden change, the reference value of the real-time trend is greater, at this time the first weight coefficient will be increased accordingly.

[0066] Then, the system arithmetically multiplies the second traffic flow by the first weight coefficient to obtain a first target traffic flow; at the same time, arithmetically multiplies the third traffic flow by the second weight coefficient to obtain a second target traffic flow. This weighted calculation method can reasonably allocate the influence proportion of different prediction results in the final prediction result according to the reliability of the prediction results.

[0067] Finally, the system arithmetically adds the first target traffic flow and the second target traffic flow to obtain the target traffic flow of the target area within the second preset time length.

[0068] S104, obtain the reservation outpatient volume data of the target hospital within the second time length, determine the target traffic flow of the target area within the second preset time length in combination with the second traffic flow, the third traffic flow and the reservation outpatient volume data, and calculate the second congestion index of the target area according to the target traffic flow.

[0069] Specifically, the application first obtains the reservation outpatient volume data within the second preset time length (such as the next 2 hours) through a hospital information system interface, and the data includes the number of patients for reservation in each department and the reservation time period distribution. For example, in a hospital, there are 50 patients for reservation in internal medicine, 30 patients for reservation in surgery, and 20 patients for reservation in each specialty in the 9:00-11:00 time period, and the total reservation outpatient volume is 100. At the same time, the system obtains the second traffic flow (predicted based on real-time data) and the third traffic flow (predicted based on historical data) calculated in the previous step.

[0070] In determining the target traffic flow, the application adopts a weighted fusion method. The reservation outpatient volume data is given a higher weight (such as 0.4) due to its deterministic characteristics; the second traffic flow is given a medium weight (such as 0.35) as it reflects the real-time situation; and the third traffic flow is given a corresponding weight (such as 0.25) as it embodies the historical law. The specific allocation of the weights will be dynamically adjusted according to the proportion of the reservation outpatient volume in the total traffic flow. For example, when the reservation outpatient volume is large, the weight is correspondingly increased. The target traffic flow obtained by the weighted calculation takes into account the determinism of the reservation information, and also takes into account the real-time changes and historical laws.

[0071] In calculating the second congestion index, the second preset formula is used for calculation, and the second preset formula is: In the formula, CI2 is the second congestion index, γ is the second adjustment coefficient, P is the target traffic flow, H is the maximum traffic flow that the target area can accommodate per unit time, β3 is the traffic density correction coefficient of the target area, and β4 is the total area of the target area. t

[0072] The formula is composed of three parts, the first part is This term reflects the basic use saturation of the area. Among them, P t ​The target person flow rate, i.e., the predicted actual passing person number; H represents the maximum person flow rate that the region can accommodate per unit time, which is the theoretical carrying capacity determined during the planning design of the region; and β3 is a person flow density correction coefficient, because the actual passing efficiency of different regions (such as squares, pedestrian streets, and passages) is different, the coefficient needs to be corrected. For example: open space: β3 = 1.2 (person flow dispersion); general region: β3 = 1.0 (reference value); passage region: β3 = 0.9 (person flow is relatively concentrated); key node: β3 = 0.8 (person flow is highly concentrated). The combination of these parameters can accurately reflect the actual use degree of the region.

[0073] The second part is γ:

[0074] The coefficient is used to adapt to the characteristics of person flow in different periods. For example, during the morning and evening peak periods, even the same person flow rate will cause more serious congestion, and at this time, the value of γ is larger (such as 1.3-1.5); during the flat peak period, the person flow is smoother, and the value of γ is smaller (such as 0.8-1.0). The specific values are as follows: morning peak period (7:30-9:00): γ = 1.3-1.5; noon period (11:30-13:30): γ = 1.1-1.3; afternoon peak period (17:00-18:30): γ = 1.2-1.4; flat peak period: γ = 0.8-1.0. This dynamic adjustment makes the congestion evaluation more in line with the actual situation.

[0075] The third part is 1 / A:

[0076] A is the total area of the region, and the reciprocal 1 / A is used as a correction term because the same person flow rate is more likely to cause congestion in a smaller space; in a larger space, the crowd can be relatively dispersed, and the congestion degree is relatively lighter; this term reflects the influence of the space scale on the formation of congestion; and makes the congestion evaluation results of different scale regions comparable.

[0077] On the basis of the above embodiment, as an optional implementation manner, in S104, the target person flow rate of the target region in the second preset time period is determined in combination with the second person flow rate, the third person flow rate, and the appointment outpatient quantity data, specifically including S41-S44: S41, obtaining a third weight coefficient corresponding to the second person flow rate and a fourth weight coefficient corresponding to the third person flow rate.

[0078] S42, the second person flow rate is multiplied by the third weight coefficient to obtain a first target person flow rate; and the third person flow rate is multiplied by the fourth weight coefficient to obtain a second target person flow rate.

[0079] S43, the first target person flow rate and the second target person flow rate are added to obtain the target person flow rate of the target region in the second preset time period.

[0080] In determining the target passenger flow of the target area, the second passenger flow based on the change trend prediction and the third passenger flow based on the historical data prediction need to be comprehensively considered. By setting a reasonable weight coefficient for weighted fusion, the advantages of real-time data and historical experience can be fully utilized, and the accuracy of passenger flow prediction can be improved.

[0081] In the target area passenger flow prediction process, the system first obtains a third weight coefficient corresponding to the second passenger flow and a fourth weight coefficient corresponding to the third passenger flow. The setting of the weight coefficient is based on the reliability and applicable scene of the prediction method, and is determined through a large amount of data analysis and verification. When the target area passenger flow presents regular change, the reference value of historical data is higher, and at this time the fourth weight coefficient will be increased accordingly; when the passenger flow presents irregular fluctuation, the reference value of real-time trend is greater, and the system will automatically increase the proportion of the third weight coefficient.

[0082] Subsequently, the system performs arithmetic multiplication on the second passenger flow and the third weight coefficient to obtain a first target passenger flow; at the same time, the system performs arithmetic multiplication on the third passenger flow and the fourth weight coefficient to obtain a second target passenger flow. This weighted calculation method can reasonably allocate the influence weight of different prediction results in the final prediction result according to the reliability of the prediction results.

[0083] Finally, the system performs arithmetic addition on the first target passenger flow and the second target passenger flow to obtain the target passenger flow of the target area in the second preset time length. Through this weighted fusion method, both the dynamic characteristics of the real-time change trend and the stability of the historical law are retained, so that the prediction result is more accurate and reliable.

[0084] S44, adjusting the target passenger flow to be adjusted according to the appointment outpatient quantity data to obtain the target passenger flow of the target area in the second preset time length.

[0085] On the basis of the above embodiment, as an optional implementation manner, in S44, adjusting the target passenger flow to be adjusted according to the appointment outpatient quantity data to obtain the target passenger flow of the target area in the second preset time length specifically includes S51-S52:

[0086] S51, determining the number of visits to the target hospital in the second preset time length according to the appointment outpatient quantity data.

[0087] S52, adjusting the target passenger flow to be adjusted according to the number of visits to obtain the target passenger flow of the target area in the second preset time length, wherein the number of visits and the target passenger flow are positively correlated.

[0088] In the crowd flow prediction of the hospital surrounding area, in addition to considering the general flow change, the influence of the hospital number of visits on the crowd flow also needs to be specially concerned. By incorporating the appointment outpatient volume data into the prediction model, the actual flow situation of the hospital surrounding area can be more accurately predicted, thereby providing more accurate basis for crowd flow management.

[0089] Firstly, the system determines the number of visits to the target hospital within the second preset time length according to the appointment outpatient volume data. This process not only needs to count the direct appointment data in the appointment system, but also needs to consider the visit time distribution characteristics. For example, the system analyzes the specific visit time distribution of the appointment patients, and considers the accompanying personnel factor, so as to more accurately estimate the actual number of visits.

[0090] Subsequently, the system adjusts the target crowd flow to be adjusted according to the calculated number of visits, to obtain the target crowd flow of the target area within the second preset time length. The number of visits and the target crowd flow show a clear positive correlation, that is, the more the number of visits, the more the final target crowd flow increases. This adjustment mechanism takes into account the superposition effect of the visit crowd on the regional crowd flow, so that the prediction result is more in line with the actual situation.

[0091] S105, in combination with the first congestion index and the second congestion index, determines the target congestion index of the target area, and generates an adjustment scheme when the target congestion index exceeds the preset index.

[0092] In the congestion management of the hospital area, the target congestion index of the hospital area can be determined by combining the vehicle congestion index and the crowd congestion index. This method is particularly suitable for hospital environment, because the hospital not only faces the traffic pressure brought by emergency vehicles, private cars, taxis and other vehicles, but also has to deal with the crowd pressure caused by outpatients, hospital visits, medical staff and other types of people.

[0093] Taking a certain hospital as an example, when determining the target congestion index, considering the characteristics of dense flow in the hospital, the weight of the crowd congestion index is set to 65%, and the weight of the vehicle congestion index is set to 35%. This weight configuration highlights the people-oriented service characteristics of the hospital, while also ensuring sufficient attention to the vehicle traffic conditions.

[0094] During the morning outpatient peak on weekdays, the vehicle congestion index is monitored to be 0.72 (indicating that the hospital area road is severely congested), and the crowd congestion index is 0.68 (indicating that the personnel gather densely). Through weighted calculation, the target congestion index is: 0.72 x 35% + 0.68 x 65% = 0.694, which exceeds the preset 0.65 warning line. At this time, the system automatically generates an adjustment scheme, mainly including vehicle shunting and personnel dredging.

[0095] In terms of vehicle management, priority is given to emergency vehicles, and time-limited flow control measures are taken for non-emergency vehicles, while a standby parking lot is opened and a person is arranged to guide the orderly parking of vehicles. In terms of people flow management, temporary clinics and convenient service windows are opened, time-limited appointment for medical treatment is implemented, the layout of the diagnosis area is optimized, and the waiting area of each department is reasonably arranged.

[0096] Based on the above method, the application also discloses a smart city service system based on cloud computing and big data, as shown in Figure 2 Figure 2 is a structural schematic diagram of a smart city service system based on cloud computing and big data provided by an embodiment of the application. The system comprises an acquisition module, a determination module, a first combination module, a second combination module and a generation module. The acquisition module is configured to acquire a first vehicle flow and a first people flow of a target area of a target hospital within a first preset time length, and predict a second vehicle flow and a second people flow of the target area within a second preset time length according to the first vehicle flow and the first people flow, the first preset time length being before the second preset time length. The determination module is configured to acquire historical vehicle flow data and historical people flow data of the target area, and determine a third vehicle flow and a third people flow of the target area within the second preset time length according to the historical vehicle flow data and the historical people flow data. The first combination module is configured to combine the second vehicle flow and the third vehicle flow to determine a target vehicle flow of the target area within the second preset time length, and calculate a first congestion index of the target area according to the target vehicle flow. The second combination module is configured to acquire appointment outpatient quantity data of the target hospital within a second time length, combine the second people flow, the third people flow and the appointment outpatient quantity data to determine a target people flow of the target area within the second preset time length, and calculate a second congestion index of the target area according to the target people flow. The generation module is configured to combine the first congestion index and the second congestion index to determine a target congestion index of the target area, and generate an adjustment scheme when the target congestion index exceeds a preset index.

[0097] It should be noted that the system provided in the above embodiments only uses the above division of functional modules as an example to implement its functions, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is described in detail in the method embodiments, which will not be described here.

[0098] Please refer to Figure 3 , which is a structural schematic diagram of an electronic device provided by an embodiment of the application. As shown in Figure 3 ​As shown, the electronic device 1000 can include at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, at least one communication bus 1002.

[0099] The communication bus 1002 is configured to realize the connection communication between the components.

[0100] The user interface 1003 can include a display, a camera, and optionally a standard wired interface and a wireless interface.

[0101] The network interface 1004 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0102] The processor 1001 can include one or more processing cores. The processor 1001 connects various parts of the server through various interfaces and lines, executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 1001 can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU is mainly used to process the operating system, user interface and application programs; the GPU is used to render and draw the content to be displayed on the display; and the modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 1001, but can be realized by a separate chip.

[0103] The memory 1005 can include a random access memory (RAM) and can also include a read-only memory (ROM). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 1005 can include a program storage area and a data storage area, where the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area can store data involved in the various method embodiments described above, etc. The memory 1005 can also be at least one storage device located away from the aforementioned processor 1001. As shown in Figure 3 The memory 1005 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program of a smart city service method based on cloud computing and big data.

[0104] In the electronic device 1000 shown above, the user interface 1003 is mainly used to provide an interface for user input and obtain user input data; and the processor 1001 can be used to call the application program of the smart city service method based on cloud computing and big data stored in the memory 1005, and when executed by one or more processors, make the electronic device execute the method described in one or more of the above embodiments. Figure 3 An electronic device readable storage medium stores instructions. When executed by one or more processors, the electronic device performs the method described in one or more of the above embodiments.

[0105] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the application is not limited by the described action sequence, because according to the application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the application.

[0106] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0107]

[0108] ​In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments is merely illustrative, and the units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0109] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they can be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0110] In addition, the functional units in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0111] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable memory. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0112] The above is only exemplary embodiments of the present disclosure, which cannot limit the scope of the present disclosure. Any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practicing the present disclosure. The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional techniques in the art that are not described in the present disclosure. The specification and embodiments are only considered exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A smart city service method based on cloud computing and big data, characterized in that, The method comprises: acquiring first traffic flow and first passenger flow of a target area of a target hospital within a first preset time period, and predicting second traffic flow and second passenger flow of the target area within a second preset time period according to the first traffic flow and the first passenger flow, the first preset time period being before the second preset time period; acquiring historical traffic flow data and historical passenger flow data of the target area, and determining third traffic flow and third passenger flow of the target area within the second preset time period according to the historical traffic flow data and the historical passenger flow data; combining the second traffic flow and the third traffic flow to determine target traffic flow of the target area within the second preset time period, and calculating a first congestion index of the target area according to the target traffic flow; acquiring appointment outpatient quantity data of the target hospital within the second preset time period, combining the second passenger flow, the third passenger flow and the appointment outpatient quantity data to determine target passenger flow of the target area within the second preset time period, and calculating a second congestion index of the target area according to the target passenger flow; combining the first congestion index and the second congestion index to determine a target congestion index of the target area, and generating an adjustment scheme when the target congestion index exceeds a preset index. 2.The cloud-computing and big-data based smart city service method of claim 1, wherein, The method comprises: determining a first change trend of traffic flow within the first preset time period according to the first traffic flow, and predicting second traffic flow of the target area within the second preset time period according to the first change trend of the traffic flow; determining a first change trend of passenger flow within the first preset time period according to the first passenger flow, and predicting second passenger flow of the target area within the second preset time period according to the first change trend of the passenger flow. 3.The cloud-computing and big-data based smart city service method of claim 1, wherein, The method comprises: acquiring a first weight coefficient corresponding to the second traffic flow and a second weight coefficient corresponding to the third traffic flow; arithmetic multiplying the second traffic flow and the first weight coefficient to obtain first target traffic flow, and arithmetic multiplying the third traffic flow and the second weight coefficient to obtain second target traffic flow; arithmetic adding the first target traffic flow and the second target traffic flow to obtain target traffic flow of the target area within the second preset time period. 4.The cloud-computing and big-data based smart city service method of claim 1, wherein, The method comprises: acquiring maximum traffic flow, lane number, vehicle type and total road length that the target area can accommodate within a unit time, and substituting the maximum traffic flow, the lane number, the vehicle type, the total road length and the target traffic flow into a first preset formula to calculate the first congestion index of the target area; wherein, The first preset formula is: In the formula, CI1 is a first congestion index, a is a first adjustment coefficient, V t is a target traffic flow, C is a maximum traffic flow that a target area can accommodate per unit time, β1 is a road grade coefficient of the target area, n is a number of lanes of the target area, σ v is a traffic flow fluctuation coefficient, β2 is a vehicle type proportion correction coefficient, and L is a total length of roads of the target area. 5.The cloud-computing and big-data based smart city service method of claim 1, wherein, The method comprises: obtain a third weight coefficient corresponding to the second traffic flow and a fourth weight coefficient corresponding to the third traffic flow; multiply the second traffic flow by the third weight coefficient to obtain a first target traffic flow, and multiply the third traffic flow by the fourth weight coefficient to obtain a second target traffic flow; add the first target traffic flow and the second target traffic flow to obtain a target traffic flow to be adjusted of the target area in the second preset time period; adjust the target traffic flow to be adjusted according to the appointment outpatient volume data to obtain a target traffic flow of the target area in the second preset time period. 6.The cloud-computing and big-data based smart city service method of claim 5, wherein, The method comprises the following steps: determine the number of patients in the target hospital in the second preset time period according to the appointment outpatient volume data; adjust the target traffic flow to be adjusted according to the number of patients to obtain a target traffic flow of the target area in the second preset time period, wherein the number of patients is positively correlated with the target traffic flow. 7.The cloud-computing and big-data based smart city service method of claim 1, wherein, The method comprises the following steps: obtain the maximum traffic flow that the target area can accommodate in a unit of time; substitute the maximum traffic flow and the target traffic flow into a second preset formula to calculate the second congestion index of the target area; wherein, The second preset formula is: In the formula, CI2 is a second congestion index, γ is a second adjustment coefficient, P t is a target passenger flow, H is a maximum passenger flow that the target area can accommodate per unit time, β3 is a passenger flow density correction coefficient of the target area, and β4 is a total area of the target area.

8. A smart city service system based on cloud computing and big data, characterized in that, The system comprises an acquisition module, a determination module, a first combination module, a second combination module, and a generation module; wherein, The acquisition module is configured to acquire a first traffic flow and a first traffic flow of a target area of a target hospital in a first preset time period, and predict a second traffic flow and a second traffic flow of the target area in a second preset time period according to the first traffic flow and the first traffic flow, respectively, wherein the first preset time period is before the second preset time period; The determination module is configured to acquire historical traffic flow data and historical traffic flow data of the target area, and determine a third traffic flow and a third traffic flow of the target area in the second preset time period according to the historical traffic flow data and the historical traffic flow data, respectively; The first combination module is configured to combine the second traffic flow and the third traffic flow to determine a target traffic flow of the target area in the second preset time period, and calculate a first congestion index of the target area according to the target traffic flow; The second combination module is configured to acquire appointment outpatient volume data of the target hospital in the second preset time period, combine the second traffic flow, the third traffic flow, and the appointment outpatient volume data to determine a target traffic flow of the target area in the second preset time period, and calculate a second congestion index of the target area according to the target traffic flow; The generation module is configured to combine the first congestion index and the second congestion index to determine a target congestion index of the target area, and generate an adjustment scheme when the target congestion index exceeds a preset index.

9. An electronic device, comprising: An electronic device comprising a processor, a memory for storing instructions, a user interface and a network interface for communicating with other devices, the processor being configured to execute the instructions stored in the memory to cause the electronic device to perform the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program stored in a memory and loadable into the working memory of a digital computer, comprising software code portions arranged to make the computer execute the method of any one of claims 1-7 when said product is run on the computer.

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