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

Abstract

The invention provides a smart city service method and system based on cloud computing and big data, and relates to the technical field of smart cities, and the method comprises the steps: respectively predicting a second traffic flow and a second pedestrian flow of a target region within a second preset duration according to a first traffic flow and a first pedestrian flow; according to the historical traffic flow data and the historical people flow data, respectively determining a third traffic flow and a third people flow of the target area within a second preset duration; determining a target traffic flow by combining the second traffic flow and the third traffic flow, and calculating a first congestion index; in combination with the second visitor flow rate, the third visitor flow rate and the reserved outpatient quantity data, determining a target visitor flow rate, and calculating a second congestion index; and in combination with the first congestion index and the second congestion index, determining a target congestion index, and when the target congestion index exceeds a preset index, generating an adjustment scheme. The method has the technical effects that the traffic condition of the surrounding area of the hospital is accurately predicted, and an effective preventive adjustment scheme is formulated in time.

Description

Technical Field

[0001] The present application relates to the field of smart city technology, and specifically to a smart city service method and system based on cloud computing and big data. Background Art

[0002] With the continuous advancement of urbanization, traffic congestion in the areas around large hospitals has become increasingly prominent. Especially during the peak period of outpatient visits, a large number of patients and vehicles flock in, which not only affects the traffic efficiency of roads around hospitals, but also causes chaos inside hospitals, seriously affecting patients' medical experience. Therefore, how to accurately predict and promptly prevent congestion in areas around hospitals has become a technical problem that needs to be solved urgently.

[0003] At present, some smart city systems have been used to predict the traffic status of the surrounding areas by obtaining real-time vehicle and pedestrian flow data in the areas surrounding the hospital. Although the above methods can reflect the regional traffic conditions to a certain extent, it is difficult to fully reflect the traffic change trend in a complex dynamic environment by relying solely on a single real-time data, resulting in low prediction accuracy of the traffic conditions in the area surrounding the hospital, and it is impossible to formulate effective preventive adjustment plans in a timely manner. Summary of the invention

[0004] The present application provides a smart city service method and system based on cloud computing and big data, which is used to accurately predict the traffic conditions in the area around the hospital and formulate effective preventive adjustment plans in a timely manner.

[0005] In a first aspect, the present application provides a smart city service method based on cloud computing and big data, the method comprising: obtaining a first vehicle flow and a first pedestrian flow in a target area of ​​a target hospital within a first preset time period, and predicting a second vehicle flow and a second pedestrian flow in the target area within a second preset time period based on the first vehicle flow and the first pedestrian flow, respectively, wherein the first preset time period is before the second preset time period; obtaining historical vehicle flow data and historical pedestrian flow data of the target area, and determining a third vehicle flow and a third pedestrian flow in the target area within the second preset time period based on the historical vehicle flow data and the historical pedestrian flow data; combining the second vehicle flow and the first pedestrian flow The target traffic volume of the target area within the second preset time period is determined by combining the second traffic volume, the third traffic volume and the scheduled outpatient volume data, and the target traffic volume of the target area within the second preset time period is determined, and the first congestion index of the target area is calculated according to the target traffic volume; the appointment outpatient volume data of the target hospital within the second time period is obtained, and the target passenger volume of the target area within the second preset time period is determined by combining the second passenger volume, the third passenger volume and the appointment outpatient volume data, and the second congestion index of the target area is calculated according to the target passenger volume; the target congestion index of the target area is determined by combining the first congestion index and the second congestion index, and when the target congestion index exceeds the preset index, an adjustment plan is generated.

[0006] By adopting the above technical solution, the real-time data of the target hospital target area within the first preset time period is obtained for prediction, and the historical data is combined for analysis, and the appointment outpatient volume data is included in the prediction model, so as to calculate the congestion index in the two dimensions of vehicle flow and pedestrian flow. By comprehensively considering multi-dimensional data and combining two congestion indexes, it is possible to more comprehensively reflect the traffic change trend of the target area, accurately predict the traffic conditions in the area around the hospital, and formulate effective preventive adjustment plans in a timely manner.

[0007] Optionally, the second vehicle flow and the second pedestrian flow of the target area within a second preset time period are predicted respectively based on the first vehicle flow and the first pedestrian flow, including: determining a first changing trend of the vehicle flow within the first preset time period based on the first vehicle flow, and predicting the second vehicle flow in the target area within the second preset time period based on the first changing trend of the vehicle flow; determining a first changing trend of the pedestrian flow within the first preset time period based on the first pedestrian flow, and predicting the second pedestrian flow in the target area within the second preset time period based on the first changing trend of the pedestrian flow.

[0008] By adopting the above technical solution, by analyzing the changing trends of the first vehicle flow and the first pedestrian flow within the first preset time period, the first changing trends of the vehicle flow and the pedestrian flow are obtained respectively, and the second vehicle flow and the second pedestrian flow within the second preset time period are predicted based on these changing trends, so as to more accurately grasp the dynamic changing laws of the vehicle flow and the pedestrian flow in the target area and improve the accuracy of predicting the traffic conditions in subsequent time periods.

[0009] Optionally, the combination of the second traffic flow and the third traffic flow to determine the target traffic flow of the target area within the second preset time duration includes: obtaining a first weight coefficient corresponding to the second traffic flow and a second weight coefficient corresponding to the third traffic flow; 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; and arithmetically adding 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 duration.

[0010] By adopting the above technical solution, different weight coefficients are assigned to the second traffic flow obtained by real-time prediction and the third traffic flow obtained based on historical data, and the target traffic flow is obtained through weighted calculation. This not only takes into account the immediacy of the current real-time data, but also combines the regularity of historical data, so that the final target traffic flow can more comprehensively and accurately reflect the actual traffic conditions in the target area.

[0011] Optionally, calculating the first congestion index of the target area according to the target traffic flow includes: obtaining the maximum traffic flow, the number of lanes, the type of vehicle, and the total length of the road that the target area can accommodate within a unit time; substituting the maximum traffic flow, the number of lanes, the type of vehicle, the total length of the road, 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, CI 1 is the first congestion index, α is the first adjustment coefficient, V t is the target traffic flow, C is the maximum traffic flow that the target area can accommodate in unit time, β 1 is the road grade coefficient of the target area, n is the number of lanes in the target area, σ v is the traffic flow fluctuation coefficient, β 2 is the vehicle type ratio correction coefficient, and L is the total road length of the target area.

[0012] By adopting the above technical solution, a first preset formula is introduced to calculate the first congestion index, which comprehensively considers multiple influencing factors such as the ratio of target traffic flow to maximum traffic flow, road grade coefficient, number of lanes, traffic flow fluctuations, proportion of vehicle types and total road length, and is corrected by the first adjustment coefficient, so that the vehicle congestion situation in the target area can be quantified more scientifically and accurately, making the congestion prediction result more in line with the actual traffic conditions.

[0013] Optionally, the target flow of people in the target area within the second preset time period is determined by combining the second flow of people, the third flow of people and the scheduled outpatient volume data, including: obtaining a third weight coefficient corresponding to the second flow of people, and a fourth weight coefficient corresponding to the third flow of people; arithmetically multiplying the second flow of people by the third weight coefficient to obtain a first target flow of people; arithmetically multiplying the third flow of people by the fourth weight coefficient to obtain a second target flow of people; arithmetically adding the first target flow of people and the second target flow of people to obtain a target flow of people to be adjusted for the target area within the second preset time period; adjusting the target flow of people to be adjusted according to the scheduled outpatient volume data to obtain the target flow of people in the target area within the second preset time period.

[0014] By adopting the above technical solution, different weight coefficients are assigned to the second flow of people obtained by real-time prediction and the third flow of people obtained based on historical data, and weighted calculation is used to obtain the target flow of people to be adjusted. This is further adjusted in combination with the appointment outpatient volume data. In this way, while taking into account the immediacy of real-time data and the regularity of historical data, the hospital's unique appointment medical information is also taken into consideration, so that the target flow of people finally determined can more accurately reflect the actual population density in the area surrounding the hospital.

[0015] Optionally, adjusting the target flow rate to be adjusted based on the scheduled outpatient volume data to obtain the target flow rate of the target area within the second preset time period includes: determining the number of patients visiting the target hospital within the second preset time period based on the scheduled outpatient volume data; adjusting the target flow rate to be adjusted based on the number of patients to obtain the target flow rate of the target area within the second preset time period, wherein the number of patients is positively correlated with the target flow rate.

[0016] By adopting the above technical solution, by converting the appointment outpatient volume data into the specific number of patients and establishing a positive correlation between the number of patients and the target flow of people, the target flow of people to be adjusted is adjusted, so that the final target flow of people not only reflects the changes in regular flow of people, but also accurately reflects the changes in flow of people caused by hospital visits, thereby better fitting the actual flow characteristics of the hospital surrounding areas and improving the accuracy of flow of people prediction.

[0017] Optionally, calculating the second congestion index of the target area according to the target flow rate includes: obtaining a maximum flow rate that the target area can accommodate within a unit time; substituting the maximum flow rate and the target flow rate into a second preset formula to calculate the second congestion index of the target area; wherein the second preset formula is: In the formula, CI 2 is the second congestion index, γ is the second adjustment coefficient, P t is the target flow of people, H is the maximum flow of people that the target area can accommodate in unit time, β 3 is the crowd density correction coefficient of the target area, β 4 is the total area of ​​the target region.

[0018] By adopting the above technical solution, a second preset formula is introduced to calculate the second congestion index. The formula comprehensively considers key factors such as the ratio of target passenger flow to maximum passenger flow, passenger density correction coefficient and total area of ​​the target area, and is corrected by the second adjustment coefficient. This makes it possible to more reasonably quantify the degree of personnel congestion in the target area, making the passenger congestion prediction result more in line with the actual crowd gathering characteristics in the area surrounding the hospital.

[0019] In the second aspect, the present application provides a smart city service system based on cloud computing and big data, the system comprising: an acquisition module, a determination module, a first combination module, a second combination module and a generation module; wherein the acquisition module is used to acquire a first vehicle flow and a first pedestrian flow in a target area of ​​a target hospital within a first preset time period, and predict a second vehicle flow and a second pedestrian flow in the target area within a second preset time period based on the first vehicle flow and the first pedestrian flow, respectively, and the first preset time period is before the second preset time period; the determination module is used to acquire historical vehicle flow data and historical pedestrian flow data in the target area, and determine a third vehicle flow and a third pedestrian flow in the target area within the second preset time period based on the historical vehicle flow data and the historical pedestrian flow data. traffic; the first combining module is used to combine the second vehicle flow and the third vehicle flow to determine the target vehicle flow of the target area within the second preset time period, and calculate the first congestion index of the target area according to the target vehicle flow; the second combining module is used to obtain the appointment outpatient volume data of the target hospital within the second time period, combine the second passenger flow, the third passenger flow and the appointment outpatient volume data, determine the target passenger flow of the target area within the second preset time period, and calculate the second congestion index of the target area according to the target passenger flow; the generating module is used to combine the first congestion index and the second congestion index to determine the target congestion index of the target area, and generate an adjustment plan when the target congestion index exceeds the preset index.

[0020] In the third aspect, the present application provides an electronic device, adopting the following technical solution: including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as 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 following technical solution: storing a computer program that can be loaded by a processor and execute 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: The prediction is made by obtaining real-time data within the first preset time period of the target hospital's target area, combining it with historical data for analysis, and incorporating the appointment outpatient volume data into the prediction model, so as to calculate the congestion index in the two dimensions of vehicle flow and pedestrian flow. By comprehensively considering multi-dimensional data and combining the two congestion indexes, it is possible to more comprehensively reflect the traffic change trend in the target area, accurately predict the traffic conditions in the area surrounding the hospital, and formulate effective preventive adjustment plans in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of a smart city service method based on cloud computing and big data provided by an embodiment of the present application; Figure 2 It is a structural diagram of a smart city service system based on cloud computing and big data provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0027] Figure 1 1 is a flow chart of a smart city service method based on cloud computing and big data provided by an embodiment of the present application. Figure 1 As shown, the method includes S101-S105: S101, obtaining a first vehicle flow and a first passenger flow in a target area of ​​a target hospital within a first preset time period, and predicting a second vehicle flow and a second passenger flow in the target area within a second preset time period based on the first vehicle flow and the first passenger flow, respectively, the first preset time period being before the second preset time period.

[0028] In conjunction with the embodiments of the present invention, existing smart city service methods usually only make predictions based on historical data, and fail to fully consider the current real-time traffic conditions and changes in the flow of people in the target area, resulting in a large deviation between the prediction results and the actual situation. In order to solve the above problems, the present invention provides a prediction method based on real-time data.

[0029] Specifically, firstly, the first vehicle flow and the first human flow within the first preset time period (for example, 2 hours before the current time) are obtained through data acquisition equipment such as video surveillance equipment, vehicle flow detectors and human flow detectors set up in the target area of ​​the target hospital. Among them, the video surveillance equipment is used to identify and count vehicles and pedestrians; the vehicle flow detector records the number of passing vehicles through sensors such as geomagnetic induction, infrared or ultrasonic waves; and the human flow detector counts the number of people in the area through technologies such as infrared detection or Wi-Fi probes. All collected data are uploaded to the cloud computing platform in real time through the Internet of Things devices for processing and storage.

[0030] After acquiring the real-time data within the first preset time period, the present invention analyzes the traffic flow change trend based on the first traffic flow data. Specifically, the rate of increase or decrease of traffic flow is determined by calculating the difference in traffic flow between adjacent time periods within the first preset time period. 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.

[0031] On the basis of the above embodiment, as an optional implementation, in S101, based on the first vehicle flow and the first pedestrian flow, respectively predicting the second vehicle flow and the second pedestrian flow of the target area within the second preset time period specifically includes S11-S12: S11, based on the first vehicle flow, determining the first change trend of the vehicle flow within the first preset time period, and based on the first change trend of the vehicle flow, predicting the second vehicle flow of the target area within the second preset time period.

[0032] First, the traffic flow data of the road section for nearly two hours (the first preset duration) is collected as the first traffic flow. The number of vehicles passing through is recorded every 5 minutes through the intelligent monitoring system, and combined with information such as vehicle type and speed to form a complete traffic flow data sequence. The system analyzes these data and identifies the first change trend of traffic flow. For example, during the morning rush hour on weekdays, monitoring shows that the traffic flow from 7:00 to 9:00 shows a change characteristic of first rising rapidly and then stabilizing, and reaches a peak at 8:15 to 8:45.

[0033] Based on the analysis results of the first change trend, the system predicts the second traffic flow in the next 30 minutes (the second preset time). The prediction not only takes into account the current change trend, but also combines historical data for the same period, weather conditions, surrounding activities and other influencing factors to improve the accuracy of the prediction. For example, when the traffic flow from 7:00 to 9:00 is monitored to reach 2,800 vehicles / hour and shows a continuous upward trend, the system will predict that the traffic flow from 9:00 to 9:30 may reach 3,200 vehicles / hour.

[0034] S12, determining a first change trend of the flow of people within a first preset time period based on the first flow of people, and predicting a second flow of people in the target area within a second preset time period based on the first change trend of the flow of people.

[0035] The system first collects the flow data of people in the target area within the first preset time as the first flow. Through video surveillance, infrared sensors and other monitoring equipment, the number of people passing through is recorded every 5 minutes, and information such as the speed and density of people moving is collected to form a complete sequence of flow data. The system analyzes these data and identifies the first change trend of the flow of people. Within the first preset time, the flow of people may show a variety of change trends such as rapid growth, slow rise, stable fluctuation or gradual decline.

[0036] Based on the analysis results of the first change trend, the system predicts the second flow of people within the second preset time. The prediction process not only considers the current change trend, but also combines historical data for the same period, weather factors, surrounding activities and other multi-dimensional information to improve the accuracy of the prediction. When the system detects that the flow of people shows a continuous growth trend within the first preset time, it will predict the possible changes in the flow of people within the second preset time accordingly.

[0037] S102, obtaining historical vehicle flow data and historical pedestrian flow data of the target area, and determining a third vehicle flow and a third pedestrian flow of the target area within a second preset time period according to the historical vehicle flow data and the historical pedestrian flow data.

[0038] In one example, the traffic and pedestrian flows in the target hospital area often have certain periodic and regular characteristics, such as differences in traffic between weekdays and weekends, rush hour, etc. Relying solely on real-time data for prediction may ignore these inherent laws, resulting in incomplete and inaccurate prediction results. To solve the above problems, the present invention proposes a prediction method based on historical data.

[0039] Specifically, the present invention obtains the historical traffic 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 traffic flow and passenger flow records in different time periods (weekdays, weekends and holidays) and under different weather conditions. The data is collected every 5 minutes to ensure 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 the morning peak period of 7:00-9:00, the morning outpatient period of 9:00-11:00, the afternoon outpatient period of 14:00-16:00, the evening peak period of 17:00-19:00, etc.).

[0040] When determining the third vehicle flow within the second preset time period (e.g., the next 2 hours), the system first identifies the time attribute of the second preset time period. For example, if the predicted time period is from 10 a.m. to 12 p.m. on Tuesday, the system will filter out all the vehicle flow data from 10 a.m. to 12 p.m. on Tuesday in the historical data. 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 human flow data in the same way to calculate the third human flow.

[0041] S103, determining a target traffic flow of the target area within a second preset time period by combining the second traffic flow and the third traffic flow, and calculating a first congestion index of the target area according to the target traffic flow.

[0042] Specifically, the present invention adopts a weighted average method to determine the target traffic flow within the second preset time length. The system first calculates the difference between the second traffic flow and the third traffic flow. When the difference is small (such as the difference does not exceed 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 factors such as the current weather conditions and whether it is a special period (such as holidays, hospital specialist clinic days, etc.). For example, in rainy and snowy weather, the second traffic flow predicted in real time is given a higher weight (such as 0.7), and the weight of the third traffic flow predicted by historical data is correspondingly reduced (such as 0.3). The target traffic flow obtained by weighted calculation takes into account both historical laws and real-time changes.

[0043] 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: 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, 1,000 vehicles per hour), the number of lanes n (for example, 4 lanes in both directions), the type of vehicle (including small cars, medium-sized cars, large cars, etc.) and the total length of the road L (for example, 2 kilometers). These parameters reflect the basic traffic capacity of the road.

[0044] After obtaining the basic parameters, the present invention substitutes these parameters together with the target vehicle flow calculated in the above steps into the first preset formula: In the equation, α is the first adjustment coefficient, which is used to modify the congestion index according to different time periods (such as morning and evening peaks, off-peak hours, etc.); β 1 is the road grade coefficient, which reflects the impact of the road design grade on the traffic capacity; σ v is the traffic flow fluctuation coefficient, indicating the instability of traffic flow; β 2 It is the vehicle type ratio correction factor, which is used to consider the impact of large vehicles on road capacity.

[0045] The first congestion index CI calculated by this formula 1 A number of influencing factors were taken into consideration: reflects the basic saturation, (1+σ v ) takes into account the impact of traffic fluctuations, β 2 It reflects the influence of vehicle composition, while 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 1,000 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 ratio correction coefficient is 1.1, and the road length is 2 kilometers, assuming that the first adjustment coefficient is 1.0, the first congestion index can be calculated.

[0046] The formula consists of five parts, the first part is: This item reflects the basic usage of the road. t represents the target traffic flow, that is, the predicted actual number of vehicles passing through; C represents the maximum traffic flow that can be accommodated per unit time, which is the theoretical traffic capacity determined during road design; β 1 Is the road grade coefficient. Because the actual traffic efficiency of roads of different grades (such as expressways, main roads, and secondary roads) is different, it is necessary to use this coefficient to make corrections. For example, expressways: β 1 =1.0 (base value); trunk road: β 1 =0.95; Secondary trunk road: β 1 =0.9; branch: β 1 =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.

[0047] The second part is α: This coefficient is used to adapt to traffic characteristics at different times. For example, during peak hours in the morning and evening, even the same traffic volume will cause more serious congestion, and α is larger (such as 1.2); during off-peak hours, traffic flow is smoother, and α is smaller (such as 0.8). This dynamic adjustment makes congestion assessment more in line with actual conditions.

[0048] The third part is (1+σ v ): σ v is the traffic flow fluctuation coefficient, reflecting the stability of traffic flow. When the traffic flow is uniform, σ v Close to 0; when the traffic speeds up and slows down, stops and starts, σ v In the hospital area, the traffic flow fluctuates greatly due to factors such as ambulances and emergencies. The introduction of this item can reflect the impact of this instability on congestion.

[0049] The fourth part is β 2 : Different types of vehicles have different impacts on road capacity. For example, large vehicles not only take up more road space, but also reduce overall traffic efficiency. 2 According to the proportion of each type of vehicle, when the proportion of large vehicles increases, β 2 A larger value (such as 1.2) indicates that congestion is more likely to occur.

[0050] The fifth part is 1 / L: 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 section, while on a longer section, vehicles can be relatively dispersed and the congestion is relatively light. This reflects the impact of section length on congestion formation.

[0051] For example, when the target area has the following situation during the morning rush hour: the target traffic volume V t = 800 vehicles / hour, maximum traffic volume C = 1000 vehicles / hour, road grade coefficient β 1 =0.9 (secondary road), number of lanes n = 4, first adjustment coefficient α = 1.2 (morning peak), traffic flow fluctuation coefficient σ v =0.3 (large fluctuation), vehicle type proportion correction coefficient β 2 =1.1 (including 20% ​​large vehicles), total road length L = 2 kilometers. Substitute into the formula: CI 1 =1.2×(800 / (1000×0.9×4))×(1+0.3)×1.1×1 / 2=0.87.

[0052] Based on the above embodiment, as an optional implementation, in S103, combining the second vehicle flow and the third vehicle flow to determine the target vehicle flow of the target area within the second preset time period specifically includes S31-S33: S31, obtaining a first weight coefficient corresponding to the second vehicle flow and a second weight coefficient corresponding to the third vehicle flow.

[0053] S32, arithmetically multiplying the second vehicle flow by the first weight coefficient to obtain a first target vehicle flow; arithmetically multiplying the third vehicle flow by the second weight coefficient to obtain a second target vehicle flow.

[0054] S33, arithmetically adding the first target vehicle flow rate and the second target vehicle flow rate to obtain the target vehicle flow rate of the target area within a second preset time period.

[0055] When determining the target traffic volume in the target area, in order to improve the prediction accuracy, it is necessary to consider the second traffic volume predicted based on the change trend and the third traffic volume predicted based on historical data. By setting the weight coefficient reasonably and weighted fusion of the results of the two prediction methods, we can make full use of real-time data and historical experience to obtain more accurate prediction results.

[0056] First, the system automatically obtains the first weight coefficient corresponding to the second traffic flow and the second weight coefficient corresponding to the third traffic flow. The setting of the weight coefficient is based on the reliability and applicability of the prediction method and is determined through a large amount of data analysis and verification. For example, when the traffic flow in the target area changes relatively steadily, the reference value of historical data is higher, and the second weight coefficient may be set larger; when the traffic flow shows sudden changes, the reference value of the real-time trend is greater, and the first weight coefficient will increase accordingly.

[0057] Then, the system multiplies the second vehicle flow by the first weight coefficient to obtain the first target vehicle flow, and multiplies the third vehicle flow by the second weight coefficient to obtain the second target vehicle flow. This weighted calculation method can reasonably allocate the influence proportion of different prediction results in the final prediction result according to their reliability.

[0058] Finally, the system 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. This weighted fusion method not only retains the dynamic characteristics of the real-time change trend, but also takes into account the stability of historical laws, making the prediction results more comprehensive and accurate.

[0059] S104, obtaining the outpatient appointment volume data of the target hospital within the second time period, combining the second passenger flow, the third passenger flow and the outpatient appointment volume data, determining the target passenger flow of the target area within the second preset time period, and calculating the second congestion index of the target area based on the target passenger flow.

[0060] Specifically, the present invention first obtains the outpatient appointment volume data within the second preset time period (such as the next 2 hours) through the hospital information system interface. These data include the number of appointment patients in each department and their appointment time distribution. For example, in a hospital, during the period of 9:00-11:00, there are 50 appointment patients in the internal medicine department, 30 appointment patients in the surgical department, and 20 appointment patients in various specialist departments, and the total appointment outpatient volume is 100. At the same time, the system obtains the second flow of people (based on real-time data prediction) and the third flow of people (based on historical data prediction) calculated in the previous steps.

[0061] When determining the target flow of people, the present invention adopts a weighted fusion method. The appointment outpatient volume data is given a higher weight (such as 0.4) due to its deterministic characteristics; the second flow of people reflects the real-time situation and is given a medium weight (such as 0.35); the third flow of people reflects historical laws and is given a corresponding weight (such as 0.25). The specific distribution of weights will be dynamically adjusted according to the proportion of appointment outpatient volume to total flow of people. For example, when the appointment outpatient volume is large, its weight is increased accordingly. The target flow of people obtained by weighted calculation takes into account both the certainty of the appointment information and the real-time changes and historical laws.

[0062] When calculating the second congestion index, the second preset formula is used for calculation, and the second preset formula is: In the formula, CI 2 is the second congestion index, γ is the second adjustment coefficient, P t is the target flow of people, H is the maximum flow of people that the target area can accommodate in unit time, β 3 is the crowd density correction coefficient of the target area, β 4 is the total area of ​​the target region.

[0063] The formula consists of three parts. The first part is This item reflects the basic usage saturation of the area. t represents the target flow of people, that is, the predicted actual number of people passing through; H represents the maximum flow of people that the area can accommodate per unit time, which is the theoretical carrying capacity determined during regional planning and design; β 3 It is the correction coefficient of the density of human traffic. Because the actual traffic efficiency of different areas (such as squares, pedestrian streets, and passages) is different, it is necessary to use this coefficient for correction. For example: open space: β3 = 1.2 (dispersed human traffic); general area: β3 = 1.0 (benchmark value); passage area: β3 = 0.9 (relatively concentrated human traffic); key node: β3 = 0.8 (highly concentrated human traffic). The combination of these parameters can accurately reflect the actual utilization degree of the area.

[0064] The second part is γ: This coefficient is used to adapt to the characteristics of passenger flow in different time periods. For example, during peak hours in the morning and evening, even the same passenger flow will cause more serious congestion, and the value of γ is larger (such as 1.3-1.5); during off-peak hours, the passenger 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; off-peak period: γ = 0.8-1.0. This dynamic adjustment makes the congestion assessment more in line with the actual situation.

[0065] The third part is 1 / A: A is the total area of ​​the region. The reason why its reciprocal 1 / A is used as a correction term is that the same flow of people is more likely to cause congestion in a smaller space; in a larger space, the crowd can be relatively dispersed and the degree of congestion is relatively light; this term reflects the impact of spatial scale on the formation of congestion; and makes the congestion assessment results of regions of different sizes comparable.

[0066] On the basis of the above embodiment, as an optional implementation, in S104, the second flow of people, the third flow of people and the scheduled outpatient volume data are combined to determine the target flow of people in the target area within the second preset time period, specifically including S41-S44: S41, obtaining the third weight coefficient corresponding to the second flow of people, and the fourth weight coefficient corresponding to the third flow of people.

[0067] S42, arithmetically multiplying the second passenger flow by the third weight coefficient to obtain a first target passenger flow; arithmetically multiplying the third passenger flow by the fourth weight coefficient to obtain a second target passenger flow.

[0068] S43, arithmetically adding the first target flow rate of people and the second target flow rate of people to obtain the target flow rate of people to be adjusted in the target area within the second preset time period.

[0069] When determining the target flow of people in the target area, it is necessary to comprehensively consider the second flow of people based on the change trend prediction and the third flow of people based on the historical data prediction. By setting a reasonable weight coefficient for weighted fusion, the advantages of real-time data and historical experience can be fully utilized to improve the accuracy of the flow prediction.

[0070] In the process of predicting the flow of people in the target area, the system first obtains the third weight coefficient corresponding to the second flow of people and the fourth weight coefficient corresponding to the third flow of people. The setting of the weight coefficient is based on the reliability and applicable scenarios of the prediction method, and is determined through a large amount of data analysis and verification. When the flow of people in the target area shows regular changes, the reference value of historical data is higher, and the fourth weight coefficient will increase accordingly; when the flow of people shows unconventional fluctuations, the reference value of the real-time trend is greater, and the system will automatically increase the proportion of the third weight coefficient.

[0071] Subsequently, the system multiplies the second flow of people by the third weight coefficient to obtain the first target flow of people, and multiplies the third flow of people by the fourth weight coefficient to obtain the second target flow of people. This weighted calculation method can reasonably allocate the influence weight of different prediction results in the final prediction results according to their reliability.

[0072] Finally, the system adds the first target flow rate and the second target flow rate to obtain the target flow rate to be adjusted in the target area within the second preset time. This weighted fusion method not only retains the dynamic characteristics of the real-time change trend, but also takes into account the stability of historical laws, making the prediction results more accurate and reliable.

[0073] S44, adjusting the target flow of people to be adjusted according to the appointment outpatient volume data to obtain the target flow of people in the target area within the second preset time period.

[0074] Based on the above embodiment, as an optional implementation, in S44, adjusting the target flow of people to be adjusted according to the appointment outpatient volume data, and obtaining the target flow of people in the target area within the second preset time period specifically includes S51-S52: S51, determining the number of patients visiting the target hospital within a second preset time period based on the scheduled outpatient volume data.

[0075] S52, adjusting the target flow of people to be adjusted according to the number of patients, and obtaining the target flow of people in the target area within the second preset time period, wherein the number of patients is positively correlated with the target flow of people.

[0076] In the prediction of the flow of people in the area around the hospital, in addition to considering the general changes in the flow of people, special attention should be paid to the impact of the number of patients visiting the hospital on the flow of people. By incorporating the data of the number of appointments into the prediction model, the actual flow of people in the area around the hospital can be predicted more accurately, thus providing a more accurate basis for the management of the flow of people.

[0077] First, the system determines the number of patients visiting the target hospital within the second preset time period based on the appointment volume data. This process not only counts the direct appointment data in the appointment system, but also needs to consider the distribution characteristics of the appointment time. For example, the system will analyze the specific appointment time distribution of the scheduled patients, while taking into account the factors of the accompanying personnel, so as to more accurately estimate the actual number of patients.

[0078] Subsequently, the system adjusts the target flow rate to be adjusted based on the calculated number of patients, and obtains the target flow rate of the target area within the second preset time. The number of patients and the target flow rate show an obvious positive correlation, that is, the more patients there are, the higher the final target flow rate will be. This adjustment mechanism takes into account the superposition effect of the number of patients on the regional flow rate, making the prediction results more in line with the actual situation.

[0079] S105, combining the first congestion index and the second congestion index to determine a target congestion index of the target area, and generating an adjustment plan when the target congestion index exceeds a preset index.

[0080] In the congestion management of hospital areas, the target congestion index of the hospital area can be determined by combining the vehicle congestion index and the human congestion index for comprehensive analysis. This method is particularly suitable for hospital environments because hospitals are faced with traffic pressure from multiple vehicles such as emergency vehicles, private cars, and taxis, as well as human pressure from multiple groups of people such as outpatients, inpatient visitors, and medical staff.

[0081] Taking a hospital as an example, when determining the target congestion index, considering the dense flow of people in the hospital, the weight of the human flow congestion index was set to 65% and the weight of the vehicle congestion index was set to 35%. This weight configuration highlights the people-oriented service characteristics of the hospital, while also ensuring full attention to vehicle traffic conditions.

[0082] During the morning outpatient peak on a weekday, the vehicle congestion index was monitored to be 0.72 (indicating serious congestion on the roads in the hospital area), and the human congestion index was 0.68 (indicating dense gathering of people). The target congestion index was obtained through weighted calculation: 0.72×35%+0.68×65%=0.694, exceeding the preset 0.65 warning line. At this time, the system automatically generates an adjustment plan, which mainly includes two aspects: vehicle diversion and personnel guidance.

[0083] In terms of vehicle management, emergency vehicles are given priority, and time-limited flow control measures are adopted for non-emergency vehicles. At the same time, spare parking lots are opened, and special personnel are arranged to guide vehicles to park in an orderly manner. In terms of crowd management, temporary clinics and convenience service windows are added, medical appointments are implemented in different time periods, the layout of the clinic area is optimized, and the waiting areas of each department are reasonably arranged.

[0084] Based on the above method, the present application also discloses a smart city service system based on cloud computing and big data, such as Figure 2 As shown, Figure 2It 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, and the system includes: an acquisition module, a determination module, a first combination module, a second combination module and a generation module; wherein the acquisition module is used to acquire a first vehicle flow and a first human flow in a target area of ​​a target hospital within a first preset time period, and predict a second vehicle flow and a second human flow in the target area within a second preset time period according to the first vehicle flow and the first human flow, respectively, and the first preset time period is before the second preset time period; the determination module is used to acquire historical vehicle flow data and historical human flow data of the target area, and determine the target area within the second preset time period according to the historical vehicle flow data and the historical human flow data. The third vehicle flow and the third passenger flow in the area; the first combining module, used to combine the second vehicle flow and the third vehicle flow, determine the target vehicle flow of the target area within the second preset time period, and calculate the first congestion index of the target area according to the target vehicle flow; the second combining module, used to obtain the appointment outpatient volume data of the target hospital within the second time period, combine the second passenger flow, the third passenger flow and the appointment outpatient volume data, determine the target passenger flow of the target area within the second preset time period, and calculate the second congestion index of the target area according to the target passenger flow; the generating module, used to combine the first congestion index and the second congestion index to determine the target congestion index of the target area, and generate an adjustment plan when the target congestion index exceeds the preset index.

[0085] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0086] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0087] The communication bus 1002 is used to realize the connection and communication between these components.

[0088] The user interface 1003 may include a display screen (Display) and a camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.

[0089] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0090] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and 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 hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0091] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). 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 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may 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.

[0092] exist Figure 3 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program stored in the memory 1005 for a smart city service method based on cloud computing and big data. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0093] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0094] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0095] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0100] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as 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: Obtaining a first vehicle flow and a first passenger flow in a target area of ​​a target hospital within a first preset time period, and predicting a second vehicle flow and a second passenger flow in the target area within a second preset time period, respectively, based on the first vehicle flow and the first passenger flow, wherein the first preset time period is before the second preset time period; Acquire historical vehicle flow data and historical pedestrian flow data of the target area, and determine 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; Determine a target traffic flow of the target area within the second preset time period by combining the second traffic flow and the third traffic flow, and calculate a first congestion index of the target area according to the target traffic flow; Obtaining the outpatient appointment volume data of the target hospital within the second duration, combining the second passenger flow, the third passenger flow and the outpatient appointment volume data, determining the target passenger flow of the target area within the second preset duration, and calculating the second congestion index of the target area according to the target passenger flow; The target congestion index of the target area is determined in combination with the first congestion index and the second congestion index, and an adjustment plan is generated when the target congestion index exceeds a preset index.

2. The smart city service method based on cloud computing and big data according to claim 1, characterized in that: The predicting, according to the first vehicle flow and the first pedestrian flow, respectively a second vehicle flow and a second pedestrian flow of the target area within a second preset time period includes: Determine, based on the first vehicle flow rate, a first change trend of the vehicle flow rate within the first preset time period, and predict, based on the first change trend of the vehicle flow rate, a second vehicle flow rate of the target area within a second preset time period; A first change trend of the human flow within the first preset time period is determined based on the first human flow, and a second human flow in the target area within a second preset time period is predicted based on the first change trend of the human flow.

3. The smart city service method based on cloud computing and big data according to claim 1, characterized in that: The determining the target vehicle flow rate of the target area within the second preset time period by combining the second vehicle flow rate and the third vehicle flow rate includes: Obtaining a first weight coefficient corresponding to the second vehicle flow and a second weight coefficient corresponding to the third vehicle flow; Arithmetically multiplying the second vehicle flow rate by the first weight coefficient to obtain a first target vehicle flow rate; Arithmetically multiplying the third vehicle flow rate by the second weight coefficient to obtain a second target vehicle flow rate; The first target vehicle flow rate and the second target vehicle flow rate are arithmetically added to obtain the target vehicle flow rate of the target area within the second preset time period.

4. The smart city service method based on cloud computing and big data according to claim 1 is characterized in that: Calculating a first congestion index of the target area according to the target vehicle flow includes: The maximum traffic flow, number of lanes, vehicle type and total road length that the target area can accommodate in a unit time are obtained; the maximum traffic flow, number of lanes, vehicle type, total road length and the target traffic flow are substituted into a first preset formula to calculate a first congestion index of the target area; wherein, The first preset formula is: Where CI1 is the first congestion index, α is the first adjustment coefficient, V t is the target traffic flow, C is the maximum traffic flow that the target area can accommodate per unit time, β1 is the road grade coefficient of the target area, n is the number of lanes in the target area, σ v is the traffic flow fluctuation coefficient, β2 is the vehicle type proportion correction coefficient, and L is the total road length of the target area.

5. The smart city service method based on cloud computing and big data according to claim 1 is characterized in that: The step of combining the second human flow, the third human flow and the scheduled outpatient volume data to determine the target human flow of the target area within the second preset time period includes: Obtaining a third weight coefficient corresponding to the second pedestrian flow and a fourth weight coefficient corresponding to the third pedestrian flow; Arithmetically multiplying the second flow of people by the third weight coefficient to obtain a first target flow of people; Arithmetically multiplying the third flow of people by the fourth weight coefficient to obtain a second target flow of people; arithmetically adding the first target human flow and the second target human flow to obtain the target human flow to be adjusted in the target area within the second preset time period; The target flow of people to be adjusted is adjusted according to the scheduled outpatient volume data to obtain the target flow of people in the target area within the second preset time period.

6. The smart city service method based on cloud computing and big data according to claim 5 is characterized in that: The step of adjusting the target flow of people to be adjusted according to the scheduled outpatient volume data to obtain the target flow of people in the target area within the second preset time period includes: Determine the number of patients visiting the target hospital within the second preset time period according to the scheduled outpatient volume data; According to the number of patients, the target flow of people to be adjusted is adjusted to obtain the target flow of people in the target area within the second preset time period, wherein the number of patients is positively correlated with the target flow of people.

7. The smart city service method based on cloud computing and big data according to claim 1, characterized in that: Calculating the second congestion index of the target area according to the target passenger flow includes: Obtain the maximum flow of people that the target area can accommodate within a unit time; Substituting the maximum passenger flow and the target passenger flow into a second preset formula to calculate a second congestion index of the target area; wherein, The second preset formula is: Where CI2 is the second congestion index, γ is the second adjustment coefficient, P t is the target flow of people, H is the maximum flow of people that the target area can accommodate per unit time, β3 is the correction coefficient of the flow density of the target area, and β4 is the 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 used to acquire a first vehicle flow and a first human flow in a target area of ​​a target hospital within a first preset time period, and predict a second vehicle flow and a second human flow in the target area within a second preset time period according to the first vehicle flow and the first human flow, respectively, wherein the first preset time period is before the second preset time period; The determination module is used to obtain historical vehicle flow data and historical pedestrian flow data of the target area, and determine 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; The first combining module is used 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 period, and calculate a first congestion index of the target area according to the target vehicle flow; The second combining module is used to obtain the outpatient appointment volume data of the target hospital within the second time period, combine the second passenger flow, the third passenger flow and the outpatient appointment volume data, determine the target passenger flow of the target area within the second preset time period, and calculate the second congestion index of the target area according to the target passenger flow; The generating module is used to determine a target congestion index of the target area by combining the first congestion index and the second congestion index, and to generate an adjustment plan when the target congestion index exceeds a preset index.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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