Terminal data acquisition system and method based on 5G edge collaboration, and storage medium

Abstract

The invention relates to the technical field of terminal data acquisition, and particularly discloses a terminal data acquisition system and method based on 5G edge collaboration and a storage medium, the system comprises a data acquisition module, a type judgment module, a transfer cost confirmation module and a dynamic transfer module; according to the method, the historical garbage throwing data, the traffic flow and the pedestrian flow of the garbage throwing points are comprehensively analyzed, the required throwing number of the garbage cans can be accurately analyzed, then the throwing points to be added and the excessive throwing points are accurately screened out, and the requirement trend index is calculated through the secondary accumulation and the primary exponential smoothing method; a demand putting coefficient is determined through a linear regression algorithm in combination with a circulation value, the actual demand of a garbage putting point can be more scientifically reflected, finally, dynamic transferring is performed under the condition that the garbage can number constraint condition is met by adopting a linear programming algorithm and taking the minimum total transferring cost as a target function, and scientific allocation of the garbage can putting number is achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of terminal data acquisition, and relates to a terminal data acquisition system and method based on 5G edge cooperation and a storage medium. BACKGROUND

[0002] With the development of smart parks, the requirement for the refinement and intelligence of garbage disposal management is increasing. In the garbage disposal management process, the actual situation of each garbage disposal point needs to be comprehensively mastered, such as the actual number of garbage cans, the historical garbage disposal weight and volume, the traffic flow and the passenger flow, and other information, so as to reasonably plan the number and distribution of garbage cans, improve the garbage disposal efficiency, and optimize the resource allocation. At the same time, the development of 5G technology provides technical support for the rapid transmission and edge cooperative processing of data, making it possible to build an efficient terminal data acquisition system.

[0003] The patent with publication number CN112465346A discloses a smart city garbage classification system and method, which includes a central processor, a garbage classification big data processing platform, a garbage classification disposal scanning module, a garbage disposal time recording module, a disposal peak period statistical module, a garbage disposal location recording module, and a disposal location garbage can allocation module. The garbage disposal time is planned by combining the garbage classification big data processing platform, the garbage classification disposal scanning module, the garbage disposal time recording module, and the disposal peak period statistical module, realizing the timed disposal of garbage. The appropriate garbage disposal location is set by combining the garbage classification big data processing platform, the garbage classification disposal scanning module, and the garbage disposal location recording module, realizing the fixed-point disposal of garbage, and reasonably allocating the number of garbage cans, realizing the full use of garbage can resources, avoiding the garbage pollution that cannot be avoided at the garbage collection point, and improving the overall environment of the community.

[0004] The patent with publication number CN115796804B discloses a multi-dimensional linkage environmental data intelligent monitoring management method and system. The method sets a detection device on the garbage can to detect the amount of garbage in the garbage can. Multiple cleaning robots clean the ground in different areas of the park according to a plurality of set routes. When the server detects that the amount of garbage in the garbage can in the park reaches a preset amount, a garbage cleaning prompt corresponding to the garbage can is generated, and the nearest cleaning robot is mobilized to clean the area where the garbage can is located. The present application can realize real-time understanding of the situation of each garbage can in the park and cleaning. Moreover, the technical solution also sets the cleaning robot to clean the ground in different areas of the park, and the cleaning robot can be controlled by the server to clean the area where the garbage can is located in time when the garbage can is full, so as to clean the newly generated garbage in the park in time.

[0005] There are still the following problems in the prior art: 1. The prior art mainly processes data related to garbage classification, putting time and location, does not involve traffic flow and passenger flow, which are important factors affecting garbage putting demand, does not deeply analyze historical garbage weight and volume to assess demand trends, data utilization is not sufficient, it is difficult to accurately grasp the actual demand of the garbage putting point, leading to unreasonable number of garbage cans at some putting points, and garbage accumulation or garbage can idling.

[0006] 2. The prior art mainly relies on detection devices on the garbage can to obtain garbage amount data, the data source is single, and the garbage can triggers the cleaning robot to clean when the garbage can is full, without analyzing the demand for putting the number of garbage cans, screening for points to be increased and over-putting points, and unable to achieve more scientific garbage can layout planning, thus lacking in foresight and rationality in garbage can layout planning. SUMMARY

[0007] In view of this, in order to solve the problems raised in the background art, a terminal data acquisition system and method based on 5G edge cooperation and storage medium are proposed.

[0008] The object of the present application can be achieved by the following technical solutions: The first aspect of the present application provides a terminal data acquisition system based on 5G edge cooperation, comprising: a data acquisition module for acquiring the actual number of garbage cans at each garbage putting point in a target smart park, the garbage weight and volume of each historical putting in each cleaning cycle, and the traffic flow and passenger flow in a set detection period.

[0009] A type screening module is used to screen each point to be increased and each over-putting point in the target smart park.

[0010] A mobilization cost confirmation module is used to acquire the garbage weight increase and garbage volume increase of each point to be increased in a set detection period per unit time, and to acquire the mobilization distance between each over-putting point and each point to be increased, and to confirm the mobilization cost of each over-putting point to each point to be increased.

[0011] A dynamic mobilization module is used to dynamically mobilize the number of garbage cans at each garbage putting point in the target smart park, and to display the mobilized number of garbage cans.

[0012] The second aspect of the present application provides a terminal data acquisition method based on 5G edge cooperation, comprising:

[0013] S1, data acquisition: acquiring the actual number of garbage cans at each garbage putting point in a target smart park, the garbage weight and volume of each historical putting in each cleaning cycle, and the traffic flow and passenger flow in a set detection period.

[0014] S2, type screening: screening each to-be-increased delivery point and each over-delivery point in the target smart park.

[0015] S3, mobilization cost confirmation: obtaining the garbage weight increase and the garbage volume increase of each to-be-increased delivery point in a unit time within a set detection period, and obtaining the mobilization distance between each over-delivery point and each to-be-increased delivery point, and confirming the mobilization cost of each over-delivery point to each to-be-increased delivery point.

[0016] S4, dynamic mobilization: dynamically mobilizing the garbage can delivery quantity of the garbage delivery point in the target smart park, and displaying the mobilized garbage can delivery quantity.

[0017] The third aspect of the application provides a storage medium, the storage medium stores one or more programs, the one or more programs can be executed by one or more processors to implement the terminal data acquisition system of the application.

[0018] Compared with the prior art, the embodiments of the application have at least the following advantages or beneficial effects:

[0019] (1) The application can accurately analyze the historical garbage delivery data, traffic flow and passenger flow of the garbage delivery point, accurately analyze the garbage can demand delivery quantity, and accurately screen the to-be-increased delivery point and the over-delivery point, thereby providing a reliable basis for subsequent garbage can allocation.

[0020] (2) The application can more scientifically reflect the actual demand of the garbage delivery point by using the quadratic accumulation and the first exponential smoothing method to calculate the demand trend index, and combining the flow value to determine the demand delivery coefficient by the linear regression algorithm, so that the calculation of the garbage can demand delivery quantity is more reasonable.

[0021] (3) The application calculates the mobilization demand urgency by comprehensively considering the garbage weight and volume increase of the to-be-increased delivery point, and then determines the distance cost weight, and calculates the comprehensive mobilization cost combined with the mobilization distance, so that the cost calculation is more in line with the actual situation, and is conducive to optimizing the garbage can mobilization scheme.

[0022] (4) The application adopts the linear programming algorithm to minimize the total mobilization cost as the objective function, dynamically mobilizes under the condition of satisfying the garbage can quantity constraint, can realize scientific allocation of the garbage can delivery quantity, and display the mobilization result, improve the efficiency and resource utilization rate of garbage delivery management. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings described in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.

[0024] Figure 1 The system structure connection diagram of the present application.

[0025] Figure 2 The method step diagram of the present application.

[0026] Figure 3 The structure diagram of the computer readable storage medium of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present application.

[0028] Embodiment 1

[0029] Please refer to Figure 1 As shown in the figure, the present application provides a terminal data acquisition system based on 5G edge cooperation, which comprises a data acquisition module, a type judgment module, a mobilization cost confirmation module and a dynamic mobilization module.

[0030] The data acquisition module and the type judgment module are connected, the type judgment module and the mobilization cost confirmation module are connected, and the data acquisition module, the type judgment module and the mobilization cost confirmation module are all connected with the dynamic mobilization module.

[0031] The data acquisition module is used to acquire the actual number of garbage cans of each garbage disposal point in the target smart park, the weight and volume of garbage in each historical disposal of each cleaning cycle, and the traffic and passenger flow in the set detection period.

[0032] It should be noted that the actual number of garbage cans of each garbage disposal point in the target smart park is obtained from the garbage disposal point management platform of the target smart park.

[0033] The method for obtaining the weight and volume of waste collected at each waste disposal point during each collection cycle is as follows: Intelligent weighing devices are installed at the waste disposal points. These devices automatically measure the weight of the waste upon disposal and record the weight data for each disposal, storing it locally or uploading it to a cloud management system. 3D laser scanning equipment scans the shape and outline of the waste by emitting a laser beam, thereby calculating its volume. Historical data can be directly extracted from a database that previously stored this data. The database can be categorized and stored according to dimensions such as collection cycle and disposal time for easy querying and statistics.

[0034] The method for obtaining vehicle and pedestrian traffic flow data at each garbage disposal point during the set detection period is as follows: Vehicle sensors, such as geomagnetic sensors or radar sensors, are installed on roads near the garbage disposal points. These sensors can detect vehicle passage, record the number of vehicles and their passage time, thereby obtaining vehicle traffic flow data. Pedestrian counting devices, such as infrared sensor counters or video surveillance statistical equipment, are installed at the garbage disposal points. Infrared sensor counters count pedestrians by sensing human infrared signals. Video surveillance statistical equipment uses video analysis technology to identify and count the number of pedestrians in the footage, thereby obtaining pedestrian traffic flow data.

[0035] The deployment point type filtering module is used to filter each deployment point to be added and each over-deployed deployment point within the target smart park.

[0036] In a specific embodiment of the present invention, the specific analysis method for screening each garbage disposal point to be added and each excessive disposal point in the target smart park is as follows: based on the weight and volume of garbage disposed of at each garbage disposal point in the target smart park in each cleaning cycle and the traffic flow and pedestrian flow in the set detection cycle, the required number of garbage bins to be disposed of at each garbage disposal point in the target smart park is analyzed.

[0037] In a specific embodiment of the present invention, the method for analyzing the required number of trash cans at each trash collection point within the target smart park is as follows: based on the weight and volume of trash collected at each trash collection point in each cleaning cycle, the demand trend index of each trash collection point within the target smart park is analyzed. ,in, This indicates the number of the garbage disposal point. .

[0038] In a specific embodiment of the present invention, the specific process of analyzing the demand trend index of each garbage disposal point in the target smart park is as follows: the weight and volume of garbage disposed of at each garbage disposal point in the target smart park during each cleaning cycle are summed twice, and then the average of the summed results is calculated to obtain the historical average garbage disposal weight of each garbage disposal point. with the historical average garbage disposal volume .

[0039] The garbage disposal weight prediction value and the garbage disposal volume prediction value of each garbage disposal point are obtained by using the exponential smoothing method . .

[0040] In an embodiment of the present application, the calculation process of obtaining the garbage disposal weight prediction value and the garbage disposal volume prediction value of each garbage disposal point by using the exponential smoothing method is as follows: the calculation formula for obtaining the garbage disposal weight prediction value of each garbage disposal point is: , wherein, is the garbage disposal weight prediction value of the previous cleaning cycle of the i-th garbage disposal point, is the actual garbage disposal weight value of the previous cleaning cycle of the i-th garbage disposal point, is the smoothing coefficient, and the value range is . In an embodiment of the present application, the and

[0041] can be obtained as shown in the following Table 1, and it is assumed that the garbage disposal weight data of a certain garbage disposal point in a target smart park for three consecutive cleaning cycles are shown in Table 1: Table 1 Garbage disposal weight data

[0042] Table 1 Garbage disposal weight data

[0043]

[0044] In the exponential smoothing method, we need to set the smoothing coefficient , and it is assumed that , then according to the data in the table, the actual garbage disposal weight value of the third cleaning cycle of the garbage disposal point is 15, the garbage disposal weight prediction value of the third cleaning cycle of the garbage disposal point is , and the garbage disposal weight prediction value of the fourth cleaning cycle of the garbage disposal point is: .

[0045] It should be noted that if the garbage disposal data fluctuates slightly and the trend is stable, a smaller value such as 0.1-0.3 is suitable, because a smaller value gives more weight to historical data, which can make full use of the stable trend of historical data for prediction. In a stable target smart park, the living habits of residents are stable, and the garbage disposal volume fluctuates slightly, so a smaller value can better reflect the long-term trend, and therefore the smoothing coefficient is 0.3.

[0046] ​It also needs to be explained that the one exponential smoothing method has significant advantages in obtaining the garbage disposal weight and volume prediction value of each garbage disposal point. It can effectively capture the dynamic trend of data, give higher weight to recent data, reflect the fluctuation of garbage disposal in time, and provide more realistic prediction results. At the same time, the method is relatively simple to calculate, and the required data is less, without the need for complex model training process, which greatly improves the calculation efficiency on the basis of ensuring a certain prediction accuracy.

[0047] The garbage disposal volume prediction value of each garbage disposal point is obtained in the same way as the garbage disposal weight prediction value of each garbage disposal point.

[0048] Calculate the demand trend index of each garbage disposal point in the target smart park , , wherein and respectively represent the garbage disposal weight demand trend and the garbage disposal volume demand trend corresponding demand trend evaluation proportion weight .

[0049] It needs to be explained that the derivation process of the demand trend index formula is as follows: 1) Dimensionless processing of single indicators: In order to be able to combine the two different indicators of weight and volume, they are processed respectively first. The difference between the prediction value and the historical average value is divided by the historical average value, that is represents the relative change of garbage disposal weight demand, represents the relative change of garbage disposal volume demand. 2) Weight distribution and comprehensive calculation: Since the importance of garbage disposal weight and volume in demand trend evaluation may be different, weights and respectively represent their proportion in demand trend evaluation, and . After multiplying the above two dimensionless relative changes by the corresponding weights and adding them, the demand trend index is obtained. This formula considers the change trend of garbage disposal weight and volume and their relative importance, so it can more comprehensively evaluate the demand trend of garbage disposal points.

[0050] In specific embodiments of the present application, The set value of is 0.5, The set value of the parameter is 0.5. When the demand trend index of each garbage disposal point in the target smart park is calculated, the importance of the garbage disposal weight demand trend and the garbage disposal volume demand trend to the demand trend evaluation proportion cannot be distinguished absolutely, and when the demand trend index is greater than 0, it indicates that the future garbage disposal amount has an upward trend, and the number of garbage cans of the garbage disposal point may need to be increased. When the demand trend index is less than 0, it indicates that the future garbage disposal amount has a downward trend, and the number of garbage cans can be appropriately reduced to save resources. When the demand trend index is close to 0, it indicates that the garbage disposal amount is relatively stable, and the existing resource allocation can be maintained.

[0051] Based on the vehicle flow and the passenger flow of each garbage disposal point in the target smart park in a set detection period, the flow value of each garbage disposal point in the target smart park is analyzed .

[0052] In specific embodiments of the present application, the specific way of analyzing the flow value of each garbage disposal point in the target smart park is: based on the vehicle flow and the passenger flow of each garbage disposal point in the target smart park in a set detection period, the flow value of each garbage disposal point in the target smart park is analyzed , , wherein , respectively represent the vehicle flow and the passenger flow of the i-th garbage disposal point in a set detection period.

[0053] It should be noted that the formula is derived based on the normalization idea. In actual scenarios, the numerical ranges of the vehicle flow and the passenger flow of different garbage disposal points may differ greatly. In order to measure the relative flow of each garbage disposal point on a unified scale, the normalization method is adopted. Assuming that the vehicle flow part is considered first, represents the total vehicle flow of all garbage disposal points, this form is similar to normalizing the vehicle flow of the i-th garbage disposal point , and the denominator can be regarded as an adjustment to the total amount (plus 1 to avoid the denominator being 0), so that the vehicle flow contribution of each garbage disposal point is measured within a relatively reasonable range. The passenger flow part Similarly, finally, the sum of the two is obtained , which comprehensively considers the influence of vehicle flow and passenger flow on the flow value.

[0054] ​It also needs to be explained that through normalization processing, the calculated flow value facilitates the comparison of the flow of different garbage disposal points, and no matter how the original traffic flow and passenger flow values of each garbage disposal point are, the relative flow degree can be reflected under the unified standard, which helps the park managers to intuitively distinguish which disposal point is more busy, and the operation of adding 1 in the denominator increases the stability of the formula to a certain extent, avoids the situation that the denominator is 0 or the calculation result is abnormal due to too large traffic flow or passenger flow of a garbage disposal point, and ensures the reliability and rationality of the calculation result.

[0055] Adopting a linear regression algorithm, the demand trend index and the flow value are used as independent variables, and the demand disposal coefficient is used as a dependent variable, and a linear regression model is constructed to calculate the demand disposal coefficient of each garbage disposal point, wherein the linear regression model is in the form of wherein , are regression coefficients, respectively representing the influence degree of the demand trend index and the flow value on the demand disposal coefficient, and the regression coefficients are obtained through experimental data fitting, is a constant term.

[0056] It needs to be explained that in one specific embodiment of the present application, the specific values of , and can be obtained through the specific experimental data in Table 2 as follows:

[0057] Table 2 Experimental data

[0058]

[0059] The dependent variable , the matrix , and the linear equation group are solved according to the least square method, and = 0.5, = 0.3, = 1.2 are obtained.

[0060] It should be noted that the linear regression model is simple in form, and by establishing a linear equation between the demand trend index, the circulation value and the demand putting coefficient, the relationship between the variables can be clearly shown. This intuitive expression makes it easy for management personnel to understand the meaning of the model and explain the reasons for the change in the demand putting coefficient. The linear regression model can not only calculate the demand putting coefficient according to the current demand trend index and circulation value, but also can be used to predict the future demand putting coefficient. When new demand trend index and circulation value data are obtained, they are substituted into the trained model to obtain the corresponding predicted value. In this way, the future garbage putting demand of the smart park can be estimated, helping the park to make resource allocation and management planning in advance.

[0061] The demand putting coefficient of each garbage putting point is multiplied by the preset unit demand putting coefficient corresponding to the garbage can putting quantity, and is rounded up to obtain the garbage can demand putting quantity of each garbage putting point in the target smart park.

[0062] It should be noted that the garbage can putting quantity corresponding to the preset unit demand putting coefficient is stored in the database.

[0063] The demand trend index is calculated by using the quadratic accumulation and the exponential smoothing method, and the demand putting coefficient is determined by using the linear regression algorithm combined with the circulation value, so that the actual demand of the garbage putting point can be more scientifically reflected, and the calculation of the garbage can demand putting quantity is more reasonable.

[0064] The actual garbage can putting quantity of each garbage putting point in the target smart park is compared with the garbage can demand putting quantity, if the actual garbage can putting quantity of a garbage putting point in the target smart park is less than the garbage can demand putting quantity, the garbage putting point is recorded as a to-be-increased putting point, if the actual garbage can putting quantity of a garbage putting point is greater than the garbage can demand putting quantity, the garbage putting point is recorded as an over-putting point, and then each to-be-increased putting point and each over-putting point in the target smart park are screened out.

[0065] The historical garbage putting data, traffic flow and passenger flow of the garbage putting point are comprehensively analyzed, so that the garbage can demand putting quantity can be accurately analyzed, and then the to-be-increased putting point and the over-putting point can be accurately screened out, thereby providing a reliable basis for subsequent garbage can allocation.

[0066] The mobilization cost confirmation module is configured to obtain the garbage weight increase and the garbage volume increase of each to-be-increased putting point per unit time within a set detection period, and obtain the mobilization distance between each over-putting point and each to-be-increased putting point, and confirm the mobilization cost of each over-putting point to each to-be-increased putting point.

[0067] It should be noted that the specific way of obtaining the garbage weight increase amount and the garbage volume increase amount of each to-be-increased disposal point in a unit time in the set detection period is: collecting the garbage weight and the garbage volume of each to-be-increased disposal point at each detection time point in the set detection period, subtracting the garbage weight at the previous detection time point from the garbage weight at the next detection time point, and then dividing by the time interval to obtain the garbage weight increase amount in a unit time, and similarly, the garbage volume increase amount in a unit time is calculated according to the garbage volume data at different time points.

[0068] It should be further noted that the specific way of obtaining the mobilization distance between each over-disposal point and each to-be-increased disposal point is: importing the map data of the target smart park through the GIS software platform, including the geographic position information of each garbage disposal point, directly measuring the actual path distance between each over-disposal point and each to-be-increased disposal point by using the distance measurement tool of GIS, and taking the actual path distance as the mobilization distance.

[0069] In the specific embodiments of the present application, the specific process of confirming the mobilization cost of each over-disposal point to each to-be-increased disposal point is: based on the garbage weight increase amount and the garbage volume increase amount of each to-be-increased disposal point in a unit time in the set detection period, calculating the mobilization demand urgency of each to-be-increased disposal point, and multiplying it by the increased weight of the distance cost corresponding to the preset unit mobilization demand urgency to obtain the increased weight of the distance cost corresponding to each to-be-increased disposal point.

[0070] The mobilization distance between each over-disposal point and each to-be-increased disposal point is multiplied by the distance cost corresponding to the preset unit mobilization distance to obtain the distance cost of each over-disposal point to each to-be-increased disposal point, and the distance cost corresponding to each to-be-increased disposal point is multiplied by the increased weight to obtain the comprehensive mobilization cost of each over-disposal point to each to-be-increased disposal point , wherein, indicates the number of the to-be-increased disposal point, , indicates the number of the over-disposal point, .

[0071] It should be noted that the specific way of calculating the mobilization demand urgency of each to-be-increased disposal point is: taking the garbage weight increase amount and the garbage volume increase amount of each to-be-increased disposal point in a unit time in the set detection period as and respectively.

[0072] The mobilization demand urgency of each to-be-increased disposal point is calculated , , wherein, and respectively represent the garbage weight increase amount and the garbage volume increase amount in a unit time set as a reference.

[0073] It should be noted that in the field of garbage management, many documents provide basis for standard data of garbage generation for the relevant industry, such as the garbage generation standard data mentioned in 'Organic Garbage Generation Characteristics and Present Situation of Dispersed Treatment Mode'. In the specific embodiments of the present application, the garbage weight increase amount and the garbage volume increase amount in a unit time set as a reference are respectively taken as 20 kg and 3 cubic meters.

[0074] It should be further noted that the derivation of the formula of the mobilization demand urgency degree is based on the concept of relative deviation, which is commonly used to measure the deviation between an actual value and an expected value. In statistics, relative deviation is often used to measure the difference between an actual value and an expected value. Using this formula can intuitively reflect the garbage generation speed of the to-be-increased drop-off point. The faster the speed, the higher the demand urgency degree, and the slower the speed, the lower the demand urgency degree. Meanwhile, the formula is simple and easy to understand, and the calculation process is clear and unambiguous.

[0075] It should be further noted that the distance cost increased weight corresponding to the preset unit mobilization demand urgency degree and the distance cost corresponding to the preset unit mobilization distance are stored in the database.

[0076] The embodiments of the present application calculate the mobilization demand urgency degree by comprehensively considering the garbage weight and volume increase amount of the to-be-increased drop-off point, and then determine the distance cost increased weight, and calculate the comprehensive mobilization cost in combination with the mobilization distance, so that the cost calculation is more in line with the actual situation, which is beneficial to optimize the garbage can mobilization scheme.

[0077] The dynamic mobilization module is configured to dynamically mobilize the number of garbage cans dropped at the garbage drop-off points in the target smart park, and display the number of garbage cans dropped after mobilization.

[0078] In the specific embodiments of the present application, the specific manner of dynamically mobilizing the number of garbage cans dropped at the garbage drop-off points in the target smart park is: obtaining the excess garbage can quantity and the lack garbage can quantity corresponding to each over-supply drop-off point and each to-be-increased drop-off point, respectively, and denoted as and .

[0079] It should be noted that the excess garbage can quantity of each over-supply drop-off point is obtained by subtracting the actual number of garbage cans dropped from the demand number of garbage cans dropped, and the lack garbage can quantity of each to-be-increased drop-off point is obtained by subtracting the actual number of garbage cans dropped from the demand number of garbage cans dropped.

[0080] A linear programming algorithm is used to minimize the total mobilization cost as the objective function, and the dynamic mobilization is carried out under the condition of meeting the garbage can quantity constraint to obtain the dynamic mobilization result , wherein the objective function is: minimizing the total mobilization cost , and the constraint condition is: , Let be the number of garbage cans mobilized from the over-provision point to the to-be-increased provision point , .

[0081] In one specific embodiment of the present application, it is assumed that there are 3 to-be-increased provision points A, B and C in the target smart park, the number of missing garbage cans is 5, 8 and 6 respectively, there are 2 over-provision points X and Y, the number of excess garbage cans is 7 and 12 respectively, and the comprehensive mobilization cost from the over-provision point X to the to-be-increased provision points A, B and C is 2, 3 and 4 respectively, and the comprehensive mobilization cost from the over-provision point Y to the to-be-increased provision points A, B and C is 3, 2 and 3 respectively.

[0082] A linear programming model is constructed, and the objective function is: minimizing the total mobilization cost , wherein , and represent the number of garbage cans mobilized from the over-provision point X to the to-be-increased provision points A, B and C respectively, , and represent the number of garbage cans mobilized from the over-provision point Y to the to-be-increased provision points A, B and C respectively, and the constraint condition is: meeting the demand of the to-be-increased provision point , not exceeding the excess number of the over-provision point , a linear programming solver is used to solve the linear programming problem, and the result obtained is , the final mobilization scheme is: mobilizing 5 garbage cans from X to A, mobilizing 8 garbage cans from Y to B, mobilizing 2 garbage cans from X to C, and mobilizing 4 garbage cans from Y to C, and the total mobilization cost is 5*2+8*2+2*4+4*3=10+16+8+12=46.

[0083] The embodiment of the present application can realize scientific deployment of the number of garbage cans provision by using a linear programming algorithm to minimize the total mobilization cost as the objective function, and carrying out dynamic mobilization under the condition of meeting the garbage can quantity constraint, and display the mobilization result, thereby improving the efficiency and resource utilization rate of garbage provision management.

[0084] Embodiment 2

[0085] Referring to Figure 2As shown, the present application provides a terminal data acquisition method based on 5G edge cooperation, comprising: S1, obtaining point data: obtaining the actual number of garbage cans of each garbage disposal point in the target smart park, the weight and volume of garbage in each historical disposal of each cleaning cycle, and the traffic and passenger flow in the set detection period.

[0086] S2, type screening: screening each to-be-increased disposal point and each over-disposal point in the target smart park.

[0087] S3, mobilization cost confirmation: obtaining the garbage weight increase and garbage volume increase of each to-be-increased disposal point in the set detection period, and obtaining the mobilization distance between each over-disposal point and each to-be-increased disposal point, and confirming the mobilization cost of each over-disposal point to each to-be-increased disposal point.

[0088] S4, dynamic mobilization of garbage cans: dynamically mobilizing the number of garbage cans of the garbage disposal point in the target smart park, and displaying the mobilized number of garbage cans.

[0089] Embodiment 3

[0090] Please refer to Figure 3 As shown, the present application provides a terminal data acquisition method based on 5G edge cooperation, comprising: S1, obtaining point data: obtaining the actual number of garbage cans of each garbage disposal point in the target smart park, the weight and volume of garbage in each historical disposal of each cleaning cycle, and the traffic and passenger flow in the set detection period.

[0091] The above is only an example and description of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the concept of the present application or exceed the scope defined by the present application, which shall belong to the protection scope of the present application.

Claims

1. A terminal data acquisition system based on 5G edge collaboration, characterized in that, include: The data acquisition module is used to acquire the actual number of trash cans disposed of at each trash disposal point in the target smart park, the weight and volume of trash disposed of in each historical disposal during each cleaning cycle, and the traffic flow and pedestrian flow during the set detection cycle. The type filtering module is used to filter the various deployment points to be added and the various over-deployed points within the target smart park; The relocation cost confirmation module is used to obtain the increase in waste weight and volume per unit time of each additional disposal point within a set detection period, and to obtain the relocation distance between each over-disposal point and each additional disposal point, and to confirm the relocation cost from each over-disposal point to each additional disposal point. The dynamic adjustment module is used to dynamically adjust the number of trash cans at the garbage disposal points within the target smart park and display the adjusted number of trash cans.

2. The terminal data acquisition system based on 5G edge collaboration according to claim 1, characterized in that: The specific analysis method for each potential deployment point and each over-deployed point within the target smart park is as follows: Based on the weight and volume of garbage disposed of at each garbage disposal point in the target smart park during each cleaning cycle, as well as the traffic flow and pedestrian flow during the set detection cycle, the required number of garbage bins to be disposed of at each garbage disposal point in the target smart park is analyzed. The actual number of trash cans disposed of at each trash disposal point within the target smart park is compared with the required number of trash cans. If the actual number of trash cans disposed of at a certain trash disposal point within the target smart park is less than the required number of trash cans, then that trash disposal point is marked as a trash disposal point to be added. If the actual number of trash cans disposed of at a certain trash disposal point is greater than the required number of trash cans, then that trash disposal point is marked as an over-disposal point. This process is used to identify each trash disposal point to be added and each over-disposal point within the target smart park.

3. The terminal data acquisition system based on 5G edge collaboration according to claim 2, characterized in that: The specific method for analyzing the required number of trash cans at each trash collection point within the target smart park is as follows: Based on the historical garbage weight and volume of each garbage disposal point in each cleaning cycle within the target smart park, the demand trend index of each garbage disposal point within the target smart park is analyzed. Based on the vehicle and pedestrian traffic at each waste disposal point within the target smart park during a set detection cycle, analyze the circulation value of each waste disposal point within the target smart park. Using a linear regression algorithm, with the demand trend index and circulation value as independent variables and the demand placement coefficient as the dependent variable, the demand placement coefficient corresponding to each waste disposal point is calculated by constructing a linear regression model. Multiply the demand coefficient corresponding to each waste disposal point by the number of waste bins corresponding to the preset unit demand coefficient, and round up to obtain the number of waste bins required for each waste disposal point in the target smart park.

4. A terminal data acquisition system based on 5G edge collaboration according to claim 3, characterized in that: The specific process for analyzing the demand trend index of each waste disposal point within the target smart park is as follows: The weight and volume of garbage disposed of at each garbage disposal point in the target smart park during each cleaning cycle are summed twice, and then the average of the summation results is calculated to obtain the historical average weight and volume of garbage disposed of at each garbage disposal point. The predicted values ​​of waste disposal weight and volume at each waste disposal point were obtained using a single exponential smoothing method. Calculate the demand trend index for each waste disposal point within the target smart park.

5. A terminal data acquisition system based on 5G edge collaboration according to claim 4, characterized in that: The calculation process for obtaining the predicted weight and volume of waste at each waste disposal point using the first exponential smoothing method is as follows: The formula for calculating the predicted weight of waste at each waste disposal point is as follows: ,in, It is the first The predicted weight of waste disposed of at each waste disposal point during the previous cleaning cycle. It is the first The actual weight of garbage disposed of at each garbage collection point during the previous cleaning cycle. It is a smoothing coefficient, and its value range is... ; Similarly, the predicted volume of waste at each waste disposal point is obtained by using the same method as the predicted weight of waste disposed of at each waste disposal point.

6. A terminal data acquisition system based on 5G edge collaboration according to claim 3, characterized in that: The specific method for analyzing the circulation value of each garbage disposal point within the target smart park is as follows: based on the vehicle and pedestrian traffic at each garbage disposal point within the target smart park during a set detection period, the circulation value of each garbage disposal point within the target smart park is analyzed.

7. A terminal data acquisition system based on 5G edge collaboration according to claim 2, characterized in that: The specific process for determining the mobilization cost from each over-deployment point to each additional deployment point is as follows: Based on the increase in waste weight and volume per unit time within the set detection period for each additional collection point, the urgency of the mobilization demand for each additional collection point is calculated, and multiplied by the preset weight of the distance cost increase corresponding to the unit urgency of the mobilization demand to obtain the weight of the distance cost increase corresponding to each additional collection point. Multiply the transfer distance between each over-deployment point and each additional deployment point by the preset distance cost per unit transfer distance to obtain the distance cost from each over-deployment point to each additional deployment point. Then multiply this cost by the weighted distance cost of each additional deployment point to obtain the comprehensive transfer cost from each over-deployment point to each additional deployment point.

8. A terminal data acquisition system based on 5G edge collaboration according to claim 7, characterized in that: The specific method for dynamically adjusting the number of trash cans at the garbage disposal points within the target smart park is as follows: Obtain the number of excess trash cans and the number of missing trash cans for each excessive disposal point and each disposal point to be added; A linear programming algorithm is used to minimize the total relocation cost as the objective function. Dynamic relocation is carried out under the constraint of the number of trash cans, and the dynamic relocation result is obtained.

9. A terminal data acquisition method based on 5G edge collaboration, characterized in that, Includes the following steps: S1. Data Acquisition: Acquire the actual number of trash cans disposed of at each trash disposal point within the target smart park, the weight and volume of trash disposed of in each historical disposal during each cleaning cycle, and the traffic flow and pedestrian flow during the set detection cycle; S2. Type Filtering: Filter each additional deployment point and each over-deployed point within the target smart park; S3. Confirmation of adjustment costs: Obtain the increase in waste weight and volume per unit time for each additional disposal point within the set detection period, and obtain the adjustment distance between each over-disposal point and each additional disposal point to confirm the adjustment cost from each over-disposal point to each additional disposal point. S4. Dynamic Adjustment: Dynamically adjust the number of trash cans at the garbage disposal points within the target smart park and display the adjusted number of trash cans.

10. A storage medium, characterized in that: The storage medium stores one or more programs, which can be executed by one or more processors to implement the terminal data acquisition system as described in any one of claims 1-8.

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