CN116108996B - Sampling point layout optimization method, system, intelligent terminal and storage medium - Google Patents
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Abstract
The invention discloses a sampling point layout optimization method, a system, an intelligent terminal and a storage medium, which are used for converting the waiting time of sampling points into equivalent waiting time according to a tolerance time threshold value based on real-time state data and population distribution data of the sampling points, calculating the reachability of the sampling points relative to resident points according to the path distance and the equivalent waiting time of the sampling points, calculating the comprehensive reachability of the sampling points according to the reachability and population data, taking the comprehensive reachability as a sampling point measurement index to accurately measure the service capacity of the sampling points, and maximizing the layout optimization of the sampling points by taking the accumulated value of the comprehensive reachability of all the sampling points as a target. Compared with the prior art, the road accessibility, waiting time and population data are considered during the layout of the sampling points, and the index for accurately measuring the service capacity of the sampling points is established, so that the layout of the sampling points is more accurate, and the comprehensive efficiency of the sampling points is higher.
Description
Technical Field
The invention relates to the technical field of network layout optimization, in particular to a sampling point layout optimization method, a sampling point layout optimization system, an intelligent terminal and a storage medium.
Background
When a large number of biological sampling demands are suddenly made in a large city, such as under the condition of nucleic acid detection, whether the layout of the nucleic acid sampling points is reasonable or not can ensure that each sampling point operates efficiently, and the method has important significance for rapidly inhibiting epidemic situation development.
While some nucleic acid sampling points are crowded and have too long waiting time. Therefore, the distribution of sampling points needs to be optimized.
At present, research on layout optimization of sampling points is mainly focused on road accessibility, consideration factors are single, service capacity of the sampling points cannot be accurately measured, and accurate layout of the sampling points is difficult to realize.
Accordingly, there is a need for improvement and advancement in the art.
Disclosure of Invention
The invention mainly aims to provide a sampling point layout optimization method, a system, an intelligent terminal and a computer readable storage medium, and aims to solve the problems that the service capacity of sampling points cannot be accurately measured and the accurate layout of the sampling points is difficult to realize during the optimization of the sampling point layout.
In order to achieve the above object, a first aspect of the present invention provides a sampling point layout optimization method, including:
acquiring population distribution data, sampling point distribution data and real-time state data of sampling points of a target area;
Calculating the distance between each sampling point and each resident point based on the sampling point distribution data and the population distribution data;
comparing the waiting time in the real-time state data of the sampling points with a preset tolerance time threshold value, and obtaining the equivalent waiting time of each sampling point according to the comparison result;
calculating the accessibility of each sampling point relative to each resident point based on the distance and the equivalent waiting time;
calculating a comprehensive reachability for each sampling point based on all of the reachability and the demographic data for each sampling point;
and carrying out layout optimization on the sampling points by taking the maximization of the integrated accessibility of all the sampling points as a target, obtaining and outputting optimized sampling point distribution data.
Optionally, the calculating the comprehensive reachability of each sampling point based on all the reachability and the population distribution data of each sampling point includes the following specific expression:
wherein A is i For the integrated reachability of sample point i, p j Is population number, k of the residential point j ij As a weight value, a is used for representing the weight between the sampling point i and the sampling point of the maximum accessibility of the residential point j ij For reachability between the sampling point i and the residential point j, n is the total number of residential points.
Optionally, the layout optimization is performed on the sampling points by increasing the number of the sampling points and changing the positions of the sampling points, and the layout optimization includes:
optimizing the positions of sampling points under each quantity based on the set quantity range of the sampling points, and calculating the integrated reachability value of all the sampling points under each quantity;
obtaining a fitting curve by taking the number of sampling points as independent variables and the accumulated value as dependent variables;
calculating the absolute value of the second derivative of each point on the fitting curve, and taking the maximum value, wherein the maximum value is the number of the optimal sampling points;
and outputting the positions of all the sampling points when the optimal sampling point number is output.
Optionally, the layout optimization is performed on the sampling points by changing the positions of the sampling points, and the layout optimization includes:
obtaining an alternative position of a target sampling point of the position to be changed according to a sampling point alternative position searching algorithm;
calculating an accumulated revenue value of the reachability of the sampling point added at the alternative position relative to each resident point;
calculating an accumulated loss value of reachability of each residential point after the increased sampling point is substituted for the target sampling point;
calculating the difference value between the accumulated benefit value and the accumulated loss value to obtain replacement total benefit;
When the total replacement benefit is greater than zero, updating the position of the target sampling point to the alternative position;
and acquiring the next alternative position according to the alternative position searching algorithm of the sampling point, and carrying out iterative updating on the position of the target sampling point until the total replacement benefit of all the alternative positions of the target sampling point is less than or equal to zero, and outputting the position of the target sampling point.
Optionally, the layout optimization is performed on the sampling points by increasing the number of the sampling points, and the layout optimization includes:
screening out sampling points with poor accessibility relative to nearby residential points from all the sampling points to obtain a sampling point set;
based on the sampling point distribution data, clustering the sampling points in the sampling point set according to a clustering algorithm to obtain a plurality of center positions;
setting a sampling point at the central position, and optimizing the position of the newly added sampling point according to a sampling point alternative position searching algorithm;
outputting the position of each sampling point after optimization.
Optionally, the calculating, based on the distance and the equivalent waiting time, the reachability of each sampling point relative to each residential point includes the following specific expression:
wherein alpha and beta are gravitation attenuation coefficients, d ij For the distance between the sampling point i and the resident point j, τ i Equivalent latency for sample point i, n i The number of samples is the sampling point i.
A second aspect of the present invention provides a sampling point layout optimization system, where the system includes:
the data acquisition module is used for acquiring population distribution data, sampling point distribution data and real-time state data of the sampling points of the target area;
the distance module is used for calculating the distance between each sampling point and each resident point based on the sampling point distribution data and the population distribution data;
the equivalent waiting time module is used for comparing the waiting time in the real-time state data of the sampling points with a preset tolerance time threshold value and obtaining the equivalent waiting time of each sampling point according to the comparison result;
the reachability module is used for calculating the reachability of each sampling point relative to each resident point based on the distance and the equivalent waiting time;
a comprehensive reachability module for calculating a comprehensive reachability of each sampling point based on all of the reachability and the demographic data for each sampling point;
and the optimization module is used for carrying out layout optimization on the sampling points with the aim of maximizing the integrated reachability value of all the sampling points, obtaining and outputting the optimized sampling point distribution data.
Optionally, the optimizing module further includes;
the position optimization unit is used for obtaining the alternative position of the target sampling point of the position to be changed according to the sampling point alternative position searching algorithm; calculating an accumulated revenue value of the reachability of the sampling point added at the alternative position relative to each resident point; calculating an accumulated loss value of reachability of each residential point after the increased sampling point is substituted for the target sampling point; calculating the difference value between the accumulated benefit value and the accumulated loss value to obtain replacement total benefit; when the total replacement benefit is greater than zero, updating the position of the target sampling point to the alternative position; acquiring the next alternative position according to a sampling point alternative position searching algorithm, and performing iterative updating of the position of the target sampling point until the total benefit of replacement of all the alternative positions is less than or equal to zero, and outputting the position of the target sampling point;
the quantity optimizing unit is used for screening out sampling points with poor accessibility relative to nearby residential points from all the sampling points to obtain a sampling point set; based on the sampling point distribution data, clustering the sampling points in the sampling point set according to a clustering algorithm to obtain a plurality of center positions; setting a sampling point at the central position, and optimizing the position of the newly added sampling point according to a sampling point alternative position searching algorithm; outputting the position of each sampling point after optimization;
The comprehensive optimization unit is used for optimizing the positions of the sampling points under each quantity based on the set quantity range of the sampling points and calculating the integrated reachability value of all the sampling points under each quantity; obtaining a fitting curve by taking the number of sampling points as independent variables and the accumulated value as dependent variables; calculating the absolute value of the second derivative of each point on the fitting curve, and taking the maximum value, wherein the maximum value is the number of the optimal sampling points; and outputting the positions of all the sampling points when the optimal sampling point number is output.
A third aspect of the present invention provides an intelligent terminal, where the intelligent terminal includes a memory, a processor, and a sampling point layout optimization program stored in the memory and capable of running on the processor, where the sampling point layout optimization program, when executed by the processor, implements any one of the steps of the sampling point layout optimization method.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a sampling point layout optimization program that, when executed by a processor, implements the steps of any one of the above-described sampling point layout optimization methods.
From the above, the invention converts the waiting time of the sampling point into equivalent waiting time according to the tolerance time threshold based on the real-time state data and population distribution data of the sampling point, calculates the reachability of the sampling point relative to the resident point according to the path distance and equivalent waiting time of the sampling point, calculates the comprehensive reachability of the sampling point according to the reachability and population data, and takes the comprehensive reachability as the measurement index of the sampling point to accurately measure the service capability of the sampling point. And then carrying out layout optimization on the sampling points with the integrated value of the integrated reachability of all the sampling points as a target. Compared with the prior art, the road accessibility, waiting time and population data are considered during the layout of the sampling points, and the index for accurately measuring the service capacity of the sampling points is established, so that the layout of the sampling points is more accurate, and the comprehensive efficiency of the sampling points is higher.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a sample point layout optimization method according to an embodiment of the present invention;
FIG. 2 is a detailed flowchart of a first embodiment of step S600 of the embodiment of FIG. 1;
FIG. 3 is a detailed flow chart of a second embodiment of step S600 of the embodiment of FIG. 1;
FIG. 4 is a detailed flowchart of a third embodiment of step S600 of the embodiment of FIG. 1;
FIG. 5 is a schematic diagram of a sample point layout optimization system according to an embodiment of the present invention;
fig. 6 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted in context as "when …" or "upon" or "in response to a determination" or "in response to detection. Similarly, the phrase "if a condition or event described is determined" or "if a condition or event described is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a condition or event described" or "in response to detection of a condition or event described".
The following description of the embodiments of the present invention will be made more fully hereinafter with reference to the accompanying drawings, in which embodiments of the invention are shown, it being evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Aiming at the problems that the service capacity of sampling points cannot be accurately measured and the accurate layout of the sampling points is difficult to realize due to single consideration factor of the layout optimization of the related sampling points at present, the invention provides a sampling point layout optimization method. Therefore, the sampling point layout optimization method realizes the data-driven-based sampling point optimization layout and management based on the real-time state of the sampling points, the accessibility of residents and the waiting time. The problem that the traditional method cannot consider the actual space distribution situation of residents, the accessibility of sampling points and the waiting time is solved, and a more scientific layout optimization scheme of the sampling points is provided.
Exemplary method
The embodiment provides a sampling point layout optimization method for nucleic acid detection, which is installed on electronic equipment such as an intelligent terminal or a sampling server. It should be noted that, although the present embodiment uses nucleic acid detection as an example, the sampling point layout optimization method of the present invention may be applied to other biological sampling situations.
As shown in fig. 1, the present embodiment specifically includes the following steps:
step S100: acquiring population distribution data, sampling point distribution data and real-time state data of sampling points of a target area;
specifically, the target area may be one or more administrative areas such as province and city, one or more villages and towns, and the like, and may be an area within a geographical range set as required. The population distribution data of the target area can be obtained by searching statistics from the population statistics data, the sampling point distribution data of the target area can be obtained according to the existing sampling point data statistics of the target area, and the real-time state data of the sampling points can be read from a preset sampling background server.
In this embodiment, the target area is city Q. Assuming that there are m sampling points in city Q, the sampling point distribution data f= { F 1 ，f 2 ，...，f m }. Wherein each sampling point f i Comprising the following attributes: number i, sample point name f i 1 Name f of the belonging area i 2 The name f of the belonging street i 3 Name f of community to which i 4 Sampling point address f i 5 Service time f i 6 Number of sampling stations n i Longitude flng i Latitude flat i Etc. Each sampling point f i The attributes comprising set f i ＝{i，f i 1 ，f i 2 ，f i 3 ，f i 4 ，f i 5 ，f i 6 ，n i ，fln g i ，flat i ，…}。
Assuming that there are n populated points within the city Q, the demographic data u= { U 1 ，u 2 ，...，u n Each residential point u j Containing the number j, the name of the residentName of the belonging area->The name of the street->Community name->Resident address->Population p of residents j Longitude ulngj, latitude ulat j Equal attribute of each residential point u j Attribute of (c) constitutes a collection
The real-time status data of the sampling point mainly comprises the current waiting time t of the nucleic acid detection point i Other real-time status data may also be included, such as: service state: the service is also suspended, and the surrounding road is jammed.
Furthermore, the sampling point distribution data, the real-time state data and the urban resident population distribution data can be preprocessed; for example: and (3) carrying out data cleaning on the sampling point distribution data F and the population distribution data U, removing the data of the sampling point positions and the resident point positions which are not in the target area, removing the repeated data, the data with missing attributes and the like. For example, the resident data whose distance from any one of the sampling points in the sampling point distribution data F exceeds a set threshold value is cleared.
Step S200: calculating the distance between each sampling point and each resident point based on the sampling point distribution data and population distribution data;
specifically, for the sampling point distribution data F, the population distribution data U, a distance function d is defined: F×U→R + And representing the travel distance from the sampling point position to the resident point position. Based on any sampling point f i With any residential point u j Calculating the travel distance d ij 。
When the actual road network data is available, A can be adopted * The algorithm or Dijkstra algorithm calculates the sampling point f i And residential point u j Is the shortest real distance of travel distance d ij 。
When the actual road network data cannot be obtained, the sampling point f can be calculated in consideration of the fact that the roads of most cities are designed to be grid-shaped i And residential point u j The Manhattan distance between the two is used as the travel distance d instead of the real path distance ij . The specific calculation formula is as follows:
d ij ＝|flng i -ulng j |+|flat i -nlat j |，
wherein flng i For longitude of sampling point i, ulng j For the longitude, flat of the resident j i For the latitude of the sampling point i, nlat j Is the latitude of the residential point j.
Step S300: comparing the waiting time in the real-time state data of the sampling points with a preset tolerance time threshold value, and obtaining the equivalent waiting time of each sampling point according to the comparison result;
Specifically, according to the real-time state data of the sampling points, the waiting time of each sampling point is obtained, and then each sampling point f is calculated according to a preset tolerance time threshold value i Equivalent latency τ of (2) i 。
Wherein the tolerance time threshold represents a generally accepted latency, as an ideal value for post-optimization latency andinstead of values. Tolerance time threshold t A The method can be set empirically, or can be determined by carrying out questionnaire investigation and data statistical analysis on a large number of nucleic acid detection sampling groups. Let t be A Is a tolerance time threshold. In general, the tolerance time threshold t of nucleic acid points that are generally accepted by residents A Since it is 10 minutes, t is set A =10 min.
The equivalent waiting time of each sampling point is obtained after the waiting time in the real-time state data of the sampling point is processed according to the tolerance time threshold value. Specifically, the sampling point f in this embodiment i Is to wait for time t of (2) i At t A The following values are all noted as 1, t A The above value is denoted as t i -t A +1, i.e. τ i ＝max(t i -t A +1,1). For example: tolerance time threshold of 10 minutes, waiting time t i For 8 minutes, τ is then i Set to 1; if waiting time t i For 12 minutes, τ is then applied i Set to 3. Waiting time t i Is not limited, for example: the sampling may be performed every 5 minutes, i.e. the equivalent waiting time of the sampling point is updated every 5 minutes.
Step S400: calculating the accessibility of each sampling point relative to each resident point based on the distance and the equivalent waiting time;
in general, reachability is measured by path length, time spent on path, and congestion at sampling points is not considered. The invention is different from the traditional accessibility, and takes the actual operation condition of the sampling points into consideration, wherein the queuing time and the number of sampling stations at the sampling points are taken into the calculated reference index of the accessibility, and the queuing time and the number of the sampling stations represent the capability of providing services for the sampling points. Therefore, the accessibility meaning of the invention is wider, and the capability of sampling point service residents can be measured more accurately.
In this embodiment, a sampling point f is calculated i And residential point u j Reachability a between ij The specific calculation formula is as follows:
wherein, alpha and beta are gravitation attenuation coefficients for restricting the attenuation degree of reachability along with the increase of space resistance, and specific values can be set by referring to a gravitation model; d, d ij For the distance between the sampling point i and the resident point j, τ i Equivalent latency for sample point i, n i The number of samples is the sampling point i.
Step S500: calculating the comprehensive reachability of each sampling point based on all reachability and population distribution data of each sampling point;
Specifically, for n populated points, n reachability is calculated for each sampling point. And taking the population data in the population distribution data as weight according to all the reachability of each sampling point, weighting and counting all the reachability to obtain the comprehensive reachability of each sampling point.
Further, based on the most likely choice of residents to sample nucleic acid from the sampling point with the highest accessibility to the resident point, the accessibility a between the sampling point i and the resident point j is calculated ij Then, the weight between the sampling point i and the sampling point of the maximum reachability of the resident point j is also calculated to weight the reachability. For example: resetting the accessibility weight of the sampling point with the maximum accessibility of the residential point j to 1, and setting other sampling points to be: and calculating the ratio of the two distances according to the distance between the sampling point and the resident point j and the distance between the resident point j and the sampling point with the maximum accessibility corresponding to the resident point j, and determining the weight of the sampling point.
In this embodiment, the weight value k ij The expression of (2) is:
where k represents the serial number of the sampling point and j is the serial number of the residential point. Namely: if the sampling point f k And if the sampling point with the highest accessibility with the resident point j is the sampling point with the highest accessibility with the resident point j, the weight value is 1, otherwise, the weight value is 0.
Calculating the comprehensive reachability A of the sampling point i i Specific calculation ofThe formula is:
wherein p is j Is population number, k of the residential point j ij As the weight value, a ij For reachability between the sampling point i and the residential point j, n is the total number of residential points.
Step S600: and carrying out layout optimization on the sampling points by taking the maximization of the integrated accessibility of all the sampling points as a target, obtaining and outputting optimized sampling point distribution data.
Specifically, after the integrated reachability of each sampling point is calculated, the integrated reachability of the sampling points may evaluate the service capability of the sampling points, and the cumulative value of the integrated reachability of all the sampling points may be used to evaluate the accuracy of the sampling point layout of the target area. The means adopted for carrying out layout optimization on the sampling points are not limited, and the positions of the existing sampling points can be changed, the number of the sampling points can be increased, and meanwhile, the positions of the sampling points can be changed. When the means is adopted to carry out layout optimization on the sampling points, the aim is to maximize the integrated accessibility cumulative value of all the optimized sampling points. Its objective function can be expressed as:wherein A is i For the integrated reachability of sample point i, m is the total number of sample points.
Taking the position of the sampling point as an example, the optimization process is as follows: the integrated accessibility integrated value of all sampling points under the current layout is calculated firstly, then a certain sampling point is assumed to be updated to a new position, the integrated accessibility integrated value of all the updated sampling points is calculated again, when the integrated value after the position is updated is larger than the integrated value before the position is updated, the optimization is effective, otherwise, the new position is inaccurate, and the service capability is poor instead. And when the optimization is effective, the sampling point is actually updated to a new position. And then taking down one sampling point to carry out iterative updating of the position until the sampling point distribution at the moment is the optimal sampling point layout when the integrated accessibility cumulative value of all the sampling points is maximum, outputting sampling point distribution data or calling the sampling point distribution data by other administrative platforms to complete layout transformation of the sampling points.
As described above, the sampling point layout optimization method of the present embodiment converts the waiting time of the sampling points into the equivalent waiting time according to the tolerance time threshold based on the real-time status data and population distribution data of the sampling points, calculates the reachability of the sampling points relative to the residential points according to the path distance and waiting time of the sampling points, calculates the comprehensive reachability of the sampling points according to the reachability and population data, and establishes the sampling point measurement index. And then carrying out layout optimization on the sampling points with the integrated value of the integrated reachability of all the sampling points as a target. The layout of the sampling points is more accurate, and accessibility, waiting time, cost and adaptability are considered.
In some embodiments, as shown in fig. 2, the layout optimization is performed on the sampling points by changing the positions of the sampling points, and the specific steps include:
step a610: obtaining an alternative position of a target sampling point of the position to be changed according to a sampling point alternative position searching algorithm;
specifically, a sampling point alternative location search algorithm is used to determine alternative locations of target sampling points for which locations are to be modified. The sampling points can be set up in the central position or the set position of the community or the street on the basis that at least one sampling point is set up in each community or street. The center position of the community, the street or the position of the set position, where the sampling point is not set, is an alternative position in the target area. Then, by adopting any local search algorithm at present, searching for the alternative position closest to the target sampling point in all alternative positions according to the position of the target sampling point of the position to be changed, and obtaining the sampling point of the alternative position i 。
In this embodiment, according to the positions of the target sampling points of the positions to be changed, adjacent candidate positions are searched, and then a clustering algorithm is adopted to obtain the central positions of the candidate positions as candidate positions of the target sampling points. The clustering algorithm is not limited, and any clustering algorithm such as the existing K-means, mini-Batch K-means, gaussian mixture, DBSCAN and the like can be adopted.
Alternatively, the neighboring residents may be searched according to the positions of the target sampling points of which the positions are to be changed, and then a clustering algorithm is adopted for the residents to obtain the central positions of the residents as the alternative positions of the target sampling points.
Step a620: calculating an accumulated revenue value of the reachability of the sampling point added at the alternative position relative to each resident point;
specifically, for each residential point, the reachability between the sampling point added at the alternative position and the residential point and the maximum reachability between the residential point and each sampling point before replacement are calculated, then the difference of the reachability and the reachability is calculated, the profit value relative to the residential point is obtained, the profit values of all the residential points are accumulated, and the accumulated profit value is obtained.
Assume gain (f i ) To add the sampling point f i Is to (1) the benefits of:
wherein, the liquid crystal display device comprises a liquid crystal display device,for the pre-substitution sum u j The sample point with the highest accessibility.
Step a630: calculating an accumulated loss value of the accessibility of the increased sampling point relative to each resident point after replacing the target sampling point;
specifically, for each residential point, the maximum value between the increased reachability of the residential point and the residential point at the alternative location and the next largest value of reachability between the residential point and each of the sampling points before replacement is calculated, subtracted from the maximum reachability between the residential point and each of the sampling points before replacement, net losses with respect to the residential point are obtained, net losses of all the residential points are accumulated, and an accumulated loss value is obtained.
Assume netloss (f i ，f r ) To use f i Replacement f r Net loss of (c):
wherein, the liquid crystal display device comprises a liquid crystal display device,for the pre-substitution sum u j Sampling point with maximum accessibility, +.>For the pre-substitution sum u j Is low in accessibility and is only lower thanIs a sampling point of (a).
Step A640: calculating the difference value between the accumulated gain value and the accumulated loss value to obtain replacement total gain;
step a650: when the total replacement benefit is greater than zero, updating the position of the target sampling point to be an alternative position;
specifically, the cumulative benefit value gain (f i ) Subtracting netloss (f) from the cumulative loss value i ，f r ) Obtaining replacement of total profit profits (f i ，f r ) By f i Replacement f r The specific expression of the replacement total profit of (a) is: profit (f) i ，f r )＝gain(f i )-netloss(f i ，f r ). If alternative position f i Is profit (f) i ，f r ) Greater than zero, then indicates that the optimization is effective, using sample point f i Replacing the target sampling point f r Otherwise, not replacing.
Step a660: and acquiring the next alternative position according to the alternative position searching algorithm of the sampling point, and carrying out iterative updating on the position of the target sampling point until the total benefit of replacement of all the alternative positions is less than or equal to zero, and outputting the position of the target sampling point.
Specifically, after updating the position of the target sampling point to the candidate position, the next candidate position may be obtained according to the sampling point candidate position searching algorithm, where the candidate position may be the next candidate position of the position before replacement, or may be the candidate position obtained according to the position after replacement. And carrying out iterative updating on the positions of the target sampling points by using the alternative positions until the total benefit of replacement of all the alternative positions is less than or equal to zero when the target sampling points reach a certain position, and obtaining the optimized positions of the target sampling points. By traversing all the possible alternative locations where sampling points are set up, the accuracy of the sampling point layout can be improved.
And (C) when the optimization effect of the alternative position obtained in the step (A) 610 is not good, the position of the target sampling point is not changed, the next alternative position of the target sampling point is obtained according to a sampling point alternative position searching algorithm, and then the alternative position is used for carrying out iterative updating on the position of the target sampling point.
In specific implementation, layout optimization can be performed on one or more target sampling points according to requirements. The objective function of layout optimization can be expressed as:
the constraint conditions are as follows: />
By the above, the target sampling point is replaced to the alternative position by obtaining the alternative position of the target sampling point, and then the total replacement benefit is calculated according to the accessibility to all the residential points, so as to evaluate the optimization effect. So that the cumulative value of the integrated reachability of all the sampling points increases. And continuously optimizing the positions of the sampling points, and gradually maximizing the integrated accessibility cumulative value of all the sampling points. The layout of the sampling points is more accurate, and the overall efficiency of the sampling point network is improved.
In some embodiments, the layout optimization is performed on the sampling points by increasing the number of the sampling points, as shown in fig. 3, and the specific steps include:
step B610: screening out sampling points with poor accessibility relative to nearby residential points from all the sampling points to obtain a sampling point set;
Specifically, the basis for increasing the position selection of the sampling points is to improve the accessibility of the sampling points with poor accessibility compared with the original accessibility of the residential points so as to improve the comprehensive service capability. Therefore, it is necessary to screen out the sampling points according to preset conditions, such as the reachability being lower than the set value, or the distance exceeding the set threshold, or the waiting time exceeding the set value. For example, all sampling points in the target area with waiting time exceeding 15 minutes are screened out.
Step B620: based on the sampling point distribution data, clustering the sampling points in the sampling point set according to a clustering algorithm to obtain a plurality of center positions;
specifically, a clustering algorithm is adopted for all the screened sampling points to obtain a plurality of center positions. The clustering algorithm is not limited, and any clustering algorithm such as the existing K-means, mini-Batch K-means, gaussian mixture, DBSCAN and the like can be adopted.
Step B630: setting a sampling point at the central position, and optimizing the position of the newly added sampling point according to a sampling point alternative position searching algorithm;
step B640: outputting the position of each sampling point after optimization.
Specifically, a sampling point is added at the central position, and the position of the newly added sampling point is optimized. The position optimization method comprises the following steps: and obtaining an alternative position of the central position according to a sampling point alternative position searching algorithm, calculating the accumulated benefits of the central position and the alternative position, and taking the position with the largest accumulated benefits as the position of the finally determined additional sampling point. In calculating the cumulative benefit, gain (f) i ) To add the sampling point f i Is to (1) the benefits of:wherein (1)>For the pre-substitution sum u j The sample point with the highest accessibility. F is continuously optimized through sampling point alternative position searching algorithm i Is positioned such that gain (f i ) Maximum. The sampling point alternative location searching algorithm may refer to the description in step a610, and will not be described herein.
And (3) carrying out the optimization process on each central position, and finally outputting the position data of each optimized sampling point.
In particular implementations, the objective function of layout optimization can be expressed as:
the constraint conditions are as follows: p is E [1, m]，
By the above, the sampling points are firstly screened, and then a plurality of central positions are obtained by adopting a clustering algorithm according to the positions of the screened sampling points, so that the initial positions of the additionally-arranged sampling points are better. And then optimizing the preliminary positions of the additional sampling points according to the sampling point alternative position searching algorithm, so that the integrated value of the integrated accessibility of the additional sampling points is maximized, the additional sampling points are more accurately distributed, and the overall efficiency of the sampling point network is improved.
In some embodiments, the layout optimization is performed on the sampling points by simultaneously changing the number and the positions of the sampling points, as shown in fig. 4, and the specific steps include:
Step C610: optimizing the positions of sampling points under each quantity based on the set quantity range of the sampling points, and calculating the integrated reachability value of all the sampling points under each quantity;
specifically, assuming that the total number of existing sampling points is m, the set number of sampling points is preferably in the range of [1,2×m ], and then assuming that the sampling points p are continuously increased from 1 to 2*m, the sampling point candidate position search algorithm is used for optimizing the positions of all the sampling points for each value p, and the process of position optimization can be referred to the descriptions in steps a610 to a660, which are not repeated herein. And storing the integrated value of the integrated reachability of all sampling points under each quantity p obtained after optimization and the distribution data of all sampling points under each quantity p.
Step C620: the number of sampling points is taken as an independent variable, and the accumulated value is taken as an independent variable, so that a fitting curve is obtained;
specifically, 2*m discrete points are made by taking p as an independent variable (horizontal axis) and C as a dependent variable (vertical axis), and fitting is performed by a least square method to obtain a fitted curve.
Step C630: calculating the absolute value of the second derivative of each point on the fitting curve, and taking the maximum value, wherein the maximum value is the number of the optimal sampling points;
Specifically, the absolute values of the second derivatives of the respective points on the fitted curve are calculated, and the maximum value among these absolute values is found. The maximum value of the absolute value of the second derivative, i.e. the p-value closest to its inflection point, is the optimal number of sampling points.
And (4) recording a fitting function corresponding to the fitting curve as theta (p), wherein the objective function is as follows:the constraint conditions are as follows: p.epsilon. {1,2,3,..2m }.
Step C640: and outputting the positions of all the sampling points when the optimal sampling point number is output.
Specifically, according to the determined optimal sampling point number, distribution data of each sampling point at the optimal sampling point number is found from the data saved in step C610, and then the sampling point distribution data is output.
In particular implementations, the objective function of layout optimization can be expressed as:
the constraint conditions are as follows:
by calculating the integrated value of the integrated reachability after the position optimization under each sampling point number, fitting the sampling point number, the integrated value of the integrated reachability and obtaining the maximum value of the absolute value of the second derivative, the optimal sampling point number is obtained, namely, the optimal sampling point number of the target area is determined. And then outputting the position-optimized sampling point distribution data under the optimal sampling point quantity. The number and layout of the sampling points are more suitable for population and traffic conditions of a target area, the layout is more accurate, and the overall efficiency of the sampling point network is higher.
In some embodiments, static data of population distribution data can be replaced by movement track data of residents, so that distribution of sampling points is better bound with daily life of the residents, and efficiency of a sampling point network is higher.
Exemplary apparatus
As shown in fig. 5, corresponding to the above-mentioned sampling point layout optimization method, an embodiment of the present invention further provides a sampling point layout optimization system, where the sampling point layout optimization system includes:
the data acquisition module 600 is configured to acquire population distribution data, sampling point distribution data, and real-time status data of sampling points of the target area;
a distance module 610, configured to calculate a distance between each sampling point and each resident point based on the sampling point distribution data and the population distribution data;
the equivalent waiting time module 620 is configured to compare the waiting time in the real-time status data of the sampling points with a preset tolerance time threshold, and obtain an equivalent waiting time of each sampling point according to the comparison result;
a reachability module 630 for calculating reachability of each sampling point with respect to each resident point, based on the distance and the equivalent wait time;
a comprehensive reachability module 640 for calculating a comprehensive reachability for each sample point based on all of said reachability and said demographic data for each sample point;
And the optimizing module 650 is configured to perform layout optimization on the sampling points with the integrated reachability value maximization of all the sampling points as a target, obtain optimized sampling point distribution data, and output the optimized sampling point distribution data.
Optionally, the optimizing module 650 includes;
the position optimization unit is used for obtaining the alternative position of the target sampling point of the position to be changed according to the sampling point alternative position searching algorithm; calculating an accumulated revenue value of the reachability of the sampling point added at the alternative position relative to each resident point; calculating an accumulated loss value of reachability of each residential point after the increased sampling point is substituted for the target sampling point; calculating the difference value between the accumulated benefit value and the accumulated loss value to obtain replacement total benefit; when the total replacement benefit is greater than zero, updating the position of the target sampling point to the alternative position; acquiring the next alternative position according to a sampling point alternative position searching algorithm, and performing iterative updating of the position of the target sampling point until the total benefit of replacement of all the alternative positions is less than or equal to zero, and outputting the position of the target sampling point;
the quantity optimizing unit is used for screening out sampling points with poor accessibility relative to nearby residential points from all the sampling points to obtain a sampling point set; based on the sampling point distribution data, clustering the sampling points in the sampling point set according to a clustering algorithm to obtain a plurality of center positions; setting a sampling point at the central position, and optimizing the position of the newly added sampling point according to a sampling point alternative position searching algorithm; outputting the position of each sampling point after optimization;
The comprehensive optimization unit is used for optimizing the positions of the sampling points under each quantity based on the set quantity range of the sampling points and calculating the integrated reachability value of all the sampling points under each quantity; obtaining a fitting curve by taking the number of sampling points as independent variables and the accumulated value as dependent variables; calculating the absolute value of the second derivative of each point on the fitting curve, and taking the maximum value, wherein the maximum value is the number of the optimal sampling points; and outputting the positions of all the sampling points when the optimal sampling point number is output.
Specifically, in this embodiment, specific functions of each module of the above-mentioned sampling point layout optimization system may refer to corresponding descriptions in the above-mentioned sampling point layout optimization method, which are not described herein again.
Based on the embodiment, the invention also provides an intelligent terminal, which comprises a processor and a memory. Wherein the processor is configured to provide computing and control capabilities. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a sample point layout optimization program. The internal memory provides an environment for the operating system and the sample point layout optimization program to run in the non-volatile storage medium. The sampling point layout optimization program, when executed by the processor, implements the steps of any one of the sampling point layout optimization methods described above.
Based on the above embodiment, the present invention also provides an intelligent terminal, and a functional block diagram thereof may be shown in fig. 6. The intelligent terminal comprises a processor, a memory, a network interface and a display screen which are connected through a system bus. The processor of the intelligent terminal is used for providing computing and control capabilities. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a sample point layout optimization program. The internal memory provides an environment for the operating system and the sample point layout optimization program to run in the non-volatile storage medium. The network interface of the intelligent terminal is used for communicating with an external terminal through network connection. The sampling point layout optimization program, when executed by the processor, implements the steps of any one of the sampling point layout optimization methods described above. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen.
It will be appreciated by those skilled in the art that the schematic block diagram shown in fig. 6 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the smart terminal to which the present inventive arrangements are applied, and that a particular smart terminal may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores a sampling point layout optimization program, which realizes the steps of any one of the sampling point layout optimization methods provided by the embodiment of the invention when being executed by a processor.
It should be understood that the sequence number of each step in the above embodiment does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not be construed as limiting the implementation process of the embodiment of the present invention.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units described above is merely a logical function division, and may be implemented in other manners, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed.
The integrated modules/units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of each method embodiment may be implemented. The computer program comprises computer program code, and the computer program code can be in a source code form, an object code form, an executable file or some intermediate form and the like. The computer readable medium may include: any entity or device capable of carrying the computer program code described above, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. The content of the computer readable storage medium can be appropriately increased or decreased according to the requirements of the legislation and the patent practice in the jurisdiction.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions are not intended to depart from the spirit and scope of the various embodiments of the invention, which are also within the spirit and scope of the invention.
Claims (8)
1. The sampling point layout optimization method is characterized by comprising the following steps:
acquiring population distribution data, sampling point distribution data and real-time state data of sampling points of a target area;
calculating the distance between each sampling point and each resident point based on the sampling point distribution data and the population distribution data;
comparing the waiting time in the real-time state data of the sampling points with a preset tolerance time threshold value, and obtaining the equivalent waiting time of each sampling point according to the comparison result;
calculating the accessibility of each sampling point relative to each resident point based on the distance and the equivalent waiting time;
Calculating a comprehensive reachability for each sampling point based on all of the reachability and the demographic data for each sampling point;
carrying out layout optimization on the sampling points by taking the maximization of the integrated accessibility of all the sampling points as a target, obtaining optimized sampling point distribution data and outputting the optimized sampling point distribution data;
the obtaining the equivalent waiting time of each sampling point according to the comparison result comprises the following steps:
when the waiting time in the real-time state data of the sampling point is below a preset tolerance time threshold, the equivalent waiting time is a preset value;
when the waiting time in the real-time state data of the sampling point is above a preset tolerance time threshold, the equivalent waiting time is the sum of the difference value between the waiting time in the real-time state data of the sampling point and the preset tolerance time threshold and the preset value;
based on the distance and the equivalent waiting time, calculating the accessibility of each sampling point relative to each resident point, wherein the specific expression is as follows:
wherein alpha and beta are gravitation attenuation coefficients, d ij For the distance between the sampling point i and the resident point j, τ i Equivalent latency for sample point i, n i The sampling number is the sampling number of the sampling point i;
and calculating the comprehensive reachability of each sampling point based on all the reachability and the population distribution data of each sampling point, wherein the specific expression is as follows:
Wherein A is i For the integrated reachability of sample point i, p j Is population number, k of the residential point j ij As a weight value, a is used for representing the weight between the sampling point i and the sampling point of the maximum accessibility of the residential point j ij For reachability between the sampling point i and the residential point j, n is the total number of residential points.
2. The sampling point layout optimization method according to claim 1, wherein the sampling points are layout-optimized by increasing the number of sampling points and changing the positions of the sampling points, the layout optimization comprising:
optimizing the positions of sampling points under each quantity based on the set quantity range of the sampling points, and calculating the integrated reachability value of all the sampling points under each quantity;
obtaining a fitting curve by taking the number of sampling points as independent variables and the accumulated value as dependent variables;
calculating the absolute value of the second derivative of each point on the fitting curve, and taking the maximum value, wherein the maximum value is the number of the optimal sampling points;
and outputting the positions of all the sampling points when the optimal sampling point number is output.
3. The sampling point layout optimization method according to claim 1, wherein the sampling points are subjected to layout optimization by changing the positions of the sampling points, the layout optimization comprising:
Obtaining an alternative position of a target sampling point of the position to be changed according to a sampling point alternative position searching algorithm;
calculating an accumulated revenue value of the reachability of the sampling point added at the alternative position relative to each resident point;
calculating an accumulated loss value of reachability of each residential point after the increased sampling point is substituted for the target sampling point;
calculating the difference value between the accumulated benefit value and the accumulated loss value to obtain replacement total benefit;
when the total replacement benefit is greater than zero, updating the position of the target sampling point to the alternative position;
and acquiring the next alternative position according to the alternative position searching algorithm of the sampling point, and carrying out iterative updating on the position of the target sampling point until the total replacement benefit of all the alternative positions of the target sampling point is less than or equal to zero, and outputting the position of the target sampling point.
4. The sampling point layout optimization method according to claim 1, wherein the sampling points are layout-optimized by increasing the number of sampling points, the layout optimization comprising:
screening out sampling points with poor accessibility relative to nearby residential points from all the sampling points to obtain a sampling point set;
based on the sampling point distribution data, clustering the sampling points in the sampling point set according to a clustering algorithm to obtain a plurality of center positions;
Setting a sampling point at the central position, and optimizing the position of the newly added sampling point according to a sampling point alternative position searching algorithm;
outputting the position of each sampling point after optimization.
5. A sampling point layout optimization system, the system comprising:
the data acquisition module is used for acquiring population distribution data, sampling point distribution data and real-time state data of the sampling points of the target area;
the distance module is used for calculating the distance between each sampling point and each resident point based on the sampling point distribution data and the population distribution data;
the equivalent waiting time module is used for comparing the waiting time in the real-time state data of the sampling points with a preset tolerance time threshold value and obtaining the equivalent waiting time of each sampling point according to the comparison result;
the reachability module is used for calculating the reachability of each sampling point relative to each resident point based on the distance and the equivalent waiting time;
a comprehensive reachability module for calculating a comprehensive reachability of each sampling point based on all of the reachability and the demographic data for each sampling point;
the optimizing module is used for carrying out layout optimization on the sampling points with the aim of maximizing the integrated accessibility accumulated value of all the sampling points, obtaining optimized sampling point distribution data and outputting the optimized sampling point distribution data;
The obtaining the equivalent waiting time of each sampling point according to the comparison result comprises the following steps:
when the waiting time in the real-time state data of the sampling point is below a preset tolerance time threshold, the equivalent waiting time is a preset value;
when the waiting time in the real-time state data of the sampling point is above a preset tolerance time threshold, the equivalent waiting time is the sum of the difference value between the waiting time in the real-time state data of the sampling point and the preset tolerance time threshold and the preset value;
based on the distance and the equivalent waiting time, calculating the accessibility of each sampling point relative to each resident point, wherein the specific expression is as follows:
wherein alpha and beta are gravitation attenuation coefficients, d ij For the distance between the sampling point i and the resident point j, τ i Equivalent latency for sample point i, n i The sampling number is the sampling number of the sampling point i;
and calculating the comprehensive reachability of each sampling point based on all the reachability and the population distribution data of each sampling point, wherein the specific expression is as follows:
wherein A is i For the integrated reachability of sample point i, p j Is population number, k of the residential point j ij As a weight value, a is used for representing the weight between the sampling point i and the sampling point of the maximum accessibility of the residential point j ij For reachability between the sampling point i and the residential point j, n is the total number of residential points.
6. The sample point layout optimization system of claim 5, wherein the optimization module comprises;
the position optimization unit is used for obtaining the alternative position of the target sampling point of the position to be changed according to the sampling point alternative position searching algorithm; calculating an accumulated revenue value of the reachability of the sampling point added at the alternative position relative to each resident point; calculating an accumulated loss value of reachability of each residential point after the increased sampling point is substituted for the target sampling point; calculating the difference value between the accumulated benefit value and the accumulated loss value to obtain replacement total benefit; when the total replacement benefit is greater than zero, updating the position of the target sampling point to the alternative position; acquiring the next alternative position according to a sampling point alternative position searching algorithm, and performing iterative updating of the position of the target sampling point until the total benefit of replacement of all the alternative positions is less than or equal to zero, and outputting the position of the target sampling point;
the quantity optimizing unit is used for screening out sampling points with poor accessibility relative to nearby residential points from all the sampling points to obtain a sampling point set; based on the sampling point distribution data, clustering the sampling points in the sampling point set according to a clustering algorithm to obtain a plurality of center positions; setting a sampling point at the central position, and optimizing the position of the newly added sampling point according to a sampling point alternative position searching algorithm; outputting the position of each sampling point after optimization;
The comprehensive optimization unit is used for optimizing the positions of the sampling points under each quantity based on the set quantity range of the sampling points and calculating the integrated reachability value of all the sampling points under each quantity; obtaining a fitting curve by taking the number of sampling points as independent variables and the accumulated value as dependent variables; calculating the absolute value of the second derivative of each point on the fitting curve, and taking the maximum value, wherein the maximum value is the number of the optimal sampling points; and outputting the positions of all the sampling points when the optimal sampling point number is output.
7. The intelligent terminal, characterized by comprising a memory, a processor and a sampling point layout optimization program stored on the memory and capable of running on the processor, wherein the sampling point layout optimization program, when executed by the processor, implements the steps of the sampling point layout optimization method according to any of claims 1-4.
8. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a sample point layout optimization program, which when executed by a processor, implements the steps of the sample point layout optimization method according to any of claims 1-4.
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