CN117352137A - Method, device and storage medium for evaluating detection point - Google Patents

Method, device and storage medium for evaluating detection point Download PDF

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CN117352137A
CN117352137A CN202210725243.6A CN202210725243A CN117352137A CN 117352137 A CN117352137 A CN 117352137A CN 202210725243 A CN202210725243 A CN 202210725243A CN 117352137 A CN117352137 A CN 117352137A
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queuing
preset
predicted
initial
determining
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冯海涛
雷俊阳
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Beijing Xiaomi Mobile Software Co Ltd
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Beijing Xiaomi Mobile Software Co Ltd
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C11/00—Arrangements, systems or apparatus for checking, e.g. the occurrence of a condition, not provided for elsewhere
    • G07C2011/04—Arrangements, systems or apparatus for checking, e.g. the occurrence of a condition, not provided for elsewhere related to queuing systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
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Abstract

The disclosure relates to a method, a device and a storage medium for evaluating a detection point. The evaluation method comprises the following steps: acquiring reference data; according to the reference data, determining the initial predicted queuing number of the detection points in a preset time period; acquiring preset pre-reference information; and determining the predicted queuing number in the preset time period according to the preset reference information and the initial queuing number. The evaluation method of the detection point can enable the user client to reasonably arrange the nucleic acid detection time according to the actual condition of the detection point, improve the nucleic acid detection efficiency and improve the user experience.

Description

Method, device and storage medium for evaluating detection point
Technical Field
The disclosure relates to the field of computer technology, and in particular, to a method and a device for evaluating a detection point, and a storage medium.
Background
In the related art, the infectious disease detection establishes an independent detection point to avoid the risk of spread of the infectious disease. However, the detection performed in each independent detection point is still in a disordered state, some detection points have more people, some detection points have less people, the time required for detecting the infectious diseases is different in different detection points, if the detection is performed at a certain detection point, the waiting time is too long, so that the time of the personnel to be detected is wasted, and the possible risk of being infected is increased.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a method, an apparatus, and a storage medium for evaluating a detection point.
According to a first aspect of embodiments of the present disclosure, there is provided an evaluation method of a detection point, the evaluation method including:
acquiring reference data;
according to the reference data, determining the initial predicted queuing number of the detection points in a preset time period;
acquiring preset reference information;
and determining the predicted queuing number in the preset time period according to the preset reference information and the initial queuing number.
In some exemplary embodiments of the present disclosure, determining the predicted queuing number within the preset time period according to the preset reference information and the initial queuing number includes:
determining a first weight affecting the number of people in the predicted queue in the preset time period according to the preset reference information;
and determining the predicted queuing number in the preset time period according to the first weight and the initial predicted queuing number.
In some exemplary embodiments of the present disclosure, when the preset reference information includes a plurality of pieces, the determining, according to the preset reference information, a first weight affecting the predicted queuing number within the preset time period includes:
Determining initial weight of each piece of preset reference information;
and determining a first weight affecting the number of predicted queuing people in the preset time period according to the initial weight of each preset reference information.
In some exemplary embodiments of the present disclosure, determining the predicted queuing number within the preset time period according to the preset reference information and the initial queuing number includes:
acquiring an initial reference value related to the preset reference information;
determining an influence factor of the initial reference value;
determining a second weight affecting the predicted queuing number in the preset time period according to the initial reference value and the affecting factor;
and determining the predicted queuing number in the preset time period according to the second weight and the initial queuing number.
In some exemplary embodiments of the present disclosure, when the preset reference information includes a plurality of pieces, a sum of initial reference values related to the preset reference information is 1.
In some exemplary embodiments of the disclosure, the checkpoint comprises a nucleic acid checkpoint.
In some exemplary embodiments of the present disclosure, the preset reference information includes one or more of the following information:
Infectious disease epidemic situation related information, infectious disease prevention and control policy information, diagnosis time and track information of positive diagnosis cases, geographic position related information and environment related information.
In some exemplary embodiments of the present disclosure, the reference data includes historical queuing data and/or current queuing data.
In some exemplary embodiments of the present disclosure, when the reference data includes historical queuing data, the historical queuing data includes historical queuing data for a corresponding preset time period within a preset time period.
In some exemplary embodiments of the present disclosure, the evaluation method includes:
and determining the predicted queuing time consumption according to the predicted queuing number.
In some exemplary embodiments of the present disclosure, the prediction method further includes:
acquiring the position of a user;
determining a detection point which is within a preset range from the position;
and determining a detection point with queuing time less than a preset threshold according to the determined predicted queuing time of the detection point.
According to a second aspect of the embodiments of the present disclosure, there is provided an evaluation device of a detection point, the evaluation device including:
the first acquisition module acquires reference data;
The first determining module is configured to determine the initial predicted queuing number of the detection points in a preset time period according to the reference data;
the second acquisition module is configured to acquire preset reference information;
and the second determining module is configured to determine the predicted queuing number in the preset time period according to the preset reference information and the initial queuing number.
In some exemplary embodiments of the present disclosure, the second determination module is configured to:
determining a first weight affecting the number of people in the predicted queue in the preset time period according to the preset reference information;
and determining the predicted queuing number in the preset time period according to the first weight and the initial predicted queuing number.
In some exemplary embodiments of the present disclosure, when the preset reference information includes a plurality of pieces, the second determining module is configured to:
determining initial weight of each piece of preset reference information;
and determining a first weight affecting the number of predicted queuing people in the preset time period according to the initial weight of each preset reference information.
In some exemplary embodiments of the present disclosure, the second determination module is configured to:
Acquiring an initial reference value related to the preset reference information;
determining an influence factor of the initial reference value;
determining a second weight affecting the predicted queuing number in the preset time period according to the initial reference value and the affecting factor;
and determining the predicted queuing number in the preset time period according to the second weight and the initial queuing number.
In some exemplary embodiments of the present disclosure, when the preset reference information includes a plurality of pieces, a sum of initial reference values related to the preset reference information is 1.
In some exemplary embodiments of the disclosure, the checkpoint comprises a nucleic acid checkpoint.
In some exemplary embodiments of the present disclosure, the preset reference information includes one or more of the following information:
infectious disease epidemic situation related information, infectious disease prevention and control policy information, diagnosis time and track information of positive diagnosis cases, geographic position related information and environment related information.
In some exemplary embodiments of the present disclosure, the reference data includes historical queuing data and/or current queuing data.
In some exemplary embodiments of the present disclosure, when the reference data includes historical queuing data, the historical queuing data includes historical queuing data for a corresponding preset time period within a preset time period. In some exemplary embodiments of the present disclosure, the evaluation apparatus further includes a third determination module configured to:
And determining the predicted queuing time consumption according to the predicted queuing number.
In some exemplary embodiments of the present disclosure, the evaluation apparatus further includes a fourth determination module configured to:
acquiring the position of a user;
determining a detection point which is within a preset range from the position;
and determining a detection point with queuing time less than a preset threshold according to the determined predicted queuing time of the detection point.
According to a third aspect of the embodiments of the present disclosure, there is provided an evaluation device for a detection point, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the methods provided by the exemplary embodiments of the present disclosure.
According to a fourth aspect of the disclosed embodiments, there is provided a non-transitory computer readable storage medium, which when executed by a processor of an apparatus, causes the apparatus to perform the method provided by the exemplary embodiments of the disclosure.
The method has the following beneficial effects: according to the evaluation method of the detection point provided by the exemplary embodiment of the disclosure, according to historical queuing data, the number of queuing people in a preset time period is predicted by combining the preset reference information which can influence the number of queuing people in the preset time period, so that a user client reasonably arranges the nucleic acid detection time according to the actual condition of the detection point, the nucleic acid detection efficiency is improved, and the user experience is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart illustrating a method of evaluating a detection point according to an exemplary embodiment;
FIG. 2 is a flowchart illustrating a method of determining a predicted number of people in a predetermined period of time based on predetermined reference information and an initial number of people in a queue in step S103 according to an exemplary embodiment;
FIG. 3 is a flowchart illustrating a method of determining a first weight affecting a predicted number of people in a preset time period based on preset reference information in step S201, according to an exemplary embodiment;
FIG. 4 is a flowchart illustrating a method of determining a predicted number of people in a predetermined period of time based on predetermined reference information and an initial number of people in a queue in step S103 according to an exemplary embodiment;
FIG. 5 is a flowchart illustrating a method of evaluating a detection point, according to an exemplary embodiment;
FIG. 6 is a block diagram of an evaluation device for a detection point, according to an exemplary embodiment;
Fig. 7 is a block diagram showing an evaluation apparatus for a detection point according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
In an exemplary embodiment of the present disclosure, a method of evaluating a detection point is provided. The evaluation method can be used for any electronic device, such as a terminal. As shown in fig. 1, fig. 1 is a flowchart illustrating an evaluation method of a detection point according to an exemplary embodiment:
step S101, obtaining reference data;
step S102, determining the initial predicted queuing number of detection points in a preset time period according to reference data;
step S103, obtaining preset reference information;
step S104, according to the preset reference information and the initial queuing number, the predicted queuing number in the preset time period is determined.
In an exemplary embodiment of the present disclosure, in order to evaluate a detection point, an initial predicted queuing number of the detection point in a preset time period may be predicted according to the acquired reference data, and preset reference information for evaluating and adjusting the initial predicted queuing number may be acquired, so as to determine the predicted queuing number in the preset time period.
According to the evaluation method of the detection point provided by the exemplary embodiment of the disclosure, according to the reference data, the preset reference information which can influence the predicted queuing number in the predicted preset time period is combined to predict the queuing number in the preset time period, so that a user client reasonably arranges the nucleic acid detection time according to the actual condition of the detection point, the nucleic acid detection efficiency is improved, and the user experience is improved.
In an illustrative example of the present disclosure, the reference data may be any data that may be used to predict an initial predicted number of queuing people at the detection point within a preset period of time. For example historical queuing data for the detection point and/or current queuing data for the detection point may be included. The preset time period may be a given time period including the current time point, or may be a certain time period in which the future user wishes to perform detection. When predicting the number of people in the initial predicted queue of the detection point in the preset time period, only the historical queuing data or only the current queuing data can be used, and the historical queuing data and the current queuing data can also be used simultaneously.
The historical queuing data may include historical queuing data for a corresponding preset time period within a preset time period. For example, when a user expects nucleic acid detection within a certain time period during the day, historical queuing data for that time period may be selected as reference data. For example, if the user has time to detect nucleic acid within 1-2 noon in the day, the historical data of nucleic acid detection within 1-2 noon corresponding to the preset duration of the detection point is selected as the reference data. The historical queuing data corresponding to the preset time period is selected as the reference data, so that the number of queuing people in the preset time period can be predicted more accurately.
The current queuing data of the detection point can be used as reference data, the current queuing data can reflect the busyness degree of the detection point to a certain extent, and the current queuing data can be used as the reference data of the initial predicted queuing number of the detection point in a preset time period.
In exemplary embodiments of the present disclosure, queuing time may also be determined based on a predicted number of queuing people. Determining queuing time based on the predicted number of queuing people may be implemented in any manner. For example, the historical average detection time required to complete a person based on detection of a detection point may be multiplied by the predicted queuing number. The queuing time consumption is determined according to the number of predicted queuing people, so that a more accurate time consumption reference can be provided for a user, and the user can conveniently schedule detection.
The detection point can be a nucleic acid detection point, when a user predicts nucleic acid detection within a preset time period, preliminary prediction of the number of people in line can be performed according to historical queuing data, and then the preliminary predicted number of people in line is adjusted according to preset reference information which possibly affects the number of people in line, so that the final number of people in line is determined.
The preset reference information can be any information which can influence the number of people at the detection point. For example, when the checkpoint is a nucleic acid checkpoint, the preset reference information may include one or more of the following:
infectious disease epidemic situation related information, infectious disease prevention and control policy information, track information of positive diagnosis cases, geographic position related information and environment related information.
The information related to the epidemic situation can comprise any information related to the epidemic situation, such as the number of nucleic acid positives in the city, the time of determining the nucleic acid positives, the distribution area, the number of medium-low risk areas in the city, and the like. The policy information for preventing and controlling infectious diseases can comprise policy information of the city according to the epidemic situation characteristics of the infectious diseases, such as the policy of detecting and isolating nucleic acid required for entering the city, the period of detecting nucleic acid, the isolation mode and period of close-connected personnel, etc. The diagnosis time and trajectory information of the positive diagnosis case may include the diagnosis time of the positive diagnosis case and the trajectory of the action within a given period of time before the diagnosis. The geographic location related information may include a geographic location in a city in which the user is located, such as in a residential area, mall, office, etc. The environment-related information may include environment-related information of an area where the user is located, for example, a sunny day, a rainy day, etc. These information can be used as information that affects the prediction of the number of people queued for the nucleic acid detection point within a preset period of time.
The information affecting the prediction of the number of people in the queue of the nucleic acid detection point in the preset time period is used as the preset reference information, so that the number of people in the queue of the nucleic acid detection point in the preset time period is predicted, and the accuracy of the number of people in the queue is improved.
In an exemplary embodiment of the present disclosure, a method of determining a predicted number of people in a preset time period based on preset reference information and an initial number of people in a queue is provided in consideration of an influence of the preset reference information on the number of people in a queue. As shown in fig. 2, fig. 2 is a flowchart illustrating a method for determining a predicted number of queuing people in a preset time period according to preset reference information and an initial number of queuing people in step S103 according to an exemplary embodiment:
step S201, determining a first weight affecting the number of people in the predicted queue in a preset time period according to preset reference information;
step S202, according to the first weight and the initial queuing number, the predicted queuing number in a preset time period is determined.
In an exemplary embodiment of the present disclosure, a first weight for a preset time period affecting a detection point may be determined according to preset reference information, and a predicted queuing number for a preset time period of a final detection point may be determined according to the first weight and the initial predicted queuing number.
The predicted number of people in the predicted time period of the final detection point may be determined in any manner based on the first weight and the initial predicted number of people in the queue. For example, the initial predicted number of people in the queue may be multiplied by a first weight to obtain the final predicted number of people in the queue. The interval in which the number of people increases or decreases may also be determined according to the range of different weight values, for example, when the value of the first weight belongs to the first preset range, the number of people in the predicted queue increases by 10% more than the number of people in the initial predicted queue. When the value of the first weight belongs to the second preset range, the predicted queuing number is increased by 20% more than the initial predicted queuing number. The method is not limited herein, and the predicted queuing number within the preset time period of the detection point can be determined to be within the protection scope of the disclosure as long as the predicted queuing number can be determined according to the first weight and the initial predicted queuing number.
In an exemplary embodiment of the present disclosure, the first weight is determined according to the preset reference information, and the preset reference information may be quantized into parameter information that may affect the number of people in the queue in the preset time period for determining the detection point, so as to improve accuracy of a prediction result of the number of people in the queue in the preset time period for determining the detection point.
In an exemplary embodiment of the present disclosure, considering that the preset reference information may include a plurality, a method of determining the first weight according to the preset reference information when the parameter information includes the plurality is provided. As shown in fig. 3, fig. 3 is a flowchart illustrating a method for determining a first weight affecting a predicted queuing number within a preset time period according to preset reference information in step S201 according to an exemplary embodiment:
Step S301, determining initial weight of each preset reference information;
step S302, according to the initial weight of each preset reference information, determining a first weight affecting the number of predicted queuing people in a preset time period.
In an exemplary embodiment of the present disclosure, in consideration of a case where the preset reference information may include a plurality of pieces, an initial weight is given to each preset reference information, and a predicted queuing number within a preset period of time affecting a detection point is determined according to the initial weight of each preset reference information.
When the detection point is a nucleic acid detection point, the preset reference information affecting the prediction of the number of people in line in the preset time period of the nucleic acid detection point may include a plurality of information such as information related to epidemic situation, information about policy of preventing and controlling infectious disease, detection time and track information of positive diagnosis cases, information related to geographical location, information related to environment, etc. Each preset reference information may be given an initial weight when determining the first weight of the preset reference information. In determining the weight of each preset reference information, it may be determined according to the state of each preset reference information. For example, when the number of nucleic acid positives in the city in which the epidemic situation related information is located is large and the number of medium-high risk areas is large, it is possible to determine that the infectious disease epidemic situation related information has a high initial weight value. Conversely, it may be determined that the infectious disease prevalence-related information has a lower initial weight value. The weight value of each corresponding sub-parameter in the related information of the epidemic situation can also be determined, and the initial weight value of the related information of the epidemic situation can be determined according to the weight value of each sub-parameter. The sub-parameters of the information about the epidemic situation of the infectious disease may include, for example, the number of nucleic acid positives in the city, the time of determining the nucleic acid positives, the distribution area, the number of medium and low risk areas in the city, etc.
When the nucleic acid and isolation policies required for entering the city in the infectious disease prevention and control policy information are strict and the nucleic acid detection period is short, the infectious disease prevention and control policy information can be determined to have higher initial weight. Conversely, it may be determined that the infectious disease prevention policy information has a lower initial weight value. The weight value of each corresponding sub-parameter in the infectious disease prevention and control policy information can also be determined, and the initial weight of the epidemic situation policy parameter is determined according to the weight value of each sub-parameter. The sub-parameters of the infection control policy information may include the policy of nucleic acid and isolation required for entering the city, the period of nucleic acid detection, the isolation mode and period of close-coupled personnel, etc.
When the detection time and the track information of the positive diagnosis cases are shorter, and the distance between the detection time and the nucleic acid detection point in the track information is shorter, the initial weight of the detection time and the track information of the positive diagnosis cases can be determined. Conversely, the detection time and trajectory information of a positive diagnosis case can be determined to have a lower initial weight value. The detection time of the positive diagnosis case and the weight value of each corresponding sub-parameter in the track information can also be determined, and the initial weight of the detection time of the positive diagnosis case and the track information can be determined according to the weight value of each sub-parameter. The sub-parameters of the detection time and trajectory information of the positive diagnosis case may include the diagnosis time of the positive case and the action trajectory of the positive case.
When the user is in the geographic position in the city, for example, the user is in a residential area, the personnel density is high, the number of the nucleic acid detection people is high, and the initial weight of the related information of the geographic position can be determined. Instead, it may be determined that the initial weight of the geographic location related information is low.
When the user needs the environmental related information of the day on which the nucleic acid detection is performed, for example, on a sunny day, the number of people for the nucleic acid detection may be high, and the initial weight of the environmental related information may be determined to be high. In the case of rain, the number of people with nucleic acid detection may be small, and a low initial weight of the environment-related information may be determined.
When the initial weight of each preset reference information is determined, the predicted first weight affecting the number of queuing people in the preset period of time of the detection point may be determined in any manner. For example, the initial weights of each preset reference information may be added to obtain the first initial weight.
In the exemplary embodiment of the disclosure, the first weight is determined according to the initial weight of each piece of preset reference information, so that the preset reference information which can influence the prediction of the number of people in the queuing in the preset time period of the detection point is fully considered, and the accuracy of the prediction of the number of people in the queuing is improved.
In an exemplary embodiment of the present disclosure, a method of determining a predicted number of people in a preset time period based on preset reference information and an initial number of people in a queue is provided in consideration of an influence of the preset reference information on the number of people in a queue. As shown in fig. 4, fig. 4 is a flowchart illustrating a method for determining a predicted number of people in a preset time period according to preset reference information and an initial number of people in a queue in step S103 according to an exemplary embodiment:
step S401, obtaining an initial reference value related to preset reference information;
step S402, determining an influence factor of the initial reference value;
step S403, determining a second weight affecting the predicted queuing number in the preset time period according to the initial reference value and the affecting factor;
and step S404, determining the predicted queuing number in the preset time period according to the second weight and the initial queuing number.
In an exemplary embodiment of the present disclosure, an initial reference value related to preset reference information may be determined, an impact factor of a preset time period to the initial reference value detected according to a user need, a second weight may be determined according to the initial reference value and the impact factor, and a predicted queuing number in the preset time period may be determined according to the second weight and the initial queuing number.
When the detection point is a nucleic acid detection point, the preset reference information affecting the prediction of the number of people in line within the preset time period of the nucleic acid detection point may include a plurality of information such as information related to epidemic situation, information about policy of preventing and controlling infectious disease, information about time and track of positive diagnosis cases, information related to geographical location, information related to environment, etc.
The initial reference value associated with the preset reference information may be an initially determined value. The initially determined value may be determined according to any rule. For example, the predicted influence of the preset reference information on the number of people in the queue in the preset time period of the detection point can be obtained. For example, the related information of epidemic situation, the policy information of preventing and controlling infectious diseases, the diagnosis time and track information of positive diagnosis cases have a larger influence on the prediction of the number of people in the queuing time period of the detection point, and a larger initial reference value can be determined. The geographic position related information and the environment related information have smaller predicted influence on the queuing number of the detection points in a preset time period, and a smaller initial reference value can be determined. When the preset reference information includes a plurality of pieces, the sum of initial reference values related to the preset reference information may be 1 in consideration of the influence of the preset reference information on the number of people in the queuing in the preset time period of the detection point. When the predicted influence of different preset reference information on the queuing number of the detection point is changed in different time periods, the initial reference value is adjusted through the influence factor, so that the second weight of the predicted queuing number in the preset time period can be determined, and further the adjustment or correction of the initial predicted queuing number is realized, so that the accuracy of queuing number prediction is improved.
Considering the current state in which the user needs to perform nucleic acid detection, for example, the state of change of the preset reference information, an influence factor that influences the initial reference value of the preset reference information may be determined. For example, when the number of nucleic acid positives in the city in which the information related to the epidemic situation is located is large and the number of medium-high risk areas is large, the corresponding value of the influence factor is high, and conversely, the value of the influence factor is small. When the policy of nucleic acid and isolation needed for entering the city in the infectious disease prevention and control policy information is strict, the nucleic acid detection period is short, and the corresponding influence factor is high. Conversely, the value of the influential factor is small. When the detection time and the track information of the positive diagnosis cases are shorter, the distance between the detection point and the nucleic acid in the track information is shorter, and the corresponding influence factor is higher. Conversely, the value of the influential factor is small. When the user is located in a geographic position in a city, such as a residential area, the user is high in personnel density, the number of the nucleic acid detection people is large, and the corresponding influence factor is high. Conversely, the value of the influential factor is small. When the user needs the environmental information of the day on which the nucleic acid detection is performed, for example, on a sunny day, the number of people for the nucleic acid detection may be high, and the corresponding influence factor is high. Conversely, the value of the influential factor is small.
The determination of the second weight affecting the predicted number of people in the preset time period may be performed according to any manner, based on the initial reference value of the preset reference information and the influencing factor. For example, the product of the initial reference value and the influence factor may be used as a second weight that affects the number of predicted queuing people within a preset time period. When the preset reference information includes a plurality of pieces, a sum of products of initial reference values of the plurality of pieces of preset reference information and the influence factor may be used as a second weight that affects the number of predicted queuing people within the preset time period.
The predicted number of people in the predicted time period of the final detection point may be determined in any manner based on the second weight and the initial predicted number of people in the queue. For example, the initial predicted number of people in the queue may be multiplied by a second weight to obtain the final predicted number of people in the queue. The interval in which the number of people increases or decreases may also be determined according to a range of weight values of different second weights, for example, when the value of the second weight belongs to a third preset range, the predicted queuing number is increased by 10% more than the initial predicted queuing number. When the value of the second weight belongs to a fourth preset range, the predicted queuing number is increased by 20% more than the initial predicted queuing number. The method is not limited herein, and the predicted queuing number within the preset time period of the detection point can be determined to be within the protection scope of the disclosure as long as the predicted queuing number can be determined according to the second weight and the initial predicted queuing number.
When the related initial reference value of the preset reference information is obtained, the initial reference value of the sub-parameter in each preset reference information is also obtained, an influence factor influencing the initial reference value of the sub-parameter is determined, and a second weight of the predicted queuing number in a preset time period of the preset reference information is determined according to the initial reference value of the sub-parameter of each sub-parameter and the corresponding influence factor. For example, the sum of the products of the initial reference value of each sub-parameter and the corresponding influence factor may be used as a second weight that affects the number of people in the predicted queue for the preset period of time.
In the exemplary embodiment of the disclosure, in different time periods, the predicted influence of different preset reference information on the number of queuing people at the detection point is changed, and then the initial reference value is adjusted through the influence factor, so that the second weight affecting the predicted number of queuing people in the preset time period can be determined, and further the adjustment or correction of the initial predicted number of queuing people is realized, so that the accuracy of the prediction of the number of queuing people is improved.
In the exemplary embodiment of the disclosure, considering the position of the user, a proper detection point can be selected according to the need, the queuing number in a preset time period is predicted, and the proper detection point is selected according to the need and pushed to the user for the user to select. As shown in fig. 5, fig. 5 is a flowchart illustrating an evaluation method of a detection point according to an exemplary embodiment:
Step S501, obtaining the position of a user;
step S502, determining detection points with positions within a preset range;
step S503, determining a detection point with queuing time less than a preset threshold according to the determined preset queuing time of the detection point.
In an exemplary embodiment of the present disclosure, the detection point within the preset range may be determined according to the location where the user is located. And predicting the queuing number of the detection points. After a preset time period selected by a user, determining a detection point with queuing time less than a preset threshold according to the number of people in the predicted queuing in the preset time period, so as to be selected by the user.
By the method for evaluating the detection points, which is provided by the exemplary embodiment of the disclosure, accurate prediction of the number of people in line of the nucleic acid detection points and possible time-consuming prediction can be provided for the user, effective reference is provided for the nucleic acid detection of the user, and the efficiency of the nucleic acid detection of the user is improved.
In an exemplary embodiment of the present disclosure, an evaluation device for a detection point is provided. As shown in fig. 6, fig. 6 is a block diagram of an evaluation device of a detection point according to an exemplary embodiment. The evaluation device comprises a first acquisition module 601, a first determination module 602, a second acquisition module 603, a second determination module 604, a third determination module 605 and a fourth determination module 606.
In some exemplary embodiments of the present disclosure, there is provided an evaluation apparatus including:
a first acquisition module 601 that acquires reference data;
a first determining module 602 configured to determine, according to the reference data, an initial predicted queuing number of the detection points within a preset period of time;
a second acquisition module 603 configured to acquire preset reference information;
a second determining module 604 configured to determine a predicted number of people queued for the preset time period according to the preset reference information and the initial number of people queued.
In some exemplary embodiments of the present disclosure, the second determining module 604 is configured to:
determining a first weight affecting the number of people in the predicted queue in the preset time period according to the preset reference information;
and determining the predicted queuing number in the preset time period according to the first weight and the initial predicted queuing number.
In some exemplary embodiments of the present disclosure, when the preset reference information includes a plurality of pieces, the second determining module 604 is configured to:
determining initial weight of each piece of preset reference information;
and determining a first weight affecting the number of predicted queuing people in the preset time period according to the initial weight of each preset reference information.
In some exemplary embodiments of the present disclosure, the second determining module 604 is configured to:
acquiring an initial reference value related to the preset reference information;
determining an influence factor of the initial reference value;
determining a second weight affecting the predicted queuing number in the preset time period according to the initial reference value and the affecting factor;
and determining the predicted queuing number in the preset time period according to the second weight and the initial queuing number.
In some exemplary embodiments of the present disclosure, when the preset reference information includes a plurality of pieces, a sum of initial reference values related to the preset reference information is 1.
In some exemplary embodiments of the disclosure, the checkpoint comprises a nucleic acid checkpoint.
In some exemplary embodiments of the present disclosure, the preset reference information includes one or more of the following information:
infectious disease epidemic situation related information, infectious disease prevention and control policy information, diagnosis time and track information of positive diagnosis cases, geographic position related information and environment related information.
In some exemplary embodiments of the present disclosure, the reference data includes historical queuing data and/or current queuing data.
In some exemplary embodiments of the present disclosure, when the reference data includes historical queuing data, the historical queuing data includes historical queuing data for a corresponding preset time period within a preset time period.
In some exemplary embodiments of the present disclosure, the evaluation apparatus further comprises a third determination module 605 configured to:
and determining the predicted queuing time consumption according to the predicted queuing number.
In some exemplary embodiments of the present disclosure, the evaluation apparatus further comprises a fourth determination module 606 configured to:
acquiring the position of a user;
determining a detection point which is within a preset range from the position;
and determining a detection point with queuing time less than a preset threshold according to the determined predicted queuing time of the detection point.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 7 is a block diagram of an evaluation device 700 for a detection point according to an exemplary embodiment. Referring to fig. 7, an apparatus 700 may include one or more of the following components: a processing component 702, a memory 704, a power component 706, a multimedia component 708, an audio component 710, an input/output (I/O) interface 712, a sensor component 714, and a communication component 716.
The processing component 702 generally controls overall operation of the apparatus 700, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 702 may include one or more processors 720 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 702 can include one or more modules that facilitate interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate interaction between the multimedia component 708 and the processing component 702.
The memory 704 is configured to store various types of data to support operations at the apparatus 700. Examples of such data include instructions for any application or method operating on the apparatus 700, contact data, phonebook data, messages, pictures, videos, and the like. The memory 704 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 706 provides power to the various components of the apparatus 700. The power components 706 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 700.
The multimedia component 708 includes a screen between the device 700 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 708 includes a front-facing camera and/or a rear-facing camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the apparatus 700 is in an operational mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 710 is configured to output and/or input audio signals. For example, the audio component 710 includes a Microphone (MIC) configured to receive external audio signals when the device 700 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 704 or transmitted via the communication component 716. In some embodiments, the audio component 710 further includes a speaker for outputting audio signals.
The I/O interface 712 provides an interface between the processing component 702 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 714 includes one or more sensors for providing status assessment of various aspects of the apparatus 700. For example, the sensor assembly 714 may detect an on/off state of the device 700, a relative positioning of the components, such as a display and keypad of the device 700, a change in position of the device 700 or a component of the device 700, the presence or absence of user contact with the device 700, an orientation or acceleration/deceleration of the device 700, and a change in temperature of the device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 714 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 716 is configured to facilitate communication between the apparatus 700 and other devices in a wired or wireless manner. The apparatus 700 may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one exemplary embodiment, the communication component 716 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 704, including instructions executable by processor 720 of apparatus 700 to perform the above-described method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
An evaluation device for a detection point, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the methods provided by the exemplary embodiments of the present disclosure.
A non-transitory computer readable storage medium, which when executed by a processor of an apparatus, enables the apparatus to perform the method provided by the exemplary embodiments of the present disclosure.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims (14)

1. A method of evaluating a detection point, the method comprising:
acquiring reference data;
according to the reference data, determining the initial predicted queuing number of the detection points in a preset time period;
acquiring preset reference information;
and determining the predicted queuing number in the preset time period according to the preset reference information and the initial queuing number.
2. The method of evaluating a detection point according to claim 1, wherein determining a predicted number of people in a predetermined period of time based on the predetermined reference information and the initial number of people in a queue includes:
determining a first weight affecting the number of people in the predicted queue in the preset time period according to the preset reference information;
and determining the predicted queuing number in the preset time period according to the first weight and the initial predicted queuing number.
3. The method according to claim 2, wherein when the preset reference information includes a plurality of pieces, the determining a first weight affecting the predicted number of people in line for the preset time period based on the preset reference information includes:
determining initial weight of each piece of preset reference information;
And determining a first weight affecting the number of predicted queuing people in the preset time period according to the initial weight of each preset reference information.
4. The method of evaluating a detection point according to claim 1, wherein determining a predicted number of people in a predetermined period of time based on the predetermined reference information and the initial number of people in a queue includes:
acquiring an initial reference value related to the preset reference information;
determining an influence factor of the initial reference value;
determining a second weight affecting the predicted queuing number in the preset time period according to the initial reference value and the affecting factor;
and determining the predicted queuing number in the preset time period according to the second weight and the initial queuing number.
5. The method of evaluating a detection point according to claim 4, wherein when the preset reference information includes a plurality of pieces, a sum of initial reference values associated with the preset reference information is 1.
6. The method of evaluating a checkpoint of any one of claims 1-5, wherein the checkpoint comprises a nucleic acid checkpoint.
7. The method of evaluating a detection point according to claim 6, wherein the preset reference information includes one or more of the following information:
Infectious disease epidemic situation related information, infectious disease prevention and control policy information, diagnosis time and track information of positive diagnosis cases, geographic position related information and environment related information.
8. The method of evaluating a detection point according to claim 1, wherein the reference data comprises historical queuing data and/or current queuing data.
9. The method of evaluating a detection point according to claim 8, wherein when the reference data includes historical queuing data, the historical queuing data includes historical queuing data for a corresponding preset time period within a preset time period.
10. The evaluation method of a detection point according to claim 1, characterized in that the evaluation method comprises:
and determining the predicted queuing time consumption according to the predicted queuing number.
11. The method of evaluating a detection point according to claim 10, wherein the prediction method further comprises:
acquiring the position of a user;
determining a detection point which is within a preset range from the position;
and determining a detection point with queuing time less than a preset threshold according to the determined predicted queuing time of the detection point.
12. An evaluation device for a detection point, characterized in that the evaluation device comprises:
The first determining module is configured to determine the initial predicted queuing number of the detection points in a preset time period according to the historical queuing data;
the acquisition module is configured to acquire preset reference information;
and the second determining module is configured to determine the predicted queuing number in the preset time period according to the preset reference information and the initial queuing number.
13. An evaluation device for a detection point, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method of any of claims 1-11.
14. A non-transitory computer readable storage medium, which when executed by a processor of an apparatus, causes the apparatus to perform the method of any of claims 1-11.
CN202210725243.6A 2022-06-24 2022-06-24 Method, device and storage medium for evaluating detection point Pending CN117352137A (en)

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