Sample sampling analysis algorithm based on Internet of Things and big data

Abstract

The present invention discloses a sample sampling analysis algorithm based on the Internet of Things and big data, relates to the technical field of sample sampling analysis, solves the technical problem that it is impossible to perform multi-faceted accurate analysis in the sample sampling process in the prior art, and can determine whether the current sampling requires sampling or comprehensive testing through real-time cardinality analysis, accurately control the use of sample sampling, determine whether sample sampling is needed, and prevent inaccurate use of sample sampling from leading to reduced efficiency of subject testing and increased testing costs; accurately analyze the target group, judge the current sample sampling operation intensity, reasonably control the input cost, and prevent the input cost from being unable to meet the current operation intensity, resulting in reduced accuracy and reliability of sample sampling, and unable to guarantee the efficiency of sample sampling.

Description

Technical Field

[0001] The present invention relates to the technical field of sample sampling analysis, and specifically to a sample sampling analysis algorithm based on the Internet of Things and big data. Background Art

[0002] Sample sampling is also called sample sampling. It is to extract a part of the sample units from all the samples to be studied. Its basic requirement is to ensure that the sample units extracted are fully representative of all the samples. The purpose of sampling is to estimate and infer the characteristics of all samples from the analysis and research results of the sample units extracted. It is an economical and effective work and research method commonly used in scientific experiments, quality inspections, and social surveys.

[0003] However, in the prior art, it is impossible to conduct accurate multi-faceted analysis during the sample sampling process, it is impossible to reasonably match the sampling type and it is impossible to determine the error risk of the current sampling. In addition, the sampling cannot be compared and the qualification of the real-time results cannot be guaranteed.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose a sample sampling analysis algorithm based on the Internet of Things and big data. Through real-time cardinality analysis, it can determine whether the current sampling requires sampling or comprehensive testing. It can accurately control the use of sample sampling and determine whether sample sampling is needed to prevent inaccurate use of sample sampling, which will lead to reduced efficiency of subject testing and increased testing costs. By accurately analyzing the target group, it can judge the current operating intensity of sample sampling, and can reasonably control the input cost to prevent the input cost from being unable to meet the current operating intensity, resulting in reduced accuracy and reliability of sample sampling, and the inability to guarantee the efficiency of sample sampling.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The sample sampling analysis algorithm based on the Internet of Things and big data, the specific sample sampling analysis method steps are as follows:

[0008] Step 1: Real-time cardinality analysis: perform analysis based on real-time sample sampling requirements, collect the subjects and samples corresponding to the current sample sampling process, analyze the current subject cardinality, and determine whether sample sampling is needed;

[0009] Step 2: Target group analysis: Analyze the current subject and select the appropriate target group within the subject. After determining the target group within the current subject, analyze the corresponding target group and analyze the sampling error risk of the current target group.

[0010] Step 3: Sampling type analysis: After determining the target group, analyze the current sampling type and determine the sampling type based on the nature of the sample sampling corresponding to the real-time target group;

[0011] Step 4: Sampling result analysis: After the sampling type is determined and the current real-time target group has completed the sample sampling, the corresponding sample sampling results are compared with each other to determine whether there is any error in the current sampling result.

[0012] As a preferred embodiment of the present invention, the real-time cardinality analysis process in step 1 is as follows:

[0013] Obtain the limiting conditions for the analysis of the current subject, which are expressed as the conditions for the subject analysis; perform real-time cardinality analysis on the current subject, collect the total number of individuals involved in the current subject under the limiting conditions and the average time consumed when the corresponding individuals execute the limiting conditions, and mark the total number of individuals involved in the current subject under the limiting conditions and the average time consumed when the corresponding individuals execute the limiting conditions as ZSL and PHS respectively;

[0014] By formula Obtain the corresponding cardinality analysis coefficient JS of the subject under the current limiting conditions, where a1 and a2 are both preset proportional coefficients, and a1>a2>0, and β is the error correction factor, which is 0.98;

[0015] Compare the corresponding cardinality analysis coefficient JS of the subject under the current limiting conditions with the corresponding cardinality analysis coefficient threshold:

[0016] If the corresponding cardinality analysis coefficient JS of the subject under the current limiting condition exceeds the corresponding cardinality analysis coefficient threshold, it is determined that the current subject needs sample sampling to execute the corresponding limiting condition, a sampling execution signal is generated, and the execution mode of the current subject limiting condition is set to the sampling mode;

[0017] If the corresponding cardinality analysis coefficient JS of the subject under the current limiting conditions does not exceed the corresponding cardinality analysis coefficient threshold, it is determined that the current subject does not need sample sampling to execute the corresponding limiting conditions, a non-sampling execution signal is generated and the execution mode of the current subject limiting conditions is set to the non-sampling mode.

[0018] As a preferred embodiment of the present invention, the specific steps of sampling type analysis in step 2 are as follows:

[0019] Analyze the samples corresponding to the limited conditions of the current subject, collect the characteristic data of the samples, and form the collected sample characteristic data into a sample characteristic set; the characteristic data is expressed as characteristic parameters of the sample and the subject; divide the subject into i sub-bodies, then collect the characteristic data of each sub-bodies, and form the collected sub-body characteristic data into i sub-body characteristic sets; compare the sample characteristic set with the sub-body characteristic set: if the sample characteristic set and the sub-body characteristic set have the same characteristic data, the corresponding sub-body is marked as the target group; if the sample characteristic set and the sub-body characteristic set do not have the same characteristic data, the corresponding sub-body is marked as a non-target group;

[0020] Obtain the ratio of the corresponding number of the target group to the number of subjects and the number of intersections between the sub-body feature set corresponding to the target group and the sample feature set, and compare the ratio of the corresponding number of the target group to the number of subjects and the number of intersections between the sub-body feature set corresponding to the target group and the sample feature set with the ratio threshold and the intersection threshold respectively:

[0021] If the ratio of the corresponding number of the target group to the number of subjects does not exceed the number ratio threshold, or the intersection number of the corresponding sub-body feature set of the target group and the sample feature set does not exceed the intersection number threshold, then the error risk of sampling the current target group is determined to be high, and a high-risk execution signal is generated and sent to the executor's mobile terminal;

[0022] If the ratio of the corresponding number of the target group to the number of subjects exceeds the number ratio threshold and the intersection number of the sub-body feature set corresponding to the target group and the sample feature set exceeds the intersection number threshold, then it is determined that the error risk of sampling the current target group is low, and a low-risk execution signal is generated and sent to the executor's mobile terminal.

[0023] As a preferred embodiment of the present invention, the specific steps of sampling type analysis in step 3 are as follows:

[0024] After the target group and sampling samples within the subject are determined, the sampling type is judged, and the sampling samples are sampled within the selected target group. During the sampling process, the number of individual categories corresponding to the sampling samples drawn within the selected target group is obtained when the current limiting conditions are obtained, and the corresponding number of individual categories is marked as the sampling result object. If the number of categories of the corresponding sampling result object fluctuates, the corresponding sampling type is set to exploratory sampling; if the number of categories of the corresponding sampling result object does not fluctuate, the corresponding sampling type is set to descriptive sampling.

[0025] As a preferred embodiment of the present invention, the specific steps of analyzing the sampling results in step 4 are as follows:

[0026] Analyze the sampling result object of the sample sampling, set the sampling result object to be labeled o, where o is a natural number greater than 1, and collect the sampling hard parameters of the corresponding sampling result object, where the sampling hard parameters are represented as hard information of the sampling;

[0027] The sampling result objects are matched with the individuals in the target group in pairs, and they are marked as matching individual groups. The sampling hard parameters of the matching individual groups in the current sampling process are analyzed. If the corresponding data in the sampling hard parameters of the matching individual groups are consistent, the matching individual groups are marked as risky individual groups; if the corresponding data in the sampling hard parameters of the matching individual groups are not consistent, the matching individual groups are marked as safe individual groups.

[0028] The risky individual groups and the safe individual groups are analyzed. If the number of risky individual groups exceeds the number of safe individual groups, and the number of risky individual groups is not less than the threshold number of risky individual groups, it is determined that there is an error in the current sampling result, an error signal is generated and the error signal is sent to the executor's mobile terminal; if the number of risky individual groups does not exceed the number of safe individual groups, and the number of risky individual groups is less than the threshold number of risky individual groups, it is determined that there is no error in the current sampling result, an accurate signal is generated and the accurate signal is sent to the executor's mobile terminal.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. In the present invention, through real-time cardinality analysis, it is possible to determine whether the current sampling requires sampling or comprehensive testing, and the use of sample sampling can be accurately controlled to determine whether sample sampling is needed, so as to prevent the inaccurate use of sample sampling from reducing the efficiency of the subject detection and increasing the detection cost; by accurately analyzing the target group, it is possible to judge the operating intensity of the current sample sampling, and it is possible to reasonably control the input cost to prevent the input cost from failing to meet the current operating intensity, resulting in reduced accuracy and reliability of sample sampling, and failure to ensure the efficiency of sample sampling;

[0031] 2. In the present invention, the current sampling type is analyzed, and the sampling type is determined according to the properties of the sample sampling corresponding to the real-time target group; the accuracy of the sample sampling result is analyzed according to the sampling type, thereby improving the accuracy of the sampling and ensuring the qualification of the sampling; after the sampling type is determined and the sample sampling of the current real-time target group is completed, the corresponding sample sampling results are compared with each other to determine the accuracy of the current sampling result. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0033] Figure 1This is a method flow chart of the sample sampling analysis algorithm based on the Internet of Things and big data of the present invention. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1 As shown in the figure, the sample sampling analysis algorithm based on the Internet of Things and big data, the specific sample sampling analysis method steps are as follows:

[0036] Step 1: Real-time cardinality analysis: perform analysis based on the real-time sample sampling requirements, collect the subjects and samples corresponding to the current sample sampling process, analyze the current subject cardinality, and determine whether sample sampling is needed; in this application, the subjects and samples corresponding to sample sampling are represented as the sampling group and the sampling characteristics, such as: sampling red balls from ten colored balls, that is, ten colored balls are the subjects and red balls are the samples; through real-time cardinality analysis, it can be determined whether the current sampling requires sampling or comprehensive testing, and the use of sample sampling can be accurately controlled to determine whether sample sampling is needed, so as to prevent the inaccurate use of sample sampling from reducing the efficiency of subject detection and increasing the detection cost;

[0037] Step 2: Target group analysis: analyze the current subject and select a suitable target group within the subject. After determining the target group within the current subject, analyze the corresponding target group to analyze the error risk of the sample sampling of the current target group. In this application, the target group is represented as a part of the subject. For example, the ten colored balls on the table are the subject, and the red balls are the samples. When sampling, the colored balls in the table area with patterns are sampled as red balls, so the colored balls in the specific pattern area are the target group. Accurately analyzing the target group can determine the current operating intensity of sample sampling, and can reasonably control the input cost to prevent the input cost from failing to meet the current operating intensity, resulting in reduced accuracy and reliability of sample sampling, and unable to guarantee the efficiency of sample sampling.

[0038] Step 3: Sampling type analysis. After determining the target group, analyze the current sampling type. According to the nature of the sample sampling corresponding to the real-time target group, determine the sampling type. According to the sampling type, analyze the accuracy of the sample sampling result, thereby improving the accuracy of the sampling and ensuring the eligibility of the sampling. The sampling types are divided into exploratory and descriptive types. The exploratory type sampling means that the result of the current sample sampling is the result of the unknown subject, such as: for class cadre election, the class students are the subject, and the top ten students are the target group, and the class cadres are voted for, and the class cadres are unknown; while the descriptive type sampling means that the result of the current sample sampling is the result of the known subject; such as: for student homework quality inspection, the class students are the subject, and the top twenty students are the target group, and the student homework quality sampling is carried out, that is, the quality of the student homework in the target group is known.

[0039] Step 4: Sampling result analysis: after the sampling type is determined and the current real-time target group has completed the sample sampling, the corresponding sample sampling results are compared with each other to determine the accuracy of the current sampling results;

[0040] The real-time cardinality analysis process in step 1 is as follows:

[0041] Get the limiting conditions for the current subject to analyze. The limiting conditions are expressed as the conditions for the subject analysis. For example, ten colored balls are analyzed for red balls, so the limiting condition is to extract red balls for analysis. Perform real-time cardinality analysis on the current subject, collect the total number of individuals involved in the current subject under the limiting conditions and the average time consumed by the corresponding individuals to execute the limiting conditions, and mark the total number of individuals involved in the current subject under the limiting conditions and the average time consumed by the corresponding individuals to execute the limiting conditions as ZSL and PHS respectively. Obtain the corresponding cardinality analysis coefficient JS of the subject under the current limiting conditions, where a1 and a2 are both preset proportional coefficients, and a1>a2>0, and β is the error correction factor, which is 0.98;

[0042] Compare the corresponding cardinality analysis coefficient JS of the subject under the current limiting conditions with the corresponding cardinality analysis coefficient threshold:

[0043] If the corresponding cardinality analysis coefficient JS of the subject under the current limiting condition exceeds the corresponding cardinality analysis coefficient threshold, it is determined that the current subject needs sample sampling to execute the corresponding limiting condition, a sampling execution signal is generated, and the execution mode of the current subject limiting condition is set to the sampling mode; if the corresponding cardinality analysis coefficient JS of the subject under the current limiting condition does not exceed the corresponding cardinality analysis coefficient threshold, it is determined that the current subject does not need sample sampling to execute the corresponding limiting condition, a non-sampling execution signal is generated, and the execution mode of the current subject limiting condition is set to the non-sampling mode; the non-sampling mode means that all individuals in the subject are tested when the current subject executes the corresponding limiting condition;

[0044] The specific steps of sampling type analysis in step 2 are as follows:

[0045] Analyze the samples corresponding to the limited conditions of the current subject, collect the characteristic data of the samples, and form the collected sample characteristic data into a sample characteristic set; the characteristic data is represented by the characteristic parameters of the sample and the subject, such as: if the sample is a red ball, its characteristic data is red, spherical and other related parameters, and if the subject is a color ball, its characteristic data is red, yellow and other related parameters;

[0046] The subject is divided into i sub-bodies, and then the feature data of each sub-bodies is collected, and the collected sub-body feature data is combined into i sub-body feature sets; the sample feature set is compared with the sub-body feature set: if the sample feature set and the sub-body feature set have the same feature data, the corresponding sub-body is marked as the target group; if the sample feature set and the sub-body feature set do not have the same feature data, the corresponding sub-body is marked as the non-target group;

[0047] Obtain the ratio of the corresponding number of the target group to the number of subjects and the number of intersections between the sub-body feature set corresponding to the target group and the sample feature set, and compare the ratio of the corresponding number of the target group to the number of subjects and the number of intersections between the sub-body feature set corresponding to the target group and the sample feature set with the ratio threshold and the intersection threshold respectively:

[0048] If the ratio of the corresponding number of the target group to the number of subjects does not exceed the number ratio threshold, or the number of intersections between the sub-body feature set corresponding to the target group and the sample feature set does not exceed the intersection number threshold, then the error risk of sampling the current target group is determined to be high, a high-risk execution signal is generated and the high-risk execution signal is sent to the executor's mobile terminal; if the ratio of the corresponding number of the target group to the number of subjects exceeds the number ratio threshold and the number of intersections between the sub-body feature set corresponding to the target group and the sample feature set exceeds the intersection number threshold, then the error risk of sampling the current target group is determined to be low, a low-risk execution signal is generated and the low-risk execution signal is sent to the executor's mobile terminal;

[0049] The specific steps for sampling type analysis in step 3 are as follows:

[0050] After the target group and the sampling sample in the subject are determined, the sampling type is judged, and the sampling sample is sampled within the selected target group. When the sampling process is executed under the current limiting conditions, the number of individual types corresponding to the sampling sample drawn from the selected target group is selected, and the corresponding number of individual types is marked as the sampling result object. If the number of types of the corresponding sampling result object fluctuates, the corresponding sampling type is set to exploratory sampling; if the number of types of the corresponding sampling result object does not fluctuate, the corresponding sampling type is set to descriptive sampling; the sampling result object in this application is taken as an example: voting for class cadres and sampling tickets. The target group is the student's corresponding voting name, and the sample is the class cadre name. In the sampling process of the class cadre name, the corresponding sampling name will increase with sampling, so this type is exploratory sampling;

[0051] The specific steps for analyzing the sampling results in step 4 are as follows:

[0052] Analyze the sampling result object of the sample sampling, set the sampling result object to be labeled o, where o is a natural number greater than 1, and collect the sampling hard parameters of the corresponding sampling result object. The sampling hard parameters represent the hard information of the sampling. For example, in the voting sampling process, the sampling hard parameters represent the time and place of voting;

[0053] The sampling result objects are matched with the individuals in the target group in pairs, and they are marked as matching individual groups. The sampling hard parameters of the matching individual groups in the current sampling process are analyzed. If the corresponding data in the sampling hard parameters of the matching individual groups are consistent, the matching individual groups are marked as risky individual groups; if the corresponding data in the sampling hard parameters of the matching individual groups are not consistent, the matching individual groups are marked as safe individual groups.

[0054] The risky individual groups and the safe individual groups are analyzed. If the number of risky individual groups exceeds the number of safe individual groups, and the number of risky individual groups is not less than the threshold number of risky individual groups, it is determined that there is an error in the current sampling result, an error signal is generated and the error signal is sent to the executor's mobile terminal; if the number of risky individual groups does not exceed the number of safe individual groups, and the number of risky individual groups is less than the threshold number of risky individual groups, it is determined that there is no error in the current sampling result, an accurate signal is generated and the accurate signal is sent to the executor's mobile terminal.

[0055] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions;

[0056] When the present invention is in use, it analyzes according to the real-time sample sampling demand, collects the subject and sample corresponding to the current sample sampling process, analyzes the current subject cardinality, and thus determines whether sample sampling is needed; analyzes the current subject and selects a suitable target group within the subject, and after the target group is determined within the current subject, analyzes the corresponding target group, and analyzes the error risk corresponding to the sample sampling of the current target group; after the target group is determined, analyzes the current sampling type, and determines the sampling type based on the properties of the sample sampling corresponding to the real-time target group; after the sampling type is determined and the sample sampling of the current real-time target group is completed, the corresponding sample sampling results are feature compared, and thus determines whether there is an error in the current sampling result.

[0057] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A sample sampling analysis algorithm based on the Internet of Things and big data, characterized in that: The specific steps of sample sampling and analysis are as follows: Step 1: Real-time cardinality analysis: Analyze according to the real-time sample sampling requirements, collect the subjects and samples corresponding to the current sample sampling process, analyze the current subject cardinality, and determine whether sample sampling is required; the real-time cardinality analysis process in step 1 is as follows: Obtain the limiting conditions for the analysis of the current subject, which are expressed as the conditions for the subject analysis; perform real-time cardinality analysis on the current subject, collect the total number of individuals involved in the current subject under the limiting conditions and the average time consumed when the corresponding individuals execute the limiting conditions, and mark the total number of individuals involved in the current subject under the limiting conditions and the average time consumed when the corresponding individuals execute the limiting conditions as ZSL and PHS respectively; By formula Obtain the corresponding cardinality analysis coefficient JS of the subject under the current limiting conditions, where a1 and a2 are both preset proportional coefficients, and a1>a2>0, and β is the error correction factor, which is 0.98; Compare the corresponding cardinality analysis coefficient JS of the subject under the current limiting conditions with the corresponding cardinality analysis coefficient threshold: If the corresponding cardinality analysis coefficient JS of the subject under the current limiting condition exceeds the corresponding cardinality analysis coefficient threshold, it is determined that the current subject needs sample sampling to execute the corresponding limiting condition, a sampling execution signal is generated, and the execution mode of the current subject limiting condition is set to the sampling mode; If the corresponding cardinality analysis coefficient JS of the subject under the current limiting condition does not exceed the corresponding cardinality analysis coefficient threshold, it is determined that the current subject does not need sample sampling to execute the corresponding limiting condition, a non-sampling execution signal is generated, and the execution mode of the current subject limiting condition is set to non-sampling mode; Step 2: Target group analysis: Analyze the current subject and select the appropriate target group within the subject. After determining the target group within the current subject, analyze the corresponding target group and analyze the sampling error risk of the current target group. Step 3: Sampling type analysis: After determining the target group, analyze the current sampling type and determine the sampling type based on the nature of the sample sampling corresponding to the real-time target group; Step 4: Sampling result analysis: After the sampling type is determined and the current real-time target group has completed the sample sampling, the corresponding sample sampling results are compared with each other to determine whether there is any error in the current sampling result.

2. The sample sampling analysis algorithm based on the Internet of Things and big data according to claim 1 is characterized in that: The specific steps of sampling type analysis in step 2 are as follows: Analyze the samples corresponding to the limited conditions of the current subject, collect the characteristic data of the samples, and form the collected sample characteristic data into a sample characteristic set; the characteristic data is expressed as characteristic parameters of the sample and the subject; divide the subject into i sub-bodies, then collect the characteristic data of each sub-bodies, and form the collected sub-body characteristic data into i sub-body characteristic sets; compare the sample characteristic set with the sub-body characteristic set: if the sample characteristic set and the sub-body characteristic set have the same characteristic data, the corresponding sub-body is marked as the target group; if the sample characteristic set and the sub-body characteristic set do not have the same characteristic data, the corresponding sub-body is marked as a non-target group; Obtain the ratio of the corresponding number of the target group to the number of subjects and the number of intersections between the sub-body feature set corresponding to the target group and the sample feature set, and compare the ratio of the corresponding number of the target group to the number of subjects and the number of intersections between the sub-body feature set corresponding to the target group and the sample feature set with the ratio threshold and the intersection threshold respectively: If the ratio of the corresponding number of the target group to the number of subjects does not exceed the number ratio threshold, or the intersection number of the corresponding sub-body feature set of the target group and the sample feature set does not exceed the intersection number threshold, then the error risk of sampling the current target group is determined to be high, and a high-risk execution signal is generated and sent to the executor's mobile terminal; If the ratio of the corresponding number of the target group to the number of subjects exceeds the number ratio threshold and the intersection number of the sub-body feature set corresponding to the target group and the sample feature set exceeds the intersection number threshold, then it is determined that the error risk of sampling the current target group is low, and a low-risk execution signal is generated and sent to the executor's mobile terminal.

3. The sample sampling analysis algorithm based on the Internet of Things and big data according to claim 1 is characterized in that: The specific steps for sampling type analysis in step 3 are as follows: After the target group and sampling samples within the subject are determined, the sampling type is judged, and the sampling samples are sampled within the selected target group. During the sampling process, the number of individual categories corresponding to the sampling samples drawn within the selected target group is obtained when the current limiting conditions are obtained, and the corresponding number of individual categories is marked as the sampling result object. If the number of categories of the corresponding sampling result object fluctuates, the corresponding sampling type is set to exploratory sampling; if the number of categories of the corresponding sampling result object does not fluctuate, the corresponding sampling type is set to descriptive sampling.

4. The sample sampling analysis algorithm based on the Internet of Things and big data according to claim 1 is characterized in that: The specific steps for analyzing the sampling results in step 4 are as follows: Analyze the sampling result object of the sample sampling, set the sampling result object to be labeled o, where o is a natural number greater than 1, and collect the sampling hard parameters of the corresponding sampling result object, where the sampling hard parameters are represented as the hard information of the sampling; The sampling result objects are matched with the individuals in the target group in pairs, and they are marked as matching individual groups. The sampling hard parameters of the matching individual groups in the current sampling process are analyzed. If the corresponding data in the sampling hard parameters of the matching individual groups are consistent, the matching individual groups are marked as risky individual groups; if the corresponding data in the sampling hard parameters of the matching individual groups are not consistent, the matching individual groups are marked as safe individual groups. The risky individual groups and the safe individual groups are analyzed. If the number of risky individual groups exceeds the number of safe individual groups, and the number of risky individual groups is not less than the threshold number of risky individual groups, it is determined that there is an error in the current sampling result, an error signal is generated and the error signal is sent to the executor's mobile terminal; if the number of risky individual groups does not exceed the number of safe individual groups, and the number of risky individual groups is less than the threshold number of risky individual groups, it is determined that there is no error in the current sampling result, an accurate signal is generated and the accurate signal is sent to the executor's mobile terminal.

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