Biological environment monitoring data management system based on Internet of Things

Abstract

The invention discloses a biological environment monitoring data management system based on the Internet of Things, and relates to the technical field of monitoring data management. Comprising the steps that a data cleaning module collects original data through the Internet of Things, and primary data is obtained after data cleaning; the demand screening module obtains data demands of users and deletes completely useless elementary data to obtain medium data; the truth judgment module extracts biological environment information according to the medium data, judges whether the medium data is truth or not, and obtains a truth judgment result; the traceability judgment module obtains the importance degree of medium data and judges whether data traceability is carried out or not in combination with a real judgment result; the data tracing module performs data tracing on the medium data, and performs screening and deletion after tracing to obtain high-level data; and the data storage module stores the higher data to obtain a biological environment monitoring database. According to the invention, the accuracy of biological environment monitoring data management based on the Internet of Things is improved.

Description

Technical Field

[0001] The present application relates to the technical field of monitoring data management, and in particular to a biological environment monitoring data management system based on the Internet of Things. Background Art

[0002] Bio-environment monitoring data management refers to the use of modern information technology and database technology to effectively manage various types of data generated during the bio-environment monitoring process. These data usually include data on the response of organisms to environmental pollutants, data on changes in the structure of biological communities, and data on the accumulation of pollutants in organisms. Through effective data management, the accuracy, integrity and accessibility of data can be ensured, providing a scientific basis for environmental protection and decision-making. However, due to the variability of the biological environment and the non-standardization of monitoring methods, not all data in the bio-environment monitoring data are valid data, and the existing technology is difficult to filter out useless data through conventional data cleaning, which brings inconvenience to the use of bio-environment monitoring data. Summary of the invention

[0003] The purpose of the present invention is to provide a biological environment monitoring data management system based on the Internet of Things to solve the problems raised in the above background technology.

[0004] This application provides a biological environment monitoring data management system based on the Internet of Things, which adopts the following technical solutions: The data cleaning module collects biological environment monitoring data through the Internet of Things and records it as raw data. After data cleaning, the raw data is obtained to obtain primary data; The demand screening module obtains the user's data requirements, deletes completely useless primary data according to the user's data requirements, and obtains medium data; A truth judgment module extracts biological environment information based on the medium data, judges whether the medium data is true based on the biological environment information, and obtains a truth judgment result; The traceability judgment module obtains the importance of medium data according to user needs, and determines whether to perform data traceability based on the actual judgment result to obtain the traceability judgment result; The data tracing module traces the medium-level data according to the tracing judgment results, and then screens and deletes them to obtain the high-level data; The data storage module stores the advanced data to obtain the biological environment monitoring database.

[0005] Preferably, the steps of extracting biological environment information based on the medium data, judging whether the medium data is true based on the biological environment information, and obtaining a true judgment result are specifically: Extract biological environmental information from medium-sized data and determine whether the biological environmental information violates biological laws; If the biological environment information does not violate biological laws, then obtain the historical information database corresponding to the biological environment information; Determine whether the biological environment information appears in the historical information database. If the biological environment information appears in the historical information database, find the corresponding historical information and record it as reference information. Obtain the similarity of the control information, and obtain the data authenticity based on the similarity evaluation of the control information; If the biological environment information does not appear in the historical information database, the occurrence probability of the biological environment information is obtained and the occurrence probability is used as the data authenticity; If the biological environment information violates the biological laws, the medium data is judged to be erroneous data and the truthfulness is 0.

[0006] Preferably, the step of obtaining the similarity of the control information and evaluating the authenticity of the data according to the similarity of the control information is specifically as follows: Compare the similarities between all the control information, calculate the average of the similarities, and obtain the standard similarity; The average similarity between the biological environment information and all control information was compared and recorded as information similarity; Obtain the total number of occurrences of the control information and record it as the total amount of information; Obtain the monitoring location corresponding to the biological environment information, find the ratio of the number of control information at the monitoring location to the total amount of information, and record it as the location occurrence frequency; The data authenticity is obtained by combining standard similarity, information similarity, total amount of information and frequency of occurrence.

[0007] Preferably, the step of obtaining the importance of the medium data according to the user's needs, determining whether to perform data tracing in combination with the real judgment result, and obtaining the tracing judgment result is specifically as follows: Obtain user needs and extract the user's research objectives based on the user needs; Determine whether the biological environment information is the biological environment information of the research target, if not, extract the monitoring target corresponding to the biological environment information; Obtain the correlation between the monitoring target and the research target and record it as the target correlation; If it is the biological environment information of the research target, the target relevance is the maximum value, and the information weight ratio of the target relevance and the data authenticity is set respectively, and the data traceability is calculated according to the information weight ratio; Determine whether the data authenticity of the medium data is 0. If the data authenticity is 0, do not perform data tracing and delete the medium data. If the data authenticity of the medium data is not 0, then a authenticity threshold is set to determine whether the data authenticity of the medium data reaches the authenticity threshold; If the authenticity threshold is reached, the medium data will not be traced and recorded as high-level data; If the authenticity threshold is not reached, a data traceability threshold is set, and medium data whose data traceability reaches the data traceability threshold is traced, and medium data that does not reach the data traceability threshold is not traced and is deleted.

[0008] Preferably, the step of obtaining the correlation between the monitoring target and the research target and recording it as the target correlation is specifically as follows: Determine whether the monitoring target and the research target are in a predator-prey relationship. If so, distinguish the predator target and the prey target from the monitoring target and the research target; Obtain the prey records of the prey target, count the frequency of the prey target in the prey records and record it as the target relevance; If it is not a predator-prey relationship, then determine whether the monitoring target and the research target are in a symbiotic relationship. If they are a symbiotic relationship, obtain the symbiotic frequency as the target association degree; If it is not a symbiotic relationship, determine whether there is a predator-prey biological chain between the monitoring target and the research target; If there is a predator-prey chain, obtain the difference between the trophic level of the monitoring target and the trophic level of the research target; The niche overlap between the monitoring target and the research target was obtained, and the target correlation was obtained by combining the grade difference.

[0009] Preferably, the step of tracing back the medium data according to the tracing judgment result, and filtering and deleting the high-level data after tracing back is specifically as follows: Tracing back the data source of the intermediate data, and filtering the intermediate data according to the data source to obtain the first data; Tracing back the storage method of the intermediate data, and filtering the first data according to the storage method to obtain the second data; Trace the usage of the medium data and filter the second data according to the usage to obtain the high-level data.

[0010] Preferably, the step of tracing the data source of the intermediate data and filtering the intermediate data according to the data source to obtain the first data is specifically: Tracing the data sources of medium-level data, including monitoring equipment, monitoring personnel, monitoring locations and monitoring time points; Extract the data accuracy of the medium data and determine whether the monitoring equipment has reached the data accuracy. If the data accuracy is not reached, delete the medium data; If the data accuracy is achieved, the personnel monitoring error rate of the monitoring personnel is obtained; Obtain historical monitoring data of the monitoring location, calculate the monitoring error rate of the historical monitoring data and record it as the location monitoring error rate; Obtain the biological information type of the medium data, calculate the monitoring error rate of the biological information type at the monitoring time point and record it as the time monitoring error rate; The monitoring weight ratios of the personnel monitoring error rate, the location monitoring error rate and the time monitoring error rate are respectively set, and the information monitoring error rate is calculated according to the monitoring weight ratios; According to the information monitoring error rate, medium data reaching a preset error rate threshold is screened and deleted to obtain first data.

[0011] Preferably, the storage method of the traceable medium data, and the step of filtering the first data to obtain the second data according to the storage method, are specifically: Trace the storage method of medium data and calculate the data loss rate of storage method; Count the storage duration of medium-sized data storage methods and combine it with the data loss rate to get the data security; The second data is obtained by screening the first data according to the data security level.

[0012] Preferably, the step of tracing back the usage of the medium data and filtering the second data to obtain the high-level data according to the usage is specifically: Track the usage of medium data, including the team, project, and frequency of use; Get the highest authority of the user team and calculate the average project size of the used projects; Obtain data monitored at the same time, by the same team, and in the same batch as the medium data as a complete set of monitoring data; Extract the usage frequency of the entire set of monitoring data according to the usage frequency and record it as the entire set of usage frequency; The ratio of the usage frequency of the medium data to the usage frequency of the whole set is extracted and recorded as the data usage frequency; The data usage value is obtained by combining the highest authority, average project size value, whole set usage frequency and data usage frequency; The second data is filtered according to the data usage value to obtain the higher-level data.

[0013] In summary, the present application includes at least one of the following beneficial technical effects: 1. The data cleaning module collects raw data through the Internet of Things, and obtains primary data after data cleaning. The demand screening module deletes completely useless primary data according to user data requirements to obtain medium data. The truth judgment module judges whether the medium data is true based on the biological environment information and obtains the true judgment result. The traceability judgment module determines whether to trace the data based on the importance of the medium data and the true judgment result to obtain the traceability judgment result. The data traceability module traces the source, storage method and usage of the medium data, and screens and deletes it to obtain high-level data. Finally, the data storage module stores the high-level data to obtain a biological environment monitoring database. It effectively reduces the interference of untrue and inaccurate data on users and improves the accuracy of biological monitoring environment data.

[0014] 2. Confirm the authenticity of the intermediate data based on whether the biological environment information extracted from the intermediate data violates biological laws and the similarity with the corresponding historical information. Combined with the target correlation between the monitoring target and the research target, confirm whether to trace the intermediate data. For the intermediate data that are not traceable, set it to be deleted or set it as high-level data, and trace the data. While ensuring the accuracy and authenticity of the data, reduce the waste of resources in the tracing process, and improve the environmental protection of the biological environment monitoring data management based on the Internet of Things.

[0015] 3. Generate different target correlations for the predator-prey relationship, symbiotic relationship, and predator-prey food chain relationship between the monitoring target and the research target, which is more in line with the actual situation of the monitoring target and the research target, and obtains more accurate results, which is conducive to the selection of traceability and subsequent data processing. It reduces the interference of invalid data, provides convenience for users to use biological environment monitoring data, and improves the convenience of biological environment monitoring data management based on the Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a module connection diagram of an embodiment of a biological environment monitoring data management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0017] Below is a combination of the embodiments and Figure 1 The present invention is further described in detail, but the embodiments of the present invention are not limited thereto.

[0018] The present invention discloses a biological environment monitoring data management system based on the Internet of Things, which specifically includes: The data cleaning module collects biological environment monitoring data through the Internet of Things and records it as raw data. After data cleaning, the raw data is obtained to obtain primary data.

[0019] Data cleaning is an important step in data processing. Its purpose is to identify and correct errors, duplications, outliers, and inconsistencies in the data set to improve the quality and reliability of the data. Data cleaning here means deleting data with obvious duplications, errors, and other basic problems, and performing simple data processing.

[0020] The demand screening module obtains the user's data requirements, deletes completely useless primary data according to the user's data requirements, and obtains intermediate data.

[0021] For example, if the user needs to study air pollution, then the heavy metals and pesticide residues in the soil are not related to air pollution monitoring, so this type of data will be deleted. If the user only wants to study the air pollution in area A, the air data in area B, which is far away, is useless for the study and also needs to be deleted.

[0022] The real judgment module obtains biological environment information based on the medium data extraction, judges whether the medium data is real based on the biological environment information, and obtains a real judgment result.

[0023] The traceability judgment module obtains the importance of medium data according to user needs, and determines whether to perform data traceability based on the actual judgment results to obtain the traceability judgment results.

[0024] The data tracing module traces the medium-level data according to the tracing judgment results, and then screens and deletes them to obtain the high-level data.

[0025] The data storage module stores the advanced data to obtain the biological environment monitoring database.

[0026] In practical application, IoT refers to a network system that connects various physical devices, sensors, software and other technologies through the Internet, enabling them to communicate and exchange data with each other. Monitoring biological environmental data has become more convenient through the Internet of Things, but it is also difficult to distinguish the authenticity of the data. The application of false data will cause serious troubles to projects and results. Therefore, confirming and processing the data and extracting more useful and accurate data can improve the accuracy of biological environmental monitoring data, reduce the process of data screening by users, and improve the convenience of applying biological environmental monitoring data.

[0027] The steps of extracting biological environment information according to the medium data, judging whether the medium data is true according to the biological environment information, and obtaining a true judgment result are specifically as follows: Extract biological environmental information from medium-sized data and determine whether the biological environmental information violates biological laws.

[0028] For example, the biological environment information extracted is that a bird stays on a tree. This phenomenon is normal and does not violate biological laws.

[0029] If the biological environment information does not violate biological laws, then a historical information database corresponding to the biological environment information is obtained.

[0030] For example, if the biological environment information is that plant No. 1 is found in an arid area, then the corresponding historical information is whether plant No. 1 is found in an arid area.

[0031] It is determined whether the biological environment information appears in the historical information database. If the biological environment information appears in the historical information database, the corresponding historical information is found and recorded as the reference information.

[0032] The similarity of the control information is obtained, and the authenticity of the data is obtained based on the similarity evaluation of the control information.

[0033] If the biological environment information does not appear in the historical information database, the occurrence probability of the biological environment information is obtained and the occurrence probability is used as the data authenticity.

[0034] If biological environmental information does not appear in the historical information database, it means that this situation does not exist yet. However, this possibility cannot be ruled out due to reasons such as gene mutation. Therefore, the probability of occurrence is used as the authenticity of the data. These probabilities can be inferred based on current scientific knowledge and observations.

[0035] If the biological environment information violates the biological laws, the medium data is judged to be erroneous data and the truthfulness is 0.

[0036] In actual application, some biological environmental information violates biological laws, and it is considered that the data errors are caused by equipment, monitoring personnel errors, etc., so the authenticity is 0. Some biological environmental information has not yet been proven by historical records, but it may occur due to gene mutations, biological purification, etc., so the data authenticity is obtained based on the probability of occurrence. For example, if organism A grows in a high-temperature area for a long time, it may slowly evolve to be resistant to high temperatures, realizing biological phenomena that can also occur in high-temperature areas. Some biological environmental information has historical records, indicating that it is a normal biological phenomenon that can be discovered, so the authenticity is determined based on the control information. For example, there is a probability that four-leaf clover will appear in three-leaf clover, so the discovery of four-leaf clover is also a normal biological phenomenon, and it is necessary to judge whether the data is true.

[0037] The steps of obtaining the similarity of the control information and evaluating the authenticity of the data according to the similarity of the control information are as follows: Compare the similarities between all the control information, calculate the average of the similarities, and obtain the standard similarity.

[0038] Comparing the average similarity between all the control information can reflect whether the biological environment information is changeable. For example, if the biological environment information is that plant No. 1 was found at 25 degrees Celsius, and the control information is the temperature at which plant No. 1 was found, if the average similarity is lower, it means that plant No. 1 is more adaptable to temperature and can survive at both low and high temperatures.

[0039] The average similarity between the biological environment information and all control information is compared and recorded as information similarity.

[0040] Comparing the average similarity between the biological environment information and the control information can reflect whether the extracted real-time biological environment information is similar to the historical information.

[0041] The total number of occurrences of the control information is obtained and recorded as the total amount of information.

[0042] Obtain the monitoring location corresponding to the biological environment information, find the ratio of the number of control information at the monitoring location to the total amount of information, and record it as the location occurrence frequency.

[0043] The data authenticity is obtained by combining standard similarity, information similarity, total amount of information and frequency of occurrence.

[0044] The weight ratios of standard similarity, information similarity, total information and frequency of occurrence are set respectively, and the data authenticity is calculated based on the weight ratios.

[0045] In actual application, when the standard similarity is higher, it means that the biological environment information is more variable, the possibility of obtaining medium data is greater, and the authenticity of the medium data is higher. The higher the information similarity, the greater the possibility of the existence of medium data, and the higher the authenticity of the data. The larger the total amount of information and the greater the frequency of occurrence, the greater the possibility of the occurrence of the biological information, and therefore the greater the authenticity of the data. For example, sika deer have been photographed many times in place A, so the biological environment information is that there are sika deer in place A, and this information is very likely to be true. However, no sika deer has been photographed in place B, so the information that there are sika deer in place B is very likely to be false. Similarly, for example, penguins basically live in cold areas, so if the biological environment information is that penguins appear in high temperature areas, the information similarity is low, which means that the authenticity of the information is not high.

[0046] The importance of medium data is obtained according to user needs, and combined with the actual judgment results, it is determined whether to perform data tracing, and the steps to obtain the tracing judgment results are as follows: Obtain user needs and extract user research objectives based on user needs.

[0047] Biological environment monitoring mainly monitors the living environment of organisms on Earth, their distribution and activities. Because the biological environment is relatively large, users have their own research goals, such as sika deer, air pollution and other specific research goals.

[0048] Determine whether the biological environment information is the biological environment information of the research target. If it is not the biological environment information of the research target, extract the monitoring target corresponding to the biological environment information.

[0049] Sometimes, other data is needed to assist in the research process, so multi-target monitoring is carried out, and targets other than the research target are recorded as monitoring targets. For example, the research target is tigers, but because tigers eat prey such as rabbits, rabbits and other prey also need to be monitored, and these are monitoring targets.

[0050] Obtain the correlation between the monitoring target and the research target and record it as the target correlation.

[0051] If it is the biological environment information of the research target, the target relevance is the maximum value, and the information weight ratio of the target relevance and the data authenticity is set respectively, and the data traceability is calculated according to the information weight ratio.

[0052] If the biological environment information is the information of the research target itself, then this information is definitely needed, so the target relevance of this information is considered to be the maximum value.

[0053] It is determined whether the data authenticity of the medium data is 0. If the data authenticity is 0, data tracing is not performed and the medium data is deleted.

[0054] If the data authenticity is 0, it means that the data is wrong data or false data, and no further confirmation and verification is required, so it can be deleted directly.

[0055] If the data authenticity of the medium data is not 0, a authenticity threshold is set to determine whether the data authenticity of the medium data reaches the authenticity threshold.

[0056] If the authenticity threshold is reached, the medium data will not be traced and will be recorded as high-level data.

[0057] If the data authenticity reaches the authenticity threshold, it means that the data credibility is high, so there is no need to verify and confirm again, and it can be directly recorded as high-level data.

[0058] If the authenticity threshold is not reached, a data traceability threshold is set, and medium data whose data traceability reaches the data traceability threshold is traced, and medium data that does not reach the data traceability threshold is not traced and is deleted.

[0059] In actual use, if the authenticity threshold is not reached, the need for tracing is determined based on the data traceability. Some data have low authenticity and low target relevance, so they are not very useful for research and can be discarded. Therefore, there is no need to waste time tracing and can be deleted directly. Some data have low authenticity but high target relevance, so they are more useful to users, so tracing confirmation is performed to confirm whether the data is real and available. If it is real and available, it will not be deleted.

[0060] The steps to obtain the correlation between the monitoring target and the research target and record it as the target correlation are as follows: Determine whether the monitoring target and the research target are in a predator-prey relationship. If so, distinguish the predator target and the prey target from the monitoring target and the research target.

[0061] If the monitoring target and the research target are in a predator-prey relationship, it is necessary to distinguish who preys on whom. For example, if the monitoring target and the research target are rabbits and tigers, the predator is the tiger, that is, the prey target, and the rabbit is the prey target.

[0062] The prey records of the prey target are obtained, and the frequency of the prey target in the prey records is counted and recorded as the target relevance.

[0063] In the predator-prey relationship, since the prey of the predator target is usually diverse, the more prey targets are preyed, the higher the target association. For example, the tiger has many prey, such as deer, sheep, wild boar, etc. The probability of a tiger preying on a wild boar is 40%, the probability of preying on a deer is 30%, and the probability of preying on a rabbit is 5%. When the prey target is a rabbit, the tiger does not take rabbit as its main food, so the association with the tiger is not as high as that with wild boar and deer.

[0064] If it is not a predator-prey relationship, determine whether the monitoring target and the research target are in a symbiotic relationship. If it is a symbiotic relationship, obtain the symbiotic frequency as the target association degree.

[0065] Some organisms have symbiotic relationships with each other, which also have an impact on the research target. For example, cattle egrets form symbiotic relationships with a variety of large herbivores, such as cows, horses, and sheep. They hang on the backs of these animals or near them to obtain food, such as insects. Therefore, these large herbivores will affect cattle egrets, and the probability of cattle egrets coexisting with these animals is also different. Cattle egrets have a higher probability of coexisting with cattle, so the target relevance of cattle will be higher.

[0066] If it is not a symbiotic relationship, determine whether there is a predator-prey food chain between the monitoring target and the research target.

[0067] If there is a predator-food chain, the difference between the nutritional level of the monitored target and the nutritional level of the research target is obtained.

[0068] If there is a predator-prey chain between the targets, the targets and the organisms are also related. For example, if a cow eats grass and a tiger eats a cow, then the grass will also have an impact on the tiger's survival, so the grass data will also play a role. When the difference in nutritional level is greater, the correlation will be smaller.

[0069] The niche overlap between the monitoring target and the research target was obtained, and the target correlation was obtained by combining the grade difference.

[0070] Set the proportional coefficients of niche overlap and grade difference respectively, and calculate the target correlation according to the proportional coefficients. If there is no predator-prey chain, the niche overlap is used as the target correlation.

[0071] In practical application, niche overlap refers to the phenomenon that two or more species with similar niches share or compete for common resources when living in the same space. Niche overlap occurs when two species use the same resources or share a certain resource factor (such as food, nutrients, space, etc.). In this case, part of the space will be shared by the two niches. If the two species have exactly the same niche, it is called complete overlap. But in most cases, there will only be partial overlap between niches, that is, part of the resources are used together, while the other part is occupied by each species.

[0072] The degree of niche overlap is closely related to the competitive relationship between organisms. The higher the degree of niche overlap, the more intense the competition between two species may be, because they need to compete for the same resources. Organisms with competitive relationships will also affect their monitoring data and play an important role in research, so the target correlation is also relatively large.

[0073] According to the tracing judgment results, the medium data is traced back, and the steps of screening and deleting to obtain high-level data are as follows: Trace back the data source of the intermediate data, and filter the intermediate data according to the data source to obtain the first data.

[0074] The source of data can reflect the authenticity of the data. Tracing the source of the intermediate data that needs to be traced can screen out inaccurate and untrue data, thereby improving the accuracy of the data.

[0075] The storage method of the intermediate data is traced back, and the second data is obtained by filtering the first data according to the storage method.

[0076] When the data storage environment is unsafe and unreliable, the data will be illegally accessed or tampered with, thus affecting the authenticity of the data. If the data storage method is indeed unsafe, then for some medium-level data with low authenticity, tracing can be used to verify whether the data is safe and reliable.

[0077] Trace the usage of the medium data and filter the second data according to the usage to obtain the high-level data.

[0078] The usage of data can also reflect the authenticity of the data. The authenticity of the data can also be obtained through the usage of medium data, thereby screening the data.

[0079] In actual application, data tracing for some relatively important data with low authenticity can confirm the status of the verified data, thereby deleting some untrue and unreliable data, improving the accuracy of biological environment monitoring data, and providing convenience for users.

[0080] The steps of tracing the data source of the intermediate data and filtering the intermediate data according to the data source to obtain the first data are as follows: Trace the sources of medium-level data, including monitoring equipment, monitoring personnel, monitoring locations and monitoring time points.

[0081] The data accuracy of the medium data is extracted to determine whether the monitoring device has achieved the data accuracy. If the data accuracy has not been achieved, the medium data is deleted.

[0082] If the monitoring device does not achieve data accuracy, it means that the data is wrong or has been tampered with, and the data is false. For example, the data monitored by the device can only be accurate to two decimal places, but the actual data is accurate to three decimal places, which means that the data is not the data monitored by the monitoring device, or it has been tampered with later and is not the original real data. The data should be deleted.

[0083] If the data accuracy is achieved, the personnel monitoring error rate of the monitoring personnel is obtained.

[0084] Different monitors have different monitoring levels and experiences, so the error rates of monitoring data are also different.

[0085] The historical monitoring data of the monitoring location is obtained, and the monitoring error rate of the historical monitoring data is calculated and recorded as the location monitoring error rate.

[0086] The accuracy of some biological environmental data will change due to environmental changes. The error rate of monitoring will also vary when monitoring in different locations and environments. For example, the monitoring equipment is affected by the environment, the accuracy will be affected by the environment, and the error rate of monitoring will vary.

[0087] The biological information type of the medium data is obtained, and the monitoring error rate of the biological information type at the monitoring time point is counted and recorded as the time monitoring error rate.

[0088] The monitoring error rate of some biological environmental data will also be affected by time. For example, biological environmental data such as images are prone to monitoring errors during nighttime monitoring due to lighting issues, which increases the monitoring error rate.

[0089] The monitoring weight ratios of the personnel monitoring error rate, location monitoring error rate and time monitoring error rate are set respectively, and the information monitoring error rate is calculated according to the monitoring weight ratios.

[0090] According to the information monitoring error rate, medium data reaching a preset error rate threshold is screened and deleted to obtain first data.

[0091] In actual application, for some data with low authenticity, if monitoring errors are easy to occur during the monitoring process, the authenticity and reliability of the data will be even lower. Therefore, data with high monitoring error rates can be deleted to reduce the impact of inaccurate data.

[0092] Tracing back the storage method of the medium data, the steps of filtering the first data according to the storage method to obtain the second data are specifically as follows: Trace the storage method of medium data and count the data loss rate of the storage method.

[0093] The storage duration of medium-sized data is calculated and combined with the data loss rate to obtain the data security.

[0094] Set the proportional coefficients of storage duration and data loss rate respectively, and calculate the data security based on the proportional coefficients. The longer the storage duration, the greater the possibility of data loss, and therefore the lower the data security.

[0095] The second data is obtained by screening the first data according to the data security level.

[0096] In actual application, appropriate storage methods can ensure the integrity of biological environment monitoring data, that is, the data will not be destroyed or lost during the storage process. If the storage method is inappropriate, such as storage device failure, insufficient data backup, etc., it may cause data loss or damage, thus affecting the authenticity of the data. Tracing the storage method of biological environment monitoring data, the security of the data can be reflected by the storage method, so as to judge whether the data is reliable and true, and screen the data. Set the data security threshold, delete the medium data that does not reach the data security threshold, and obtain the second data.

[0097] The steps of tracing the usage of the medium data and filtering the second data to obtain the high-level data according to the usage are as follows: Track the usage of medium data, including the using team, using project and using frequency.

[0098] Get the highest authority of the using team and count the average project size of the using projects.

[0099] The degree of authority can be determined based on the beneficial effects of the user team and the number of people who follow it, while the project scale can be determined based on the number of project participants and the cost.

[0100] Obtain data monitored at the same time, by the same team, and in the same batch as the medium data as a complete set of monitoring data.

[0101] The usage frequency of the entire set of monitoring data is extracted based on the usage frequency and recorded as the entire set of usage frequency.

[0102] The ratio of the usage frequency of the medium data to the usage frequency of the whole set is extracted and recorded as the data usage frequency.

[0103] The data usage value is obtained by combining the highest authority, the average project size value, the frequency of use of the entire set, and the frequency of data use.

[0104] The second data is filtered according to the data usage value to obtain the higher-level data.

[0105] The highest authority, average project size, the ratio coefficient of the whole set usage frequency and the data usage frequency are set respectively, and the data usage value is calculated according to the ratio coefficient. The data usage value threshold is set, and the second data that does not reach the data usage value threshold is deleted to obtain high-level data.

[0106] In actual use, authoritative teams will check the data more carefully because of the authority of the information they release. Therefore, the higher the authority of the team, the lower the error rate, which means the data is more authentic and reliable. The larger the scale of the project, the greater the loss caused by using wrong data. Therefore, the data will be more strictly reviewed to ensure the authenticity and accuracy of the data. Therefore, the larger the scale, the higher the accuracy of the data. At the same time, the higher the frequency of use of the whole set of data in the same batch, the more the data is recognized by the public and the more authentic and reliable the data is. The higher the frequency of use of a single data in the whole set, the higher the user's trust in the data. For example, temperature, humidity and atmospheric data were monitored at the same time, but most users did not use temperature data, which means that temperature data may be flawed and inaccurate, while humidity data and atmospheric data are used more frequently and are more authentic and reliable.

[0107] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A biological environment monitoring data management system based on the Internet of Things, characterized in that: include: The data cleaning module collects biological environment monitoring data through the Internet of Things and records it as raw data. After data cleaning, the raw data is obtained to obtain primary data; The demand screening module obtains the user's data requirements, deletes completely useless primary data according to the user's data requirements, and obtains medium data; A truth judgment module extracts biological environment information based on the medium data, judges whether the medium data is true based on the biological environment information, and obtains a truth judgment result; The traceability judgment module obtains the importance of medium data according to user needs, and determines whether to perform data traceability based on the actual judgment result to obtain the traceability judgment result; The data tracing module traces the medium-level data according to the tracing judgment results, and then screens and deletes them to obtain the high-level data; The data storage module stores the advanced data to obtain the biological environment monitoring database.

2. According to the biological environment monitoring data management system based on the Internet of Things according to claim 1, it is characterized in that: The steps of extracting biological environment information based on the medium data, judging whether the medium data is true based on the biological environment information, and obtaining a true judgment result are specifically: Extract biological environmental information from medium-sized data and determine whether the biological environmental information violates biological laws; If the biological environment information does not violate biological laws, then obtain the historical information database corresponding to the biological environment information; Determine whether the biological environment information appears in the historical information database. If the biological environment information appears in the historical information database, find the corresponding historical information and record it as reference information. Obtaining the similarity of the control information, and evaluating the authenticity of the data based on the similarity of the control information; If the biological environment information does not appear in the historical information database, the occurrence probability of the biological environment information is obtained and the occurrence probability is used as the data authenticity; If the biological environment information violates the biological laws, the medium data is judged to be erroneous data and the truthfulness is 0.

3. The biological environment monitoring data management system based on the Internet of Things according to claim 2 is characterized in that: The step of obtaining the similarity of the control information and evaluating the authenticity of the data according to the similarity of the control information is specifically as follows: Compare the similarities between all the control information, calculate the average of the similarities, and obtain the standard similarity; The average similarity between the biological environment information and all control information was compared and recorded as information similarity; Obtain the total number of occurrences of the control information and record it as the total amount of information; Obtain the monitoring location corresponding to the biological environment information, find the ratio of the number of control information at the monitoring location to the total amount of information, and record it as the location occurrence frequency; The data authenticity is obtained by combining standard similarity, information similarity, total amount of information and frequency of occurrence.

4. The biological environment monitoring data management system based on the Internet of Things according to claim 3 is characterized in that: The steps of obtaining the importance of the medium data according to the user's needs, determining whether to perform data tracing in combination with the real judgment result, and obtaining the tracing judgment result are specifically: Obtain user needs and extract the user's research objectives based on the user needs; Determine whether the biological environment information is the biological environment information of the research target, if not, extract the monitoring target corresponding to the biological environment information; Obtain the correlation between the monitoring target and the research target and record it as the target correlation; If it is the biological environment information of the research target, the target relevance is the maximum value, and the information weight ratio of the target relevance and the data authenticity is set respectively, and the data traceability is calculated according to the information weight ratio; Determine whether the data authenticity of the medium data is 0. If the data authenticity is 0, do not perform data tracing and delete the medium data. If the data authenticity of the medium data is not 0, then a authenticity threshold is set to determine whether the data authenticity of the medium data reaches the authenticity threshold; If the authenticity threshold is reached, the medium data will not be traced and recorded as high-level data; If the authenticity threshold is not reached, a data traceability threshold is set, and medium data whose data traceability reaches the data traceability threshold is traced, and medium data that does not reach the data traceability threshold is not traced and is deleted.

5. The biological environment monitoring data management system based on the Internet of Things according to claim 4 is characterized in that: The step of obtaining the correlation between the monitoring target and the research target and recording it as the target correlation is specifically as follows: Determine whether the monitoring target and the research target are in a predator-prey relationship. If so, distinguish the predator target and the prey target from the monitoring target and the research target; Obtain the prey records of the prey target, count the frequency of the prey target in the prey records and record it as the target relevance; If it is not a predator-prey relationship, then determine whether the monitoring target and the research target are in a symbiotic relationship. If they are a symbiotic relationship, obtain the symbiotic frequency as the target association degree; If it is not a symbiotic relationship, determine whether there is a predator-prey biological chain between the monitoring target and the research target; If there is a predator-prey chain, obtain the difference between the trophic level of the monitoring target and the trophic level of the research target; The niche overlap between the monitoring target and the research target was obtained, and the target correlation was obtained by combining the grade difference.

6. The biological environment monitoring data management system based on the Internet of Things according to claim 5 is characterized in that: The steps of tracing back the medium data according to the tracing judgment result and filtering and deleting the high-level data after tracing back are specifically as follows: Tracing back the data source of the intermediate data, and filtering the intermediate data according to the data source to obtain the first data; Tracing back the storage method of the intermediate data, and filtering the first data according to the storage method to obtain the second data; Trace the usage of the medium data and filter the second data according to the usage to obtain the high-level data.

7. The biological environment monitoring data management system based on the Internet of Things according to claim 6 is characterized in that: The step of tracing the data source of the intermediate data and filtering the intermediate data according to the data source to obtain the first data is specifically as follows: Tracing the data sources of medium-level data, including monitoring equipment, monitoring personnel, monitoring locations and monitoring time points; Extract the data accuracy of the medium data and determine whether the monitoring equipment has reached the data accuracy. If the data accuracy is not reached, delete the medium data; If the data accuracy is achieved, the personnel monitoring error rate of the monitoring personnel is obtained; Obtain historical monitoring data of the monitoring location, calculate the monitoring error rate of the historical monitoring data and record it as the location monitoring error rate; Obtain the biological information type of the medium data, calculate the monitoring error rate of the biological information type at the monitoring time point and record it as the time monitoring error rate; The monitoring weight ratios of the personnel monitoring error rate, the location monitoring error rate and the time monitoring error rate are respectively set, and the information monitoring error rate is calculated according to the monitoring weight ratios; According to the information monitoring error rate, medium data reaching a preset error rate threshold is screened and deleted to obtain first data.

8. The biological environment monitoring data management system based on the Internet of Things according to claim 7 is characterized in that: The storage method of the tracing medium data, and the step of filtering the first data to obtain the second data according to the storage method, are specifically: Trace the storage method of medium data and calculate the data loss rate of storage method; Count the storage duration of medium-sized data storage methods and combine it with the data loss rate to get the data security; The second data is obtained by screening the first data according to the data security level.

9. The biological environment monitoring data management system based on the Internet of Things according to claim 8, characterized in that: The step of tracing back the usage of the medium data and filtering the second data according to the usage to obtain the high-level data is specifically as follows: Track the usage of medium data, including the team, project, and frequency of use; Get the highest authority of the user team and calculate the average project size of the used projects; Obtain data monitored at the same time, by the same team, and in the same batch as the medium data as a complete set of monitoring data; Extract the usage frequency of the entire set of monitoring data according to the usage frequency and record it as the entire set of usage frequency; The ratio of the usage frequency of the medium data to the usage frequency of the whole set is extracted and recorded as the data usage frequency; The data usage value is obtained by combining the highest authority, average project size value, whole set usage frequency and data usage frequency; The second data is filtered according to the data usage value to obtain the higher-level data.

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