CN117237145A - Sensor data processing method and system based on big data - Google Patents
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- CN117237145A CN117237145A CN202311508731.2A CN202311508731A CN117237145A CN 117237145 A CN117237145 A CN 117237145A CN 202311508731 A CN202311508731 A CN 202311508731A CN 117237145 A CN117237145 A CN 117237145A
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- 238000003860 storage Methods 0.000 description 7
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Abstract
The application discloses a sensor data processing method and system based on big data, and relates to the technical field of big data processing. The method comprises the following steps: registering the internet of things cluster in a cloud server, and performing security verification on the internet of things cluster by the cloud server; the cloud server collects sensor data uploaded by each Internet of things cluster; acquiring attribute characteristics affecting irrigation quantity from sensor data, and acquiring comprehensive irrigation attribute characteristics according to the attribute characteristics of each sensor; calculating a predicted soil index of the irrigation area according to the comprehensive irrigation attribute characteristics; comparing the predicted soil index with a preset threshold, if the predicted soil index is smaller than the preset threshold, sending an irrigation instruction to the corresponding internet of things cluster, otherwise, continuing to analyze the sensor data. By adopting the technical scheme of the application, the intelligent agricultural irrigation sensor data can be safely processed, the irrigation strategy can be accurately specified, the irrigation waste is reduced, and the method has remarkable effect on intelligent agricultural irrigation progress.
Description
Technical Field
The application relates to the technical field of big data processing, in particular to a sensor data processing method and system based on big data.
Background
Agricultural irrigation mainly refers to irrigation operation performed on agricultural cultivation areas. Agricultural irrigation methods can be generally classified into conventional ground irrigation, ordinary spray irrigation and micro irrigation. Ancient Chinese agricultural irrigation relies on precipitation and on ground rivers, and agricultural cultivation is mainly concentrated in areas with abundant precipitation and developed river water networks, and farmers in the areas often engage in agricultural production according to solar terms.
The intelligent agriculture is the current trend of agricultural management, and the intelligent agriculture is the combination of modern science and technology and agricultural planting, so that unmanned, automatic and intelligent management is realized.
However, the existing smart agriculture has reduced the drawbacks of the production by the day to a certain extent, but is still in a sprouting state in terms of safety and intelligence, and more remarkable effects on the smart agriculture are needed. Based on the method and the system, the application provides a sensor data processing method and a system based on big data, which are used for realizing intelligent irrigation in intelligent agriculture.
Disclosure of Invention
The application provides a sensor data processing method based on big data, which comprises the following steps:
the internet of things cluster is registered in a cloud server, the cloud server performs security verification on the internet of things cluster, and if the security verification is passed, the internet of things cluster is allowed to upload sensor data to the cloud server;
the cloud server collects sensor data uploaded by each Internet of things cluster;
acquiring attribute characteristics affecting irrigation quantity from sensor data, and acquiring comprehensive irrigation attribute characteristics according to the attribute characteristics of each sensor;
calculating a predicted soil index of the irrigation area according to the comprehensive irrigation attribute characteristics;
comparing the predicted soil index with a preset threshold, if the predicted soil index is smaller than the preset threshold, sending an irrigation instruction to the corresponding internet of things cluster, otherwise, continuing to analyze the sensor data.
According to the sensor data processing method based on the big data, the irrigation area is divided into the sub-area test fields, independent Internet of things clusters are arranged in each sub-area test field, the plurality of sensors are installed in the Internet of things clusters of each sub-area test field, and the plurality of sensors are used for carrying out overall data acquisition on the sub-area test fields.
The sensor data processing method based on big data, as described above, wherein the cloud server performs security assessment on the internet of things cluster and the transmission channel thereof, specifically includes:
collecting equipment risk feature data of sensors in all the Internet of things clusters and channel risk features of transmission channels;
calculating an Internet of things cluster risk value according to the sensor equipment risk feature data and channel risk features of the transmission channel;
comparing the risk value of the Internet of things cluster with a preset safety threshold, if the risk value of the Internet of things cluster is smaller than the preset safety threshold, passing the safety evaluation, otherwise, failing the safety evaluation.
The sensor data processing method based on big data, as described above, wherein the comprehensive irrigation attribute feature is calculated, specifically comprising the following sub-steps:
acquiring sensor data in t time;
eliminating deviation points in sensor data to obtain a sensor effective data set;
extracting attribute features affecting irrigation quantity from the effective data set of the sensor;
and carrying out fusion treatment on the attribute characteristics influencing the irrigation quantity according to the attributes to obtain the comprehensive characteristics of all the attributes.
According to the sensor data processing method based on the big data, the sensor data in the t time comprises the acquired data of the plurality of sensors uploaded by the sensor groups of each Internet of things cluster, the attribute characteristics of each sensor group are acquired, the attribute characteristics of each sensor group are subjected to fusion processing, the comprehensive attribute characteristics in the time are obtained, and the comprehensive attribute characteristics are formed.
A big data based sensor data processing system comprising: the internet of things cluster and the cloud server; the internet of things cluster is registered in a cloud server, the cloud server performs security verification on the internet of things cluster, and if the security verification is passed, the internet of things cluster is allowed to upload sensor data to the cloud server;
the cloud server specifically includes:
the acquisition module is used for acquiring the sensor data uploaded by each Internet of things cluster;
the predicted soil index calculation module is used for acquiring attribute characteristics affecting irrigation quantity from sensor data and obtaining comprehensive irrigation attribute characteristics according to the attribute characteristics of each sensor; calculating a predicted soil index of the irrigation area according to the comprehensive irrigation attribute characteristics;
and the comparison module is used for comparing the predicted soil index with a preset threshold value, sending an irrigation instruction to the corresponding internet of things cluster if the predicted soil index is smaller than the preset threshold value, and if not, continuing to analyze the sensor data.
According to the sensor data processing system based on big data, the irrigation area is divided into the sub-area test fields, the independent Internet of things clusters are arranged in each sub-area test field, the plurality of sensors are installed in the Internet of things clusters of each sub-area test field, and the plurality of sensors are used for carrying out overall data acquisition on the sub-area test fields.
The sensor data processing system based on big data as described above, wherein the cloud server performs security assessment on the internet of things cluster and the transmission channel thereof, specifically includes:
collecting equipment risk feature data of sensors in all the Internet of things clusters and channel risk features of transmission channels;
calculating an Internet of things cluster risk value according to the sensor equipment risk feature data and channel risk features of the transmission channel;
comparing the risk value of the Internet of things cluster with a preset safety threshold, if the risk value of the Internet of things cluster is smaller than the preset safety threshold, passing the safety evaluation, otherwise, failing the safety evaluation.
The sensor data processing system based on big data, wherein, calculate the comprehensive irrigation attribute characteristic, concretely include the following substeps:
acquiring sensor data in t time;
eliminating deviation points in sensor data to obtain a sensor effective data set;
extracting attribute features affecting irrigation quantity from the effective data set of the sensor;
and carrying out fusion treatment on the attribute characteristics influencing the irrigation quantity according to the attributes to obtain the comprehensive characteristics of all the attributes.
The sensor data processing system based on big data comprises the sensor data uploaded by each sensor group of each Internet of things cluster in t time, the attribute characteristics of each sensor group are obtained, the attribute characteristics of each sensor group are subjected to fusion processing, the comprehensive attribute characteristics in the time are obtained, and the comprehensive attribute characteristics are formed.
The beneficial effects of the application are as follows: by adopting the technical scheme of the application, the intelligent agricultural irrigation sensor data can be safely processed, the irrigation strategy can be accurately specified, the irrigation waste is reduced, and the method has remarkable effect on intelligent agricultural irrigation progress.
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For a clearer description of the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the description below are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art, wherein:
fig. 1 is a flowchart of a sensor data processing method based on big data according to an embodiment of the present application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be clear and complete, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
As shown in fig. 1, an embodiment of the present application provides a sensor data processing method based on big data, which is applied in the field of intelligent agriculture and is used for analyzing agricultural irrigation. The sensor data processing method based on big data specifically comprises the following steps:
step 110, registering the internet of things cluster in a cloud server, performing security verification on the internet of things cluster by the cloud server, and if the security verification is passed, allowing the internet of things cluster to upload sensor data to the cloud server;
dividing the irrigation area into a plurality of sub-area test fields, setting an independent Internet of things cluster in each sub-area test field, installing a plurality of sensors in the Internet of things cluster of each sub-area test field, and carrying out comprehensive data acquisition and the like on the sub-area test fields by adopting the plurality of sensors. Because the area of the test field is larger, and in order to improve the accurate control of soil irrigation, N soil water quantity measuring sensors are correspondingly arranged in the test field. And each internet of things cluster gathers the sensor data in the corresponding subarea, and the control center uploads the sensor data to the cloud server for sensor data analysis.
Specifically, before each internet of things cluster sends data to a cloud server through the internet of things, the cloud server performs security assessment on the internet of things cluster and a transmission channel thereof, and specifically includes:
and step 111, collecting equipment risk feature data and channel risk features of transmission channels of sensors in all the Internet of things clusters.
The device risk profile data includes: the number of vulnerabilities, hardware security risk values, software security risk values, network security risk values, etc. The channel risk features include malicious codes carried in communication data, gateway hardware security risk values, gateway software security risk values, network security risk values and the like, and the transmission channel can have various forms, such as a wireless transmission way, a limited transmission way and the like.
And step 112, calculating the risk value of the Internet of things cluster according to the risk characteristic data of the sensor equipment and the channel risk characteristic of the transmission channel.
Specifically, the formula is adoptedAn internet of things cluster risk value, wherein +.>Representing an Internet of things cluster risk value; />Representing the influence weight of sensor equipment risks on the cluster risks of the internet of things, and +.>The influence weight of the transmission channel risk on the cluster risk of the Internet of things is represented; />The j-th equipment risk characteristic data of the i-th sensor equipment are represented, wherein the value of i is 1 to n, n is the total number of the sensor equipment in the internet of things cluster, the value of j is 1 to m, and m is the total number of the equipment risk characteristics; />The influence factor of the j-th equipment risk characteristic data of the i-th sensor equipment on the risk value of the internet of things cluster is represented; />The method comprises the steps that the risk characteristics of a channel B of a transmission channel a are represented, the value of a is 1 to A, A is the number of channels with the selected channel risk smaller than the preset risk, A is less than or equal to n, the value range of B is 1 to B, B is the total number of channel risk characteristics corresponding to the channels with the selected channel risk smaller than the preset risk, and B is less than or equal to m; />Is a preset risk; />And the influence factor of the risk characteristic of the b-th channel of the a-th transmission channel on the risk value of the Internet of things cluster is represented.
And 113, comparing the risk value of the internet of things cluster with a preset safety threshold, if the risk value of the internet of things cluster is smaller than the preset safety threshold, passing the safety evaluation, otherwise, failing the safety evaluation.
According to the method, the security evaluation is carried out on the Internet of things cluster connected with the cloud server, so that the security of the area of the Internet of things is guaranteed, the security and the reliability of the sensor data are improved, and the transmitted sensor data are prevented from being stolen or tampered.
The sensor data processing method based on big data specifically comprises the following steps:
step 120, the cloud server collects sensor data uploaded by each Internet of things cluster;
130, acquiring attribute characteristics affecting irrigation quantity from sensor data, and acquiring comprehensive irrigation attribute characteristics according to the attribute characteristics of each sensor;
specifically, the comprehensive irrigation attribute characteristic is calculated, and the method specifically comprises the following substeps:
step 131, acquiring sensor data in t time;
as an embodiment of the present application, in order to reduce the data processing amount of the cloud server, the cloud server acquires sensor data for a period of time t after several times of last irrigation, for example, when the time of last irrigation is a, after three days (the value can be set) after the time a, starts to acquire sensor data within two hours after two days.
Step 132, eliminating deviation points in sensor data to obtain a sensor effective data set;
specifically, the sensor data are arranged in time series, and the index of each sensor data is markedCalculating the overall deviation value of the sensor time seriesN is the sequence->Is a function of the number of data items. If the integral deviation value R is smaller than the preset value, the sensor time sequence is indicated to have no deviation point, and if the integral deviation value R is larger than the preset value, the whole time is continuously searchedThe point of deviation of the sequences. And carrying out multi-section segmentation on the time sequences, calculating the local deviation value of each local time sequence, searching the local time sequence larger than a preset value, and finding the deviation point in the local time sequence according to the trend of the sequence from the local time sequence.
Step 133, extracting attribute features affecting irrigation quantity from the sensor effective data set;
step 134, carrying out fusion processing on attribute characteristics influencing irrigation quantity according to attributes to obtain comprehensive characteristics of all the attributes;
the sensor data in the t time comprises acquisition data of a plurality of sensors uploaded by each sensor group of each Internet of things cluster, the attribute characteristics of each sensor group are obtained, the attribute characteristics of each sensor group are fused, the comprehensive attribute characteristics in the t time are obtained, and the comprehensive attribute characteristic set is formed.
Step 140, calculating a predicted soil index of the irrigation area according to the comprehensive irrigation attribute characteristics;
in the embodiment of the application, a formula is adoptedCalculating a predicted soil index of the irrigation area, i.e. a predicted water content of the soil, wherein +_>Normalized values for the kth integrated irrigation attribute feature; />The influence weight of the kth comprehensive irrigation attribute characteristic on the soil index is given; the value of K is 1 to K, and K is the total number of comprehensive irrigation attribute characteristics; />The predicted deviation amount for the kth integrated irrigation attribute feature.
And 140, comparing the predicted soil index with a preset threshold, if the predicted soil index is smaller than the preset threshold, sending an irrigation instruction to the corresponding internet of things cluster, otherwise, continuing to analyze the sensor data.
As a specific embodiment of the application, if the calculated predicted soil index is smaller than a preset threshold value (the value can be set as a corresponding threshold value according to the type of the plant), the soil moisture content of the current test field is lower than a normal level, irrigation treatment is needed, the irrigation quantity is the preset threshold value minus the predicted moisture content of the soil, a signal needing to be irrigated is sent to a control center, the control center responds to the received signal needing to be irrigated and sends an irrigation instruction to irrigation equipment, and the irrigation equipment is requested to irrigate the soil according to the corresponding irrigation quantity.
Example two
The second embodiment of the application provides a sensor data processing system based on big data, comprising: the internet of things cluster and the cloud server; the internet of things cluster is registered in a cloud server, the cloud server performs security verification on the internet of things cluster, and if the security verification is passed, the internet of things cluster is allowed to upload sensor data to the cloud server; dividing the irrigation area into a plurality of sub-area test fields, setting an independent Internet of things cluster in each sub-area test field, installing a plurality of sensors in the Internet of things cluster of each sub-area test field, and carrying out comprehensive data acquisition on the sub-area test fields by adopting the plurality of sensors.
The cloud server specifically includes:
the acquisition module is used for acquiring the sensor data uploaded by each Internet of things cluster;
the predicted soil index calculation module is used for acquiring attribute characteristics affecting irrigation quantity from sensor data and obtaining comprehensive irrigation attribute characteristics according to the attribute characteristics of each sensor; calculating a predicted soil index of the irrigation area according to the comprehensive irrigation attribute characteristics;
and the comparison module is used for comparing the predicted soil index with a preset threshold value, sending an irrigation instruction to the corresponding internet of things cluster if the predicted soil index is smaller than the preset threshold value, and if not, continuing to analyze the sensor data.
The cloud server firstly carries out security assessment on the internet of things cluster and a transmission channel thereof, and specifically comprises the following steps:
collecting equipment risk feature data of sensors in all the Internet of things clusters and channel risk features of transmission channels;
calculating an Internet of things cluster risk value according to the sensor equipment risk feature data and channel risk features of the transmission channel;
comparing the risk value of the Internet of things cluster with a preset safety threshold, if the risk value of the Internet of things cluster is smaller than the preset safety threshold, passing the safety evaluation, otherwise, failing the safety evaluation.
The comprehensive irrigation attribute characteristic is calculated, and the method specifically comprises the following substeps:
acquiring sensor data in t time;
eliminating deviation points in sensor data to obtain a sensor effective data set;
extracting attribute features affecting irrigation quantity from the effective data set of the sensor;
and carrying out fusion treatment on the attribute characteristics influencing the irrigation quantity according to the attributes to obtain the comprehensive characteristics of all the attributes.
Specifically, the sensor data in the t time includes collected data of a plurality of sensors uploaded by each sensor group of each internet of things cluster, attribute characteristics of each sensor group are obtained, the attribute characteristics of each sensor group are fused, comprehensive attribute characteristics in the time are obtained, and a comprehensive attribute characteristic set is formed.
Corresponding to the above embodiments, an embodiment of the present application provides a sensor data processing device based on big data, including: at least one memory and at least one processor;
the memory is used for storing one or more program instructions;
a processor for executing one or more program instructions for performing a sensor data processing method based on big data.
In accordance with the embodiments described above, embodiments of the present application provide a computer-readable storage medium having one or more program instructions embodied therein for execution by a processor of a sensor data processing method based on big data.
The disclosed embodiments provide a computer readable storage medium having stored therein computer program instructions which, when run on a computer, cause the computer to perform a big data based sensor data processing method as described above.
In the embodiment of the application, the processor may be an integrated circuit chip with signal processing capability. The processor may be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP for short), an application specific integrated circuit (Application Specific f ntegrated Circuit ASIC for short), a field programmable gate array (FieldProgrammable Gate Array FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The processor reads the information in the storage medium and, in combination with its hardware, performs the steps of the above method.
The storage medium may be memory, for example, may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable ROM (Electrically EPROM, EEPROM), or a flash Memory.
The volatile memory may be a random access memory (Random Access Memory, RAM for short) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (Double Data RateSDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and direct memory bus RAM (directracram, DRRAM).
The storage media described in embodiments of the present application are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the present application may be implemented in a combination of hardware and software. When the software is applied, the corresponding functions may be stored in a computer-readable medium or transmitted as one or more instructions or code on the computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application in further detail, and are not to be construed as limiting the scope of the application, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the application.
Claims (10)
1. A sensor data processing method based on big data, comprising:
the internet of things cluster is registered in a cloud server, the cloud server performs security verification on the internet of things cluster, and if the security verification is passed, the internet of things cluster is allowed to upload sensor data to the cloud server;
the cloud server collects sensor data uploaded by each Internet of things cluster;
acquiring attribute characteristics affecting irrigation quantity from sensor data, and acquiring comprehensive irrigation attribute characteristics according to the attribute characteristics of each sensor;
calculating a predicted soil index of the irrigation area according to the comprehensive irrigation attribute characteristics;
comparing the predicted soil index with a preset threshold, if the predicted soil index is smaller than the preset threshold, sending an irrigation instruction to the corresponding internet of things cluster, otherwise, continuing to analyze the sensor data.
2. The sensor data processing method based on big data according to claim 1, wherein the irrigation area is divided into a plurality of sub-area test fields, each sub-area test field is provided with an independent internet of things cluster, a plurality of sensors are installed in the internet of things cluster of each sub-area test field, and the plurality of sensors are used for carrying out overall data acquisition on the sub-area test fields.
3. The method for processing sensor data based on big data according to claim 1, wherein the cloud server performs security assessment on the internet of things cluster and a transmission channel thereof, specifically comprising:
collecting equipment risk feature data of sensors in all the Internet of things clusters and channel risk features of transmission channels;
calculating an Internet of things cluster risk value according to the sensor equipment risk feature data and channel risk features of the transmission channel;
comparing the risk value of the Internet of things cluster with a preset safety threshold, if the risk value of the Internet of things cluster is smaller than the preset safety threshold, passing the safety evaluation, otherwise, failing the safety evaluation.
4. A method of sensor data processing based on big data according to claim 1, characterized in that the calculation of the general irrigation property features comprises the following sub-steps:
acquiring sensor data in t time;
eliminating deviation points in sensor data to obtain a sensor effective data set;
extracting attribute features affecting irrigation quantity from the effective data set of the sensor;
and carrying out fusion treatment on the attribute characteristics influencing the irrigation quantity according to the attributes to obtain the comprehensive characteristics of all the attributes.
5. The sensor data processing method based on big data according to claim 4, wherein the sensor data in the t time includes collected data of a plurality of sensors uploaded by each sensor group of each internet of things cluster, attribute characteristics of each sensor group are obtained, and the attribute characteristics of each sensor group are subjected to fusion processing to obtain comprehensive attribute characteristics in the time period, so as to form a comprehensive attribute characteristic set.
6. A big data based sensor data processing system, comprising: the internet of things cluster and the cloud server; the internet of things cluster is registered in a cloud server, the cloud server performs security verification on the internet of things cluster, and if the security verification is passed, the internet of things cluster is allowed to upload sensor data to the cloud server;
the cloud server specifically includes:
the acquisition module is used for acquiring the sensor data uploaded by each Internet of things cluster;
the predicted soil index calculation module is used for acquiring attribute characteristics affecting irrigation quantity from sensor data and obtaining comprehensive irrigation attribute characteristics according to the attribute characteristics of each sensor; calculating a predicted soil index of the irrigation area according to the comprehensive irrigation attribute characteristics;
and the comparison module is used for comparing the predicted soil index with a preset threshold value, sending an irrigation instruction to the corresponding internet of things cluster if the predicted soil index is smaller than the preset threshold value, and if not, continuing to analyze the sensor data.
7. The big data based sensor data processing system of claim 6, wherein the irrigation area is divided into a plurality of sub-area test fields, each sub-area test field is provided with an independent internet of things cluster, a plurality of sensors are installed in the internet of things cluster of each sub-area test field, and the plurality of sensors are used for overall data acquisition of the sub-area test fields.
8. The big data-based sensor data processing system of claim 6, wherein the cloud server is configured to perform security assessment on the internet of things cluster and the transmission channel thereof, and specifically comprises:
collecting equipment risk feature data of sensors in all the Internet of things clusters and channel risk features of transmission channels;
calculating an Internet of things cluster risk value according to the sensor equipment risk feature data and channel risk features of the transmission channel;
comparing the risk value of the Internet of things cluster with a preset safety threshold, if the risk value of the Internet of things cluster is smaller than the preset safety threshold, passing the safety evaluation, otherwise, failing the safety evaluation.
9. A big data based sensor data processing system according to claim 6, characterized in that the calculation of the integrated irrigation property features comprises the following sub-steps:
acquiring sensor data in t time;
eliminating deviation points in sensor data to obtain a sensor effective data set;
extracting attribute features affecting irrigation quantity from the effective data set of the sensor;
and carrying out fusion treatment on the attribute characteristics influencing the irrigation quantity according to the attributes to obtain the comprehensive characteristics of all the attributes.
10. The big data-based sensor data processing system of claim 9, wherein the sensor data in the t time includes collected data of a plurality of sensors uploaded by each sensor group of each internet of things cluster, attribute characteristics of each sensor group are obtained, and the attribute characteristics of each sensor group are subjected to fusion processing to obtain comprehensive attribute characteristics in the time period, so as to form a comprehensive attribute characteristic set.
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