SaaS-based multi-level environment detection data fusion method

Abstract

The invention discloses a multi-level environment detection data fusion method based on SaaS, and relates to the technical field of data fusion, a data acquisition layer is constructed to acquire environment parameter data of a monitoring area, a data transmission layer is constructed to carry out transmission initialization configuration and dynamic data encryption on the environment parameter data, a configuration ciphertext file is generated, and the configuration ciphertext file is stored in a database; the method comprises the following steps: constructing a data processing layer to pre-process a configuration ciphertext file, converting the configuration ciphertext file into a standard file, performing semantic analysis to obtain a semantic word set, constructing a data analysis layer to analyze the semantic word set, and obtaining environment trend data of a plurality of time nodes in different data dimensions in a monitoring area; the method comprises the steps of constructing data dimensions, constructing data mapping layers of which the number is the same as that of the data dimensions, mapping environment trend data of a plurality of time nodes under each data dimension into the data mapping layers, constructing to-be-fused data layers of different hierarchies, and performing data fusion on the different to-be-fused data layers based on an SaaS technology to generate final environment detection comprehensive data.

Description

Technical Field

[0001] The present invention relates to the technical field of data fusion, and specifically to a multi-level environmental detection data fusion method based on SaaS. Background Art

[0002] SaaS (Software as a Service) is a cloud computing service model that allows users to access and use software applications provided by third-party providers via the Internet. In this model, the software applications are hosted on cloud servers, and users do not need to install or maintain the software locally, but only need to connect to the Internet to use these services.

[0003] At present, environmental detection technology has become an important part of China's environmental protection cause. However, in the field of environmental detection, how to efficiently and accurately process and analyze a large amount of environmental data remains a difficult problem. The traditional methods for processing environmental detection data have the following deficiencies: low data processing efficiency, unable to meet the requirements of real-time monitoring; single data analysis dimension, difficult to comprehensively reflect the environmental situation; poor data security, prone to data leakage. Therefore, it is necessary to study a new environmental detection data fusion method to improve data processing efficiency, enrich data analysis dimensions, and ensure data security. Summary of the Invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a multi-level environmental detection data fusion method based on SaaS.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A multi-level environmental detection data fusion method based on SaaS, including the following steps:

[0006] Step S1: Construct a data acquisition layer to collect environmental parameter data of the monitoring area through the data acquisition layer;

[0007] Step S2: Construct a data transmission layer to perform transmission initialization configuration and dynamic data encryption on the environmental parameter data, and generate a configured ciphertext file;

[0008] Step S3: Construct a data processing layer to preprocess the configured ciphertext file through the data processing layer, convert the configured ciphertext file into a specification file, perform semantic analysis on the specification file, and obtain the semantic word set corresponding to the specification file;

[0009] Step S4: Construct a data analysis layer to analyze the semantic word set of the specification file through the data analysis layer, and further obtain environmental trend data at several time nodes in the current monitoring area under different data dimensions;

[0010] Step S5: Construct a data mapping layer with the same number of data dimensions, and map the environmental trend data of several time nodes under each data dimension into the data mapping layer to construct different levels of data layers to be fused;

[0011] Step S6: Based on SaaS technology, perform data fusion on different data layers to be fused, and then generate the final comprehensive environmental detection data.

[0012] Furthermore, construct a data acquisition layer. The process of acquiring environmental parameter data of the monitoring area through the data acquisition layer includes:

[0013] Construct a data acquisition layer and configure the working parameters of the data acquisition layer. The working parameters include specifying the data acquisition rate, specifying the data acquisition delay interval, and specifying the information set. The information set is used to record the information format, information source address, and information text type of the data information allowed to be acquired within the monitoring area for environmental monitoring;

[0014] Real-time acquire the environmental parameter data of the monitoring area through the data acquisition layer, obtain the data acquisition rate and data acquisition delay when acquiring the environmental parameter data. When the rate difference between the data acquisition rate and the specified data acquisition rate does not meet the preset rate difference range, or the data acquisition delay is not within the specified data acquisition delay interval, or any of the information format, information source address, and information text type of the data information acquired from the monitoring area does not meet the requirements of the specified information set, then re-acquire the environmental parameter data, otherwise, do not perform any operation.

[0015] Furthermore, construct a data transmission layer. The process of performing transmission initialization configuration and dynamic data encryption on the environmental parameter data through the data transmission layer to generate a configured ciphertext file includes:

[0016] Construct a data transmission layer and configure the transmission protocol. Deploy the hardware device for data transmission for the data transmission layer, and perform transmission initialization configuration on the environmental parameter data transmitted by the data transmission layer through the hardware device;

[0017] The transmission initialization configuration is: set the port number, transmission rate, and data packet size for each data transmission of the environmental parameter data of the data transmission layer, construct the retransmission rule corresponding to the data transmission, and judge the application scenario for executing the retransmission rule;

[0018] Dynamically encrypt the environmental parameter data for which the transmission initialization configuration is completed, convert the environmental parameter data into a binary character sequence, convert the binary character sequence into data stream data, set the header data length, data body length, and tail data length, divide the data stream data into streaming data one, streaming data two, and streaming data three, and perform AES encryption, RSA encryption, and ECC encryption respectively, and set the respective encryption maintenance durations of streaming data one, streaming data two, and streaming data three;

[0019] If the frequencies of data attacks suffered by streaming data one, streaming data two, and streaming data three respectively are higher than the preset frequency upper limit before the end of their respective encryption maintenance durations, or the encryption durations of streaming data one, streaming data two, and streaming data three reach the encryption maintenance durations, re-encrypt streaming data one, streaming data two, and streaming data three, merge the encrypted streaming data one, streaming data two, and streaming data three to generate the corresponding ciphertext data, and import the ciphertext data into a preset blank text to generate the final configuration ciphertext file.

[0020] Further, the process of determining the application scenario for executing the retransmission rule includes:

[0021] When the data transmission layer performs data transmission on the environmental parameter data, monitor the transmission rate in real time, accumulate the durations of all time periods when the transmission rate is lower than the preset rate lower threshold, and then obtain the fluctuating transmission duration. When the fluctuating transmission duration exceeds the preset duration upper limit, determine that the current data transmission layer is in abnormal data transmission, and synchronously perform data retransmission on the current data transmission layer according to the retransmission rule. When the fluctuating transmission duration does not exceed the duration upper limit, do not perform any operation.

[0022] Further, construct a data processing layer, and preprocess the configuration ciphertext file through the data processing layer to convert the configuration ciphertext file into a specification file. The process of performing semantic analysis on the specification file to obtain the semantic word set corresponding to the specification file includes:

[0023] The data processing layer preprocesses the configuration ciphertext file. The preprocessing includes data cleaning and format correction. Remove the partial data irrelevant to the monitoring requirements in the configuration ciphertext file through data cleaning, fill in the missing partial data in the configuration ciphertext file, and delete the partial data belonging to duplicate content in the configuration ciphertext file. After all the missing, duplicate, and irrelevant partial data in the configuration ciphertext file are processed, convert the configuration ciphertext file into the corresponding specification file;

[0024] Construct a semantic analysis model through natural language processing technology, input the specification document into the semantic analysis model to be segmented into several text segments, count the word frequency, part of speech, inverse document frequency, and sentence length of each text segment in the specification document, and then generate several semantic clusters. Summarize all the semantic clusters generated after processing the specification document, and then obtain the semantic word set corresponding to the specification document.

[0025] Furthermore, construct a data analysis layer. The process of analyzing the semantic word set of the specification document through the data analysis layer to obtain the environmental trend data of several time nodes in different data dimensions in the current monitoring area includes:

[0026] Construct a data analysis layer and monitor the real-time intra-layer environment in the data analysis layer. If at least one of the data information in the data blacklist and the data source address is detected in the intra-layer environment, the data reception channel corresponding to the configuration of the data analysis layer will not be opened, and the reception of the semantic word set corresponding to the specification document will be prohibited;

[0027] If neither the data information in the data blacklist nor the data source address is detected in the intra-layer environment, the semantic word set will be input through the data reception channel, and the data analysis layer will start to analyze the semantic word set, and set the high-frequency dimension semantic cluster word bag, medium-frequency dimension semantic cluster word bag, and low-frequency dimension semantic cluster word bag. The high-frequency dimension semantic cluster word bag, medium-frequency dimension semantic cluster word bag, and low-frequency dimension semantic cluster word bag are each associated with different semantic cluster features;

[0028] The data analysis layer sequentially matches the semantic cluster features to which different semantic clusters in the semantic word set belong, and then classifies the semantic clusters into the high-frequency dimension semantic cluster word bag, medium-frequency dimension semantic cluster word bag, and low-frequency dimension semantic cluster word bag according to the semantic cluster features to which the semantic clusters belong;

[0029] Perform time node annotation on the semantic clusters classified into the high-frequency dimension semantic cluster word bag, medium-frequency dimension semantic cluster word bag, and low-frequency dimension semantic cluster word bag, and then obtain the point mapping data corresponding to each data dimension under several time nodes. Construct a two-dimensional coordinate system, and draw the point mapping data corresponding to different data dimensions onto the two-dimensional coordinate system in the order from the earliest to the latest time node, and then construct the environmental trend data corresponding to each data dimension.

[0030] Furthermore, construct a data mapping layer with the same number as the number of data dimensions, and map the environmental trend data of several time nodes under each data dimension into the data mapping layer. The process of constructing different levels of data layers to be fused includes:

[0031] According to the number of data dimensions, construct the same number of data mapping layers, and label the data mapping layers as high-dimensional mapping layer, medium-dimensional mapping layer, and low-dimensional mapping layer. Input the environmental trend data of several time nodes corresponding to the data dimensions classified into the high-frequency dimension semantic cluster word bag into the high-dimensional mapping layer, input the environmental trend data of several time nodes corresponding to the data dimensions classified into the medium-frequency dimension semantic cluster word bag into the medium-dimensional mapping layer, and input the environmental trend data of several time nodes corresponding to the data dimensions classified into the low-frequency dimension semantic cluster word bag into the low-dimensional mapping layer. Then, construct the data layers to be fused at the high-dimensional level, medium-dimensional level, and low-dimensional level respectively.

[0032] Further, based on the SaaS technology, the process of data fusion for different data layers to be fused and then generating the final comprehensive environmental detection data includes:

[0033] Denote the number of layer channels of the data layers to be fused at the high-dimensional level, medium-dimensional level, and low-dimensional level as Td1, Td2, and Td3 respectively. Among them, Td1, Td2, and Td3 are all natural numbers greater than 0. Based on the SaaS technology, construct corresponding service request links for the data layers to be fused at the high-dimensional level, medium-dimensional level, and low-dimensional level, and unify the number of layer channels of the data layers to be fused at the high-dimensional level, medium-dimensional level, and low-dimensional level respectively. Then, complete the data fusion between the environmental trend data corresponding to the data layers to be fused at different layer dimensions. Among them, fuse the environmental trend data at the high-dimensional level, medium-dimensional level, and low-dimensional level at the same time node to generate the final comprehensive environmental detection data.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: By constructing a data acquisition layer, a data transmission layer, a data processing layer, a data analysis layer, and a data mapping layer, the rapid acquisition, transmission, processing, and analysis of environmental detection data are realized, meeting the requirements of real-time monitoring. By analyzing the semantic word set of the specification document, the environmental trend data in different data dimensions of the current monitoring area can be obtained, thus more comprehensively reflecting the environmental situation. Using the dynamic data encryption technology to encrypt the environmental parameter data effectively prevents data leakage and ensures data security. Description of the Drawings

[0035] Figure 1 It is a flowchart of the present invention. Detailed Embodiments

[0036] As Figure 1 shown, the multi-level environmental detection data fusion method based on SaaS includes the following steps:

[0037] Step S1: Construct a data acquisition layer, and collect the environmental parameter data of the monitoring area through the data acquisition layer;

[0038] Step S2: Construct a data transmission layer, and perform transmission initialization configuration and dynamic data encryption on the environmental parameter data through the data transmission layer to generate a configured ciphertext file;

[0039] Step S3: Construct a data processing layer, preprocess the configured ciphertext file through the data processing layer, convert the configured ciphertext file into a specification file, perform semantic analysis on the specification file, and obtain a semantic word set corresponding to the specification file;

[0040] Step S4: Construct a data analysis layer, analyze the semantic word set of the specification file through the data analysis layer, and further obtain environmental trend data of several time nodes in the current monitoring area under different data dimensions;

[0041] Step S5: Construct data mapping layers with the same number as the number of data dimensions, map the environmental trend data of several time nodes under each data dimension into the data mapping layers, and construct different-level data layers to be fused;

[0042] Step S6: Based on the SaaS technology, perform data fusion on different data layers to be fused, and further generate the final comprehensive environmental detection data.

[0043] It should be further noted that in the specific implementation process, the process of constructing a data acquisition layer and collecting environmental parameter data of the monitoring area through the data acquisition layer includes:

[0044] Construct a data acquisition layer and configure the working parameters of the data acquisition layer. The working parameters include the standard data acquisition rate, the standard data acquisition delay interval, and the standard information set. Among them, the standard information set is used to record the information format, information source address, and information text type of the data information allowed to be collected in the monitoring area for environmental monitoring;

[0045] Real-time collect the environmental parameter data in the monitoring area through the data acquisition layer. The environmental parameter data includes meteorological parameters, air quality parameters, water quality parameters, soil quality parameters, sound environment parameters, radiation parameters, and ecological parameters; Meteorological parameters include temperature, humidity, wind speed and direction, air pressure, precipitation, ultraviolet index, and sunshine intensity, etc.; Water quality parameters include pH value, dissolved oxygen content, conductivity, turbidity, and heavy metal content, etc.; Soil quality parameters include soil humidity, soil temperature, soil pH value, organic matter content, nutrient element content, and soil salinity, etc.; Sound environment parameters include environmental noise decibels, noise frequency spectrum, and sound pressure level; Radiation parameters include γ radiation dose rate, β radiation dose rate, radiation type, and radiation energy, etc.; Ecological parameters include vegetation coverage, biodiversity index, soil erosion rate, and soil and water loss rate, etc.;

[0046] Obtain the data acquisition rate and data acquisition delay when acquiring environmental parameter data. When the rate difference between the data acquisition rate and the specified data acquisition rate does not meet the preset rate difference range, or the data acquisition delay is not within the specified data acquisition delay interval, or any of the information format, information source address, and information text type of the data information collected from the monitoring area does not meet the requirements of the specified information set, then re-acquire the environmental parameter data; otherwise, do nothing.

[0047] It should be further noted that in the specific implementation process, to construct the data transmission layer and perform transmission initialization configuration and dynamic data encryption on the environmental parameter data through the data transmission layer, the process of generating the configuration ciphertext file includes:

[0048] Construct the data transmission layer and configure the transmission protocol of the data transmission layer. The transmission protocols include the HTTP protocol, MQTT protocol, and TCP / IP protocol. Deploy the hardware devices for data transmission to the data transmission layer. The types of hardware devices include network routers, switches, and gateway centers. Through the hardware devices, perform transmission initialization configuration on the environmental parameter data that needs to be transmitted by the data transmission layer;

[0049] The content of the transmission initialization configuration is: set the port number, transmission rate, and the size of each data packet for transmitting the environmental parameter data of the data transmission layer, construct the retransmission rule corresponding to the data transmission, and judge the application scenario for executing the retransmission rule;

[0050] When the data transmission layer transmits the environmental parameter data, monitor the transmission rate in real time, and accumulate the durations of all time periods when the transmission rate is lower than the preset rate lower limit threshold to obtain the fluctuating transmission duration. When the fluctuating transmission duration exceeds the preset duration upper limit, it is judged that the current data transmission layer is in abnormal data transmission, and synchronously execute data retransmission on the current data transmission layer according to the retransmission rule. When the fluctuating transmission duration does not exceed the duration upper limit, do nothing.

[0051] Perform dynamic data encryption on the environmental parameter data that has completed the transmission initialization configuration. The content of the dynamic data encryption is as follows:

[0052] Convert the environmental parameter data into a binary character sequence, convert the binary character sequence into data stream data, and set the header data length, data body length, and tail data length. According to the header data length, data body length, and tail data length, divide the data stream data into streaming data one, streaming data two, and streaming data three;

[0053] Perform AES encryption, RSA encryption, and ECC encryption on streaming data one, streaming data two, and streaming data three respectively, and set the respective encryption maintenance durations for streaming data one, streaming data two, and streaming data three.

[0054] If the frequencies of data attacks suffered by streaming data one, streaming data two, and streaming data three respectively are higher than the preset frequency upper limit before the end of their respective encryption maintenance durations, or the encryption durations of streaming data one, streaming data two, and streaming data three reach the encryption maintenance durations, then re-encrypt streaming data one, streaming data two, and streaming data three.

[0055] Merge the encrypted streaming data one, streaming data two, and streaming data three to generate corresponding ciphertext data, and import the ciphertext data into a preset blank text to generate a final configured ciphertext file.

[0056] It should be further noted that in the specific implementation process, a data processing layer is constructed. The process of preprocessing the configured ciphertext file through the data processing layer, converting the configured ciphertext file into a specification file, and performing semantic analysis on the specification file to obtain the semantic word set corresponding to the specification file includes:

[0057] Construct a data processing layer. The preprocessing of the configured ciphertext file through the data processing layer includes data cleaning and format correction. Through data cleaning, remove partial data in the configured ciphertext file that is irrelevant to the monitoring requirements, fill in the missing partial data in the configured ciphertext file, and delete the partial data in the configured ciphertext file that belongs to duplicate content.

[0058] After all the missing, duplicate, and irrelevant partial data in the configured ciphertext file are processed, convert the configured ciphertext file into a corresponding specification file.

[0059] Construct a semantic analysis model through natural language processing technology. Input the specification file into the semantic analysis model, and then split the specification file into several text segments. Count the word frequency, part of speech, inverse document frequency, and sentence length of each text segment in the specification file, and then generate several semantic clusters. Summarize all the semantic clusters generated after processing the specification file, and then obtain the semantic word set corresponding to the specification file.

[0060] It should be further noted that in the specific implementation process, a data analysis layer is constructed. The process of analyzing the semantic word set of the specification file through the data analysis layer to obtain the environmental trend data of several time nodes in different data dimensions of the current monitoring area includes:

[0061] Build a data analysis layer and monitor the in-layer environment in real time in the data analysis layer. If at least one of the data information in the data blacklist and the data source address is detected in the in-layer environment, the data reception channel corresponding to the configuration of the data analysis layer is not opened, and the reception of the semantic word set corresponding to the specification file is prohibited;

[0062] If neither the data information in the data blacklist nor the data source address is detected in the in-layer environment, it is determined that the current in-layer environment is in a safe state. The data reception channel corresponding to the data analysis layer is opened, the semantic word set is entered through the data reception channel, and the data analysis layer starts to analyze the semantic word set;

[0063] Set up a high-frequency dimension semantic cluster word bag, a medium-frequency dimension semantic cluster word bag, and a low-frequency dimension semantic cluster word bag. Each of the high-frequency dimension semantic cluster word bag, the medium-frequency dimension semantic cluster word bag, and the low-frequency dimension semantic cluster word bag is associated with different semantic cluster features;

[0064] The data analysis layer sequentially matches the semantic cluster features to which different semantic clusters in the semantic word set belong. Then, according to the semantic cluster features to which the semantic clusters belong, the semantic clusters are classified into the high-frequency dimension semantic cluster word bag, the medium-frequency dimension semantic cluster word bag, and the low-frequency dimension semantic cluster word bag;

[0065] Perform time node annotation on the semantic clusters classified into the high-frequency dimension semantic cluster word bag, the medium-frequency dimension semantic cluster word bag, and the low-frequency dimension semantic cluster word bag, and then obtain the point mapping data corresponding to each data dimension under several time nodes. Build a two-dimensional coordinate system, and draw the point mapping data corresponding to different data dimensions onto the two-dimensional coordinate system in the order from the earliest to the latest time node, and then build the environmental trend data corresponding to each data dimension;

[0066] The environmental change trends represented by the environmental trend data include environmental improvement change trends, environmental deterioration change trends, and environmental maintenance trends. In the environmental improvement change trend, it indicates that the environment of the monitored area is gradually improving. In the environmental deterioration change trend, it indicates that the environment of the monitored area is gradually deteriorating. In the environmental maintenance trend, it indicates that the environment of the monitored area remains at the original level and does not evolve in the direction of deterioration or improvement.

[0067] It should be further noted that in the specific implementation process, the process of building a data mapping layer with the same number as the number of data dimensions and mapping the environmental trend data of several time nodes under each data dimension into the data mapping layer to build different levels of data layers to be fused includes:

[0068] According to the number of data dimensions, construct a data mapping layer with the same number of data dimensions, and label the data mapping layer as a high-dimensional mapping layer, a medium-dimensional mapping layer, and a low-dimensional mapping layer, and set the data input rates corresponding to the high-dimensional mapping layer, the medium-dimensional mapping layer, and the low-dimensional mapping layer respectively;

[0069] According to their respective data input rates, input the environmental trend data of several time nodes corresponding to the data dimensions classified into the high-frequency dimension semantic cluster word bag into the high-dimensional mapping layer, input the environmental trend data of several time nodes corresponding to the data dimensions classified into the medium-frequency dimension semantic cluster word bag into the medium-dimensional mapping layer, and input the environmental trend data of several time nodes corresponding to the data dimensions classified into the low-frequency dimension semantic cluster word bag into the low-dimensional mapping layer, and then respectively construct the data layers to be fused at the high-dimensional level, the medium-dimensional level, and the low-dimensional level.

[0070] It should be further noted that in the specific implementation process, based on the SaaS technology, the process of data fusion for different data layers to be fused to generate the final comprehensive environmental detection data includes:

[0071] Denote the number of layer channels of the data layers to be fused at the high-dimensional level, the medium-dimensional level, and the low-dimensional level as Td1, Td2, and Td3 respectively, where Td1, Td2, and Td3 are all natural numbers greater than 0. Based on the SaaS technology, construct corresponding service request links for the data layers to be fused at the high-dimensional level, the medium-dimensional level, and the low-dimensional level, and unify the number of layer channels of the data layers to be fused at the high-dimensional level, the medium-dimensional level, and the low-dimensional level, and then complete the data fusion between the environmental trend data corresponding to the data layers to be fused at different layer dimensions. Among them, the environmental trend data at the high-dimensional level, the medium-dimensional level, and the low-dimensional level at the same time node are fused to generate the final comprehensive environmental detection data.

[0072] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-level environment detection data fusion method based on SaaS, characterized in that: The following steps are involved: Step S1: construct a data collection layer, and collect environmental parameter data of the monitoring area through the data collection layer; Step S2: construct a data transmission layer, perform initialization configuration of environmental parameter data transmission and dynamic data encryption through the data transmission layer, and generate a configuration ciphertext file; Step S3: construct a data processing layer, pre-process the configuration ciphertext file through the data processing layer, convert the configuration ciphertext file into a standard file, perform semantic analysis on the standard file, and obtain a semantic word set corresponding to the standard file; Step S4: construct a data analysis layer, analyze the semantic word set of the specification document through the data analysis layer, and then obtain the environmental trend data of several time nodes in the current monitoring area under different data dimensions; Step S5: constructing data mapping layers with the same number as the data dimensions, and mapping the environmental trend data of several time nodes under each data dimension into the data mapping layer, so as to construct different levels of data layers to be fused; Step S6: Based on SaaS technology, different data layers to be integrated are integrated to generate the final comprehensive environmental detection data.

2. The multi-level environment detection data fusion method based on SaaS according to claim 1 is characterized in that: Construct a data collection layer. The process of collecting environmental parameter data of the monitoring area through the data collection layer includes: Construct a data collection layer and configure the working parameters of the data collection layer. The working parameters include a standardized data collection rate, a standardized data collection delay interval, and a standardized information set. The standardized information set is used to record the information format, information source address, and information text type of the data information allowed to be collected within the monitoring area for environmental monitoring. The environmental parameter data in the monitoring area is collected in real time through the data collection layer, and the data collection rate and data collection delay when collecting environmental parameter data are obtained. When the rate difference between the data collection rate and the standard data collection rate does not meet the preset rate difference range, or the data collection delay is not within the standard data collection delay range, or the information format, information source address and information text type of the data information collected from the monitoring area do not meet the requirements of the standard information set, the environmental parameter data is collected again, otherwise, no operation is performed.

3. The multi-level environment detection data fusion method based on SaaS according to claim 2 is characterized in that: Construct a data transmission layer, use the data transmission layer to perform initialization configuration of environmental parameter data and dynamic data encryption, and the process of generating a configuration ciphertext file includes: Build a data transmission layer and configure the transmission protocol, deploy hardware devices for data transmission for the data transmission layer, and perform transmission initialization configuration on the environmental parameter data of the data transmission performed by the data transmission layer through the hardware devices; The transmission initialization configuration is as follows: setting the port number of the data transmission layer, the transmission rate, and the data packet size for each data transmission of the environmental parameter data, constructing the retransmission rules corresponding to the data transmission, and determining the application scenarios for executing the retransmission rules; Dynamically encrypt the environment parameter data that has completed the transmission initialization configuration, convert the environment parameter data into a binary character sequence, convert the binary character sequence into data stream data, set the header data length, the data body length, and the tail data length, divide the data stream data into streaming data one, streaming data two, and streaming data three, and perform AES encryption, RSA encryption, and ECC encryption respectively, and set the encryption maintenance time of each of streaming data one, streaming data two, and streaming data three; If the frequency of data attack on streaming data one, streaming data two and streaming data three is higher than the preset frequency upper limit before the end of the encryption maintenance period, or the encryption period of streaming data one, streaming data two and streaming data three reaches the encryption maintenance period, then streaming data one, streaming data two and streaming data three are re-encrypted, and the encrypted streaming data one, streaming data two and streaming data three are merged to generate corresponding ciphertext data, and the ciphertext data is imported into the preset blank text to generate the final configuration ciphertext file.

4. The multi-level environment detection data fusion method based on SaaS according to claim 3 is characterized in that: The process of determining the application scenario for executing the retransmission rule includes: When the data transmission layer transmits environmental parameter data, the transmission rate is monitored in real time, and the duration of all time periods in which the transmission rate is lower than the preset rate lower limit threshold is accumulated to obtain the fluctuating transmission duration. When the fluctuating transmission duration exceeds the preset duration upper limit, it is determined that the current data transmission layer is in a data transmission abnormality, and data retransmission is performed on the current data transmission layer synchronously according to the retransmission rules. When the fluctuating transmission duration does not exceed the duration upper limit, no operation is performed.

5. The multi-level environment detection data fusion method based on SaaS according to claim 4 is characterized in that: The process of constructing a data processing layer, preprocessing the configuration ciphertext file through the data processing layer, converting the configuration ciphertext file into a standard file, and performing semantic analysis on the standard file to obtain the semantic word set corresponding to the standard file includes: Construct a data processing layer to preprocess the configuration ciphertext file. The preprocessing includes data cleaning and format correction. Through data cleaning, some data in the configuration ciphertext file that is irrelevant to the monitoring needs is removed, some data in the configuration ciphertext file that is true is filled, and some data in the configuration ciphertext file that is duplicate content is deleted. When the missing, duplicated and irrelevant data in the configuration ciphertext file are processed, the configuration ciphertext file is converted into a corresponding standard file. A semantic analysis model is constructed through natural language processing technology. The standard file is input into the semantic analysis model and divided into several text segments. The word frequency, part of speech, inverse document frequency and sentence length of each text segment in the standard file are counted, and then several semantic clusters are generated. All semantic clusters generated after processing the standard file are summarized to obtain the semantic word set corresponding to the standard file.

6. The multi-level environment detection data fusion method based on SaaS according to claim 5 is characterized in that: The process of constructing a data analysis layer and analyzing the semantic word set of the specification document through the data analysis layer to obtain the environmental trend data of several time nodes in the current monitoring area under different data dimensions includes: Construct a data analysis layer and monitor the real-time intra-layer environment in the data analysis layer. If at least one of the data information and data source address in the data blacklist is detected in the intra-layer environment, the corresponding data receiving channel of the data analysis layer will not be opened, and the reception of the semantic word set corresponding to the specification file will be prohibited; If the data information and data source address in the data blacklist are not detected in the intra-layer environment, the semantic word set is recorded by the data receiving channel, and the data analysis layer starts the analysis of the semantic word set, setting the high-frequency dimension semantic cluster word bag, the medium-frequency dimension semantic cluster word bag and the low-frequency dimension semantic cluster word bag, and the high-frequency dimension semantic cluster word bag, the medium-frequency dimension semantic cluster word bag and the low-frequency dimension semantic cluster word bag are each associated with different semantic cluster features; The data analysis layer sequentially matches the semantic cluster features of different semantic clusters in the semantic word set, and then classifies the semantic clusters into high-frequency dimension semantic cluster word bags, medium-frequency dimension semantic cluster word bags, and low-frequency dimension semantic cluster word bags according to the semantic cluster features to which the semantic clusters belong; The semantic clusters classified into the high-frequency dimension semantic cluster bag-of-words, the medium-frequency dimension semantic cluster bag-of-words and the low-frequency dimension semantic cluster bag-of-words are labeled with time nodes, and then the point mapping data corresponding to each data dimension under several time nodes are obtained, and a two-dimensional coordinate system is constructed. In the order of time nodes from early to late, the point mapping data corresponding to different data dimensions are plotted on the two-dimensional coordinate system, and then the corresponding environmental trend data under each data dimension are constructed.

7. The SaaS-based multi-level environment detection data fusion method according to claim 6 is characterized in that: The process of constructing data mapping layers with the same number of data dimensions and mapping the environmental trend data of several time nodes under each data dimension into the data mapping layer to construct different levels of data layers to be fused includes: According to the number of data dimensions, the same number of data mapping layers are constructed, and the data mapping layers are marked as high-dimensional mapping layer, medium-dimensional mapping layer and low-dimensional mapping layer. The environmental trend data of several time nodes of the data dimension classified into the high-frequency dimension semantic cluster word bag corresponding to the data dimension are entered into the high-dimensional mapping layer, the environmental trend data of several time nodes of the data dimension classified into the medium-frequency dimension semantic cluster word bag corresponding to the data dimension are entered into the medium-dimensional mapping layer, and the environmental trend data of several time nodes of the data dimension classified into the low-frequency dimension semantic cluster word bag corresponding to the data dimension are entered into the low-dimensional mapping layer, thereby respectively constructing the data layers to be fused at the high-dimensional level, the medium-dimensional level and the low-dimensional level.

8. The SaaS-based multi-level environment detection data fusion method according to claim 7 is characterized in that: Based on SaaS technology, the process of fusing different data layers to be fused and generating the final comprehensive environmental detection data includes: The number of hierarchical channels of the data layers to be fused at the high-dimensional level, the middle-dimensional level and the low-dimensional level are denoted as Td1, Td2 and Td3 respectively, where Td1, Td2 and Td3 are all natural numbers greater than 0. Based on SaaS technology, corresponding service request links are constructed for the data layers to be fused at the high-dimensional level, the middle-dimensional level and the low-dimensional level, and the number of hierarchical channels of the data layers to be fused at the high-dimensional level, the middle-dimensional level and the low-dimensional level are unified, thereby completing the data fusion between the corresponding environmental trend data of the data layers to be fused at different levels and dimensions, where the environmental trend data of the high-dimensional level, the middle-dimensional level and the low-dimensional level at the same time node are fused to generate the final comprehensive environmental detection data.

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