Internet of Things informatization data acquisition and monitoring system

Abstract

The invention relates to the technical field of data acquisition and processing, in particular to an Internet of Things informatization data acquisition and monitoring system, which comprises a data acquisition module; a data processing and transmission module; and a data monitoring and application module. According to the invention, the accuracy and stability of collected data are improved through preprocessing technologies such as noise filtering, standardization and data compression; block transmission and compression transmission strategies are adopted, dynamic adjustment is carried out according to network conditions, and the stability and safety of data transmission are improved; based on a linear regression prediction model, trend prediction of an environment state is realized, and the intelligence degree of decision making is improved; through an encryption mechanism and a data authentication mechanism, data integrity is ensured, and data loss or tampering is prevented; and according to a prediction result, an equipment control instruction is automatically generated, and intelligent management, such as equipment early warning, execution stopping or daily maintenance prompting, is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition and processing, and specifically to an Internet of Things (IoT) information data acquisition and monitoring system. Background Art

[0002] With the development of Internet of Things (IoT) technology, data acquisition and remote monitoring have become core requirements in fields such as industry, agriculture, and smart cities. Efficient data acquisition and intelligent analysis can improve equipment management levels, achieve precise control and prediction, and enhance production efficiency and safety. However, at present, the following problems still exist:

[0003] Traditional data acquisition systems have problems of low data quality and large noise interference, affecting the accuracy of subsequent analysis;

[0004] Low data transmission efficiency, and network fluctuations can cause data loss or delay, affecting real-time monitoring capabilities;

[0005] Existing monitoring systems mostly display data statically, lack intelligent prediction capabilities, and cannot respond to emergencies in a timely manner;

[0006] It is difficult to ensure data security and integrity, and there is a risk of data being tampered with or lost. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed.

[0008] To solve the above technical problems, the present invention provides the following technical solution: An Internet of Things information data acquisition and monitoring system, including,

[0009] A data acquisition module, specifically:

[0010] Collect data through sensors. Through a sensor network deployed in the monitoring area, collect environmental data in real time, and perform preliminary processing on the collected raw data to improve the quality and reliability of the data. Finally, pack the processed data and prepare to transmit it to the data processing and transmission module;

[0011] A data processing and transmission module, specifically:

[0012] Further clean, format, and optimize the data received from the data acquisition module, and through an encryption and authentication mechanism, ensure that the data can be efficiently transmitted to the central processing unit;

[0013] And a data monitoring and application module, specifically:

[0014] Convert the processed data into corresponding chart forms, and monitor the environmental status in real time. At the same time, analyze the received data in real time, build a prediction model for trend prediction; and generate control instructions according to the prediction results. The execution device performs operations according to the control instructions to achieve the intelligent management of the execution device.

[0015] As a preferred solution of the Internet of Things information-based data acquisition and monitoring system described in the present invention, specifically: Build a sensor network to collect environmental data in real time, as follows:

[0016] Deploy multiple sensor nodes inside the monitoring area to ensure that the sensors can cover all monitoring areas. Then, there is S = {S1, S2,..., S n}, where S i represents the sensor corresponding to the i-th sensor node. And, the deployment ranges of all sensors overlap to cover the entire monitoring area;

[0017] Collect data within the monitoring area based on the built sensor network, as follows:

[0018] For the nodes in the sensor network, each sensor node collects environmental data at a fixed time interval Δt. Then, there is D = {S1(t1), S2(t2),.., S n (t n )}, where S i (t i ) represents the environmental data collected by the i-th sensor at time t i , and the time interval between time t i and time t i-1 is a fixed length Δt.

[0019] As a preferred solution of the Internet of Things information-based data acquisition and monitoring system described in the present invention, specifically: The preliminary processing of the collected raw data is as follows:

[0020] Filter the noise of the raw data to remove outliers or interference data. Then, there is D filed = f(D), where D filed represents the data after filtering, and f represents the filtering function, including median filtering and mean filtering;

[0021] For the filtered data D filed , convert the data into a unified format and unit through data standardization, specifically:

[0022]

[0023] where D filedrepresents the data after filtering, μ represents the mean of the data after filtering, σ represents the standard deviation of the data after filtering, and D std represents the data after normalization;

[0024] For the data after normalization, data compression is performed, then D comp = f comp (D std ), where D std represents the data after normalization, and f comp represents the compression function, including Huffman coding and LZW compression, and D comp represents the compressed data for subsequent data storage.

[0025] As a preferred solution of the Internet of Things information-based data acquisition and monitoring system described in the present invention, wherein: the authentication mechanism is specifically as follows:

[0026] D valid = f valid (D form ), where D form represents the converted data, D valid represents the verified data, and f valid represents the verification function, specifically, the data after formatting is sorted according to the time nodes of data acquisition. If the verified data cannot increase as the acquisition time increases, it means that the data verification fails and the data is missing, and the data cleaning process is restarted until the data verification is satisfied.

[0027] As a preferred solution of the Internet of Things information-based data acquisition and monitoring system described in the present invention, wherein: the data after passing the verification is encrypted to ensure the security of data transmission, then D encrg = E(D form , K), where D form represents the converted data, D encrg represents the encrypted data, E represents the encryption algorithm, including symmetric encryption algorithm and asymmetric encryption algorithm, and K represents the encryption key corresponding to the adopted encryption algorithm;

[0028] For the encrypted data, it is transmitted to the central processing unit through a secure protocol communication. At the same time, the network fluctuation state is monitored in real time, and the data transmission strategy is dynamically adjusted according to the monitored network fluctuation state. For the adjustment of the transmission strategy, it is based on the monitoring of the data transmission efficiency, specifically:

[0029] If it is detected that the data transmission efficiency is lower than the conventional transmission threshold, the transmission strategy is adjusted to block transmission;

[0030] If the monitored data transmission efficiency is higher than the conventional transmission threshold, the transmission strategy will be adjusted to compressed transmission.

[0031] As a preferred solution of the Internet of Things information-based data acquisition and monitoring system described in the present invention, wherein: the conversion of the processed data into the corresponding chart form is specifically as follows:

[0032] For the encrypted data D encrg , decrypt the data using the decryption key. At the same time, for the decrypted data D dec perform data information extraction, then there is, D parsed = f parsed (D dec ), where D dec represents the decrypted data, represents the data information extracted from the decrypted data, and f parsed represents the decryption function;

[0033] For the data information extracted from the data, store the data in the database, and establish a data index for the stored data to improve the data query efficiency. The establishment of the data index assigns index serial numbers successively according to the time sequence nodes when the data is stored in the database;

[0034] For the data information stored in the data according to the serial number, display the extracted data information in the data display terminal in the form of a chart. Specifically, use the extracted data information as the input data of the echarts data table, and display it in the data display terminal in different table forms through format conversion of the input data. The forms of the data table include column charts, line charts, and pie charts.

[0035] As a preferred solution of the Internet of Things information-based data acquisition and monitoring system described in the present invention, wherein: the construction of the prediction model for trend prediction is specifically as follows:

[0036] The prediction model is constructed based on the linear regression algorithm, and data prediction is performed through the linear regression coefficient. Then there is,

[0037] Y = β0 + β1x1 + β2x2 +... + β n x n + ε

[0038] Wherein, β represents the linear regression coefficient, and the specific value is set by the implementer according to the actual application scenario. x i represents the display result of the i-th data information in the data table in the database, ε represents the error term, which is used for positive correction of the prediction result, and Y represents the prediction result of the prediction model, which is used to generate a control instruction to realize real-time response control of the execution device.

[0039] As a preferred solution of the Internet of Things information-based data acquisition and monitoring system described in the present invention, specifically: the implementation of the intelligent management of the execution device is as follows:

[0040] For the prediction result, use historical data to verify the prediction result. Take the historical data as the input data of the prediction model. According to the output result of the model, compare the output result with the historical actual data. If the comparison result exceeds 1 / 2 of the historical actual data, it means that the prediction result of the current prediction model is inaccurate. Readjust the linear regression coefficient until the comparison result is lower than 1 / 2 of the historical actual data, which means that the prediction result of the constructed prediction model is accurate. At the same time, generate a control instruction according to the prediction result to realize the control of the execution device, specifically:

[0041] If the prediction result indicates a risk of failure, generate an instruction to stop execution, control the execution device to stop the task being executed, and notify relevant personnel for repair;

[0042] If the prediction result shows no risk of failure, generate an instruction for daily maintenance, control the execution device to continue executing the task being executed, and notify the daily staff for the daily maintenance of the device.

[0043] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it realizes the Internet of Things information-based data acquisition and monitoring system.

[0044] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it realizes the Internet of Things information-based data acquisition and monitoring system.

[0045] Advantages of the present invention:

[0046] Through preprocessing technologies such as noise filtering, standardization, and data compression, the present invention improves the accuracy and stability of the acquired data;

[0047] Adopt a block transfer and compression transfer strategy, dynamically adjust according to the network condition, and improve the stability and security of data transmission;

[0048] Based on a linear regression prediction model, realize the trend prediction of the environmental state and improve the intelligence level of decision-making;

[0049] Through an encryption mechanism and a data authentication mechanism, ensure the integrity of the data and prevent data loss or tampering;

[0050] According to the prediction result, automatically generate device control instructions to realize intelligent management, such as device early warning, stop execution, or daily maintenance reminder. Description of the Drawings

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings. Among them:

[0052] Figure 1 It is a schematic diagram of the overall system step structure of an Internet of Things information-based data acquisition and monitoring system of the present invention. Specific embodiments

[0053] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0055] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0056] The present invention will be described in detail in conjunction with the schematic diagrams. When detailing the embodiments of the present invention, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0057] At the same time, in the description of the present invention, it should be noted that the terms "first, second, or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0058] Unless otherwise clearly defined and limited in the present invention, the terms "installation, connection, and coupling" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or it may be indirectly connected through an intermediate medium, or it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0059] Embodiment 1

[0060] Referring to Figure 1 , an embodiment of the present invention provides an Internet of Things information data acquisition and monitoring system, including a data acquisition module, a data processing and transmission module, and a data monitoring and application module;

[0061] Specifically, the data acquisition module collects data through sensors and transmits the collected data to the data processing and transmission module based on Internet of Things technology. After receiving the data collected by the sensors, the data processing and transmission module processes the collected data and transmits the processed result to the data monitoring and application module through Internet of Things technology. The data monitoring and application module performs real-time data monitoring on the processed data result, and at the same time, deploys the processed data result to relevant applications.

[0062] Furthermore, the data acquisition module collects environmental data (such as temperature, humidity, light, air quality, etc.) in real time through a sensor network deployed in the monitoring area, and performs preliminary processing on the collected raw data (such as noise filtering, data standardization, etc.) to improve the quality and reliability of the data. Finally, the processed data is packaged and prepared to be transmitted to the data processing and transmission module, providing a basis for subsequent data analysis and monitoring. The specific implementation is as follows:

[0063] Construct a sensor network to ensure comprehensive data collection in the monitoring area, specifically as follows:

[0064] According to the actual application requirements, determine the area range to be monitored and divide the deployment positions of sensor nodes;

[0065] Select corresponding target sensors according to the monitoring objectives, including temperature sensors, humidity sensors, and light sensors;

[0066] Deploy multiple sensor nodes in the monitoring area to ensure that the sensors can cover all the monitoring areas. Then, there is S = {S1, S2,..., S n}, where S i represents the sensor corresponding to the i-th sensor node, and the deployment ranges of all sensors overlap to cover the entire monitoring area.

[0067] Collect data within the monitoring area based on the constructed sensor network, as follows:

[0068] For the nodes in the sensor network, each sensor node collects environmental data at a fixed time interval Δt. Then, D = {S1(t1), S2(t2),.., S n (t n )}, where S i (t i ) represents the environmental data collected by the i-th sensor at time t i , and the time interval between time t i and time t i-1 is a fixed length Δt;

[0069] For the environmental data collected by the sensor network, store the collected raw data in the local cache of the sensor node for subsequent data processing.

[0070] Perform data preprocessing on the environmental data stored in the local cache. The specific processing process is as follows:

[0071] Filter the noise from the raw data and remove outliers or interfering data. Then, D filed = f(D), where D filed represents the data after filtering, and f represents the filtering function, including median filtering and mean filtering. The specific filtering function is set by the implementer according to the actual application scenario;

[0072] For the filtered data D filed , convert the data to a unified format and unit through data standardization for subsequent data processing. Specifically:

[0073]

[0074] where D filed represents the filtered data, μ represents the mean of the filtered data, σ represents the standard deviation of the filtered data, and D std represents the data after standardization;

[0075] Compress the data after standardization for subsequent data storage. Then, D comp = f comp (D std ), where D std represents the data after standardization, f comp represents the compression function, including Huffman coding and LZW compression. The specific compression function is set by the implementer according to the actual application scenario, and D compRepresents the compressed data for subsequent data storage.

[0076] Before data transmission based on the Internet of Things technology for the compressed data, the preparations are as follows:

[0077] Pack the compressed data using a packing function, then P = f pack (D comp ), where D comp represents the compressed data, and f back represents the packing function. The specific compression function is set by the implementer according to the actual application scenario. P represents the data after packing;

[0078] Encrypt the packed data and transmit the encrypted data to the data processing and transmission module through the Internet of Things technology, specifically as follows:

[0079] For the packed data P, encrypt the data through end-to-end encryption technology, and simultaneously generate a decryption key. Transmit the generated decryption key and the packed data to the data processing and transmission module through a wireless network.

[0080] Furthermore, the data processing and transmission module further cleans, formats, and optimizes the data received from the data acquisition module to ensure the accuracy and consistency of the data, and through encryption and authentication mechanisms, ensures the security and integrity of the data during transmission. At the same time, it optimizes the efficiency of data transmission, reduces network latency and bandwidth occupancy, and ensures that the data can be efficiently transmitted to the central processing unit. The specific implementation is as follows:

[0081] Receive the encrypted data packet from the data acquisition module, use the decryption key transmitted together with the data to decrypt the encrypted data packet, and then extract the original data. For the decrypted data, further clean the data to remove invalid data and abnormal data to improve the accuracy of the data;

[0082] Convert the cleaned data into a unified format for subsequent data processing and analysis, then D form = f form (D clean ), where D clean represents the cleaned data, D form represents the converted data, including JSON format and CSV format, and f form represents the conversion function, and the implementer automatically selects the corresponding conversion function according to the format to be converted;

[0083] For the formatted data, use a data verification function to perform data verification to ensure data integrity and consistency. Then, we have D valid = f valid (D form ), where D form represents the converted data, D valid represents the verified data, and f valid represents the verification function. Specifically, the formatted data is sorted according to the data collection time nodes. If the verified data does not increase as the collection time increases, it means the data verification fails and data is missing. Then, the data cleaning process is restarted until the data verification is satisfied;

[0084] Encrypt the data that has passed the verification to ensure data transmission security. Then, we have D encrg = E(D form , K), where D form represents the converted data, D encrg represents the encrypted data, E represents the encryption algorithm, including symmetric encryption algorithms and asymmetric encryption algorithms, which are set by the implementer according to the actual application scenario, and K represents the encryption key corresponding to the adopted encryption algorithm;

[0085] For the encrypted data, it is transmitted to the central processing unit through a secure protocol. At the same time, the network fluctuation status is monitored in real time, and the data transmission strategy is dynamically adjusted according to the monitored network fluctuation status. For the adjustment of the transmission strategy, the data transmission efficiency is monitored. If the monitored data transmission efficiency is lower than the normal transmission threshold, the transmission strategy is adjusted to block transmission. If the monitored data transmission efficiency is higher than the normal transmission threshold, the transmission strategy is adjusted to compressed transmission.

[0086] Furthermore, the data monitoring and application module converts the processed data into a corresponding chart form, monitors the environmental status in real time, and at the same time, performs real-time analysis on the received data, constructs a prediction model for trend prediction; and generates control instructions according to the prediction results. The execution device performs operations according to the control instructions to achieve intelligent management of the execution device. The specific implementation is as follows:

[0087] For the encrypted data D encrg , use the decryption key to decrypt the data. At the same time, for the decrypted data D dec , extract data information. Then, we have D parsed = f parsed (D dec ), where D dec represents the decrypted data, represents the data information extracted from the decrypted data, and f parsedRepresents the decryption function, and the specific function is set by the implementer according to the actual application scenario;

[0088] For the data information extracted from the data, store the data in the database, and establish a data index for the stored data to improve the data query efficiency. The establishment of the data index assigns index serial numbers successively according to the time sequence nodes when the data is stored in the database;

[0089] For the data information stored in the data according to the serial number, display the extracted data information in the data display terminal in the form of a chart. Specifically, use the extracted data information as the input data of the echarts data table, and through format conversion of the input data, display it in the data display terminal in different table forms. The forms of the data tables include bar charts, line charts, and pie charts. The specific table form is selected by the implementer according to the actual needs from different echarts tables;

[0090] Based on the data tables in the data display terminal, perform statistical analysis on the data tables and generate an analysis report. At the same time, based on historical data, use machine learning algorithms to predict the future change trend of the data in order to achieve the actual monitoring of the data and give real-time warnings about faults according to the monitoring results. The specific implementation is as follows:

[0091] Use the statistical data as the input data of the prediction model, and generate corresponding control instructions according to the output results of the prediction model. Perform real-time response control according to the execution devices of the control instructions. Specifically:

[0092] The prediction model is constructed based on the linear regression algorithm, and data prediction is performed through the linear regression coefficient. Then,

[0093] Y = β0 + β1x1 + β2x2 +... + β n x n + ε

[0094] Among them, β represents the linear regression coefficient, and the specific value is set by the implementer according to the actual application scenario. x i represents the display result of the i-th data information in the database in the data table. ε represents the error term, which is used for positive correction of the prediction result. Y represents the prediction result of the prediction model, which is used to generate control instructions to achieve real-time response control of the execution device. Specifically:

[0095] For the prediction results, historical data is used to verify the prediction results. The historical data is used as the input data of the prediction model. According to the output results of the model, the output results are compared with the historical actual data. If the comparison result exceeds 1 / 2 of the historical actual data, it means that the prediction results of the current prediction model are inaccurate, and the linear regression coefficients are adjusted again until the comparison result is lower than 1 / 2 of the historical actual data, which means that the prediction results of the constructed prediction model are accurate. At the same time, control instructions are generated according to the prediction results to achieve the control of the execution device. Specifically:

[0096] If the prediction results indicate a risk of failure, an instruction to stop execution is generated to control the execution device to stop the task being executed and notify the relevant personnel for maintenance;

[0097] If the prediction results do not indicate a risk of failure, an instruction for daily maintenance is generated to control the execution device to continue executing the task being executed and notify the daily staff for the daily maintenance of the device.

[0098] Furthermore, if the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the system described in various embodiments of the present invention. And the aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0099] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0100] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0101] Moreover, in order to provide a concise description of the exemplary embodiments, all features of the actual embodiments may not be described (i.e., those features that are not relevant to the currently contemplated best mode of carrying out the invention or those features that are not relevant to the implementation of the invention).

[0102] It should be understood that in the development of any actual implementation, as in any engineering or design project, numerous specific implementation decisions may be made. Such development efforts may be complex and time-consuming, but for those of ordinary skill in the art who benefit from this disclosure, without undue experimentation, such development efforts will be a routine task of design, fabrication, and production.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. 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 solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An Internet of Things information-based data acquisition and monitoring system, characterized in that: including, a data acquisition module, specifically: collect data through sensors. Through the sensor network deployed in the monitoring area, collect environmental data in real time, and perform preliminary processing on the collected raw data to improve the quality and reliability of the data. Finally, pack the processed data and prepare to transmit it to the data processing and transmission module; a data processing and transmission module, specifically: further clean, format, and optimize the data received from the data acquisition module, and through encryption and authentication mechanisms, ensure that the data can be efficiently transmitted to the central processing unit; and a data monitoring and application module, specifically: convert the processed data into corresponding chart forms, and monitor the environmental status in real time. At the same time, perform real-time analysis on the received data, build a prediction model for trend prediction; and generate control instructions according to the prediction results, and the execution device executes operations according to the control instructions to achieve intelligent management of the execution device.

2. An Internet of Things information-based data acquisition and monitoring system according to claim 1, characterized in that: Build a sensor network to collect environmental data in real time, specifically as follows: Deploy multiple sensor nodes within the monitoring area to ensure that the sensors can cover all of the monitoring area. Then, we have S = {S1, S2,..., S n}, where S i represents the sensor corresponding to the i-th sensor node, and moreover, the deployment ranges of all sensors overlap to cover the entire monitoring area; Collect data in the monitoring area based on the built sensor network, specifically as follows: For the nodes in the sensor network, each sensor node collects environmental data at fixed time intervals Δt. Then, D = {S1(t1), S2(t2),.., S n (t n )}, where S i (t i ) represents the environmental data collected by the i-th sensor at time t i , and the time interval between time t i and time t i-1 is a fixed length Δt.

3. An Internet of Things information-based data acquisition and monitoring system according to claim 2, characterized in that: The preliminary processing of the collected raw data is specifically as follows: Noise filtering is performed on the original data to remove outliers or interfering data, so there is D filed = f(D), where D filed represents the data after filtering, and f represents the filtering function, including median filtering and mean filtering; For the filtered data D filed , the data is converted into a unified format and unit through data standardization, specifically as follows: Among them, D filed represents the data after filtering, μ represents the mean value of the data after filtering, σ represents the standard deviation of the data after filtering, and D std represents the data after normalization; For the data after standardization, data compression is performed, so there is D comp = f comp (D std ), where D std represents the data after standardization, and f comp represents the compression function, including Huffman coding and LZW compression. D comp represents the compressed data for subsequent data storage.

4. The Internet of Things information-based data acquisition and monitoring system according to claim 3, characterized in that: The authentication mechanism is specifically as follows: D valid = f valid (D form ), where D form represents the converted data, and D valid represents the verified data, and f valid represents the verification function. Specifically, the formatted data is sorted according to the time nodes of data collection. If the verified data does not increase as the collection time increases, it means that the data verification fails and data is missing, and the data cleaning process is restarted until the data verification is satisfied.

5. An Internet of Things information-based data acquisition and monitoring system according to claim 4, characterized in that: Encrypt the data after passing the verification to ensure the security of data transmission. Then, there is D encrg = E(D form , K), where D form represents the converted data, D encrg represents the encrypted data, E represents the encryption algorithm, including symmetric encryption algorithm and asymmetric encryption algorithm, and K represents the encryption key corresponding to the adopted encryption algorithm; For the encrypted data, transmit it to the central processing unit through secure protocol communication. At the same time, monitor the network fluctuation status in real time, and dynamically adjust the data transmission strategy according to the monitored network fluctuation status. For the adjustment of the transmission strategy, based on the efficiency of monitoring data transmission, specifically: If it is detected that the data transmission efficiency is lower than the normal transmission threshold, then adjust the transmission strategy to block transmission; If the detected data transmission efficiency is higher than the normal transmission threshold, then adjust the transmission strategy to compressed transmission.

6. An Internet of Things information-based data acquisition and monitoring system according to claim 5, characterized in that: The conversion of the processed data into corresponding chart forms is specifically as follows: For the encrypted data D encrg , the data is decrypted using the decryption key. At the same time, for the decrypted data D dec , data information is extracted. Then, D parsed = f parsed (D dec ), where D dec represents the decrypted data, and represents the data information extracted from the decrypted data. f parsed represents the decryption function; For the data information extracted from the data, store the data in the database, and establish a data index for the stored data to improve the data query efficiency. The establishment of the data index is based on the time sequence nodes when the data is stored in the database, and the index serial numbers are sequentially assigned; For the data information stored in the data according to the serial number, display the extracted data information in the data display terminal in the form of a chart. Specifically, use the extracted data information as the input data of the echarts data table, and through the format conversion of the input data, display it in the data display terminal in different table forms. The forms of the data tables include column charts, line charts, and pie charts.

7. An Internet of Things information-based data acquisition and monitoring system according to claim 6, characterized in that: The construction of the prediction model for trend prediction is specifically as follows: The prediction model is constructed based on the linear regression algorithm, and data prediction is performed through the linear regression coefficient. Then, Y = β0 + β1x1 + β2x2 +... + β n x n + ε Among them, β represents the linear regression coefficient, and the specific value is set by the implementer according to the actual application scenario. x i represents the display result of the i-th data information in the database in the data table. ε represents the error term, which is used for positive correction of the prediction result. Y represents the prediction result of the prediction model, which is used to generate a control instruction to achieve real-time response control of the execution device.

8. An Internet of Things information-based data acquisition and monitoring system according to claim 7, characterized in that, The realization of the intelligent management of the execution device is specifically as follows: For the prediction results, historical data is used to verify the prediction results. The historical data is used as the input data of the prediction model. According to the output results of the model, the output results are compared with the historical actual data. If the comparison result exceeds 1 / 2 of the historical actual data, it means that the prediction results of the current prediction model are inaccurate, and the linear regression coefficients are adjusted again until the comparison result is lower than 1 / 2 of the historical actual data, which means that the prediction results of the constructed prediction model are accurate. At the same time, control instructions are generated according to the prediction results to realize the control of the execution device. Specifically: If the prediction results indicate a risk of failure, an instruction to stop execution is generated to control the execution device to stop the task being executed and notify relevant personnel for maintenance; If the prediction results do not indicate a risk of failure, an instruction for daily maintenance is generated to control the execution device to continue executing the task being executed and notify the daily staff for daily maintenance of the device.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the system according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the system according to any one of claims 1 to 8.

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