An Internet of Things element data management system

Abstract

The present invention relates to the technical field of Internet of Things data management, and in particular to an Internet of Things element data management system, comprising an information acquisition module for acquiring sensor monitoring data in real time, and also for acquiring data transmission information within a monitoring period, an environmental data analysis module for analyzing production environment abnormality according to environmental data, an operation data analysis module for analyzing production monitoring data abnormality and analyzing fluctuation state, the operation data analysis module also for analyzing production state, a data transmission module for judging the data transmission mode of each sensor, and transmitting to a server according to the analysis result of abnormality of production monitoring data and production state analysis result, and a data storage module for storing the abnormality analysis result of the transmitted production monitoring data. The present invention effectively improves the transmission efficiency of Internet of Things element data.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data management, and in particular to an Internet of Things element data management system. Background Art

[0002] With the rapid development of Internet of Things technology, the Internet of Everything has become a reality. However, the collection, storage, processing and security management of massive data have become bottlenecks that restrict the in-depth development of Internet of Things applications. Existing Internet of Things data management systems often lack effective control of data quality, support for data standardization models, and flexible data security strategies, resulting in low data processing efficiency and difficulty in application expansion.

[0003] Chinese patent publication number CN112019626A discloses an industrial Internet of Things system and data processing method. The system includes a controller and a cloud server. The controller is used to obtain Internet of Things data and mark the Internet of Things data as real-time data or trigger data. The controller is also used to send the real-time data to the cloud server. The controller is also used to read the trigger identification bit state corresponding to the trigger data. When the trigger identification bit state is acquisition, the controller is also used to sample the trigger data and save it to the trigger data storage area corresponding to the trigger data. When the trigger identification bit state is trigger, the controller is also used to send the data stored in the trigger data storage area corresponding to the trigger data to the cloud server. The cloud server is used to receive real-time data and trigger data. The cloud server is also used to process the real-time data and trigger data and generate processing results. The system can meet the processing requirements of different types of data by processing different types of data separately, while ensuring data integrity and real-time performance; it can be seen that the invention does not specifically analyze the process of transmitting data to the server, and cannot achieve efficient transmission of real-time data and trigger data to the cloud server, and there is a problem of low efficiency of Internet of Things data transmission. Summary of the invention

[0004] To this end, the present invention provides an Internet of Things element data management system to overcome the problem of low Internet of Things data transmission efficiency in the prior art.

[0005] To achieve the above object, the present invention provides an Internet of Things element data management system, comprising:

[0006] An information acquisition module is used to acquire sensor monitoring data in real time and also to acquire data transmission information within a monitoring period;

[0007] An environmental data analysis module is used to analyze the abnormality of the production environment according to the environmental data, and adjust the analysis process of the abnormality of the production environment according to the signal power density and transmission bandwidth;

[0008] An operation data analysis module is used to analyze the abnormality of the production monitoring data according to the production monitoring data of each sensor, and to analyze the fluctuation state according to the abnormality analysis results of the production monitoring data of each sensor and the historical abnormal data, and the operation data analysis module is also used to analyze the production state according to the fluctuation state analysis results;

[0009] The data transmission module is used to determine the data transmission mode of each sensor according to the abnormality analysis results of the production environment and the data transmission information within the monitoring period, and transmit the abnormality analysis results of the production monitoring data and the production status analysis results to the server according to the data transmission mode;

[0010] The data storage module is used to store the abnormality analysis results of the transmitted production monitoring data as historical abnormality data in the controller.

[0011] Furthermore, the environmental data analysis module is provided with a device signal propagation monitoring unit, and the signal propagation monitoring unit is used to analyze the abnormality of signal propagation according to the real-time temperature and real-time humidity;

[0012] The signal propagation monitoring unit compares the propagation impact factor α with the preset impact factor A, and analyzes the signal propagation abnormality according to the comparison result, wherein:

[0013] When α≥A, the signal propagation monitoring unit determines that the signal propagation abnormality is propagation abnormality;

[0014] When α<A, the signal propagation monitoring unit determines that the signal propagation abnormality is normal;

[0015] The environmental data analysis module is further provided with an electromagnetic monitoring unit, which compares the electromagnetic interference coefficient β with the preset electromagnetic interference coefficient B, and analyzes the environmental electromagnetic anomaly according to the comparison result, wherein:

[0016] When β<B, the electromagnetic monitoring unit determines that the electromagnetic abnormality is normal;

[0017] When β≥B, the electromagnetic monitoring unit determines that the electromagnetic abnormality is abnormal.

[0018] Furthermore, the environmental data analysis module is further provided with an environmental data analysis unit, and the environmental data analysis unit is used to analyze the abnormality of the production environment according to the electromagnetic abnormality analysis results and the signal propagation abnormality analysis results, wherein:

[0019] When the signal propagation abnormality is normal and the electromagnetic abnormality is normal, the environmental data analysis unit determines that the current production environment abnormality is normal, and sets the first environmental abnormality coefficient γ1, setting γ1=1;

[0020] When the signal propagation abnormality is abnormal and the electromagnetic abnormality is normal, the environmental data analysis module determines that the production environment abnormality is a signal abnormality, and sets a second environmental abnormality coefficient γ2, setting γ2=1-ln[1+(α-A) / A];

[0021] When the signal propagation abnormality is normal and the electromagnetic abnormality is abnormal, the environmental data analysis unit determines that the current production environment abnormality is a transmission abnormality, and sets a third environmental abnormality coefficient γ3, setting γ3=cos[(β-B) / B];

[0022] When the signal propagation abnormality is abnormal and the electromagnetic abnormality is abnormal, the environmental data analysis unit determines that the current production environment abnormality is abnormal, and sets the fourth environmental abnormality coefficient γ4, setting γ4=b1×{1-ln[1+(α-A) / A]}+b2×cos[(β-B) / B].

[0023] Furthermore, the environmental data analysis module is further provided with an adjustment unit, which is used to calculate the signal power density ρ of the production area according to the number n of signal transmitting devices in the production area and the area D of the production area, and set ρ=n×P / D, where P is the average transmission power of each signal transmitting device;

[0024] The adjustment unit compares the signal power density ρ of the production area with the preset density C, and adjusts the analysis process of the abnormality of the production environment according to the comparison result, wherein:

[0025] When ρ<C, the adjustment unit determines that the signal power density is normal and does not make any adjustment;

[0026] When ρ≥C, the adjustment unit determines that the signal power density is abnormal, and adjusts the environmental abnormality coefficient to γi', setting γi'=γi×sin[(ρ-C) / C];

[0027] Among them, i=1,2,3,4.

[0028] Furthermore, the environmental data analysis module is further provided with an optimization unit, which compares the server transmission bandwidth k with the server processing data rate K during the monitoring period, and optimizes the adjustment process of the key production abnormality according to the comparison result, wherein:

[0029] When k<K, the optimization unit determines that the data transmission volume within the monitoring period is normal and does not perform optimization;

[0030] When k≥K, the optimization unit determines that the data transmission volume in the monitoring period is abnormal, and optimizes the preset density to C', setting C'=C×{1-arctan[(kK) / K]}.

[0031] Further, the operation data analysis module is provided with a data anomaly analysis unit, and the data anomaly analysis unit is used to analyze the anomaly of the production monitoring data according to the production monitoring data;

[0032] The data anomaly analysis unit compares the production monitoring data h(j, z) of each sensor with each production threshold, and analyzes the anomaly of the production monitoring data according to the comparison result, wherein:

[0033] When h(j, z)≥H2(j) or h(j, z)

[0034] When H1(j)<h(j,z)<H2(j), the data abnormality analysis unit determines that the production monitoring data is normal data;

[0035] Among them, h(j,z) represents the zth production monitoring data transmitted by the jth sensor, H1(j) is the left value of the normal range of the production monitoring data of the jth sensor, and H2(j) is the right value of the normal range of the production monitoring data of the jth sensor.

[0036] Furthermore, the operation data analysis module is also provided with a fluctuation analysis unit, and the fluctuation analysis unit is used to analyze the fluctuation state of each sensor according to the production monitoring data within the monitoring period;

[0037] The fluctuation analysis unit counts the abnormal data in the production monitoring data of each sensor, and calculates the coefficient of variation η(j) of the abnormal data of each sensor according to the statistical results. The calculation formula of the coefficient of variation η(j) of the abnormal data of each sensor is as follows:

[0038] η(j)=σ(j) 1 / 2 / E(j);

[0039] σ(j)={[Y(j,1)-E(j)] 2 +[Y(j,2)-E(j)] 2 +...+[Y(j,Nj)-E(j)] 2} / Nj;

[0040] E(j)=[Y(j,1)+Y(j,2)+...+Y(j,Nj)] / Nj;

[0041] ​Among them, Y(j,1) is the production monitoring data currently monitored by the j-th sensor, Y(j,2) is the 1st historical abnormal data of the j-th sensor, Y(j,Nj) is the Nj-1th historical abnormal data of the j-th sensor, Nj is the number of historical abnormal data, σ(j) is the variance of the abnormal data of the j-th sensor, and E(j) is the mean of the abnormal data of the j-th sensor;

[0042] The fluctuation analysis unit compares the coefficient of variation η(j) of the abnormal data of each sensor with the coefficient of variation threshold R, and analyzes the fluctuation state of each sensor according to the comparison result, wherein:

[0043] When η(j)<R, the fluctuation analysis unit determines that the fluctuation state of the sensor is normal;

[0044] When η(j)≥R, the fluctuation analysis unit determines that the fluctuation state of the sensor is abnormal.

[0045] Furthermore, the operation data analysis module is also provided with a production status analysis unit, and the production status analysis unit is used to analyze the production status according to the fluctuation status analysis results of each sensor within the monitoring period;

[0046] The production status analysis unit counts the number NY of sensors whose sensor fluctuation status is abnormal;

[0047] When NY / N×Σ{[η(j)-R] / R}<U, the production status analysis unit determines that the production status of the equipment within the monitoring period is normal;

[0048] When NY / N×Σ{[η(j)-R] / R}≥U, the production status analysis unit determines that the production status of the equipment within the monitoring period is abnormally fluctuating;

[0049] Where U is the preset production status index and N is the number of equipment sensors.

[0050] Furthermore, the data transmission module is provided with a pattern analysis unit, and the pattern analysis unit is used to analyze the data transmission pattern within the monitoring period according to the data transmission information within the monitoring period and the production environment abnormality analysis result;

[0051] The mode analysis unit calculates the server load index q according to the data transmission information in the monitoring period. The calculation formula of the network status index q is as follows:

[0052] q={arctan[k×log2(1+S / F)]×c1+ln(τ)×c2+ln(μ)×c3};

[0053] Where k is the server data transmission bandwidth, τ is the data transmission delay, μ is the data transmission packet loss rate, S is the signal power, F is the noise power, c1 is the network bandwidth weight, c2 is the network delay weight, and c3 is the packet loss rate weight;

[0054] The mode analysis unit analyzes the data transmission mode within the monitoring period according to the server load index q and the production environment abnormality analysis result within the monitoring period, wherein:

[0055] When the abnormality of the production environment is normal, if q<Q, the mode analysis unit sets the data transmission mode within the monitoring period to the normal data transmission mode; if q≥Q, the mode analysis unit sets the data transmission mode within the monitoring period to the balanced data transmission mode;

[0056] When the production environment abnormality is a signal abnormality, the mode analysis unit sets the data transmission mode within the monitoring period to a redundant transmission mode;

[0057] When the production environment abnormality is a transmission abnormality, if q<Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to a balanced transmission mode; if q≥Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to a redundant transmission mode;

[0058] When the production environment abnormality is abnormal, if q<Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to the redundant transmission mode; if q≥Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to the balanced redundant transmission mode.

[0059] Furthermore, the data transmission module is further provided with a data transmission unit, which performs data transmission management on the production monitoring data abnormality analysis result and the production status analysis result according to the data transmission mode analysis result within the monitoring period, wherein:

[0060] When the data transmission mode in the monitoring period is the normal transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W1, and sets W1=W;

[0061] When the data transmission mode in the monitoring period is the balanced transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W2, and sets W2=W×cos[(qQ) / Q];

[0062] When the data transmission mode in the monitoring period is a redundant transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W3, and sets W3=W×{1-arctan[(qQ×γi) / Q]};

[0063] When the data transmission mode in the monitoring period is the balanced redundant transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W4, and sets W4=W×{1-arctan[(qQ×γi) / Q]-sin[(qQ) / Q]};

[0064] Where W is the maximum transmission packet size;

[0065] The data transmission unit transmits the production monitoring data abnormality analysis results and the production status analysis results to the server with a transmission data packet size Gv, and v is set to 1, 2, 3, 4.

[0066] Compared with the prior art, the beneficial effects of the present invention are that the information acquisition module improves the accuracy and completeness of information acquisition by acquiring the information required by the system, analyzes the abnormality of the production environment through the information environment data analysis module, and improves the accuracy of the signal state analysis of the production environment, analyzes the abnormality of the production monitoring data through the operation data analysis module, and judges the abnormal data, thereby improving the accuracy of the abnormality analysis of the production monitoring data, the operation data analysis module also analyzes the production status according to the fluctuation state analysis results of each sensor during the monitoring period, thereby improving the accuracy of the production status analysis, and the data transmission module improves the transmission efficiency of the Internet of Things element data by performing data transmission management on the abnormality analysis results of the production monitoring data and the production status analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a structural diagram of the Internet of Things element data management system of this embodiment.

[0068] Figure 2 Schematic diagram of the structure of the environmental data analysis module of this embodiment.

[0069] Figure 3 This is a schematic diagram of the structure of the data analysis module running in this embodiment.

[0070] Figure 4 Schematic diagram of the structure of the data transmission module in this embodiment. DETAILED DESCRIPTION

[0071] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0072] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0073] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0074] See also Figure 1 As shown, it is a structural diagram of the Internet of Things element data management system of this embodiment, including:

[0075] The information acquisition module is used to acquire sensor monitoring data in real time and to acquire data transmission information within the monitoring period; the sensor monitoring data includes environmental data and production monitoring data of each sensor; the environmental data includes real-time temperature, real-time humidity, electromagnetic monitoring data, the number of signal transmitting devices in the production area and the area of ​​the production area; the electromagnetic monitoring data includes transmitting power, receiving power, gain of transmitting antenna, gain of receiving antenna, radio wave wavelength and test distance, and the electromagnetic monitoring data is specifically the data obtained by electromagnetic antenna monitoring in the production area; the production monitoring data in this embodiment is the transmission data of each sensor in the production equipment; the data transmission information includes server data transmission bandwidth, data transmission delay, data transmission packet loss rate, signal Power, noise power and server processing data rate; this embodiment does not specifically limit the acquisition method of sensor monitoring data and data transmission information, and those skilled in the art can freely set it. This embodiment acquires data through sensors and transmits the data to the controller by wire; this embodiment does not specifically limit the time interval for real-time data acquisition, and those skilled in the art can freely set it, and only needs to meet the value requirements of the time interval for real-time data acquisition, such as the value of the time interval for real-time data acquisition can be set to 1s, 2s, 3s, etc.; this embodiment does not specifically limit the value of the monitoring period, and those skilled in the art can freely set it, and only needs to meet the value requirements of the monitoring period, such as the monitoring period can be set to 10min, 15min, 20min, etc.;

[0076] An environmental data analysis module, used to analyze the abnormality of the production environment according to the environmental data, and to adjust the analysis process of the abnormality of the production environment according to the signal power density and the transmission bandwidth, the environmental analysis module is connected to the information acquisition module;

[0077] An operation data analysis module is used to analyze the abnormality of the production monitoring data according to the production monitoring data of each sensor, and to analyze the fluctuation state according to the abnormality analysis results of the production monitoring data of each sensor and the historical abnormal data. The operation data analysis module is also used to analyze the production state according to the fluctuation state analysis results. The operation data analysis module is connected to the information acquisition module; the historical abnormal data is the abnormality analysis results of the production monitoring data stored historically;

[0078] A data transmission module, used to judge the data transmission mode of each sensor according to the production environment abnormality analysis result and the data transmission information within the monitoring period, and transmit the production monitoring data abnormality analysis result and the production status analysis result to the server according to the data transmission mode, and the data transmission module is connected with the operation data analysis module and the environment data analysis module;

[0079] The data storage module is used to store the abnormality analysis results of the transmitted production monitoring data as historical abnormality data in the controller, and the data storage module is connected to the data transmission module.

[0080] See also Figure 2 As shown, it is a schematic diagram of the structure of the environmental data analysis module of this embodiment, including:

[0081] A signal propagation monitoring unit is used to analyze signal propagation anomalies based on real-time temperature and real-time humidity;

[0082] An electromagnetic monitoring unit is used to calculate the electromagnetic interference coefficient and analyze the environmental electromagnetic anomaly according to the calculation result of the electromagnetic interference coefficient;

[0083] An environmental data analysis unit, used to analyze production environment abnormality according to the electromagnetic abnormality analysis results and the signal propagation abnormality analysis results, the environmental data analysis unit is connected to the signal propagation monitoring unit and the electromagnetic monitoring unit;

[0084] An adjustment unit, used to calculate the signal power density of the production area and adjust the analysis process of the production environment abnormality according to the signal power density calculation result, and the adjustment unit is connected to the environmental data analysis unit;

[0085] The optimization unit is used to optimize the adjustment process of the production key abnormality according to the server transmission bandwidth within the monitoring period, and the optimization unit is connected to the adjustment unit.

[0086] See also Figure 3 As shown, it is a structural diagram of the operation data analysis module of this embodiment, including:

[0087] A data anomaly analysis unit, used to analyze the anomaly of the production monitoring data based on the production monitoring data;

[0088] A fluctuation analysis unit, used to analyze the fluctuation state of each sensor according to the production monitoring data within the monitoring period, and the fluctuation analysis unit is connected to the data abnormality analysis unit;

[0089] The production status analysis unit is used to analyze the production status according to the fluctuation status analysis results of each sensor within the monitoring period, and the production status analysis unit is connected to the fluctuation analysis unit.

[0090] See also Figure 4 As shown, it is a schematic diagram of the structure of the data transmission module of this embodiment, including:

[0091] A pattern analysis unit, used to analyze the data transmission pattern within the monitoring period according to the data transmission information within the monitoring period and the abnormality analysis result of the production environment;

[0092] The data transmission unit is used to perform data transmission management on the production monitoring data abnormality analysis results and the production status analysis results according to the data transmission pattern analysis results within the monitoring period, and the data transmission unit is connected to the pattern analysis unit.

[0093] Specifically, the Internet of Things element data management system described in this embodiment is applied to the management of industrial Internet of Things element data in the controller of production equipment; the industrial Internet of Things element data described in this embodiment is specifically the equipment data and environmental data acquired by various sensors in the production process of high-power electrical equipment; the specific function of the controller described in this embodiment is to receive sensor monitoring data, analyze the sensor monitoring data, and then transmit the data to the server; the present invention improves the transmission efficiency of Internet of Things element data by analyzing the abnormal state of the production environment at the production site, the abnormal data in the production monitoring data, and the production status of the equipment, and transmitting the abnormal data and the production status of the equipment according to the analysis results.

[0094] Specifically, the environmental data analysis module is provided with a device signal propagation monitoring unit, and the signal propagation monitoring unit analyzes the abnormality of signal propagation according to the real-time temperature and real-time humidity;

[0095] The signal propagation monitoring unit calculates the propagation impact factor α according to the real-time temperature and the real-time humidity. The calculation formula of the propagation impact factor α is as follows:

[0096] α=|(t-T1) / t+(t-T2) / t|+|(sS) / S|;

[0097] Where t is the real-time temperature of the environment, T1 is the lowest temperature suitable for the sensor to work, T2 is the highest temperature suitable for the sensor to work, T1<T2, s is the real-time humidity of the environment, and S is the absolute humidity threshold;

[0098] The signal propagation monitoring unit compares the propagation impact factor α with the preset impact factor A, and analyzes the signal propagation abnormality according to the comparison result, wherein:

[0099] When α≥A, the signal propagation monitoring unit determines that the signal propagation abnormality is propagation abnormality;

[0100] When α<A, the signal propagation monitoring unit determines that the signal propagation abnormality is normal.

[0101] Specifically, the signal propagation monitoring unit analyzes the abnormality of signal propagation according to the real-time temperature and real-time humidity, thereby improving the accuracy of abnormal analysis of signal propagation in the controller, thereby improving the accuracy of signal state analysis of the production environment, thereby improving the accuracy of data transmission mode analysis, and ultimately improving the transmission efficiency of IoT element data; the present embodiment does not make specific limitations on the method for obtaining the minimum working temperature T1 of the sensor and the maximum working temperature T2 of the sensor, and those skilled in the art can set them freely, such as obtaining the operating temperature range of the controller through the controller working parameter table, and setting the left value of the range to T1 and the right value of the range to T2; at the same time, the present embodiment does not make specific limitations on the value of the absolute humidity threshold S, and those skilled in the art can set it freely, such as setting S to 75%. It is worth noting that the real-time humidity of the environment described in this embodiment is the absolute humidity.

[0102] Specifically, the environmental data analysis module is further provided with an electromagnetic monitoring unit, and the electromagnetic monitoring unit calculates the electromagnetic interference coefficient β according to the environmental data. The calculation formula of the electromagnetic interference coefficient β is as follows:

[0103] β = [PR-PT × (GT × GR × λ 2 ) / (4×π×R) 2 ] / (PR-PT);

[0104] Where PR is the transmit power, PT is the receive power, GT is the gain of the transmit antenna, GR is the gain of the receive antenna, λ is the wavelength of the radio wave, and R is the test distance;

[0105] The electromagnetic monitoring unit compares the electromagnetic interference coefficient β with the preset electromagnetic interference coefficient B, and analyzes the environmental electromagnetic anomaly according to the comparison result, wherein:

[0106] When β<B, the electromagnetic monitoring unit determines that the electromagnetic abnormality is normal;

[0107] When β≥B, the electromagnetic monitoring unit determines that the electromagnetic abnormality is abnormal.

[0108] Specifically, the electromagnetic monitoring unit improves the accuracy of the electromagnetic interference intensity analysis in the production environment area by calculating the electromagnetic interference coefficient and analyzing the environmental electromagnetic abnormality according to the calculation result of the electromagnetic interference coefficient, thereby improving the accuracy of the production environment abnormality analysis, thereby improving the accuracy of the data transmission mode analysis, and ultimately improving the transmission efficiency of the Internet of Things element data; it can be understood that this embodiment does not make specific limitations on the value of the preset electromagnetic interference coefficient B, and technical personnel in this field can set it freely, as long as the value requirements of the preset electromagnetic interference coefficient B are met, such as the preset electromagnetic interference coefficient B can be set to 0.3.

[0109] Specifically, the environmental data analysis module is further provided with an environmental data analysis unit, and the environmental data analysis unit analyzes the abnormality of the production environment according to the electromagnetic abnormality analysis results and the signal propagation abnormality analysis results, wherein:

[0110] When the signal propagation abnormality is normal and the electromagnetic abnormality is normal, the environmental data analysis unit determines that the current production environment abnormality is normal, and sets the first environmental abnormality coefficient γ1, setting γ1=1;

[0111] When the signal propagation abnormality is abnormal and the electromagnetic abnormality is normal, the environmental data analysis module determines that the production environment abnormality is a signal abnormality, and sets a second environmental abnormality coefficient γ2, setting γ2=1-ln[1+(α-A) / A];

[0112] When the signal propagation abnormality is normal and the electromagnetic abnormality is abnormal, the environmental data analysis unit determines that the current production environment abnormality is a transmission abnormality, and sets a third environmental abnormality coefficient γ3, setting γ3=cos[(β-B) / B];

[0113] When the signal propagation abnormality is abnormal and the electromagnetic abnormality is abnormal, the environmental data analysis unit determines that the current production environment abnormality is abnormal, and sets the fourth environmental abnormality coefficient γ4, setting γ4=b1×{1-ln[1+(α-A) / A]}+b2×cos[(β-B) / B];

[0114] Among them, b1 is the signal anomaly weight value, b2 is the electromagnetic anomaly weight value, b1>b2 and b1+b2=1.

[0115] Specifically, the environmental data analysis unit analyzes the abnormalities of the production environment based on the electromagnetic abnormality analysis results and the signal propagation abnormality analysis results, thereby improving the accuracy of the signal status analysis of the production environment, and then improving the accuracy of the data transmission mode analysis, thereby improving the accuracy of the management of the data transmission process, and ultimately improving the transmission efficiency of the Internet of Things element data.

[0116] Specifically, the environmental data analysis module is further provided with an adjustment unit, which calculates the signal power density ρ of the production area according to the number n of signal transmitting devices in the production area and the area D of the production area, and sets ρ=n×P / D, where P is the average transmission power of each signal transmitting device;

[0117] The adjustment unit compares the signal power density ρ of the production area with the preset density C, and adjusts the analysis process of the abnormality of the production environment according to the comparison result, wherein:

[0118] When ρ<C, the adjustment unit determines that the signal power density is normal and does not make any adjustment;

[0119] When ρ≥C, the adjustment unit determines that the signal power density is abnormal, and adjusts the environmental abnormality coefficient to γi', setting γi'=γi×sin[(ρ-C) / C];

[0120] Among them, i=1,2,3,4.

[0121] Specifically, the adjustment unit improves the accuracy of electromagnetic interference analysis in the production environment by calculating the signal power density in the production area and adjusting the analysis process of production environment abnormalities according to the calculation result of the signal power density, thereby improving the accuracy of signal status analysis in the production environment, and further improving the accuracy of data transmission mode analysis, thereby improving the accuracy of management of the data transmission process, and ultimately improving the transmission efficiency of IoT element data; it can be understood that this embodiment does not make specific limitations on the value of the preset density C, and technical personnel in this field can set it freely, as long as the value requirements of the preset density C are met, such as the preset density C can be set to 30mW.

[0122] Specifically, the environmental data analysis module is further provided with an optimization unit, which compares the server transmission bandwidth k with the server processing data rate K during the monitoring period, and optimizes the adjustment process of the key production abnormality according to the comparison result, wherein:

[0123] When k<K, the optimization unit determines that the data transmission volume within the monitoring period is normal and does not perform optimization;

[0124] When k≥K, the optimization unit determines that the data transmission volume in the monitoring period is abnormal, and optimizes the preset density to C', setting C'=C×{1-arctan[(kK) / K]}.

[0125] Specifically, the optimization unit optimizes the adjustment process of key production abnormalities according to the server transmission bandwidth during the monitoring period, improves the accuracy of data transmission mode analysis, thereby improving the accuracy of data transmission process management, and ultimately improving the transmission efficiency of IoT element data.

[0126] Specifically, the operation data analysis module is provided with a data anomaly analysis unit, and the data anomaly analysis unit analyzes the anomaly of the production monitoring data according to the production monitoring data;

[0127] The data anomaly analysis unit compares the production monitoring data h(j, z) of each sensor with each production threshold, and analyzes the anomaly of the production monitoring data according to the comparison result, wherein:

[0128] When h(j, z)≥H2(j) or h(j, z)

[0129] When H1(j)<h(j,z)<H2(j), the data abnormality analysis unit determines that the production monitoring data is normal data;

[0130] Among them, h(j,z) represents the zth production monitoring data transmitted by the jth sensor, H1(j) is the left value of the normal range of the production monitoring data of the jth sensor, and H2(j) is the right value of the normal range of the production monitoring data of the jth sensor.

[0131] Specifically, the data anomaly analysis unit analyzes the abnormality of the production monitoring data based on the production monitoring data, and judges the abnormal data, thereby improving the accuracy of the abnormality analysis of the production monitoring data, and then improving the accuracy of the fluctuation state analysis of each sensor, thereby improving the accuracy of the production state analysis, and ultimately improving the transmission efficiency of the Internet of Things element data.

[0132] Specifically, the operation data analysis module is further provided with a fluctuation analysis unit, which analyzes the fluctuation state of each sensor according to the production monitoring data within the monitoring period;

[0133] The fluctuation analysis unit counts the abnormal data in the production monitoring data of each sensor, and calculates the coefficient of variation η(j) of the abnormal data of each sensor according to the statistical results. The calculation formula of the coefficient of variation η(j) of the abnormal data of each sensor is as follows:

[0134] ​η(j)=σ(j) 1 / 2 / E(j);

[0135] σ(j)={[Y(j,1)-E(j)] 2 +[Y(j,2)-E(j)] 2 +...+[Y(j,Nj)-E(j)] 2} / Nj;

[0136] E(j)=[Y(j,1)+Y(j,2)+...+Y(j,Nj)] / Nj;

[0137] Among them, Y(j,1) is the production monitoring data currently monitored by the j-th sensor, Y(j,2) is the 1st historical abnormal data of the j-th sensor, Y(j,Nj) is the Nj-1th historical abnormal data of the j-th sensor, Nj is the number of historical abnormal data, σ(j) is the variance of the abnormal data of the j-th sensor, and E(j) is the mean of the abnormal data of the j-th sensor;

[0138] The fluctuation analysis unit compares the coefficient of variation η(j) of the abnormal data of each sensor with the coefficient of variation threshold R, and analyzes the fluctuation state of each sensor according to the comparison result, wherein:

[0139] When η(j)<R, the fluctuation analysis unit determines that the fluctuation state of the sensor is normal;

[0140] When η(j)≥R, the fluctuation analysis unit determines that the fluctuation state of the sensor is abnormal.

[0141] Specifically, the fluctuation analysis unit analyzes the fluctuation state of each sensor according to the production monitoring data within the monitoring period, thereby improving the accuracy of the fluctuation state analysis of the abnormal data of each sensor, thereby improving the accuracy of the production state analysis, and ultimately improving the transmission efficiency of the IoT element data; it can be understood that this embodiment does not make a specific limitation on the value of the coefficient of variation threshold R, and technical personnel in this field can set it freely, as long as the value requirements of the coefficient of variation threshold R are met, such as the coefficient of variation threshold R can be set to 30%.

[0142] Specifically, the operation data analysis module is further provided with a production status analysis unit, which analyzes the production status according to the fluctuation status analysis results of each sensor within the monitoring period;

[0143] The production status analysis unit counts the number NY of sensors whose sensor fluctuation status is abnormal;

[0144] When NY / N×Σ{[η(j)-R] / R}<U, the production status analysis unit determines that the production status of the equipment within the monitoring period is normal;

[0145] When NY / N×Σ{[η(j)-R] / R}≥U, the production status analysis unit determines that the production status of the equipment within the monitoring period is abnormally fluctuating;

[0146] Among them, U is the preset production status index, and N is the number of equipment sensors;

[0147] The production status analysis unit integrates the abnormal data of the production monitoring data of each sensor and the production status analysis result into a data dictionary.

[0148] Specifically, the production status analysis unit analyzes the production status according to the fluctuation status analysis results of each sensor during the monitoring period, thereby improving the accuracy of the production status analysis, and then improving the accuracy of the data transmission mode analysis, thereby improving the accuracy of the management of the data transmission process, and ultimately improving the transmission efficiency of the Internet of Things element data; it can be understood that this embodiment does not make specific limitations on the value of the preset production status index U, and technical personnel in this field can set it freely as long as the value requirements of U are met, such as U can be set to 1.

[0149] Specifically, the data transmission module is provided with a pattern analysis unit, and the pattern analysis unit analyzes the data transmission pattern within the monitoring period according to the data transmission information within the monitoring period and the production environment abnormality analysis result;

[0150] The mode analysis unit calculates the server load index q according to the data transmission information in the monitoring period. The calculation formula of the network status index q is as follows:

[0151] q={arctan[k×log2(1+S / F)]×c1+ln(τ)×c2+ln(μ)×c3};

[0152] Wherein, τ is the data transmission delay, μ is the data transmission packet loss rate, S is the signal power, F is the noise power, c1 is the network bandwidth weight, c2 is the network delay weight, c3 is the packet loss rate weight, c1+c2+c3=1 and 0<c1<c3<c2<1;

[0153] The mode analysis unit analyzes the data transmission mode within the monitoring period according to the server load index q and the production environment abnormality analysis result within the monitoring period, wherein:

[0154] When the abnormality of the production environment is normal, if q<Q, the mode analysis unit sets the data transmission mode within the monitoring period to the normal data transmission mode; if q≥Q, the mode analysis unit sets the data transmission mode within the monitoring period to the balanced data transmission mode;

[0155] When the production environment abnormality is a signal abnormality, the mode analysis unit sets the data transmission mode within the monitoring period to a redundant transmission mode;

[0156] When the production environment abnormality is a transmission abnormality, if q<Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to a balanced transmission mode; if q≥Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to a redundant transmission mode;

[0157] When the production environment abnormality is abnormal, if q<Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to the redundant transmission mode; if q≥Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to the balanced redundant transmission mode;

[0158] Wherein, Q is the server load status threshold.

[0159] Specifically, the pattern analysis unit analyzes the data transmission pattern within the monitoring period according to the data transmission information within the monitoring period and the production environment abnormality analysis results, thereby improving the analysis of the data transmission pattern, improving the accuracy of managing the data transmission process, and ultimately improving the transmission efficiency of IoT element data; it can be understood that this embodiment does not make specific limitations on the value of the server load status threshold Q, and technical personnel in this field can set it freely, as long as the value requirements of the server load status threshold Q are met, such as the server load status threshold Q can be set to 0.4.

[0160] Specifically, the data transmission module is further provided with a data transmission unit, and the data transmission unit performs data transmission management on the production monitoring data abnormality analysis result and the production status analysis result according to the data transmission mode analysis result within the monitoring period, wherein:

[0161] When the data transmission mode in the monitoring period is the normal transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W1, and sets W1=W;

[0162] When the data transmission mode in the monitoring period is the balanced transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W2, and sets W2=W×cos[(qQ) / Q];

[0163] When the data transmission mode in the monitoring period is a redundant transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W3, and sets W3=W×{1-arctan[(qQ×γi) / Q]};

[0164] When the data transmission mode in the monitoring period is the balanced redundant transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W4, and sets W4=W×{1-arctan[(qQ×γi) / Q]-sin[(qQ) / Q]};

[0165] Where W is the maximum transmission packet size;

[0166] The data transmission unit transmits the production monitoring data abnormality analysis results and the production status analysis results to the server with a transmission data packet size Gv, and v is set to 1, 2, 3, 4.

[0167] Specifically, the data transmission management of the production monitoring data abnormality analysis results and the production status analysis results is performed according to the data transmission mode analysis results within the monitoring period, thereby improving the transmission efficiency of the IoT element data; it can be understood that this embodiment does not make a specific limitation on the value of the maximum transmission data packet size W, and technical personnel in this field can set it freely, as long as the value requirement of the maximum transmission data packet size W is met. In this embodiment, the maximum transmission data packet size W can be set to 1460 bits.

[0168] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An Internet of Things element data management system, characterized in that: include, An information acquisition module is used to acquire sensor monitoring data in real time and also to acquire data transmission information within a monitoring period; An environmental data analysis module is used to analyze the abnormality of the production environment according to the environmental data, and adjust the analysis process of the abnormality of the production environment according to the signal power density and transmission bandwidth; An operation data analysis module is used to analyze the abnormality of the production monitoring data according to the production monitoring data of each sensor, and to analyze the fluctuation state according to the abnormality analysis results of the production monitoring data of each sensor and the historical abnormal data, and the operation data analysis module is also used to analyze the production state according to the fluctuation state analysis results; The data transmission module is used to determine the data transmission mode of each sensor according to the abnormality analysis results of the production environment and the data transmission information within the monitoring period, and transmit the abnormality analysis results of the production monitoring data and the production status analysis results to the server according to the data transmission mode; A data storage module, used to store the abnormality analysis results of the transmitted production monitoring data as historical abnormality data in the controller; The sensor monitoring data includes environmental data and production monitoring data of each sensor; the environmental data includes real-time temperature, real-time humidity, electromagnetic monitoring data, the number of signal transmitting devices in the production area and the area of ​​the production area; the electromagnetic monitoring data includes transmission power, receiving power, gain of the transmitting antenna, gain of the receiving antenna, radio wave wavelength and test distance, and the electromagnetic monitoring data is specifically data obtained by electromagnetic antenna monitoring in the production area; the data transmission information includes server data transmission bandwidth, data transmission delay, data transmission packet loss rate, signal power, noise power and server processing data rate; The environmental data analysis module is provided with a signal propagation monitoring unit, and the signal propagation monitoring unit is used to analyze the abnormality of signal propagation according to the real-time temperature and real-time humidity; The signal propagation monitoring unit calculates the propagation impact factor α according to the real-time temperature and the real-time humidity. The calculation formula of the propagation impact factor α is as follows: α=|(t-T1) / t+(t-T2) / t|+|(sS) / S|; Where t is the real-time temperature of the environment, T1 is the lowest temperature suitable for the sensor to work, T2 is the highest temperature suitable for the sensor to work, T1<T2, s is the real-time humidity of the environment, and S is the absolute humidity threshold; The signal propagation monitoring unit compares the propagation impact factor α with the preset impact factor A, and analyzes the signal propagation abnormality according to the comparison result; The electromagnetic monitoring unit calculates the electromagnetic interference coefficient β according to the environmental data. The calculation formula of the electromagnetic interference coefficient β is as follows: β=[PR-PT×(GT×GR×λ 2 ) / (4×π×R) 2 ] / (PR-PT); Where PR is the transmit power, PT is the receive power, GT is the gain of the transmit antenna, GR is the gain of the receive antenna, λ is the wavelength of the radio wave, and R is the test distance; The environmental data analysis module is further provided with an electromagnetic monitoring unit, which compares the electromagnetic interference coefficient β with a preset electromagnetic interference coefficient B, and analyzes the environmental electromagnetic anomaly according to the comparison result; The environmental data analysis module is further provided with an environmental data analysis unit, and the environmental data analysis unit is used to analyze the abnormality of the production environment according to the electromagnetic abnormality analysis results and the signal propagation abnormality analysis results; The environmental data analysis module is further provided with an adjustment unit, which is used to calculate the signal power density ρ of the production area according to the number n of signal transmitting devices in the production area and the area D of the production area, and set ρ=n×P / D, where P is the average transmission power of each signal transmitting device; The adjustment unit compares the signal power density ρ of the production area with the preset density C, and adjusts the analysis process of the abnormality of the production environment according to the comparison result; The operation data analysis module is provided with a data anomaly analysis unit, and the data anomaly analysis unit is used to analyze the anomaly of the production monitoring data according to the production monitoring data; The data anomaly analysis unit compares the production monitoring data h(j, z) of each sensor with each production threshold, and analyzes the anomaly of the production monitoring data according to the comparison result; The operation data analysis module is further provided with a fluctuation analysis unit, which is used to analyze the fluctuation state of each sensor according to the production monitoring data within the monitoring period; The fluctuation analysis unit counts abnormal data in the production monitoring data of each sensor, and calculates the coefficient of variation η(j) of the abnormal data of each sensor according to the statistical results; The fluctuation analysis unit compares the coefficient of variation η(j) of the abnormal data of each sensor with the coefficient of variation threshold R, and analyzes the fluctuation state of each sensor according to the comparison result; The data transmission module is provided with a pattern analysis unit, and the pattern analysis unit is used to analyze the data transmission pattern within the monitoring period according to the data transmission information within the monitoring period and the production environment abnormality analysis result; The mode analysis unit calculates the server load index q according to the data transmission information in the monitoring period. The calculation formula of the network status index q is as follows: q={arctan[k×log2(1+S / F)]×c1+ln(τ)×c2+ln(μ)×c3}; Where k is the server data transmission bandwidth, τ is the data transmission delay, μ is the data transmission packet loss rate, S is the signal power, F is the noise power, c1 is the network bandwidth weight, c2 is the network delay weight, and c3 is the packet loss rate weight; The mode analysis unit analyzes the data transmission mode within the monitoring period according to the server load index q and the production environment abnormality analysis result within the monitoring period, wherein: When the abnormality of the production environment is normal, if q<Q, the mode analysis unit sets the data transmission mode within the monitoring period to the normal data transmission mode; if q≥Q, the mode analysis unit sets the data transmission mode within the monitoring period to the balanced data transmission mode; When the production environment abnormality is a signal abnormality, the mode analysis unit sets the data transmission mode within the monitoring period to a redundant transmission mode; When the production environment abnormality is a transmission abnormality, if q<Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to a balanced transmission mode; if q≥Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to a redundant transmission mode; When the production environment abnormality is abnormal, if q<Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to the redundant transmission mode; if q≥Q×γi, the mode analysis unit sets the data transmission mode within the monitoring period to the balanced redundant transmission mode; Wherein, Q is the server load status threshold.

2. The Internet of Things element data management system according to claim 1, characterized in that: When α≥A, the signal propagation monitoring unit determines that the signal propagation abnormality is propagation abnormality; When α<A, the signal propagation monitoring unit determines that the signal propagation abnormality is normal; When β<B, the electromagnetic monitoring unit determines that the electromagnetic abnormality is normal; When β≥B, the electromagnetic monitoring unit determines that the electromagnetic abnormality is abnormal.

3. The Internet of Things element data management system according to claim 2, characterized in that: When the signal propagation abnormality is normal and the electromagnetic abnormality is normal, the environmental data analysis unit determines that the current production environment abnormality is normal, and sets the first environmental abnormality coefficient γ1, setting γ1=1; When the signal propagation abnormality is abnormal and the electromagnetic abnormality is normal, the environmental data analysis module determines that the production environment abnormality is a signal abnormality, and sets a second environmental abnormality coefficient γ2, setting γ2=1-ln[1+(α-A) / A]; When the signal propagation abnormality is normal and the electromagnetic abnormality is abnormal, the environmental data analysis unit determines that the current production environment abnormality is a transmission abnormality, and sets a third environmental abnormality coefficient γ3, setting γ3=cos[(β-B) / B]; When the signal propagation abnormality is abnormal and the electromagnetic abnormality is abnormal, the environmental data analysis unit determines that the current production environment abnormality is abnormal, and sets the fourth environmental abnormality coefficient γ4, setting γ4=b1×{1-ln[1+(α-A) / A]}+b2×cos[(β-B) / B].

4. The Internet of Things element data management system according to claim 3, characterized in that: When ρ<C, the adjustment unit determines that the signal power density is normal and does not make any adjustment; When ρ≥C, the adjustment unit determines that the signal power density is abnormal, and adjusts the environmental abnormality coefficient to γi', setting γi'=γi×sin[(ρ-C) / C]; Among them, i=1,2,3,4.

5. The Internet of Things element data management system according to claim 4, characterized in that: The environmental data analysis module is further provided with an optimization unit, which compares the server transmission bandwidth k with the server processing data rate K during the monitoring period, and optimizes the adjustment process of the key production abnormality according to the comparison result, wherein: When k<K, the optimization unit determines that the data transmission volume within the monitoring period is normal and does not perform optimization; When k≥K, the optimization unit determines that the data transmission volume in the monitoring period is abnormal, and optimizes the preset density to C', setting C'=C×{1-arctan[(kK) / K]}.

6. The Internet of Things element data management system according to claim 1, characterized in that: When h(j, z)≥H2(j) or h(j, z)<H1(j), the data anomaly analysis unit determines that the production monitoring data is abnormal data; When H1(j)<h(j,z)<H2(j), the data abnormality analysis unit determines that the production monitoring data is normal data; Among them, h(j,z) represents the zth production monitoring data transmitted by the jth sensor, H1(j) is the left value of the normal range of the production monitoring data of the jth sensor, and H2(j) is the right value of the normal range of the production monitoring data of the jth sensor.

7. The Internet of Things element data management system according to claim 6, characterized in that: The calculation formula of the coefficient of variation η(j) of abnormal data of each sensor is as follows: η(j)=σ(j) 1 / 2 / E(j); σ(j)={[Y(j,1)-E(j)] 2 +[Y(j,2)-E(j)] 2 +...+[Y(j,Nj)-E(j)] 2 } / Nj; E(j)=[Y(j,1)+Y(j,2)+...+Y(j,Nj)] / Nj; Among them, Y(j,1) is the production monitoring data currently monitored by the j-th sensor, Y(j,2) is the 1st historical abnormal data of the j-th sensor, Y(j,Nj) is the Nj-1th historical abnormal data of the j-th sensor, Nj is the number of historical abnormal data, σ(j) is the variance of the abnormal data of the j-th sensor, and E(j) is the mean of the abnormal data of the j-th sensor; When η(j)<R, the fluctuation analysis unit determines that the fluctuation state of the sensor is normal; When η(j)≥R, the fluctuation analysis unit determines that the fluctuation state of the sensor is abnormal.

8. The Internet of Things element data management system according to claim 7, characterized in that: The operation data analysis module is further provided with a production status analysis unit, which is used to analyze the production status according to the fluctuation status analysis results of each sensor within the monitoring period; The production status analysis unit counts the number NY of sensors whose sensor fluctuation status is abnormal; When NY / N×Σ{[η(j)-R] / R}<U, the production status analysis unit determines that the production status of the equipment within the monitoring period is normal; When NY / N×Σ{[η(j)-R] / R}≥U, the production status analysis unit determines that the production status of the equipment within the monitoring period is abnormally fluctuating; Where U is the preset production status index and N is the number of equipment sensors.

9. The Internet of Things element data management system according to claim 1, characterized in that: The data transmission module is further provided with a data transmission unit, which performs data transmission management on the production monitoring data abnormality analysis result and the production status analysis result according to the data transmission mode analysis result within the monitoring period, wherein: When the data transmission mode in the monitoring period is the normal transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W1, and sets W1=W; When the data transmission mode in the monitoring period is the balanced transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W2, and sets W2=W×cos[(qQ) / Q]; When the data transmission mode in the monitoring period is a redundant transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W3, and sets W3=W×{1-arctan[(qQ×γi) / Q]}; When the data transmission mode in the monitoring period is the balanced redundant transmission mode, the data transmission unit sets the transmission data packet size in the monitoring period to W4, and sets W4=W×{1-arctan[(qQ×γi) / Q]-sin[(qQ) / Q]}; Where W is the maximum transmission packet size; The data transmission unit transmits the production monitoring data abnormality analysis results and the production status analysis results to the server with a transmission data packet size Gv, and v is set to 1, 2, 3, 4.

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