A remote monitoring method and system for efficient material feeding production based on industrial Internet of Things

Abstract

The present invention discloses a remote monitoring method and system for efficient feeding production based on industrial Internet of Things, which relates to the technical field of industrial production monitoring. The method includes counting the feeding equipment connected to the industrial Internet of Things and marking them as Internet of Things feeding equipment; obtaining the currently deployed sensor data and basic application data of the Internet of Things feeding equipment, and importing them into the cloud computing center to analyze the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment; analyzing and obtaining the integrated sensor replacement evaluation result of the Internet of Things feeding equipment according to the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment, and performing an integrated sensor replacement warning; remotely monitoring the Internet of Things feeding equipment after the integrated sensor is replaced, analyzing the production coordination indicator coefficient of the Internet of Things feeding equipment, and performing management and control warning reminders. The present invention can conduct targeted analysis and processing on different production links, improve the accuracy of monitoring the feeding production process and the efficiency of responding to abnormal feeding production situations.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production monitoring, and in particular to a method and system for remote monitoring of efficient material feeding production based on industrial Internet of Things. Background Art

[0002] With the development of industrial automation and the increase in information technology needs, traditional feeding production monitoring methods can no longer meet the current real-time and accuracy requirements of the feeding production process. With the maturity and popularization of Internet of Things technology, feeding equipment is also constantly expanding its application. Remote monitoring can help optimize the feeding production process and reduce management costs. Therefore, it is necessary to establish an efficient feeding production remote monitoring method and system based on the industrial Internet of Things to remotely monitor feeding production, improve production efficiency, and achieve efficient control and optimization of the production process.

[0003] The prior art, such as the invention patent with announcement number: CN116757546B, is a production monitoring and early warning system based on the industrial Internet, including: involving the field of data processing technology, obtaining the expected processing quality evaluation index and the physical state baseline of the production equipment of the first production node, optimizing the processing control parameters according to the preset optimization rules based on the basic information of the conveyed product of the second production node combined with the expected processing quality evaluation index, obtaining the optimized state of the processing control parameters, and performing production monitoring and early warning on the processing control parameter state and the physical state of the production equipment based on the physical state baseline of the production equipment and the optimized state of the processing control parameters. This solves the technical problem that the traditional monitoring and early warning method in the prior art mainly monitors the historical state of the isolated manufacturer, the monitoring level is relatively superficial and the accuracy cannot be guaranteed. It optimizes the state according to the production needs for different warning dimensions, and accurately determines the critical state of the warning judgment, so as to effectively monitor the real-time production and provide accurate and timely early warning.

[0004] The prior art, such as the invention patent with announcement number: CN117291582B, is an industrial production interconnection monitoring system based on data analysis, including: involving the technical field of monitoring systems, the processing module pre-processes the monitoring data, operation data and product data, and then obtains the abnormal coefficient by comprehensive calculation, and the abnormal coefficient information is sent to the analysis module. The analysis module compares the abnormal coefficient with the abnormal threshold, and judges whether there is an abnormality in the product processing based on the comparison result of the abnormal coefficient and the abnormal threshold. The judgment result is sent to the control module, and when the received judgment result is that there is an abnormality in the product processing. This invention obtains multiple data in the product production process through the interconnection of multiple monitoring devices, and judges whether there is an abnormality in the product processing based on the comparison result of the abnormal coefficient and the abnormal threshold. The analysis is more comprehensive and can effectively control the production line to stop running in time when an abnormality occurs in the product processing, which not only ensures the quality of product production, but also avoids greater accidents or economic losses caused by the spread of abnormalities.

[0005] It can be seen from the above scheme that the current production monitoring system often only performs detection and analysis on the production equipment itself, and pays relatively little attention to the sensor devices used to monitor the equipment, which reduces the efficiency and accuracy of the monitoring process, increases the cost of enterprise feeding production monitoring, and indirectly increases the error rate of the monitoring process. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an efficient remote monitoring method and system for feeding production based on industrial Internet of Things, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a remote monitoring method for efficient feeding production based on industrial Internet of Things, comprising the following steps:

[0008] S1, counts the feeding devices connected to the Industrial Internet of Things, marked as IoT feeding devices.

[0009] S2, obtain the currently deployed sensor data and basic application data of the IoT feeding equipment, and import them into the cloud computing center to analyze the integrated sensor replacement characterization coefficient of the IoT feeding equipment.

[0010] S3, according to the integrated sensor replacement characterization coefficient of the IoT feeding equipment, the integrated sensor replacement evaluation result of the IoT feeding equipment is analyzed and an integrated sensor replacement warning is issued.

[0011] S4, remotely monitor the IoT feeding equipment after the integrated sensor is replaced, analyze the production coordination indicator coefficient of the IoT feeding equipment, and issue management and control warnings.

[0012] As an optimal technical solution, the currently deployed sensor data of the IoT feeding equipment is obtained, and the specific acquisition process is as follows: statistics are collected on each production process link of the IoT feeding equipment, wherein each production process link includes a first feeding link, a second storage link, and a third distribution link.

[0013] According to the preset evaluation period, several time points are divided into equal intervals to obtain each time point. The data collection frequency, data conversion speed and data transmission speed of the built-in sensors in each production process link at each time point are collected respectively, and the first sub-element data of the currently deployed sensor as the IoT feeding equipment is combined.

[0014] The accumulated data collection volume, average application response delay, and average application energy consumption value of built-in sensors in each production process link are extracted from the industrial Internet of Things background management center, and combined with the second sub-element data of the currently deployed sensors as IoT feeding equipment.

[0015] The first sub-element data of the currently deployed sensor of the Internet of Things feeding device and the second sub-element data of the currently deployed sensor of the Internet of Things feeding device are taken together as the currently deployed sensor data of the Internet of Things feeding device.

[0016] As a preferred technical solution, the basic application data includes the rated service life, current application life, cumulative power-on operation time, average daily power energy consumption and total number of failures of the IoT feeding equipment.

[0017] As a preferred technical solution, the integrated sensor replacement characterization coefficient of the IoT feeding equipment is analyzed, and the specific process is as follows: based on the first sub-element data of the currently deployed sensor of the IoT feeding equipment, the reference data collection frequency, reference data conversion speed and reference data transmission speed of the sensor are extracted from the cloud database, and the first characterization factor of the degree of use of the built-in sensors in each production process link is obtained through analysis and processing.

[0018] According to the second sub-element data of the currently deployed sensors of the IoT feeding equipment, the reference cumulative data collection volume of the sensors is extracted from the cloud database, the average application response delay is defined, and the average application energy consumption value is defined. The second characterization factor of the degree of use of the built-in sensors in each production process link is obtained through analysis and processing.

[0019] Based on the basic application data, the rated power-on operation time, reference average daily power energy consumption and fault number threshold of the IoT feeding equipment are extracted from the cloud database, and the application degree characterization factor of the IoT feeding equipment is obtained through analysis and processing.

[0020] Based on the first characterization factor of the usage degree of built-in sensors in each production process link and the second characterization factor of the usage degree of built-in sensors in each production process link, the sensor usage degree characterization factors of the IoT feeding equipment are comprehensively analyzed.

[0021] According to the application degree characterization factor of the IoT feeding equipment and the sensor usage degree characterization threshold value corresponding to each application degree characterization factor interval of the IoT feeding equipment stored in the cloud database, the sensor usage degree characterization threshold value of the IoT feeding equipment is obtained by matching.

[0022] According to the sensor usage level characterization threshold value of the IoT feeding equipment and according to the sensor usage level characterization factor of the IoT feeding equipment, the integrated sensor replacement characterization coefficient of the IoT feeding equipment is obtained through analysis.

[0023] The integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment is specifically obtained by processing the currently deployed sensor data and basic application data of the Internet of Things feeding equipment to obtain a numerical result for analyzing the application production degree of the Internet of Things feeding equipment, and is used as an analysis basis for the integrated sensor replacement evaluation result of the Internet of Things feeding equipment.

[0024] As an optimal technical solution, the analysis obtains an integrated sensor replacement evaluation result of the Internet of Things feeding equipment. The specific analysis process is as follows: extract the integrated sensor replacement characterization coefficient threshold of the Internet of Things feeding equipment in the cloud database, and compare it with the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment. If the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment is lower than the integrated sensor replacement characterization coefficient threshold of the Internet of Things feeding equipment, then the integrated sensor replacement evaluation result of the Internet of Things feeding equipment is defined as not executing replacement.

[0025] If the integrated sensor replacement characterization coefficient of the IoT feeding device is higher than or equal to the integrated sensor replacement characterization coefficient threshold of the IoT feeding device, the integrated sensor replacement evaluation result of the IoT feeding device is defined as demand replacement, and the demand replacement request is transmitted to the cloud computing center.

[0026] The cloud computing center receives requests for demand changes and simultaneously counts the usage characteristics of the built-in sensors in each production process link.

[0027] The reference range of the usage degree characterization factor of the built-in sensor in each production process link of the integrated feeding equipment is extracted from the cloud database, and the usage degree characterization factor of the built-in sensor in each production process link is compared with the reference range of the usage degree characterization factor of the built-in sensor in each production process link. If the usage degree characterization factor of the built-in sensor in a production process link exceeds the reference range of the usage degree characterization factor of the built-in sensor in that production process link, the integrated sensor replacement evaluation result of that production process link is defined as the execution replacement.

[0028] The usage characterization factors of the built-in sensors in each production process link are traversed in turn to obtain the integrated sensor replacement evaluation results of each production process link.

[0029] As a preferred technical solution, the production coordination indicator coefficient of the IoT feeding equipment is analyzed, and the specific analysis process is as follows: the state parameters of the IoT feeding equipment in the first feeding link are obtained, and the state parameters include the pressure difference, raw material height difference and flow rate difference in each sensing time period, and the allowable pressure difference, allowable raw material height difference and allowable flow rate difference of the IoT feeding equipment in the first feeding link stored in the cloud database are extracted, and the production coordination indicator coefficient of the first feeding link of the IoT feeding equipment is obtained by analysis and processing.

[0030] The operating parameters of the IoT feeding equipment in the second material storage link are obtained, and the operating parameters include the temperature difference, humidity difference and gas concentration difference in each sensing period. The defined temperature difference, defined humidity difference and defined gas concentration difference of the IoT feeding equipment in the second material storage link stored in the cloud database are extracted, and the production coordination indicator coefficient of the second material storage link of the IoT feeding equipment is obtained through analysis and processing.

[0031] The transmission and distribution data of the IoT feeding equipment in the third distribution link are obtained, and the transmission and distribution data include the average raw material transmission and distribution speed, the average perceived pressure of raw material transmission and distribution, and the average perceived load of raw material transmission and distribution in each perception period. The reference raw material transmission and distribution speed, the reference raw material transmission and distribution perceived pressure, and the reference raw material transmission and distribution perceived load of the IoT feeding equipment in the third distribution link stored in the cloud database are extracted, and the production coordination indicator coefficient of the third distribution link of the IoT feeding equipment is obtained through analysis and processing.

[0032] According to the production coordination indicator coefficient of the first feeding link of the Internet of Things feeding equipment, the production coordination indicator coefficient of the second storage link of the Internet of Things feeding equipment and the production coordination indicator coefficient of the third distribution link of the Internet of Things feeding equipment, the production coordination indicator coefficient of the Internet of Things feeding equipment is obtained through comprehensive analysis.

[0033] The production coordination indicator coefficient of the Internet of Things feeding equipment is specifically a numerical result obtained by remotely monitoring the Internet of Things feeding equipment after the integrated sensor is replaced, which is used to comprehensively analyze the production coordination degree of the feeding equipment, and is used as a basis for judging the feeding equipment management and control warning reminders.

[0034] As a preferred technical solution, the specific process of the management and control warning reminder is as follows: extract the production coordination indicator coefficient threshold of the Internet of Things feeding equipment stored in the cloud database, and compare it with the production coordination indicator coefficient of the Internet of Things feeding equipment. When the production coordination indicator coefficient of the Internet of Things feeding equipment is less than the production coordination indicator coefficient threshold of the Internet of Things feeding equipment, the feeding equipment is calibrated as an abnormal feeding equipment, and the warning reminder signal generated by the abnormal feeding equipment is uploaded to the cloud computing center and a management and control warning reminder is performed.

[0035] As a preferred technical solution, the integrated sensor replacement characterization coefficient of the IoT feeding equipment is specifically analyzed by the formula: Among them, D is the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment, Y is the sensor usage degree characterization factor of the Internet of Things feeding equipment, Y0 is the sensor usage degree characterization threshold value of the Internet of Things feeding equipment, y is the preset sensor usage degree characterization correction coefficient of the Internet of Things feeding equipment, and e is a natural constant.

[0036] As a preferred technical solution, the production coordination index coefficient of the IoT feeding equipment is specifically formulated as follows: H = tan -1(E*φ1+F*φ2+G*φ3), where H is the production coordination indicator coefficient of the Internet of Things feeding equipment, E is the production coordination indicator coefficient of the first feeding link of the Internet of Things feeding equipment, F is the production coordination indicator coefficient of the second storage link of the Internet of Things feeding equipment, G is the production coordination indicator coefficient of the third distribution link of the Internet of Things feeding equipment, φ1 is the preset production coordination indicator coefficient weight of the first feeding link, φ2 is the preset production coordination indicator coefficient weight of the second storage link, and φ3 is the preset production coordination indicator coefficient weight of the third distribution link.

[0037] The second aspect of the present invention provides an efficient remote monitoring system for feeding production based on the industrial Internet of Things, including: a feeding equipment statistics module, which is used to count the feeding equipment connected to the industrial Internet of Things, marked as IoT feeding equipment.

[0038] The data acquisition and analysis module obtains the currently deployed sensor data and basic application data of the IoT feeding equipment, and imports them into the cloud computing center to analyze the integrated sensor replacement characterization coefficient of the IoT feeding equipment.

[0039] The integrated sensor replacement assessment module is used to analyze and obtain the integrated sensor replacement assessment result of the Internet of Things feeding equipment according to the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment, and to issue an integrated sensor replacement warning.

[0040] The remote monitoring and control module of feeding equipment remotely monitors the feeding equipment after the integrated sensor is replaced, analyzes the production coordination indicator coefficient of the IoT feeding equipment, and issues control warning reminders.

[0041] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0042] (1) The present invention provides an efficient remote monitoring method and system for material feeding production based on the industrial Internet of Things, which can effectively combine the characteristic performance of equipment sensors for detailed monitoring and processing, and can help enterprises to accurately monitor the material feeding production process remotely, discover abnormal situations in time and respond to them, thereby reducing risk losses.

[0043] (2) The present invention analyzes the integrated sensor replacement characterization coefficient of the IoT feeding equipment and analyzes and processes the currently deployed sensor data and basic application data of the IoT feeding equipment. It can reflect the application production level of the IoT feeding equipment in a targeted manner, improve the accuracy of the feeding production monitoring process, and provide guarantee for the stable operation of the IoT feeding equipment.

[0044] (3) The present invention analyzes the integrated sensor replacement evaluation results of the IoT feeding equipment, analyzes and processes the sensors in different production links, can promptly detect abnormal sensors, and provide targeted replacement warnings, which not only improves the accuracy of the subsequent feeding production monitoring process, but also helps to reduce the replacement or maintenance costs of the feeding equipment.

[0045] (4) The present invention analyzes the production coordination indicator coefficient of the IoT feeding equipment and performs targeted processing on the production performance of different production links of the IoT feeding equipment, making the judgment of management and control warning more accurate.

[0046] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0048] Figure 2 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "all around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0051] See also Figure 1 As shown, an embodiment of the present invention provides a remote monitoring method for efficient feeding production based on industrial Internet of Things, comprising the following steps:

[0052] Statistics on the feeding equipment connected to the Industrial Internet of Things are marked as IoT feeding equipment.

[0053] The currently deployed sensor data and basic application data of the IoT feeding equipment are obtained, and imported into the cloud computing center to analyze the integrated sensor replacement characterization coefficient of the IoT feeding equipment.

[0054] Specifically, the cloud computing center refers to a data center dedicated to providing cloud computing services, and in this embodiment is used to analyze the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment.

[0055] In this embodiment, the specific process of obtaining the currently deployed sensor data of the Internet of Things feeding equipment is as follows: statistics are collected on each production process link of the Internet of Things feeding equipment, where each production process link includes a first feeding link, a second storage link, and a third distribution link.

[0056] According to the preset evaluation period, several time points are divided into equal intervals to obtain each time point. The data collection frequency, data conversion speed and data transmission speed of the built-in sensors in each production process link at each time point are collected respectively, and the first sub-element data of the currently deployed sensor as the IoT feeding equipment is combined.

[0057] It should be understood that the data conversion speed refers to the speed at which the sensor converts an analog signal into a digital signal, and the data transmission speed refers to the data transmission speed of the wireless communication module integrated in the sensor.

[0058] The accumulated data collection volume, average application response delay, and average application energy consumption value of built-in sensors in each production process link are extracted from the industrial Internet of Things background management center, and combined with the second sub-element data of the currently deployed sensors as IoT feeding equipment.

[0059] In this embodiment, the industrial Internet of Things background management center is used to store the cumulative data collection volume, average application response delay and average application energy consumption value of the built-in sensors in each production process link.

[0060] It should be understood that the cumulative data collection amount of the built-in sensors in each production process link refers to the total amount of data points collected by the built-in sensors in each production process link within the total application cycle of the built-in sensors in each production process link.

[0061] It should be understood that the average application response delay of the built-in sensors in each production process link refers to the average value of the time interval from the built-in sensors in each production process link receiving the input signal to generating the corresponding output.

[0062] The first sub-element data of the currently deployed sensor of the Internet of Things feeding device and the second sub-element data of the currently deployed sensor of the Internet of Things feeding device are taken together as the currently deployed sensor data of the Internet of Things feeding device.

[0063] In this embodiment, the basic application data includes the rated service life of the IoT feeding equipment, the current application life of the IoT feeding equipment, the cumulative power-on operation time of the IoT feeding equipment, the average daily power energy consumption of the IoT feeding equipment and the total number of failures of the IoT feeding equipment.

[0064] According to the integrated sensor replacement characterization coefficient of the IoT feeding equipment, the integrated sensor replacement evaluation result of the IoT feeding equipment is analyzed and an integrated sensor replacement warning is issued.

[0065] In this embodiment, the integrated sensor replacement characterization coefficient of the IoT feeding equipment is analyzed, and the specific process is as follows: based on the first sub-element data of the currently deployed sensor of the IoT feeding equipment, the reference data collection frequency, reference data conversion speed and reference data transmission speed of the sensor are extracted from the cloud database, and the first characterization factor of the degree of use of the built-in sensors in each production process link is obtained through analysis and processing.

[0066] In this embodiment, the cloud database is used to store indicator data one, indicator data two and indicator data three, wherein indicator data one includes a reference data collection frequency, a reference data conversion speed and a reference data transmission speed of the sensor, and also includes a reference cumulative data collection amount, a reference average application response delay and a reference average application energy consumption value of the sensor.

[0067] Data indicator two includes the rated power-on operating time of the IoT feeding equipment, the reference average daily power consumption and the fault number threshold, as well as the sensor usage level characterization threshold value of the IoT feeding equipment, the integrated sensor replacement characterization coefficient threshold value and the reference range of the usage level characterization factor of the built-in sensors in each production process link.

[0068] Data indicator three includes the allowable pressure difference, allowable raw material height difference and allowable flow difference of the Internet of Things feeding equipment in the first feeding link, as well as the defined temperature difference, defined humidity difference and defined gas concentration difference of the Internet of Things feeding equipment in the second storage link, and also includes the reference raw material distribution average speed, reference raw material distribution average perceived pressure, reference raw material distribution average perceived load and the production coordination indicator coefficient threshold of the Internet of Things feeding equipment in the third distribution link.

[0069] In this embodiment, the first characterization factor of the usage level of the built-in sensors in each production process link is obtained by analyzing the first sub-element data of the currently deployed sensors of the Internet of Things feeding equipment, and the numerical analysis results on the sensor usage level of the Internet of Things feeding equipment are obtained, and are used as the analysis basis for the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment.

[0070] In a specific embodiment, the first characterization factor of the degree of use of the built-in sensors in each production process link can be obtained by establishing a deep learning model and comprehensively analyzing the sensor usage data of the IoT feeding equipment, and can also be obtained by calculation. The specific calculation formula is as follows: Among them, A i is the first characterization factor of the degree of use of built-in sensors in the i-th production process link, αij is the data collection frequency of the built-in sensor in the i-th production process link at the j-th time point, α0 is the reference data collection frequency of the sensor, β ij is the data conversion speed of the built-in sensor in the i-th production process link at the j-th time point, β0 is the reference data conversion speed of the sensor, δ ij is the data transmission speed of the built-in sensor in the i-th production process link at the j-th time point, δ0 is the reference data transmission speed of the sensor, is the preset data collection frequency weight, is the preset data conversion speed weight, is the preset data transmission speed weight, i is the number of the production link, i=1, 2, 3, when i=1, it represents the first feeding link, when i=2, it represents the second storage link, when i=3, it represents the third distribution link, j is the number of each time point, j=1, 2, ..., n, n is the number of time points, and e is a natural constant.

[0071] According to the second sub-element data of the currently deployed sensors of the IoT feeding equipment, the reference cumulative data collection volume of the sensors is extracted from the cloud database, the average application response delay is defined, and the average application energy consumption value is defined. The second characterization factor of the degree of use of the built-in sensors in each production process link is obtained through analysis and processing.

[0072] In this embodiment, the second characterization factor of the usage degree of the built-in sensors in each production process link is obtained by analyzing the second sub-element data of the currently deployed sensors of the Internet of Things feeding equipment, and the numerical analysis results on the sensor usage degree of the Internet of Things feeding equipment are obtained, and are used as the analysis basis for the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment.

[0073] In a specific embodiment, the second characterization factor of the usage degree of the built-in sensor in each production process link can be obtained by establishing a long short-term memory network model and analyzing and processing the sensor usage degree data, and can also be obtained by calculation. The specific calculation formula is as follows: Among them, B i is the second characterization factor of the degree of use of built-in sensors in the i-th production process link, γ i is the cumulative data collection of the built-in sensors in the production process, γ0 is the reference cumulative data collection of the sensors, τ i is the average application response delay of the built-in sensor in the production process, τ0 is the reference average application response delay of the sensor, ω i is the average energy consumption value of the built-in sensor in the production process, ω0 is the reference average energy consumption value of the sensor, is the preset cumulative data collection amount weight, is the preset average application response delay weight, is the preset average application energy consumption value weight, and e is a natural constant.

[0074] Based on the basic application data, the rated power-on operation time, reference average daily power energy consumption and fault number threshold of the IoT feeding equipment are extracted from the cloud database, and the application degree characterization factor of the IoT feeding equipment is obtained through analysis and processing.

[0075] In this embodiment, the application degree characterization factor of the Internet of Things feeding equipment is specifically a numerical analysis result of the application degree of the Internet of Things feeding equipment obtained by analyzing the basic application data of the Internet of Things feeding equipment, and is used as an analysis basis for the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment.

[0076] In a specific embodiment, the application degree characterization factor of the IoT feeding device can be obtained by applying statistical methods and establishing a distribution function, or by calculation. The specific calculation formula is as follows: Among them, C is the application degree characterization factor of the Internet of Things feeding equipment, ε is the current application period of the Internet of Things feeding equipment, ε0 is the rated service life of the Internet of Things feeding equipment, θ is the cumulative power-on operation time of the Internet of Things feeding equipment, θ0 is the rated power-on operation time of the Internet of Things feeding equipment, μ is the average daily power energy consumption of the Internet of Things feeding equipment, μ0 is the reference average daily power energy consumption of the Internet of Things feeding equipment, σ is the total number of failures of the Internet of Things feeding equipment, σ0 is the failure number threshold of the Internet of Things feeding equipment, x1 is the preset application period weight, x2 is the preset power-on operation time weight, x3 is the preset daily average power energy consumption weight, x4 is the preset total number of failures weight, and e is a natural constant.

[0077] Based on the first characterization factor of the usage degree of built-in sensors in each production process link and the second characterization factor of the usage degree of built-in sensors in each production process link, the sensor usage degree characterization factors of the IoT feeding equipment are comprehensively analyzed.

[0078] According to the application degree characterization factor of the IoT feeding equipment and the sensor usage degree characterization threshold value corresponding to the application degree characterization factor interval of the IoT feeding equipment stored in the cloud database, the sensor usage degree characterization threshold value of the IoT feeding equipment is obtained by matching.

[0079] In this embodiment, the sensor usage degree characterization factor of the Internet of Things feeding equipment is obtained by analyzing the first sub-element data of the currently deployed sensors of the Internet of Things feeding equipment and the second sub-element data of the currently deployed sensors of the Internet of Things feeding equipment, and the numerical analysis results on the sensor usage degree of the Internet of Things feeding equipment are obtained, and are used as the analysis basis for the application degree characterization threshold value of the Internet of Things feeding equipment.

[0080] In a specific embodiment, the sensor usage degree characterization factor of the IoT feeding device can be obtained by cluster analysis or by calculation. The specific calculation formula is as follows: Among them, Y is the sensor usage factor of the IoT feeding equipment, A i is the first characterization factor of the degree of use of built-in sensors in the i-th production process link, B i is the second characterization factor of the degree of use of built-in sensors in the i-th production process link, is the weight of the first characterization factor of the preset usage degree, is the preset weight of the second characterization factor of the degree of use, i is the number of the production link, i=1, 2, 3, when i=1, it represents the first feeding link, when i=2, it represents the second storage link, when i=3, it represents the third distribution link.

[0081] In this embodiment, the sensor usage level characterization threshold value of the Internet of Things feeding equipment is specifically obtained through the following process: the numerical results of the application level characterization factors of the Internet of Things feeding equipment are allocated to corresponding intervals in the cloud database, and then the corresponding sensor usage level characterization threshold value is extracted therefrom.

[0082] According to the sensor usage level characterization threshold value of the IoT feeding equipment and according to the sensor usage level characterization factor of the IoT feeding equipment, the integrated sensor replacement characterization coefficient of the IoT feeding equipment is obtained through analysis.

[0083] The integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment is specifically obtained by processing the currently deployed sensor data and basic application data of the Internet of Things feeding equipment to obtain a numerical result for analyzing the application production degree of the Internet of Things feeding equipment, and is used as an analysis basis for the integrated sensor replacement evaluation result of the Internet of Things feeding equipment.

[0084] In a specific embodiment, the integrated sensor replacement characterization coefficient of the IoT feeding device is specifically analyzed by the formula: Among them, D is the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment, Y is the sensor usage degree characterization factor of the Internet of Things feeding equipment, Y0 is the sensor usage degree characterization threshold value of the Internet of Things feeding equipment, y is the preset sensor usage degree characterization correction coefficient of the Internet of Things feeding equipment, and e is a natural constant.

[0085] In this embodiment, the integrated sensor replacement characterization coefficient of the analysis of the Internet of Things feeding equipment can be used to analyze the application production level of the Internet of Things feeding equipment through analysis and processing of the currently deployed sensor data and basic application data of the Internet of Things feeding equipment, thereby improving the accuracy of the feeding production monitoring process and providing guarantee for the stable operation of the Internet of Things feeding equipment.

[0086] In this embodiment, the analysis obtains the integrated sensor replacement evaluation result of the Internet of Things feeding equipment. The specific analysis process is as follows: extract the integrated sensor replacement characterization coefficient threshold of the Internet of Things feeding equipment in the cloud database, and compare it with the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment. If the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment is lower than the integrated sensor replacement characterization coefficient threshold of the Internet of Things feeding equipment, the integrated sensor of the Internet of Things feeding equipment is defined as not to be replaced.

[0087] If the integrated sensor replacement characterization coefficient of the IoT feeding device is higher than or equal to the integrated sensor replacement characterization coefficient threshold of the IoT feeding device, the integrated sensor replacement evaluation result of the IoT feeding device is defined as demand replacement, and the demand replacement request is transmitted to the cloud computing center.

[0088] In this embodiment, the integrated sensor replacement assessment results of the IoT feeding equipment are analyzed and processed for sensors in different production links, abnormal sensors can be discovered in a timely manner, and targeted replacement warnings can be provided, which not only improves the accuracy of the feeding production monitoring process, but also reduces the cost of equipment replacement.

[0089] The cloud computing center receives requests for demand changes and simultaneously counts the usage characteristics of the built-in sensors in each production process link.

[0090] In a specific embodiment, the statistical usage characterization factor of the built-in sensors in each production process link can be obtained by establishing a distribution function model or by calculation. The specific formula is as follows: Among them, Y i is the factor representing the degree of use of built-in sensors in the i-th production process link, A i is the first characterization factor of the degree of use of built-in sensors in the i-th production process link, B i is the second characterization factor of the degree of use of built-in sensors in the i-th production process link, is the weight of the first characterization factor of the preset usage degree, It is the weight of the second characterization factor of the preset usage degree.

[0091] The reference range of the usage degree characterization factor of the built-in sensor in each production process link of the integrated feeding equipment is extracted from the cloud database, and the usage degree characterization factor of the built-in sensor in each production process link is compared with the reference range of the usage degree characterization factor of the built-in sensor in each production process link. If the usage degree characterization factor of the built-in sensor in a production process link exceeds the reference range of the usage degree characterization factor of the built-in sensor in that production process link, the integrated sensor replacement evaluation result of that production process link is defined as the execution replacement.

[0092] The usage characterization factors of the built-in sensors in each production process link are traversed in turn to obtain the integrated sensor replacement evaluation results of each production process link.

[0093] Remotely monitor the IoT feeding equipment after the integrated sensor is replaced, analyze the production coordination indicator coefficient of the IoT feeding equipment, and issue management and control warnings.

[0094] In this embodiment, the production coordination indicator coefficient of the IoT feeding equipment is analyzed, and the specific analysis process is as follows: the state parameters of the IoT feeding equipment in the first feeding link are obtained, and the state parameters include the pressure difference, raw material height difference and flow rate difference in each sensing time period, and the allowable pressure difference, allowable raw material height difference and allowable flow rate difference of the IoT feeding equipment in the first feeding link stored in the cloud database are extracted, and the production coordination indicator coefficient of the first feeding link of the IoT feeding equipment is obtained by analysis and processing.

[0095] The state parameters of the IoT feeding equipment in the first feeding link are collected by the built-in integrated sensor in the first feeding link of the IoT feeding equipment. The built-in integrated sensor in the first feeding link of the feeding production includes a pressure sensor, a height sensor and a flow sensor.

[0096] The production coordination indicator coefficient of the first feeding link of the Internet of Things feeding equipment is specifically obtained by analyzing and processing the state parameters of the Internet of Things feeding equipment in the first feeding link, thereby obtaining a numerical result for analyzing the production coordination degree of the Internet of Things feeding equipment in the first feeding link, and used as a basis for analyzing the production coordination indicator coefficient of the Internet of Things feeding equipment.

[0097] In a specific embodiment, the production coordination index coefficient of the first feeding link of the IoT feeding equipment can be obtained by calculation in addition to analysis and processing by establishing a convolutional neural network model. The specific formula is as follows: Among them, E is the production coordination index coefficient of the first feeding link of the IoT feeding equipment, p k is the pressure difference of the IoT feeding device in the kth sensing period of the first feeding link, Δp is the allowable pressure difference of the IoT feeding device in the first feeding link, and h k is the raw material height difference of the IoT feeding equipment in the kth sensing period of the first feeding link, Δh is the allowable raw material height difference of the IoT feeding equipment in the first feeding link, a k is the flow difference of the kth sensing period of the IoT feeding equipment in the first feeding link, Δa is the allowable flow difference of the IoT feeding equipment in the first feeding link, z1 is the preset pressure difference weight, z2 is the preset raw material height difference weight, z3 is the preset flow difference weight, k is the number of each sensing period, k=1, 2, ..., g, g is the number of sensing periods, and e is a natural constant.

[0098] The operating parameters of the IoT feeding equipment in the second material storage link are obtained, and the operating parameters include the temperature difference, humidity difference and gas concentration difference in each sensing period. The defined temperature difference, defined humidity difference and defined gas concentration difference of the IoT feeding equipment in the second material storage link stored in the cloud database are extracted, and the production coordination indicator coefficient of the second material storage link of the IoT feeding equipment is obtained through analysis and processing.

[0099] The operating parameters of the Internet of Things feeding equipment in the second material storage link are collected by the built-in integrated sensors in the second material storage link of the Internet of Things feeding equipment. The built-in integrated sensors in the second material storage link of the Internet of Things feeding equipment include temperature sensors, humidity sensors and gas concentration sensors.

[0100] The production coordination indicator coefficient of the second material storage link of the Internet of Things feeding equipment is specifically obtained by analyzing and processing the operating parameters of the Internet of Things feeding equipment in the second material storage link, thereby obtaining a numerical result for analyzing the degree of production coordination of the Internet of Things feeding equipment in the second material storage link, and used as a basis for analyzing the production coordination indicator coefficient of the Internet of Things feeding equipment.

[0101] In a specific embodiment, the production coordination index coefficient of the second material storage link of the IoT feeding equipment can be obtained by analyzing and processing by establishing a cyclic sequence model, or by calculation. The specific formula is as follows: Among them, F is the production coordination index coefficient of the second storage link of the IoT feeding equipment, b k is the temperature difference of the IoT feeding equipment in the kth sensing period of the second storage link, Δb is the defined temperature difference of the IoT feeding equipment in the second storage link, c k is the humidity difference of the IoT feeding device in the kth sensing period of the second storage link, Δc is the defined humidity difference of the IoT feeding device in the second storage link, d k is the gas concentration difference of the kth sensing period of the IoT feeding equipment in the second storage link, Δd is the defined gas concentration difference of the IoT feeding equipment in the second storage link, z4 is the preset temperature weight, z5 is the preset humidity weight, z6 is the preset gas concentration weight, k is the number of each sensing period, k=1, 2, ..., g, g is the number of sensing periods.

[0102] The transmission and distribution data of the IoT feeding equipment in the third distribution link are obtained, and the transmission and distribution data include the average raw material transmission and distribution speed, the average perceived pressure of raw material transmission and distribution, and the average perceived load of raw material transmission and distribution in each perception period. The reference raw material transmission and distribution speed, the reference raw material transmission and distribution perceived pressure, and the reference raw material transmission and distribution perceived load of the IoT feeding equipment in the third distribution link stored in the cloud database are extracted, and the production coordination indicator coefficient of the third distribution link of the IoT feeding equipment is obtained through analysis and processing.

[0103] The transmission and distribution data of the IoT feeding equipment in the third distribution link is collected by the built-in integrated sensor of the IoT feeding equipment in the third distribution link. The built-in integrated sensor of the IoT feeding equipment in the third distribution link includes a speed sensor, a pressure sensor and a load sensor.

[0104] The production coordination indicator coefficient of the third distribution link of the Internet of Things feeding equipment is specifically obtained by analyzing and processing the transmission and distribution data of the Internet of Things feeding equipment in the third distribution link, and is used to analyze the numerical result of the production coordination degree of the Internet of Things feeding equipment in the third distribution link, and is used as the analysis basis of the production coordination indicator coefficient of the Internet of Things feeding equipment.

[0105] In a specific embodiment, the production coordination index coefficient of the third distribution link of the IoT feeding equipment can be obtained by comprehensive analysis of data by establishing a BETR pre-training model, or by calculation. The specific formula is as follows:

[0106] Among them, G is the production coordination index coefficient of the third distribution link of IoT feeding equipment, v k is the average speed of raw material delivery and distribution of the IoT feeding equipment in the kth sensing period of the third distribution link, Δv is the average speed of reference raw material delivery and distribution of the IoT feeding equipment in the third distribution link, and f k is the average perceived pressure of raw material distribution of the IoT feeding equipment in the kth sensing period of the third distribution link, Δf is the average perceived pressure of reference raw material distribution of the IoT feeding equipment in the third distribution link, m k is the average perceived load of raw material distribution of the IoT feeding equipment in the kth perception period of the third distribution link, Δm is the reference average perceived load of raw material distribution of the IoT feeding equipment in the third distribution link, z7 is the preset speed weight, z8 is the preset pressure weight, and z9 is the preset load weight.

[0107] According to the production coordination indicator coefficient of the first feeding link of the Internet of Things feeding equipment, the production coordination indicator coefficient of the second storage link of the Internet of Things feeding equipment and the production coordination indicator coefficient of the third distribution link of the Internet of Things feeding equipment, the production coordination indicator coefficient of the Internet of Things feeding equipment is obtained through comprehensive analysis.

[0108] The production coordination indicator coefficient of the Internet of Things feeding equipment is specifically a numerical result obtained by remotely monitoring the Internet of Things feeding equipment after the integrated sensor is replaced, which is used to comprehensively analyze the production coordination degree of the feeding equipment, and is used as a basis for judging the feeding equipment management and control warning reminders.

[0109] In a specific embodiment, the production coordination index coefficient of the IoT feeding equipment is specifically formulated as follows: H = tan -1(E*φ1+F*φ2+G*φ3), where H is the production coordination indicator coefficient of the Internet of Things feeding equipment, E is the production coordination indicator coefficient of the first feeding link of the Internet of Things feeding equipment, F is the production coordination indicator coefficient of the second storage link of the Internet of Things feeding equipment, G is the production coordination indicator coefficient of the third distribution link of the Internet of Things feeding equipment, φ1 is the preset production coordination indicator coefficient weight of the first feeding link, φ2 is the preset production coordination indicator coefficient weight of the second storage link, and φ3 is the preset production coordination indicator coefficient weight of the third distribution link.

[0110] In this embodiment, the production coordination indicator coefficient of the IoT feeding equipment is analyzed, and the production coordination degree data of different production links of the IoT feeding equipment are processed in a targeted manner, so that the judgment on whether the feeding equipment is abnormal is more accurate.

[0111] In this embodiment, the specific process of performing the management and control warning reminder is as follows: extracting the production coordination indicator coefficient threshold of the Internet of Things feeding equipment stored in the cloud database, and comparing it with the production coordination indicator coefficient of the Internet of Things feeding equipment; when the production coordination indicator coefficient of the Internet of Things feeding equipment is less than the production coordination indicator coefficient threshold of the Internet of Things feeding equipment, the feeding equipment is calibrated as an abnormal feeding equipment, and the warning reminder signal generated by the abnormal feeding equipment is uploaded to the cloud computing center and a management and control warning reminder is performed.

[0112] It should be understood that when the production coordination indicator coefficient of the Internet of Things feeding equipment is greater than or equal to the production coordination indicator coefficient threshold of the Internet of Things feeding equipment, the current feeding equipment is in a normal operating state and can continue to be used.

[0113] It should be understood that the generation of the warning reminder signal refers to the use of an alarm and a pop-up window to remind personnel to handle the abnormal feeding equipment in a timely manner after the abnormal feeding equipment is detected.

[0114] See also Figure 2 As shown, the second aspect of an embodiment of the present invention provides an efficient remote monitoring system for feeding production based on industrial Internet of Things, including: a feeding equipment statistics module, a data acquisition and analysis module, an integrated sensor replacement evaluation module and a feeding equipment remote monitoring and control module.

[0115] The feeding equipment statistics module is used to count the feeding equipment connected to the Industrial Internet of Things, which is marked as IoT feeding equipment.

[0116] The data acquisition and analysis module obtains the currently deployed sensor data and basic application data of the IoT feeding equipment, and imports them into the cloud computing center to analyze the integrated sensor replacement characterization coefficient of the IoT feeding equipment.

[0117] The integrated sensor replacement assessment module is used to analyze and obtain the integrated sensor replacement assessment result of the Internet of Things feeding equipment according to the integrated sensor replacement characterization coefficient of the Internet of Things feeding equipment, and to issue an integrated sensor replacement warning.

[0118] The remote monitoring and control module of feeding equipment remotely monitors the feeding equipment after the integrated sensor is replaced, analyzes the production coordination indicator coefficient of the IoT feeding equipment, and issues control warning reminders.

[0119] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0120] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. An efficient remote monitoring method for feeding production based on industrial Internet of Things, characterized by: The following steps are involved: S1, counts the feeding devices connected to the industrial Internet of Things, marked as IoT feeding devices; S2, obtaining the currently deployed sensor data and basic application data of the IoT feeding equipment, and importing them into the cloud computing center to analyze the integrated sensor replacement characterization coefficient of the IoT feeding equipment; S3, analyzing and obtaining the integrated sensor replacement evaluation result of the IoT feeding equipment according to the integrated sensor replacement characterization coefficient of the IoT feeding equipment, and issuing an integrated sensor replacement warning; S4, remotely monitor the IoT feeding equipment after the integrated sensor is replaced, analyze the production coordination indicator coefficient of the IoT feeding equipment, and issue management and control warning reminders; The basic application data includes the rated service life, current application life, cumulative power-on operation time, average daily power energy consumption and total number of failures of the IoT feeding equipment; The integrated sensor replacement characterization coefficient of the analysis IoT feeding device is specifically processed as follows: According to the first sub-element data of the currently deployed sensors of the IoT feeding equipment, the reference data collection frequency, reference data conversion speed and reference data transmission speed of the sensors are extracted from the cloud database, and the first characterization factor of the degree of use of the built-in sensors in each production process link is obtained through analysis and processing; According to the second sub-element data of the currently deployed sensors of the IoT feeding equipment, the reference cumulative data collection volume of the sensors is extracted from the cloud database, the average application response delay is defined, and the average application energy consumption value is defined. The second characterization factor of the degree of use of the built-in sensors in each production process link is obtained through analysis and processing; Based on the basic application data, the rated power-on operation time, reference daily average power energy consumption and fault number threshold of the IoT feeding equipment are extracted from the cloud database, and the application degree characterization factor of the IoT feeding equipment is obtained through analysis and processing; According to the first characterization factor of the degree of use of built-in sensors in each production process link and the second characterization factor of the degree of use of built-in sensors in each production process link, the sensor use degree characterization factor of the IoT feeding equipment is comprehensively analyzed; According to the application degree characterization factor of the IoT feeding device, and according to the sensor usage degree characterization threshold value corresponding to each application degree characterization factor interval of the IoT feeding device stored in the cloud database, the sensor usage degree characterization threshold value of the IoT feeding device is matched therefrom; According to the sensor usage level characterization threshold value of the IoT feeding equipment, and according to the sensor usage level characterization factor of the IoT feeding equipment, the integrated sensor replacement characterization coefficient of the IoT feeding equipment is obtained through analysis; The integrated sensor replacement characterization coefficient of the IoT feeding device is specifically obtained by processing the currently deployed sensor data and basic application data of the IoT feeding device to obtain a numerical result for analyzing the application production degree of the IoT feeding device, and is used as an analysis basis for the integrated sensor replacement evaluation result of the IoT feeding device; Analyze the production coordination index coefficient of IoT feeding equipment. The specific analysis process is as follows: Obtain the state parameters of the IoT feeding equipment in the first feeding link, which include the pressure difference, raw material height difference and flow rate difference in each sensing period, and extract the permissible pressure difference, permissible raw material height difference and permissible flow rate difference of the IoT feeding equipment in the first feeding link stored in the cloud database, and analyze and process to obtain the production coordination indicator coefficient of the first feeding link of the IoT feeding equipment; Obtaining the operating parameters of the IoT feeding equipment in the second material storage link, the operating parameters include the temperature difference, humidity difference and gas concentration difference in each sensing period, and extracting the defined temperature difference, defined humidity difference and defined gas concentration difference of the IoT feeding equipment in the second material storage link stored in the cloud database, and analyzing and processing to obtain the production coordination indicator coefficient of the IoT feeding equipment in the second material storage link; Acquire the transmission and distribution data of the IoT feeding equipment in the third distribution link, which includes the average speed of raw material transmission and distribution, the average perceived pressure of raw material transmission and distribution, and the average perceived load of raw material transmission and distribution in each perception period, and extract the reference raw material transmission and distribution speed, reference raw material transmission and distribution perceived pressure, and reference raw material transmission and distribution perceived load of the IoT feeding equipment in the third distribution link stored in the cloud database, and analyze and process to obtain the production coordination indicator coefficient of the third distribution link of the IoT feeding equipment; According to the production coordination index coefficient of the first feeding link of the Internet of Things feeding equipment, the production coordination index coefficient of the second storage link of the Internet of Things feeding equipment and the production coordination index coefficient of the third distribution link of the Internet of Things feeding equipment, the production coordination index coefficient of the Internet of Things feeding equipment is obtained by comprehensive analysis; The production coordination indicator coefficient of the IoT feeding equipment is specifically a numerical result obtained by remotely monitoring the IoT feeding equipment after the integrated sensor is replaced, which is used to comprehensively analyze the production coordination degree of the feeding equipment, and is used as a basis for judging the warning reminder of the feeding equipment management and control; The integrated sensor replacement characterization coefficient of the IoT feeding equipment is specifically analyzed by the formula: ； in, It is the integrated sensor replacement characterization coefficient of the IoT feeding equipment. It is the sensor usage level characterization factor of IoT feeding equipment. It is the threshold value representing the sensor usage level of IoT feeding equipment. Characterize the correction factor for the sensor usage level of the preset IoT feeding equipment. is a natural constant; The production coordination index coefficient of the IoT feeding equipment is specifically formulated as follows: ； in, is the production coordination index coefficient of IoT feeding equipment, It is the production coordination index coefficient of the first feeding link of IoT feeding equipment. It is the production coordination index coefficient of the second material storage link of IoT feeding equipment. It is the production coordination index coefficient of the third distribution link of IoT feeding equipment. is the preset production coordination index coefficient weight of the first feeding link, is the preset production coordination index coefficient weight of the second storage link, It is the production coordination indicator coefficient weight of the preset third allocation link.

2. The method for remote monitoring of efficient material feeding production based on industrial Internet of Things according to claim 1 is characterized in that: The specific acquisition process of obtaining the currently deployed sensor data of the IoT feeding device is as follows: Count the various production process links of the IoT feeding equipment, where each production process link includes the first feeding link, the second storage link and the third distribution link; According to the preset evaluation period, several time points are divided at equal intervals to obtain each time point, and the data collection frequency, data conversion speed and data transmission speed of the built-in sensors in each production process link at each time point are collected respectively, and the first sub-element data of the currently deployed sensor as the IoT feeding equipment is combined; Extract the accumulated data collection volume, average application response delay, and average application energy consumption value of the built-in sensors in each production process link from the industrial Internet of Things background management center, and combine it with the second sub-element data of the currently deployed sensors as IoT feeding equipment; The first sub-element data of the currently deployed sensor of the Internet of Things feeding device and the second sub-element data of the currently deployed sensor of the Internet of Things feeding device are taken together as the currently deployed sensor data of the Internet of Things feeding device.

3. The method for remote monitoring of efficient material feeding production based on industrial Internet of Things according to claim 1 is characterized in that: The analysis obtained the integrated sensor replacement evaluation results of the IoT feeding equipment. The specific analysis process is as follows: Extract the integrated sensor replacement coefficient threshold of the IoT feeding device in the cloud database, and compare it with the integrated sensor replacement coefficient of the IoT feeding device. If the integrated sensor replacement coefficient of the IoT feeding device is lower than the integrated sensor replacement coefficient threshold of the IoT feeding device, define the integrated sensor replacement evaluation result of the IoT feeding device as not performing replacement. If the integrated sensor replacement characterization coefficient of the IoT feeding device is higher than or equal to the integrated sensor replacement characterization coefficient threshold of the IoT feeding device, the integrated sensor replacement evaluation result of the IoT feeding device is defined as demand replacement, and the demand replacement request is transmitted to the cloud computing center; The cloud computing center receives demand replacement requests and simultaneously counts the usage level characterization factors of the built-in sensors in each production process link; Extract the reference range of the usage degree characterization factor of the built-in sensor of each production process link of the integrated feeding equipment from the cloud database, and compare the usage degree characterization factor of the built-in sensor of each production process link with the reference range of the usage degree characterization factor of the built-in sensor of each production process link. If the usage degree characterization factor of the built-in sensor of a production process link exceeds the reference range of the usage degree characterization factor of the built-in sensor of the production process link, the integrated sensor replacement evaluation result of the production process link is defined as the execution replacement; The usage characterization factors of the built-in sensors in each production process link are traversed in turn to obtain the integrated sensor replacement evaluation results of each production process link.

4. The method for remote monitoring of efficient material feeding production based on industrial Internet of Things according to claim 1 is characterized in that: The specific process of the control warning reminder is as follows: The production coordination indicator coefficient threshold of the IoT feeding equipment stored in the cloud database is extracted and compared with the production coordination indicator coefficient of the IoT feeding equipment. When the production coordination indicator coefficient of the IoT feeding equipment is less than the production coordination indicator coefficient threshold of the IoT feeding equipment, the feeding equipment is calibrated as an abnormal feeding equipment, and the warning reminder signal generated by the abnormal feeding equipment is uploaded to the cloud computing center for management and control warning reminders.

5. A system using the method for remote monitoring of efficient material feeding production based on industrial Internet of Things as described in any one of claims 1 to 4, characterized in that: include: Feeding equipment statistics module, used to count the feeding equipment connected to the industrial Internet of Things, marked as IoT feeding equipment; The data acquisition and analysis module obtains the currently deployed sensor data and basic application data of the IoT feeding equipment, and imports them into the cloud computing center to analyze the integrated sensor replacement characterization coefficient of the IoT feeding equipment; An integrated sensor replacement assessment module is used to analyze and obtain an integrated sensor replacement assessment result of the IoT feeding equipment according to the integrated sensor replacement characterization coefficient of the IoT feeding equipment, and to issue an integrated sensor replacement warning; The remote monitoring and control module of feeding equipment remotely monitors the feeding equipment after the integrated sensor is replaced, analyzes the production coordination indicator coefficient of the IoT feeding equipment, and issues control warning reminders.

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