Production data management and analysis system based on Internet of Things

Abstract

The invention discloses a production data management and analysis system based on the Internet of Things. The production data management and analysis system based on the Internet of Things comprises a production data acquisition module, a production data processing module and a production data management and analysis module. According to the invention, the production data acquisition module acquires the production data of the to-be-monitored production workshop and sends the data to the production data processing module, and then the production data processing module acquires the reference production data in combination with the historical production data; judging whether a benefit risk, an environment risk and an equipment risk exist in the to-be-monitored production workshop in combination with the corresponding reference production data, if so, processing, and if not, obtaining second production data according to the first production data and sending the second production data to a production data management analysis module; and finally, the production data management and analysis module performs visual analysis and visual display on the received data, so that the production data management and analysis efficiency is improved, and the problem of low production data management and analysis efficiency in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a production data management and analysis system based on the Internet of Things. Background Art

[0002] Production data refers to various information and digital records generated during the manufacturing process, covering all aspects of production, such as production plan data, production process data, production quality data, equipment maintenance data, personnel data, etc. Specific production plan data includes production task data, planned output data, and production progress data, etc. Production process data involves equipment data, environmental data, raw material data, etc. during the production process. Therefore, the management and analysis of production data are very important. Reasonable management and analysis of production data can promote production efficiency, production quality, and production benefits, etc., and can also help managers make timely decisions on emergencies, etc. Internet of Things technology (IoT) is a technology that connects and exchanges data through the Internet, which can enable various devices and systems to achieve real-time communication. In the production field, Internet of Things technology can be applied to physical devices such as production equipment and sensors to achieve interconnection and interoperability between devices, enabling enterprises to better understand and optimize the production process.

[0003] In the prior art, the management and analysis of production data are often achieved through the following methods: data warehouse, which stores production data in a data warehouse and combines data mining technology to discover the laws between production data; real-time analysis, which uses real-time data processing technology to perform real-time analysis on production data; big data and machine learning, which use big data technology to process and analyze production data to discover the relevance of data, and use machine learning to predict and classify production technology, etc.

[0004] For example, a cable production quality data management system announced with the publication number: CN115983721B includes: obtaining production quality data and quality standard ranges of each process based on Internet of Things big data; obtaining the quality grade of each cable according to the production quality data; using the LOF algorithm to obtain the deviation factor of the production quality data, and obtaining the deviation index and clustering cluster of the target process according to the deviation factor; obtaining the quality difference index of the target process according to the numerical distribution of the production quality data in the clustering cluster; obtaining the outlier index according to the deviation index and the quality difference index, adjusting the outlier density in the LOF algorithm according to the outlier index, and determining the quality improvement value of the target process according to the optimal deviation factor; adjusting the quality standard range of the target process according to the quality improvement value.

[0005] For example, an enterprise big data management system disclosed in the patent with publication number CN110490486B includes a big data collection subsystem, a big data classification subsystem, a big data processing subsystem, a big data warning subsystem, and a visualization subsystem. The big data collection subsystem is used to collect enterprise data. The big data classification subsystem divides enterprise data into production equipment data and market data. The big data processing subsystem analyzes and processes production equipment data and market data. The big data warning subsystem monitors and warns the production equipment and the market based on the analysis results of production equipment data and market data respectively. The visualization subsystem is used to display the analysis results of production equipment data and market data.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it is found that the above technology has at least the following technical problems:

[0007] In the prior art, the construction and maintenance costs of data warehouses are relatively high. Real-time data processing requires high-performance hardware and software support, and may face challenges in processing large-scale data. Big data analysis requires powerful computing and storage resources. Artificial intelligence and machine learning require a large amount of labeled data and computing resources for training complex models. All of these lead to slow processing speed in the management and analysis of production data, and there is a problem of low efficiency in the management and analysis of production data. Summary of the Invention

[0008] The embodiments of the present application provide a production data management and analysis system based on the Internet of Things, which solves the problem of low efficiency in the management and analysis of production data in the prior art and realizes the improvement of the efficiency of production data management and analysis.

[0009] The embodiments of the present application provide a production data management and analysis system based on the Internet of Things, including a production data acquisition module, a production data processing module, and a production data management and analysis module. Among them, the production data acquisition module is used to acquire the production data of the production workshop to be monitored and send the acquired production data to the production data processing module. The production data is used to describe the situation of the production workshop to be monitored during the production process in the first monitoring time period. The production data processing module is used to receive the production data sent by the production data acquisition module, acquire second production data according to the received production data, and at the same time send the acquired data to the production data management and analysis module. The second production data is used to describe the situation of the production workshop to be monitored during the production process in the second monitoring time period. The duration of the second monitoring time period is an integer multiple of the duration of the first monitoring time period. The production data management and analysis module is used to receive the second production data sent by the production data processing module and analyze the situation of the production workshop to be monitored during the production process in the second monitoring time period according to the second production data.

[0010] Further, the production data acquisition module includes a production benefit data acquisition unit, a production environment data acquisition unit, and a production equipment data acquisition unit; the production benefit data acquisition unit: used to acquire the production benefit data of the production workshop to be monitored, and the production benefit data is used to describe the production benefit situation of various products produced by the production workshop to be monitored within the first monitoring time period; the production environment data acquisition unit: used to acquire the production environment data of the production workshop to be monitored, and the production environment data is used to describe the production environment situation of the production workshop to be monitored within the first monitoring time period; the production equipment data acquisition unit: used to acquire the production equipment data of the production workshop to be monitored, and the production equipment data is used to describe the working conditions of various production equipment in the production workshop to be monitored within the first monitoring time period; the production data includes production benefit data, production environment data, and production equipment data.

[0011] Further, the production data processing module includes a reference production data storage unit, a production benefit data processing unit, a production environment data processing unit, a production equipment data processing unit, and an alarm unit; the reference production data storage unit: used to acquire and store the reference production data of the production workshop to be monitored, and the reference production data is used to describe the production situation of the production workshop to be monitored under the reference working state; the production benefit data processing unit: used to analyze whether there is a benefit risk in the production workshop to be monitored within the first monitoring time period according to the received production benefit data. If there is a benefit risk, it sends a production risk alarm message to the alarm unit. Otherwise, it sums up the production benefit data of each first monitoring time period within the second monitoring time period obtained to get the second production benefit data, and deletes the production benefit data; the production environment data processing unit: used to analyze whether there is an environmental risk in the production workshop to be monitored within the first monitoring time period according to the received production environment data. If there is an environmental risk, it sends an environmental risk alarm message to the alarm unit. Otherwise, it sums up the production environment data of each first monitoring time period within the second monitoring time period obtained and takes the average value to get the second production environment data, and deletes the production environment data; the production equipment data processing unit: used to analyze whether there is an equipment risk in the production workshop to be monitored within the first monitoring time period according to the received production equipment data. If there is an equipment risk, it sends an equipment risk alarm message to the alarm unit. Otherwise, it sums up the production equipment data of each first monitoring time period within the second monitoring time period obtained to get the second production equipment data, and deletes the production equipment data; the alarm unit: used to listen to the alarm messages sent by the production benefit data processing unit, the production environment data processing unit, and the production equipment data processing unit, and feedback the listening result to the preset management personnel, and the preset management personnel take corresponding treatment measures.

[0012] Further, the reference production data includes a reference production average value and a reference production deviation. The specific acquisition method is as follows: Obtain historical production data, which is used to describe the production situation of the production workshop to be monitored in each historical monitoring time period; Combine the duration corresponding to the historical production data to obtain corresponding first historical production data, which is used to describe the production situation of the production workshop to be monitored per unit time in each historical time period; According to the first historical production data, obtain the reference production average value of the production workshop to be monitored, which is used to describe the average production situation per unit time of the production workshop to be monitored in the reference working environment, specifically including reference production benefit data, reference production environment data, and reference production equipment data; According to the reference production average value and the first historical production data, obtain the production absolute deviation data corresponding to the first historical production data, and perform sorting on the obtained production absolute deviation data to calculate the reference production deviation of the production workshop to be monitored. The reference production deviation is used to describe the maximum reference deviation amount of the production situation per unit time of the production workshop to be monitored in the reference working environment, specifically including reference production benefit deviation, reference production environment deviation, and reference production equipment deviation.

[0013] Further, the historical production data includes historical production benefit data, historical production environment data, and historical production equipment data; The historical production benefit data includes historical production cost data and historical sales revenue data; The historical production environment data includes historical temperature, historical humidity, and historical dust concentration; The historical production equipment data includes historical equipment maintenance times, historical equipment working hours, and historical equipment external temperature.

[0014] Further, the specific analysis method for whether there is a benefit risk in the production workshop to be monitored in the first monitoring time period is as follows: Obtain the production benefit data, reference production benefit data, and reference production benefit deviation of the production workshop to be monitored; Calculate the benefit risk assessment index of the production workshop to be monitored according to the obtained data. The benefit risk assessment index is used to evaluate the degree of benefit risk of the production workshop to be monitored in the first monitoring time period, and determine whether the calculated production risk assessment index is less than the reference benefit risk assessment index. If it is less, there is no benefit risk in the production workshop to be monitored, otherwise there is a benefit risk; The benefit risk assessment index is calculated using the following formula: In the formula, h is the number of the first monitoring time period, h = 1, 2,..., H, where H is the total number of the first monitoring time periods, and PE h is the benefit risk assessment index of the production workshop to be monitored in the hth first monitoring time period, T h , C h and R hThey are the monitoring duration, production cost data, and sales revenue data of the production workshop to be monitored in the h-th first monitoring time period respectively. C0 and R0 are the reference production cost data and reference sales revenue data of the production workshop to be monitored respectively. ΔC and ΔR are the reference production cost deviation and reference sales revenue deviation of the production workshop to be monitored respectively. W1 and W2 are the weight factors of the production cost deviation ratio and sales revenue deviation ratio of the production workshop to be monitored respectively. The production cost deviation ratio is used to describe the ratio of the production cost data deviation of the production workshop to be monitored to the reference production cost deviation. The sales revenue deviation ratio is used to describe the ratio of the sales revenue data deviation of the production workshop to be monitored to the reference sales revenue deviation.

[0015] Further, the specific analysis method for whether there is an environmental risk in the production workshop to be monitored in the first monitoring time period is as follows: Obtain the production environment data, reference production environment data, and reference production environment deviation of the production workshop to be monitored. Calculate the environmental risk assessment index of the production workshop to be monitored based on the obtained data. The environmental risk assessment index is used to evaluate the environmental risk level of the production workshop to be monitored in the first monitoring time period, and determine whether the calculated environmental risk assessment index is less than the reference environmental risk assessment index. If it is less, there is no environmental risk in the production workshop to be monitored; otherwise, there is an environmental risk. The environmental risk assessment index is calculated using the following formula: In the formula, CE h is the environmental risk assessment index of the production workshop to be monitored in the h-th first monitoring time period, HT h and D h are the temperature and humidity risk assessment index and dust concentration of the production workshop to be monitored in the h-th first monitoring time period respectively. HT0 and D0 are the reference temperature and humidity risk assessment index and reference dust concentration of the production workshop to be monitored respectively. ΔD is the reference dust concentration deviation of the production workshop to be monitored. α is the correction factor of the temperature and humidity risk assessment index. The temperature and humidity risk assessment index is used to describe the temperature and humidity risk level of the production workshop to be monitored in the first monitoring time period. The temperature and humidity risk assessment index is calculated using the following formula: In the formula, HT h is the temperature and humidity risk assessment index of the production workshop to be monitored in the h-th first monitoring time period, TP h and HP hThey are respectively the temperature and humidity of the production workshop to be monitored during the hth first monitoring time period. TP0 and HP0 are respectively the reference temperature and reference humidity of the production workshop to be monitored. ΔTP and ΔHP are respectively the reference temperature deviation and reference humidity deviation of the production workshop to be monitored. λ1 and λ2 are respectively the weight factors of the temperature deviation ratio and the humidity deviation ratio of the production workshop to be monitored. The temperature deviation ratio is used to describe the ratio of the temperature deviation of the production workshop to be monitored to the reference temperature deviation. The humidity deviation ratio is used to describe the ratio of the humidity deviation of the production workshop to be monitored to the reference humidity deviation.

[0016] Furthermore, the specific analysis method for whether there is equipment risk in the production workshop to be monitored during the first monitoring time period is as follows: Obtain the production equipment data, reference production equipment data, and reference production equipment deviation of the production workshop to be monitored. Calculate the equipment risk assessment index of the production workshop to be monitored based on the obtained data. The equipment risk assessment index is used to evaluate the equipment risk degree of each production equipment in the production workshop to be monitored during the first monitoring time period, and determine whether the calculated equipment risk assessment index is less than the reference equipment risk assessment index. If it is less, there is no equipment risk in the production workshop to be monitored; otherwise, there is equipment risk. The equipment risk assessment index is calculated using the following formula: In the formula, j is the production equipment number of the production workshop to be monitored, j = 1, 2,..., J, and J is the total number of production equipment. DE h is the equipment risk assessment index of the production workshop to be monitored during the hth first monitoring time period. and are respectively the first risk index, second risk index, and third risk index of the jth production equipment in the production workshop to be monitored during the hth first monitoring time period. β, χ, and δ are respectively the correction factors of the first risk index, second risk index, and third risk index. μ1, μ2, and μ3 are respectively the weight factors of the first risk index, second risk index, and third risk index. The first risk index is used to describe the risk degree caused by insufficient maintenance of each production equipment. The second risk index is used to describe the risk degree caused by excessive working hours of each production equipment. The third risk index is used to describe the risk degree caused by too high external temperature of each production equipment.

[0017] Furthermore, the first risk index is calculated using the following formula: In the formula, is the equipment maintenance times of the jth production equipment in the production workshop to be monitored during the hth first monitoring time period. DM j is the reference equipment maintenance times of the jth production equipment in the production workshop to be monitored. ΔDM jis the deviation of the reference equipment maintenance times of the j-th production equipment in the production workshop to be monitored; the second risk index is calculated using the following formula: In the formula, is the equipment working duration of the j-th production equipment in the production workshop to be monitored during the h-th first monitoring period, WT j is the reference equipment working duration of the j-th production equipment in the production workshop to be monitored, ΔWT j is the deviation of the reference equipment working duration of the j-th production equipment in the production workshop to be monitored; the third risk index is calculated using the following formula: In the formula, is the external temperature of the j-th production equipment in the production workshop to be monitored during the h-th first monitoring period, DT j is the reference external temperature of the j-th production equipment in the production workshop to be monitored, ΔDT j is the deviation of the reference external temperature of the j-th production equipment in the production workshop to be monitored.

[0018] Further, the production data management and analysis module includes a production data management and analysis unit and a production data visualization unit; the production data management and analysis unit: is used to receive the second production benefit data, the second production environment data, and the second production equipment data of the production workshop to be monitored, then perform visual analysis on the received data, and send the analysis results to the production data visualization unit; the production data visualization unit: is used to receive the analysis results sent by the production data management and analysis unit, and perform visual display through a preset visualization device.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. By obtaining the production data of the production workshop to be monitored, then combining the historical production data to obtain the reference production data, and then combining the corresponding reference production data to determine whether there are benefit risks, environmental risks, and equipment risks in the production workshop to be monitored. If there are, they are processed; otherwise, the second production data is obtained according to the first production data, and finally, visual analysis and display are performed on the second production data, thereby realizing the reduction of the amount of management analysis data, and then realizing the improvement of the production data management and analysis efficiency, effectively solving the problem of low production data management and analysis efficiency in the prior art.

[0021] 2. By obtaining the production cost data and sales revenue data of the production workshop to be monitored during the first monitoring time period, then obtaining the corresponding reference production data, then calculating the corresponding benefit risk assessment index in combination with the duration of the first monitoring time period, and finally judging whether there is a benefit risk in the production workshop to be monitored during the corresponding first monitoring time in combination with the reference benefit risk assessment index, the quantification of the benefit risk degree of the production workshop to be monitored is realized, and further, a more accurate judgment of whether there is a benefit risk in the production workshop to be monitored is realized.

[0022] 3. By obtaining the temperature and humidity of the production workshop to be monitored during the first monitoring time period, then calculating the temperature and humidity risk assessment index in combination with the corresponding reference production data, then calculating the environmental risk assessment index in combination with the dust concentration and the corresponding reference production data, and finally judging whether there is an environmental risk in the production workshop to be monitored during the corresponding first monitoring time in combination with the reference environmental risk assessment index, the quantification of the environmental risk degree of the production workshop to be monitored is realized, and further, a more accurate judgment of whether there is an environmental risk in the production workshop to be monitored is realized. Brief Description of the Drawings

[0023] Figure 1 It is a schematic structural diagram of a production data management and analysis system based on the Internet of Things provided by an embodiment of the present application;

[0024] Figure 2 It is a schematic structural diagram of a production data acquisition module in a production data management and analysis system based on the Internet of Things provided by an embodiment of the present application;

[0025] Figure 3 It is a schematic structural diagram of a production data processing module in a production data management and analysis system based on the Internet of Things provided by an embodiment of the present application;

[0026] Figure 4 It is a schematic structural diagram of a production data management and analysis module in a production data management and analysis system based on the Internet of Things provided by an embodiment of the present application. Detailed Embodiments

[0027] Embodiments of the present application provide a production data management and analysis system based on the Internet of Things, which solves the problem of low efficiency in production data management and analysis in the prior art. The production benefit data acquisition unit, production environment data acquisition unit, and production equipment data acquisition unit in the production data acquisition module respectively acquire the production benefit data, production environment data, and production equipment data of the production workshop to be monitored in the first monitoring time period, and send the acquired data to the production data processing module. Then, the reference production data storage unit in the production data processing module obtains the reference production average value and reference production deviation of the production workshop to be monitored according to the acquired historical production data and combines the duration corresponding to the historical production data, and stores them. Next, the production benefit data processing unit, production environment data processing unit, and production equipment data processing unit in the production data processing module respectively combine the corresponding reference production average value and reference production deviation to determine whether there are benefit risks, environmental risks, and equipment risks in the production workshop to be monitored. If so, corresponding alarm information is generated and sent to the alarm unit for processing. Otherwise, the second production benefit data, second production environment data, and second production equipment data of the production workshop to be monitored in the second monitoring time period are obtained according to the production benefit data, production environment data, and production equipment data in the first monitoring time period, and sent to the production data management and analysis module. Then, the production data management and analysis unit in the production data management and analysis module performs visual analysis on the received data, and sends the analysis result to the production data visualization unit. Finally, the production data visualization unit performs visual display on the received analysis result through a preset visualization device, achieving an improvement in the efficiency of production data management and analysis.

[0028] The technical solution in the embodiments of the present application to solve the above problem of low efficiency in production data management and analysis is generally as follows:

[0029] The production data acquisition module is used to acquire the production data of the production workshop to be monitored in the first monitoring time period, and send the acquired data to the production data processing module. Then, the production data processing module combines the historical production data to obtain the reference production data of the production workshop to be monitored. Next, it combines the corresponding reference production data to determine whether there are benefit risks, environmental risks, and equipment risks in the production workshop to be monitored. If so, it is processed. Otherwise, the second production data of the production workshop to be monitored in the second monitoring time period is obtained according to the first production data and sent to the production data management and analysis module. Then, the production data management and analysis module performs visual analysis on the received data, and finally performs visual display on the received analysis result through a preset visualization device, achieving the effect of improving the efficiency of production data management and analysis.

[0030] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0031] As Figure 1 shown, it is a schematic structural diagram of a production data management and analysis system based on the Internet of Things provided by an embodiment of the present application. The production data management and analysis system based on the Internet of Things provided by an embodiment of the present application includes a production data acquisition module, a production data processing module, and a production data management and analysis module: Among them, the production data acquisition module is used to acquire the production data of the production workshop to be monitored and send the acquired production data to the production data processing module. The production data is used to describe the situation of the production workshop to be monitored during the production process in the first monitoring time period; the production data processing module is used to receive the production data sent by the production data acquisition module, acquire second production data according to the received production data, and at the same time send the acquired data to the production data management and analysis module. The second production data is used to describe the situation of the production workshop to be monitored during the production process in the second monitoring time period. The duration of the second monitoring time period is an integer multiple of the duration of the first monitoring time period; the production data management and analysis module is used to receive the second production data sent by the production data processing module and analyze the situation of the production workshop to be monitored during the production process in the second monitoring time period according to the second production data.

[0032] In this embodiment, in combination with the common edge computing concept in the Internet of Things, the production data processing module is connected to the production data acquisition module to judge the range of the production data at the data end, and then acquire the production data of a longer monitoring time period, that is, the second production data of the second monitoring time period, and then delete the production data, which improves the data transmission efficiency and reduces the difficulty of storing the production data. Finally, the production situation of the production workshop to be monitored is managed and analyzed according to the second production data; the improvement of the production data management and analysis efficiency is realized.

[0033] Further, as Figure 2 shown, it is a schematic structural diagram of the production data acquisition module in a production data management and analysis system based on the Internet of Things provided by an embodiment of the present application. The production data acquisition module includes a production benefit data acquisition unit, a production environment data acquisition unit, and a production equipment data acquisition unit; the production benefit data acquisition unit: is used to acquire the production benefit data of the production workshop to be monitored. The production benefit data is used to describe the production benefit situation of various products produced by the production workshop to be monitored in the first monitoring time period; the production environment data acquisition unit: is used to acquire the production environment data of the production workshop to be monitored. The production environment data is used to describe the production environment situation of the production workshop to be monitored in the first monitoring time period; the production equipment data acquisition unit: is used to acquire the production equipment data of the production workshop to be monitored. The production equipment data is used to describe the working conditions of various production equipment in the production workshop to be monitored in the first monitoring time period; the production data includes production benefit data, production environment data, and production equipment data.

[0034] In this embodiment, the production benefit data specifically includes production cost data and sales revenue data; the production environment data includes temperature, humidity, and dust concentration, where the dust concentration can also be specified as the concentration of toxic dust and non-toxic dust according to the actual situation; the production equipment data includes the number of equipment maintenance times, the equipment working hours, and the external temperature of the equipment; more accurate production data of the production workshop to be monitored is obtained.

[0035] Further, as Figure 3 shown, it is a schematic structural diagram of a production data processing module in a production data management and analysis system based on the Internet of Things provided by an embodiment of the present application. The production data processing module includes a reference production data storage unit, a production benefit data processing unit, a production environment data processing unit, a production equipment data processing unit, and an alarm unit; the reference production data storage unit: is used to obtain and store the reference production data of the production workshop to be monitored, and the reference production data is used to describe the production situation of the production workshop to be monitored in the reference working state; the production benefit data processing unit: is used to analyze whether there is a benefit risk in the production workshop to be monitored during the first monitoring time period according to the received production benefit data. If there is a benefit risk, it sends a production risk alarm message to the alarm unit. Otherwise, it sums up the production benefit data of each first monitoring time period within the second monitoring time period obtained to get the second production benefit data, and deletes the production benefit data; the production environment data processing unit: is used to analyze whether there is an environmental risk in the production workshop to be monitored during the first monitoring time period according to the received production environment data. If there is an environmental risk, it sends an environmental risk alarm message to the alarm unit. Otherwise, it sums up the production environment data of each first monitoring time period within the second monitoring time period obtained and takes the average to get the second production environment data, and deletes the production environment data; the production equipment data processing unit: is used to analyze whether there is an equipment risk in the production workshop to be monitored during the first monitoring time period according to the received production equipment data. If there is an equipment risk, it sends an equipment risk alarm message to the alarm unit. Otherwise, it sums up the production equipment data of each first monitoring time period within the second monitoring time period obtained to get the second production equipment data, and deletes the production equipment data; the alarm unit: is used to monitor the alarm messages sent by the production benefit data processing unit, the production environment data processing unit, and the production equipment data processing unit, and feedback the monitoring results to the preset management personnel, and the preset management personnel take corresponding treatment measures.

[0036] In this embodiment, processing and analysis are performed at the data end, which can enable the production situation of the production workshop to be monitored in the shortest time within each first monitoring time period, and can also discover risks and handle them in the shortest time. After obtaining the corresponding second production data, the first production data is deleted, which can not only ensure the monitoring of the workshop, but also reduce the pressure of data transmission, management analysis and storage; and realize the faster control of the benefit risk, environmental risk and equipment risk of the production workshop to be monitored.

[0037] Furthermore, the reference production data includes a reference production average value and a reference production deviation. The specific acquisition method is as follows: Obtain historical production data, which is used to describe the production situation of the production workshop to be monitored within each historical monitoring time period; Combine the duration corresponding to the historical production data to obtain the corresponding first historical production data, which is used to describe the production situation of the production workshop to be monitored per unit time within each historical time period; According to the first historical production data, obtain the reference production average value of the production workshop to be monitored, which is used to describe the average production situation of the production workshop to be monitored per unit time under the reference working environment, and specifically includes reference production benefit data, reference production environment data and reference production equipment data; According to the reference production average value and the first historical production data, obtain the production absolute deviation data corresponding to the first historical production data, and perform sorting on the obtained production absolute deviation data to calculate the reference production deviation of the production workshop to be monitored. The reference production deviation is used to describe the maximum reference deviation amount of the production situation of the production workshop to be monitored per unit time under the reference working environment, and specifically includes reference production benefit deviation, reference production environment deviation and reference production equipment deviation.

[0038] In this embodiment, the duration of the first monitoring time period may be different, so it is necessary to obtain the first historical production data, and what is obtained is the production situation per unit time; The reference production average value generally represents the best value, and can also be considered as the ideal value, but there will be certain deviations in the actual situation, while the reference production deviation gives the maximum deviation amount of each data; The production absolute deviation data has positive and negative values, so it is necessary to take the absolute value and then perform sorting to obtain the reference production deviation; It realizes the faster management and analysis of the production situation of the production workshop to be monitored.

[0039] Furthermore, the historical production data includes historical production benefit data, historical production environment data and historical production equipment data; The historical production benefit data includes historical production cost data and historical sales revenue data; The historical production environment data includes historical temperature, historical humidity and historical dust concentration; The historical production equipment data includes historical equipment maintenance times, historical equipment working hours and historical external temperature of the equipment.

[0040] In this embodiment, the historical production cost data includes historical raw material costs, historical employee costs, historical equipment operation costs, and historical workshop operation costs; the more frequently the production equipment is maintained usually, the relatively lower the probability of equipment risks, because during each maintenance process, various components of the equipment are inspected; the longer the equipment operates, the greater the probability of equipment risks, because continuous operation for a long time will accelerate the wear and tear of the production equipment; and if the corresponding external temperature of the equipment remains at a high level all the time, it may also cause premature aging and other phenomena of the production equipment; by obtaining the reference production data from the historical production data, a more comprehensive assessment of the production situation of the production workshop to be monitored is achieved.

[0041] Furthermore, the specific analysis method for whether there is a benefit risk in the production workshop to be monitored during the first monitoring time period is as follows: Obtain the production benefit data, reference production benefit data, and reference production benefit deviation of the production workshop to be monitored; calculate the benefit risk assessment index of the production workshop to be monitored based on the obtained data. The benefit risk assessment index is used to evaluate the degree of benefit risk of the production workshop to be monitored during the first monitoring time period, and determine whether the calculated production risk assessment index is less than the reference benefit risk assessment index. If it is less, there is no benefit risk in the production workshop to be monitored; otherwise, there is a benefit risk. The benefit risk assessment index is calculated using the following formula: In the formula, h is the number of the first monitoring time period, h = 1, 2,..., H, where H is the total number of the first monitoring time periods, and PE h is the benefit risk assessment index of the production workshop to be monitored in the h-th first monitoring time period, T h , C h and R h are respectively the monitoring duration, production cost data, and sales revenue data of the production workshop to be monitored in the h-th first monitoring time period, C0 and R0 are respectively the reference production cost data and reference sales revenue data of the production workshop to be monitored, ΔC and ΔR are respectively the reference production cost deviation and reference sales revenue deviation of the production workshop to be monitored, and W1 and W2 are respectively the weight factors of the production cost deviation ratio and the sales revenue deviation ratio of the production workshop to be monitored; the production cost deviation ratio is used to describe the ratio of the production cost data deviation of the production workshop to be monitored to the reference production cost deviation; the sales revenue deviation ratio is used to describe the ratio of the sales revenue data deviation of the production workshop to be monitored to the reference sales revenue deviation.

[0042] In this embodiment, when the production benefit data is within the reference range, the corresponding benefit risk assessment index is 0, that is, there is no benefit risk; otherwise, according to the deviation degree between the production benefit data and the reference range, the corresponding benefit risk degree is evaluated. The greater the deviation degree, the greater the benefit risk assessment index and the higher the corresponding benefit risk degree. It is necessary to preset the management personnel to analyze and process in combination with specific data; when the benefit risk assessment index is less than the reference benefit risk assessment index, it indicates that the production workshop to be monitored temporarily does not need to carry out benefit risk treatment; W1 and W2 describe the influence degrees of the production cost deviation ratio and the sales revenue deviation ratio of the production workshop to be monitored on the benefit risk assessment index, realizing the numerical and accurate assessment of the benefit risk degree of the production workshop to be monitored.

[0043] Further, the specific analysis method for whether there is an environmental risk in the production workshop to be monitored during the first monitoring time period is as follows; obtain the production environment data, reference production environment data and reference production environment deviation of the production workshop to be monitored; calculate the environmental risk assessment index of the production workshop to be monitored according to the obtained data. The environmental risk assessment index is used to evaluate the environmental risk degree of the production workshop to be monitored during the first monitoring time period, and judge whether the calculated environmental risk assessment index is less than the reference environmental risk assessment index. If it is less, there is no environmental risk in the production workshop to be monitored, otherwise there is an environmental risk; the environmental risk assessment index is calculated using the following formula: In the formula, CE h is the environmental risk assessment index of the production workshop to be monitored in the hth first monitoring time period, HT h and D h are respectively the temperature and humidity risk assessment index and the dust concentration of the production workshop to be monitored in the hth first monitoring time period, HT0 and D0 are respectively the reference temperature and humidity risk assessment index and the reference dust concentration of the production workshop to be monitored, ΔD is the reference dust concentration deviation of the production workshop to be monitored, and α is the correction factor of the temperature and humidity risk assessment index; the temperature and humidity risk assessment index is used to describe the temperature and humidity risk degree of the production workshop to be monitored during the first monitoring time period, and the temperature and humidity risk assessment index is calculated using the following formula: In the formula, HT h is the temperature and humidity risk assessment index of the production workshop to be monitored in the hth first monitoring time period, TP h and HP hThey are the temperature and humidity of the production workshop to be monitored in the h-th first monitoring time period respectively. TP0 and HP0 are the reference temperature and reference humidity of the production workshop to be monitored respectively. ΔTP and ΔHP are the reference temperature deviation and reference humidity deviation of the production workshop to be monitored respectively. λ1 and λ2 are the weight factors of the temperature deviation ratio and humidity deviation ratio of the production workshop to be monitored respectively. The temperature deviation ratio is used to describe the ratio of the temperature deviation of the production workshop to be monitored to the reference temperature deviation. The humidity deviation ratio is used to describe the ratio of the humidity deviation of the production workshop to be monitored to the reference humidity deviation.

[0044] In this embodiment, when the temperature and humidity are within the reference range, the corresponding temperature and humidity risk assessment index is 0, and when the dust concentration is within the reference range, the corresponding environmental risk assessment index is 0, that is, there is no environmental risk. Otherwise, according to the deviation degree between the production environment data and the reference range, the corresponding environmental risk degree is evaluated. The greater the deviation degree, the greater the environmental risk assessment index, and the higher the corresponding environmental risk degree. It is necessary for the preset management personnel to analyze and process in combination with specific data. When the environmental risk assessment index is less than the reference environmental risk assessment index, it indicates that the production workshop to be monitored does not need to carry out environmental risk treatment temporarily. The weight factors in the temperature and humidity risk assessment index describe the influence degree of the corresponding data on the temperature and humidity risk assessment index, realizing the numerical and accurate assessment of the environmental risk degree of the production workshop to be monitored.

[0045] Furthermore, the specific analysis method for whether there is equipment risk in the production workshop to be monitored in the first monitoring time period is as follows: Obtain the production equipment data, reference production equipment data and reference production equipment deviation of the production workshop to be monitored. Calculate the equipment risk assessment index of the production workshop to be monitored according to the obtained data. The equipment risk assessment index is used to evaluate the equipment risk degree of each production equipment in the production workshop to be monitored in the first monitoring time period, and judge whether the calculated equipment risk assessment index is less than the reference equipment risk assessment index. If it is less, there is no equipment risk in the production workshop to be monitored, otherwise there is equipment risk. The equipment risk assessment index is calculated by the following formula: In the formula, j is the production equipment number of the production workshop to be monitored, j = 1, 2,..., J, J is the total number of production equipment, DE h is the equipment risk assessment index of the production workshop to be monitored in the h-th first monitoring time period, and They are respectively the first risk index, the second risk index, and the third risk index of the j-th production equipment in the production workshop to be monitored during the h-th first monitoring time period. β, χ, and δ are respectively the correction factors of the first risk index, the second risk index, and the third risk index. μ1, μ2, and μ3 are respectively the weights of the first risk index, the second risk index, and the third risk index. The first risk index is used to describe the risk degree of each production equipment caused by insufficient maintenance. The second risk index is used to describe the risk degree of each production equipment caused by excessive working hours. The third risk index is used to describe the risk degree of each production equipment caused by excessive external temperature of the equipment.

[0046] In this embodiment, when the actual first risk index, second risk index, and third risk index of each equipment in the production workshop to be monitored are all 0, that is, the corresponding At this time, the equipment risk assessment index is 0, indicating that there is no equipment risk in the production workshop to be monitored during this first monitoring time period. When the equipment risk assessment index is less than the reference equipment risk assessment index, it indicates that the production workshop to be monitored temporarily does not need to perform equipment risk treatment. The first risk index, the second risk index, and the third risk index are all non-negative numbers, and the equipment risk assessment index increases as the first risk index, the second risk index, and the third risk index increase. The numerical evaluation of the equipment risk degree in the production workshop to be monitored is realized.

[0047] Furthermore, the first risk index is calculated using the following formula: In the formula, is the equipment maintenance times of the j-th production equipment in the production workshop to be monitored during the h-th first monitoring time period, DM j is the reference equipment maintenance times of the j-th production equipment in the production workshop to be monitored, ΔDM j is the deviation of the reference equipment maintenance times of the j-th production equipment in the production workshop to be monitored. The second risk index is calculated using the following formula: In the formula, is the equipment working hours of the j-th production equipment in the production workshop to be monitored during the h-th first monitoring time period, WT j is the reference equipment working hours of the j-th production equipment in the production workshop to be monitored, ΔWT j is the deviation of the reference equipment working hours of the j-th production equipment in the production workshop to be monitored. The third risk index is calculated using the following formula: In the formula, is the external temperature of the equipment of the j-th production equipment in the production workshop to be monitored during the h-th first monitoring time period, DT j$T_{ref,j}$ is the reference external temperature of the $j$-th production equipment in the production workshop to be monitored, and $\Delta DT_j$ is the deviation of the reference external temperature of the $j$-th production equipment in the production workshop to be monitored.

[0048] In this embodiment, when the production equipment data of each production equipment is within the reference range, the corresponding first risk index, second risk index, and third risk index are all 0, that is, there is no corresponding risk; otherwise, according to the deviation degree between the production equipment data and the reference range, the corresponding risk degree is evaluated. The greater the deviation degree, the higher the corresponding risk degree; the first risk index decreases as the number of equipment maintenance increases, and the increase in the number of equipment maintenance within a certain range will reduce the corresponding risk; the second risk index increases as the equipment working hours increase, and the increase in the equipment working hours within a certain range will increase the corresponding risk; the third risk index increases as the external temperature of the equipment increases, and the increase in the external temperature of the equipment within a certain range will increase the corresponding risk; a more comprehensive evaluation of the risk degree of the equipment in the production workshop to be monitored is realized.

[0049] Furthermore, as Figure 4 shown, it is a schematic structural diagram of the production data management and analysis module in a production data management and analysis system based on the Internet of Things provided by the embodiment of the present application. The production data management and analysis module includes a production data management and analysis unit and a production data visualization unit; the production data management and analysis unit: is used to receive the second production benefit data, second production environment data, and second production equipment data of the production workshop to be monitored, then perform visual analysis on the received data, and send the analysis result to the production data visualization unit; the production data visualization unit: is used to receive the analysis result sent by the production data management and analysis unit, and perform visual display through a preset visualization device.

[0050] In this embodiment, the visual analysis can be in the form of graphs, tables, etc. The visualization of the analysis result can fully display the change trend and overall change situation, and realize a more intuitive grasp of the production situation of the production workshop to be monitored.

[0051] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages: Compared with a cable production quality data management system based on Internet of Things big data disclosed in the patent with publication number CN115983721B, in the embodiments of the present application, by obtaining the production cost data and sales revenue data of the production workshop to be monitored during the first monitoring time period, then obtaining the corresponding reference production data, then calculating the corresponding benefit risk assessment index in combination with the duration of the first monitoring time period, and finally judging whether there is a benefit risk in the production workshop to be monitored during the corresponding first monitoring time in combination with the reference benefit risk assessment index, the numerical value of the benefit risk degree of the production workshop to be monitored is realized, and further a more accurate judgment on whether there is a benefit risk in the production workshop to be monitored is realized; Compared with an enterprise big data management system disclosed in the patent with publication number CN110490486B, in the embodiments of the present application, by obtaining the temperature and humidity of the production workshop to be monitored during the first monitoring time period, then calculating the temperature and humidity risk assessment index in combination with the corresponding reference production data, then calculating the environmental risk assessment index in combination with the dust concentration and the corresponding reference production data, and finally judging whether there is an environmental risk in the production workshop to be monitored during the corresponding first monitoring time in combination with the reference environmental risk assessment index, the numerical value of the environmental risk degree of the production workshop to be monitored is realized, and further a more accurate judgment on whether there is an environmental risk in the production workshop to be monitored is realized.

[0052] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0054] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction means that implements the functions specified in one or more of the processes Figure 1 and / or boxes Figure 1 specified in one or more of the processes and / or boxes.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes Figure 1 and / or boxes Figure 1 specified in one or more of the boxes.

[0056] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0057] It is obvious that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An Internet of Things-based production data management and analysis system, characterized in that It includes a production data acquisition module, a production data processing module, and a production data management and analysis module: Among them, the production data acquisition module is used to acquire the production data of the production workshop to be monitored and send the acquired production data to the production data processing module. The production data is used to describe the situation of the production workshop to be monitored during the production process in the first monitoring time period; The production data processing module is used to receive the production data sent by the production data acquisition module, acquire second production data according to the received production data, and at the same time send the acquired data to the production data management and analysis module. The second production data is used to describe the situation of the production workshop to be monitored during the production process in the second monitoring time period, and the duration of the second monitoring time period is an integer multiple of the duration of the first monitoring time period; The production data management and analysis module is used to receive the second production data sent by the production data processing module and analyze the situation of the production workshop to be monitored during the production process in the second monitoring time period according to the second production data.

2. The production data management and analysis system based on the Internet of Things according to claim 1, characterized in that: The production data acquisition module includes a production benefit data acquisition unit, a production environment data acquisition unit, and a production equipment data acquisition unit; The production benefit data acquisition unit: is used to acquire the production benefit data of the production workshop to be monitored. The production benefit data is used to describe the production benefit situation of various products produced by the production workshop to be monitored in the first monitoring time period; The production environment data acquisition unit: is used to acquire the production environment data of the production workshop to be monitored. The production environment data is used to describe the production environment situation of the production workshop to be monitored in the first monitoring time period; The production equipment data acquisition unit: is used to acquire the production equipment data of the production workshop to be monitored. The production equipment data is used to describe the working conditions of various production equipment in the production workshop to be monitored in the first monitoring time period; The production data includes production benefit data, production environment data, and production equipment data.

3. The production data management and analysis system based on the Internet of Things according to claim 1, characterized in that: The production data processing module includes a reference production data storage unit, a production benefit data processing unit, a production environment data processing unit, a production equipment data processing unit, and an alarm unit; The reference production data storage unit: is used to acquire and store the reference production data of the production workshop to be monitored. The reference production data is used to describe the production situation of the production workshop to be monitored in the reference working state; The production benefit data processing unit: is used to analyze whether there is a benefit risk in the production workshop to be monitored in the first monitoring time period according to the received production benefit data. If there is a benefit risk, it sends a production risk alarm message to the alarm unit. Otherwise, it sums up the production benefit data of each first monitoring time period in the acquired second monitoring time period to obtain second production benefit data and deletes the production benefit data; The production environment data processing unit: is used to analyze whether there is an environmental risk in the production workshop to be monitored during the first monitoring time period according to the received production environment data. If there is an environmental risk, it sends an environmental risk alarm message to the alarm unit. Otherwise, it sums up the production environment data of each first monitoring time period within the obtained second monitoring time period and takes the average value to obtain the second production environment data, and deletes the production environment data; The production equipment data processing unit: is used to analyze whether there is an equipment risk in the production workshop to be monitored during the first monitoring time period according to the received production equipment data. If there is an equipment risk, it sends an equipment risk alarm message to the alarm unit. Otherwise, it sums up the production equipment data of each first monitoring time period within the obtained second monitoring time period to obtain the second production equipment data, and deletes the production equipment data; The alarm unit: is used to monitor the alarm messages sent by the production benefit data processing unit, the production environment data processing unit and the production equipment data processing unit, and feedback the monitoring results to the preset management personnel, and the preset management personnel take corresponding treatment measures.

4. The production data management and analysis system based on the Internet of Things according to claim 3, wherein The reference production data includes a reference production average value and a reference production deviation, and the specific acquisition method is as follows: Obtain historical production data, where the historical production data is used to describe the production situation of the production workshop to be monitored during each historical monitoring time period; Combined with the duration corresponding to the historical production data, obtain the corresponding first historical production data, where the first historical production data is used to describe the production situation of the production workshop to be monitored per unit time during each historical time period; According to the first historical production data, obtain the reference production average value of the production workshop to be monitored, where the reference production average value is used to describe the average production situation per unit time of the production workshop to be monitored under the reference working environment, and specifically includes reference production benefit data, reference production environment data and reference production equipment data; According to the reference production average value and the first historical production data, obtain the production absolute deviation data corresponding to the first historical production data, and sort the obtained production absolute deviation data to calculate the reference production deviation of the production workshop to be monitored. The reference production deviation is used to describe the maximum reference deviation amount of the production situation per unit time of the production workshop to be monitored under the reference working environment, and specifically includes reference production benefit deviation, reference production environment deviation and reference production equipment deviation.

5. The production data management and analysis system based on the Internet of Things according to claim 4, wherein: The historical production data includes historical production benefit data, historical production environment data and historical production equipment data; The historical production benefit data includes historical production cost data and historical sales revenue data; The historical production environment data includes historical temperature, historical humidity and historical dust concentration; The historical production equipment data includes historical equipment maintenance times, historical equipment working hours and historical equipment external temperature; 6. The production data management and analysis system based on the Internet of Things according to claim 3, characterized in that The specific analysis method for whether there is a benefit risk in the production workshop to be monitored during the first monitoring time period is as follows: Obtain the production benefit data, reference production benefit data and reference production benefit deviation of the production workshop to be monitored; Calculate the benefit risk assessment index of the production workshop to be monitored based on the acquired data. The benefit risk assessment index is used to evaluate the benefit risk level of the production workshop to be monitored during the first monitoring time period, and determine whether the calculated production risk assessment index is less than the reference benefit risk assessment index. If it is less, there is no benefit risk in the production workshop to be monitored; otherwise, there is a benefit risk. The benefit risk assessment index is calculated using the following formula: In the formula, h is the number of the first monitoring time period, h = 1, 2,..., H, where H is the total number of the first monitoring time periods. PEh is the benefit risk assessment index of the production workshop to be monitored in the h-th first monitoring time period. Th, Ch, and Rh are the monitoring duration, production cost data, and sales revenue data of the production workshop to be monitored in the h-th first monitoring time period respectively. C0 and R0 are the reference production cost data and reference sales revenue data of the production workshop to be monitored respectively. ΔC and ΔR are the reference production cost deviation and reference sales revenue deviation of the production workshop to be monitored respectively. W1 and W2 are the weight factors of the production cost deviation ratio and the sales revenue deviation ratio of the production workshop to be monitored respectively. The production cost deviation ratio is used to describe the ratio of the production cost data deviation of the production workshop to be monitored to the reference production cost deviation. The sales revenue deviation ratio is used to describe the ratio of the sales revenue data deviation of the production workshop to be monitored to the reference sales revenue deviation.

7. The production data management and analysis system based on the Internet of Things according to claim 6, characterized in that, The specific analysis method for whether there is an environmental risk in the production workshop to be monitored during the first monitoring time period is as follows: Obtain the production environment data, reference production environment data, and reference production environment deviation of the production workshop to be monitored. Calculate the environmental risk assessment index of the production workshop to be monitored based on the acquired data. The environmental risk assessment index is used to evaluate the environmental risk level of the production workshop to be monitored during the first monitoring time period, and determine whether the calculated environmental risk assessment index is less than the reference environmental risk assessment index. If it is less, there is no environmental risk in the production workshop to be monitored; otherwise, there is an environmental risk. The environmental risk assessment index is calculated using the following formula: In the formula, CEh is the environmental risk assessment index of the production workshop to be monitored in the h-th first monitoring time period. HTh and Dh are the temperature and humidity risk assessment index and dust concentration of the production workshop to be monitored in the h-th first monitoring time period respectively. HT0 and D0 are the reference temperature and humidity risk assessment index and reference dust concentration of the production workshop to be monitored respectively. ΔD is the reference dust concentration deviation of the production workshop to be monitored. α is the correction factor of the temperature and humidity risk assessment index. The temperature and humidity risk assessment index is used to describe the temperature and humidity risk level of the production workshop to be monitored during the first monitoring time period. The temperature and humidity risk assessment index is calculated using the following formula: Wherein, HTh is the temperature and humidity risk assessment index of the production workshop to be monitored in the h-th first monitoring time period, TPh and HPh are the temperature and humidity of the production workshop to be monitored in the h-th first monitoring time period respectively, TP0 and HP0 are the reference temperature and reference humidity of the production workshop to be monitored respectively, ΔTP and ΔHP are the reference temperature deviation and reference humidity deviation of the production workshop to be monitored respectively, and λ1 and λ2 are the weight factors of the temperature deviation ratio and humidity deviation ratio of the production workshop to be monitored respectively; The temperature deviation ratio is used to describe the ratio of the temperature deviation of the production workshop to be monitored to the reference temperature deviation; The humidity deviation ratio is used to describe the ratio of the humidity deviation of the production workshop to be monitored to the reference humidity deviation.

8. The production data management and analysis system based on the Internet of Things according to claim 6, characterized in that The specific analysis method for whether there is equipment risk in the production workshop to be monitored during the first monitoring time period is as follows; Obtain the production equipment data, reference production equipment data and reference production equipment deviation of the production workshop to be monitored; Calculate the equipment risk assessment index of the production workshop to be monitored according to the obtained data. The equipment risk assessment index is used to evaluate the equipment risk degree of each production equipment in the production workshop to be monitored during the first monitoring time period, and judge whether the calculated equipment risk assessment index is less than the reference equipment risk assessment index. If it is less, there is no equipment risk in the production workshop to be monitored, otherwise there is equipment risk; The equipment risk assessment index is calculated using the following formula: In the formula, j is the production equipment number of the production workshop to be monitored, j = 1, 2,..., J, where J is the total number of production equipment, DEh is the equipment risk assessment index of the production workshop to be monitored in the h-th first monitoring time period, and are respectively the first risk index, the second risk index and the third risk index of the j-th production equipment in the production workshop to be monitored in the h-th first monitoring time period, β, χ and δ are respectively the correction factors of the first risk index, the second risk index and the third risk index, and μ1, μ2 and μ3 are respectively the weights of the first risk index, the second risk index and the third risk index; The first risk index is used to describe the risk degree caused by insufficient maintenance of each production equipment; The second risk index is used to describe the risk degree caused by too long working hours of each production equipment; The third risk index is used to describe the risk degree caused by too high external temperature of each production equipment.

9. The production data management and analysis system based on the Internet of Things according to claim 8, characterized in that, The first risk index is calculated using the following formula: Wherein, is the equipment maintenance times of the j-th production equipment in the production workshop to be monitored during the h-th first monitoring time period, DMj is the reference equipment maintenance times of the j-th production equipment in the production workshop to be monitored, and ΔDMj is the deviation of the reference equipment maintenance times of the j-th production equipment in the production workshop to be monitored; The second risk index is calculated using the following formula: Wherein, is the equipment working duration of the j-th production equipment in the production workshop to be monitored during the h-th first monitoring time period, WTj is the reference equipment working duration of the j-th production equipment in the production workshop to be monitored, and ΔWTj is the deviation of the reference equipment working duration of the j-th production equipment in the production workshop to be monitored; The third risk index is calculated using the following formula: In the formula, is the external temperature of the j-th production equipment in the production workshop to be monitored during the h-th first monitoring time period, DTj is the reference external temperature of the j-th production equipment in the production workshop to be monitored, and ΔDTj is the reference external temperature deviation of the j-th production equipment in the production workshop to be monitored.

10. The production data management and analysis system based on the Internet of Things according to claim 1, wherein: The production data management and analysis module includes a production data management and analysis unit and a production data visualization unit; The production data management and analysis unit: is used to receive the second production benefit data, second production environment data and second production equipment data of the production workshop to be monitored, then perform visual analysis on the received data, and send the analysis results to the production data visualization unit; The production data visualization unit: is used to receive the analysis results sent by the production data management and analysis unit, and perform visual display through a preset visualization device.

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