Smart factory production system based on industrial Internet of Things

Abstract

The invention relates to the technical field of factory production, and discloses a smart factory production system based on industrial Internet of Things, which comprises a production data acquisition module, a production data analysis module and an early warning module, and is characterized in that the production data acquisition module is used for acquiring equipment operation data, equipment environment data, equipment production data and personnel feedback data; and the production data analysis module calculates an equipment fault index, an environmental influence index, equipment production pressure, an operation satisfaction number, an equipment comprehensive fault rate and a production delay rate according to the received data, judges whether the current equipment needs to be overhauled or not and whether a production delay problem is caused or not, and sends a corresponding signal to the early warning module. The system analyzes and knows the progress in the production process and the operation indexes of the equipment from multiple aspects, discovers potential equipment problems in advance, reduces the non-planned downtime, and improves the continuity and efficiency of the production line.

Description

Technical Field

[0001] The present invention relates to the field of factory production technology, and in particular to a smart factory production system based on industrial Internet of Things. Background Art

[0002] Smart factories integrate advanced information technology and manufacturing technology to realize a modern factory model that automates, intelligentizes and makes the production process more efficient. It not only includes automated equipment on the production line, but also covers all aspects of the entire production process, such as supply chain management, quality control, equipment maintenance, etc. As an important development direction of modern manufacturing, smart factories are gradually changing the production methods and business operation models. By integrating advanced information technology and manufacturing technology, smart factories have realized the automation, intelligence and efficiency of the production process, bringing huge economic and social benefits to enterprises. In the future, with the continuous advancement of technology and the expansion of application scenarios, smart factories will play an important role in more fields and promote the continuous innovation and development of the manufacturing industry.

[0003] The equipment involved in smart factories is of many types and large in number, and the maintenance of the equipment becomes complicated and cumbersome. When equipment fails frequently, it will not only affect production efficiency, but may also cause production interruptions and product quality problems. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a smart factory production system based on the industrial Internet of Things, which has the advantages of multi-faceted analysis to understand the progress of the production process and the operating indicators of the equipment, fully understand the operating health of the equipment and the frequency of equipment failures, and be able to discover potential equipment problems in advance, so as to perform preventive maintenance before failures occur, reduce unplanned downtime, and improve the continuity and efficiency of the production line.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a smart factory production system based on industrial Internet of Things, including a production data acquisition module, a production data analysis module and an early warning module.

[0008] The production data acquisition module is used to collect equipment operation data SByx, equipment environment data SBhj, equipment production data SBsc and personnel feedback data RYfk. The production data acquisition module forms a corresponding data set of the collected data and sends it to the production data analysis module through a connection network;

[0009] The production data analysis module calculates the equipment failure index SBzs according to the data set of the equipment operation data SByx, calculates the environmental impact index HJzs according to the data set of the equipment environment data SBhj, calculates the equipment production pressure SCyl according to the data set of the equipment production data SBsc, and calculates the operation satisfaction number CZmy according to the data set of the personnel feedback data RYfk through an algorithm formula based on the received data set;

[0010] The production data analysis module calculates the equipment comprehensive failure rate ZHgz according to the equipment failure index SBzs, the environmental impact index HJzs, and the equipment production pressure SCyl, and then calculates the production delay rate SCywl according to the equipment comprehensive failure rate ZHgz and the operation satisfaction number CZmy;

[0011] The production data analysis module determines whether the current equipment needs to be repaired based on the equipment comprehensive failure rate ZHgz, and sends a signal to the early warning module if maintenance is required;

[0012] The production data analysis module determines whether production delays will occur based on the production delay rate SCywl, and sends a signal to the early warning module if production delays will occur;

[0013] The warning module generates a corresponding warning report according to the corresponding signal.

[0014] Preferably, the corresponding data set expression is as follows:

[0015] The data set of the equipment operation data SByx includes the number of equipment startup times, the average equipment operation time, the equipment operation status, the equipment failure frequency, and the equipment energy consumption data, and the expression is: {KJcs, YXsc, YXzt, GZpl, SBnh};

[0016] The data set of the equipment environment data SBhj includes equipment environment temperature data, equipment environment humidity data, and equipment environment air quality data, and the expression is: {HJwd, HJsd, HJkq};

[0017] The data set of the equipment production data SBsc includes equipment production capacity data, equipment production progress data, and equipment production quality, and the expression is: {SCcn, SCjd, SBzl};

[0018] The data set expression of the personnel feedback data RYfk is: {RYfk1, RYfk2, ..., RYfk n}, where n represents the number of people providing feedback information in the personnel feedback data RYfk.

[0019] Preferably, the calculation formula of the equipment failure index SBzs is:

[0020]

[0021] Preferably, the calculation formula of the environmental impact index HJzs is:

[0022]

[0023] In the above formula, α, β, and γ represent the corresponding weight coefficients of the ambient temperature data, the equipment ambient humidity data, and the equipment ambient air quality data, respectively. b 、HJsd b 、HJkq b They represent the corresponding standard values ​​in the equipment environment temperature data, equipment environment humidity data, and equipment environment air quality data respectively.

[0024] Preferably, the calculation formula of the equipment production pressure SCyl is:

[0025]

[0026] Preferably, the operation satisfaction number CZm y The calculation formula is:

[0027]

[0028] In the above formula, The sum of all personnel feedback data in the data set representing the personnel feedback data RYfk.

[0029] Preferably, the calculation formula of the equipment comprehensive failure rate ZHgz is:

[0030]

[0031] In the above formula, BZ represents the failure rate of the equipment under standard conditions.

[0032] Preferably, the production data analysis module compares the equipment comprehensive failure rate ZHgz with the comprehensive failure threshold, and sends a maintenance signal to the early warning module when the comprehensive failure threshold is exceeded.

[0033] Preferably, the calculation formula of the production delay rate SCywl is:

[0034] SCywl=ZHgz*CZmy

[0035] Preferably, the production data analysis module compares the production delay rate SCywl with the production delay threshold, and sends a production delay signal to the early warning module when the production delay threshold is exceeded.

[0036] Compared with the prior art, the present invention provides a smart factory production system based on industrial Internet of Things, which has the following beneficial effects:

[0037] 1. The present invention collects equipment operation data SByx, equipment environment data SBhj, equipment production data SBsc and personnel feedback data RYfk, comprehensively calculates equipment failure index SBzs, environmental impact index HJzs, equipment production pressure SCyl, and operation satisfaction number CZmy, analyzes and understands the progress of the production process and the operation indicators of the equipment from multiple aspects, fully understands the operating health status of the equipment and the frequency of equipment failures, reduces the occurrence of equipment failures caused by environmental factors, can timely discover problems in the production process, can identify bottlenecks and inefficient links in the production process, and then carry out targeted optimization, thereby improving the operating efficiency of the production line, reducing resource waste, avoiding unplanned downtime due to failures, and improving the continuity and efficiency of the production line.

[0038] 2. The present invention calculates the equipment comprehensive failure rate ZHgz and the production delay rate SCywl through the equipment failure index SBzs, the environmental impact index HJzs, the equipment production pressure SCyl, and the operation satisfaction number CZmy, evaluates the overall status and potential risks in the factory production process, and can discover potential equipment problems in advance, so as to perform preventive maintenance before the failure occurs and reduce unplanned downtime. This not only improves the utilization rate of the equipment, but also ensures the continuous operation of the production line. It can implement more accurate preventive maintenance strategies, avoid unnecessary maintenance work and excessive maintenance, thereby reducing maintenance costs, timely predict production progress, avoid the problem of delayed order delivery due to production delays, and facilitate timely production planning adjustments according to actual conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the structural system of the present invention; DETAILED DESCRIPTION

[0040] 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.

[0041] See also Figure 1 , a smart factory production system based on industrial Internet of Things, including a production data acquisition module, a production data analysis module and an early warning module.

[0042] The production data acquisition module is used to collect equipment operation data SByx, equipment environment data SBhj, equipment production data SBsc and personnel feedback data RYfk. The production data acquisition module forms a corresponding data set of the collected data and sends it to the production data analysis module through the connection network;

[0043] The corresponding data set expression is as follows:

[0044] The data set of equipment operation data SByx includes the number of equipment startup times, the average equipment operation time, the equipment operation status, the equipment failure frequency, and the equipment energy consumption data, and the expression is: {KJcs, Yxsc, YXzt, GZpl, SBnh};

[0045] The data set of the equipment environment data SBhj includes equipment environment temperature data, equipment environment humidity data, and equipment environment air quality data, and the expression is: {HJwd, HJsd, HJkq};

[0046] Equipment production data SBs c The data set includes equipment production capacity data, equipment production progress data, and equipment production quality, and the expression is: {SCcn, SCjd, SBzl};

[0047] The data set expression of the personnel feedback data RYfk is: {RYfk1, RYfk2, ..., RYfk n}, where n represents the number of people providing feedback information in the personnel feedback data RYfk;

[0048] The production data analysis module calculates the equipment failure index SBzs according to the received data set and the data set of the equipment operation data SByx through an algorithm formula;

[0049] The calculation formula of equipment failure index SBzs is:

[0050]

[0051] By combining the equipment startup times, average equipment operation time, equipment operation status, equipment failure frequency, and equipment energy consumption data, the equipment failure index SBzs is calculated comprehensively to fully understand the equipment's operating health status, reflect the frequency of equipment failures, and thus evaluate its reliability;

[0052] Calculate the environmental impact index HJzs based on the data set of equipment environment data SBhj;

[0053] The calculation formula of environmental impact index HJzs is:

[0054]

[0055] In the above formula, α, β, and γ represent the corresponding weight coefficients of the ambient temperature data, the equipment ambient humidity data, and the equipment ambient air quality data, respectively. b 、HJsd b 、HJkq b They represent the corresponding standard values ​​of the equipment environment temperature data, equipment environment humidity data, and equipment environment air quality data respectively;

[0056] Combined with the environmental factors such as temperature, humidity and air quality, the environmental impact index HJzs is calculated to reduce the occurrence of equipment failures caused by environmental factors, avoid unplanned downtime caused by failures, and improve the continuity and efficiency of the production line;

[0057] Calculate the equipment production pressure SCyl based on the data set of equipment production data SBsc;

[0058] The calculation formula of equipment production pressure SCyl is:

[0059]

[0060] By calculating the production pressure of the equipment, it can be ensured that it operates within the design range and avoid equipment damage caused by overpressure;

[0061] Calculate the operation satisfaction number CZmy based on the data set of personnel feedback data RYfk;

[0062] The calculation formula of the operation satisfaction number CZmy is:

[0063]

[0064] In the above formula, The sum of all personnel feedback data in the data set representing personnel feedback data RYfk;

[0065] By combining multiple personnel to provide feedback on production equipment, problems in the production process can be discovered in a timely manner, bottlenecks and inefficient links in the production process can be identified, and targeted optimization can be carried out, thereby improving the operating efficiency of the production line and reducing resource waste;

[0066] The production data analysis module calculates the comprehensive equipment failure rate ZHgz based on the equipment failure index SBzs, environmental impact index HJzs, and equipment production pressure SCyl;

[0067] The calculation formula of equipment comprehensive failure rate ZHgz is:

[0068]

[0069] In the above formula, BZ represents the failure rate of the equipment under standard conditions;

[0070] The production data analysis module compares the equipment comprehensive failure rate ZHgz with the comprehensive failure threshold. If the comprehensive failure threshold is exceeded, a maintenance signal is sent to the early warning module.

[0071] The comprehensive equipment failure rate ZHgz can evaluate the overall status and potential risks of the factory production process, and can detect potential equipment problems in advance, so as to carry out preventive maintenance before failure occurs and reduce unplanned downtime. This not only improves the utilization rate of equipment, but also ensures the continuous operation of the production line. It can implement more accurate preventive maintenance strategies, avoid unnecessary maintenance work and excessive maintenance, and thus reduce maintenance costs;

[0072] The production data analysis module calculates the production delay rate SCywl based on the equipment comprehensive failure rate ZHgz and the operation satisfaction number CZmy;

[0073] The calculation formula of production delay rate SCywl is:

[0074] SCywl=ZHgz*CZmy

[0075] The production data analysis module compares the production delay rate SCywl with the production delay threshold. If the production delay threshold is exceeded, a production delay signal is sent to the early warning module.

[0076] The production delay rate can timely predict the production progress when equipment fails or affects production, avoid the problem of delayed order delivery due to production delays, and facilitate timely production planning adjustments based on actual conditions;

[0077] The early warning module generates a corresponding early warning report according to the corresponding signal.

[0078] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart factory production system based on industrial Internet of Things, characterized by: It includes production data collection module, production data analysis module and early warning module. The production data acquisition module is used to collect equipment operation data SByx, equipment environment data SBhj, equipment production data SBsc and personnel feedback data RYfk. The production data acquisition module forms a corresponding data set of the collected data and sends it to the production data analysis module through a connection network; The production data analysis module calculates the equipment failure index SBzs according to the data set of the equipment operation data SByx, calculates the environmental impact index HJzs according to the data set of the equipment environment data SBhj, calculates the equipment production pressure SCyl according to the data set of the equipment production data SBsc, and calculates the operation satisfaction number CZmy according to the data set of the personnel feedback data RYfk through an algorithm formula based on the received data set; The production data analysis module calculates the equipment comprehensive failure rate ZHgz according to the equipment failure index SBzs, the environmental impact index HJzs, and the equipment production pressure SCyl, and then calculates the production delay rate SCywl according to the equipment comprehensive failure rate ZHgz and the operation satisfaction number CZmy; The production data analysis module determines whether the current equipment needs to be repaired based on the equipment comprehensive failure rate ZHgz, and sends a signal to the early warning module if maintenance is required; The production data analysis module determines whether production delays will occur based on the production delay rate SCywl, and sends a signal to the early warning module if production delays will occur; The warning module generates a corresponding warning report according to the corresponding signal.

2. According to claim 1, a smart factory production system based on industrial Internet of Things is characterized by: The corresponding data set expression is as follows: The data set of the equipment operation data SByx includes the number of equipment startup times, the average equipment operation time, the equipment operation status, the equipment failure frequency, and the equipment energy consumption data, and the expression is: {KJcs, YXsc, YXzt, GZpl, SBnh}; The data set of the equipment environment data SBhj includes equipment environment temperature data, equipment environment humidity data, and equipment environment air quality data, and the expression is: {HJwd, HJsd, HJkg}; The data set of the equipment production data SBsc includes equipment production capacity data, equipment production progress data, and equipment production quality, and the expression is: {SCcn, SCjd, SBzl}; The data set expression of the personnel feedback data RYfk is: Among them, n represents the personnel feedback data RYfk in which n personnel provide feedback information data.

3. The smart factory production system based on industrial Internet of Things according to claim 1 is characterized in that: The calculation formula of the equipment failure index SBzs is:

4. The smart factory production system based on industrial Internet of Things according to claim 1 is characterized in that: The calculation formula of the environmental impact index HJzs is: In the above formula, α, β, and γ represent the corresponding weight coefficients of the ambient temperature data, the equipment ambient humidity data, and the equipment ambient air quality data, respectively. b ,HJSd b 、HJkq b They represent the corresponding standard values ​​in the equipment environment temperature data, equipment environment humidity data, and equipment environment air quality data respectively.

5. The smart factory production system based on industrial Internet of Things according to claim 1 is characterized in that: The calculation formula of the equipment production pressure SCyl is:

6. The smart factory production system based on industrial Internet of Things according to claim 1 is characterized in that: The calculation formula of the operation satisfaction number CZmy is: In the above formula, The sum of all personnel feedback data in the data set representing the personnel feedback data RYfk.

7. The smart factory production system based on industrial Internet of Things according to claim 1 is characterized in that: The calculation formula of the equipment comprehensive failure rate ZHgz is: In the above formula, BZ represents the failure rate of the equipment under standard conditions.

8. The smart factory production system based on industrial Internet of Things according to claim 1 is characterized in that: The production data analysis module compares the equipment comprehensive failure rate ZHgz with the comprehensive failure threshold, and sends a maintenance signal to the early warning module when the comprehensive failure threshold is exceeded.

9. The smart factory production system based on industrial Internet of Things according to claim 1 is characterized in that: The calculation formula of the production delay rate SCywl is: SCywl=ZHgz*CZmy 10. The smart factory production system based on industrial Internet of Things according to claim 1, characterized in that: The production data analysis module compares the production delay rate SCywl with the production delay threshold, and sends a production delay signal to the early warning module when the production delay threshold is exceeded.

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