Visual business management system based on big data platform

Abstract

The invention discloses a visual business management system based on a big data platform, and belongs to the technical field of management systems, the system comprises a plurality of production devices, each production device has its own coordinate in the same coordinate system; the acquisition module is used for acquiring condition information of each production device during working; the detection module is used for analyzing the condition information acquired by each sub-acquisition unit so as to generate a state signal according to an analysis result; and the visual management platform is used for managing the equipment according to the generated state signal. According to the invention, the detection module analyzes and judges the acquired condition information of the production equipment during working, so that the operation condition of the equipment can be acquired more conveniently and accurately, the operation condition of single equipment can be known without routine inspection of multiple detection personnel every day, and management of factories is more convenient.

Description

Technical Field

[0001] The present invention belongs to the technical field of management systems, and in particular relates to a visual business management system based on a big data platform. Background Art

[0002] A big data platform is a network platform that provides services through content sharing, resource sharing, channel co-construction, and data sharing. With the continuous development of cloud technology, big data platforms are widely used in factory management. Through the information in big data and the monitoring data during factory operation, through the analysis process, the factory status information can be displayed to managers in a visual manner, thereby assisting in abnormal supervision and management of the factory.

[0003] When the factory assembly line is operating, there will be a large number of production equipment to process the products in batches. In order to ensure the quality of the products produced, routine inspection and management of the production equipment is required. The current management of equipment is generally done by inspectors who conduct relevant inspections on the production equipment in the area they are responsible for every day, and then upload the records to the company's management platform, combined with the relevant content of the big data platform, to judge the operating status of the production equipment, so as to manage the equipment. However, due to the large number of production equipment in the factory, inspection is more laborious, requiring a large number of inspectors to conduct inspections, and the inspection results are easily affected by the inspection personnel's professional experience, resulting in inaccurate results, which is not convenient for subsequent management; and manual inspection can only preliminarily inspect the operating status of the equipment, and cannot determine the potential risk factors of the production equipment. Summary of the invention

[0004] The purpose of the present invention is to provide a visual business management system based on a big data platform to solve the problems faced in the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A visual business management system based on a big data platform, the system comprising:

[0007] Multiple production equipment, each of which has its own coordinates in the same coordinate system;

[0008] An acquisition module, which is composed of a plurality of sub-acquisition units, which are respectively installed in each production equipment and are used to acquire the working status information of each production equipment;

[0009] A detection module, which is used to analyze the status information acquired by each sub-acquisition unit, thereby generating a status signal according to the analysis result and uploading it to the visualization management platform;

[0010] A visual management platform manages the device according to the generated status signal.

[0011] Furthermore, the operating status information of the production equipment includes noise information and temperature information generated by the production equipment during operation.

[0012] Furthermore, the status signal includes a primary status signal, a secondary status signal and a tertiary status signal.

[0013] Furthermore, the detection module analysis method is:

[0014] Get the temperature change function T of the production equipment during processing vr (t) and the temperature variation function of the production equipment during idle operation T vo (t);

[0015] Obtain the noise variation function S of the production equipment during processing vr (t) and the noise variation function S of the production equipment during idle operation vo (t);

[0016] By the formula Status = w 1 *T st +w 2 *S st Calculate the running status value Status of the device;

[0017] Compare the obtained operating status value Status with the operating status threshold Status set by the system th Make comparisons;

[0018] If Status < Status th , the equipment is considered to be operating normally and a first-level status signal is generated;

[0019] Otherwise, the equipment is considered to be operating abnormally and a level 3 status signal is generated;

[0020] Among them, T st is the temperature deviation value, S st is the noise deviation value, w 1 and w 2 is the proportionality coefficient.

[0021] Furthermore, the method for obtaining the temperature deviation value is:

[0022] By formula Find out;

[0023] in, is the function of the historical temperature change of the production equipment during processing over time, is the historical temperature variation function of the production equipment during idle operation, & 1 as well as& 2 is the proportionality coefficient.

[0024] Furthermore, the method for obtaining the noise deviation value is:

[0025] By formula Find out;

[0026] in, is the function of the historical noise of the production equipment during processing over time, is the function of the historical noise of the production equipment during idle operation over time, α 1 and α 2 is the proportionality coefficient.

[0027] Furthermore, the detection module analysis method also includes:

[0028] When the equipment is operating normally, obtain the daily average temperature deviation value of the production equipment when it is working And the daily average noise deviation In this way, the daily average temperature deviation value of the production equipment within n days and the daily average noise deviation value change curve with the number of days are obtained. as well as

[0029] Will obtain as well as Compare with the temperature deviation threshold value changing with the number of days T(d) and the noise deviation threshold value changing with the number of days S(d) preset in the system respectively;

[0030] The upper side of T(d) and The area enclosed below is marked as W T , connect the top of S(d) with The area enclosed below is marked as W S ;

[0031] By formula Calculate the potential risk value F;

[0032] When F ≥ 1, a secondary state signal is generated;

[0033] Otherwise, no status signal is generated;

[0034] Among them, η 1 and η 2 is the weight coefficient, F th The risk threshold preset for the system.

[0035] Furthermore, the visual management platform management method is:

[0036] When a primary status signal is generated, the production equipment is marked green on the platform;

[0037] When a secondary status signal is generated, the production equipment is marked yellow on the platform;

[0038] When a level 3 status signal is generated, the production equipment is marked in red on the platform.

[0039] Furthermore, the detection module is also used to judge the operational stability of the production equipment in the entire plant area, and the judgment method is:

[0040] Count the average operating status of the entire factory's production equipment And the operating status value of each production device Status i ;

[0041] By formula Find the discrete value P B ;

[0042] Among them, i∈m, m is the number of production equipment in the factory;

[0043] The discrete value P B Discrete thresholds preset by the system Make a comparison;

[0044] If the discrete value P B Less than discrete threshold It is believed that the operation stability of the production equipment in the entire plant is good;

[0045] Otherwise, the stability is considered poor.

[0046] Beneficial effects of the present invention:

[0047] The present invention uses a detection module to analyze and judge the acquired status information of the production equipment during operation, and can more conveniently and accurately obtain the operating status of the equipment. It is not necessary for multiple detection personnel to perform routine inspections every day to know the operating status of a single device, thereby making it more convenient for factories to manage.

[0048] The present invention compares and analyzes the detected temperature and noise with the historical temperature and noise of the equipment, and can make judgments based on the situation of each device itself, so as to make the detection result more accurate. At the same time, by comparing and analyzing the temperature and noise when the equipment is running idle with the historical temperature and noise when the equipment is running idle, the interference of the processed products on the equipment itself can be reduced, thereby reducing the occurrence of misjudgment. Through the combination of the two, the detection result can be made more accurate.

[0049] The present invention can timely conduct further detection on equipment in normal operating conditions, can predict in advance whether the equipment has potential risks, and can timely pre-process the equipment to reduce subsequent losses and maintenance costs.

[0050] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0053] 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.

[0054] In one embodiment, Figure 1 As shown, a visual business management system based on a big data platform is disclosed, and the management system includes:

[0055] Multiple production equipment, each of which has its own coordinates in the same coordinate system;

[0056] An acquisition module, which is composed of several sub-acquisition units and is installed in each production equipment to acquire the working status information of each production equipment;

[0057] A detection module is used to analyze the status information obtained by each sub-acquisition unit, thereby generating a status signal according to the analysis result and uploading it to the visualization management platform;

[0058] Visual management platform,The visual management platform manages the equipment according to the generated status signals.

[0059] Through the above technical scheme, this embodiment provides a method for judging the operating status of production equipment in a factory area. By analyzing and judging the acquired status information of the production equipment during operation through the detection module, the operating status of the equipment can be obtained more conveniently and accurately. There is no need for multiple detection personnel to conduct routine inspections every day to know the operating status of a single device, which makes it more convenient for factory management.

[0060] As an implementation manner of the present invention, the operating status information of the production equipment includes noise information and temperature information generated by the production equipment during operation.

[0061] Through the above technical solution, generally speaking, when factory equipment has an abnormality, the noise it produces during operation will increase and the temperature during production will also rise. Therefore, by obtaining the noise information and temperature information generated by the production equipment during operation, the operating status of the equipment can be judged more accurately, and the noise information of the equipment can be obtained through the sound detector installed in the equipment, and the temperature information can be obtained through the temperature sensor installed in the equipment.

[0062] As an implementation manner of the present invention, the status signal includes a primary status signal, a secondary status signal and a tertiary status signal.

[0063] Through the above technical solution, the detection results of the detection module are divided into primary status signals, secondary status signals and tertiary status signals, which makes it easier for the subsequent visual management platform to manage the production equipment.

[0064] As an embodiment of the present invention, the method of detecting module analysis is:

[0065] Get the temperature change function T of the production equipment during processing vr (t) and the temperature variation function of the production equipment during idle operation T vo (t);

[0066] Obtain the noise variation function S of the production equipment during processing vr (t) and the noise variation function S of the production equipment during idle operation vo (t);

[0067] By the formula Status = w 1 *T st +w 2 *S st Calculate the running status value Status of the device;

[0068] Compare the obtained operating status value Status with the operating status threshold Status set by the system th Make comparisons;

[0069] If Status < Status th , the equipment is considered to be operating normally and a first-level status signal is generated;

[0070] Otherwise, the equipment is considered to be operating abnormally and a level 3 status signal is generated;

[0071] Among them, T st is the temperature deviation value, S st is the noise deviation value, w 1 and w 2 is the proportionality coefficient.

[0072] Through the above technical solution, this embodiment provides a specific detection method for the detection module to detect the operating status of the production equipment, and obtains the temperature deviation value and the noise deviation value through the detected noise information and temperature information. Since errors are easily generated in single judgments, for example, when both the noise and the temperature deviation change, and the changes of both are not large, the system cannot detect it at this time, and it will be assumed that there is no problem with the operation status of the equipment, thereby generating judgment errors. Therefore, through the formula Status = w 1 *T st +w 2 *S st Comprehensively analyze and judge the detected temperature deviation value and noise deviation value to obtain the equipment's operating status value Status, which can more accurately determine the operating status of the production equipment, and then compare the obtained operating status value Status with the operating status threshold Status set by the system. th Compare, when Status<Status th If the operating status threshold set by the system is not exceeded at this time, the equipment is considered to be in normal operation and a first-level status signal is generated; otherwise, the equipment is considered to be in abnormal operation and a third-level status signal is generated and uniformly transmitted to the visualization management platform for management; this method can more accurately detect the operating status of production equipment to reduce detection errors and facilitate subsequent management.

[0073] In the above technical solution, the proportionality coefficient w 1 and w 2 The operating status threshold is determined based on historical data and experience. th It can be formulated after comprehensive analysis based on historical data combined with relevant equipment data in the big data platform, which will not be elaborated here.

[0074] As an implementation manner of the present invention, a method for obtaining a temperature deviation value is:

[0075] By formula Find out;

[0076] in, is the function of the historical temperature change of the production equipment during processing over time, is the historical temperature variation function of the production equipment during idle operation, & 1 as well as& 2 is the proportionality coefficient;

[0077] The method for obtaining the noise deviation value is:

[0078] By formula Find out;

[0079] in, is the function of the historical noise of the production equipment during processing over time, is the function of the historical noise of the production equipment during idle operation over time, α 1 and α 2 is the proportionality coefficient.

[0080] Through the above technical solution, this embodiment provides a specific method for obtaining the temperature deviation value and the noise deviation value. During the production and processing of the production equipment, the temperature or noise of the equipment may change due to some factors of the processed product itself, which may cause misjudgment. In addition, due to the difference of each device's parts and other factors, the operating conditions of each device are also different. If a unified judgment is made, errors will also occur. Therefore, this application obtains the temperature change function T of the production equipment during processing. vr (t) and the temperature variation function of the production equipment during idle operation T vo (t), obtain the noise variation function S of the production equipment during processing vr (t) and the noise variation function S of the production equipment during idle operation vo (t), and compare them with the historical data functions respectively. First, we use the formula Calculate the temperature deviation value, where the formula is The difference between the temperature change of the equipment during processing and the historical temperature change can be obtained through the formula The difference between the temperature change of the equipment during idle operation and the historical temperature change during idle operation can be obtained, and a comprehensive judgment can be made by combining the two. It can be seen that the larger the difference, the larger the temperature change difference, the worse the operating condition of the equipment; at the same time, through the formula Find the noise deviation value, obviously The larger the difference, or The larger the difference, the larger the noise deviation value, and the more it indicates that the equipment is in poor operating condition. By comparing the detected temperature and noise with the historical temperature and noise of the equipment, judgment can be made based on the situation of each device, making the test results more accurate. At the same time, by comparing the temperature and noise of the equipment when it is running idle with the historical temperature and noise of the equipment when it is running idle, the interference of the processed products on the equipment itself can be reduced, thereby reducing the occurrence of misjudgment. The combination of the two can make the test results more accurate.

[0081] In the above technical solution, can be obtained based on historical data, the proportionality coefficient & 1 as well as& 2 , α 1 and α 2 This is formulated based on historical data and experience, so I will not go into details here.

[0082] As an embodiment of the present invention, the method of detecting module analysis further includes:

[0083] When the equipment is operating normally, obtain the daily average temperature deviation value of the production equipment when it is working And the daily average noise deviation In this way, the daily average temperature deviation value of the production equipment within n days and the daily average noise deviation value change curve with the number of days are obtained. as well as

[0084] Will obtain as well as Compare with the temperature deviation threshold value changing with the number of days T(d) and the noise deviation threshold value changing with the number of days S(d) preset in the system respectively;

[0085] The upper side of T(d) and The area enclosed below is marked as W T , connect the top of S(d) with The area enclosed below is marked as W S ;

[0086] By formula Calculate the potential risk value F;

[0087] When F ≥ 1, a secondary state signal is generated;

[0088] Otherwise, no status signal is generated;

[0089] Among them, η 1 and η 2 is the weight coefficient, F th The risk threshold preset for the system.

[0090] Through the above technical solution, this embodiment provides a specific analysis method for the potential risk problems of equipment operation status. Since the changes in the operation status of some production equipment occur slowly, it is impossible to detect it at the beginning, and it is also impossible to judge its potential risk status. Therefore, the daily average temperature deviation value of the production equipment when it is working is first obtained in the production equipment with normal operation status. And the daily average noise deviation In this way, the daily average temperature deviation value of the production equipment within n days and the daily average noise deviation value change curve with the number of days are obtained. as well as And compare them with the temperature deviation threshold value changing with the number of days T(d) and the noise deviation threshold value changing with the number of days S(d) preset in the system, and compare the upper side of T(d) with The area enclosed below is marked as W T , connect the top of S(d) with The area enclosed below is marked as W S , in combination with the formula Calculate the potential risk value F, and you can see that when the enclosed area W T , W S The larger the value, the higher the potential risk value F. When F≥1, it is considered that the production equipment has potential risks and a secondary status signal is generated. In this way, further inspections can be carried out on equipment that is operating normally in a timely manner, and whether the equipment has potential risks can be predicted in advance. The equipment can be processed in advance to reduce subsequent losses and maintenance costs.

[0091] In the above technical solution, the preset temperature deviation threshold value changes with the number of days T(d) and the noise deviation threshold value changes with the number of days S(d), and the risk threshold F th The weight coefficient η can be analyzed and formulated based on the relevant data in the big data platform. 1 and η 2 This is formulated based on historical data and experience, so I will not go into details here.

[0092] As an implementation mode of the present invention, the visual management platform management method is:

[0093] When a primary status signal is generated, the production equipment is marked green on the platform;

[0094] When a secondary status signal is generated, the production equipment is marked yellow on the platform;

[0095] When a level 3 status signal is generated, the production equipment is marked in red on the platform.

[0096] Through the above technical solution, this embodiment can visualize the three status signals generated to facilitate management by workers. Specifically, when a first-level status signal is generated, the production equipment is marked green on the platform, indicating that the equipment is normal; when a second-level status signal is generated, the production equipment is marked yellow on the platform, indicating that the equipment has potential risks and requires timely maintenance and repair; when a third-level status signal is generated, the production equipment is marked red on the platform, indicating that the equipment is not operating normally and maintenance and inspection can be carried out when necessary, thereby facilitating workers to manage the production equipment.

[0097] As an implementation mode of the present invention, the detection module is also used to judge the operation stability of the production equipment in the entire plant area, and the judgment method is:

[0098] Count the average operating status of the entire factory's production equipment And the operating status value of each production device Status i ;

[0099] By formula Find the discrete value P B ;

[0100] Among them, i∈m, m is the number of production equipment in the factory;

[0101] The discrete value P B Discrete thresholds preset by the system Make a comparison;

[0102] If the discrete value P B Less than discrete threshold It is believed that the operation stability of the production equipment in the entire plant is good;

[0103] Otherwise, the stability is considered poor.

[0104] Through the above technical solution, this embodiment provides a method for the detection module to judge the operating stability of the production equipment in the entire factory area. First, the average operating status value of the production equipment in the entire factory area is counted. And the operating status value of each production device Status i , so that through the formula Find the discrete value P B , so that the operating status fluctuation of the entire plant equipment can be known, and the obtained discrete value P B Discrete thresholds preset by the system For comparison, the discrete threshold preset by the system It can be selected based on historical data. If the discrete value P B Less than discrete threshold In this way, we can know the fluctuation of the operation status of the production equipment in the whole plant, and thus know the operation stability of the production equipment in the whole plant, so as to timely understand the overall production efficiency of the plant.

[0105] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A visual business management system based on a big data platform, characterized by: The system comprises: Multiple production equipment, each of which has its own coordinates in the same coordinate system; An acquisition module, which is composed of a plurality of sub-acquisition units, which are respectively installed in each production equipment and are used to acquire the working status information of each production equipment; A detection module, which is used to analyze the status information acquired by each sub-acquisition unit, thereby generating a status signal according to the analysis result and uploading it to the visualization management platform; A visual management platform manages the device according to the generated status signal.

2. The visual business management system based on the big data platform according to claim 1 is characterized in that: The working condition information of the production equipment includes noise information and temperature information generated by the production equipment when it is working.

3. The visual business management system based on the big data platform according to claim 2 is characterized in that: The status signal includes a primary status signal, a secondary status signal and a tertiary status signal.

4. The visual business management system based on the big data platform according to claim 3 is characterized in that: The method of the detection module analysis is: Get the temperature change function T of the production equipment during processing vr (t) and the temperature variation function of the production equipment during idle operation T vo (t); Obtain the noise variation function S of the production equipment during processing vr (t) and the noise variation function S of the production equipment during idle operation vo (t); By formula Status = w1*T st +w2*S st Calculate the running status value Status of the device; Compare the obtained operating status value Status with the operating status threshold Status set by the system th Make comparisons; If Status < Status th , the equipment is considered to be operating normally and a first-level status signal is generated; Otherwise, the equipment is considered to be operating abnormally and a level 3 status signal is generated; Among them, T st is the temperature deviation value, S st is the noise deviation value, w1 and w2 are proportional coefficients.

5. The visual business management system based on the big data platform according to claim 4 is characterized in that: The method for obtaining the temperature deviation value is: By formula Find out; Among them, T vr0 (t) is the function of the historical temperature of the production equipment during processing over time, T vo0 (t) is the historical temperature variation function of the production equipment during idle operation, &1 and &2 are proportional coefficients.

6. The visual business management system based on the big data platform according to claim 5 is characterized in that: The method for obtaining the noise deviation value is: By formula Find out; in, is the function of the historical noise of the production equipment during processing over time, is the function of the historical noise variation over time when the production equipment is running idle, and α1 and α2 are proportional coefficients.

7. The visual business management system based on the big data platform according to claim 6 is characterized in that: The method of the detection module analysis also includes: When the equipment is operating normally, obtain the daily average temperature deviation value of the production equipment when it is working And the daily average noise deviation In this way, the daily average temperature deviation value of the production equipment within n days and the daily average noise deviation value change curve with the number of days are obtained. as well as Will obtain as well as Compare with the temperature deviation threshold value changing with the number of days T(d) and the noise deviation threshold value changing with the number of days S(d) preset in the system respectively; The upper side of T(d) and The area enclosed below is marked as W T , connect the top of S(d) with The area enclosed below is marked as W S ; By formula Calculate the potential risk value F; When F ≥ 1, a secondary state signal is generated; Otherwise, no status signal is generated; Among them, η1 and η2 are weight coefficients, F th The risk threshold preset for the system.

8. The visual business management system based on the big data platform according to claim 7 is characterized in that: The method of management of the visualization management platform is: When a primary status signal is generated, the production equipment is marked green on the platform; When a secondary status signal is generated, the production equipment is marked yellow on the platform; When a level 3 status signal is generated, the production equipment is marked in red on the platform.

9. The visual business management system based on the big data platform according to claim 8 is characterized in that: The detection module is also used to judge the operational stability of the production equipment in the entire plant area, and the judgment method is: Count the average operating status of the entire factory's production equipment And the operating status value of each production device Status i ; By formula Find the discrete value P B ; Among them, i∈m, m is the number of production equipment in the factory; The discrete value P B Discrete thresholds preset by the system Make a comparison; If the discrete value P B Less than discrete threshold It is believed that the operation stability of the production equipment in the entire plant is good; Otherwise, the stability is considered poor.

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