Intelligent MES data analysis system and method for workshop production

Abstract

An intelligent MES data analysis system and method for workshop production, relating to the technical field of MESs. The system comprises a data collection module, a data processing module and an optimization scheduling module. The data collection module is used for acquiring key data from a workshop device and ambient data of a workshop in real time. The data processing module comprises a prediction unit and a quality management unit, wherein the prediction unit is used for predicting a possible device fault on the basis of data of the data collection module, which is beneficial to scheduling maintenance in advance. By evaluating and calculating the remaining useful life of a production device in the workshop, the time when a next fault occurs in the production device in the workshop can be predicted in advance, thereby facilitating subsequent maintenance, and reducing the loss caused by the fault; and according to production requirements and resource conditions, resource allocation on a production line, such as manpower, materials and machinery, can be automatically adjusted, thereby improving the resource utilization rate, reducing waste, and quickly responding to production changes.

Description

Workshop production intelligent MES data analysis system and analysis method TECHNICAL FIELD

[0001] The present application relates to the technical field of MES, more specifically, to a workshop production intelligent MES data analysis system and analysis method. BACKGROUND

[0002] The workshop production intelligent MES (manufacturing execution system) data analysis system is mainly used for improving production efficiency, optimizing resource allocation, ensuring product quality, reducing production cost, and enhancing the flexibility and response capability of production to market changes. The system collects various data in the production process in real time, such as device status, production progress, quality detection results, etc., to provide real-time information for production decision-making; records quality detection data to timely find and handle quality problems and improve product quality.

[0003] However, as the requirements of industrial workshops are becoming higher and higher, workshops are becoming more and more special due to different products. The general MES system may not meet the specific process and flow requirements of special workshops, and some workshops may operate in extreme temperature, humidity, corrosive or radioactive environments. The existing intelligent MES is difficult to adapt to the use requirements.

[0004] SUMMARY

[0005] To solve the problem that workshops are becoming more and more special due to different products, the general MES system may not meet the specific process and flow requirements of special workshops, and some workshops may operate in extreme temperature, humidity, corrosive or radioactive environments, and the existing intelligent MES is difficult to adapt to the use requirements, the present application provides a workshop production intelligent MES data analysis system and analysis method.

[0006] In order to achieve the above purpose, a workshop production intelligent MES data analysis system comprises a data acquisition module, a data processing module and an optimization scheduling module.

[0007] The acquisition module is used to acquire key data and workshop environment data from workshop equipment in real time.

[0008] The data processing module comprises a prediction unit and a quality management unit. The prediction unit is used to predict possible equipment failures according to the data of the data acquisition module, which is beneficial to arrange maintenance work in advance. Specifically:

[0009] The interval time T1 from the last failure to the last-but-one failure of the production equipment in the workshop is obtained.

[0010] The average failure interval time T2 of the production equipment in the workshop is obtained.

[0011] Obtaining the failure rate γ of the production equipment in the workshop per unit time;

[0012] Obtaining the reliability R of the production equipment in the workshop;

[0013] According to the formula Calculating the remaining service life Q of the production equipment in the workshop; the quality management unit is used to monitor the product quality in real time according to the data of the data acquisition module;

[0014] The optimization scheduling module is used to generate production demand and resource status according to the data of the data acquisition module and the judgment result of the data processing module, and automatically adjust the resource allocation on the production line.

[0015] Preferably, the reliability R of the production equipment in the workshop can be obtained in the following way:

[0016] According to the formula R = e -γt , calculating the reliability R of the production equipment in the workshop, wherein e is the base of natural logarithm, and t is the time elapsed since the last maintenance of the production equipment in the workshop.

[0017] Preferably, the specific working mode of the quality management unit is as follows:

[0018] Obtaining the average value X of the current batch size of the production products in the workshop;

[0019] Obtaining the standard deviation σ of the current batch of production products in the workshop;

[0020] Setting the upper limit specification Y1 and the lower limit specification Y2 of the production products in the workshop in advance;

[0021] According to the formula Calculating the real-time evaluation value P of the production products in the workshop;

[0022] Obtaining the threshold value of the real-time evaluation value in advance, if the real-time evaluation value P of the production products is lower than the threshold value of the real-time evaluation value, an abnormal signal is generated and transmitted to the optimization scheduling module, otherwise, it is normal.

[0023] Preferably, the specific working mode of the average value X of the current batch size of the production products in the workshop and the standard deviation σ of the current batch of production products in the workshop is as follows:

[0024] According to the formula Calculating the average value X of the current batch size of the production products in the workshop, wherein X i is the measurement value of the i-th product in the batch, and n is the number of current measurement values;

[0025] Updating the standard deviation σ of the current batch with each new measurement value.

[0026] According to the formula Calculate the standard deviation σ of the current batch of workshop production products, wherein Hl i is the average value of the size of the current batch of products.

[0027] Preferably, the quality management unit is also used to monitor the abnormalities of the workshop in real time, in particular:

[0028] According to the formula Calculate the comprehensive safety environment value K of the workshop, wherein the ω c B1 is the first preset weight coefficient, ω c B2 is the second adjusted weight coefficient, m is the number of key indicators, δ c is the value of the cth key indicator, g c is the adjusted value of the cth key indicator.

[0029] A threshold value of the comprehensive safety environment value is set in advance, if the comprehensive safety environment value K of the workshop is lower than the threshold value of the comprehensive safety environment value, an abnormal signal is generated and transmitted to the optimization scheduling module, otherwise, it is normal operation.

[0030] Preferably, the key indicators include workshop indicators and environmental indicators, and the workshop indicators include:

[0031] Accident rate δ1, which is obtained by dividing the number of historical accidents by the number of historical working hours and multiplying by one million, can be converted into the number of accidents per million working hours, and is an important indicator for measuring safety;

[0032] Accident severity δ2, which is obtained by dividing the total loss caused by historical accidents by the number of historical accidents, can help assess the average impact of each accident;

[0033] Safety inspection frequency δ3, which is obtained by dividing the number of historical safety inspections by the number of historical total working hours, is an important means of preventing accidents through regular safety inspections;

[0034] The environmental indicators include:

[0035] Air quality index δ4, which is the concentration value of pollutants monitored in the workshop;

[0036] Temperature and humidity index δ5, which is the sum of the temperature and humidity values in the workshop;

[0037] Noise level δ6, which is the noise intensity in the workshop;

[0038] Harmful substance concentration δ7, which is the concentration of harmful substances measured in the workshop divided by the safety standard concentration.

[0039] Preferably, the first weight coefficient ω c B1 and the key indicators are one-to-one correspondence, specifically ω1B1, ω2B1, ω3B1, ω4B1, ω5B1, ω6B1 and ω7B1, and the specific values are 0.029, 0.192, 0.386, 0.102, 0.123, 0.097 and 0.071;

[0040] According to the formula g c = V c ×(1+α*ω c B1), the adjustment value of the cth key indicator is calculated, wherein V c is the value of the cth key indicator minus the average value of all key indicators and then divided by the standard deviation of all key indicators, and α is a preset adjustment coefficient.

[0041] The importance value Z i of all equipment in the workshop is obtained.

[0042] According to the formula , the adjustment value μ i of each production equipment according to the remaining service life Q of the production equipment in the workshop is obtained, wherein ∈ i is the risk score of the ith equipment in the workshop, ∈1 is the comprehensive risk score of all equipment in the workshop, and θ is the total production plan in the workshop.

[0043] According to the calculated adjustment value μ, the resources on the production line are redistributed.

[0044] Preferably, the risk score ∈ i of the ith equipment in the workshop is obtained by dividing the remaining service life of the ith equipment by the importance of the ith equipment.

[0045] Preferably, the optimization scheduling module further comprises an emergency processing unit, and the working mode of the emergency processing unit is as follows:

[0046] When the emergency processing unit receives the abnormal information of the quality management unit:

[0047] If the real-time evaluation value is abnormal, adjust the production parameters and increase the quality control measures;

[0048] If the comprehensive safety environment value is abnormal, adjust the environment.

[0049] Beneficial effects: By evaluating and calculating the remaining service life of the production equipment in the workshop, the time of the next failure of the production equipment in the workshop can be predicted in advance, which is beneficial to subsequent maintenance and can reduce the loss caused by failure;

[0050] According to the production demand and resource condition, the resource allocation on the production line, such as manpower, material and machine, is automatically adjusted, which can improve the resource utilization rate, reduce waste and quickly respond to production changes. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is a flow chart of the intelligent MES data analysis system of the present application. DETAILED DESCRIPTION

[0052] As shown in Figure 1: a workshop production intelligent MES data analysis system, comprising a data acquisition module, a data processing module and an optimization scheduling module; it should be noted that the workshop production intelligent MES (manufacturing execution system) data analysis system is mainly used to improve production efficiency, optimize resource allocation, ensure product quality, reduce production cost, and enhance the flexibility and response ability of production to market changes, the system collects various data in the production process in real time, such as equipment state, production progress, quality detection result, etc., to provide real-time information for production decision; record quality detection data, find and handle quality problems in time, and improve product quality; but with the increasing requirements of industrial workshops, workshops are becoming more and more special because of different products, and general MES system may not meet the special process and flow requirements of special workshops, and some workshops may operate in extreme temperature, humidity, corrosive or radioactive environment, and existing intelligent MES is difficult to adapt to the use requirements;

[0053] The acquisition module is configured to acquire key data and environment data of the workshop in real time from the workshop equipment; it should be noted that in the embodiment, the key data includes production state data: equipment running state (such as opening, closing, failure, etc.); working state of the production line (such as idle, running, maintenance, etc.); performance index data: working efficiency and performance parameters (such as speed, temperature, pressure, etc.) of the equipment; throughput and capacity utilization of the production line; quality control data: product quality test results, including size, weight, appearance, etc.; number and type of defects and defective products; material tracking data: consumption and inventory level of raw materials; location and state of work-in-process (WIP); order and work order data; order progress and completion; work order allocation and execution status; maintenance and repair data: equipment maintenance history and repair records; preventive maintenance plan and execution; energy consumption data; usage of energy such as electricity, water, gas; energy use efficiency and cost; personnel management data: employee attendance and work records; skill and training needs; safety and environmental data: safety event records of the workshop; environmental monitoring data such as air quality, noise level, etc.; supply chain data: supplier delivery and inventory levels; logistics and distribution status; customer feedback data: customer feedback and complaints on products or services; market trends and customer demand changes; financial data: production cost and profit analysis; budget and actual expenditure comparison, etc., which are mainly obtained through sensors and various questionnaires;

[0054] It should be further noted that the environment data of the workshop includes temperature, humidity and air pressure of the workshop;

[0055] The data processing module includes a prediction unit and a quality management unit, the prediction unit is configured to predict possible equipment failures according to the data of the data acquisition module, which is beneficial to arrange maintenance work in advance, specifically:

[0056] Obtain the interval time T1 from the last failure to the last-but-one failure of the production equipment in the workshop;

[0057] Obtain the average failure interval time T2 of the production equipment in the workshop; it should be noted that in the embodiment, T2 is the average value of all failure interval times;

[0058] Obtain the failure rate γ of the production equipment in the workshop per unit time; it should be noted that in the embodiment, the failure rate γ is calculated according to the formula:

[0059] Obtain the reliability R of the production equipment in the workshop; it should be noted that in the embodiment, R is used to quantitatively evaluate whether the production equipment will fail by comprehensively analyzing historical data;

[0060] According to the formula The remaining service life Q of the production equipment in the workshop is calculated; it should be noted that the remaining service life of the production equipment in the workshop is calculated by evaluation, which can predict the time of the next failure of the production equipment in the workshop, is beneficial to subsequent maintenance, and can reduce the loss caused by failure;

[0061] The quality management unit is used to monitor product quality in real time according to the data of the data acquisition module;

[0062] The optimization scheduling module is used to generate production demand and resource status according to the data of the data acquisition module and the judgment result of the data processing module, and automatically adjust the resource allocation on the production line. It should be noted that the resource utilization rate is improved, waste is reduced, and production changes are quickly responded.

[0063] As an optional embodiment, the reliability R of the production equipment in the workshop can be obtained in the following way:

[0064] According to the formula R = e -γt , the reliability R of the production equipment in the workshop is calculated, wherein e is the base of natural logarithm, and t is the time elapsed since the last maintenance of the production equipment in the workshop. It should be noted that the reliability R represents the probability that the equipment can still operate normally after time t, wherein the reliability R decreases with the increase of time, because the equipment is more and more likely to fail as time goes on.

[0065] As an optional embodiment, the specific working mode of the quality management unit is as follows:

[0066] The average value X of the current batch size of the production products in the workshop is obtained;

[0067] The standard deviation σ of the current batch of production products in the workshop is obtained;

[0068] The upper limit specification Y1 and the lower limit specification Y2 of the production products in the workshop are set in advance;

[0069] According to the formula The real-time evaluation value P of the production products in the workshop is calculated;

[0070] The threshold value of the real-time evaluation value is obtained in advance, if the real-time evaluation value P of the production products is lower than the threshold value of the real-time evaluation value, an abnormal signal is generated and transmitted to the optimization scheduling module, otherwise, it is normal. It should be noted that in this embodiment, the threshold value of the real-time evaluation value is usually determined by industry standards, customer requirements or company's internal quality targets, for example, the threshold value of the real-time evaluation value is usually 1, and the threshold value of the real-time evaluation value is gradually increased according to the requirements.

[0071] As an optional embodiment, the specific working mode of the average value X of the current batch size of the workshop production product and the standard deviation σ of the current batch of the workshop production product is as follows:

[0072] The average value X of the current batch size of the workshop production product is calculated according to the formula i wherein X is the average value of the current batch size of the workshop production product, and n is the number of current measurement values;

[0073] The standard deviation σ of the current batch is updated as each new measurement value is obtained;

[0074] The standard deviation σ of the current batch of the workshop production product is calculated according to the formula i wherein H1 is the average value of the current batch size of the workshop production product.

[0075] As an optional embodiment, the quality management unit is further used to monitor the abnormality of the workshop in real time, and specifically:

[0076] The comprehensive safety environment value K of the workshop is calculated according to the formula c wherein B1 is a preset first weight coefficient, ω c B2 is an adjusted second weight coefficient, m is the number of key indicators, δ c is the value of the cth key indicator, and g c is the adjusted value of the cth key indicator.

[0077] A threshold value of the comprehensive safety environment value is set in advance, and if the comprehensive safety environment value K of the workshop is lower than the threshold value of the comprehensive safety environment value, an abnormal signal is generated and transmitted to the optimization scheduling module, otherwise, the workshop is in normal operation.

[0078] As an optional embodiment, the key indicators include workshop indicators and environmental indicators, and the workshop indicators include:

[0079] The accident occurrence rate δ1, which is obtained by dividing the historical number of accidents by the historical number of working hours and multiplying by one million, can be converted into the number of accidents per million working hours, and is an important indicator for measuring safety;

[0080] The accident severity δ2, which is obtained by dividing the total loss caused by historical accidents by the historical number of accidents, can help to assess the average impact of each accident;

[0081] The safety inspection frequency δ3, which is obtained by dividing the historical number of safety inspections by the historical total number of working hours, is an important means of preventing accidents through regular safety inspections;

[0082] ​​​The environmental indicators include:

[0083] An air quality index δ4, the air quality index δ4 being a concentration value of a pollutant monitored in the workshop;

[0084] A temperature and humidity index δ5, the temperature and humidity index δ5 being a sum of a temperature value and a humidity value in the workshop;

[0085] A noise level δ6, the noise level δ6 being a noise intensity in the workshop;

[0086] A harmful substance concentration δ7, the harmful substance concentration δ7 being a concentration of a harmful substance measured in the workshop divided by a safety standard concentration.

[0087] As an optional embodiment, the first weight coefficient ω c B1 and the key indicators are in one-to-one correspondence, specifically ω1B1, ω2B1, ω3B1, ω4B1, ω5B1, ω6B1 and ω7B1, and the specific values are 0.029, 0.192, 0.386, 0.102, 0.123, 0.097 and 0.071;

[0088] According to the formula g c = V c × (1 + α * ω c B1), the adjusted value of the cth key indicator is obtained, where V c is obtained by subtracting the average value of all key indicators from the value of the cth key indicator and dividing by the standard deviation of all key indicators, and α is a preset adjustment coefficient. It should be noted that in this embodiment, the value of α can be 1.023, and it should also be noted that the second weight coefficient ω c B2 and the adjusted value of the key indicator are in one-to-one correspondence, specifically ω1B2, ω2B2, ω3B2, ω4B2, ω5B2, ω6B2 and ω7B2, and the specific values are 0.129, 0.196, 0.282, 0.107, 0.123, 0.084 and 0.079.

[0089] As an optional embodiment, the optimization scheduling module includes a feedback adjustment unit, which is configured to adjust the production equipment in the workshop according to the result of the prediction unit, and the specific working mode is as follows:

[0090] The importance value Z i of all equipment in the workshop is obtained; it should be noted that in this embodiment, Z i represents the importance value Z i of the ith equipment, which is obtained by staff evaluation and is any integer from 1 to 10;

[0091] According to the formula The adjustment value μ of each production device is obtained according to the remaining service life Q of the production device in the workshop i wherein ∈ i is the risk score of the i-th device in the workshop, ∈1 is the comprehensive risk score of all devices in the workshop, and θ is the total production plan in the workshop.

[0092] According to the calculated adjustment value μ, the resources on the production line are redistributed. It should be noted that the new production target is obtained by subtracting the adjustment value μ from the current production target of the workshop, and the manpower and material resources are rearranged in this way. In this way, the production plan can be dynamically adjusted according to the remaining service life and risk score of the device, the resource allocation can be optimized, and the production efficiency can be improved.

[0093] As an optional embodiment: the risk score ∈ i is obtained by dividing the remaining service life of the i-th device by the importance of the i-th device.

[0094] As an optional embodiment: the optimization scheduling module further comprises an emergency processing unit, and the working mode of the emergency processing unit is as follows:

[0095] When the emergency processing unit receives the abnormal information of the quality management unit:

[0096] If the real-time evaluation value is abnormal, adjust the production parameters and increase the quality control measures; it should be noted that in this embodiment, the quality score can be analyzed: determine the specific factors that cause the low product quality score, such as size deviation, defect rate, performance not meeting standards, etc.; parameter adjustment; according to the analysis result, adjust the key parameters in the production process, such as temperature, pressure, speed, etc.; process control is strengthened: increase the monitoring frequency of key production steps to ensure the strictness of process control; quality control measures: introduce or enhance online quality detection system to realize real-time quality feedback and control; operator training: additional training for operators to improve their understanding of quality control points and operation skills; equipment maintenance and calibration: ensure that all production equipment is properly maintained and calibrated to ensure production accuracy; raw material inspection: strengthen the quality inspection of raw materials to ensure that only qualified raw materials enter the production process; improve the process: according to the quality data feedback, optimize the production process to reduce quality fluctuations.

[0097] If the comprehensive safety environment value is abnormal, the environment is adjusted.It should be noted that in the present embodiment, the environment score analysis can be: identifying factors that cause low environment condition scores, such as excessively high temperature, unsuitable humidity, poor air quality, etc.; the environment monitoring system can be: strengthening the environment monitoring system to realize real-time monitoring of key environmental parameters; and the environment control system adjustment can be: adjusting the air conditioning system, humidity control system, air filtration system, etc. according to the monitoring data to improve the environmental conditions.

[0098] Working principle:

[0099] By evaluating the remaining service life of the production equipment in the workshop, the time of the next failure of the production equipment in the workshop can be predicted in advance, which is beneficial to subsequent maintenance and can reduce the loss caused by the failure.

[0100] According to the production demand and resource status, the resource allocation on the production line, such as manpower, materials and machines, is automatically adjusted, which can improve the resource utilization rate, reduce waste and quickly respond to production changes.

[0101] The above is only a preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments only, and any technical solution falling within the scope of the present application shall be considered as falling within the protection scope of the present application. It should be noted that for ordinary technicians in the technical field, some improvements and refinements without departing from the principles of the present application are also considered as falling within the protection scope of the present application.

Claims

1. A shop floor production intelligent MES data analysis system, characterized by, The system comprises a data acquisition module, a data processing module and an optimized scheduling module. The acquisition module is configured to acquire real-time key data and environmental data of the workshop from the workshop equipment. The data processing module comprises a prediction unit and a quality management unit. The prediction unit is configured to predict possible equipment failures according to the data of the data acquisition module, so as to arrange maintenance work in advance. The interval time T1 between the last failure and the last-but-one failure of the production equipment in the workshop is acquired. The average failure interval time T2 of the production equipment in the workshop is acquired. The failure rate γ of the production equipment in the workshop per unit time is acquired. According to the formula The reliability R of the production equipment in the workshop is acquired. The residual service life Q of the production equipment in the workshop is calculated.

2. The intelligent MES data analysis system for shop floor production according to claim 1, characterized in that, The quality management unit is configured to monitor product quality in real time according to the data of the data acquisition module. According to the formula R = e -γt , the reliability R of the production equipment in the workshop is calculated, where e is the base of natural logarithm, and t is the time elapsed since the last maintenance of the production equipment in the workshop.

3. The intelligent MES data analysis system for shop floor production according to claim 1, characterized in that, The optimized scheduling module is configured to generate production demand and resource status according to the data of the data acquisition module and the judgment result of the data processing module, and automatically adjust resource allocation on the production line. The reliability R of the production equipment in the workshop can be acquired in the following manner. The specific working mode of the quality management unit is as follows. The average value X of the current batch size of the production product in the workshop is acquired. According to the formula The standard deviation σ of the current batch of the production product in the workshop is acquired. The upper limit specification Y1 and the lower limit specification Y2 of the production product in the workshop are set in advance.

4. The intelligent MES data analysis system for shop floor production according to claim 3, characterized in that, The real-time evaluation value P of the production product in the workshop is calculated. According to the formula The average value X of the current batch size of the products produced in the workshop is calculated, wherein X i is the measurement value of the ith product in the batch in the workshop, and n is the number of current measurement values; The threshold value of the real-time evaluation value is acquired in advance. If the real-time evaluation value P of the production product is lower than the threshold value of the real-time evaluation value, an abnormal signal is generated and transmitted to the optimized scheduling module. Otherwise, the system runs normally. According to the formula Calculate the standard deviation σ of the current batch of products produced in the workshop, where Hl i It is the average size of the current batch of products.

5. The intelligent MES data analysis system for shop floor production according to claim 4, characterized in that, The specific working mode of the average value X of the current batch size of the production product in the workshop and the standard deviation σ of the current batch of the production product in the workshop is as follows. According to the formula The comprehensive safety environment value K of the workshop is calculated, wherein ω c B1 is a preset first weight coefficient, ω c B2 is an adjusted second weight coefficient, m is the number of key indicators, δ c is the value of the cth key indicator, g c is the adjusted value of the cth key indicator With each new measurement value, the standard deviation σ of the current batch is updated.

6. The intelligent MES data analysis system for shop floor production according to claim 5, characterized in that, The quality management unit is also configured to monitor the abnormality of the workshop in real time, specifically as follows. A threshold value of the comprehensive safety environment value K is set in advance. If the comprehensive safety environment value K of the workshop is lower than the threshold value of the comprehensive safety environment value, an abnormal signal is generated and transmitted to the optimized scheduling module. Otherwise, the system runs normally. The key indicators include workshop indicators and environmental indicators. The accident occurrence rate δ1 is obtained by dividing the historical number of accidents by the historical number of working hours and multiplying by one million. It can be converted into the number of accidents per million working hours, which is an important indicator of safety. The accident severity δ2 is obtained by dividing the total loss caused by historical accidents by the number of historical accidents, which can help evaluate the average impact of each accident. The safety inspection frequency δ3 is obtained by dividing the historical number of safety inspections by the historical total number of working times. Regular safety inspections are an important means of preventing accidents. The environmental indicators include: The air quality index δ4 is the concentration value of pollutants monitored in the workshop. The temperature and humidity index δ5 is the sum of the temperature and humidity values in the workshop. The noise level δ6 is the noise intensity in the workshop. The harmful substance concentration δ7 is the harmful substance concentration measured in the workshop divided by the safety standard concentration.

7. The intelligent MES data analysis system for shop floor production according to claim 6, characterized in that, The first weight coefficient ω c B1 and the key indicators are one-to-one correspondence, specifically ω1B1, ω2B1, ω3B1, ω4B1, ω5B1, ω6B1 and ω7B1, and the specific values are 0.029, 0.192, 0.386, 0.102, 0.123, 0.097 and 0.071; According to the formula g c = V c × (1 + a * ω c B1), the adjustment value of the cth key indicator is calculated, wherein V c is obtained by subtracting the average value of all key indicators from the value of the cth key indicator and dividing by the standard deviation of all key indicators, and a is a preset adjustment coefficient.

8. The intelligent MES data analysis system for shop floor production according to claim 1, characterized in that, The optimization scheduling module comprises a feedback adjustment unit, which is configured to adjust the production equipment in the workshop according to the result of the prediction unit, and the specific working mode is as follows: Obtaining the important value Z of all equipment in the workshop i ; According to the formula The adjustment value μ of each production equipment obtained according to the residual service life Q of the production equipment in the workshop is calculated i wherein ∈ i is the risk score of the i-th equipment in the workshop, ∈1 is the comprehensive risk score of all equipment in the workshop, and θ is the total production plan in the workshop. According to the calculated adjustment value μ, the resources on the production line are redistributed.

9. The intelligent MES data analysis system for shop floor production according to claim 8, characterized in that, the risk score of the ith equipment in the workshop i is obtained by dividing the remaining service life of the ith equipment by the importance of the ith equipment.

10. A laboratory refrigeration and heating thermostat management method, characterized by, The optimization scheduling module further comprises an emergency processing unit, and the working mode of the emergency processing unit is as follows: When the emergency processing unit receives the abnormal information of the quality management unit: If the real-time evaluation value is abnormal, the production parameters are adjusted and the quality control measures are increased; If the comprehensive safety environment value is abnormal, the environment is adjusted.

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