Method and system for monitoring operation state of enterprise production intelligent equipment

Abstract

The invention relates to the technical field of production monitoring, and particularly discloses an enterprise production intelligent equipment operation state monitoring method and system, and the method comprises the steps: dividing a product production process into a plurality of stages, obtaining the number of defective products in each stage in a monitoring period, calculating to obtain a stage deviation ratio and a stage defective rate, and calculating to obtain an equipment state value; acquiring equipment state values in different monitoring periods, calculating to obtain a state anomaly ratio, drawing a period-state anomaly ratio change curve, calculating a linear reference value, comparing the linear reference value with a linear reference threshold value, judging a curve change type, and if the curve is non-linear change, acquiring an equipment maintenance deviation ratio; and drawing a period-equipment state value and period-maintenance deviation ratio change curve, calculating a correlation degree, comparing the correlation degree with a threshold value, and determining whether the equipment is overhauled or not. The device operation state can be mastered in time, and the production efficiency and the product quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production monitoring, and in particular to a method and system for monitoring the operating status of intelligent production equipment in an enterprise. Background Art

[0002] In modern enterprise production, the widespread use of intelligent production equipment has greatly improved production efficiency and product quality, but the monitoring and management of equipment operating status also faces many challenges.

[0003] In the existing technology, monitoring technology is not capable of predicting and analyzing equipment failures. Usually, maintenance is performed only after the equipment has obvious failures, which lacks foresight. This post-maintenance method not only affects production progress and causes product delivery delays, but may also cause a large number of defective products due to sudden equipment failures, increase production costs, and reduce the economic benefits of the enterprise. Moreover, during the maintenance process, due to the lack of effective analysis of the equipment's historical operating data, maintenance personnel often find it difficult to quickly and accurately locate the cause of the failure, affecting production efficiency.

[0004] By obtaining the number of defective products at multiple stages of the production process, calculating the equipment status value Zt, the abnormal stage ratio, the deviation mean ratio and the status abnormality ratio, and drawing the cycle-status abnormality ratio change curve, in-depth mining of the changing trend of the equipment operation status can comprehensively and accurately evaluate the equipment's operating status at each production stage. This multi-indicator comprehensive analysis method can more promptly discover abnormal conditions in equipment operation, warn of potential problems in advance, and avoid sudden equipment failures, thereby ensuring production continuity and reducing the defective rate. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for monitoring the operating status of intelligent production equipment in an enterprise to solve the problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A method and system for monitoring the operating status of intelligent equipment in an enterprise, comprising: Obtain the production process of products manufactured by intelligent production equipment, divide the production process into multiple stages, obtain the production stages, and obtain the number of defective products in each production stage; Based on the number of defective products in each stage, the defective target range of each stage is obtained, the stage deviation ratio Pc and the stage defective rate Cp are calculated, and the equipment state value Zt is calculated. If the equipment state value Zt is less than the equipment state threshold, a state analysis signal is generated; The production stage that generates the state analysis signal is marked as the production abnormality stage, and the abnormal stage ratio is calculated. The deviation mean ratio of the equipment state value Zt and the equipment state threshold is calculated, and the deviation mean ratio is calculated with the abnormal stage ratio to obtain the state abnormality ratio. The state abnormality ratio of different monitoring periods is obtained, and the change curve of the cycle-state abnormality ratio is drawn. The linear reference value Ck is calculated by the change curve, and compared with the linear reference threshold. If the change curve is a nonlinear change, a trend analysis signal is generated; Based on the trend analysis signal, the maintenance deviation ratio of the production equipment during the abnormal production stage is calculated, the equipment status value Zt and the maintenance deviation ratio in different monitoring periods are obtained, the corresponding change curve is drawn in the same coordinate system, the cycle time of the curve peak overlap is obtained, the overlap time ratio Sj and the average number of overlaps Jz are calculated, and the correlation Gl is calculated. The correlation Gl is compared with the correlation threshold. If the correlation GI exceeds the threshold, production must be stopped and the production intelligent equipment must be repaired.

[0007] As a further solution of the present invention: the device status value Zt is obtained in the following manner: Calculate the equipment status value Zt based on the stage deviation ratio Pc and the stage defective rate Cp; By formula: , the device state value Zt is calculated, where a1 and a2 are preset proportional coefficients.

[0008] As a further solution of the present invention: the stage deviation ratio Pc is obtained as follows: Based on the number of defective products at each production stage, obtain the target range of defective products at each stage; Subtract the number of defective products in each production stage from the two endpoints of the defective product target range in each stage and take the absolute value, and mark the minimum value obtained as the stage defective product difference; The stage defective difference is processed by ratio with the length of the defective target range to obtain the stage deviation ratio, which is marked as Pc; The method for obtaining the defective rate Cp in the above stage is: Obtain the total number of products manufactured in each production stage during the monitoring period, and calculate the ratio of the number of defective products to the total number of products to obtain the stage defective rate, which is marked as Cp.

[0009] As a further solution of the present invention: the linear reference value Ck is obtained in the following manner: Based on the linear threshold ratio Xb and the linear variance Xf, a linear reference value Ck of the change curve of the cycle-state abnormality ratio is calculated; By formula: , the linear reference value Ck is calculated, where ln(b1*Xb+b2*Cf+1.112) is a logarithmic function with base e, and b1 and b2 are preset proportional coefficients.

[0010] As a further solution of the present invention: the linear variance Xf is obtained as follows: Put the linear parameter differences of all sub-curve segments into the data set as elements, calculate the variance of the elements in the data set, obtain the linear variance, and mark the linear variance as Xf; The linear threshold ratio Xb is obtained as follows: Obtain the linear parameter differences of all sub-curve segments, sum and average the linear parameter differences of all sub-curve segments, and obtain the linear difference mean; The linear difference mean is processed with the linear difference threshold to obtain the linear threshold difference, and the linear threshold difference is processed with the linear difference threshold to obtain the linear threshold ratio, which is marked as Xb.

[0011] As a further solution of the present invention: the linear deviation is obtained by: Obtain the state anomaly ratios of different monitoring periods, and draw a change curve of the period-state anomaly ratio in a two-dimensional rectangular coordinate system; Connect the two end points of the change curve of the cycle-state anomaly ratio to obtain a linear reference line, and divide the change curve into multiple curve segments to obtain sub-curve segments; Obtain the state anomaly ratio of the sub-curve segment, perform summation and mean processing on the state anomaly ratio of the sub-curve segment, and obtain the curve segment anomaly mean; Draw a straight line parallel to the Y axis with the curve anomaly mean, and obtain the state anomaly ratio corresponding to the intersection of the straight line and the linear reference line; The difference between the segment anomaly mean and the state anomaly ratio is taken and the absolute value is taken to obtain the linear parameter difference.

[0012] As a further solution of the present invention: the state abnormality ratio is obtained in the following manner: Marking the production stage that generates the status analysis signal as the production abnormality stage, obtaining the number of the production abnormality stages, and summing up the numbers of all the production abnormality stages to obtain the total number of abnormal stages; The number of all production stages is obtained, and the ratio of the sum of abnormal stages to the number of all production stages is processed to obtain the abnormal stage ratio; The device state value Zt is subtracted from the device state threshold and the absolute value is taken to obtain the state value difference; The state value difference is processed by ratio with the device state threshold to obtain the state deviation ratio; Obtain the state deviation ratios of all abnormal production stages, perform summation and mean processing on the state deviation ratios, and obtain the deviation mean ratio; The deviation mean ratio and the abnormal stage ratio are processed as ratios to obtain the state abnormality ratio.

[0013] As a further solution of the present invention: the method of obtaining the correlation index GI is as follows: Based on the overlap time ratio Sj and the average overlap times Jz, the correlation degree Gl is calculated; By formula: , calculate the association index GI.

[0014] As a further solution of the present invention: the acquisition method of the overlap time ratio Sj and the overlap number mean Jz is: Obtain the equipment maintenance times during the abnormal production stage, perform subtraction processing on the equipment maintenance times and the maintenance target times, and obtain the maintenance times difference; The maintenance deviation ratio is obtained by ratioing the maintenance frequency difference with the maintenance target frequency. It should be noted that the maintenance deviation ratio reflects the degree of deviation between the actual maintenance of the equipment during the abnormal production stage and the expected maintenance target; Obtain the equipment status value Zt and maintenance deviation ratio of each production abnormality stage in different monitoring cycles, and draw the cycle-equipment status value Zt change curve and cycle-maintenance deviation ratio change curve in the same two-dimensional rectangular coordinate system; Obtain the cycle-equipment status value Zt change curve, cycle-maintenance deviation ratio change curve, and the cycle time when the peaks overlap, and obtain the overlap duration; Obtain the overlap duration of all monitoring cycles to obtain the total overlap time, perform ratio processing on the total overlap time and the duration of all monitoring cycles to obtain the overlap time ratio, and mark the overlap time ratio as Sj; The number of overlap durations is obtained, and the number of overlap durations of all monitoring cycles is summed and averaged to obtain the average number of overlap times, which is marked as Jz.

[0015] An enterprise production intelligent equipment operation status monitoring system, comprising: Parameter acquisition module: obtains the production process of products manufactured by intelligent production equipment, divides the production process into multiple stages, obtains the production stages, and obtains the number of defective products in each production stage; State analysis module: based on the number of defective products in each stage, obtain the defective target range of each stage, calculate the stage deviation ratio Pc, stage defective rate Cp, and calculate the equipment state value Zt. If the equipment state value Zt is less than the equipment state threshold, generate a state analysis signal; Trend judgment module: Based on the state analysis signal, the production stage that generates the state analysis signal is marked as the production abnormality stage, and the abnormal stage ratio is calculated. The equipment state value Zt and the equipment state threshold are calculated to obtain the deviation mean ratio. The deviation mean ratio is calculated with the abnormal stage ratio to obtain the state abnormality ratio, and the state abnormality ratio of different monitoring periods is obtained. The change curve of the cycle-state abnormality ratio is drawn, and the linear reference value Ck is calculated by the change curve. By comparing it with the linear reference threshold, if the change curve is a nonlinear change, a trend analysis signal is generated; Correlation analysis module: Based on the trend analysis signal, calculate the maintenance deviation ratio of production equipment in the abnormal production stage, obtain the equipment status value Zt and maintenance deviation ratio in the abnormal production stage in different monitoring cycles, draw the corresponding change curve in the same coordinate system, obtain the cycle time of the curve peak overlap, calculate the overlap time ratio Sj, the average number of overlaps Jz, and calculate the correlation Gl. Compare the correlation Gl with the correlation threshold. If the correlation GI exceeds the threshold, it is necessary to stop production and repair the intelligent production equipment.

[0016] Beneficial effects of the present invention: (1) By dividing the production process into multiple stages and comprehensively considering multiple key indicators such as the number of defective products, stage deviation ratio Pc, stage defective product Cp, and equipment status value Zt at each stage, a comprehensive and accurate monitoring of the operating status of intelligent production equipment is achieved. This multi-dimensional analysis method can promptly detect subtle anomalies in equipment operation and warn of potential problems before equipment failure occurs. If a key component of the equipment begins to wear out and has not yet caused a serious failure, it can be detected in time through changes in the stage deviation ratio Pc and the stage defective rate Cp, so that the company has enough time to arrange equipment maintenance and avoid sudden equipment shutdowns that cause production interruptions.

[0017] (2) In the abnormal production stage, by obtaining the equipment maintenance deviation ratio and correlating it with the equipment status value Zt, the overlap time Sj, the average overlap number Jz and the correlation Gl are calculated, which provides a scientific basis for equipment maintenance decision-making. When the correlation GI exceeds the threshold, the close relationship between equipment failure and maintenance status can be accurately located, prompting the enterprise to stop the production process in time for targeted maintenance, thus avoiding the waste of time and resources caused by blind maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below in conjunction with the accompanying drawings.

[0019] Figure 1 This is a flow chart of a method for monitoring the operating status of intelligent equipment in enterprise production according to the present invention; Figure 2 It is a module diagram of an enterprise production intelligent equipment operation status monitoring system in the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] Example 1, please refer to Figure 1 As shown, the present invention is a method for monitoring the operating status of intelligent equipment in an enterprise, comprising: Step 1: Obtain the production process of the products manufactured by the intelligent production equipment, divide the production process into multiple stages, obtain the production stages, and obtain the number of defective products in each production stage; Obtain the production process of the products manufactured by the enterprise's intelligent equipment, divide the production process into multiple stages, and obtain the production stage; Use infrared sensors and counting sensors to obtain the number of unqualified products at each production stage during the monitoring period and obtain the number of defective products; In some embodiments, an infrared sensor is used to obtain a product image and compare it with a target image to classify the product into qualified products and unqualified products; Step 2: Based on the number of defective products in each stage, obtain the defective target range of each stage, calculate the stage deviation ratio Pc, the stage defective rate Cp, and calculate the equipment state value Zt. If the equipment state value Zt is less than the equipment state threshold, generate a state analysis signal; Based on the number of defective products at each production stage, obtain the target range of defective products at each stage; Subtract the number of defective products in each production stage from the two endpoints of the defective product target range in each stage and take the absolute value, and mark the minimum value obtained as the stage defective product difference; The stage defective difference is processed by ratio with the length of the defective target range to obtain the stage deviation ratio, which is marked as Pc; Obtain the total number of products manufactured in each production stage during the monitoring period, and calculate the ratio of the number of defective products to the total number of products to obtain the stage defective rate, which is marked as Cp; Calculate the equipment status value Zt based on the stage deviation ratio Pc and the stage defective rate Cp; By formula: , calculate the device state value Zt, where a1 and a2 are preset proportional coefficients; Compare the device state value Zt with the device state threshold; If the equipment status value Zt ≥ the equipment status threshold, it means that in the current monitoring period, the equipment status value Zt of the production intelligent equipment is within the target expectation. Since the operating status of the equipment may change at any time, it is still necessary to continuously monitor the changes in the output deviation value Cp of each production stage; If the equipment status value Zt is less than the equipment status threshold, it indicates that the equipment status value Zt in the current production stage is lower than expected, and a status analysis signal is generated to further analyze the production process; It should be noted that the equipment status value Zt is lower than the expected production stage, and the stage defective rate Cp or stage deviation ratio Pc of the equipment is higher, indicating that there may be potential problems in the operation status of the equipment, resulting in the equipment status value Zt being lower than expected; The technical solution of this embodiment is: obtain the production process of the products manufactured by the intelligent production equipment, divide the production process into multiple stages, obtain the production stage, obtain the number of defective products in each production stage, based on the number of defective products in each stage, obtain the defective target range for each stage, calculate the stage deviation ratio Pc, the stage defective rate Cp, and calculate the equipment state value Zt. If the equipment state value Zt is less than the equipment state threshold, generate a state analysis signal.

[0022] Embodiment 2, based on embodiment 1, a method for monitoring the operating status of intelligent equipment in an enterprise production further includes: Step 3: Based on the state analysis signal, the production stage that generates the state analysis signal is marked as the production abnormality stage, and the abnormal stage ratio is calculated. The equipment state value Zt and the equipment state threshold are calculated to obtain the deviation mean ratio, and the deviation mean ratio is calculated with the abnormal stage ratio to obtain the state abnormality ratio. The state abnormality ratio of different monitoring periods is obtained, and the change curve of the cycle-state abnormality ratio is drawn. The linear reference value Ck is calculated by the change curve, and the change curve is divided into linear change and nonlinear change by comparison with the linear reference threshold. For the nonlinear change curve, a trend analysis signal is generated; Marking the production stage that generates the status analysis signal as the production abnormality stage, obtaining the number of the production abnormality stages, and summing up the numbers of all the production abnormality stages to obtain the total number of abnormal stages; The number of all production stages is obtained, and the ratio of the sum of abnormal stages to the number of all production stages is processed to obtain the abnormal stage ratio; The device state value Zt is subtracted from the device state threshold and the absolute value is taken to obtain the state value difference; The state value difference is processed by ratio with the device state threshold to obtain the state deviation ratio; Obtain the state deviation ratios of all abnormal production stages, perform summation and mean processing on the state deviation ratios, and obtain the deviation mean ratio; The deviation mean ratio and the abnormal stage ratio are processed as ratios to obtain the state abnormality ratio; Obtain the state anomaly ratios of different monitoring periods, and draw a change curve of the period-state anomaly ratio in a two-dimensional rectangular coordinate system with the monitoring period as the X-axis and the state anomaly ratio as the Y-axis; Connect the two end points of the change curve of the cycle-state anomaly ratio to obtain a linear reference line, and divide the change curve into multiple curve segments to obtain sub-curve segments; Obtain the state anomaly ratio of the sub-curve segment, perform summation and mean processing on the state anomaly ratio of the sub-curve segment, and obtain the curve segment anomaly mean; Draw a straight line parallel to the Y axis with the curve anomaly mean, and obtain the state anomaly ratio corresponding to the intersection of the straight line and the linear reference line; The difference between the mean value of the curve segment anomaly and the state anomaly ratio is taken and the absolute value is taken to obtain the linear parameter difference; Obtain the linear parameter differences of all sub-curve segments, sum and average the linear parameter differences of all sub-curve segments, and obtain the linear difference mean; Perform difference processing on the linear difference mean and the linear difference threshold to obtain the linear threshold difference, perform ratio processing on the linear threshold difference and the linear difference threshold to obtain the linear threshold ratio, and mark the linear threshold ratio as Xb; It should be noted that the linear difference threshold is set by professionals in this field based on experience; Put the linear parameter differences of all sub-curve segments into the data set as elements, calculate the variance of the elements in the data set, obtain the linear variance, and mark the linear variance as Xf; Based on the linear threshold ratio Xb and the linear variance Xf, a linear reference value Ck of the change curve of the cycle-state abnormality ratio is calculated; By formula: , the linear reference value Ck is calculated, where ln(b1*Xb+b2*Cf+1.112) is a logarithmic function with base e, and b1 and b2 are preset proportional coefficients; Compare the linear reference value Ck with the linear reference threshold; If the linear reference value Ck ≥ the linear reference threshold, the cycle-state anomaly ratio is fitted using the least squares method to obtain a fitting straight line, and the slope of the fitting straight line is calculated; If the slope of the fitted straight line is greater than 0, it means that the cycle-state abnormality ratio change curve is increasing linearly, and the production process needs to be stopped and the production intelligent equipment needs to be repaired and maintained; If the linear reference value Ck is less than the linear reference threshold, it indicates that the cycle-state abnormality ratio change curve is nonlinear, and a trend analysis signal is generated; It should be noted that the slope of the fitting straight line is greater than 0. As the monitoring period increases, the state abnormality ratio gradually increases according to the linear trend, indicating that the operating state of the production intelligent equipment is gradually deteriorating, and the production process needs to be stopped and the production intelligent equipment needs to be repaired. For the cycle-state abnormality ratio change curve with nonlinear trend changes, a trend analysis signal is generated to further analyze the production intelligent equipment. Step 4: Based on the trend analysis signal, calculate the maintenance deviation ratio of the production equipment in the abnormal production stage, obtain the equipment status value Zt and the maintenance deviation ratio in the abnormal production stage in different monitoring cycles, draw the corresponding change curve in the same coordinate system, obtain the cycle time of the curve peak overlap, calculate the overlap time ratio Sj, the average number of overlaps Jz, and calculate the correlation Gl, compare the correlation Gl with the correlation threshold, if the correlation GI exceeds the threshold, it is necessary to stop production and overhaul the production intelligent equipment; Obtain the equipment maintenance times during the abnormal production stage, perform subtraction processing on the equipment maintenance times and the maintenance target times, and obtain the maintenance times difference; The maintenance deviation ratio is obtained by ratioing the maintenance frequency difference with the maintenance target frequency. It should be noted that the maintenance deviation ratio reflects the degree of deviation between the actual maintenance of the equipment during the abnormal production stage and the expected maintenance target; Obtain the equipment status value Zt and maintenance deviation ratio of each production abnormality stage in different monitoring cycles, and draw the cycle-equipment status value Zt change curve and cycle-maintenance deviation ratio change curve in the same two-dimensional rectangular coordinate system; Obtain the cycle-equipment status value Zt change curve, cycle-maintenance deviation ratio change curve, and the cycle time when the peaks overlap, and obtain the overlap duration; Obtain the overlap duration of all monitoring cycles to obtain the total overlap time, perform ratio processing on the total overlap time and the duration of all monitoring cycles to obtain the overlap time ratio, and mark the overlap time ratio as Sj; Obtain the number of coincidence durations, sum and average the number of coincidence durations of all monitoring cycles to obtain the mean number of coincidences, and mark the mean number of coincidences as Jz; Based on the overlap time ratio Sj and the average overlap times Jz, the correlation degree Gl is calculated; By formula: , calculate the correlation GI; Compare the relevance GI with the relevance threshold; If the correlation GI of the current production abnormality stage exceeds the correlation threshold, it indicates that the equipment state value Zt of the production abnormality stage of the production intelligent equipment has a large correlation with the maintenance deviation ratio, and it is necessary to stop the current production process and perform maintenance on the production intelligent equipment; If the correlation GI of the current production abnormality stage is within the correlation threshold, it is still necessary to continuously monitor the changes in the correlation GI; Embodiment 3, as Figure 2 As shown, a system for monitoring the operation status of intelligent equipment in enterprise production also includes: Parameter acquisition module: obtains the production process of products manufactured by intelligent production equipment, divides the production process into multiple stages, obtains the production stages, and obtains the number of defective products in each production stage; State analysis module: based on the number of defective products in each stage, obtain the defective target range of each stage, calculate the stage deviation ratio Pc, stage defective rate Cp, and calculate the equipment state value Zt. If the equipment state value Zt is less than the equipment state threshold, generate a state analysis signal; Trend judgment module: Based on the state analysis signal, the production stage that generates the state analysis signal is marked as the production abnormality stage, and the abnormal stage ratio is calculated. The equipment state value Zt and the equipment state threshold are calculated to obtain the deviation mean ratio. The deviation mean ratio is calculated with the abnormal stage ratio to obtain the state abnormality ratio, and the state abnormality ratio of different monitoring periods is obtained. The change curve of the cycle-state abnormality ratio is drawn, and the linear reference value Ck is calculated by the change curve. By comparing it with the linear reference threshold, if the change curve is a nonlinear change, a trend analysis signal is generated; Correlation analysis module: Based on the trend analysis signal, calculate the maintenance deviation ratio of production equipment in the abnormal production stage, obtain the equipment status value Zt and maintenance deviation ratio in the abnormal production stage in different monitoring cycles, draw the corresponding change curve in the same coordinate system, obtain the cycle time of the curve peak overlap, calculate the overlap time ratio Sj, the average number of overlaps Jz, and calculate the correlation Gl. Compare the correlation Gl with the correlation threshold. If the correlation GI exceeds the threshold, it is necessary to stop production and repair the intelligent production equipment.

[0023] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for monitoring the operating status of intelligent equipment in an enterprise, characterized in that: include: Obtain the production process of products manufactured by intelligent production equipment, divide the production process into multiple stages, obtain the production stages, and obtain the number of defective products in each production stage; Based on the number of defective products in each stage, the defective target range of each stage is obtained, the stage deviation ratio Pc and the stage defective rate Cp are calculated, and the equipment state value Zt is calculated. If the equipment state value Zt is less than the equipment state threshold, a state analysis signal is generated; The production stage that generates the state analysis signal is marked as the production abnormality stage, and the abnormal stage ratio is calculated. The deviation mean ratio of the equipment state value Zt and the equipment state threshold is calculated, and the deviation mean ratio is calculated with the abnormal stage ratio to obtain the state abnormality ratio. The state abnormality ratio of different monitoring periods is obtained, and the change curve of the cycle-state abnormality ratio is drawn. The linear reference value Ck is calculated through the change curve. If the linear reference value Ck is less than the linear reference threshold, the change curve is a nonlinear change, and a trend analysis signal is generated; Based on the trend analysis signal, the maintenance deviation ratio of the production equipment during the abnormal production stage is calculated, the equipment status value Zt and the maintenance deviation ratio in different monitoring periods are obtained, the corresponding change curve is drawn in the same coordinate system, the cycle time of the curve peak overlap is obtained, the overlap time ratio Sj and the average number of overlaps Jz are calculated, and the correlation Gl is calculated. The correlation Gl is compared with the correlation threshold. If the correlation GI exceeds the threshold, production must be stopped and the production intelligent equipment must be repaired.

2. The method for monitoring the operation status of intelligent production equipment in an enterprise according to claim 1, characterized in that: The device status value Zt is obtained as follows: Calculate the equipment status value Zt based on the stage deviation ratio Pc and the stage defective rate Cp; By formula: , the device state value Zt is calculated, where a1 and a2 are preset proportional coefficients.

3. The method for monitoring the operation status of enterprise production intelligent equipment according to claim 2, characterized in that: The stage deviation ratio Pc is obtained as follows: Based on the number of defective products at each production stage, obtain the target range of defective products at each stage; Subtract the number of defective products in each production stage from the two endpoints of the defective product target range in each stage and take the absolute value, and mark the minimum value obtained as the stage defective product difference; The stage defective difference is processed by ratio with the length of the defective target range to obtain the stage deviation ratio, which is marked as Pc; The method for obtaining the defective rate Cp in the above stage is: Obtain the total number of products manufactured in each production stage during the monitoring period, and calculate the ratio of the number of defective products to the total number of products to obtain the stage defective rate, which is marked as Cp.

4. The method for monitoring the operation status of enterprise production intelligent equipment according to claim 1, characterized in that: The linear reference value Ck is obtained as follows: Based on the linear threshold ratio Xb and the linear variance Xf, a linear reference value Ck of the change curve of the cycle-state abnormality ratio is calculated; By formula: , the linear reference value Ck is calculated, where b1 and b2 are preset proportional coefficients.

5. The method for monitoring the operation status of intelligent production equipment in an enterprise according to claim 4, characterized in that: The linear variance Xf is obtained as follows: Put the linear parameter differences of all sub-curve segments into the data set as elements, calculate the variance of the elements in the data set, obtain the linear variance, and mark the linear variance as Xf; The linear threshold ratio Xb is obtained as follows: Obtain the linear parameter differences of all sub-curve segments, sum and average the linear parameter differences of all sub-curve segments, and obtain the linear difference mean; The linear difference mean is processed with the linear difference threshold to obtain the linear threshold difference, and the linear threshold difference is processed with the linear difference threshold to obtain the linear threshold ratio, which is marked as Xb.

6. The method for monitoring the operation status of intelligent production equipment in an enterprise according to claim 5, characterized in that: The linear parameter difference is obtained as follows: Obtain the state anomaly ratios of different monitoring periods, and draw a change curve of the period-state anomaly ratio in a two-dimensional rectangular coordinate system; Connect the two end points of the change curve of the cycle-state anomaly ratio to obtain a linear reference line, and divide the change curve into multiple curve segments to obtain sub-curve segments; Obtain the state anomaly ratio of the sub-curve segment, perform summation and mean processing on the state anomaly ratio of the sub-curve segment, and obtain the curve segment anomaly mean; Draw a straight line parallel to the Y axis with the curve anomaly mean, and obtain the state anomaly ratio corresponding to the intersection of the straight line and the linear reference line; The difference between the segment anomaly mean and the state anomaly ratio is taken and the absolute value is taken to obtain the linear parameter difference.

7. The method for monitoring the operation status of intelligent production equipment in an enterprise according to claim 6, characterized in that: The state abnormality ratio is obtained in the following manner: Marking the production stage that generates the status analysis signal as the production abnormality stage, obtaining the number of the production abnormality stages, and summing up the numbers of all the production abnormality stages to obtain the total number of abnormal stages; The number of all production stages is obtained, and the ratio of the sum of abnormal stages to the number of all production stages is processed to obtain the abnormal stage ratio; The device state value Zt is subtracted from the device state threshold and the absolute value is taken to obtain the state value difference; The state value difference is processed by ratio with the device state threshold to obtain the state deviation ratio; Obtain the state deviation ratios of all abnormal production stages, perform summation and mean processing on the state deviation ratios, and obtain the deviation mean ratio; The deviation mean ratio and the abnormal stage ratio are processed as ratios to obtain the state abnormality ratio.

8. The method for monitoring the operation status of enterprise production intelligent equipment according to claim 1, characterized in that: The method for obtaining the association index GI is as follows: Based on the coincidence time ratio Sj and the mean number of coincidence times Jz; By formula: , calculate the association index GI.

9. The method for monitoring the operation status of intelligent production equipment in an enterprise according to claim 8, characterized in that: The acquisition method of the overlap time ratio Sj and the overlap times mean value Jz is as follows: Obtain the equipment maintenance times during the abnormal production stage, perform subtraction processing on the equipment maintenance times and the maintenance target times, and obtain the maintenance times difference; The maintenance deviation ratio is obtained by ratioing the maintenance frequency difference with the maintenance target frequency. The maintenance deviation ratio reflects the degree of deviation between the actual maintenance of equipment and the expected maintenance target during the abnormal production stage; Obtain the equipment status value Zt and maintenance deviation ratio of each production abnormality stage in different monitoring cycles, and draw the cycle-equipment status value Zt change curve and cycle-maintenance deviation ratio change curve in the same two-dimensional rectangular coordinate system; Obtain the cycle-equipment status value Zt change curve, cycle-maintenance deviation ratio change curve, and the cycle time when the peaks overlap, and obtain the overlap duration; Obtain the overlap duration of all monitoring cycles to obtain the total overlap time, perform ratio processing on the total overlap time and the duration of all monitoring cycles to obtain the overlap time ratio, and mark the overlap time ratio as Sj; The number of overlap durations is obtained, and the number of overlap durations of all monitoring cycles is summed and averaged to obtain the average number of overlap times, which is marked as Jz.

10. An enterprise production intelligent equipment operation status monitoring system, characterized by: include: Parameter acquisition module: obtains the production process of products manufactured by intelligent production equipment, divides the production process into multiple stages, obtains the production stages, and obtains the number of defective products in each production stage; State analysis module: based on the number of defective products in each stage, obtain the defective target range of each stage, calculate the stage deviation ratio Pc, stage defective rate Cp, and calculate the equipment state value Zt. If the equipment state value Zt is less than the equipment state threshold, generate a state analysis signal; Trend judgment module: Based on the state analysis signal, the production stage that generates the state analysis signal is marked as the production abnormality stage, and the abnormal stage ratio is calculated. The equipment state value Zt and the equipment state threshold are calculated to obtain the deviation mean ratio. The deviation mean ratio is calculated with the abnormal stage ratio to obtain the state abnormality ratio, and the state abnormality ratio of different monitoring periods is obtained. The change curve of the cycle-state abnormality ratio is drawn, and the linear reference value Ck is calculated through the change curve. If the linear reference value Ck is less than the linear reference threshold, the curve is nonlinear, and a trend analysis signal is generated; Correlation analysis module: Based on the trend analysis signal, calculate the maintenance deviation ratio of production equipment in the abnormal production stage, obtain the equipment status value Zt and maintenance deviation ratio in the abnormal production stage in different monitoring cycles, draw the corresponding change curve in the same coordinate system, obtain the cycle time of the curve peak overlap, calculate the overlap time ratio Sj, the average number of overlaps Jz, and calculate the correlation Gl. Compare the correlation Gl with the correlation threshold. If the correlation GI exceeds the threshold, it is necessary to stop production and repair the intelligent production equipment.

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