Production scheduling control method and system based on equipment state sensing and storage medium

Abstract

The invention relates to an equipment state sensing production scheduling control method and system and a storage medium, and relates to the technical field of production management, and the method comprises the steps: carrying out the operation temperature monitoring and operation amplitude monitoring of a plurality of pieces of equipment, obtaining an equipment temperature array and an equipment amplitude array, carrying out the temperature amplitude verification analysis, and obtaining a sensing accuracy parameter array; obtaining a service information array, predicting and classifying to obtain an ideal perception accuracy parameter array, and calculating to obtain a perception abnormal parameter array; according to the equipment temperature array and the equipment amplitude array, carrying out equipment load prediction to obtain a predicted equipment load array, and carrying out compensation to obtain a compensated equipment load array; and obtaining current production planning information, performing production scheduling optimization in combination with the compensation equipment load array, obtaining optimal production scheduling information, and performing control. The technical problem that production scheduling management based on equipment state monitoring is inaccurate and unreliable in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and in particular to a production scheduling control method, system and storage medium based on equipment status awareness. Background Art

[0002] In modern industrial production management, production scheduling and scheduling based on equipment status monitoring have become an important means to improve production efficiency and reduce costs. However, the existing technology mainly relies on information such as sensor data and equipment operation logs to judge the operating status of the equipment and conduct production management accordingly. However, due to problems such as aging, wear, and potential failures during the long-term operation of the equipment, the accuracy of equipment status monitoring is affected. For example, sensor sensitivity decline, data drift, false alarms and missed alarms may lead to deviations in equipment status assessment, thereby affecting the rationality of the production plan, resulting in waste of resources or insufficient production capacity. Therefore, there are technical problems in the existing technology that production scheduling management based on equipment status monitoring is inaccurate and unreliable, which affects production quality. Summary of the invention

[0003] The present invention aims to solve the technical problem that production scheduling management based on equipment status monitoring in the prior art is inaccurate and unreliable, and provides a production scheduling control method, system and storage medium with equipment status awareness to solve the problem.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a production scheduling control method for equipment status perception, comprising: performing operating temperature monitoring and operating amplitude monitoring on a plurality of equipment to obtain an equipment temperature array and an equipment amplitude array, performing temperature amplitude verification analysis on each equipment respectively, and obtaining a perception accuracy parameter array; Acquire the service information array of the plurality of devices, predict and classify to obtain an ideal perception accuracy parameter array, and calculate and obtain a perception abnormality parameter array by combining the perception accuracy parameter array; According to the device temperature array and the device amplitude array, device load prediction is performed to obtain a predicted device load array, and based on the sensed abnormality parameter array, the predicted device load array is compensated to obtain a compensated device load array; The current production planning information is obtained, and combined with the compensation equipment load array, the production scheduling is optimized to obtain the optimal production scheduling information for control.

[0005] In a second aspect, the present invention provides a production scheduling control system with equipment status perception, including: an equipment perception module, which is used to monitor the operating temperature and operating amplitude of multiple equipment, obtain an equipment temperature array and an equipment amplitude array, perform temperature amplitude verification analysis on each equipment respectively, and obtain a perception accuracy parameter array; A perception anomaly analysis module, used to obtain the service information array of the plurality of devices, predict and classify to obtain an ideal perception accuracy parameter array, and calculate and obtain a perception anomaly parameter array in combination with the perception accuracy parameter array; An equipment load prediction module is used to perform equipment load prediction based on the equipment temperature array and the equipment amplitude array to obtain a predicted equipment load array, and to compensate the predicted equipment load array based on the sensed abnormality parameter array to obtain a compensated equipment load array; The production scheduling optimization module is used to obtain the current production planning information, combine the compensation equipment load array, optimize the production scheduling, obtain the optimal production scheduling information, and perform control.

[0006] In a third aspect, the present invention provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the production scheduling control method with equipment status awareness as described in the first aspect is implemented.

[0007] The beneficial effects of the present invention are as follows: the present invention can reflect the operating status of the equipment in real time by monitoring the equipment temperature array and the equipment amplitude array, and provide basic data for subsequent analysis. Secondly, the temperature amplitude verification analysis is carried out to obtain the perception accuracy parameter array, and the credibility of the monitoring data is improved through historical data comparison and index classification, and the error degree caused by sensor aging, data drift and other problems is known. On this basis, the service information array of the equipment is obtained, and the ideal perception accuracy parameters are predicted, and the perception abnormality parameter array is calculated, which can quantify the error abnormality degree of equipment status monitoring and provide a correction basis for subsequent equipment load prediction and production scheduling. The present invention also predicts the equipment load array and compensates based on the perception abnormality parameters to effectively compensate for the inaccurate load prediction caused by the equipment state perception error and improve the rationality of load prediction. Finally, by combining the compensation equipment load array to optimize the production scheduling, it is ensured that the production scheduling plan can fully consider the actual operating capacity of the equipment, improve the equipment utilization rate, avoid the scheduling imbalance problem caused by the misjudgment of the state, and comprehensively reduce the problems of equipment damage and production quality decline caused by inappropriate scheduling as much as possible, thereby optimizing production efficiency and reducing production losses. In summary, the present invention achieves more accurate equipment status perception and production scheduling control through multi-level data monitoring, error correction and optimization calculation, solves the problem of inaccurate scheduling management caused by equipment status monitoring errors in the prior art, and improves the intelligence and adaptability of production scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of a process flow of a production scheduling control method based on equipment status awareness provided by the present invention; Figure 2 A schematic structural diagram of a production scheduling control system with equipment status awareness provided by the present invention.

[0009] In the accompanying drawings, the components represented by the reference numerals are described as follows: Equipment perception module 11, perception anomaly analysis module 12, equipment load prediction module 13, production scheduling optimization module 14. DETAILED DESCRIPTION

[0010] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0011] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0012] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0013] Embodiment 1, as Figure 1 As shown, an embodiment of the present invention provides a production scheduling control method with equipment status awareness, which specifically includes the following steps: S10: Perform operating temperature monitoring and operating amplitude monitoring on multiple devices to obtain a device temperature array and a device amplitude array, perform temperature amplitude verification analysis on each device respectively, and obtain a perception accuracy parameter array.

[0014] In the embodiment of the present application, the operating temperature and operating amplitude of multiple devices currently in production scheduling are first monitored to obtain a device temperature array and a device amplitude array. The temperature and amplitude of the device can reflect the load level of the current device operation and the change in lubrication state.

[0015] Furthermore, based on the monitored device temperature array and device amplitude array, a temperature amplitude verification analysis is performed on each device to determine whether the temperature and amplitude of each monitored device are accurate, thereby obtaining a perception accuracy parameter array.

[0016] Step S10 in the method provided in the embodiment of the present application includes: Perform operating temperature monitoring and operating amplitude monitoring on multiple devices to obtain an operating temperature set and an operating amplitude set; The sequence of the plurality of devices is obtained, and the operating temperature sets and the operating amplitude sets are arranged to obtain a device temperature array and a device amplitude array.

[0017] In the embodiment of the present application, the multiple devices are, for example, multiple CNC machine tools of the same model. During operation, the temperature and amplitude of the CNC machine tools will change significantly with the increase of operating time and load.

[0018] First, the operating temperature and operating amplitude of multiple devices are monitored to obtain the operating temperature set and operating amplitude set respectively, providing basic data support for subsequent status analysis. The operating temperature refers to the temperature value measured during the operation of the device, which is usually collected in real time by infrared temperature sensors, thermocouples or other temperature detection devices. For example, the operating temperature of a certain device may be 65°C. The operating amplitude refers to the vibration amplitude caused by mechanical movement or load changes during the operation of the device. It can be measured by an acceleration sensor or a vibration monitoring system. For example, the operating amplitude of a certain device may be 0.15 mm or 0.25 mm. Optionally, the temperature and amplitude of the spindle bearings, tools or motors in CNC machine tools can be monitored and collected.

[0019] Furthermore, after obtaining the temperature and amplitude data of each device, it is necessary to arrange these data in sequence to form a device temperature array and a device amplitude array. The order of multiple devices is obtained, for example, the spatial order of multiple devices in the factory or the order of pre-numbered devices, and the operating temperature set and the operating amplitude set are arranged. For example, the measured temperatures of devices A, B, and C are 65°C, 72°C, and 68°C respectively, and the device temperature array can be expressed as [65,72,68]. In this way, the device temperature array and the device amplitude array are obtained.

[0020] Step S10 in the method provided in the embodiment of the present application includes: Obtain historical temperature amplitude monitoring data of similar equipment, collect a sample equipment temperature set, and collect the average amplitude of the equipment at different sample equipment temperatures to obtain a sample equipment amplitude set; Using the sample device temperature set as an index header and the sample device amplitude set as an index value, constructing a device verification index table; Inputting a plurality of temperatures in the device temperature array into the device verification index table, and obtaining an index device amplitude array by index classification; Calculating and obtaining a verification amplitude deviation amplitude array according to the index device amplitude array and the device amplitude array; According to the verification amplitude deviation magnitude array, a perception accuracy parameter array is calculated and obtained, wherein the magnitude of the perception accuracy parameter is negatively correlated with the magnitude of the verification amplitude deviation magnitude.

[0021] In the embodiment of the present application, in order to obtain the accuracy of the currently monitored temperatures and amplitudes of multiple devices, it is necessary to perform temperature amplitude verification analysis on each device based on the device temperature array and the device amplitude array.

[0022] In the embodiment of the present application, the historical temperature amplitude monitoring data of the same type of equipment is first obtained. These data can come from the equipment operation data log recorded during long-term operation, including the temperature-amplitude correspondence in the operation of the same type of equipment. First, the sample equipment temperature is collected, for example, the equipment operation temperature data of different equipment is collected at different time points, such as [42℃, 55℃, 65℃, 72℃], to form a sample equipment temperature set.

[0023] Furthermore, the average amplitude of the device at different sample device temperatures is collected. For example, when the temperature of the same type of device is monitored multiple times at 40°C, the device amplitude of the same type of device is calculated to be 0.08mm, and the sample device amplitude corresponding to 40°C is 0.08mm. In this way, the average amplitude of the device at different sample device temperatures is collected to obtain a sample device amplitude set.

[0024] Further, the sample device temperature set is used as the index header, and the sample device amplitude set is used as the index value. Based on the mapping relationship between each sample device temperature and the corresponding sample device amplitude, a device verification index table is constructed. The device verification index table includes a mapping relationship between multiple sample device temperatures and multiple sample device amplitudes. When the sample device temperature is input, the index can be mapped to the corresponding sample device amplitude. Exemplarily, some data in the device verification index table is shown in Table 1.

[0025] In this way, multiple temperatures in the current device temperature array are input into the device verification index table. When the same sample device temperature exists, the corresponding device amplitude is indexed and output. If the same sample device temperature does not exist, the device amplitude corresponding to the sample device temperature with the smallest difference is obtained and indexed and output. For example, if 39°C is input and the sample device temperature with the smallest difference is 40°C, the device amplitude corresponding to 40°C is indexed and output. In this way, the index output obtains the indexed device amplitude array.

[0026] Furthermore, temperature amplitude verification analysis and calculation are performed based on the index device amplitude array and the device amplitude array. Specifically, the index device amplitude array includes the ideal device amplitude size of multiple devices under the current device temperature array, while the device amplitude array includes the device amplitude size actually monitored by multiple devices. Due to possible errors in sensors and potential hidden faults that may exist when the equipment runs for too long, the actual device amplitude is inconsistent with the index device amplitude. By calculating the deviation between the actual device amplitude and the index device amplitude, the accuracy of the current multiple devices in detecting and sensing temperature and amplitude, that is, the perception accuracy parameter, can be analyzed.

[0027] Exemplarily, based on the index device amplitude array and the device amplitude array, the amplitude difference between the index device amplitude and the device amplitude of each device is calculated, and the ratio of the absolute value of the amplitude difference to the index device amplitude is calculated as the verification amplitude deviation amplitude, so that the verification amplitude deviation amplitude array is calculated. Exemplarily, if the device amplitude of a certain device is 0.10mm and the index device amplitude is 0.08mm, then the verification amplitude deviation amplitude is (0.10-0.08) / 0.08=0.25.

[0028] Then, according to the verification amplitude deviation array, the perception accuracy parameter array is calculated, wherein the larger the verification amplitude deviation, the less accurate the perception, and the smaller the perception accuracy parameter, and the size of the perception accuracy parameter is negatively correlated with the size of the verification amplitude deviation. For example, 1 minus the verification amplitude deviation is used as the perception accuracy parameter. For example, if the verification amplitude deviation is 0.25, the perception accuracy parameter is 1-0.25=0.75. In this way, the perception accuracy parameter array is calculated. The larger the perception accuracy parameter, the more accurate the equipment status parameter monitoring perception. Conversely, the inaccurate equipment status parameter monitoring perception may be caused by sensor errors or hidden equipment failures (such as lubrication failure).

[0029] Through the above steps, the accuracy of the equipment's operating status perception can be effectively evaluated, which can then serve as the data basis for subsequent production scheduling of multiple devices. In the production scheduling, not only the monitored equipment operating parameters can be considered, but also the accuracy of the implicit operating parameters caused by monitoring errors or implicit equipment failures can be analyzed, thereby improving the reliability and accuracy of production scheduling.

[0030] S20: Acquire the service information array of the plurality of devices, predict and classify to obtain an ideal perception accuracy parameter array, and calculate and obtain a perception anomaly parameter array by combining the perception accuracy parameter array.

[0031] In the embodiment of the present application, as the service time of the equipment increases, the equipment will gradually age, which will lead to inaccurate monitoring perception of equipment status parameters, that is, the perception accuracy parameter decreases. The embodiment of the present application obtains a service information array of multiple devices, predicts ideal perception accuracy parameters under different service information, obtains an ideal perception accuracy parameter array, and then combines the perception accuracy parameter array calculated by the current verification analysis to analyze whether the perception accuracy parameters of the current multiple devices are reasonable, and then analyzes whether an abnormality occurs, such as an abnormal situation where premature aging leads to inaccurate perception, and obtains a perception abnormality parameter array.

[0032] Step S20 in the method provided in the embodiment of the present application includes: Obtaining the service time of the plurality of devices as service information, and obtaining a service information array; According to each service information, the prediction classification obtains an array of ideal perception accuracy parameters; According to the ideal perception accuracy parameter array and the perception accuracy parameter array, the deviation amplitude of each ideal perception accuracy parameter and each perception accuracy parameter is calculated as a perception abnormality parameter to obtain a perception abnormality parameter array.

[0033] In the embodiment of the present application, the service time of the multiple devices is obtained, which is the operating time of the devices from the time they are put into use to the present, and has a direct impact on the state perception accuracy of the devices. Generally, as the use time of the devices increases, the components age and wear aggravates, resulting in increased volatility of state parameters such as temperature and amplitude, resulting in reduced perception accuracy, that is, reduced perception accuracy parameters.

[0034] In order to quantitatively analyze the perception accuracy parameters under different service times, the service time of multiple devices is obtained as service information, and a service information array is constructed, which records the operating time data of multiple devices. For example, the service time of devices A, B, and C is 12 months, 24 months, and 36 months.

[0035] Furthermore, according to each service information, the perception accuracy parameters of the device in the ideal state under different service information are obtained by prediction and classification, and an ideal perception accuracy parameter array is obtained.

[0036] The step of "predicting and classifying to obtain an ideal perception accuracy parameter array according to each service information" in the method provided in the embodiment of the present application includes: According to the perception monitoring data of the same type of equipment at different service times, a sample service information set is collected, and the average perception accuracy parameters of the equipment under different sample service information are collected to obtain a sample ideal perception accuracy parameter set; Constructing a device perception index table using the sample service information set as an index header and the sample ideal perception accuracy parameter set as an index value; Each service information in the service information array is input into the device perception index table, and the index classification is performed to obtain an ideal perception accuracy parameter array.

[0037] In the embodiments of the present application, during the long-term operation of similar devices, as the service time (i.e., the cumulative use time of the device) increases, factors such as the measurement accuracy of the sensor and the mechanical stability of the device will change. Therefore, devices with different service times may show different accuracy in state perception. For example, the longer the service time, the smaller the perception accuracy parameter.

[0038] To collect the perception monitoring data of the same type of equipment at different service times, first collect the different service times of the same type of equipment as sample service information to obtain a sample service information set. Then extract the average perception accuracy parameters of the same type of equipment under different sample service information (i.e. service time), and calculate the mean of the perception accuracy parameters of the same type of equipment with the same service time. In this way, the ideal perception accuracy parameter set of the samples is collected.

[0039] Further, in order to facilitate the query of the ideal perception accuracy parameters of different service times, the service information set is used as the index header, and the sample ideal perception accuracy parameter array is used as the index value to construct a device perception index table, which includes a mapping index relationship between the sample service information set and the sample ideal perception accuracy parameter set. Exemplarily, some data in the device perception index table is shown in Table 2.

[0040] In an embodiment of the present application, based on the constructed device perception index table, each service information in the current service information array is input, and index prediction classification is performed to obtain the ideal perception accuracy parameter corresponding to each service information, or the ideal perception accuracy parameter corresponding to the sample service information with the closest index, so as to obtain an ideal perception accuracy parameter array.

[0041] The ideal perception accuracy parameter array reflects the perception accuracy parameters of multiple devices under normal conditions based on their service time. If the actual perception accuracy parameter of the device is less than the perception accuracy parameter, it means that the device has an implicit fault due to premature aging or excessive current production load, which causes its perception accuracy parameter to be abnormal. The probability of damage to the device is high, and it needs to be considered in the subsequent production scheduling to avoid damage to the device or affect the processing quality. On the contrary, if the actual perception accuracy parameter of the device is consistent with the perception accuracy parameter, it means that the monitoring perception accuracy of the device is normal and the device is operating normally, and it can be considered as a general consideration in the subsequent production scheduling.

[0042] The embodiment of the present application can reasonably calculate the perception accuracy of the equipment according to its service time, and provide data support for subsequent production scheduling optimization.

[0043] In the embodiment of the present application, perception anomaly analysis of multiple devices is performed based on the ideal perception accuracy parameter array of the predicted classification and the perception accuracy parameter array actually obtained by verification analysis and calculation, as perception anomaly parameters.

[0044] Exemplarily, the deviation amplitude between the ideal perception accuracy parameter and the perception accuracy parameter of each device is calculated as the perception abnormality parameter. For example, the ideal perception accuracy parameter of a device is 0.95, and the perception accuracy parameter calculated by actual verification analysis is 0.92. Then the deviation amplitude is (0.95-0.92) / 0.95=3.1%. As the perception abnormality parameter. The larger the perception abnormality parameter, the higher the probability of failure in the device operation and status monitoring perception, which needs to be considered in the subsequent production scheduling.

[0045] S30: performing equipment load prediction according to the equipment temperature array and the equipment amplitude array to obtain a predicted equipment load array, and compensating the predicted equipment load array based on the sensed abnormality parameter array to obtain a compensated equipment load array.

[0046] In the embodiment of the present application, in order to perform production scheduling for multiple devices, load prediction for multiple devices is also required as reference data for production scheduling. The device load prediction is performed based on the device temperature array and the device amplitude array to obtain a predicted device load array.

[0047] Furthermore, if the perceived abnormality parameter of the equipment is larger, the probability that its previous production load was too high, resulting in a failure or inaccurate monitoring perception, is greater. Therefore, based on the perceived abnormality parameter array, the predicted equipment load of each equipment is compensated and corrected, so that the load of the equipment with a larger perceived abnormality parameter is amplified, thereby improving the rationality of subsequent production scheduling and avoiding equipment damage caused by excessive scheduling load of equipment with a larger degree of perceived abnormality.

[0048] Step S30 in the method provided in the embodiment of the present application includes: Using the sample device temperature set and the sample device amplitude set as input features, using the sample device load set as output features, and using machine learning to train a device load predictor; According to the device temperature array and the device amplitude array, the load prediction input characteristics of each device are obtained by dividing and combining, and the characteristics are input into the device load predictor, and the predicted device load array is obtained by output; The predicted equipment load array is compensated according to the perceived abnormality parameter array to obtain a compensated equipment load array.

[0049] In the embodiment of the present application, in order to accurately predict the operating load of the equipment, it is necessary to establish an equipment load predictor based on the historical load monitoring data of similar equipment, use machine learning, and compensate the predicted load in combination with perceived abnormal parameters, so as to obtain a more accurate compensation equipment load array.

[0050] In the embodiment of the present application, the operating load monitoring data of the same type of equipment is collected, specifically the temperature and amplitude of the same type of equipment during operation, and the load degree of the equipment under different sample equipment temperatures and sample equipment amplitudes. The load degree can be the proportional coefficient of equipment maintenance required under different sample equipment temperatures and sample equipment amplitudes as the equipment load, so that the sample equipment load set is obtained by collection and annotation.

[0051] For example, among the operating parameters of the same type of equipment, the number of devices monitored under a certain sample device temperature and sample device amplitude is 10, and in subsequent maintenance it is found that the number of devices that need to be repaired is 6, then the sample device load corresponding to the sample device temperature and sample device amplitude is 6 / 10=0.6.

[0052] Furthermore, a set of sample device temperatures and a set of sample device amplitudes are used as input features, and a set of sample device loads are used as output features. Machine learning is used to train a device load predictor for predicting device loads. Exemplarily, a feedforward neural network in machine learning is used to construct a device load predictor, which includes an input layer, a hidden layer, and an output layer. The dimensions of the input layer and the output layer are 2 and 1, respectively. The hidden layer uses a ReLU activation function. During the training process, the sample device temperature and the sample device amplitude are input, the output device load is obtained, and the difference with the corresponding sample device load is calculated as the loss. Then, according to the loss, the network parameters such as weights are adjusted to reduce the loss until it is less than the preset requirement, for example, less than 0.05, and the training is completed.

[0053] According to the currently collected equipment temperature array and equipment amplitude array, division and combination are performed to obtain the current equipment temperature and equipment amplitude of each equipment, which are used as input data and input into the trained equipment load predictor. The corresponding equipment load is output to obtain the predicted equipment load array.

[0054] Furthermore, the predicted device load array is compensated and corrected according to the sensed abnormal parameter array to obtain a compensated device load array of a plurality of devices after compensation and correction.

[0055] The step of "compensating the predicted device load array according to the sensed abnormal parameter array to obtain a compensated device load array" in the method provided in the embodiment of the present application includes: Generating an array of equipment load compensation coefficients according to the array of abnormal perception parameters; According to the equipment load compensation coefficient array, compensation calculation is performed on the predicted equipment load array to obtain a compensated equipment load array.

[0056] In an embodiment of the present application, an array of equipment load compensation coefficients for equipment load compensation correction is generated based on an array of perceived abnormality parameters. Exemplarily, 1 plus each perceived abnormality parameter is used as the equipment load compensation coefficient. For example, if the perceived abnormality parameter is 3.1%, the equipment load compensation coefficient is 1+3.1%=1.031. In this way, the larger the perceived abnormality parameter, the larger the equipment load compensation coefficient, and the larger the equipment load after compensation. The equipment load with perceived abnormality, that is, the equipment load that may have a fault, can be amplified to reduce its production volume in subsequent production scheduling, optimize the rationality of scheduling, and try to avoid equipment damage and ensure production quality.

[0057] Furthermore, multiple equipment load compensation coefficients in the equipment load compensation coefficient array are used to perform compensation correction calculations on multiple predicted equipment loads in the predicted equipment load array. For example, the equipment load compensation coefficient is multiplied by the predicted equipment load to obtain a compensated equipment load array after compensation correction. For example, if the equipment load compensation coefficient is 1.031 and the predicted equipment load is 0.6, then the compensated equipment load is .

[0058] S40: Acquire current production planning information, optimize production scheduling in combination with the compensation equipment load array, obtain optimal production scheduling information, and perform control.

[0059] In an embodiment of the present application, the production planning information of the current production scheduling is obtained, and the production scheduling of multiple equipment is optimized in combination with the compensation equipment load array of multiple equipment for prediction compensation, so as to obtain the optimal production scheduling information and perform production control.

[0060] Step S40 in the method provided in the embodiment of the present application includes: Acquire current production planning information, wherein the production planning information includes production volume; Based on the compensation equipment load array and the abnormal perception parameter array, a production scheduling function is constructed as follows: ; in, is the scheduling adaptability, N is the number of multiple devices, is the weight of the ith device assigned according to the perceived abnormality parameters of each device in the perceived abnormality parameter array, is the proportion of the equipment production volume allocated to the i-th equipment in the production planning information, is the equipment production coefficient allocated according to the compensation equipment load of the i-th equipment, wherein the size of the equipment production coefficient is negatively correlated with the compensation equipment load; Randomly assigning the production planning information to a plurality of devices to obtain a plurality of first device production quantities as first production scheduling information; Based on the production scheduling function, calculating and obtaining a first scheduling fitness of the first production scheduling information; Continue to randomly allocate and optimize the production schedule information until convergence, and output the optimal production schedule information with the largest schedule fitness for control.

[0061] In the embodiment of the present application, first, the current production planning information is obtained, including the total production volume. For example, 10,000 products need to be produced.

[0062] Furthermore, based on the compensation equipment load array and the perception abnormality parameter array, a production scheduling function is constructed as follows: ; in, is the scheduling adaptability, N is the number of multiple devices, for example, 10, The weight of the ith device is assigned according to the perceived abnormality parameters of each device in the perceived abnormality parameter array. Specifically, the ratio of the perceived abnormality parameters of each device to the sum of the perceived abnormality parameter array is calculated as the weight. The larger the perceived abnormality parameter, the greater the weight of the device, and the greater the importance it has in production scheduling optimization. It is the ratio of the equipment production volume assigned to the i-th equipment to the production planning information, for example, the ratio of the equipment productivity assigned to the i-th equipment to the total productivity.

[0063] is the equipment production coefficient allocated according to the compensation equipment load of the i-th equipment, wherein the size of the equipment production coefficient is negatively correlated with the compensation equipment load. The larger the compensation equipment load, the smaller the equipment production amount that needs to be allocated to the equipment should be, to avoid equipment failure damage due to excessive equipment load. Therefore, the larger the compensation equipment load, the smaller the equipment production coefficient. For example, the reciprocal of the compensation equipment load of multiple equipment can be calculated, and then the ratio of each reciprocal to the sum of multiple reciprocals can be calculated as the equipment production coefficient. In this way, multiple equipment production coefficients of multiple equipment are calculated.

[0064] Furthermore, the production planning information is randomly distributed to multiple devices, that is, the total production volume is randomly distributed to multiple devices, and multiple first device production volumes are obtained as the first production scheduling information. The sum of the multiple first device production volumes is the production volume in the production planning information.

[0065] Furthermore, the ratio of the production volume of each first device to the total production volume is calculated as the production proportion of multiple first devices, and then the equipment production coefficients of multiple devices are combined and substituted into the production scheduling function to calculate the first scheduling fitness. The closer the production proportion of each device and the equipment production coefficient are, the more reasonable the production scheduling allocation is and the greater the scheduling fitness is.

[0066] Furthermore, continue to randomly assign production planning information, generate new production scheduling information, calculate the scheduling fitness, and optimize the production scheduling information until convergence. For example, after 100 production scheduling information are randomly generated and convergence occurs, the optimization is completed, and the production scheduling information with the largest scheduling fitness is output as the optimal production scheduling information. The optimization is completed and the production scheduling is controlled.

[0067] The embodiment of the present application optimizes the scheduling information by allocating equipment production coefficients according to the compensating equipment loads of multiple devices. This allows equipment with a larger equipment load to produce less quantity, and the larger the perceived abnormal parameter, the greater the weight of attention in the optimization, that is, the greater the weight of equipment that may be prematurely aged or faulty. In this way, the reliability and rationality of equipment production scheduling control are improved, damage to equipment due to excessive load is avoided, and production efficiency and quality are guaranteed.

[0068] An embodiment of the present invention provides a production scheduling control method based on equipment status awareness, which has at least the following technical effects: The embodiment of the present invention can reflect the operating status of the equipment in real time by monitoring the equipment temperature array and the equipment amplitude array, and provide basic data for subsequent analysis. Secondly, the temperature amplitude verification analysis is performed to obtain the perception accuracy parameter array, and the credibility of the monitoring data is improved through historical data comparison and index classification, and the error degree caused by sensor aging, data drift and other problems is known. On this basis, the service information array of the equipment is obtained, and the ideal perception accuracy parameters are predicted, and the perception abnormality parameter array is calculated, which can quantify the error abnormality degree of equipment status monitoring and provide a correction basis for subsequent equipment load prediction and production scheduling. The present invention also predicts the equipment load array and compensates based on the perception abnormality parameters to effectively compensate for the inaccurate load prediction caused by the equipment status perception error and improve the rationality of load prediction. Finally, by combining the compensation equipment load array to optimize production scheduling, it is ensured that the production scheduling plan can fully consider the actual operating capacity of the equipment, improve equipment utilization, avoid scheduling imbalance problems caused by misjudgment of the state, and comprehensively minimize equipment damage and production quality decline caused by inappropriate scheduling, thereby optimizing production efficiency and reducing production losses. In summary, the present invention achieves more accurate equipment status perception and production scheduling control through multi-level data monitoring, error correction and optimization calculation, solves the problem of inaccurate scheduling management caused by equipment status monitoring errors in the prior art, and improves the intelligence and adaptability of production scheduling.

[0069] Embodiment 2, as Figure 2 As shown, based on the same inventive concept of a production scheduling control method with equipment status awareness provided in Embodiment 1, an embodiment of the present invention further provides a production scheduling control system with equipment status awareness, including: The device sensing module 11 is used to monitor the operating temperature and operating amplitude of multiple devices, obtain the device temperature array and the device amplitude array, perform temperature amplitude verification analysis on each device respectively, and obtain the sensing accuracy parameter array; A perception anomaly analysis module 12 is used to obtain the service information array of the plurality of devices, predict and classify to obtain an ideal perception accuracy parameter array, and calculate and obtain a perception anomaly parameter array in combination with the perception accuracy parameter array; The equipment load prediction module 13 is used to perform equipment load prediction based on the equipment temperature array and the equipment amplitude array to obtain a predicted equipment load array, and to compensate the predicted equipment load array based on the sensed abnormality parameter array to obtain a compensated equipment load array; The production scheduling optimization module 14 is used to obtain the current production planning information, combine the compensation equipment load array, optimize the production scheduling, obtain the optimal production scheduling information, and perform control.

[0070] Furthermore, the equipment status-aware production scheduling control system is also used for: Perform operating temperature monitoring and operating amplitude monitoring on multiple devices to obtain an operating temperature set and an operating amplitude set; The sequence of the plurality of devices is obtained, and the operating temperature sets and the operating amplitude sets are arranged to obtain a device temperature array and a device amplitude array.

[0071] Furthermore, the equipment status-aware production scheduling control system is also used for: Obtain historical temperature amplitude monitoring data of similar equipment, collect a sample equipment temperature set, and collect the average amplitude of the equipment at different sample equipment temperatures to obtain a sample equipment amplitude set; Using the sample device temperature set as an index header and the sample device amplitude set as an index value, constructing a device verification index table; Inputting a plurality of temperatures in the device temperature array into the device verification index table, and obtaining an index device amplitude array by index classification; Calculating and obtaining a verification amplitude deviation amplitude array according to the index device amplitude array and the device amplitude array; According to the verification amplitude deviation magnitude array, a perception accuracy parameter array is calculated and obtained, wherein the magnitude of the perception accuracy parameter is negatively correlated with the magnitude of the verification amplitude deviation magnitude.

[0072] Furthermore, the equipment status-aware production scheduling control system is also used for: Obtaining the service time of the plurality of devices as service information, and obtaining a service information array; According to each service information, the prediction classification obtains an array of ideal perception accuracy parameters; According to the ideal perception accuracy parameter array and the perception accuracy parameter array, the deviation amplitude of each ideal perception accuracy parameter and each perception accuracy parameter is calculated as a perception abnormality parameter to obtain a perception abnormality parameter array.

[0073] Furthermore, the equipment status-aware production scheduling control system is also used for: According to the perception monitoring data of the same type of equipment at different service times, a sample service information set is collected, and the average perception accuracy parameters of the equipment under different sample service information are collected to obtain a sample ideal perception accuracy parameter set; Constructing a device perception index table using the sample service information set as an index header and the sample ideal perception accuracy parameter set as an index value; Each service information in the service information array is input into the device perception index table, and the index classification is performed to obtain an ideal perception accuracy parameter array.

[0074] Furthermore, the equipment status-aware production scheduling control system is also used for: According to the operating load monitoring data of similar equipment, a sample equipment temperature set and a sample equipment amplitude set are collected, and the load degree of the equipment under different sample equipment temperatures and sample equipment amplitudes is collected and marked to obtain a sample equipment load set; Using the sample device temperature set and the sample device amplitude set as input features, using the sample device load set as output features, and using machine learning to train a device load predictor; According to the device temperature array and the device amplitude array, the load prediction input characteristics of each device are obtained by dividing and combining, and the characteristics are input into the device load predictor, and the predicted device load array is obtained by output; The predicted equipment load array is compensated according to the perceived abnormality parameter array to obtain a compensated equipment load array.

[0075] Furthermore, the equipment status-aware production scheduling control system is also used for: Generating an array of equipment load compensation coefficients according to the array of abnormal perception parameters; According to the equipment load compensation coefficient array, compensation calculation is performed on the predicted equipment load array to obtain a compensated equipment load array.

[0076] Furthermore, the equipment status-aware production scheduling control system is also used for: Acquire current production planning information, wherein the production planning information includes production volume; Based on the compensation equipment load array and the abnormal perception parameter array, a production scheduling function is constructed as follows: ; in, is the scheduling adaptability, N is the number of multiple devices, is the weight of the ith device assigned according to the perceived abnormality parameters of each device in the perceived abnormality parameter array, is the proportion of the equipment production volume allocated to the i-th equipment in the production planning information, is the equipment production coefficient allocated according to the compensation equipment load of the i-th equipment, wherein the size of the equipment production coefficient is negatively correlated with the compensation equipment load; Randomly assigning the production planning information to a plurality of devices to obtain a plurality of first device production quantities as first production scheduling information; Based on the production scheduling function, calculating and obtaining a first scheduling fitness of the first production scheduling information; Continue to randomly allocate and optimize the production schedule information until convergence, and output the optimal production schedule information with the largest schedule fitness for control.

[0077] Embodiment 3: This embodiment provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, a production scheduling control method based on equipment status awareness as described in Embodiment 1 is implemented.

[0078] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A production scheduling control method based on equipment status perception, characterized in that: The method comprises: Perform operating temperature monitoring and operating amplitude monitoring on multiple devices to obtain device temperature arrays and device amplitude arrays, perform temperature amplitude verification analysis on each device respectively, and obtain a perception accuracy parameter array; Acquire the service information array of the plurality of devices, predict and classify to obtain an ideal perception accuracy parameter array, and calculate and obtain a perception abnormality parameter array by combining the perception accuracy parameter array; According to the device temperature array and the device amplitude array, device load prediction is performed to obtain a predicted device load array, and based on the sensed abnormality parameter array, the predicted device load array is compensated to obtain a compensated device load array; The current production planning information is obtained, and combined with the compensation equipment load array, the production scheduling is optimized to obtain the optimal production scheduling information for control.

2. The production scheduling control method based on equipment status perception according to claim 1 is characterized in that: Perform operating temperature monitoring and operating amplitude monitoring on multiple devices to obtain device temperature arrays and device amplitude arrays, including: Perform operating temperature monitoring and operating amplitude monitoring on multiple devices to obtain an operating temperature set and an operating amplitude set; The sequence of the plurality of devices is obtained, and the operating temperature sets and the operating amplitude sets are arranged to obtain a device temperature array and a device amplitude array.

3. The production scheduling control method based on equipment status perception according to claim 1 is characterized in that: Perform temperature amplitude validation analysis on each device separately to obtain an array of perception accuracy parameters, including: Obtain historical temperature amplitude monitoring data of similar equipment, collect a sample equipment temperature set, and collect the average amplitude of the equipment at different sample equipment temperatures to obtain a sample equipment amplitude set; Using the sample device temperature set as an index header and the sample device amplitude set as an index value, constructing a device verification index table; Inputting a plurality of temperatures in the device temperature array into the device verification index table, and obtaining an index device amplitude array by index classification; Calculating and obtaining a verification amplitude deviation amplitude array according to the index device amplitude array and the device amplitude array; According to the verification amplitude deviation magnitude array, a perception accuracy parameter array is calculated and obtained, wherein the magnitude of the perception accuracy parameter is negatively correlated with the magnitude of the verification amplitude deviation magnitude.

4. The production scheduling control method based on equipment status perception according to claim 1 is characterized in that: Acquiring the service information array of the plurality of devices, predicting and classifying to obtain an ideal perception accuracy parameter array, and combining the perception accuracy parameter array to calculate and obtain a perception abnormality parameter array, including: Obtaining the service time of the plurality of devices as service information, and obtaining a service information array; According to each service information, the prediction classification obtains an array of ideal perception accuracy parameters; According to the ideal perception accuracy parameter array and the perception accuracy parameter array, the deviation amplitude of each ideal perception accuracy parameter and each perception accuracy parameter is calculated as a perception abnormality parameter to obtain a perception abnormality parameter array.

5. The production scheduling control method based on equipment status perception according to claim 4 is characterized in that: Based on each service information, the prediction classification obtains an array of ideal perception accuracy parameters, including: According to the perception monitoring data of the same type of equipment at different service times, a sample service information set is collected, and the average perception accuracy parameters of the equipment under different sample service information are collected to obtain a sample ideal perception accuracy parameter set; Constructing a device perception index table using the sample service information set as an index header and the sample ideal perception accuracy parameter set as an index value; Each service information in the service information array is input into the device perception index table, and the index classification is performed to obtain an ideal perception accuracy parameter array.

6. The production scheduling control method based on equipment status perception according to claim 1 is characterized in that: According to the device temperature array and the device amplitude array, device load prediction is performed to obtain a predicted device load array, and based on the sensed abnormality parameter array, the predicted device load array is compensated to obtain a compensated device load array, including: According to the operating load monitoring data of similar equipment, a sample equipment temperature set and a sample equipment amplitude set are collected, and the load degree of the equipment under different sample equipment temperatures and sample equipment amplitudes is collected and marked to obtain a sample equipment load set; Using the sample device temperature set and the sample device amplitude set as input features, using the sample device load set as output features, and using machine learning to train a device load predictor; According to the device temperature array and the device amplitude array, the load prediction input characteristics of each device are obtained by dividing and combining, and the characteristics are input into the device load predictor, and the predicted device load array is obtained by output; The predicted equipment load array is compensated according to the perceived abnormality parameter array to obtain a compensated equipment load array.

7. The production scheduling control method based on equipment status perception according to claim 6 is characterized in that: According to the sensed abnormality parameter array, the predicted device load array is compensated to obtain a compensated device load array, including: Generating an array of equipment load compensation coefficients according to the array of abnormal perception parameters; According to the equipment load compensation coefficient array, compensation calculation is performed on the predicted equipment load array to obtain a compensated equipment load array.

8. The production scheduling control method based on equipment status perception according to claim 1 is characterized in that: Obtain the current production planning information, combine it with the compensation equipment load array, optimize the production schedule, obtain the optimal production schedule information, and perform control, including: Acquire current production planning information, wherein the production planning information includes production volume; Based on the compensation equipment load array and the abnormal perception parameter array, a production scheduling function is constructed as follows: ； in, is the scheduling adaptability, N is the number of multiple devices, is the weight of the ith device assigned according to the perceived abnormality parameters of each device in the perceived abnormality parameter array, is the proportion of the equipment production volume allocated to the i-th equipment in the production planning information, is the equipment production coefficient allocated according to the compensation equipment load of the i-th equipment, wherein the size of the equipment production coefficient is negatively correlated with the compensation equipment load; Randomly assigning the production planning information to a plurality of devices to obtain a plurality of first device production quantities as first production scheduling information; Based on the production scheduling function, calculating and obtaining a first scheduling fitness of the first production scheduling information; Continue to randomly allocate and optimize the production schedule information until convergence, and output the optimal production schedule information with the largest schedule fitness for control.

9. A production scheduling control system based on equipment status perception, characterized in that: The steps for implementing the production scheduling control method of equipment status awareness as described in any one of claims 1 to 8 include: The device perception module is used to monitor the operating temperature and operating amplitude of multiple devices, obtain the device temperature array and the device amplitude array, perform temperature amplitude verification analysis on each device respectively, and obtain the perception accuracy parameter array; A perception anomaly analysis module, used to obtain the service information array of the plurality of devices, predict and classify to obtain an ideal perception accuracy parameter array, and calculate and obtain a perception anomaly parameter array in combination with the perception accuracy parameter array; An equipment load prediction module is used to perform equipment load prediction based on the equipment temperature array and the equipment amplitude array to obtain a predicted equipment load array, and to compensate the predicted equipment load array based on the sensed abnormality parameter array to obtain a compensated equipment load array; The production scheduling optimization module is used to obtain the current production planning information, combine the compensation equipment load array, optimize the production scheduling, obtain the optimal production scheduling information, and perform control.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the production scheduling control method with equipment status awareness as described in any one of claims 1 to 8.

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