Smart factory equipment cooperative control method and system based on Internet of Things

Abstract

The invention discloses a smart factory equipment cooperative control method and system based on the Internet of Things, and relates to the technical field of smart factory equipment cooperative control, and the method comprises the steps: S1, receiving an original parameter set, collected in real time, of factory equipment, differentiated preprocessing is carried out on the original parameter set based on a pre-constructed parameter real-time requirement level rule, so that multi-dimensional operation parameters are obtained; s2, generating a plurality of non-conflicting initial strategies based on the multi-dimensional operation parameters by a pre-constructed initial strategy generation model; s3, performing multi-dimensional evaluation on each initial strategy to obtain a multi-dimensional evaluation result, and if the multi-dimensional evaluation result is consistent with a target, determining that the initial strategy is a feasible cooperative control strategy and sending the feasible cooperative control strategy; and if the multi-dimensional evaluation result is that the target is not met, marking the initial strategy as an infeasible cooperative control strategy. According to the invention, the efficiency, accuracy and stability of equipment cooperative control can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart factory equipment collaborative control, and particularly relates to a smart factory equipment collaborative control method and system based on Internet of Things. BACKGROUND

[0002] In the field of smart factory equipment collaborative control, with the expansion of production scale and the development of equipment intelligence, the demand for efficient collaborative management of equipment is increasing. However, the existing technology has the following problems:

[0003] (1) No distinction in parameter processing, and response is not timely: After the existing technology collects equipment parameters (such as temperature, power, and running time), it does not classify and process different equipment parameters according to the real-time demand differences of the production impact. Among them, high real-time parameters (such as core component temperature) need to be responded quickly. If high real-time parameters and low real-time parameters (such as running time) are mixed together for transmission and processing, it will be difficult to detect key problems such as core component overheating in time, which may cause equipment failure and production line shutdown.

[0004] (2) Single strategy generation, and collaboration is prone to conflict: The initial strategy generation of the existing technology is mostly focused on a single target (such as only considering energy consumption), without fully considering the constraints in the multi-equipment collaborative scenario. Different equipment strategies are prone to conflict in terms of execution sequence and load allocation, and fixed strategies cannot be adjusted flexibly when facing dynamic scenarios such as urgent orders and temporary equipment failures, so the production line has weak ability to respond to changes.

[0005] (3) One-sided strategy evaluation, poor execution effect: The existing technology usually only performs simple verification on the generated strategy, without evaluating it from multiple dimensions such as energy consumption, equipment life, and communication stability. Some strategies may have low energy consumption in the short term, but may increase operation and maintenance costs in the long term due to excessive equipment wear and tear or communication interference. In addition, the existing technology lacks clear criteria for matching with targets, making it difficult to accurately filter out feasible strategies that adapt to the production line, thereby affecting the efficiency and stability of collaborative control. SUMMARY

[0006] The purpose of the present application is to provide a smart factory equipment collaborative control method and system based on Internet of Things, which can improve the efficiency, accuracy and stability of equipment collaborative control.

[0007] To achieve the above object, the application provides a smart factory equipment collaborative control method based on Internet of Things, comprising the following steps: S1: receiving a real-time collected original parameter set of factory equipment, and differentially preprocessing the original parameter set based on a pre-constructed parameter real-time requirement level rule, thereby obtaining multi-dimensional operation parameters; wherein the original parameters in the original parameter set at least include original core component temperature, original load power, original operation duration, and relative distance between original devices; the multi-dimensional operation parameters at least include high real-time parameters, medium real-time parameters and low real-time parameters; the high real-time parameters at least include core component temperature and load power; the medium real-time parameters at least include relative distance between devices; and the low real-time parameters at least include operation duration; S2: generating a plurality of mutually conflicting initial strategies based on the multi-dimensional operation parameters by a pre-constructed initial strategy generation model, wherein each initial strategy comprises an execution device, an execution sequence, a start-stop timing adjustment, a load distribution ratio and a communication frequency adjustment; S3: performing multi-dimensional evaluation on each initial strategy respectively to obtain multi-dimensional evaluation results, wherein if the multi-dimensional evaluation results are in line with the target, the initial strategy is determined as a feasible collaborative control strategy and is sent; and if the multi-dimensional evaluation results are not in line with the target, the initial strategy is marked as an unfeasible collaborative control strategy.

[0008] As above, wherein the sub-step of differentially preprocessing the original parameter set based on the pre-constructed parameter real-time requirement level rule to obtain multi-dimensional operation parameters is as follows: S11: classifying the original parameter set based on the pre-constructed parameter real-time requirement level rule to obtain classification data, wherein the classification data comprises high real-time original parameters, medium real-time original parameters and low real-time original parameters; S12: differentially preprocessing the classification data to obtain multi-dimensional operation parameters.

[0009] The substep of classifying the original parameter set based on the pre-constructed parameter real-time requirement level rule to obtain classification data is as follows: S111: generating a classification serial number for each original parameter in the original parameter set in a random order, wherein the classification serial numbers are sequentially increased from early to late according to the generation order, and S112 is executed; S112: taking the original parameter with the smallest classification serial number as a current classification parameter, traversing the pre-constructed parameter attribute matching table to determine that the parameter entry in the parameter attribute matching table that is consistent with the parameter name of the current classification parameter is a target parameter entry, taking the standard production influence weight corresponding to the target parameter entry as a current production influence weight, and taking the standard response time threshold corresponding to the target parameter entry as a current response time threshold, and S113 is executed; S113: analyzing the current production influence weight and the current response time threshold according to the pre-constructed parameter real-time requirement level rule, if the current production influence weight and the current response time threshold meet the high real-time requirement level standard in the parameter real-time requirement level rule, the current classification parameter is classified as a high real-time original parameter, if the current production influence weight and the current response time threshold meet the medium real-time requirement level standard in the parameter real-time requirement level rule, the current classification parameter is classified as a medium real-time original parameter, and if the current production influence weight and the current response time threshold meet the low real-time requirement level standard in the parameter real-time requirement level rule, the current classification parameter is classified as a low real-time original parameter, and S114 is executed; S114: judging the classification serial number of the current classification parameter by using the total number of classification serial numbers, if the classification serial number of the current classification parameter is less than the total number of classification serial numbers, the classification serial number of the current classification parameter is removed, and S112 is executed, if the classification serial number of the current classification parameter is equal to the total number of classification serial numbers, the classification serial number of the current classification parameter is removed, and S115 is executed; S115: taking all the high real-time original parameters, the medium real-time original parameters, and the low real-time original parameters as classification data.

[0010] The substep of differentiating and preprocessing the classification data to obtain the multi-dimensional running parameter is as follows: S121: adopting adaptive Kalman filtering to denoise the high real-time original parameter to obtain a high real-time parameter; S122: adopting a method of median filtering combined with sliding window analysis to preprocess the medium real-time original parameter to obtain a medium real-time parameter; S123: adopting mean filtering to preprocess the low real-time original parameter to obtain a low real-time parameter; and S124: taking the high real-time parameter, the medium real-time parameter, and the low real-time parameter as the multi-dimensional running parameter.

[0011] The step of generating a plurality of initial strategies that do not conflict with each other based on the multi-dimensional operation parameters by the pre-constructed initial strategy generation model is as follows: S21: performing format standardization processing on the multi-dimensional operation parameters by the initial strategy generation model, and outputting a standardized parameter table; wherein the standardized parameter table includes: a plurality of parameter names, one parameter name corresponding to one device main body, one parameter value, one unit, and a set of device inherent threshold values; the parameter names at least include: core component temperature, load power, relative distance between devices, and operation time length; the device inherent threshold values at least include: the maximum allowable temperature of the core component and the maximum allowable load power of the device; S22: using each parameter name in the standardized parameter table, respectively traversing the adjustment rule database to determine the sub-rule data packet corresponding to the standard parameter name consistent with the parameter name in the adjustment rule database as the target sub-rule data packet, and then traversing the trigger threshold range in the target sub-rule data packet according to the parameter value corresponding to the parameter name to determine the adjustment action corresponding to the trigger threshold range to which the parameter value belongs as the adjustment rule, and then collecting all the adjustment rules corresponding to the parameter names to form an executable candidate rule pool; S23: selecting adjustment rules from the executable candidate rule pool for combination to form a plurality of strategy drafts, wherein each strategy draft includes: a set of individual devices and a set of device pairs; wherein when the set of individual devices includes: one execution device or a plurality of execution devices that cannot form a device pair, the set of device pairs is empty; when the set of individual devices includes: at least two execution devices that can form a device pair, the set of device pairs includes at least one device pair; each execution device corresponds to at least one adjustment rule; each device pair corresponds to at least one adjustment rule; S24: checking each strategy draft by the preset conflict constraint library, if there is any kind of conflict defined in the conflict constraint library in the strategy draft, the strategy draft is removed; if there is no any kind of conflict defined in the conflict constraint library in the strategy draft, the strategy draft is taken as a conflict-free strategy draft; S25: performing element integrity analysis on each conflict-free strategy draft according to the preset demand elements, if the conflict-free strategy draft has missing elements, performing element completion processing on the conflict-free strategy draft according to the preset completion method corresponding to the missing elements, thereby obtaining a plurality of initial strategies that do not conflict with each other; wherein the preset demand elements at least include: execution device, execution sequence, start-stop timing adjustment, load distribution ratio, and communication frequency adjustment.

[0012] The sub-step of the format standardization processing of the multi-dimensional operation parameter by the initial strategy generation model and outputting the standardization parameter table is as follows: S211: extracting the current equipment identity from the multi-dimensional operation parameter, and querying the pre-stored equipment inherent threshold table according to the current equipment identity; wherein the equipment inherent threshold table comprises: a plurality of standard equipment identities, each standard equipment identity corresponds to a set of standard equipment inherent threshold; the current equipment identity at least comprises: equipment code and equipment model; S212: taking the standard equipment inherent threshold corresponding to the standard equipment identity consistent with the current equipment identity as the equipment inherent threshold; S213: performing the format standardization processing and integration on the multi-dimensional operation parameter and the equipment inherent threshold, and forming the standardization parameter table.

[0013] The substep of obtaining the multi-dimensional evaluation result when the device pair exists in the initial strategy is as follows: S31: a health degree of each execution device in the initial strategy is calculated by a pre-constructed device health degree evaluation model to obtain a current device health degree, the current device health degree is analyzed by using a preset device health degree threshold to generate a first sub-evaluation result, if the current device health degree is greater than or equal to the device health degree threshold, the generated first sub-evaluation result is in line with the target, if the current device health degree is less than the device health degree threshold, the generated first sub-evaluation result is not in line with the target; if the first sub-evaluation result of each execution device in the initial strategy is in line with the target, S32 is executed; if one or more first sub-evaluation results exist in the first sub-evaluation results of all execution devices in the initial strategy, the multi-dimensional evaluation result is generated, and the multi-dimensional evaluation result is not in line with the target; S32: the correlation degree of each device pair in the initial strategy is analyzed by a pre-constructed inter-device correlation degree evaluation model to obtain a current device pair correlation degree; the current device pair correlation degree is analyzed by using a preset device pair correlation degree threshold to generate a second sub-evaluation result, if the current device pair correlation degree is greater than or equal to the device pair correlation degree threshold, the generated second sub-evaluation result is in line with the target, if the current device pair correlation degree is less than the device pair correlation degree threshold, the generated second sub-evaluation result is not in line with the target; if the second sub-evaluation result of each device pair in the initial strategy is in line with the target, S33 is executed; if one or more second sub-evaluation results exist in the second sub-evaluation results of all device pairs in the initial strategy, the multi-dimensional evaluation result is generated, and the multi-dimensional evaluation result is not in line with the target; S33: the task completion rate of all execution devices in the initial strategy is analyzed by a pre-constructed task completion rate evaluation model to obtain a current total task completion rate; the current total task completion rate is analyzed by using a preset total task completion rate threshold to generate a third sub-evaluation result; if the current total task completion rate is greater than or equal to the total task completion rate threshold, the generated third sub-evaluation result is in line with the target, and S34 is executed; if the current total task completion rate is less than the total task completion rate threshold, the generated third sub-evaluation result is not in line with the target, and the third sub-evaluation result is taken as the multi-dimensional evaluation result; S34: the total energy consumption of all execution devices and communication in the initial strategy is analyzed by a pre-constructed total collaborative energy consumption evaluation model to obtain a current total collaborative energy consumption; the current total collaborative energy consumption is analyzed by using a preset total collaborative energy consumption threshold to generate a multi-dimensional evaluation result; if the current total collaborative energy consumption is less than or equal to the total collaborative energy consumption threshold, the generated multi-dimensional evaluation result is in line with the target; if the current total collaborative energy consumption is greater than the total collaborative energy consumption threshold, the generated multi-dimensional evaluation result is not in line with the target.

[0014] The device health degree threshold is 0.6; the device pair cooperation correlation degree threshold is 0.5; and the total task completion rate threshold is 90%.

[0015] The sub-steps of obtaining the multi-dimensional evaluation result when the device pair does not exist in the initial strategy are as follows: S31': the health degree of each execution device in the initial strategy is calculated by using the pre-constructed device health degree evaluation model to obtain the current device health degree, the current device health degree is analyzed by using the preset device health degree threshold, and the health degree sub-evaluation result is generated, if the current device health degree is greater than or equal to the device health degree threshold, the health degree sub-evaluation result generated is in line with the target, and if the current device health degree is less than the device health degree threshold, the health degree sub-evaluation result generated is not in line with the target; if the health degree sub-evaluation result of each execution device in the initial strategy is in line with the target, S32' is executed; if one or more health degree sub-evaluation results exist in the health degree sub-evaluation results of all execution devices in the initial strategy, the multi-dimensional evaluation result is generated, and the multi-dimensional evaluation result is not in line with the target; S32': the task completion rate of all execution devices in the initial strategy is analyzed by using the pre-constructed task completion rate evaluation model to obtain the current total task completion rate; the current total task completion rate is analyzed by using the preset total task completion rate threshold, and the completion rate sub-evaluation result is generated; if the current total task completion rate is greater than or equal to the total task completion rate threshold, the completion rate sub-evaluation result generated is in line with the target, and S33' is executed; if the current total task completion rate is less than the total task completion rate threshold, the completion rate sub-evaluation result generated is not in line with the target, and the completion rate evaluation result is taken as the multi-dimensional evaluation result; S33': the total energy consumption of all execution devices in the initial strategy is analyzed by using the pre-constructed total energy consumption evaluation model to obtain the current total energy consumption; the current total energy consumption is analyzed by using the preset total energy consumption threshold, and the multi-dimensional evaluation result is generated; if the current total energy consumption is less than or equal to the total energy consumption threshold, the multi-dimensional evaluation result generated is in line with the target; if the current total cooperative energy consumption is greater than the total cooperative energy consumption threshold, the multi-dimensional evaluation result generated is not in line with the target.

[0016] The application also provides a smart factory device cooperative control system based on the Internet of Things, which at least comprises: a plurality of factory clients and an Internet of Things cooperative control center; wherein the factory client: collects a set of original parameters of the factory device in real time through a sensor or a device self-provided interface, and sends the set of original parameters of the factory device collected in real time to the Internet of Things cooperative control center; after receiving a feasible cooperative control strategy, determines an execution strategy based on the feasible cooperative control strategy according to a preset selection rule, and adjusts the corresponding execution device according to the execution strategy; the Internet of Things cooperative control center: is used for executing the smart factory device cooperative control method based on the Internet of Things described above.

[0017] The beneficial effects realized by the present application are as follows:

[0018] (1) The smart factory equipment collaborative control method and system based on the Internet of Things can improve the efficiency, accuracy and stability of equipment collaborative control.

[0019] (2) The smart factory equipment collaborative control method and system based on the Internet of Things can improve the timeliness of response by differentiating the real-time collected original parameter set of the factory equipment.

[0020] (3) The smart factory equipment collaborative control method and system based on the Internet of Things can generate multiple initial strategies containing execution equipment, execution sequence, load distribution ratio and other factors based on multi-dimensional operation parameters through the constructed initial strategy generation model, which can not only avoid equipment collaborative contradictions from the source, but also provide adjustment space for dynamic scenarios (such as emergency orders, equipment failure), thereby enhancing the production line's ability to adapt to changes and ensuring production continuity.

[0021] (4) The smart factory equipment collaborative control method and system based on the Internet of Things can evaluate the initial strategy from multiple dimensions such as energy consumption, equipment life, communication stability, and then screen out feasible collaborative control strategies that take into account short-term efficiency and long-term cost, which not only avoids the execution of invalid strategies and improves the accuracy of collaborative control, but also helps efficient equipment collaboration and stable operation of the production line. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0023] Figure 1 The structure diagram of an embodiment of the smart factory equipment collaborative control system based on the Internet of Things;

[0024] Figure 2 The flowchart of an embodiment of the smart factory equipment collaborative control method based on the Internet of Things. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0026] As Figure 1 shown, the present application provides a smart factory equipment collaborative control system based on Internet of Things, at least comprising: a plurality of factory clients 1 and an Internet of Things collaborative control center 2.

[0027] Among them, the factory client 1: through the sensor or the equipment self- interface real-time collection factory equipment original parameter set, and sends the real-time collection factory equipment original parameter set to the Internet of Things collaborative control center 2; after receiving the feasible collaborative control strategy, according to the preset selection rule, the feasible collaborative control strategy is determined based on the execution strategy, and the corresponding execution device is adjusted according to the execution strategy.

[0028] The Internet of Things collaborative control center 2: for executing the following smart factory equipment collaborative control method based on Internet of Things.

[0029] Specifically, the feasible collaborative control strategy is one or more.

[0030] Further, the preset selection rule is set according to the actual situation of the factory client 1, and the present application is preferably: the preset selection rule is any one or combination of direct selection, random selection and dynamic selection.

[0031] Specifically, direct selection is: based on the strategy priority (such as: the strategy priority of the lowest energy consumption, the highest task completion efficiency) directly selecting one feasible collaborative control strategy from the multiple feasible collaborative control strategies as the execution strategy, if there is only one feasible collaborative control strategy, then directly taking the feasible collaborative control strategy as the execution strategy.

[0032] Random selection is: when there is no explicit priority, randomly selecting one from the multiple feasible collaborative control strategies as the execution strategy; if there is only one feasible collaborative control strategy, then directly taking the feasible collaborative control strategy as the execution strategy.

[0033] Dynamic adjustment: combined with real-time production task change (such as: emergency order insertion), the feasible collaborative control strategy is adjusted manually and used as the execution strategy.

[0034] As Figure 2 shown, the present application provides a smart factory equipment collaborative control method based on Internet of Things, comprising the following steps:

[0035] S1: receive a raw parameter set of a factory equipment collected in real time, and perform differential preprocessing on the raw parameter set based on a pre-constructed parameter real-time requirement level rule, so as to obtain multi-dimensional running parameters; wherein, the raw parameters in the raw parameter set at least include: raw core component temperature, raw load power, raw running time length, and relative distance between raw devices; the multi-dimensional running parameters at least include: high real-time parameters, medium real-time parameters and low real-time parameters; the high real-time parameters at least include: core component temperature and load power; the medium real-time parameters at least include: relative distance between devices; and the low real-time parameters at least include: running time length.

[0036] Further, the sub-steps of performing differential preprocessing on the raw parameter set based on the pre-constructed parameter real-time requirement level rule to obtain multi-dimensional running parameters are as follows:

[0037] S11: classify the raw parameter set based on the pre-constructed parameter real-time requirement level rule to obtain classification data, wherein, the classification data includes: high real-time raw parameters, medium real-time raw parameters and low real-time raw parameters.

[0038] Further, the sub-steps of classifying the raw parameter set based on the pre-constructed parameter real-time requirement level rule to obtain classification data are as follows:

[0039] S111: generate a classification serial number for each raw parameter in the raw parameter set in random order, wherein, the classification serial numbers are sequentially increased from early to late according to the generation order, and S112 is executed.

[0040] Specifically, the classification serial number in the early generation order is smaller than the classification serial number in the late generation order.

[0041] S112: take the raw parameter with the smallest classification serial number as the current classification parameter, traverse the pre-constructed parameter attribute matching table, determine that the parameter entry in the parameter attribute matching table which is consistent with the parameter name of the current classification parameter is a target parameter entry, take the standard production influence weight corresponding to the target parameter entry as the current production influence weight, and take the standard response time threshold corresponding to the target parameter entry as the current response time threshold, and execute S113.

[0042] Specifically, the pre-constructed parameter attribute matching table includes: a plurality of parameter entries, one parameter entry corresponds to one parameter name, one standard production influence weight and one standard response time threshold.

[0043] The specific values of the parameter name, the standard production influence weight, and the standard response timeliness threshold of the parameter item are set according to historical fault cases, actual experience, and actual production requirements, such as determining the specific value of the standard production influence weight corresponding to the parameter item according to historical fault cases, for example, because the original load power overload may cause a fire, therefore, the standard production influence weight (W) corresponding to the original load power is set to 3; because the original running time only affects the maintenance cycle, therefore, the standard production influence weight (W) corresponding to the original running time is set to 1.

[0044] For example, according to the role of the parameter corresponding to the parameter item in the production chain, the specific value of the standard response timeliness threshold corresponding to the parameter item is determined, for example: the relative distance between devices is used for mechanical arm cooperation, and the position needs to be adjusted within 5 seconds, so the standard response timeliness threshold (T) is set to 5 seconds; the short-term fluctuation of the temperature of the core component does not affect the current process, so the standard response timeliness threshold (T) is set to 10 minutes.

[0045] S113: According to the pre-constructed parameter real-time demand level rule, the current production influence weight and the current response timeliness threshold are analyzed, if the current production influence weight and the current response timeliness threshold meet the high real-time demand level standard in the parameter real-time demand level rule, the current classified parameter is divided into a high real-time original parameter, if the current production influence weight and the current response timeliness threshold meet the medium real-time demand level standard in the parameter real-time demand level rule, the current classified parameter is divided into a medium real-time original parameter, if the current production influence weight and the current response timeliness threshold meet the low real-time demand level standard in the parameter real-time demand level rule, the current classified parameter is divided into a low real-time original parameter, and S114 is executed.

[0046] Further, the parameter real-time demand level is divided according to the emergency degree and the accuracy requirement of the parameter on the production process. The application preferably is: the parameter real-time demand level rule is a two-dimensional judgment standard based on the production influence weight and the response timeliness threshold, and at least includes: a high real-time demand level standard, a medium real-time demand level standard, and a low real-time demand level standard.

[0047] The high real-time demand level standard is: the production influence weight (W) = A1, and the response timeliness threshold (T) ≤ B1.

[0048] The medium real-time demand level standard is: the production influence weight (W) = A2, and B1 < the response timeliness threshold (T) ≤ B2.

[0049] The low real-time demand level standard is: the production influence weight (W) = A3, and B2 < the response timeliness threshold (T).

[0050] Wherein, A1>A2>A3, B1<B2, A1, A2, A3, B1 and B2 are positive numbers.

[0051] Specifically, the specific values of A1, A2 and A3 are set according to actual conditions, and the application is preferably: A1=3, A2=2, A3=1. The specific values of B1 and B2 are set according to actual conditions, and the application is preferably: B1=100 milliseconds, B2=5 seconds.

[0052] Further, the high real-time original parameter at least includes: original core component temperature and original load power.

[0053] Specifically, the high real-time original parameter is: once an abnormal change occurs, it will immediately have a major impact on production safety and / or product quality, and needs to be responded and handled immediately. Parameters such as: load power and key position pressure.

[0054] Further, the medium real-time original parameter at least includes: the relative distance between the original devices.

[0055] Specifically, the medium real-time original parameter is: once an abnormal change occurs, it will have an impact on the production process within a preset time period, and needs to be processed within a preset processing time. Data such as: cumulative output of device operation and batch quality data of product.

[0056] Further, the low real-time original parameter at least includes: original running time.

[0057] Specifically, the low real-time original parameter is: once an abnormal change occurs, the impact on the production process is relatively slow, and allows delayed processing of data within a preset delay processing time. Such as: running time of the device and periodic maintenance record.

[0058] S114: using the total number of classification serial numbers to judge the classification serial number of the current classification parameter, if the classification serial number of the current classification parameter is less than the total number of classification serial numbers, then the classification serial number of the current classification parameter is removed, and S112 is executed; if the classification serial number of the current classification parameter is equal to the total number of classification serial numbers, then the classification serial number of the current classification parameter is removed, and S115 is executed.

[0059] S115: taking all high real-time original parameters, medium real-time original parameters and low real-time original parameters as classification data.

[0060] S12: differentially pre-processing the classification data to obtain multi-dimensional running parameters.

[0061] Further, the sub-steps of differentially pre-processing the classification data to obtain multi-dimensional running parameters are as follows:

[0062] S121: Adopting adaptive Kalman filtering to denoise the high real-time original parameters to obtain high real-time parameters.

[0063] Further, the expression of the high real-time parameters obtained by adopting adaptive Kalman filtering to denoise the high real-time original parameters is:

[0064]

[0065] Among them, is the gth high real-time parameter at time t k . is the prediction value of the gth high real-time original parameter at time t k . is the Kalman gain of the gth high real-time original parameter at time t k . is the gth high real-time original parameter at time t k ; H is an observation matrix.

[0066] Specifically, the Kalman gain of different high real-time original parameters is different, and in the adaptive Kalman filtering, the adaptive adjustment is realized by dynamically updating the covariance matrix, that is, the Kalman gain of different high real-time original parameters can be obtained by using the prior art, and therefore no further description is made.

[0067] The observation matrix H is used to ensure the accuracy and timeliness of the data, and the observation matrix H can be obtained based on prior knowledge, and therefore no further description is made.

[0068] S122: Adopting the method of median filtering combined with sliding window analysis to pretreat the medium real-time original parameters to obtain medium real-time parameters.

[0069] Specifically, the sub-steps of adopting the method of median filtering combined with sliding window analysis to pretreat the medium real-time original parameters to obtain medium real-time parameters are:

[0070] U1: Determine the sliding window according to the pre-set preset length, the sliding window includes the current collected medium real-time original parameters and multiple groups of historical medium real-time original parameters, and the current collected medium real-time original parameters and multiple groups of historical medium real-time original parameters are continuous time series data.

[0071] Specifically, the specific value of the preset length is set according to the parameter characteristics.

[0072] U2: performing median calculation on the current acquisition of the medium real-time original parameter and the plurality of sets of historical medium real-time original parameters in the sliding window, that is, taking the middle value after sorting the current acquisition of the medium real-time original parameter and the plurality of sets of historical medium real-time original parameters in the sliding window according to size, and taking the middle value as the medium real-time parameter.

[0073] Specifically, using the middle value to replace the mean value of the medium real-time original parameter in the sliding window can eliminate occasional impulse noise (such as a jump value caused by transient interference of a sensor).

[0074] S123: performing mean filtering on the low real-time original parameter to obtain a low real-time parameter.

[0075] Specifically, the sub-step of performing mean filtering on the low real-time original parameter to obtain a low real-time parameter includes:

[0076] R1: determining a cache period according to a preset period length, the cache period including the current acquisition of the low real-time original parameter and the plurality of sets of historical low real-time original parameters, and the current acquisition of the low real-time original parameter and the plurality of sets of historical low real-time original parameters being continuous time series data.

[0077] The specific value of the preset period length is set according to the parameter characteristics.

[0078] R2: performing calculation on the current acquisition of the low real-time original parameter and the plurality of sets of historical low real-time original parameters in the cache period to obtain an average value, and taking the average value as the low real-time parameter.

[0079] S124: taking the high real-time parameter, the medium real-time parameter and the low real-time parameter as the multi-dimensional running parameters.

[0080] S2: generating a plurality of initial strategies that do not conflict with each other based on the multi-dimensional running parameters by an initial strategy generation model constructed in advance, wherein each initial strategy includes: an execution device, an execution sequence, a start-stop time sequence adjustment, a load distribution ratio and a communication frequency adjustment.

[0081] Further, the initial strategy generation model is constructed based on a rule engine and a constraint programming technology, but is not limited to the rule engine and the constraint programming technology, and can be based on a deep learning technology, a reinforcement learning technology or a genetic learning technology.

[0082] Further, as an embodiment, the sub-step of generating a plurality of initial strategies that do not conflict with each other based on the multi-dimensional running parameters by the initial strategy generation model constructed in advance includes:

[0083] S21: The initial strategy generation model performs format standardization processing on the multi-dimensional operation parameters, and outputs a standardized parameter table; wherein the standardized parameter table includes: a plurality of parameter names, one parameter name corresponding to one device main body, one parameter value, one unit, and a set of device inherent threshold values; the parameter names at least include: core component temperature, load power, relative distance between devices, and operation time length; the device inherent threshold values at least include: core component maximum allowable temperature T max , and device maximum allowable load power P max .

[0084] Further, the initial strategy generation model performs format standardization processing on the multi-dimensional operation parameters, and outputs a standardized parameter table, the sub-steps are as follows:

[0085] S211: Extract the current device identity from the multi-dimensional operation parameters, and query the pre-stored device inherent threshold value table according to the current device identity; wherein the device inherent threshold value table includes: a plurality of standard device identities, each standard device identity corresponding to a set of standard device inherent threshold values; the current device identity at least includes: device code and device model.

[0086] Specifically, the standard device inherent threshold values include: core component maximum allowable temperature T max , and device maximum allowable load power P max . The core component maximum allowable temperature T max and the device maximum allowable load power P max are hardware safety threshold values set by the manufacturer when the device is shipped.

[0087] S212: The standard device inherent threshold values corresponding to the standard device identity consistent with the current device identity are taken as the device inherent threshold values.

[0088] S213: The multi-dimensional operation parameters and the device inherent threshold values are subjected to format standardization processing and integration to form a standardized parameter table.

[0089] S22: Using each parameter name in the standardized parameter table, the adjustment rule database is traversed respectively to determine that the sub-rule data package corresponding to the standard parameter name consistent with the parameter name in the adjustment rule database is the target sub-rule data package, and then the trigger threshold range in the target sub-rule data package is traversed according to the parameter value corresponding to the parameter name to determine the adjustment action corresponding to the trigger threshold range to which the parameter value belongs as the adjustment rule, and all the adjustment rules corresponding to the parameter names are summarized to form an executable candidate rule pool.

[0090] Specifically, the adjustment rule database includes: a plurality of sub-rule data packages, one sub-rule data package corresponding to one standard parameter name, and each sub-rule data package including: a plurality of trigger threshold ranges, one trigger threshold range corresponding to one adjustment action.

[0091] The specific values of the trigger threshold range are set according to actual conditions, for example: the standard parameter name is core component temperature, and the trigger threshold range corresponding to the core component temperature includes: less than or equal to 0.5 x T max , and greater than or equal to 0.8 x T max . The standard parameter name is the relative distance between devices, and the trigger threshold range corresponding to the relative distance between devices includes: [3m, 10m), and less than or equal to 0.3m.

[0092] The adjustment action is a specific adjustment scheme formulated for the corresponding trigger threshold range (i.e. trigger condition), and the specific content of the specific adjustment scheme is set according to actual experience or experimental results. For example: the parameter value corresponding to the core component temperature in the standardized parameter table is greater than 0.8 x T max , the core component temperature corresponds to the execution device a1, and the adjustment action corresponding to the execution device a1 is to reduce the load by 15% and shut down for 5 minutes every hour. The parameter value corresponding to the relative distance between devices in the standardized parameter table is within [3m, 10m), and the relative distance between devices corresponds to the execution device a2, and the adjustment action corresponding to the execution device a2 is to increase the communication frequency to 5Hz.

[0093] S23: Select adjustment rules from the pool of executable candidate rules to form multiple strategy drafts, wherein each strategy draft includes: a set of individual devices and a set of device pairs; wherein when the set of individual devices includes: one execution device or multiple execution devices that cannot form a device pair, the set of device pairs is empty; when the set of individual devices includes: at least two execution devices that can form a device pair, the set of device pairs includes at least one device pair; each execution device corresponds to at least one adjustment rule; each device pair corresponds to at least one adjustment rule.

[0094] Specifically, unable to form a device pair means that the execution devices do not need to work together. Can form a device pair means that the execution devices need to work together.

[0095] The strategy draft meets the requirements of covering all execution devices and all device pairs, and each execution device corresponds to at least one adjustment rule, and each device pair corresponds to at least one adjustment rule.

[0096] For example: there are 3 execution devices, d1, d2 and d3; the set of individual devices is: Dgt = {d1, d2, d3}, and the set of device pairs is: Dsbd = {(d1, d2), (d2, d3), (d1, d3)}, then the structure of the strategy draft is:

[0097] The device individual rule includes:

[0098] The adjustment rule for the single execution device d1 is: if the parameter value corresponding to the core component temperature of d1 is greater than 0.8*T max , then the load is down-regulated by 15%.

[0099] The adjustment rule for the single execution device d2 is: if the load power of d2 is within the first trigger threshold range of the load power, the operation mode is switched to the energy-saving mode.

[0100] The adjustment rule for the single execution device d3 is: if the vibration frequency of d3 is abnormal, self-checking is performed once every hour.

[0101] The device association rule includes:

[0102] The adjustment rule for (d1, d2) is: if the relative distance between d1 and d2 is ∈ [3m, 10m), the communication frequency is increased to 5Hz.

[0103] The adjustment rule for (d1, d3) is: if the parameter correlation of d1 and d3 is within the first parameter correlation trigger threshold range (for example: ≤0.3), the data synchronization period is extended to 5 minutes.

[0104] The adjustment rule for (d2, d3) is: if the synergy efficiency of d2 and d3 is within the first synergy efficiency trigger threshold range (for example: less than 80%), the task allocation weight is adjusted to d2:d3=6:4.

[0105] S24: Check each strategy draft against the preset conflict constraint library. If there is any conflict defined in the conflict constraint library in the strategy draft, eliminate the strategy draft. If there is no any conflict defined in the conflict constraint library in the strategy draft, take the strategy draft as a conflict-free strategy draft.

[0106] Specifically, the specific content of the conflict defined in the conflict constraint library is set according to actual experience, research data, field knowledge and other information, and meets the actual application requirements.

[0107] The application preferably includes: the conflicts defined in the conflict constraint library at least include: intra-device conflicts, inter-device conflicts and logical self-consistent conflicts.

[0108] Specifically, the intra-device conflict refers to the contradictory adjustment action direction of the same execution device. For example: "the adjustment rule of execution device a3 is load up-regulation" and "the adjustment rule of execution device a3 is load down-regulation" cannot coexist; "the adjustment rule of execution device a4 is continuous operation" and "the adjustment rule of execution device a4 is immediate shutdown" cannot coexist.

[0109] Inter-device conflict refers to the contradiction between the adjustment actions of a pair of devices and the characteristics of the associated parameters. For example, the characteristic of "the communication frequency is increased to 5 Hz when the relative distance between the execution device a5 and the execution device a6 is greater than 10 m" conflicts with the characteristic of "the communication frequency should be reduced as the distance increases".

[0110] Logical self-consistent conflict refers to the contradiction between the execution condition of the adjustment action and the device state. For example, "the execution device a7 receives additional load when the load power of the execution device a7 has reached the maximum allowable load power P max " conflicts with "maximum load limit".

[0111] S25: According to the preset demand elements, element integrity analysis is performed on each conflict-free strategy draft. If the conflict-free strategy draft is missing, the element completion method corresponding to the missing element is used to complete the conflict-free strategy draft, thereby obtaining multiple initial strategies that do not conflict with each other; wherein the preset demand elements at least include: execution device, execution sequence, start-stop timing adjustment, load distribution ratio and communication frequency adjustment.

[0112] Specifically, the preset demand elements and the preset completion method are set according to actual conditions.

[0113] For example, if the missing element is the execution sequence, the corresponding completion method is to set the execution sequence based on device association or action dependency. For example, if the adjustment action of the execution device a8 (such as "stop cooling") will affect the load power of the execution device a9 (a9 needs to temporarily take over the task of a8), the execution sequence needs to be set as "first adjust the load upper limit of a9, then execute the stop of a8".

[0114] If the missing element is the start-stop timing adjustment, the corresponding completion method is to supplement the start-stop timing adjustment in combination with the device running period or the action effective condition. For example, the "load down 30%" action of the execution device a10 needs to be supplemented with "start time (execute immediately), duration (until the temperature is lower than the threshold) and stop interval (stop for 2 minutes every 10 minutes of operation)".

[0115] If the missing element is the load distribution ratio, the corresponding completion method is to distribute the load distribution ratio according to the preset algorithm (such as: according to the rated load ratio, efficiency weight) for inter-device cooperation scenarios. For example, the execution device a11 and the execution device a12 need to jointly undertake the total load P, if the rated load of a11 is twice that of a12, the distribution ratio is set as "a11 undertakes 2P / 3 and a12 undertakes P / 3".

[0116] If the missing element is the communication frequency adjustment, the corresponding completion method is: associate the distance between devices or the data transmission demand, and complete the communication frequency adjustment through the formula. For example: the relative distance Djl between the execution device a13 and the execution device a14 is 5m, and according to the rule "Djl∈[3m, 10m), the communication frequency is 5Hz".

[0117] S3: Perform multi-dimensional evaluation on each initial strategy respectively to obtain a multi-dimensional evaluation result, if the multi-dimensional evaluation result is in line with the target, determine the initial strategy as a feasible cooperative control strategy, and send it; if the multi-dimensional evaluation result is not in line with the target, mark the initial strategy as an infeasible cooperative control strategy.

[0118] Further, when there is a device pair in the initial strategy, the multi-dimensional evaluation of the initial strategy is as follows:

[0119] S31: Calculate the health degree of each execution device in the initial strategy by the pre-constructed device health degree evaluation model to obtain the current device health degree, analyze the current device health degree by using the preset device health degree threshold to generate a first sub-evaluation result, if the current device health degree is greater than or equal to the device health degree threshold, the generated first sub-evaluation result is in line with the target, if the current device health degree is less than the device health degree threshold, the generated first sub-evaluation result is not in line with the target; if the first sub-evaluation result of each execution device in the initial strategy is in line with the target, execute S32; if there is one or more first sub-evaluation results that are not in line with the target in the first sub-evaluation result of all execution devices in the initial strategy, generate a multi-dimensional evaluation result, and the multi-dimensional evaluation result is not in line with the target.

[0120] Specifically, the specific value of the device health degree threshold is set according to the actual situation, and the application is preferably 0.6.

[0121] Further, the formula of the pre-constructed device health degree evaluation model is:

[0122]

[0123] Wherein, Sjk i is the current device health degree of the i-th execution device in the initial strategy; and is the parameter weight of the i-th execution device, T i is the core component temperature of the i-th execution device; T i max is the maximum allowable temperature of the core component of the i-th execution device; P i is the load power of the i-th execution device; P imax is the maximum allowed load power of the i-th execution device; e is a natural constant; λ i is the attenuation coefficient of the i-th execution device; t i is the running time of the i-th execution device.

[0124] Specifically, and The specific value of λ is set according to actual conditions. i The specific value of is set according to historical data fitting calibration or experimental data of the i-th execution device.

[0125] S32: The pre-constructed inter-device cooperative correlation degree evaluation model is used to analyze the correlation degree of each device pair in the initial strategy respectively, and the current device pair cooperative correlation degree is obtained; the preset device pair cooperative correlation degree threshold is used to analyze the current device pair cooperative correlation degree, and a second sub-evaluation result is generated, if the current device pair cooperative correlation degree is greater than or equal to the device pair cooperative correlation degree threshold, the generated second sub-evaluation result is in line with the target, if the current device pair cooperative correlation degree is less than the device pair cooperative correlation degree threshold, the generated second sub-evaluation result is not in line with the target; if the second sub-evaluation result of each device pair in the initial strategy is in line with the target, S33 is executed; if there is one or more second sub-evaluation results in the second sub-evaluation result of all device pairs in the initial strategy, a multi-dimensional evaluation result is generated, and the multi-dimensional evaluation result is not in line with the target.

[0126] Specifically, the specific value of the device pair cooperative correlation degree threshold is set according to actual conditions, and the application is preferably 0.5.

[0127] Further, the formula of the pre-constructed inter-device cooperative correlation degree evaluation model is:

[0128]

[0129] Where, Sxt j is the current device pair cooperative correlation degree of the j-th device pair in the initial strategy; is the parameter weight of the j-th device pair, is the distance attenuation term of the j-th device pair; e is a natural constant; α j is the distance influence coefficient of the j-th device pair; d j is the relative distance between the execution devices of the j-th device pair; is the load power correlation of the j-th device pair, is the load power of the execution device x1 in the j-th device pair; is the load power of the execution device x2 in the j-th device pair.

[0130] Specifically, The specific value is set according to the actual situation. Distance influence coefficient α j The specific value is set based on the degree to which distance affects device collaboration in the actual scenario. α j The larger the distance, the stronger the attenuation effect of the collaborative association degree. The Pearson correlation coefficient was used to obtain Fxg. However, it is not limited to using the Pearson correlation coefficient to obtain...

[0131] S33: The pre-built task completion rate evaluation model analyzes the task completion rate of all execution devices in the initial strategy to obtain the current total task completion rate; the pre-set total task completion rate threshold is used to analyze the current total task completion rate and generate a third sub-evaluation result; if the current total task completion rate is greater than or equal to the total task completion rate threshold, the generated third sub-evaluation result is in line with the target, and S34 is executed; if the current total task completion rate is less than the total task completion rate threshold, the generated third sub-evaluation result is not in line with the target, and the third sub-evaluation result is used as the multi-dimensional evaluation result.

[0132] Specifically, the threshold value for the overall task completion rate is set according to the actual situation, and is preferably 90% in this application.

[0133] Furthermore, the formula for the pre-built task completion rate evaluation model is as follows:

[0134]

[0135] Where Qz represents the current total task completion rate of all execution devices in the initial strategy; P n T represents the load power of the nth execution device in the initial strategy, where n ∈ [1, N] and N is the total number of execution devices participating in the coordination in the initial strategy. work,n Q is the effective operating time of the nth execution device obtained by adjusting the start-stop timing of the nth execution device according to the initial strategy; plan This represents the pre-set total planned workload.

[0136] S34: The pre-built total collaborative energy consumption assessment model analyzes the total energy consumption of all execution devices and communications in the initial strategy to obtain the current total collaborative energy consumption; the preset total collaborative energy consumption threshold is used to analyze the current total collaborative energy consumption and generate multi-dimensional assessment results; if the current total collaborative energy consumption is less than or equal to the total collaborative energy consumption threshold, the generated multi-dimensional assessment results are in line with the target; if the current total collaborative energy consumption is greater than the total collaborative energy consumption threshold, the generated multi-dimensional assessment results are not in line with the target.

[0137] Specifically, the total collaborative energy consumption threshold should be set according to the actual situation.

[0138] Further, the formula of the pre-constructed total synergistic energy consumption evaluation model is:

[0139]

[0140] wherein, Ez is the current total synergistic energy consumption of all the execution devices and communication in the initial strategy; Pr is the load power of the rth execution device in the initial strategy, r∈[1, R], R is the total number of the execution devices participating in the synergy in the initial strategy; Tr is the effective working time length of the rth execution device obtained according to the start-stop timing adjustment of the rth execution device in the initial strategy; Er is the single start-up energy consumption of the rth execution device in the initial strategy; Cr is the start-up frequency of the rth execution device in the initial strategy; fh is the communication frequency of the hth device pair obtained according to the communication frequency adjustment of the hth device pair in the initial strategy, h∈Ω, Ω is a set of device pairs composed of R execution devices participating in the synergy, the number of device pairs included in Ω is r work,r start,r start,r h T comm is the total communication time length; is the communication energy consumption parameter.

[0141] Specifically, the communication energy consumption parameter is a device inherent parameter. comm is equal to the preset total time length of the entire synergistic task.

[0142] Further, when there is no device pair in the initial strategy, the multi-dimensional evaluation of the initial strategy is performed, and the sub-steps of obtaining the multi-dimensional evaluation result are as follows:

[0143] S31’: the health degree of each execution device in the initial strategy is calculated by the pre-constructed device health degree evaluation model respectively to obtain the current device health degree, the current device health degree is analyzed by using the preset device health degree threshold to generate the health degree sub-evaluation result, if the current device health degree is greater than or equal to the device health degree threshold, the generated health degree sub-evaluation result is in line with the target, if the current device health degree is less than the device health degree threshold, the generated health degree sub-evaluation result is not in line with the target; if the health degree sub-evaluation result of each execution device in the initial strategy is in line with the target, S32’ is executed; if there is one or more health degree sub-evaluation results in the health degree sub-evaluation results of all the execution devices in the initial strategy, the multi-dimensional evaluation result is generated, and the multi-dimensional evaluation result is not in line with the target.

[0144] ​​​​​Specifically, if there is only one execution device in the initial strategy, then only the current device health of that execution device is analyzed, generating a health sub-evaluation result. The specific value of the device health threshold is set according to the actual situation; in this application, it is preferably 0.6.

[0145] Furthermore, the formula for the pre-built equipment health assessment model is as follows:

[0146]

[0147] Among them, Sjk v The current device health of the v-th executing device in the initial strategy; and Let the parameter weights be those of the v-th execution device. T v The temperature of the core component of the v-th execution device; P represents the maximum allowable temperature of the core component of the v-th actuator. v Let the load power of the v-th execution device be denoted as 'v'. λ is the maximum allowable load power of the v-th executing device; e is the natural constant; λ v t is the attenuation coefficient of the v-th actuator; v The runtime of the vth execution device.

[0148] Specifically, and The specific value is set according to the actual situation. λ v The specific value is set based on the historical data fitting calibration or experimental data of the vth execution device.

[0149] S32': The pre-built task completion rate evaluation model analyzes the task completion rate of all execution devices in the initial strategy to obtain the current total task completion rate; the pre-set total task completion rate threshold is used to analyze the current total task completion rate and generate a completion rate sub-evaluation result; if the current total task completion rate is greater than or equal to the total task completion rate threshold, the generated completion rate sub-evaluation result is in line with the target, and S33' is executed; if the current total task completion rate is less than the total task completion rate threshold, the generated completion rate sub-evaluation result is not in line with the target, and the completion rate evaluation result is used as a multi-dimensional evaluation result.

[0150] Specifically, the threshold value for the overall task completion rate is set according to the actual situation, and is preferably 90% in this application.

[0151] Furthermore, the formula for the pre-built task completion rate evaluation model is as follows:

[0152]

[0153] Wherein, Qzw is the current total task completion rate of all execution devices in the initial strategy; P o is the load power of the oth execution device in the initial strategy, o∈[1, O], O is the total number of execution devices in the initial strategy; T work,o is the effective working time length of the oth execution device obtained according to the start-stop timing adjustment of the oth execution device in the initial strategy; Q plan is the preset planned total amount of work.

[0154] S33': performing total energy consumption analysis on all execution devices in the initial strategy by the pre-constructed total energy consumption evaluation model to obtain the current total energy consumption; performing analysis on the current total energy consumption by using the preset total energy consumption threshold to generate a multi-dimensional evaluation result; if the current total energy consumption is less than or equal to the total energy consumption threshold, the generated multi-dimensional evaluation result is in line with the target; if the current total collaborative energy consumption is greater than the total collaborative energy consumption threshold, the generated multi-dimensional evaluation result is not in line with the target.

[0155] Further, the formula of the pre-constructed total energy consumption evaluation model is:

[0156]

[0157] Wherein, Eqb is the current total energy consumption of all execution devices in the initial strategy; P u is the load power of the uth execution device in the initial strategy, u∈[1, U], U is the total number of execution devices in the initial strategy; T work,u is the effective working time length of the uth execution device obtained according to the start-stop timing adjustment of the uth execution device in the initial strategy; E start,u is the single start energy consumption of the uth execution device in the initial strategy; C start,u is the start number of the uth execution device in the initial strategy.

[0158] The beneficial effects realized by the present application are as follows:

[0159] (1) The intelligent factory device collaborative control method and system based on the Internet of Things can improve the efficiency, accuracy and stability of device collaborative control.

[0160] (2) The intelligent factory device collaborative control method and system based on the Internet of Things can improve the response timeliness by differentiating the real-time collected original parameter set of the factory device.

[0161] (3) The IoT-based smart factory equipment collaborative control method and system of this application generates multiple non-conflicting initial strategies based on multi-dimensional operating parameters by constructing an initial strategy generation model, which includes elements such as execution equipment, execution order, and load distribution ratio. This can not only avoid equipment collaboration conflicts from the source, but also provide adjustment space for dynamic scenarios (such as emergency orders and equipment failures), thereby enhancing the production line's adaptability and ensuring production continuity.

[0162] (4) The IoT-based smart factory equipment collaborative control method and system of this application can evaluate the initial strategy from multiple dimensions such as energy consumption, equipment lifespan, and communication stability, and then select a feasible collaborative control strategy that takes into account both short-term efficiency and long-term cost. This not only avoids the execution of ineffective strategies and improves the accuracy of collaborative control, but also helps the equipment to collaborate efficiently and the production line to operate stably.

[0163] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the scope of protection of this application is intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application. Obviously, those skilled in the art can make various alterations and variations to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of protection of this application and its equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for collaborative control of smart factory equipment based on the Internet of Things, characterized in that, Includes the following steps: S1: Receives the raw parameter set of factory equipment acquired in real time, and performs differentiated preprocessing on the raw parameter set based on pre-built parameter real-time requirement level rules to obtain multi-dimensional operating parameters; wherein, the raw parameters in the raw parameter set include at least: raw core component temperature, raw load power, raw runtime, and raw relative distance between equipment; the multi-dimensional operating parameters include at least: high real-time parameters, medium real-time parameters, and low real-time parameters; high real-time parameters include at least: core component temperature and load power; medium real-time parameters include at least: relative distance between equipment; low real-time parameters include at least: runtime; S2: The pre-built initial strategy generation model generates multiple non-conflicting initial strategies based on multi-dimensional operating parameters. Each initial strategy includes: execution device, execution order, start-stop timing adjustment, load distribution ratio, and communication frequency adjustment. S3: Perform multi-dimensional evaluation on each initial strategy to obtain the multi-dimensional evaluation results. If the multi-dimensional evaluation results meet the objectives, the initial strategy is determined to be a feasible collaborative control strategy and sent; if the multi-dimensional evaluation results do not meet the objectives, the initial strategy is marked as an infeasible collaborative control strategy.

2. The IoT-based smart factory equipment collaborative control method according to claim 1, characterized in that, The sub-steps for obtaining multi-dimensional operating parameters by performing differential preprocessing on the original parameter set based on pre-built parameter real-time requirement level rules are as follows: S11: Classify the original parameter set based on the pre-built parameter real-time requirement level rules to obtain classified data, which includes: high real-time requirement original parameters, medium real-time requirement original parameters, and low real-time requirement original parameters. S12: Perform differential preprocessing on the categorical data to obtain multidimensional operating parameters.

3. The IoT-based smart factory equipment collaborative control method according to claim 2, characterized in that, The sub-steps for classifying the original parameter set based on pre-built parameter real-time requirement level rules to obtain classified data are as follows: S111: Generate a classification number for each original parameter in the original parameter set in a random order, wherein the classification numbers are sequentially increased from first to last according to the generation order, and then execute S112; S112: Take the original parameter with the smallest classification number as the current classification parameter, traverse the pre-built parameter attribute matching table, determine the parameter entry in the parameter attribute matching table that has the same parameter name as the current classification parameter as the target parameter entry, take the standard production impact weight corresponding to the target parameter entry as the current production impact weight, take the standard response time threshold corresponding to the target parameter entry as the current response time threshold, and execute S113. S113: Analyze the current production impact weight and current response time threshold according to the pre-built parameter real-time requirement level rules. If the current production impact weight and current response time threshold meet the high real-time requirement level standard in the parameter real-time requirement level rules, then classify the current classification parameter as a high real-time original parameter. If the current production impact weight and current response time threshold meet the medium real-time requirement level standard in the parameter real-time requirement level rules, then classify the current classification parameter as a medium real-time original parameter. If the current production impact weight and current response time threshold meet the low real-time requirement level standard in the parameter real-time requirement level rules, then classify the current classification parameter as a low real-time original parameter. Execute S114. S114: Use the total number of classification numbers to judge the classification number of the current classification parameter. If the classification number of the current classification parameter is less than the total number of classification numbers, then remove the classification number of the current classification parameter and execute S112; if the classification number of the current classification parameter is equal to the total number of classification numbers, then remove the classification number of the current classification parameter and execute S115. S115: Treat all high real-time raw parameters, medium real-time raw parameters, and low real-time raw parameters as categorical data.

4. The IoT-based smart factory equipment collaborative control method according to claim 2, characterized in that, The sub-steps for performing differential preprocessing on categorical data to obtain multidimensional operating parameters are as follows: S121: Adaptive Kalman filtering is used to denoise the original parameters for high real-time performance, thereby obtaining high real-time parameters; S122: The median filtering combined with sliding window analysis is used to preprocess the original real-time parameters to obtain the real-time parameters. S123: Mean filtering is used to preprocess the original parameters with low real-time performance to obtain the parameters with low real-time performance. S124: Use high real-time parameters, medium real-time parameters, and low real-time parameters as multi-dimensional operating parameters.

5. The IoT-based smart factory equipment collaborative control method according to claim 1, characterized in that, The sub-steps for generating multiple non-conflicting initial policies from a pre-built initial policy generation model based on multi-dimensional runtime parameters are as follows: S21: The initial strategy generates a model to standardize the format of multi-dimensional operating parameters and output a standardized parameter table. The standardized parameter table includes: multiple parameter names, each parameter name corresponding to a device body, a parameter value, a unit, and a set of device inherent thresholds. The parameter names include at least: core component temperature, load power, relative distance between devices, and running time. The device inherent thresholds include at least: the maximum allowable temperature of the core component and the maximum allowable load power of the device. S22: Using each parameter name in the standardized parameter table, traverse the adjustment rule database to determine the sub-rule data package corresponding to the standard parameter name that matches the parameter name in the adjustment rule database as the target sub-rule data package. Then, based on the parameter value corresponding to the parameter name, traverse the trigger threshold range in the target sub-rule data package to determine the adjustment action corresponding to the trigger threshold range to which the parameter value belongs as the adjustment rule. Summarize the adjustment rules corresponding to all parameter names to form an executable candidate rule pool. S23: Select adjustment rules from the executable candidate rule pool and combine them to form multiple strategy drafts. Each strategy draft includes: a set of individual devices and a set of device pairs. When the set of individual devices includes: one execution device or multiple execution devices that cannot form a device pair, the set of device pairs is empty. When the set of individual devices includes: at least two execution devices that can form a device pair, the set of device pairs includes at least one device pair. Each execution device corresponds to at least one adjustment rule. Each device pair corresponds to at least one adjustment rule. S24: Verify each policy draft using the preset conflict constraint library. If any conflict defined in the conflict constraint library exists in the policy draft, the policy draft is removed. If no conflict defined in the conflict constraint library exists in the policy draft, the policy draft is treated as a conflict-free policy draft. S25: Based on the preset requirement elements, perform element integrity analysis on each conflict-free strategy draft. If there are missing elements in the conflict-free strategy draft, perform element completion processing on the conflict-free strategy draft according to the preset completion method corresponding to the missing elements, thereby obtaining multiple non-conflicting initial strategies. Among them, the preset requirement elements include at least: execution device, execution order, start-stop timing adjustment, load distribution ratio, and communication frequency adjustment.

6. The IoT-based smart factory equipment collaborative control method according to claim 5, characterized in that, The sub-steps for standardizing the format of multidimensional operating parameters and outputting a standardized parameter table from the initial strategy model are as follows: S211: Extract the current device identity from the multi-dimensional operating parameters, and query the pre-stored device inherent threshold table based on the current device identity; wherein, the device inherent threshold table includes: multiple standard device identities, each standard device identity corresponding to a set of standard device inherent thresholds; the current device identity includes at least: device code and device model; S212: Use the standard device inherent threshold corresponding to the standard device identity that is consistent with the current device identity as the device inherent threshold. S213: Standardize and integrate multi-dimensional operating parameters and inherent equipment thresholds to form a standardized parameter table.

7. The IoT-based smart factory equipment collaborative control method according to claim 1, characterized in that, When device pairs exist in the initial strategy, the initial strategy is evaluated in multiple dimensions. The sub-steps for obtaining the multi-dimensional evaluation results are as follows: S31: The pre-built device health assessment model calculates the health of each executing device in the initial strategy to obtain the current device health. A preset device health threshold is used to analyze the current device health, generating a first sub-assessment result. If the current device health is greater than or equal to the device health threshold, the generated first sub-assessment result meets the target; if the current device health is less than the device health threshold, the generated first sub-assessment result does not meet the target. If the first sub-assessment results of each executing device in the initial strategy all meet the target, then proceed to S32. If one or more first sub-assessment results of all executing devices in the initial strategy do not meet the target, then a multi-dimensional assessment result is generated, and the multi-dimensional assessment result does not meet the target. S32: The pre-built inter-device collaboration correlation evaluation model analyzes the correlation of each device pair in the initial strategy to obtain the current device pair collaboration correlation. A preset device pair collaboration correlation threshold is used to analyze the current device pair collaboration correlation, generating a second sub-evaluation result. If the current device pair collaboration correlation is greater than or equal to the device pair collaboration correlation threshold, the generated second sub-evaluation result meets the target; if the current device pair collaboration correlation is less than the device pair collaboration correlation threshold, the generated second sub-evaluation result does not meet the target. If the second sub-evaluation results of each device pair in the initial strategy meet the target, then S33 is executed. If one or more second sub-evaluation results of all device pairs in the initial strategy do not meet the target, then a multi-dimensional evaluation result is generated, and the multi-dimensional evaluation result does not meet the target. S33: The pre-built task completion rate evaluation model analyzes the task completion rate of all execution devices in the initial strategy to obtain the current total task completion rate; the pre-set total task completion rate threshold is used to analyze the current total task completion rate and generate a third sub-evaluation result; if the current total task completion rate is greater than or equal to the total task completion rate threshold, the generated third sub-evaluation result is in line with the target, and S34 is executed; if the current total task completion rate is less than the total task completion rate threshold, the generated third sub-evaluation result is not in line with the target, and the third sub-evaluation result is used as the multi-dimensional evaluation result. S34: The total energy consumption of all execution devices and communications in the initial strategy is analyzed by a pre-built total collaborative energy consumption assessment model to obtain the current total collaborative energy consumption; The current total collaborative energy consumption is analyzed using a preset total collaborative energy consumption threshold, generating multi-dimensional evaluation results; If the current total collaborative energy consumption is less than or equal to the total collaborative energy consumption threshold, the generated multi-dimensional evaluation result is in line with the target; If the current total collaborative energy consumption is greater than the total collaborative energy consumption threshold, the generated multi-dimensional evaluation result will be deemed not to meet the target.

8. The IoT-based smart factory equipment collaborative control method according to claim 7, characterized in that, The equipment health threshold is 0.6; the equipment collaboration correlation threshold is 0.5; and the overall task completion rate threshold is 90%.

9. The IoT-based smart factory equipment collaborative control method according to claim 1, characterized in that, When no device pair exists in the initial strategy, the initial strategy is evaluated in multiple dimensions. The sub-steps for obtaining the multi-dimensional evaluation results are as follows: S31': The pre-built device health assessment model calculates the health of each executing device in the initial strategy to obtain the current device health. The current device health is analyzed using a preset device health threshold to generate a health sub-assessment result. If the current device health is greater than or equal to the device health threshold, the generated health sub-assessment result meets the target. If the current device health is less than the device health threshold, the generated health sub-assessment result does not meet the target. If the health sub-assessment result of each executing device in the initial strategy meets the target, then proceed to S32'. If one or more health sub-assessment results of all execution devices in the initial strategy are not in line with the target, then a multi-dimensional assessment result is generated, and the multi-dimensional assessment result is not in line with the target. S32': The pre-built task completion rate evaluation model analyzes the task completion rate of all execution devices in the initial strategy to obtain the current total task completion rate; the pre-set total task completion rate threshold is used to analyze the current total task completion rate and generate a completion rate sub-evaluation result; if the current total task completion rate is greater than or equal to the total task completion rate threshold, the generated completion rate sub-evaluation result is in line with the target, and S33' is executed; if the current total task completion rate is less than the total task completion rate threshold, the generated completion rate sub-evaluation result is not in line with the target, and the completion rate evaluation result is used as a multi-dimensional evaluation result. S33': The total energy consumption of all execution devices in the initial strategy is analyzed by a pre-built total energy consumption assessment model to obtain the current total energy consumption; The current total energy consumption is analyzed using a preset total energy consumption threshold to generate multi-dimensional evaluation results; If the current total energy consumption is less than or equal to the total energy consumption threshold, the generated multi-dimensional evaluation result is in line with the target; If the current total collaborative energy consumption is greater than the total collaborative energy consumption threshold, the generated multi-dimensional evaluation result will be deemed not to meet the target.

10. A smart factory equipment collaborative control system based on the Internet of Things, characterized in that, It includes at least: multiple factory clients and an IoT collaborative control center; Among them, the factory client: collects the raw parameter set of the factory equipment in real time through sensors or the built-in interface of the equipment, and sends the raw parameter set of the factory equipment collected in real time to the IoT collaborative control center; after receiving the feasible collaborative control strategy, it determines the execution strategy based on the preset selection rules and adjusts the corresponding execution equipment according to the execution strategy; IoT Collaborative Control Center: Used to execute the IoT-based smart factory equipment collaborative control method as described in any one of claims 1-9.

Images