Mechanical and electrical equipment collaborative control method and system based on internet of things

Abstract

The application discloses a kind of based on Internet of Things electromechanical equipment collaborative control method and system, belong to industrial Internet of Things technical field, based on electromechanical equipment's motor real-time current, motor instantaneous power peak and motor temperature rise load obtain electrical load stress coefficient;Based on the sorting action frequency density of electromechanical equipment, continuous working time and processing interval time obtains sorting state load stress coefficient;Based on package task queue depth, special package proportion and sorting port distance offset obtains task load stress coefficient;Based on the electrical load stress coefficient under sorting state load stress coefficient and task load stress coefficient obtains electrical-task cooperativity coefficient;Based on the current package conveying speed under electrical-task cooperativity coefficient and current package conveying pitch constructs pitch optimization model and outputs target package conveying pitch;The application is fused by multi-source data, dynamically optimizes equipment operating parameter, solves load imbalance and low efficiency problem.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of industrial internet of things, and particularly relates to a mechatronic device cooperative control method and system based on an internet of things. BACKGROUND

[0002] In the current industrial sorting system, mechatronic devices often face multiple challenges such as electrical load fluctuation, mechanical fatigue and task complexity mutation. Traditional control methods usually independently monitor single parameters such as motor current, temperature or task queue, lacking cooperative analysis of electrical performance, mechanical state and task demand. This leads to device overload shutdown when load surges, or blind speed-up to cause faults when task backlog, while fixed design of package spacing is difficult to adapt to dynamic working conditions, reducing sorting efficiency and device life. Especially when handling special packages (such as oversized, overweight or irregular-shaped items), system response lag further exacerbates device stress. Therefore, a multi-dimensional cooperative control mechanism based on an internet of things is urgently needed to achieve load balancing and dynamic optimization. SUMMARY

[0003] In view of the deficiencies of the prior art, the application provides a mechatronic device cooperative control method and system based on an internet of things, which solves the above problems.

[0004] To achieve the above purpose, the application realizes the following technical scheme: a mechatronic device cooperative control method based on an internet of things, comprising:

[0005] An electrical load pressure model is constructed based on the real-time current of the motor of the mechatronic device, the instantaneous power peak value of the motor and the motor temperature rise load (the difference between the current motor temperature and the ambient temperature), and an electrical load pressure coefficient is output;

[0006] A sorting state load pressure model is constructed based on the sorting action frequency density of the mechatronic device, the continuous working time and the processing interval time, and a sorting state load pressure coefficient is output;

[0007] A task load pressure model is constructed based on the package task queue depth, the special package proportion and the sorting port distance offset, and a task load pressure coefficient is output;

[0008] An electrical-task cooperativity model is constructed based on the electrical load pressure coefficient under the sorting state load pressure coefficient and the task load pressure coefficient, and an electrical-task cooperativity coefficient is output;

[0009] A spacing optimization model is constructed based on the current package conveying speed and the current package conveying spacing under the electrical-task cooperativity coefficient, and a target package conveying spacing is output.

[0010] On the basis of the above technical scheme, the application further provides the following optional technical schemes:

[0011] Further technical solutions: the step of constructing a spacing optimization model based on the current parcel delivery speed and the current parcel delivery spacing under the electrical-task cooperativity coefficient to output the target parcel delivery spacing is:

[0012] The current parcel delivery speed is introduced into the formula The current parcel delivery speed index is obtained, wherein, represents the current parcel delivery speed, represents the maximum allowed parcel delivery speed;

[0013] The current delivery speed index and the current parcel delivery spacing are introduced into the constructed spacing optimization model to obtain the target parcel delivery spacing, and the spacing optimization model is represented as:

[0014]

[0015] wherein, represents the target parcel delivery spacing, represents the maximum allowed parcel spacing, represents the minimum allowed parcel spacing, represents the spacing adjustment gain, represents the current electrical-task cooperativity coefficient, represents the current parcel delivery speed index, represents the speed sensitivity coefficient, represents the ideal parcel delivery speed index.

[0016] Further technical solutions: the step of constructing an electrical-task cooperativity model based on the electrical load pressure coefficient and the task load pressure coefficient under the sorting state load pressure coefficient to output the electrical-task cooperativity coefficient is:

[0017] An electrical-task analysis model is constructed based on the sorting state load pressure coefficient, the electrical load pressure coefficient and the task load pressure coefficient, and the electrical-task cooperativity analysis model is represented as:

[0018]

[0019] wherein, represents the electrical-task analysis coefficient, represents the electrical load pressure coefficient, represents the sorting state load pressure coefficient, represents the task load pressure coefficient;

[0020] The electrical-task cooperativity model is obtained in the electrical-task cooperativity model constructed based on the electrical-task analysis coefficient, and the electrical-task cooperativity model is represented as:

[0021]

[0022] wherein, represents an electrical-task coordination coefficient, represents an adjustment sensitivity, represents an electrical-task analysis coefficient, represents a preset electrical-task analysis coefficient ideal value, the and the greater the value, the better the coordination;

[0023] introducing the current sorting state load pressure coefficient, the current electrical load pressure coefficient and the current task load pressure coefficient into the electrical-task analysis model to obtain a current electrical-task analysis coefficient;

[0024] introducing the current electrical-task analysis coefficient into the electrical-task coordination model to obtain a current electrical-task coordination coefficient.

[0025] Further technical solutions: based on the real-time current of the motor of the electromechanical equipment, the instantaneous power peak value of the motor and the temperature rise load of the motor (the difference between the current motor temperature and the ambient temperature), an electrical load pressure model is constructed to output an electrical load pressure coefficient, and the steps are as follows:

[0026] The maximum-minimum normalization processing is performed on the real-time current of the motor, the instantaneous power peak value of the motor and the temperature rise load of the motor to obtain a current index, a power peak value index and a temperature rise load index;

[0027] Based on the current index, the power peak value index and the temperature rise load index, an electrical load pressure model is constructed, and the electrical load pressure model is represented as:

[0028]

[0029] wherein, represents an electrical load pressure coefficient, represents a current index, represents a power peak value index, represents a temperature rise load index, represents a weight coefficient and , the and the greater the value, the greater the electrical load pressure;

[0030] introducing the current current index, the current power peak value index and the current temperature rise load index into the electrical load pressure model to obtain a current electrical load pressure coefficient.

[0031] Further technical solutions: based on the sorting action frequency density of the electromechanical equipment, the continuous working time and the processing interval time, a sorting state load model is constructed to output a sorting state load coefficient, and the steps are as follows:

[0032] The maximum-minimum normalization processing is performed on the sorting action frequency density and the processing interval time to obtain an action frequency density index and a processing interval time index;

[0033] The continuous working time is introduced into a formula to obtain a continuous working time index, wherein, represents the continuous working time, represents a maximum allowed continuous working time;

[0034] A sorting state load pressure model is constructed based on the continuous working time index, the processing interval time index and the action frequency density index, and the sorting state load pressure model is represented as:

[0035]

[0036] wherein, represents a sorting state load pressure coefficient, represents a sensitivity coefficient, represents the continuous working time index, represents the processing interval time index, represents the action frequency density index, represents a balance threshold value, represents a weight coefficient and , the and the greater the value is, the higher the sorting pressure is;

[0037] The current continuous working time index, the current processing interval time index and the current action frequency density index are introduced into the sorting state load pressure model to obtain a current sorting state load pressure coefficient.

[0038] Further technical solutions: based on the package task queue depth, the special package proportion and the package and sorting port distance offset, a task load pressure model is constructed to output a task load pressure coefficient, and the steps are as follows:

[0039] The package and sorting port distance offset is introduced into a formula to obtain a sorting port distance offset index, wherein, represents the package and sorting port distance offset, represents a maximum allowed package and sorting port offset;

[0040] The special package proportion and the maximum allowed special package proportion are processed by ratio to obtain a special package proportion index;

[0041] The package task queue depth is subjected to maximum-minimum normalization processing to obtain a task queue depth index;

[0042] A task load pressure model is constructed based on the sorting port distance offset index, the special package proportion index, and the task queue depth index, and is expressed as:

[0043]

[0044] wherein, represents a task load pressure coefficient, represents a sorting port distance offset index, represents a special package proportion index, represents a task queue depth index, represents a weight coefficient and , the and the greater the value, the more complex the sorting task is;

[0045] The current sorting port distance offset index, the current special package proportion index, and the current task queue depth index are introduced into the task load pressure model to obtain the current task load pressure coefficient.

[0046] Further technical solutions: the judgment method of the special package is:

[0047] The package volume, the package weight, and the package surface curvature are obtained and are respectively subjected to ratio processing with respective maximum allowable values to obtain a package volume index, a package weight index, and a surface curvature index.

[0048] The package volume index, the package weight index, and the surface curvature index of the current package are subjected to weighted average processing to obtain a special package index.

[0049] The obtained special package index is compared with a preset special package index threshold value, and if the special package index is not within the special package index threshold value, the current package is determined as a special package.

[0050] An electromechanical equipment collaborative control system based on the Internet of Things is constructed by using the above-mentioned electromechanical equipment collaborative control method based on the Internet of Things, and comprises:

[0051] An electrical load pressure analysis module, which is constructed based on the real-time current of the motor of the electromechanical equipment, the instantaneous power peak value of the motor, and the motor temperature rise load (the difference between the current motor temperature and the ambient temperature) to output an electrical load pressure coefficient.

[0052] A sorting state load pressure analysis module, which is constructed based on the sorting action frequency density of the electromechanical equipment, the continuous working time, and the processing interval time to output a sorting state load pressure coefficient.

[0053] A task load pressure analysis module constructs a task load pressure model based on a parcel task queue depth, a special parcel proportion, and a sorting port distance offset to output a task load pressure coefficient;

[0054] A synergy analysis module constructs an electrical-task synergy model based on an electrical load pressure coefficient under a sorting state load pressure coefficient and a task load pressure coefficient to output an electrical-task synergy coefficient;

[0055] A spacing optimization module constructs a spacing optimization model based on a current parcel conveying speed and a current parcel conveying spacing under an electrical-task synergy coefficient to output a target parcel conveying spacing.

[0056] The present application provides a kind of based on Internet of Things electromechanical equipment collaborative control method and system, compared with prior art has following beneficial effects:

[0057] 1, the present application can pass through electrical load (current, power, temperature rise), sorting state (action frequency, continuous working time length, processing interval) and task load (queue depth, special parcel proportion, sorting port offset) three models, real-time quantification equipment pressure, break single parameter limitation;

[0058] 2, the present application can pass through the construction electrical-task synergy coefficient, fusion electrical, mechanical and task load data, accurately assess system synergy state, provide core basis for decision-making, simultaneously can be based on electrical-task synergy coefficient and conveying speed, through spacing optimization model dynamically adjusts parcel spacing, increase spacing to reduce load when low synergy, expand spacing to guarantee safety when high conveying speed. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 It is the flowchart of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0061] The specific implementation of the present application is described in detail below in combination with specific examples.

[0062] Please refer to Figure 1 , a kind of based on Internet of Things electromechanical equipment collaborative control method provided by an embodiment of the present application, including the following steps:

[0063] Based on the real-time current of the motor of electromechanical equipment, the instantaneous power peak value of motor and the temperature rise load of motor (the difference between current motor temperature and ambient temperature) constructs electrical load pressure model and outputs electrical load pressure coefficient.

[0064] constructing a sorting state load pressure model based on the sorting action frequency density of the electromechanical device, the continuous working time, and the processing interval time to output a sorting state load pressure coefficient;

[0065] constructing a task load pressure model based on the package task queue depth, the special package proportion, and the sorting port distance offset to output a task load pressure coefficient;

[0066] constructing an electrical-task synergy model based on the electrical load pressure coefficient under the sorting state load pressure coefficient and the task load pressure coefficient to output an electrical-task synergy coefficient;

[0067] constructing a spacing optimization model based on the current package conveying speed and the current package conveying spacing under the electrical-task synergy coefficient to output a target package conveying spacing.

[0068] The electrical load pressure model refers to a composite evaluation system that fuses current, power, and temperature rise parameters, which can be implemented by a weighted normalization method, and is used to quantify motor overload risk. The sorting state load pressure model refers to a correlation analysis of mechanical action frequency and duration, which can be implemented by an exponential function to fuse processing interval time parameters, and is used to evaluate mechanical component fatigue state. The task load pressure model refers to a comprehensive analysis of task queue features and package attributes, which can be implemented by a composite calculation of sorting port distance offset and special package proportion, and is used to reflect sorting task complexity. The electrical-task synergy model refers to an analysis mechanism that nonlinearly couples three pressure coefficients, and is used to generate system overall operation state indicators. The spacing optimization model refers to a control algorithm that dynamically adjusts package spacing based on the synergy coefficient, and is used to balance device load and sorting efficiency.

[0069] Specifically, the method first collects motor current, power, and temperature data in real time through Internet of Things sensors, generates electrical load pressure coefficients after normalization processing, such as converting temperature rise difference into exponential values in the 0-1 interval. At the same time, the action frequency and running time of the sorting mechanical arm are monitored, and the maximum allowed value in the device maintenance record is combined to calculate the sorting state load pressure coefficient. At the task analysis level, the number of special packages is counted through a visual recognition system, and the distance offset is calculated by combining sorting port layout data to generate a task load pressure coefficient. The three coefficients are input into the synergy model for nonlinear coupling, such as using a vector space projection method to calculate the synergy coefficient. Finally, according to the dynamic relationship between the current conveying speed and the synergy coefficient, the target spacing is calculated through an optimization formula containing a sigmoid function, such as automatically increasing the package spacing to relieve device pressure when the synergy decreases.

[0070] Compared with the prior art, the traditional method only monitors a single parameter of motor current or temperature, and cannot identify the associated influence of electrical load and task queue. The present scheme can capture the causal relationship between current surge and special package processing by constructing a multi-dimensional pressure model. The prior art adopts a fixed spacing control strategy, while the present scheme is based on a dynamic adjustment mechanism of cooperativity coefficient, which automatically expands the package spacing when the device load increases, avoiding overload shutdown and maintaining sorting efficiency. Especially when processing irregular packages, the traditional system lacks task complexity assessment, resulting in delayed response. The present scheme calculates the special package proportion index in real time to trigger the delivery speed adjustment in advance.

[0071] Through the above technical scheme, the technical problem of cooperative control of electrical parameters, mechanical state and task demand is effectively solved. In the package sorting scene, when a large number of irregular parts are processed at the same time, the task load pressure model can identify the complexity increase in time, the cooperativity model can automatically reduce the delivery speed index, and the spacing optimization model can correspondingly increase the package spacing. This closed-loop control mechanism not only avoids the triggering of motor overload protection, but also maintains the sorting throughput through dynamic spacing adjustment, achieving balanced optimization of device safety and operating efficiency.

[0072] Preferably, the step of constructing an electrical load pressure model based on the real-time current of the motor of the electromechanical device, the instantaneous power peak value of the motor, and the temperature rise load of the motor (the difference between the current motor temperature and the ambient temperature) to output an electrical load pressure coefficient is:

[0073] The maximum-minimum normalization processing is performed on the real-time current of the motor, the instantaneous power peak value of the motor, and the temperature rise load of the motor to obtain a current index, a power peak value index, and a temperature rise load index.

[0074] An electrical load pressure model is constructed based on the current index, the power peak value index, and the temperature rise load index, and the electrical load pressure model is represented as:

[0075]

[0076] wherein, represents the electrical load pressure coefficient, represents the current index, represents the power peak value index, represents the temperature rise load index, represents the weight coefficient and , the and the greater the value, the greater the electrical load pressure;

[0077] The current current index, the current power peak value index, and the current temperature rise load index are introduced into the electrical load pressure model to obtain the current electrical load pressure coefficient.

[0078] The motor real-time current refers to the instantaneous current value of the motor through the winding during the operation process, and can be realized by real-time collection of a current sensor, and is used to reflect the real-time load state of the motor.

[0079] The motor instantaneous power peak value refers to the maximum output power value of the motor in a unit time, and can be realized by continuous monitoring and recording of peak value data by a power meter, and is used to represent the instantaneous overload risk of the motor.

[0080] The motor temperature rise load refers to the difference between the current temperature of the motor and the ambient temperature, and can be realized by calculating the difference between the motor surface temperature and the ambient temperature measured by a temperature sensor, and is used to evaluate the thermal accumulation state of the motor.

[0081] The maximum-minimum normalization processing refers to linear transformation of original data to the interval [0, 1], which is used to eliminate the comparability obstacles of different dimension parameters. The weight coefficient refers to the contribution parameter of each normalized index in the model, and can be realized by a dynamic adjustment strategy, for example, increasing the weight of the temperature rise load index in a high temperature environment, which is used to dynamically optimize the accuracy of electrical load evaluation according to the working conditions.

[0082] Specifically, the motor real-time current, instantaneous power peak value and temperature rise load data are collected by current sensors, power meters and temperature sensors respectively, and each parameter is subjected to maximum-minimum normalization processing to convert into current index, power peak value index and temperature rise load index of uniform dimension. For example, when the motor real-time current is 1.2 times the rated current, its current index can be calculated as 0.8. Subsequently, the three indices are linearly weighted and summed according to the preset weight coefficient to generate the electrical load pressure coefficient. In this process, the weight coefficient can be dynamically adjusted according to the device operating environment, for example, when the ambient temperature is high, the weight of the temperature rise load index is set to 0.5 to strengthen the influence of thermal stability on load pressure. The final output electrical load pressure coefficient is continuously quantified in the interval [0, 1], which provides accurate electrical state input for subsequent cooperative control.

[0083] Compared with the prior art, the traditional method only monitors a single electrical parameter and uses a fixed threshold to judge the load state, for example, only triggers the protection mechanism according to the current overrun. However, the present scheme can more comprehensively reflect the comprehensive operating state of the motor by fusing the data of current, power peak value and temperature rise load, and introducing a dynamic weight distribution mechanism. For example, when the motor is overloaded for a short time in a low temperature environment, the system can avoid misjudgment by reducing the temperature rise load weight, and in a high temperature environment, the thermal overload risk can be warned in advance by increasing the temperature rise weight.

[0084] ​Through the above technical solution, this application solves the problem of electrical load assessment deviation caused by independent monitoring of a single parameter in traditional methods, and realizes a coordinated quantitative assessment of the steady-state operating current, transient power surge, and heat accumulation state of the motor. For example, when the sorting equipment continuously processes special packages, causing intermittent motor overload, the system can accurately identify the coupling effect of instantaneous current peak and temperature rise, avoiding delayed response or malfunction caused by judging solely based on current threshold, thereby effectively preventing motor overload shutdown and extending equipment service life.

[0085] Preferably, the steps for constructing a sorting state load model and outputting sorting state load coefficients based on the sorting action frequency density, continuous working time, and processing interval time of the electromechanical equipment are as follows:

[0086] Max-min normalization is performed on the sorting action frequency density and processing interval time to obtain the action frequency density and processing interval time index.

[0087] Import continuous working time into the formula The continuous working time index is obtained from the data. Indicates continuous working time. Indicates the maximum allowed continuous working time;

[0088] A sorting state load pressure model is constructed based on the continuous working time index, the processing interval time index, and the action frequency density index. The sorting state load pressure model is expressed as follows:

[0089]

[0090] in, This indicates the load pressure coefficient during sorting. Represents the sensitivity coefficient. This represents the continuous working time index. This represents the processing interval time index. Indicates the frequency density index of actions. Indicates the equilibrium threshold. Represents the weight coefficient and The The larger the value, the higher the sorting pressure;

[0091] Import the current continuous working time index, the current processing interval time index, and the current action frequency density index into the sorting status load pressure model to obtain the current sorting status load pressure coefficient.

[0092] The sorting action frequency density refers to the number of times of executing the sorting action by the sorting mechanical arm per unit time, which can be achieved by counting the number of sorting trigger signals collected by the sensor and dividing the time window length, and is used to quantify the motion frequency of the mechanical component. The processing interval time refers to the idle time between adjacent two sorting operations, which can be achieved by recording the time difference between the sorting completion signal and the next sorting start signal by the timer, and is used to reflect the intermittent recovery ability of the mechanical component. The max-min normalization processing refers to linearly mapping the original data to the interval [0, 1], which can be achieved by using the formula (current value-minimum value) / (maximum value-minimum value), and is used to eliminate the numerical difference of different dimension parameters. The continuous working time index refers to the ratio of the device continuous running time to the preset safety threshold, which can be achieved by dividing the current running time by the maximum allowed continuous working time and taking the minimum value between 1, and is used to prevent the mechanical overheating or wear and tear. The sorting state load pressure model refers to a nonlinear mapping relationship based on the sigmoid function, which can be achieved by inputting the weighted parameter combination into the activation function, and is used to dynamically represent the response characteristics of the mechanical pressure changing with the running parameters.

[0093] Specifically, the sorting action frequency density and the processing interval time are converted into action frequency density index and processing interval time index through normalization, so that the impact of high-frequency action and abnormal interval on the mechanical structure is quantified. The continuous working time is converted into continuous working time index through the formula, and when the running time approaches the safety threshold, the index tends to 1, triggering the protection mechanism. The three indexes are input into the sorting state load pressure model, which maps the weighted combination parameters to the interval [0, 1] using the sigmoid function, and when the weighted value exceeds the balance threshold, the load pressure coefficient presents nonlinear growth. The weight coefficient is adjusted according to the device characteristics, for example, the continuous working time weight can be increased for easily-worn parts, and the action frequency weight can be strengthened for high-frequency sorting scenes. The output load pressure coefficient reflects the mechanical pressure level in real time, providing a quantitative basis for subsequent control strategies.

[0094] Compared with the prior art, the traditional method only monitors a single parameter and uses linear superposition to evaluate the mechanical load, which cannot capture the synergistic effect of high-frequency action and intermittent abnormality, resulting in lagging mechanical fatigue warning. The present scheme can identify the mechanical overload risk caused by action frequency mutation, continuous overtime operation or processing interval abnormality earlier by fusing multi-dimensional running parameters and constructing a nonlinear pressure response model, and realizes dynamic pressure evaluation.

[0095] By the technical solution, the mechanical load pressure of the sorting equipment can be dynamically quantified, the sorting rhythm can be adjusted in time when the sorting action frequency suddenly changes, the sorting interval can be optimized when the continuous working time approaches the safety threshold, and the mechanical component wear rate is reduced, the equipment shutdown caused by overload is avoided, and the running stability of the sorting system is improved.

[0096] Preferably, the step of constructing the task load pressure model to output the task load pressure coefficient based on the parcel task queue depth, the special parcel proportion, and the parcel-to-sorting port distance offset is:

[0097] The parcel-to-sorting port distance offset is introduced into the formula The sorting port distance offset index is obtained, wherein The parcel-to-sorting port distance offset is represented as The maximum allowed parcel-to-sorting port offset is represented as

[0098] The special parcel proportion is processed by ratio with the maximum allowed special parcel proportion to obtain a special parcel proportion index.

[0099] The parcel task queue depth is processed by maximum-minimum normalization to obtain a task queue depth index.

[0100] The task load pressure model is constructed based on the sorting port distance offset index, the special parcel proportion index, and the task queue depth index, and the task load pressure model is represented as

[0101]

[0102] Wherein, The task load pressure coefficient is represented as The sorting port distance offset index is represented as The special parcel proportion index is represented as The task queue depth index is represented as The weight coefficient is represented as and The The greater the value is, the more complex the sorting task is.

[0103] The current sorting port distance offset index, the current special parcel proportion index, and the current task queue depth index are introduced into the task load pressure model to obtain the current task load pressure coefficient.

[0104] The package task queue depth refers to the backlog degree of the to-be-picked packages in the queue, and can be specifically realized by adopting the ratio of the number of to-be-processed packages in the current queue to the maximum carrying capacity of the system, and the parameter is used to quantify the pressure on the system throughput caused by the task backlog. The special package proportion refers to the proportion of packages with abnormal attributes in the total task, and can be specifically realized by detecting whether the volume, weight and surface curvature of the package exceed the preset threshold and calculating the proportion of the number, and the parameter is used to identify the interference of the special package aggregation on the picking rhythm. The picking port distance offset refers to the spatial deviation of the actual position of the package from the target picking port, and can be specifically realized by using a laser ranging sensor to measure the straight-line distance between the center point of the package and the reference point of the picking port in real time, and the parameter is used to reflect the additional mechanical load caused by the adjustment of the motion trajectory of the mechanical arm.

[0105] Specifically, through the picking port distance offset index calculation, the deviation degree of the actual picking path of the package from the standard position is converted into a standardized parameter, solving the additional load caused by the frequent adjustment of the motion trajectory of the mechanical arm due to the position offset. Through the special package proportion index calculation, the abnormal attributes such as the volume and weight of the package are converted into a standardized parameter, effectively identifying the interference of the special package aggregation on the picking rhythm. Through the task queue depth index calculation, the pressure of the backlog degree of the to-be-processed package on the system throughput is reflected in real time. Finally, the three types of indexes are fused through a linear weighting model, so that the task load pressure coefficient can represent the three factors of spatial offset, abnormal package attributes and task backlog. For example, in the irregular part picking scene, the weight coefficient can be set to focus on the special package proportion index; in the high-speed picking scene, the weight coefficient can enhance the sensitivity of the distance offset index, thereby dynamically adapting to different working conditions.

[0106] Compared with the prior art, the traditional method usually only monitors a single task parameter and uses a fixed threshold to judge the task complexity, and cannot effectively cope with the multi-dimensional dynamic changes of the picking task. The prior art lacks quantitative analysis of the attributes of the package when processing special packages, and does not associate the spatial offset with the task backlog, resulting in system response lag. The present scheme cooperatively analyzes the spatial offset, attribute anomaly and task backlog by constructing a multi-dimensional task parameter fusion model, and realizes the dynamic quantitative evaluation of the task complexity.

[0107] Through the above technical scheme, the present application can realize real-time sensing of the mechanical load fluctuation caused by the position offset of the package, accurate identification of the picking rhythm disorder caused by the aggregation of special packages, and dynamic evaluation of the influence of the task backlog on the system throughput. Therefore, the picking device can automatically adjust the picking strategy according to the task load pressure coefficient, such as optimizing the motion trajectory of the mechanical arm in advance when the distance offset index is detected to be high, and triggering the speed reduction protection mechanism when the special package proportion suddenly increases, so as to avoid device overload and maintain the picking efficiency.

[0108] Preferably, the special package is determined by:

[0109] The package volume, package weight, and package surface curvature are obtained and compared with the respective maximum allowable values to obtain a package volume index, a package weight index, and a surface curvature index.

[0110] The package volume index, the package weight index, and the surface curvature index of the current package are weighted and averaged to obtain a special package index.

[0111] The obtained special package index is compared with a preset special package index threshold value. If the special package index is not within the special package index threshold value, the current package is determined to be a special package.

[0112] The package volume index is the ratio of the actual volume of the package to the preset maximum allowable volume. Specifically, the volume can be measured by a volume sensor, and then divided by the preset threshold value to achieve the volume index, which is used to eliminate the influence of different volume dimensions on the judgment result. The package weight index is the ratio of the actual weight of the package to the preset maximum allowable weight. Specifically, the weight data can be collected by a weighing sensor in real time, and then divided by the preset threshold value to achieve the weight index, which is used to quantify the degree of overweight of the package. The surface curvature index is the ratio of the surface curvature of the package to the preset maximum allowable curvature. Specifically, the surface curvature data can be obtained by a three-dimensional laser scanner, and then divided by the preset threshold value to achieve the curvature index, which is used to identify the geometric characteristics of the special-shaped package. The weighted average processing refers to linear combination of multiple indexes according to preset weights. Specifically, the normalized weight coefficients can be assigned and then weighted summation operation is performed to achieve the weighted average processing, which is used to flexibly adjust the contribution of different parameters to the judgment result. The special package index threshold value refers to the preset determination range interval. Specifically, the upper and lower limit values can be generated by a dynamic adjustment algorithm according to the real-time load state of the device to avoid boundary misjudgment caused by fixed threshold value.

[0113] Specifically, the package volume, weight, and surface curvature data are collected by Internet of Things sensors in real time, and then compared with the preset maximum allowable values to generate standardized indexes. When calculating the volume index, for example, when the maximum allowable volume is set to 1 cubic meter, the measured package volume is 0.8 cubic meters, and the corresponding volume index is 0.8. In the process of generating the weight index, if the maximum allowable weight is 50 kg, the measured package weight is 60 kg, and the weight index is 1.2. When calculating the surface curvature index, for a maximum allowable curvature of 0.5, the measured curvature is 0.6, and the corresponding index is 1.2.

[0114] Subsequently, the three indexes are weighted and averaged according to preset weights, for example, when the volume weight is 0.4, the weight weight is 0.4, and the curvature weight is 0.2, the package indexes are 0.8, 1.2, and 1.2 respectively, and the special package index is 0.8*0.4+1.2*0.4+1.2*0.2=1.04. The index is compared with a dynamically adjusted threshold range, for example, when the threshold is set to [0.9, 1.1], the package exceeding the range is determined as a special package. By dynamically adjusting the threshold range, the equipment load state at different sorting stages can be adapted, for example, the threshold range is automatically reduced to improve the screening accuracy when the equipment is highly loaded.

[0115] Compared with the prior art, the conventional method usually only uses a single weight threshold or a fixed size threshold to judge a special package, for example, an alarm is triggered only when the weight exceeds 50 kg, resulting in that the abnormal package with irregular shape or composite parameters is easily missed. The static threshold setting in the prior art cannot adapt to the real-time working condition of the equipment, for example, a conservative threshold is still used when the equipment is under low load, causing excessive screening. The prior art lacks a multi-dimensional parameter fusion mechanism, for example, only detecting volume or weight cannot identify packages with severely deformed surfaces.

[0116] Through the above technical solutions, the present application solves the problems of high misjudgment rate and response lag in the identification of special packages in traditional sorting systems due to single parameter judgment or static threshold setting. Through multi-dimensional parameter fusion processing, packages with normal volume but surface distortion can be accurately identified, avoiding missed judgment caused by relying only on weight parameters. The dynamic threshold mechanism can automatically adjust the judgment range according to the real-time running state of the equipment, reducing the threshold range to prioritize high-risk packages when the equipment is highly loaded, and expanding the range to balance the sorting efficiency when the equipment is under low load. The weighted average processing allows the operator to adjust the parameter weight according to different scenarios, for example, increasing the surface curvature weight in a scenario dominated by sorting irregular shapes, thereby enhancing the adaptability of the system.

[0117] Preferably, the step of constructing an electrical-task cooperativity model based on the electrical load pressure coefficient, the task load pressure coefficient and the sorting state load pressure coefficient to output an electrical-task cooperativity coefficient is:

[0118] An electrical-task analysis model is constructed based on the sorting state load pressure coefficient, the electrical load pressure coefficient and the task load pressure coefficient, and the electrical-task cooperativity analysis model is represented as:

[0119]

[0120] wherein, represents the electrical-task analysis coefficient, represents the electrical load pressure coefficient, represents the sorting state load pressure coefficient, represents the task load pressure coefficient.

[0121] An electrical-task coordination model is obtained based on the electrical-task analysis coefficient model, and the electrical-task coordination model is expressed as:

[0122]

[0123] wherein, represents the electrical-task coordination coefficient, represents the adjustment sensitivity, represents the electrical-task analysis coefficient, represents the preset electrical-task analysis coefficient ideal value, and the the greater the value, the better the coordination;

[0124] The current sorting state load pressure coefficient, the current electrical load pressure coefficient and the current task load pressure coefficient are introduced into the electrical-task analysis model to obtain the current electrical-task analysis coefficient;

[0125] The current electrical-task analysis coefficient is introduced into the electrical-task coordination model to obtain the current electrical-task coordination coefficient.

[0126] The sorting state load pressure coefficient refers to the mechanical state pressure index calculated by the sorting action frequency density, continuous working time and processing interval time, and can specifically adopt the action frequency density index, continuous working time index and processing interval time index after normalization processing, and is combined with the logistic function to realize weighted calculation, and is used to quantify the dynamic load of the mechanical sorting component. The electrical load pressure coefficient refers to the electrical system pressure index calculated by the motor real-time current, instantaneous power peak value and temperature rise load, and can specifically adopt the weighted summation of the normalized current index, power peak value index and temperature rise load index to realize, and is used to reflect the real-time load state of the motor electrical system. The task load pressure coefficient refers to the task complexity index calculated by the parcel task queue depth, special parcel proportion and sorting port distance offset, and can specifically adopt the weighted summation of the normalized queue depth index, special parcel proportion index and distance offset index to realize, and is used to represent the dynamic complexity of the sorting task. The electrical-task analysis coefficient refers to the dynamic coupling index calculated by the interaction of the three types of parameters of sorting state, electrical load and task pressure, and can specifically adopt the multiplication of the electrical load and the sorting state of the numerator item and the task pressure to realize, and the denominator item is normalized by double square root, and is used to balance the dimensional difference of different parameters. The electrical-task coordination coefficient refers to the coordination quantitative value mapped to the 0-1 interval by the analysis coefficient, and can specifically adopt the logistic function combined with the adjustment sensitivity and the preset ideal value to realize, and is used to dynamically evaluate the overall coordination state of the system.

[0127] Specifically, the electrical load pressure coefficient, the sorting state load pressure coefficient and the task load pressure coefficient are extracted from three dimensions of electrical performance, mechanical state and task demand, and input into the electrical-task analysis model after normalization processing to eliminate dimensional differences. In the electrical-task analysis model, the numerator term for capturing the interaction between the electrical load and the sorting state, and the denominator term is superimposed The task complexity parameter is introduced, and the denominator term is dynamically balanced by double square root operation on the three types of parameters to avoid the dominance of a single parameter in the calculation result. The analysis coefficient is further mapped to the synergy coefficient by the logistic function , wherein the adjustment sensitivity controls the steepness of the curve, and the preset ideal value adjusts the offset, so as to convert the coupling result of multi-dimensional parameters into a quantifiable synergy index. The index reflects the matching degree of electrical performance, mechanical state and task demand in real time, and provides a basis for the subsequent dynamic optimization of parcel conveying spacing.

[0128] Compared with the prior art, the traditional method independently monitors electrical parameters or task queues, and does not establish a synergistic analysis mechanism for multi-dimensional parameters, resulting in a lag in the response of the equipment when the load suddenly changes. The present scheme dynamically couples the electrical load, the mechanical state and the task complexity by constructing an electrical-task analysis model and a synergy model, and realizes real-time balancing and synergistic evaluation of the three types of parameters by using the normalization processing and nonlinear mapping of the mathematical model, solving the problems of overload risk and poor working condition adaptability caused by single parameter analysis.

[0129] Through the above technical scheme, the present application can dynamically balance the correlation between the electrical load pressure, the sorting mechanical state and the task complexity, prevent shutdown failure caused by electrical overload, avoid mechanical wear caused by blind speed-up when the task is accumulated, and accurately judge the system state through the synergy coefficient, providing a basis for real-time optimization of parcel conveying spacing, thereby improving the sorting efficiency and the equipment operation stability.

[0130] Preferably, the step of constructing a spacing optimization model based on the current parcel conveying speed and the current parcel conveying spacing under the electrical-task synergy coefficient to output a target parcel conveying spacing is:

[0131] The current parcel conveying speed is introduced into the formula to obtain the current parcel conveying speed index, wherein represents the current parcel conveying speed, represents the maximum allowed parcel conveying speed;

[0132] The current conveying speed index and the current parcel conveying interval are introduced into the constructed interval optimization model to obtain a target parcel conveying interval, which is represented as:

[0133]

[0134] wherein, represents the target parcel conveying interval, represents the maximum allowed parcel interval, represents the minimum allowed parcel interval, represents the interval adjustment gain, represents the current electrical-task cooperativity coefficient, represents the current parcel conveying speed index, represents the speed sensitivity coefficient, represents the ideal parcel conveying speed index.

[0135] The current parcel conveying speed index refers to the minimum value between the ratio of the current speed to the maximum allowed speed and 1, which can be obtained by real-time acquisition of the conveying belt speed data by a speed sensor and calculation, and is used to eliminate the dimensional differences of different devices. The maximum allowed parcel interval refers to the maximum safe distance allowed by the mechanical structure of the sorting line, which is used to constrain the interval adjustment range. The minimum allowed parcel interval refers to the minimum interval to ensure that the parcels do not collide, which is used to maintain the safety of sorting. The interval adjustment gain refers to the control parameter of the model output amplitude, which is used to control the sensitivity of interval adjustment. The electrical-task cooperativity coefficient refers to an index reflecting the matching degree of the electrical load and the task complexity of the device, which is used to guide the direction of interval adjustment. The speed sensitivity coefficient refers to the adjustment factor of the influence of speed change on interval, which is used to balance the change amplitude of interval caused by speed fluctuation. The ideal parcel conveying speed index refers to the speed ratio in the best operating state of the system, which is used to guide the system to converge to the optimal working condition.

[0136] Specifically, by monitoring the conveying speed in real time and calculating the speed index, the speed value is normalized to adapt to different device working conditions. The normalized speed index and the current interval are input into the interval optimization model, and the model structure adopts the form of combination of exponential function and linear combination. When the speed approaches the ideal value, the denominator of the exponential function tends to be stable, making the interval adjustment amplitude tend to be gentle; when the speed deviates from the ideal interval, the denominator value changes rapidly, triggering more significant interval adjustment. The model output value dynamically changes between the maximum and minimum allowed interval, and the overall adjustment amplitude is controlled by the adjustment gain. The electrical-task cooperativity coefficient serves as a reverse adjustment factor, and when the system cooperativity decreases, the coefficient value decreases, resulting in an increase in the interval, thereby reducing the device load pressure. The speed sensitivity coefficient adjusts the influence of speed change on the interval, avoiding frequent adjustment caused by slight speed fluctuations. Through the synergistic effect of multiple parameters, the balance between conveying efficiency and device load is achieved under the premise of ensuring safe interval.

[0137] Compared with the prior art, the traditional method adopts a fixed parcel spacing, which causes the device to be unable to adaptively adjust when the load suddenly changes, and easily causes overload shutdown or task backlog. The scheme cooperates the device running state and task demand coordination parameters into the calculation process by constructing a dynamic spacing optimization model, so that the parcel spacing can be automatically adjusted according to the real-time working condition. Compared with the adjustment strategy which only depends on a single parameter of speed or load, the scheme automatically increases the spacing to reduce the mechanical pressure when the device load suddenly increases, and optimizes the combination of speed and spacing to improve the processing efficiency when the task queue depth increases.

[0138] Through the above technical scheme, the application solves the problems of device overload and low efficiency caused by fixed spacing design. In the condition of sudden increase of electrical load, the parcel spacing is increased to reduce the working load of the motor, avoiding overheating protection shutdown; when the task is backlog, the combination of conveying speed and spacing is optimized to improve the processing capacity per unit time, preventing mechanical wear caused by blind speed-up; when processing special parcels, the spacing between adjacent parcels is dynamically adjusted to reserve buffer space for abnormal sorting operation. The scheme realizes the dynamic balance of sorting efficiency and device safety, and improves the system robustness under complex working conditions.

[0139] An electromechanical equipment cooperative control system based on Internet of Things is constructed by using the above-mentioned electromechanical equipment cooperative control method based on Internet of Things, comprising:

[0140] An electrical load pressure analysis module, which constructs an electrical load pressure model based on the real-time current of the motor of the electromechanical equipment, the instantaneous power peak value of the motor and the temperature rise load of the motor (the difference between the current motor temperature and the ambient temperature) to output an electrical load pressure coefficient;

[0141] A sorting state load pressure analysis module, which constructs a sorting state load pressure model based on the sorting action frequency density of the electromechanical equipment, the continuous working time and the processing interval time to output a sorting state load pressure coefficient;

[0142] A task load pressure analysis module, which constructs a task load pressure model based on the parcel task queue depth, the special parcel proportion and the sorting port distance offset to output a task load pressure coefficient;

[0143] A cooperativity analysis module, which constructs an electrical-task cooperativity model based on the electrical load pressure coefficient under the sorting state load pressure coefficient and the task load pressure coefficient to output an electrical-task cooperativity coefficient;

[0144] A spacing optimization module, which constructs a spacing optimization model based on the current parcel conveying speed under the electrical-task cooperativity coefficient and the current parcel conveying spacing to output a target parcel conveying spacing.

[0145] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, and does not necessarily require or imply any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0146] While embodiments of the present application have been shown and described with reference to particular embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. A method for collaborative control of electromechanical devices based on the Internet of Things, characterized in that, The method comprises the following steps: An electrical load pressure model is constructed based on the real-time current of the motor of the electromechanical device, the instantaneous power peak of the motor, and the temperature rise load of the motor to output an electrical load pressure coefficient; A sorting state load pressure model is constructed based on the sorting action frequency density of the electromechanical device, the continuous working time, and the processing interval time to output a sorting state load pressure coefficient; A task load pressure model is constructed based on the package task queue depth, the special package proportion, and the sorting port distance offset to output a task load pressure coefficient; An electrical-task coordination model is constructed based on the electrical load pressure coefficient under the sorting state load pressure coefficient and the task load pressure coefficient to output an electrical-task coordination coefficient; A spacing optimization model is constructed based on the current package conveying speed and the current package conveying spacing under the electrical-task coordination coefficient to output a target package conveying spacing; The step of constructing the electrical-task coordination model based on the electrical load pressure coefficient under the sorting state load pressure coefficient and the task load pressure coefficient to output an electrical-task coordination coefficient is: An electrical-task analysis model is constructed based on the sorting state load pressure coefficient, the electrical load pressure coefficient, and the task load pressure coefficient, and the electrical-task coordination analysis model is represented as: ； wherein, represents the electrical-task analysis coefficient, represents the electrical load pressure coefficient, represents the sorting state load pressure coefficient, represents the task load pressure coefficient; The electrical-task coordination model is obtained in the electrical-task coordination model constructed based on the electrical-task analysis coefficient, and the electrical-task coordination model is represented as: ； wherein, represents the electrical-task cooperativeness coefficient, represents the adjustment sensitivity, represents the electrical-task analysis coefficient, represents the preset electrical-task analysis coefficient ideal value, the and the greater the value, the better the cooperativeness; The current sorting state load pressure coefficient, the current electrical load pressure coefficient, and the current task load pressure coefficient are introduced into the electrical-task analysis model to obtain a current electrical-task analysis coefficient; The current electrical-task analysis coefficient is introduced into the electrical-task coordination model to obtain a current electrical-task coordination coefficient; The step of constructing the spacing optimization model based on the current package conveying speed and the current package conveying spacing under the electrical-task coordination coefficient to output a target package conveying spacing is: Importing the current package delivery speed into the formula Obtaining a current package delivery speed index, wherein, Indicating the current package delivery speed, Indicating the maximum allowed package delivery speed; The current conveying speed index and the current package conveying spacing are introduced into the constructed spacing optimization model to obtain a target package conveying spacing, and the spacing optimization model is represented as: ； wherein, represents a target package delivery spacing, represents a maximum allowable package spacing, represents a minimum allowable package spacing, represents a spacing adjustment gain, represents a current electrical-task cooperativity coefficient, represents a current package delivery speed index, represents a speed sensitivity coefficient, represents an ideal package delivery speed index. 2.The IoT-based collaborative control method of electromechanical devices according to claim 1, characterized in that, The step of constructing the electrical load pressure model based on the real-time current of the motor of the electromechanical device, the instantaneous power peak of the motor, and the temperature rise load of the motor to output an electrical load pressure coefficient is: The real-time current of the motor, the instantaneous power peak of the motor, and the temperature rise load of the motor are subjected to maximum-minimum normalization processing to obtain a current index, a power peak index, and a temperature rise load index; An electrical load pressure model is constructed based on the current index, the power peak index, and the temperature rise load index, and the electrical load pressure model is represented as: ； wherein represents the electrical load pressure coefficient, represents the current exponent, represents the power peak exponent, represents the temperature rise load exponent, represents the weight coefficient and , said and the greater the value the greater the electrical load pressure; The current current index, the current power peak index, and the current temperature rise load index are introduced into the electrical load pressure model to obtain a current electrical load pressure coefficient. 3.The IoT-based collaborative control method of electromechanical devices according to claim 1, characterized in that, The step of constructing the sorting state load model based on the sorting action frequency density of the electromechanical device, the continuous working time, and the processing interval time to output a sorting state load coefficient is: The sorting action frequency density and the processing interval time are subjected to maximum-minimum normalization processing to obtain an action frequency density and a processing interval time index; introducing the continuous working time into the formula to obtain the continuous working time index, wherein, denotes the continuous working time, denotes the maximum permissible continuous working time; A sorting state load pressure model is constructed based on the continuous working time index, the processing interval time index, and the action frequency density index. The sorting state load pressure model is expressed as follows: ； wherein, represents a sorting state load pressure coefficient, represents a sensitivity coefficient, represents a continuous working time index, represents a processing interval time index, represents a motion frequency density index, represents an equilibrium threshold, represents a weight coefficient and , said and the greater the value, the higher the sorting pressure; Import the current continuous working time index, the current processing interval time index, and the current action frequency density index into the sorting status load pressure model to obtain the current sorting status load pressure coefficient. 4.The IoT-based collaborative control method of electromechanical devices according to claim 1, characterized in that, The steps to construct a task load stress model and output the task load stress coefficient based on the package task queue depth, the proportion of special packages, and the distance offset between the package and the sorting point are as follows: Introducing the package to sorting portal distance offset into the equation Obtaining a sorting portal distance offset index, wherein, represents the package to sorting portal distance offset, represents the maximum allowable package to sorting portal offset; The special package percentage index is obtained by comparing the percentage of special packages with the maximum allowed percentage of special packages. The depth of the package task queue is obtained by performing a maximum-min normalization process on the depth of the task queue. A task load pressure model is constructed based on the sorting point distance offset index, the special package proportion index, and the task queue depth index. The task load pressure model is expressed as follows: ； wherein, represents a task load pressure coefficient, represents a sort gate distance offset index, represents a special package fraction index, represents a task queue depth index, represents a weight coefficient and , the and the greater the value, the more complex the sorting task; Import the current sorting point distance offset index, the current special package percentage index, and the current task queue depth index into the task load pressure model to obtain the current task load pressure coefficient. 5.The IoT-based collaborative control method of electromechanical devices according to claim 4, characterized in that, The method for determining the special package is as follows: The package volume, package weight, and package surface curvature are obtained and compared with their respective maximum allowable values ​​to obtain the package volume index, package weight index, and surface curvature index. The special package index is obtained by weighted averaging the package volume index, package weight index, and surface curvature index of the current package. The special package index is compared with the preset special package index threshold. If the special package index is not within the special package index threshold, the current package is determined to be a special package.

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