Electromechanical equipment cooperative control method and system based on Internet of Things

Abstract

The invention discloses an electromechanical equipment cooperative control method and system based on the Internet of Things, and belongs to the technical field of industrial Internet of Things, and the method comprises the steps: obtaining an electrical load pressure coefficient based on a motor real-time current, a motor instantaneous power peak value and a motor temperature rise load of electromechanical equipment; obtaining a sorting state load pressure coefficient based on the sorting action frequency density, the continuous working time and the processing interval time of the electromechanical equipment; obtaining a task load pressure coefficient based on the package task queue depth, the special package proportion and the sorting port distance offset; obtaining an electrical-task collaboration coefficient based on the electrical load pressure coefficient and the task load pressure coefficient under the sorting state load pressure coefficient; based on the current parcel conveying speed and the current parcel conveying spacing under the electrical-task collaboration coefficient, constructing a spacing optimization model to output a target parcel conveying spacing; through multi-source data fusion, equipment operation parameters are dynamically optimized, and the problems of load unbalance and low efficiency are solved.

Description

Technical Field

[0001] This invention belongs to the field of industrial Internet of Things (IoT) technology, and in particular relates to a method and system for collaborative control of electromechanical equipment based on IoT. Background Technology

[0002] In current industrial sorting systems, electromechanical equipment often faces multiple challenges, including fluctuating electrical loads, mechanical fatigue, and sudden changes in task complexity. Traditional control methods typically monitor single parameters such as motor current, temperature, or task queues independently, lacking a collaborative analysis of electrical performance, mechanical status, and task requirements. This leads to equipment being prone to overload shutdowns during load surges, or blindly accelerating during task backlogs, causing malfunctions. Furthermore, fixed package spacing designs struggle to adapt to dynamic conditions, reducing sorting efficiency and equipment lifespan. Especially when handling special packages (such as oversized, overweight, or irregularly shaped items), system response lags, further exacerbating equipment stress. Therefore, a multi-dimensional collaborative control mechanism based on the Internet of Things (IoT) is urgently needed to achieve load balancing and dynamic optimization. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and system for collaborative control of electromechanical equipment based on the Internet of Things (IoT), thus solving the aforementioned problems.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for collaborative control of electromechanical equipment based on the Internet of Things, comprising:

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

[0006] Based on the sorting action frequency density, continuous working time and processing interval of electromechanical equipment, a sorting state load pressure model is constructed, and the sorting state load pressure coefficient is output.

[0007] A task load pressure model is constructed based on the depth of the package task queue, the proportion of special packages, and the distance offset from the sorting point, and the task load pressure coefficient is output.

[0008] Based on the electrical load pressure coefficient and task load pressure coefficient under the sorting state load pressure coefficient, an electrical-task synergy model is constructed and the electrical-task synergy coefficient is output.

[0009] Based on the current package delivery speed and current package delivery distance under the electrical-task synergy coefficient, a distance optimization model is constructed to output the target package delivery distance.

[0010] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0011] Further technical solution: The steps for constructing a spacing optimization model based on the current package delivery speed and current package delivery spacing under the electrical-task coordination coefficient and outputting the target package delivery spacing are as follows:

[0012] Import the current parcel delivery speed into the formula Obtain the current package delivery speed index, where, This indicates the current package delivery speed. Indicates the maximum permissible parcel delivery speed;

[0013] The current delivery speed index and the current package delivery spacing are imported into the constructed spacing optimization model to obtain the target package delivery spacing. The spacing optimization model is expressed as follows:

[0014]

[0015] in, Indicates the distance between the target packages being transported. Indicates the maximum permissible spacing between packages. Indicates the minimum allowable package spacing. Indicates the gain of the spacing adjustment. This represents the current electrical-task synergy coefficient. This indicates the current parcel delivery speed index. Indicates the speed sensitivity coefficient. This represents the ideal parcel delivery speed index.

[0016] Further technical solution: The steps for constructing an electrical-task synergy model and outputting the electrical-task synergy coefficient based on the electrical load pressure coefficient under the sorting state load pressure coefficient are as follows:

[0017] An electrical-task analysis model is constructed based on the sorting status load pressure coefficient, electrical load pressure coefficient, and task load pressure coefficient. This electrical-task collaborative analysis model is expressed as follows:

[0018]

[0019] in, Indicates the electrical-task analysis coefficients. Indicates the electrical load pressure coefficient. This indicates the load pressure coefficient during sorting. Indicates the task load stress coefficient;

[0020] The electrical-task synergy model is obtained from the electrical-task synergy model constructed based on electrical-task analysis coefficients. The electrical-task synergy model is expressed as follows:

[0021]

[0022] in, Indicates the electrical-task synergy coefficient. Indicates the adjustment of sensitivity. Indicates the electrical-task analysis coefficients. This represents the preset ideal value of the electrical-task analysis coefficients. Furthermore, the larger the value, the better the synergy;

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

[0024] Import the current electrical-task analysis coefficients into the electrical-task synergy model to obtain the current electrical-task synergy coefficients.

[0025] Further technical solution: The steps for constructing an electrical load pressure model and outputting the electrical load pressure coefficient based on the real-time motor current, instantaneous peak motor power, and motor temperature rise load (the difference between the current motor temperature and the ambient temperature) of the electromechanical equipment are as follows:

[0026] The current index, peak power index, and temperature rise load index of the motor are obtained by performing maximum-minimum normalization on the real-time current of the motor, the instantaneous peak power of the motor, and the temperature rise load.

[0027] An electrical load stress model is constructed based on the current index, peak power index, and temperature rise load index. This electrical load stress model is expressed as follows:

[0028]

[0029] in, Indicates the electrical load pressure coefficient. Indicates the current index, Indicates the peak power index. Indicates the temperature rise load index. Represents the weight coefficient and The Furthermore, the larger the value, the greater the electrical load pressure;

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

[0031] Further technical solution: The steps for constructing a sorting state load model and outputting the sorting state load coefficient based on the sorting action frequency density, continuous working time, and processing interval of electromechanical equipment are as follows:

[0032] 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.

[0033] Import continuous working time into the formula The continuous working time index is obtained from the data. Indicates continuous working time. Indicates the maximum permissible 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. The sorting state load pressure model is expressed as follows:

[0035]

[0036] 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;

[0037] 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.

[0038] Further technical solution: The steps for constructing a task load pressure model and outputting the task load pressure 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:

[0039] Import the offset of the distance between the package and the sorting point into the formula. Obtain the sorting port distance offset index, where, This indicates the distance offset between the package and the sorting point. This indicates the maximum permissible offset between the package and the sorting area;

[0040] The special package percentage index is obtained by comparing the percentage of special packages with the maximum allowed percentage of special packages.

[0041] The depth of the package task queue is obtained by performing a maximum-min normalization process on the depth of the task queue.

[0042] 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:

[0043]

[0044] in, Indicates the task load stress coefficient. This indicates the sorting port distance offset index. This indicates the percentage of special parcels. This represents the task queue depth index. Represents the weight coefficient and The Furthermore, the larger the value, the more complex the sorting task;

[0045] 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.

[0046] Further technical solution: The method for determining the special package is as follows:

[0047] 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.

[0048] The special package index is obtained by weighted averaging the package volume index, package weight index, and surface curvature index of the current package.

[0049] 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.

[0050] An IoT-based electromechanical equipment collaborative control system, constructed using the aforementioned IoT-based electromechanical equipment collaborative control method, includes:

[0051] The electrical load stress analysis module constructs an electrical load stress model based on the real-time motor current, instantaneous peak motor power, and motor temperature rise load (the difference between the current motor temperature and the ambient temperature) of the electromechanical equipment, and outputs the electrical load stress coefficient.

[0052] The sorting status load pressure analysis module constructs a sorting status load pressure model based on the sorting action frequency density, continuous working time and processing interval of electromechanical equipment, and outputs the sorting status load pressure coefficient.

[0053] The task load pressure analysis module constructs a task load pressure model based on the package task queue depth, the proportion of special packages, and the sorting point distance offset, and outputs the task load pressure coefficient.

[0054] The coordination analysis module constructs an electrical-task coordination model based on the electrical load pressure coefficient and task load pressure coefficient under the sorting state load pressure coefficient, and outputs the electrical-task coordination coefficient.

[0055] The spacing optimization module constructs a spacing optimization model based on the current package delivery speed and current package delivery spacing under the electrical-task synergy coefficient, and outputs the target package delivery spacing.

[0056] This invention provides a method and system for collaborative control of electromechanical equipment based on the Internet of Things, which has the following advantages compared with the prior art:

[0057] 1. This invention can quantify equipment pressure in real time through three models: electrical load (current, power, temperature rise), sorting status (action frequency, continuous working time, processing interval), and task load (queue depth, special package ratio, sorting port offset), breaking through the limitations of single parameters.

[0058] 2. This invention can accurately assess the system's coordination status by constructing an electrical-task coordination coefficient and integrating electrical, mechanical, and task load data, providing a core basis for decision-making. At the same time, based on the electrical-task coordination coefficient and the conveying speed, it can dynamically adjust the package spacing through a spacing optimization model. When the coordination is low, the spacing is increased to reduce the load, and when the conveying speed is high, the spacing is expanded to ensure safety. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0061] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0062] Please see Figure 1 The present invention provides an embodiment of an IoT-based electromechanical equipment collaborative control method, comprising the following steps:

[0063] An electrical load pressure model is constructed based on the real-time motor current, instantaneous peak motor power, and motor temperature rise load (the difference between the current motor temperature and the ambient temperature) of the electromechanical equipment, and the electrical load pressure coefficient is output.

[0064] Based on the sorting action frequency density, continuous working time and processing interval of electromechanical equipment, a sorting state load pressure model is constructed, and the sorting state load pressure coefficient is output.

[0065] A task load pressure model is constructed based on the depth of the package task queue, the proportion of special packages, and the distance offset of the sorting point, and the task load pressure coefficient is output.

[0066] Based on the electrical load pressure coefficient and task load pressure coefficient under the sorting state load pressure coefficient, an electrical-task synergy model is constructed, and the electrical-task synergy coefficient is output.

[0067] Based on the current package delivery speed and current package delivery distance under the electrical-task synergy coefficient, a distance optimization model is constructed to output the target package delivery distance.

[0068] The electrical load pressure model refers to a composite evaluation system that integrates current, power, and temperature rise parameters, specifically implemented using a weighted normalization method, to quantify motor overload risk. The sorting status load pressure model uses the correlation analysis of mechanical action frequency and duration, specifically employing an exponential function to fuse interval time parameters, to assess the fatigue state of mechanical components. The task load pressure model uses a comprehensive analysis of task queue characteristics and package attributes, specifically employing a composite calculation of sorting port distance offset and the proportion of special packages, to reflect the complexity of sorting tasks. The electrical-task synergy model uses an analysis mechanism that nonlinearly couples three pressure coefficients to generate overall system operating status indicators. The spacing optimization model is a control algorithm based on synergy coefficients to dynamically adjust package spacing, used to balance equipment load and sorting efficiency.

[0069] Specifically, this method first collects real-time data on motor current, power, and temperature using IoT sensors. After normalization, an electrical load pressure coefficient is generated, for example, converting temperature rise differences into exponential values ​​in the 0-1 range. Simultaneously, the operation frequency and runtime of the sorting robotic arm are monitored, and combined with the maximum allowable values ​​from equipment maintenance records, the sorting status load pressure coefficient is calculated. At the task analysis level, the number of special packages is counted using a visual recognition system, and the distance offset is calculated based on the sorting port layout data to generate a task load pressure coefficient. These three coefficients are input into a coordination model for nonlinear coupling, for example, using a vector space projection method to calculate the coordination coefficient. Finally, based on the dynamic relationship between the current conveying speed and the coordination coefficient, the target spacing is calculated using an optimization formula incorporating a sigmoid function. For example, when coordination decreases, the package spacing is automatically increased to alleviate equipment pressure.

[0070] Compared to existing technologies, traditional methods only monitor single parameters such as motor current or temperature, failing to identify the correlation between electrical load and task queues. This solution, however, constructs a multi-dimensional stress model to capture the causal relationship between sudden current surges and special package handling. Existing technologies employ fixed-space control strategies, while this solution uses a dynamic adjustment mechanism based on a synergy coefficient to automatically expand package spacing when equipment load increases, avoiding overload shutdowns while maintaining sorting efficiency. Particularly when handling irregularly shaped packages, traditional systems suffer from response delays due to a lack of task complexity assessment; this solution calculates the special package proportion index in real time, triggering advance adjustments to the conveyor speed.

[0071] Through the above technical solution, this application effectively solves the technical challenge of coordinated control of electrical parameters, mechanical state, and task requirements. In parcel sorting scenarios, when the system processes a large number of irregularly shaped items simultaneously, it can promptly identify increases in complexity through a task load pressure model, automatically reduce the conveyor speed index through a coordination model, and correspondingly increase the parcel spacing through a spacing optimization model. This closed-loop control mechanism avoids triggering motor overload protection and maintains sorting throughput through dynamic spacing adjustment, achieving a balance between equipment safety and operational efficiency.

[0072] Preferably, the steps for constructing an electrical load pressure model and outputting the electrical load pressure coefficient based on the real-time motor current, instantaneous peak motor power, and motor temperature rise load (the difference between the current motor temperature and the ambient temperature) are as follows:

[0073] The current index, peak power index, and temperature rise load index of the motor are obtained by performing maximum-minimum normalization on the real-time current of the motor, the instantaneous peak power of the motor, and the temperature rise load.

[0074] An electrical load stress model is constructed based on the current index, peak power index, and temperature rise load index. This electrical load stress model is expressed as follows:

[0075]

[0076] in, Indicates the electrical load pressure coefficient. Indicates the current index, Indicates the peak power index. Indicates the temperature rise load index. Represents the weight coefficient and The Furthermore, the larger the value, the greater the electrical load pressure;

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

[0078] Among them, the real-time motor current refers to the instantaneous current value passing through the windings during motor operation. Specifically, it can be acquired in real time using a current sensor to reflect the real-time load status of the motor.

[0079] Among them, the instantaneous peak power of the motor refers to the maximum output power of the motor per unit time. Specifically, it can be achieved by continuously monitoring and recording the peak data through a power meter, and is used to characterize the instantaneous overload risk of the motor.

[0080] Among them, the motor temperature rise load refers to the difference between the current temperature of the motor and the ambient temperature. Specifically, it can be achieved by measuring the surface temperature of the motor and the ambient temperature separately using temperature sensors and then calculating the difference, which is used to assess the thermal accumulation state of the motor.

[0081] Max-min normalization refers to linearly transforming the original data to the [0,1] interval to eliminate comparability barriers between parameters with different dimensions. The weighting coefficients... This refers to the contribution parameters of each normalized index in the model. Specifically, it can be achieved through dynamic adjustment strategies. For example, in high-temperature environments, the weight of the temperature rise load index can be increased to dynamically optimize the accuracy of electrical load assessment based on operating conditions.

[0082] Specifically, real-time motor current, instantaneous peak power, and temperature rise load data are collected using current sensors, power meters, and temperature sensors, respectively. Each parameter undergoes maximum-minimum normalization to convert it into a unified dimension for the current index, peak power index, and temperature rise load index. For example, when the motor's 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 preset weighting coefficients to generate the electrical load pressure coefficient. During this process, the weighting coefficients can be dynamically adjusted according to the equipment's operating environment; for example, when the ambient temperature is high, the weight of the temperature rise load index is adjusted. The value is set to 0.5 to enhance the impact of thermal stability on load pressure. The final output electrical load pressure coefficient is continuously quantized within the range of [0,1], providing accurate electrical state input for subsequent coordinated control.

[0083] Compared to existing technologies, traditional methods monitor only a single electrical parameter and use fixed thresholds to determine load status, such as relying solely on current over-limit triggering protection mechanisms. This solution, however, integrates data from three dimensions—current, peak power, and temperature rise load—and introduces a dynamic weighting mechanism to more comprehensively reflect the motor's overall operating status. For example, when the motor experiences a short-term overload in a low-temperature environment, the system can reduce the weight of the temperature rise load to avoid misjudgment, while in a high-temperature environment, it can increase the weight of the temperature rise load to provide early warning of thermal overload risks.

[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 permissible 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 sorting actions performed by the robotic arm per unit time. This can be achieved by dividing the number of sorting trigger signals collected by sensors by the time window length, quantifying the movement frequency of the mechanical components. The processing interval time refers to the idle time between two adjacent sorting operations. This can be achieved by recording the time difference between the sorting completion signal and the next sorting start signal using a timer, reflecting the intermittent recovery capability of the mechanical components. Maximum-minimum normalization refers to linearly mapping the original data to the [0,1] interval. This can be achieved using the formula (current value - minimum value) / (maximum value - minimum value), eliminating numerical differences between parameters of different dimensions. The continuous working time index is the ratio of the equipment's continuous operating time to a preset safety threshold. This can be achieved by dividing the current operating time by the maximum allowable continuous working time and taking the minimum value between this and 1, preventing overheating or accelerated wear of the machinery. The sorting state load pressure model is a nonlinear mapping relationship based on the sigmoid function. This can be achieved by inputting a weighted parameter combination into an activation function, dynamically characterizing the response characteristics of mechanical pressure as operating parameters change.

[0093] Specifically, the sorting action frequency density and processing interval time are normalized into action frequency density index and processing interval time index, respectively, quantifying the impact of high-frequency actions and abnormal intervals on the mechanical structure. Continuous working time is converted into a continuous working time index using a formula; when the running time approaches a safety threshold, the index approaches 1, triggering a protection mechanism. These three indices are input into the sorting state load pressure model, which uses a sigmoid function to map the weighted combination parameters to the [0,1] interval. When the weighted value exceeds the equilibrium threshold, the load pressure coefficient exhibits non-linear growth. The weighting coefficients are adjusted according to equipment characteristics; for example, the weight of continuous working time can be increased for easily worn parts, and the weight of action frequency can be strengthened for high-frequency sorting scenarios. The output load pressure coefficient reflects the mechanical pressure level in real time, providing a quantitative basis for subsequent control strategies.

[0094] Compared to existing technologies, traditional methods monitor only a single parameter and use linear superposition to assess mechanical load, failing to capture the synergistic effects of high-frequency actions and intermittent anomalies, resulting in delayed early warning of mechanical fatigue. This solution, by integrating multi-dimensional operating parameters and constructing a nonlinear pressure response model, can identify mechanical overload risks caused by sudden changes in action frequency, continuous overtime operation, or abnormal handling intervals earlier, achieving dynamic pressure assessment.

[0095] Through the above technical solutions, this application can dynamically quantify the mechanical load pressure of sorting equipment, adjust the sorting rhythm in a timely manner when the sorting frequency changes abruptly, trigger shutdown protection when the continuous working time approaches the safety threshold, and optimize the sorting interval when the processing interval is abnormal, thereby reducing the wear rate of mechanical parts, avoiding equipment shutdown due to overload, and improving the operational stability of the sorting system.

[0096] Preferably, the steps for constructing a task load pressure model and outputting a task load pressure 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:

[0097] Import the offset of the distance between the package and the sorting point into the formula. Obtain the sorting port distance offset index, where, This indicates the distance offset between the package and the sorting point. This indicates the maximum permissible offset between the package and the sorting area;

[0098] The special package percentage index is obtained by comparing the percentage of special packages with the maximum allowed percentage of special packages.

[0099] The depth of the package task queue is obtained by performing a maximum-min normalization process on the depth of the task queue.

[0100] 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:

[0101]

[0102] in, Indicates the task load stress coefficient. This indicates the sorting port distance offset index. This indicates the percentage of special parcels. This represents the task queue depth index. Represents the weight coefficient and The Furthermore, the larger the value, the more complex the sorting task;

[0103] 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.

[0104] The package task queue depth refers to the backlog of packages waiting to be sorted in the queue. This can be achieved by real-time statistics of the ratio of the number of packages currently waiting to be processed in the queue to the system's maximum capacity. This parameter quantifies the pressure that task backlog puts on the system's throughput. The special package percentage refers to the proportion of packages with abnormal attributes in the total tasks. This can be achieved by detecting whether the package volume, weight, and surface curvature exceed preset thresholds and calculating their proportion. This parameter is used to identify the interference of concentrated special packages on the sorting rhythm. The sorting port distance offset refers to the spatial deviation between the actual position of the package and the target sorting port. This can be achieved by using a laser rangefinder to measure the straight-line distance between the center point of the package and the reference point of the sorting port in real time. This parameter reflects the additional mechanical load caused by the adjustment of the robotic arm's motion trajectory.

[0105] Specifically, the deviation between the actual sorting path and the standard position of a package is converted into a standardized parameter through the sorting port distance offset index calculation, thus mitigating the additional load caused by the frequent adjustments of the robotic arm's trajectory due to positional offset. The special package proportion index is calculated, converting abnormal attributes such as package volume and weight into standardized parameters, effectively identifying the interference of concentrated special packages on the sorting rhythm. The task queue depth index is calculated to reflect the pressure on system throughput from the backlog of pending packages in real time. Finally, a linear weighted model is used to fuse the three indices, enabling the task load pressure coefficient to simultaneously characterize spatial offset, abnormal package attributes, and task backlog. For example, in the case of sorting irregularly shaped items, the weighting coefficient can be set to emphasize the special package proportion index; in the case of high-speed sorting, the weighting coefficient can enhance the sensitivity of the distance offset index, thereby dynamically adapting to different working conditions.

[0106] Compared to existing technologies, traditional methods typically monitor only a single task parameter and use fixed thresholds to determine task complexity, failing to effectively address the multi-dimensional dynamic changes in sorting tasks. Existing technologies lack quantitative analysis of package attributes when handling special packages and do not correlate spatial offset with task backlog, leading to system response delays. This solution constructs a multi-dimensional task parameter fusion model to collaboratively analyze spatial offset, attribute anomalies, and task backlog, achieving dynamic quantitative assessment of task complexity.

[0107] Through the above technical solution, this application can perceive the mechanical load fluctuations caused by package position deviation in real time, accurately identify the sorting rhythm disorder caused by the aggregation of special packages, and dynamically assess the impact of task backlog on system throughput. Therefore, the sorting equipment can automatically adjust its sorting strategy according to the task load pressure coefficient. For example, it can optimize the robotic arm's movement trajectory in advance when an increase in the distance deviation index is detected, and trigger a speed reduction protection mechanism when the proportion of special packages suddenly increases, thereby avoiding equipment overload shutdown and maintaining sorting efficiency.

[0108] Preferably, the method for determining the special package is as follows:

[0109] 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.

[0110] The special package index is obtained by weighted averaging the package volume index, package weight index, and surface curvature index of the current package.

[0111] 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.

[0112] The package volume index is the ratio of the actual volume of the package to the preset maximum allowable volume. This can be achieved by measuring the package's three-dimensional dimensions with a volume sensor, calculating the volume, and then dividing it by a preset threshold. This eliminates the influence of different volume units on the judgment result. The package weight index is the ratio of the actual weight of the package to the preset maximum allowable weight. This can be achieved by collecting weight data in real time with a weighing sensor and then dividing it by a preset threshold. This quantifies the degree of overweight. The surface curvature index is the ratio of the package's surface curvature to the preset maximum allowable curvature. This can be achieved by acquiring surface curvature data with a 3D laser scanner and then dividing it by a preset threshold. This identifies the geometric features of irregularly shaped packages. Weighted average processing involves linearly combining multiple indices according to preset weights. This can be achieved by allocating normalized weight coefficients and then performing a weighted summation operation. This allows for flexible adjustment of the contribution of different parameters to the judgment result. The special package index threshold is a preset judgment range. This can be achieved by using a dynamic adjustment algorithm to generate upper and lower limits based on the real-time load status of the equipment. This avoids boundary misjudgments caused by fixed thresholds.

[0113] Specifically, package volume, weight, and surface curvature data are collected in real time via IoT sensors and then compared with preset maximum allowable values ​​to generate standardized indices. For example, when calculating the volume index, if the maximum allowable volume is set to 1 cubic meter, a measured package volume of 0.8 cubic meters corresponds to a volume index of 0.8. In the weight index generation process, if the maximum allowable weight is 50 kg and the actual package weight is 60 kg, the weight index is 1.2. When calculating the surface curvature index, if the maximum allowable curvature is set to 0.5, a measured curvature of 0.6 corresponds to an index of 1.2.

[0114] Subsequently, the three indices are weighted and averaged according to preset weights. For example, with a volume weight of 0.4, a weight weight of 0.4, and a curvature weight of 0.2, the indices for a certain package are 0.8, 1.2, and 1.2 respectively. Therefore, the special package index is 0.8 × 0.4 + 1.2 × 0.4 + 1.2 × 0.2 = 1.04. This index is compared with a dynamically adjusted threshold range. For example, when the threshold is set to [0.9, 1.1], packages exceeding this range are identified as special packages. By dynamically adjusting the threshold range, the system can adapt to different equipment load conditions at different sorting stages. For instance, it can automatically narrow the threshold range to improve screening accuracy when the equipment is under high load.

[0115] Compared to existing technologies, traditional methods typically rely on a single weight threshold or a fixed size threshold to identify special packages. For example, an alarm might be triggered only when the weight exceeds 50 kg, leading to the easy omission of irregularly shaped packages or packages with abnormal composite parameters. Static threshold settings in existing technologies cannot adapt to real-time equipment conditions; for instance, using conservative thresholds under low equipment load results in over-screening. Furthermore, existing technologies lack multi-dimensional parameter fusion mechanisms; for example, detecting only volume or weight may fail to identify packages with severely deformed surfaces.

[0116] Through the above technical solutions, this application solves the problems of high misjudgment rate and slow response caused by single-parameter judgment or static threshold setting in the identification of special packages in traditional sorting systems. By using multi-dimensional parameter fusion processing, packages with normal volume but surface distortion can be accurately identified, avoiding missed judgments caused by relying solely on weight parameters. The dynamic threshold mechanism can automatically adjust the judgment range according to the real-time operating status of the equipment, narrowing the threshold range under high load to prioritize high-risk packages and widening the range under low load to balance sorting efficiency. Weighted average processing allows operators to adjust parameter weights according to different scenarios, such as increasing the surface curvature weight in scenarios where sorting mainly irregularly shaped items, thereby enhancing system adaptability.

[0117] Preferably, the steps for constructing an electrical-task synergy model and outputting electrical-task synergy coefficients based on the electrical load pressure coefficient under the sorting state load pressure coefficient and the task load pressure coefficient are as follows:

[0118] An electrical-task analysis model is constructed based on the sorting status load pressure coefficient, electrical load pressure coefficient, and task load pressure coefficient. This electrical-task collaborative analysis model is expressed as follows:

[0119]

[0120] in, Indicates the electrical-task analysis coefficients. Indicates the electrical load pressure coefficient. This indicates the load pressure coefficient during sorting. Indicates the task load stress coefficient;

[0121] The electrical-task synergy model is obtained from the electrical-task synergy model constructed based on electrical-task analysis coefficients. The electrical-task synergy model is expressed as follows:

[0122]

[0123] in, Indicates the electrical-task synergy coefficient. Indicates the adjustment of sensitivity. Indicates the electrical-task analysis coefficients. This represents the preset ideal value of the electrical-task analysis coefficients. Furthermore, the larger the value, the better the synergy;

[0124] Import the current sorting status load pressure coefficient, the current electrical load pressure coefficient, and the current task load pressure coefficient into the electrical-task analysis model to obtain the current electrical-task analysis coefficient;

[0125] Import the current electrical-task analysis coefficients into the electrical-task synergy model to obtain the current electrical-task synergy coefficients.

[0126] The sorting state load pressure coefficient is a mechanical state pressure index calculated based on the sorting action frequency density, continuous working time, and processing interval time. Specifically, it can be calculated by weighting normalized action frequency density, continuous working time, and processing interval time indices using a logistic function, and is used to quantify the dynamic load of mechanical sorting components. The electrical load pressure coefficient is an electrical system pressure index calculated based on real-time motor current, instantaneous peak power, and temperature rise load. Specifically, it can be calculated by weighted summation of normalized current, peak power, and temperature rise load indices, and is used to reflect the real-time load status of the motor and electrical system. The task load pressure coefficient is a task complexity index calculated based on package task queue depth, special package ratio, and sorting port distance offset. Specifically, it can be calculated by weighted summation of normalized queue depth, special package ratio, and distance offset indices, and is used to characterize the dynamic complexity of sorting tasks. The electrical-task analysis coefficient is a dynamic coupling index obtained through the interaction calculation of three types of parameters: sorting status, electrical load, and task pressure. Specifically, it can be calculated by superimposing the product of electrical load and sorting status, plus task pressure, into the numerator, and then normalizing the denominator using double square root normalization to balance the dimensional differences of the different parameters. The electrical-task synergy coefficient maps the analysis coefficient to a synergy quantification value in the 0-1 range. Specifically, it can be achieved using a logistic function combined with sensitivity adjustment and a preset ideal value, used to dynamically evaluate the overall synergy status of the system.

[0127] Specifically, the electrical load pressure coefficient, sorting state load pressure coefficient, and task load pressure coefficient are extracted as key parameters from three dimensions: electrical performance, mechanical state, and task requirements. These parameters are then normalized to eliminate dimensional differences before being input into the electrical-task analysis model. In the electrical-task analysis model, the numerator term... Used to capture the interaction between electrical load and sorting status, superimposed A task complexity parameter is introduced, and the denominator is dynamically balanced across three types of parameters using double square root operations to prevent a single parameter from dominating the calculation result. (Analysis of coefficients follows.) Furthermore, it is mapped to the cooperability coefficient through the logistic function. Among them, adjusting sensitivity Control the steepness of the curve, preset the ideal value By adjusting the offset, the results of multi-dimensional parameter coupling are transformed into a quantifiable synergy indicator. This indicator reflects in real time the degree of matching between electrical performance, mechanical condition, and task requirements, providing a basis for dynamic optimization of subsequent package delivery spacing.

[0128] Compared with existing technologies, traditional methods independently monitor electrical parameters or task queues without establishing a collaborative analysis mechanism for multi-dimensional parameters, resulting in delayed response of equipment during sudden load changes. This solution constructs an electrical-task analysis model and a collaborative model to dynamically couple and calculate electrical load, mechanical state, and task complexity. By utilizing the normalization processing and nonlinear mapping of the mathematical model, it achieves real-time balance and collaborative evaluation of the three types of parameters, solving the problems of overload risk and poor adaptability to operating conditions caused by single-parameter analysis.

[0129] Through the above technical solution, this application can dynamically balance the correlation between electrical load pressure, sorting machine status and task complexity, prevent downtime caused by electrical overload, avoid mechanical damage caused by blindly speeding up when tasks are backlogged, and accurately judge the system status through the coordination coefficient, providing a basis for real-time optimization of package conveying distance, thereby improving sorting efficiency and equipment operation stability.

[0130] Preferably, the step of constructing a spacing optimization model based on the current package delivery speed and current package delivery spacing under the electrical-task synergy coefficient and outputting the target package delivery spacing is as follows:

[0131] Import the current parcel delivery speed into the formula Obtain the current package delivery speed index, where, This indicates the current package delivery speed. Indicates the maximum permissible package delivery speed;

[0132] The target package delivery distance is obtained by importing the current delivery speed index and the current package delivery spacing into the constructed spacing optimization model, which is expressed as follows:

[0133]

[0134] in, Indicates the distance between the target packages being transported. Indicates the maximum permissible spacing between packages. Indicates the minimum allowable package spacing. Indicates the gain of the spacing adjustment. This represents the current electrical-task synergy coefficient. This indicates the current parcel delivery speed index. Indicates the speed sensitivity coefficient. This represents the ideal parcel delivery speed index.

[0135] The current package conveying speed index refers to the minimum value between the ratio of the current speed to the maximum permissible speed and 1. This is calculated by real-time collection of conveyor belt rotation speed data from a speed sensor, used to eliminate differences in speed dimensions between different devices. The maximum permissible package spacing refers to the maximum safe distance allowed by the sorting line's mechanical structure, used to constrain the spacing adjustment range. The minimum permissible package spacing refers to the minimum interval to ensure packages do not collide, used to maintain sorting safety. The spacing adjustment gain is a control parameter for the model's output amplitude, used to control the sensitivity of spacing adjustment. The electrical-task synergy coefficient is an indicator reflecting the degree of matching between the equipment's electrical load and task complexity, used to guide the direction of spacing adjustment. The speed sensitivity coefficient is a factor adjusting the impact of speed changes on spacing, used to balance the spacing changes caused by speed fluctuations. The ideal package conveying speed index is the speed ratio under optimal system operating conditions, used to guide the system towards optimal convergence.

[0136] Specifically, by monitoring the conveying speed in real time and calculating the speed index, the speed value is normalized to adapt to different equipment operating conditions. The normalized speed index is then used in conjunction with the current spacing input to optimize the spacing model. The model structure combines exponential functions with linear combinations. When the speed approaches the ideal value, the denominator of the exponential function approaches a stable value, making the spacing adjustment range more gradual; when the speed deviates from the ideal range, the denominator changes more rapidly, triggering more significant spacing adjustments. The model output value dynamically changes between the maximum and minimum allowable spacing, and the overall adjustment range is controlled by adjusting the gain. The electrical-task synergy coefficient acts as a reverse adjustment factor; when system synergy decreases, the coefficient value decreases, leading to an increase in spacing, thereby reducing the equipment load pressure. The speed sensitivity coefficient adjusts the intensity of the impact of speed changes on spacing, avoiding frequent adjustments caused by small speed fluctuations. Through the synergistic effect of multiple parameters, a balance between conveying efficiency and equipment load is achieved while ensuring safe spacing.

[0137] Compared to existing technologies, traditional methods using fixed package spacing cannot adaptively adjust to sudden load changes, easily leading to overload shutdowns or task backlogs. This solution constructs a dynamic spacing optimization model, integrating equipment operating status and task requirements into the calculation process, enabling automatic adjustment of package spacing based on real-time operating conditions. Compared to adjustment strategies relying solely on speed or load as a single parameter, this solution uses multi-dimensional parameter coupling calculations to automatically increase spacing to reduce mechanical stress during load surges and optimize the speed-spacing combination to improve processing efficiency as task queue depth increases.

[0138] Through the above technical solution, this application solves the problems of equipment overload and low efficiency caused by fixed spacing design. Under conditions of sudden increase in electrical load, increasing the package spacing reduces the motor workload and avoids overheating and shutdown. When there is a backlog of tasks, optimizing the combination of conveyor speed and spacing increases the throughput per unit time, preventing mechanical wear caused by blindly increasing speed. When handling special packages, dynamically adjusting the spacing between adjacent packages provides buffer space for abnormal sorting operations. This solution achieves a dynamic balance between sorting efficiency and equipment safety, improving system robustness under complex conditions.

[0139] An IoT-based electromechanical equipment collaborative control system, constructed using the aforementioned IoT-based electromechanical equipment collaborative control method, includes:

[0140] The electrical load stress analysis module constructs an electrical load stress model based on the real-time motor current, instantaneous peak motor power, and motor temperature rise load (the difference between the current motor temperature and the ambient temperature) of the electromechanical equipment, and outputs the electrical load stress coefficient.

[0141] The sorting status load pressure analysis module constructs a sorting status load pressure model based on the sorting action frequency density, continuous working time and processing interval of electromechanical equipment, and outputs the sorting status load pressure coefficient.

[0142] The task load pressure analysis module constructs a task load pressure model based on the package task queue depth, the proportion of special packages, and the sorting point distance offset, and outputs the task load pressure coefficient.

[0143] The coordination analysis module constructs an electrical-task coordination model based on the electrical load pressure coefficient and task load pressure coefficient under the sorting state load pressure coefficient, and outputs the electrical-task coordination coefficient.

[0144] The spacing optimization module constructs a spacing optimization model based on the current package delivery speed and current package delivery spacing under the electrical-task synergy coefficient, and outputs the target package delivery spacing.

[0145] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0146] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for collaborative control of electromechanical equipment based on the Internet of Things, characterized in that, include: An electrical load pressure model is constructed based on the real-time motor current, instantaneous peak power, and temperature rise load of the electromechanical equipment, and the electrical load pressure coefficient is output. Based on the sorting action frequency density, continuous working time and processing interval of electromechanical equipment, a sorting state load pressure model is constructed, and the sorting state load pressure coefficient is output. A task load pressure model is constructed based on the depth of the package task queue, the proportion of special packages, and the distance offset from the sorting point, and the task load pressure coefficient is output. Based on the electrical load pressure coefficient and task load pressure coefficient under the sorting state load pressure coefficient, an electrical-task synergy model is constructed and the electrical-task synergy coefficient is output. Based on the current package delivery speed and current package delivery distance under the electrical-task synergy coefficient, a distance optimization model is constructed to output the target package delivery distance.

2. The method for collaborative control of electromechanical equipment based on the Internet of Things according to claim 1, characterized in that, The steps for constructing a spacing optimization model based on the current package delivery speed and current package delivery spacing under the electrical-task coordination coefficient, and then outputting the target package delivery spacing, are as follows: Import the current parcel delivery speed into the formula Obtain the current package delivery speed index, where, This indicates the current package delivery speed. Indicates the maximum permissible package delivery speed; The current delivery speed index and the current package delivery spacing are imported into the constructed spacing optimization model to obtain the target package delivery spacing. The spacing optimization model is expressed as follows: ; in, Indicates the distance between the target packages being transported. Indicates the maximum allowable spacing between packages. Indicates the minimum allowable package spacing. Indicates the gain of the spacing adjustment. This represents the current electrical-task synergy coefficient. This indicates the current parcel delivery speed index. Indicates the speed sensitivity coefficient. This represents the ideal parcel delivery speed index.

3. The method for collaborative control of electromechanical equipment based on the Internet of Things according to claim 2, characterized in that, The steps for constructing an electrical-task synergy model and outputting the electrical-task synergy coefficient based on the electrical load pressure coefficient under the sorting state load pressure coefficient and the task load pressure coefficient are as follows: An electrical-task analysis model is constructed based on the sorting status load pressure coefficient, electrical load pressure coefficient, and task load pressure coefficient. This electrical-task collaborative analysis model is expressed as follows: ; in, Indicates the electrical-task analysis coefficients. Indicates the electrical load pressure coefficient. This indicates the load pressure coefficient during sorting. Indicates the task load stress coefficient; The electrical-task synergy model is obtained from the electrical-task synergy model constructed based on electrical-task analysis coefficients. The electrical-task synergy model is expressed as follows: ; in, Indicates the electrical-task synergy coefficient. Indicates the adjustment of sensitivity. Indicates the electrical-task analysis coefficients. This represents the preset ideal value of the electrical-task analysis coefficients. Furthermore, the larger the value, the better the synergy; Import the current sorting status load pressure coefficient, the current electrical load pressure coefficient, and the current task load pressure coefficient into the electrical-task analysis model to obtain the current electrical-task analysis coefficient; Import the current electrical-task analysis coefficients into the electrical-task synergy model to obtain the current electrical-task synergy coefficients.

4. The method for collaborative control of electromechanical equipment based on the Internet of Things according to claim 3, characterized in that, The steps for constructing an electrical load stress model and outputting the electrical load stress coefficient based on the real-time motor current, instantaneous peak power, and temperature rise load of the electromechanical equipment are as follows: The current index, peak power index, and temperature rise load index of the motor are obtained by performing maximum-minimum normalization on the real-time current of the motor, the instantaneous peak power of the motor, and the temperature rise load. An electrical load stress model is constructed based on the current index, peak power index, and temperature rise load index. This electrical load stress model is expressed as follows: ; in, Indicates the electrical load pressure coefficient. Indicates the current index, Indicates the peak power index. Indicates the temperature rise load index. Represents the weight coefficient and The Furthermore, the larger the value, the greater the electrical load pressure; Import the current current index, the current peak power index, and the current temperature rise load index into the electrical load pressure model to obtain the current electrical load pressure coefficient.

5. The method for collaborative control of electromechanical equipment based on the Internet of Things according to claim 3, characterized in that, The steps to construct a sorting state load model based on the sorting action frequency density, continuous working time, and processing interval of electromechanical equipment, and to output the sorting state load coefficient, are as follows: 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. Import continuous working time into the formula The continuous working time index is obtained from the data. Indicates continuous working time. Indicates 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: ; 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; 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.

6. The method for collaborative control of electromechanical equipment based on the Internet of Things according to claim 3, 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: Import the offset of the distance between the package and the sorting point into the formula. Obtain the sorting port distance offset index, where, This indicates the distance offset between the package and the sorting point. This indicates the maximum permissible offset between the package and the sorting area; 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: ; in, Indicates the task load stress coefficient. Indicates the sorting port distance offset index. This indicates the percentage of special parcels. This represents the task queue depth index. Represents the weight coefficient and The Furthermore, the larger 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.

7. The method for collaborative control of electromechanical equipment based on the Internet of Things according to claim 6, 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.

8. A collaborative control system for electromechanical equipment based on the Internet of Things, characterized in that, The method for collaborative control of electromechanical equipment based on the Internet of Things, as described in any one of claims 1-7, comprises: The electrical load stress analysis module constructs an electrical load stress model based on the real-time motor current, instantaneous peak motor power, and motor temperature rise load of electromechanical equipment, and outputs the electrical load stress coefficient. The sorting status load pressure analysis module constructs a sorting status load pressure model based on the sorting action frequency density, continuous working time and processing interval of electromechanical equipment, and outputs the sorting status load pressure coefficient. The task load pressure analysis module constructs a task load pressure model based on the package task queue depth, the proportion of special packages, and the sorting point distance offset, and outputs the task load pressure coefficient. The coordination analysis module constructs an electrical-task coordination model based on the electrical load pressure coefficient and the task load pressure coefficient under the sorting state load pressure coefficient, and outputs the electrical-task coordination coefficient. The spacing optimization module constructs a spacing optimization model based on the current package delivery speed and current package delivery spacing under the electrical-task synergy coefficient, and outputs the target package delivery spacing.

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