Photovoltaic internet of things oriented cooperative control method, system, device and medium

Abstract

This invention discloses a collaborative control method, system, device, and medium for photovoltaic Internet of Things (PV IoT), belonging to the field of distributed control. The method involves: acquiring status data of several PV devices in the PV IoT; dividing the PV devices into several device clusters based on device response time and remaining power; calculating the average illuminance variation rate and network bandwidth utilization rate of each device cluster based on the illuminance intensity sequence and network latency of each PV device within each cluster; inputting the average illuminance variation rate and network bandwidth utilization rate into a preset collaborative control model to perform logical operations; determining the device adjustment rate and network data reporting frequency of each device cluster based on the logical operation results; controlling each device cluster to perform adjustments according to the device adjustment rate; and allocating communication resources according to the network data reporting frequency. Therefore, this invention can improve the accuracy of global resource allocation in complex heterogeneous systems.

Description

Technical Field

[0001] This invention relates to the field of distributed control, and more particularly to collaborative control methods, systems, devices and media for photovoltaic Internet of Things. Background Technology

[0002] Photovoltaic Internet of Things (IoT) typically comprises a large number of device nodes with varying functions, computing capabilities, and communication states, while also facing fluctuating environmental inputs (such as sunlight and load) and network quality over time. Adopting only isolated resource scheduling strategies can easily lead to a "one-sided" dilemma: for example, some nodes may fail due to energy depletion or network congestion may prevent the uploading of critical data, while other nodes may remain idle. Therefore, global resource allocation is necessary to improve the system's robustness and adaptability in extreme environments, and is also a crucial means to ensure the long-term stable operation of complex IoT systems and maximize overall task performance.

[0003] Current global resource allocation technologies for complex heterogeneous systems primarily rely on centralized monitoring and scheduling architectures. This approach typically involves a central control node collecting heterogeneous data from all devices (such as power consumption, task load, and network latency) and using rule-based or optimization model-based algorithms to generate global configuration commands. However, centralized architectures suffer from inherent network latency during information collection and command issuance, causing the system state upon which control decisions are based to be "past tense," making it difficult to match the rapid changes in the real environment and resulting in control lag. Furthermore, the modeling of complex environments is often coarse; for example, it is difficult to accurately quantify the non-linear impact on overall task progress when "minor network congestion" and "decreased device computing power" are coupled, leading to a discrepancy between decision outputs and actual physical requirements. Summary of the Invention

[0004] This invention provides a collaborative control method, system, device, and medium for photovoltaic Internet of Things (IoT), which can improve the accuracy of global resource allocation in complex heterogeneous systems.

[0005] The first aspect of this invention provides a collaborative control method for photovoltaic Internet of Things (IoT), comprising: Acquire status data of several photovoltaic devices in the photovoltaic Internet of Things, wherein the status data includes device response time, remaining power, light intensity sequence and network latency; Based on the device response time and the remaining power, the photovoltaic devices are divided into several device clusters. Based on the light intensity sequence of each photovoltaic device in each device cluster and the network latency, the average light change rate and network bandwidth utilization rate of each device cluster are calculated respectively. The average light intensity change rate and the network bandwidth occupancy rate are input into a preset collaborative control model to perform logical operations. Based on the results of the logical operations, the device adjustment rate and network data reporting frequency of each device cluster are determined. Each device cluster is then controlled to perform actions according to the device adjustment rate and to allocate communication resources according to the network data reporting frequency.

[0006] This invention clusters heterogeneous devices using device response time and remaining power, grouping devices with similar response capabilities and energy reserves into the same cluster. This lays a refined management foundation for subsequent differentiated control and effectively solves the resource mismatch problem caused by individual device differences. Furthermore, it calculates the average rate of change in illumination and network bandwidth utilization for each cluster, achieving accurate characterization of dynamic changes in the physical environment and network communication load, providing reliable data support for global resource allocation. Finally, the two types of parameters reflecting cluster characteristics are input into a preset collaborative control model for logical operations. Based on the operation results, the device adjustment rate and network data reporting frequency of each cluster are determined synchronously, enabling coordinated optimization of physical-level action adjustments and information-level communication resource allocation. Thus, in complex and heterogeneous photovoltaic IoT environments, this application can dynamically adjust device execution strategies based on illumination changes to ensure power generation efficiency, and adaptively allocate communication resources according to network status to avoid network congestion, thereby significantly improving the accuracy and coordination of resource allocation at the global level.

[0007] Further, the step of inputting the average illumination change rate and the network bandwidth occupancy rate into a preset collaborative control model to perform logical operations, and determining the device adjustment rate and network data reporting frequency of each device cluster based on the logical operation results, includes: The average illumination change rate and the network bandwidth occupancy rate are input into the collaborative control model. Through the fuzzification interface of the collaborative control model, the first membership degree of the average illumination change rate relative to a plurality of preset first fuzzy linguistic variables and the second membership degree of the network bandwidth occupancy rate relative to a plurality of preset second fuzzy linguistic variables are determined. The first membership degree and the second membership degree are input into the fuzzy inference engine of the collaborative control model. The fuzzy rule base preset in the fuzzy inference engine is used for matching and inference to generate a first fuzzy output set about the adjustment rate and a second fuzzy output set about the frequency of network data reporting. The fuzzy rule base contains several fuzzy rules, and each fuzzy rule defines the logical relationship between the combination of the first membership degree and the second membership degree in different states and the output control quantity. By using the defuzzification interface of the collaborative control model, defuzzification calculations are performed on the first fuzzy output set and the second fuzzy output set respectively to obtain the device adjustment rate and the network data reporting frequency of each device cluster.

[0008] This mechanism addresses the uncertainties of illumination variation rate and bandwidth utilization through fuzzy logic, matching fuzzy inputs to a pre-defined rule base for inference, and then defuzzifying to generate precise adjustment rates and reporting frequencies. This simulation of expert-driven decision-making ensures robust and adaptable resource allocation in dynamic environments, significantly improving the accuracy of global resource allocation.

[0009] Furthermore, by performing matching and reasoning through a preset fuzzy rule base in the fuzzy inference engine, a first fuzzy output set regarding the adjustment rate and a second fuzzy output set regarding the frequency of network data reporting are generated, including: Traverse each fuzzy rule in the fuzzy rule base, extract the corresponding membership degree based on the fuzzy linguistic variables involved in the antecedent of each fuzzy rule, and perform fuzzy intersection operation on the extracted membership degree to obtain the activation intensity corresponding to each fuzzy rule; Based on the activation intensity, the output fuzzy subset defined in the consequent of each fuzzy rule is truncated or scaled to obtain the inference output fuzzy set corresponding to each fuzzy rule; All the inference output fuzzy sets are fuzzy-joined according to the type of the output variables, and then aggregated to generate the first fuzzy output set and the second fuzzy output set.

[0010] Furthermore, the step of dividing the photovoltaic equipment into several equipment clusters based on the equipment response time and the remaining power includes: The device response time and the remaining battery power are mapped to a unified numerical range to obtain normalized response time and normalized remaining battery power. The normalized response time is compared with a preset response time threshold to obtain a first comparison result; The normalized remaining battery power is compared with a preset battery power threshold to obtain a second comparison result; Based on the first comparison result and the second comparison result, the photovoltaic equipment is divided into corresponding clusters, resulting in several equipment clusters.

[0011] By clustering heterogeneous devices using their response time and remaining power, devices with similar response capabilities and energy reserves can be grouped into the same cluster. This lays a refined management foundation for subsequent differentiated control and effectively solves the resource mismatch problem caused by individual device differences.

[0012] Furthermore, based on the first comparison result and the second comparison result, the photovoltaic equipment is divided into corresponding clusters to obtain several equipment clusters, including: If the normalized response time is less than the response time threshold and the normalized remaining power is greater than or equal to the power threshold, then the corresponding photovoltaic equipment will be assigned to the fast response high power cluster. If the normalized response time is less than the response time threshold and the normalized remaining power is less than the power threshold, then the corresponding photovoltaic equipment will be assigned to the fast response low power cluster. If the normalized response time is greater than or equal to the response time threshold and the normalized remaining power is greater than or equal to the power threshold, then the corresponding photovoltaic equipment will be assigned to the slow-response high-power cluster. If the normalized response time is greater than or equal to the response time threshold and the normalized remaining power is less than the power threshold, then the corresponding photovoltaic device will be assigned to the slow response low power cluster.

[0013] Further, the step of calculating the average illuminance variation rate and network bandwidth utilization rate of each of the photovoltaic devices in each of the device clusters based on the illuminance intensity sequence of each device and the network latency includes: Based on the light intensity sequence of each photovoltaic device in each of the aforementioned equipment clusters within a preset historical time window, the average light change rate of the equipment clusters is calculated. The device upload data volume of each photovoltaic device in each device cluster within a preset statistical period is obtained, and the first device bandwidth utilization rate of each photovoltaic device is calculated based on the rated uplink bandwidth of each photovoltaic device and the device upload data volume. If the network latency exceeds a preset latency threshold, the bandwidth utilization rate of the first device is weighted and corrected to obtain the corrected bandwidth utilization rate of the second device. The network bandwidth utilization rate of each device cluster is obtained by aggregating and calculating the second device bandwidth utilization rate of all photovoltaic devices in each device cluster.

[0014] By accurately capturing the dynamics of illumination through historical windows and calculating the utilization rate by combining rated bandwidth and data volume, while also making weighted corrections based on network latency, the authenticity of the cluster-level network load representation is ensured. Finally, the accurate cluster bandwidth utilization rate is obtained by aggregation, providing a reliable data base for the collaborative control model, thereby improving the accuracy of global resource allocation.

[0015] Further, the step of calculating the average rate of change of illumination for the equipment cluster based on the illumination intensity sequence of each photovoltaic device within a preset historical time window includes: For each photovoltaic device, the absolute value of the difference between two adjacent sampling points is calculated sequentially based on the light intensity sequence, wherein the light intensity sequence contains several sampling points arranged in chronological order; Calculate the arithmetic mean of all the absolute values ​​mentioned; Divide the arithmetic mean by the duration of the historical time window to obtain the initial rate of change of illumination for each photovoltaic device; The initial irradiance variation rate of all photovoltaic devices within each of the aforementioned device clusters is aggregated and calculated to obtain the average irradiance variation rate of the device cluster.

[0016] Another embodiment of the present invention also provides a collaborative control system for photovoltaic Internet of Things, comprising: The acquisition module is used to acquire status data of several photovoltaic devices in the photovoltaic Internet of Things, wherein the status data includes device response time, remaining power, light intensity sequence and network latency; The partitioning module is used to divide the photovoltaic equipment into several equipment clusters according to the device response time and the remaining power. Based on the light intensity sequence of each photovoltaic device in each equipment cluster and the network latency, the module calculates the average light change rate and network bandwidth utilization rate of each equipment cluster. The control module is used to input the average light change rate and the network bandwidth occupancy rate into a preset collaborative control model to perform logical operations, determine the device adjustment rate and network data reporting frequency of each device cluster based on the logical operation results, control each device cluster to perform action adjustments according to the device adjustment rate, and perform communication resource allocation according to the network data reporting frequency.

[0017] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the collaborative control method for photovoltaic Internet of Things as described in the present invention.

[0018] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the cooperative control method for photovoltaic Internet of Things as described in the present invention. Attached Figure Description

[0019] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating one embodiment of the collaborative control method for photovoltaic Internet of Things provided in this application; Figure 2 This is a flowchart illustrating one embodiment of steps S201 to S204 provided in this application; Figure 3 This is a flowchart illustrating one embodiment of steps S301 to S304 provided in this application; Figure 4 This is a flowchart illustrating one embodiment of steps S401 to S403 provided in this application; Figure 5 This is a schematic diagram of an embodiment of the collaborative control system for photovoltaic Internet of Things provided in this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0023] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0024] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0025] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0026] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0027] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0028] See Figure 1 To improve the accuracy of global resource allocation in complex heterogeneous systems, an embodiment of the present invention provides a collaborative control method for photovoltaic Internet of Things, including steps S101 to S103: Step S101: Obtain status data of several photovoltaic devices in the photovoltaic Internet of Things, wherein the status data includes device response time, remaining power, light intensity sequence and network latency; In some embodiments, firstly, status data is collected from each photovoltaic device at fixed intervals (e.g., 30 seconds) via the MQTT protocol. The collected raw data includes device response time (in milliseconds), remaining power (percentage), current solar irradiance (in watts per square meter), and network latency (in milliseconds). Then, to ensure data quality, outlier filtering is performed on the collected raw data. For example, if the response time exceeds 5000 milliseconds, or the remaining power is less than 0 or greater than 100%, it is considered invalid data and discarded, thus obtaining the status data.

[0029] It should be noted that the light intensity sequence refers to multiple light intensity values ​​arranged in chronological order of sampling time within a preset historical time window (e.g., the most recent 5 minutes), used to reflect the dynamic trend of light intensity changes. Network latency, on the other hand, is the response time of the communication link between the device and the edge node or cloud, reflecting the current level of network congestion.

[0030] Step S102: Based on the device response time and the remaining power, the photovoltaic equipment is divided into several device clusters. Based on the light intensity sequence of each photovoltaic device in each device cluster and the network delay, the average light change rate and network bandwidth utilization rate of each device cluster are calculated respectively. Please refer to Figure 2 In some embodiments, dividing the photovoltaic equipment into several equipment clusters based on the equipment response time and the remaining power includes steps S201 to S204: Step S201: Map the device response time and the remaining power to a unified numerical range to obtain normalized response time and normalized remaining power. In some embodiments, the min-max normalization method is used to process the device response time. Specifically, the maximum and minimum response times in historical data are pre-calculated. For example, the maximum response time is set to 5000 milliseconds and the minimum response time to 50 milliseconds. Then, for the actual response time t of a certain device, its normalized response time is (t - 50) / (5000 - 50), thereby compressing the original response time to the [0,1] interval. The normalization of the remaining power is relatively straightforward. Since the power is already in percentage form, simply dividing the collected remaining power value by 100 yields the normalized remaining power, which is also located in the [0,1] interval.

[0031] It should be noted that if the remaining power of a device is exactly 100% at the time of data collection, the normalized value will be 1.0; if it is 0%, the normalized value will be 0.0.

[0032] Through the above normalization operation, response time and remaining power, two indicators that originally had different dimensions and different numerical ranges, are placed on the same scale, providing a unified comparison benchmark for subsequent threshold comparison and cluster division.

[0033] Step S202: Compare the normalized response time with a preset response time threshold to obtain a first comparison result; In some embodiments, the normalized response time is compared with a preset response time threshold. If the normalized response time is less than the response time threshold, the first comparison result is "fast"; otherwise, it is "slow".

[0034] It should be noted that the setting of the response time threshold should take into account the typical response capability of the mechanical actuator of the equipment and the system's requirements for speed. For example, the normalized response time threshold can be set to 0.16, which physically corresponds to an original response time of about 800 milliseconds (calculated based on the aforementioned maximum value of 5000 milliseconds).

[0035] It should be noted that the specific value of the threshold can be adjusted according to the equipment model, geographical and climatic conditions and operating experience data of different photovoltaic power plants, and this application does not impose any restrictions.

[0036] Step S203: Compare the normalized remaining power with a preset power threshold to obtain a second comparison result; In some embodiments, the normalized remaining power is compared with a power threshold. If the normalized remaining power is greater than or equal to the power threshold, the second comparison result is "high power"; otherwise, it is "low power".

[0037] It should be noted that the power threshold reflects the minimum energy reserve required for the device to maintain high-frequency operation. For example, setting it to 0.6 corresponds to 60% of the original power.

[0038] Step S204: Based on the first comparison result and the second comparison result, the photovoltaic equipment is divided into corresponding clusters to obtain several equipment clusters.

[0039] In some embodiments, step S204 includes: if the normalized response time is less than the response time threshold and the normalized remaining power is greater than or equal to the power threshold, then the corresponding photovoltaic device is assigned to the fast response high power cluster; if the normalized response time is less than the response time threshold and the normalized remaining power is less than the power threshold, then the corresponding photovoltaic device is assigned to the fast response low power cluster; if the normalized response time is greater than or equal to the response time threshold and the normalized remaining power is greater than or equal to the power threshold, then the corresponding photovoltaic device is assigned to the slow response high power cluster; if the normalized response time is greater than or equal to the response time threshold and the normalized remaining power is less than the power threshold, then the corresponding photovoltaic device is assigned to the slow response low power cluster. Specifically, if the first comparison result is "fast" and the second comparison result is "high power," the device is assigned to the fast response high power cluster; if the first comparison result is "fast" and the second comparison result is "low power," it is assigned to the fast response low power cluster; if the first comparison result is "slow" and the second comparison result is "high power," it is assigned to the slow response high power cluster; and if the first comparison result is "slow" and the second comparison result is "low power," it is assigned to the slow response low power cluster. For example, an inverter with a normalized response time of 0.12 (fast) and a normalized remaining power of 0.78 (high power) is assigned to the fast response high power cluster; another data collector with a normalized response time of 0.27 (slow) and a normalized remaining power of 0.43 (low power) is assigned to the slow response low power cluster.

[0040] It should be noted that the four device clusters obtained through the above method—namely, the fast-response high-power cluster, the fast-response low-power cluster, the slow-response high-power cluster, and the slow-response low-power cluster—have distinctly different operating characteristics and resource endowments. To achieve refined and differentiated collaborative control of clusters with different characteristics, this invention pre-defines a collaborative control model (or fuzzy rule base) matching the characteristics of each cluster. This ensures that the control strategy can fully adapt to the energy state and response capability of each cluster. The subsequent fuzzy rule base is specifically customized according to the characteristics of a particular cluster.

[0041] By clustering heterogeneous devices using their response time and remaining power, devices with similar response capabilities and energy reserves can be grouped into the same cluster. This lays a refined management foundation for subsequent differentiated control and effectively solves the resource mismatch problem caused by individual device differences.

[0042] Please refer to Figure 3In some embodiments, the step of calculating the average illuminance variation rate and network bandwidth utilization rate of each of the photovoltaic devices in each device cluster based on the illuminance intensity sequence of each device in each device cluster and the network latency includes steps S301 to S304: Step S301: Based on the light intensity sequence of each photovoltaic device in each device cluster within a preset historical time window, calculate the average light change rate of the device cluster. Further, the step of calculating the average light intensity change rate of the equipment cluster based on the light intensity sequence of each photovoltaic device within a preset historical time window includes: for each photovoltaic device, calculating the absolute value of the difference in light intensity sequence between two adjacent sampling points based on the light intensity sequence, wherein the light intensity sequence includes several sampling points arranged in chronological order; calculating the arithmetic mean of all the absolute values; dividing the arithmetic mean by the duration of the historical time window to obtain the initial light intensity change rate of each photovoltaic device; and aggregating the initial light intensity change rates of all photovoltaic devices within each equipment cluster to obtain the average light intensity change rate of the equipment cluster. Specifically, the system presets a historical time window, the length of which can be set according to the severity of light changes and the required control precision. In this embodiment, it is preferably set to 5 minutes. The device reports the current light intensity every fixed sampling interval (e.g., 30 seconds). Therefore, each time window contains 10 sampling points arranged in chronological order, denoted as L1, L2, ..., L10, where L1 corresponds to the light intensity at the start of the window and L10 corresponds to the light intensity at the end of the window. Then, for each device, the system sequentially calculates the magnitude of the change in light intensity between two adjacent sampling points, i.e., the absolute value of the difference: |L2 - L1|, |L3 - L2|, ..., |L10 - L9|. These absolute values ​​reflect the magnitude of light fluctuation between every two adjacent sampling times. Subsequently, the arithmetic mean of these differences is calculated. The formula is: ,in The number of sampling points (n=10 in this example). Let be the light intensity at the i-th sampling point. This average value represents the average fluctuation range of the light intensity within the time window. To obtain the rate of change per unit time, this average value needs to be divided by the total duration T of the historical time window (T = 5 minutes in this embodiment; to maintain unit consistency, it can be converted to seconds or used directly in minutes) to obtain the initial rate of change of light intensity for the device. The calculation formula is: Finally, after completing the calculations for each individual device, the system aggregates the initial irradiance variation rates of all photovoltaic devices within each device cluster. The aggregation method typically uses an arithmetic mean to obtain the cluster-average irradiance variation rate. The calculation formula is: , where m is the number of devices in the cluster, and j is the j-th photovoltaic device.

[0043] It should be noted that if the number of devices in the cluster is large, the median or other robust statistics can be used instead of the arithmetic mean to eliminate interference from individual abnormal devices. In this embodiment, the arithmetic mean is preferred to simplify the calculation and ensure real-time performance.

[0044] Step S302: Obtain the amount of data uploaded by each photovoltaic device in each device cluster within a preset statistical period, and calculate the first device bandwidth utilization rate of each photovoltaic device based on the rated uplink bandwidth of each photovoltaic device and the amount of data uploaded by the device. In some embodiments, the amount of data uploaded by each photovoltaic device within a preset statistical period is obtained. After obtaining the amount of uploaded data, the rated uplink bandwidth of the device is queried. Subsequently, for each photovoltaic device, its amount of uploaded data (converted to bits) within the statistical period is divided by the duration of the statistical period (i.e., 60 seconds) to obtain the average transmission rate of that period. Then, the average transmission rate is divided by the rated uplink bandwidth of the device to obtain the first device bandwidth utilization rate of the device. The calculation formula is as follows: ,in, The amount of data uploaded (in bits) within the statistical period. The duration of the statistical period (in seconds). The rated uplink bandwidth (bits / second) represents the bandwidth utilization rate of the first device, which is between 0 and 1, indicating the actual proportion of uplink bandwidth occupied by the device in the current period.

[0045] It should be noted that the length of the statistical period can be set according to the granularity requirements of network monitoring. In this embodiment, it is preferably 1 minute, that is, the system counts the total amount of data actually transmitted by each device on the uplink every minute.

[0046] It should be noted that there are two ways to obtain the amount of data uploaded by the device: one is to perform deep parsing and byte counting on the data packets reported by the device at the edge gateway or data aggregation node, and accumulate them to obtain the total number of uploaded bytes in the period; the other is for the device itself to count the data and report it along with the status data. In this case, it is necessary to ensure that the device clock is synchronized with the system clock.

[0047] It should be noted that the rated uplink bandwidth is usually determined by the device model and network interface type. For example, the rated uplink bandwidth of a device using a 4GCAT1 module is about 5 Mbps, while that of a device using a Wi-Fi module can reach 20 Mbps. For older devices whose rated bandwidth cannot be obtained directly, it can be estimated by the highest historical transmission rate and stored in the device file.

[0048] Step S303: If the network latency exceeds a preset latency threshold, the bandwidth utilization rate of the first device is weighted and corrected to obtain the corrected bandwidth utilization rate of the second device. In some embodiments, considering the impact of network latency on actual communication efficiency, bandwidth utilization calculated solely based on data volume may not accurately reflect the degree of channel congestion. For example, when network latency is high, the probability of data packet retransmission increases, and the same amount of data will occupy a longer channel time, thereby exacerbating bandwidth competition. Therefore, it is necessary to use network latency for correction. Specifically, a latency threshold is preset, preferably 200 milliseconds in this embodiment, as a critical value for judging whether the network state has deteriorated. When the real-time network latency of a device exceeds this threshold, the system determines that it is in a high-latency state. At this time, the bandwidth utilization of the first device is weighted and corrected, that is, the correction method can adopt linear weighting, that is, the corrected bandwidth utilization of the second device is equal to the bandwidth utilization of the first device multiplied by a weighting coefficient. This coefficient is positively correlated with the degree to which the current latency exceeds the threshold. The specific calculation formula is as follows: ,in, Current network latency (milliseconds) The preset delay threshold (milliseconds) is used. The weighting coefficient is adjustable; in this embodiment, it is set to 0.3 to avoid over-correction. If the network latency does not exceed the threshold, the bandwidth utilization of the second device is directly equal to that of the first device.

[0049] Step S304: Perform aggregate calculation on the second device bandwidth utilization rate of all photovoltaic devices in each device cluster to obtain the network bandwidth utilization rate of each device cluster.

[0050] In some embodiments, considering that photovoltaic IoT devices typically access the network through a shared gateway or base station, and that there is channel contention in the uplink transmission of each device, this embodiment preferably uses a summation method for aggregation, that is, adding the bandwidth utilization rates of the second devices of all devices in the cluster to obtain the total bandwidth utilization rate of the cluster. The calculation formula is as follows: , where m is the number of devices in the cluster.

[0051] It should be noted that the network bandwidth utilization rate directly reflects the cluster's total uplink bandwidth demand in the current period. When this value is close to or exceeds 100%, it indicates that the cluster is facing a serious risk of network congestion, and intervention measures such as reducing the reporting frequency and enabling intermittent communication should be taken in subsequent collaborative control.

[0052] It should be noted that if each device in the cluster has an independent communication channel (such as a remote site using satellite communication), the arithmetic average method can also be used for aggregation to reflect the average load level of the cluster.

[0053] By accurately capturing the dynamics of illumination through historical windows and calculating the utilization rate by combining rated bandwidth and data volume, while also making weighted corrections based on network latency, the authenticity of the cluster-level network load representation is ensured. Finally, the accurate cluster bandwidth utilization rate is obtained by aggregation, providing a reliable data base for the collaborative control model, thereby improving the accuracy of global resource allocation.

[0054] Step S103: Input the average illumination change rate and the network bandwidth occupancy rate into a preset collaborative control model to perform logical operations. Based on the logical operation results, determine the device adjustment rate and network data reporting frequency of each device cluster, and control each device cluster to perform action adjustments according to the device adjustment rate and perform communication resource allocation according to the network data reporting frequency.

[0055] It should be noted that the construction of the aforementioned collaborative control model (i.e., fuzzy control model) is based on the actual operating characteristics of the photovoltaic Internet of Things (PV Internet of Things) and expert experience. The specific construction process is as follows: First, for the input variable "average light intensity variation rate," by collecting one year's worth of historical light intensity data from the PV power station, the distribution range of its variation rate is statistically analyzed. 0~0.8 W / m² / s is classified as "low" variation rate, 0.8~2.5 W / m² / s as "medium" variation rate, and greater than 2.5 W / m² / s as "high" variation rate. For the input variable "network bandwidth utilization rate," based on the rated load capacity of the communication module, 0~40% is classified as "low" load, 40%~75% as "medium" load, and above 75% as "high" load. The membership functions for each linguistic variable are all trigonometric functions, and their parameters are set according to the boundary and center values ​​of the aforementioned intervals. Secondly, the establishment of the fuzzy rule base follows the following collaborative optimization principles: (1) Energy efficiency priority principle: When the light intensity changes drastically (high), the equipment adjustment rate is prioritized to capture more solar energy; (2) Network light load principle: When the network bandwidth utilization rate is low, the data reporting frequency is prioritized to enhance monitoring accuracy; (3) Congestion avoidance principle: When the network bandwidth utilization rate is high, the data reporting frequency is prioritized to prevent network collapse; (4) Energy balance principle: Combining the equipment cluster partitioning results (such as fast response high power clusters), the adjustment rate and reporting frequency are differentiated in the rule post-processing to ensure that low power clusters do not consume excessive energy. Based on the above principles, a fuzzy rule table covering all input state combinations was constructed through expert scoring and on-site measurement data verification to ensure that reasonable control quantities can be output under any input state.

[0056] Please refer to Figure 4 In some embodiments, the step of inputting the average illumination change rate and the network bandwidth occupancy rate into a preset collaborative control model to perform logical operations, and determining the device adjustment rate and network data reporting frequency of each device cluster based on the logical operation results, includes steps S401 to S403: Step S401: Input the average illumination change rate and the network bandwidth occupancy rate into the collaborative control model, and determine the first membership degree of the average illumination change rate relative to a plurality of preset first fuzzy linguistic variables and the second membership degree of the network bandwidth occupancy rate relative to a plurality of preset second fuzzy linguistic variables through the fuzzification interface of the collaborative control model. In some embodiments, the average illumination change rate and network bandwidth utilization rate of each device cluster are used as the precise input values ​​of the model. For the average illumination change rate, multiple first fuzzy linguistic variables are preset, such as "low," "medium," and "high," corresponding to slow, moderate, and drastic illumination changes, respectively. For the network bandwidth utilization rate, multiple second fuzzy linguistic variables are preset, such as "low," "medium," and "high," corresponding to light, moderate, and congested bandwidth loads, respectively. Each fuzzy linguistic variable is determined using a membership function. Then, for the precise value of the current input, its membership degree relative to each fuzzy linguistic variable is calculated, resulting in a first membership degree set (corresponding to the average illumination change rate) and a second membership degree set (corresponding to the network bandwidth utilization rate). For example, if the average illumination change rate is a certain value, its membership degrees on the three linguistic variables "low," "medium," and "high" might be 0.2, 0.8, and 0.0, respectively, indicating that the input mainly belongs to the "medium" state.

[0057] In some embodiments, the membership function takes the form of a triangular function, a trapezoidal function, or a Gaussian function. Taking the triangular membership function μ(x) as an example, its expression is: ; Where a, b, and c are function parameters, set based on historical data or expert experience.

[0058] Step S402: Input the first membership degree and the second membership degree into the fuzzy inference engine of the collaborative control model, and perform matching and inference through the preset fuzzy rule base in the fuzzy inference engine to generate a first fuzzy output set about the adjustment rate and a second fuzzy output set about the frequency of network data reporting. The fuzzy rule base contains several fuzzy rules, and each fuzzy rule defines the logical relationship between the combination of the first membership degree and the second membership degree in different states and the output control quantity. In some embodiments, step S402 includes: traversing each fuzzy rule in the fuzzy rule base, extracting the corresponding membership degree according to the fuzzy linguistic variables involved in the antecedent of each fuzzy rule, and performing fuzzy intersection operation on the extracted membership degree to obtain the activation intensity corresponding to each fuzzy rule; truncating or scaling the output fuzzy subset defined in the consequent of each fuzzy rule according to the activation intensity to obtain the inference output fuzzy set corresponding to each fuzzy rule; and performing fuzzy union operation on all the inference output fuzzy sets according to the type of output variables to aggregate and generate the first fuzzy output set and the second fuzzy output set. Specifically, firstly, in the fuzzy inference process, the system traverses each fuzzy rule in the fuzzy rule base, extracts the corresponding membership degree value from the membership degree set output by the fuzzification interface according to the fuzzy linguistic variables involved in the antecedent of each rule. Since the antecedent of each rule is usually composed of a combination of multiple fuzzy linguistic conditions (e.g., "high average illumination change rate AND low network bandwidth utilization"), it is necessary to perform fuzzy operation on the extracted multiple membership degrees to obtain the comprehensive activation intensity of the rule. After obtaining the activation strength of each rule, the system truncates or scales the output fuzzy subset defined in the rule consequent to generate the inference output fuzzy set corresponding to that rule. Finally, after completing the inference of all rules, the inference output fuzzy sets generated by each rule are aggregated according to the type of output variable through fuzzy union to form the final first fuzzy output set (corresponding to the adjustment rate) and the second fuzzy output set (corresponding to the network data reporting frequency).

[0059] It should be noted that fuzzy intersection operations typically employ the minimum (min) or product (prod) method. In this embodiment, the minimum method is preferred, meaning that for the first membership degree μ1 and the second membership degree μ2 in the rule antecedent, their activation intensity α = min(μ1,μ2). If the rule antecedent contains more conditions, the minimum value is taken sequentially.

[0060] It should be noted that the output fuzzy subset is a membership function predefined on the output universe of discourse. For example, for the output variable of adjustment rate, its consequent may be defined as fuzzy linguistic variables such as "high", "medium", and "low", and each variable corresponds to a membership function shape (such as triangle or trapezoid).

[0061] It should be noted that the truncation operation refers to cutting the membership function of the output fuzzy subset along the vertical axis with the activation strength as the upper limit. That is, for each point in the output universe, its membership degree is the smaller of the original membership degree and the activation strength. The scaling operation, on the other hand, multiplies the membership function of the output fuzzy subset by the activation strength, reducing the peak value of the function to the activation strength value. In this embodiment, the truncation method is preferred because it can better preserve the shape characteristics of the output fuzzy subset.

[0062] It should be noted that the aggregation operation adopts a fuzzy union approach, typically using the maximum value method (max). That is, for each point in the output universe, the maximum membership value from all rule inference results is taken as the membership degree of that point in the aggregated fuzzy set. Suppose that for a point u in the output universe, there are k rules involved in the inference, and the membership degree of each rule at that point is μ_i(u) (i=1,2,…,k), then the aggregated membership degree μ_agg(u) = max(μ_1(u), μ_2(u),…, μ_k(u)).

[0063] Step S403: Through the defuzzification interface of the collaborative control model, defuzzification calculations are performed on the first fuzzy output set and the second fuzzy output set respectively to obtain the device adjustment rate and the network data reporting frequency of each device cluster.

[0064] In some embodiments, the centroid abscissa of the region enclosed by the membership functions of the output fuzzy set is calculated using the centroid method, and this abscissa is used as the precise value of the final output. For discrete universes, the centroid method calculation formula is: ,in, To output discrete points in the universe of discourse, Let N be the membership degree corresponding to the point, and N be the number of discrete points. By performing defuzzification calculations on the first fuzzy output set and the second fuzzy output set respectively, the precise device adjustment rate (the number of adjustments allowed per unit time) and network data reporting frequency (the number of reports allowed per unit time) of each device cluster can be obtained.

[0065] It should be noted that, in order to achieve differentiated control, the membership functions of the output variables (i.e., device adjustment rate and network data reporting frequency) used in the defuzzification interface are set independently for different device clusters. For example, the rate range covered by the 'fast adjustment' membership function of the 'fast response high-power cluster' is significantly higher than the 'fast adjustment' range of the 'slow response low-power cluster'. In this way, even if different clusters face the same changes in lighting and network conditions, they can obtain control commands adapted to their own physical characteristics.

[0066] This mechanism addresses the uncertainties of illumination variation rate and bandwidth utilization through fuzzy logic, matching fuzzy inputs to a pre-defined rule base for inference, and then defuzzifying to generate precise adjustment rates and reporting frequencies. This simulation of expert-driven decision-making ensures robust and adaptable resource allocation in dynamic environments, significantly improving the accuracy of global resource allocation.

[0067] In some embodiments, the system controls each of the device clusters to perform action adjustments according to the device adjustment rate and to allocate communication resources according to the network data reporting frequency. Specifically, after obtaining the device adjustment rate and network data reporting frequency of each device cluster, the system generates specific execution instructions through edge computing nodes or cloud control platforms and sends them to each photovoltaic device in the corresponding cluster. For the control of the device adjustment rate, the system converts the adjustment rate into an interval of mechanical actions or parameter optimization cycle that the device can execute. For example, if the adjustment rate of a cluster is 0.5 times per minute, the control instruction will explicitly require the device to complete a tracking angle calibration or maximum power point tracking calculation every two minutes. After receiving the instruction, the device is parsed by the embedded controller and written into the operating parameter register. Subsequently, the actuator is triggered to perform actions according to the fixed interval. At the same time, the actual progress is detected through closed-loop feedback during the execution process. If the action times out or fails to meet the standard due to mechanical jamming or motor failure, the device will actively report the abnormal status, and the upper-level system will perform compensation adjustment or downgrade processing in the next scheduling cycle.

[0068] In some embodiments, the system uses a combination of dynamic scheduling and queue management to allocate communication resources for controlling the frequency of network data reporting. Specifically, the system allocates a fixed reporting time slot or a maximum allowed number of reports to each device based on the reporting frequency requirements of each cluster. For example, if a low-priority cluster is set to report once every 15 minutes, the device will only wake up the communication module and upload status data when that time window arrives. For high-priority clusters, the reporting frequency can be shortened to once per minute, and the system reserves a high-priority queue for them on the gateway side to ensure that data packets are forwarded preferentially. If a device attempts to report in an unauthorized time slot, the gateway will directly discard the data packet or delay its processing, thereby avoiding channel congestion.

[0069] This invention clusters heterogeneous devices using device response time and remaining power, grouping devices with similar response capabilities and energy reserves into the same cluster. This lays a refined management foundation for subsequent differentiated control and effectively solves the resource mismatch problem caused by individual device differences. Furthermore, it calculates the average rate of change in illumination and network bandwidth utilization for each cluster, achieving accurate characterization of dynamic changes in the physical environment and network communication load, providing reliable data support for global resource allocation. Finally, the two types of parameters reflecting cluster characteristics are input into a preset collaborative control model for logical operations. Based on the operation results, the device adjustment rate and network data reporting frequency of each cluster are determined synchronously, enabling coordinated optimization of physical-level action adjustments and information-level communication resource allocation. Thus, in complex and heterogeneous photovoltaic IoT environments, this application can dynamically adjust device execution strategies based on illumination changes to ensure power generation efficiency, and adaptively allocate communication resources according to network status to avoid network congestion, thereby significantly improving the accuracy and coordination of resource allocation at the global level.

[0070] like Figure 5 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; One embodiment of the present invention provides a collaborative control system for photovoltaic Internet of Things, comprising: The acquisition module 100 is used to acquire status data of several photovoltaic devices in the photovoltaic Internet of Things, wherein the status data includes device response time, remaining power, light intensity sequence and network latency; The partitioning module 200 is used to divide the photovoltaic equipment into several equipment clusters according to the equipment response time and the remaining power, and to calculate the average light intensity change rate and network bandwidth utilization rate of each equipment cluster based on the light intensity sequence of each photovoltaic equipment in each equipment cluster and the network delay. The control module 300 is used to input the average light change rate and the network bandwidth occupancy rate into a preset collaborative control model to perform logical operations, determine the device adjustment rate and network data reporting frequency of each device cluster based on the logical operation results, control each device cluster to perform action adjustments according to the device adjustment rate, and perform communication resource allocation according to the network data reporting frequency.

[0071] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the collaborative control method for photovoltaic Internet of Things provided by any of the above-described method embodiments of the present invention.

[0072] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0073] Based on the above embodiments of the collaborative control method for photovoltaic Internet of Things, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the collaborative control method for photovoltaic Internet of Things according to any embodiment of the present invention.

[0074] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0075] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0076] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0077] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the collaborative control method for photovoltaic Internet of Things described in any of the above-described method embodiments of the present invention.

[0078] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0079] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A collaborative control method for photovoltaic Internet of Things, characterized in that, include: Acquire status data of several photovoltaic devices in the photovoltaic Internet of Things, wherein the status data includes device response time, remaining power, light intensity sequence and network latency; Based on the device response time and the remaining power, the photovoltaic devices are divided into several device clusters. Based on the light intensity sequence of each photovoltaic device in each device cluster and the network latency, the average light change rate and network bandwidth utilization rate of each device cluster are calculated respectively. The average light intensity change rate and the network bandwidth occupancy rate are input into a preset collaborative control model to perform logical operations. Based on the results of the logical operations, the device adjustment rate and network data reporting frequency of each device cluster are determined. Each device cluster is then controlled to perform actions according to the device adjustment rate and to allocate communication resources according to the network data reporting frequency.

2. The collaborative control method for photovoltaic Internet of Things according to claim 1, characterized in that, The step of inputting the average illumination change rate and the network bandwidth occupancy rate into a preset collaborative control model to perform logical operations, and determining the device adjustment rate and network data reporting frequency of each device cluster based on the logical operation results, includes: The average illumination change rate and the network bandwidth occupancy rate are input into the collaborative control model. Through the fuzzification interface of the collaborative control model, the first membership degree of the average illumination change rate relative to a plurality of preset first fuzzy linguistic variables and the second membership degree of the network bandwidth occupancy rate relative to a plurality of preset second fuzzy linguistic variables are determined. The first membership degree and the second membership degree are input into the fuzzy inference engine of the collaborative control model. The fuzzy rule base preset in the fuzzy inference engine is used for matching and inference to generate a first fuzzy output set about the adjustment rate and a second fuzzy output set about the frequency of network data reporting. The fuzzy rule base contains several fuzzy rules, and each fuzzy rule defines the logical relationship between the combination of the first membership degree and the second membership degree in different states and the output control quantity. By using the defuzzification interface of the collaborative control model, defuzzification calculations are performed on the first fuzzy output set and the second fuzzy output set respectively to obtain the device adjustment rate and the network data reporting frequency of each device cluster.

3. The collaborative control method for photovoltaic Internet of Things according to claim 2, characterized in that, The fuzzy inference engine performs matching and inference using a pre-set fuzzy rule base, generating a first fuzzy output set regarding the adjustment rate and a second fuzzy output set regarding the frequency of network data reporting, including: Traverse each fuzzy rule in the fuzzy rule base, extract the corresponding membership degree based on the fuzzy linguistic variables involved in the antecedent of each fuzzy rule, and perform fuzzy intersection operation on the extracted membership degree to obtain the activation intensity corresponding to each fuzzy rule; Based on the activation intensity, the output fuzzy subset defined in the consequent of each fuzzy rule is truncated or scaled to obtain the inference output fuzzy set corresponding to each fuzzy rule; All the inference output fuzzy sets are fuzzy-joined according to the type of the output variables, and then aggregated to generate the first fuzzy output set and the second fuzzy output set.

4. The collaborative control method for photovoltaic Internet of Things according to claim 1, characterized in that, The process of dividing the photovoltaic equipment into several equipment clusters based on the equipment response time and the remaining power includes: The device response time and the remaining battery power are mapped to a unified numerical range to obtain normalized response time and normalized remaining battery power. The normalized response time is compared with a preset response time threshold to obtain a first comparison result; The normalized remaining battery power is compared with a preset battery power threshold to obtain a second comparison result; Based on the first comparison result and the second comparison result, the photovoltaic equipment is divided into corresponding clusters, resulting in several equipment clusters.

5. The collaborative control method for photovoltaic Internet of Things according to claim 4, characterized in that, The photovoltaic equipment is divided into corresponding clusters based on the first comparison result and the second comparison result, resulting in several equipment clusters, including: If the normalized response time is less than the response time threshold and the normalized remaining power is greater than or equal to the power threshold, then the corresponding photovoltaic equipment will be assigned to the fast response high power cluster. If the normalized response time is less than the response time threshold and the normalized remaining power is less than the power threshold, then the corresponding photovoltaic equipment will be assigned to the fast response low power cluster. If the normalized response time is greater than or equal to the response time threshold and the normalized remaining power is greater than or equal to the power threshold, then the corresponding photovoltaic equipment will be assigned to the slow-response high-power cluster. If the normalized response time is greater than or equal to the response time threshold and the normalized remaining power is less than the power threshold, then the corresponding photovoltaic device will be assigned to the slow response low power cluster.

6. The collaborative control method for photovoltaic Internet of Things according to claim 1, characterized in that, The calculation of the average rate of change of illumination and network bandwidth utilization for each of the photovoltaic devices within each device cluster, based on the light intensity sequence of each device in each device cluster and the network latency, includes: Based on the light intensity sequence of each photovoltaic device in each of the aforementioned equipment clusters within a preset historical time window, the average light change rate of the equipment clusters is calculated. The device upload data volume of each photovoltaic device in each device cluster within a preset statistical period is obtained, and the first device bandwidth utilization rate of each photovoltaic device is calculated based on the rated uplink bandwidth of each photovoltaic device and the device upload data volume. If the network latency exceeds a preset latency threshold, the bandwidth utilization rate of the first device is weighted and corrected to obtain the corrected bandwidth utilization rate of the second device. The network bandwidth utilization rate of each device cluster is obtained by aggregating and calculating the second device bandwidth utilization rate of all photovoltaic devices in each device cluster.

7. The collaborative control method for photovoltaic Internet of Things according to claim 6, characterized in that, The step of calculating the average rate of change of illumination for each photovoltaic device within a preset historical time window, based on the illumination intensity sequence of each device in the device cluster, includes: For each photovoltaic device, the absolute value of the difference between two adjacent sampling points is calculated sequentially based on the light intensity sequence, wherein the light intensity sequence contains several sampling points arranged in chronological order; Calculate the arithmetic mean of all the absolute values ​​mentioned; Divide the arithmetic mean by the duration of the historical time window to obtain the initial rate of change of illumination for each photovoltaic device; The initial irradiance variation rate of all photovoltaic devices within each of the aforementioned device clusters is aggregated and calculated to obtain the average irradiance variation rate of the device cluster.

8. A collaborative control system for photovoltaic Internet of Things, characterized in that, include: The acquisition module is used to acquire status data of several photovoltaic devices in the photovoltaic Internet of Things, wherein the status data includes device response time, remaining power, light intensity sequence and network latency; The partitioning module is used to divide the photovoltaic equipment into several equipment clusters according to the equipment response time and the remaining power. Based on the light intensity sequence of each photovoltaic equipment in each equipment cluster and the network latency, the module calculates the average light change rate and network bandwidth utilization rate of each equipment cluster. The control module is used to input the average light change rate and the network bandwidth occupancy rate into a preset collaborative control model to perform logical operations, determine the device adjustment rate and network data reporting frequency of each device cluster based on the logical operation results, control each device cluster to perform action adjustments according to the device adjustment rate, and perform communication resource allocation according to the network data reporting frequency.

9. A terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the collaborative control method for photovoltaic Internet of Things as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the steps of the collaborative control method for photovoltaic Internet of Things as described in any one of claims 1-7.

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