Electric energy monitoring method and system based on multi-source agricultural information interaction

Abstract

The invention discloses an electric power energy monitoring method and system based on multi-source agricultural information interaction, and the method comprises the steps: obtaining equipment call data under each production unit of an agricultural park, constructing a cloud edge cooperation framework according to a preset agricultural production plan, collecting multi-source heterogeneous data in a production process, carrying out the data preprocessing, and obtaining a cloud edge cooperation framework; performing semantic association analysis by combining a cloud edge collaborative framework to obtain a semantic association relationship among heterogeneous data, and performing time sequence modeling processing according to a data acquisition time sequence of the multi-source heterogeneous data and the semantic association relationship to obtain a data fusion model for performing electric power energy scheduling on the production equipment; and acquiring electric energy consumption data corresponding to each agricultural production stage, and inputting the electric energy consumption data into the data fusion model for electric energy data fusion analysis and electric energy allocation processing to obtain electric energy monitoring data of the agricultural park. The method has the effect of promoting fine management and sustainable development in the agricultural production process.

Description

Technical Field

[0001] This invention relates to the technical field of agricultural energy monitoring, and in particular to a method and system for monitoring electric energy based on multi-source agricultural information interaction. Background Technology

[0002] Currently, with the continuous integration and development of modern agriculture and intelligent technology, the dependence on electricity in agricultural production is also increasing. How to achieve efficient energy utilization and rational allocation, and reduce energy consumption in the production process, has become an important research topic in modern agriculture. The diversification and complexity of energy consumption in agricultural production processes have placed higher demands on energy management.

[0003] Existing agricultural production often relies on electrical equipment. The entire agricultural production process is controlled by managing the operation of electrical equipment. Through the deep coupling and optimized configuration of agricultural production and energy systems, efficient production in modern agriculture can be achieved. However, current agricultural power energy management methods often monitor single devices or specific links, lacking unified monitoring and scheduling capabilities for multiple scenarios and devices. This makes it difficult to cover the diverse energy-consuming units within agricultural parks and to achieve efficient energy scheduling and management based on real-time data. There is room for further improvement and refinement. Summary of the Invention

[0004] To address the problem that existing single-device monitoring methods are insufficient to meet the intelligent optimization and scheduling needs in complex agricultural scenarios, this invention provides a power energy monitoring method and system based on multi-source agricultural information interaction. This system can perform integrated analysis of the operation of agricultural power equipment in different scenarios, promoting refined management and sustainable development in agricultural production.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution: A power energy monitoring method based on multi-source agricultural information interaction includes: Obtain equipment call data for each production unit in the agricultural park, and construct a cloud-edge collaborative framework based on the equipment call data and a preset agricultural production plan; Collect multi-source heterogeneous data from the production process in the agricultural park and perform data preprocessing. Combined with the cloud-edge collaboration framework, perform semantic association analysis on the preprocessed multi-source heterogeneous data to obtain the semantic association relationship between the heterogeneous data. Based on the data acquisition time sequence of the multi-source heterogeneous data and the semantic association relationship, time sequence modeling processing is performed to obtain a data fusion model for power energy scheduling of production equipment; The power consumption data corresponding to each stage of agricultural production is obtained and input into the data fusion model for power energy data fusion analysis and power energy allocation processing to obtain power energy monitoring data of the agricultural park.

[0006] Preferably, the step of acquiring the power consumption data corresponding to each stage of agricultural production and inputting it into the data fusion model for power data fusion analysis and power allocation processing to obtain power monitoring data for the agricultural park specifically includes: Acquire production site data under the current production unit, analyze the corresponding agricultural production stage based on the production site data, and collect the corresponding power energy consumption data; The power consumption data is input into the data fusion model to associate data with all operating equipment under the current production unit and track the operating status of each operating equipment according to the running sequence. Based on the equipment operation status analysis results, power energy allocation is carried out on the corresponding operating equipment to obtain power energy monitoring data that conforms to the agricultural production progress under the current production unit.

[0007] Preferably, the step of performing time-series modeling processing based on the data acquisition time sequence of the multi-source heterogeneous data and the semantic association relationship to obtain a data fusion model for power energy scheduling of production equipment specifically includes: Obtain the data acquisition time sequence corresponding to the multi-source heterogeneous data, and combine it with the semantic association relationship to perform simultaneous data association of the multi-source heterogeneous data according to the data acquisition time sequence to obtain simultaneous associated data; The simultaneous associated data are sequentially associated according to the data acquisition time sequence to obtain sequentially associated data; The simultaneous correlation data and the sequential correlation data are processed by time series modeling to obtain a data fusion model for power energy scheduling of production equipment.

[0008] Preferably, the process of collecting multi-source heterogeneous data during the agricultural park's production process and preprocessing the data, combined with the cloud-edge collaborative framework, involves semantic association analysis of the preprocessed multi-source heterogeneous data to obtain semantic relationships between the heterogeneous data. Specifically, this includes: Multi-source heterogeneous data from the agricultural park's production process are collected in real time, and the data format of the multi-source heterogeneous data is converted using a preset data processing mechanism to obtain multi-source preprocessed data in the same data format. Based on the cloud-edge collaboration framework, semantic similarity analysis is performed on the multi-source preprocessed data, and semantic association processing is performed on the multi-source preprocessed data that reaches the preset semantic similarity to obtain the semantic association relationship between heterogeneous data.

[0009] Preferably, the step of acquiring equipment call data for each production unit in the agricultural park and constructing a cloud-edge collaborative framework based on the equipment call data according to a preset agricultural production plan specifically includes: Obtain equipment call data for each production unit in the agricultural park, and assign edge node permissions to each device according to the preset agricultural production plan of the current production unit; Based on the edge node permission allocation results and the data transmission relationship between each device and the agricultural park management center node, a cloud-edge collaboration framework matching the preset agricultural production plan is constructed.

[0010] Preferably, the power energy monitoring method further includes: Obtain the resource usage status and resource allocation ratio of all production units in the agricultural park, and in conjunction with the production plan of each production unit, assess whether the resource allocation ratio of the next production stage meets the production needs. Based on the production demand assessment results, the resource allocation plan for all production units in the next production stage is dynamically adjusted, and the overall power energy allocation plan of the agricultural park is optimized to obtain coordinated operation data of the park.

[0011] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions: A power energy monitoring system based on multi-source agricultural information interaction, applied to the aforementioned power energy monitoring method based on multi-source agricultural information interaction, the system comprising: The framework construction module is used to obtain equipment call data under each production unit in the agricultural park, and to construct a cloud-edge collaborative framework according to the preset agricultural production plan based on the equipment call data. The semantic analysis module is used to collect multi-source heterogeneous data during the production process of agricultural parks and perform data preprocessing. Combined with the cloud-edge collaborative framework, semantic association analysis is performed on the preprocessed multi-source heterogeneous data to obtain the semantic association relationship between heterogeneous data. The model building module is used to perform time-series modeling processing based on the data acquisition time sequence and semantic association of the multi-source heterogeneous data to obtain a data fusion model for power energy scheduling of production equipment; The resource coordination module is used to acquire the power energy consumption data corresponding to each stage of agricultural production, and input it into the data fusion model for power energy data fusion analysis and power energy allocation processing to obtain power energy monitoring data of the agricultural park.

[0012] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described power energy monitoring method based on multi-source agricultural information interaction.

[0013] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described power energy monitoring method based on multi-source agricultural information interaction.

[0014] In summary, this application includes at least one of the following beneficial technical effects: 1. This invention aims to dynamically monitor and adjust the power demand of multi-source heterogeneous equipment in agricultural parks, monitor the operating status of power equipment in real time, and achieve autonomous operation of the edge layer and overall management of the central management node through the construction of a cloud-edge collaborative framework. This improves the orderly use and management of power equipment in agricultural parks and enables data sharing among different devices through a data fusion model. It also provides integrated analysis of energy consumption, equipment operation status, and production environment parameters for each production unit, improving the scientific and precise management of power energy, promoting refined and standardized management in agricultural production, and achieving sustainable development of modern agricultural production. The technical solution of this application addresses the problems of existing agricultural energy management systems, which often focus on monitoring single equipment or specific production links, lack comprehensive monitoring and intelligent scheduling functions for diverse agricultural scenarios, and are unable to effectively monitor multiple types of energy consumption points within the park.

[0015] 2. This application integrates and correlates multi-source heterogeneous data under the same production unit through horizontal correlation of multi-source heterogeneous data under the same time sequence and vertical correlation in accordance with the time sequence. This facilitates data sharing of multi-source heterogeneous data, makes it easier to search and analyze, and enables more accurate retrieval of data from all equipment under the same production unit for integrated analysis and allocation of power energy. This improves the accuracy of power energy analysis and allocation, and enables refined management of agricultural production.

[0016] 3. This application breaks down complex agricultural scenarios into production units and analyzes and optimizes the production data of each unit separately, forming unit autonomy within each unit. This helps improve the accuracy of power energy monitoring within a single production unit. Furthermore, by analyzing the overall operation of the entire agricultural production park, the application adjusts and optimizes the equipment resources of all production units in the next production stage and allocates them to the corresponding production units. This allows for comprehensive adjustment and monitoring of the overall operation of the park, achieving integrated operation and maintenance and improving the coordination of power energy adjustments within the park. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 This is a flowchart illustrating the power energy monitoring method based on multi-source agricultural information interaction in this embodiment.

[0019] Figure 2 This is a flowchart illustrating step S10 of the power energy monitoring method in this embodiment.

[0020] Figure 3 This is a flowchart illustrating step S20 of the power energy monitoring method in this embodiment.

[0021] Figure 4 This is a flowchart illustrating step S30 of the power energy monitoring method in this embodiment.

[0022] Figure 5 This is a flowchart illustrating step S40 of the power energy monitoring method in this embodiment.

[0023] Figure 6 This is a schematic diagram of the process of using the power energy monitoring method in this embodiment for comprehensive allocation of resources in the park.

[0024] Figure 7 This is a schematic diagram of environmental parameter monitoring for the power energy monitoring method in this embodiment.

[0025] Figure 8 This is a schematic diagram of power consumption monitoring in the power energy monitoring method of this embodiment.

[0026] Figure 9 This is a schematic diagram of the agricultural product processing monitoring method of the power energy monitoring method in this embodiment.

[0027] Figure 10This is a schematic diagram of the monitoring of farmed fish in the aquaponics greenhouse of this embodiment.

[0028] Figure 11 This is a schematic diagram of crop planting and monitoring in the aquaponics greenhouse of this embodiment.

[0029] Figure 12 This is a structural block diagram of the power energy monitoring system based on multi-source agricultural information interaction in this embodiment.

[0030] Figure 13 This is a schematic diagram of the internal structure of a computer device used to implement power energy monitoring methods. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0033] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0034] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0035] In one embodiment, such as Figure 1 As shown, this application discloses a power energy monitoring method based on multi-source agricultural information interaction, which specifically includes the following steps: S10: Obtain equipment call data for each production unit in the agricultural park, and build a cloud-edge collaborative framework based on the equipment call data and a preset agricultural production plan.

[0036] Specifically, such as Figure 2 As shown, step S10 includes: S101: Obtain equipment call data for each production unit in the agricultural park, and perform edge node permission allocation for each device according to the preset agricultural production plan of the current production unit.

[0037] Specifically, the agricultural production unit in this embodiment includes agricultural production, agricultural product processing, and livestock and poultry breeding, as well as other production units related to agricultural planting and agricultural product processing. Based on the different production units, usage data of the power equipment used in each scenario is collected on a unit-by-unit basis, including the power consumption and operating power of various equipment. According to the preset agricultural production plan of the current production unit, and combined with edge computing algorithms, each device is treated as an edge node and permissions are assigned, enabling the power equipment under each production unit to perform unit autonomy within the corresponding permission range.

[0038] S102: Based on the edge node permission allocation results and the data transmission relationship between each device and the agricultural park management center node, construct a cloud-edge collaborative framework that matches the preset agricultural production plan.

[0039] Specifically, based on the edge node permission allocation results and the data transmission relationship between each device and the central management node of the agricultural park, i.e. the park's central management platform, a cloud-edge collaborative framework is constructed, which integrates production unit autonomy and central management platform management. The cloud-edge collaborative framework is constrained by a preset agricultural production plan.

[0040] S20: Collect multi-source heterogeneous data from the agricultural park's production process and perform data preprocessing. Combined with the cloud-edge collaboration framework, perform semantic association analysis on the preprocessed multi-source heterogeneous data to obtain the semantic association relationships between heterogeneous data.

[0041] Specifically, such as Figure 3 As shown, step S20 includes: S201: Collect multi-source heterogeneous data in real time during the production process of agricultural parks, and combine the preset data processing mechanism to perform data format conversion processing on the multi-source heterogeneous data to obtain multi-source preprocessed data in the same data format.

[0042] Specifically, during the production process in the agricultural park, multi-source heterogeneous data is collected in real time from each production unit. This includes key environmental parameters collected by various environmental sensors, energy consumption and operating parameters of various equipment, and plant and animal growth and breeding parameters. Key environmental parameters include temperature, humidity, light intensity, carbon dioxide concentration, water temperature, dissolved oxygen concentration, and pH. The environmental parameter monitoring in this embodiment is as follows: Figure 7As shown, the equipment operating energy consumption and operating condition parameters mainly include energy data such as equipment power consumption, operating power, operating status, distributed power output, energy storage status, and purchased electricity price, as well as equipment operating status such as agricultural machinery load, greenhouse control equipment operating parameters, and water pump start / stop status. The power consumption monitoring in this embodiment is as follows: Figure 8 As shown, the parameters for the growth and breeding of plants and animals include the growth status or breeding status of crops and livestock.

[0043] Combined with a preset data processing mechanism, such as according to a preset data format, multi-source heterogeneous data is converted to obtain multi-source preprocessed data with a unified data format.

[0044] S202: Based on the cloud-edge collaboration framework, perform semantic similarity analysis on multi-source preprocessed data, and perform semantic association processing on multi-source preprocessed data that reach the preset semantic similarity to obtain the semantic association relationship between heterogeneous data.

[0045] Specifically, based on the cloud-edge collaboration framework, semantic similarity analysis is performed on multi-source preprocessed data. Semantic similarity analysis is also performed based on the frequency of occurrence and semantic similarity of production keywords in the current production unit. Semantic association is then performed on multi-source preprocessed data that reach the preset semantic similarity to obtain the semantic association relationship between heterogeneous data.

[0046] S30: Based on the data acquisition time sequence and semantic association of multi-source heterogeneous data, perform time sequence modeling to obtain a data fusion model for power energy scheduling of production equipment.

[0047] Specifically, such as Figure 4 As shown, step S30 includes: S301: Obtain the data acquisition time sequence corresponding to the multi-source heterogeneous data, and combine the semantic association relationship to perform simultaneous sequential data association of the multi-source heterogeneous data according to the data acquisition time sequence to obtain simultaneous sequential associated data.

[0048] Specifically, the data acquisition sequence of multi-source heterogeneous data is the data acquisition cycle under the current production unit. In this embodiment, a day is used as an example for explanation, but it can also be set to an hour or a month according to actual needs, and is not limited to one of the embodiments in this example.

[0049] Based on the current production unit's data acquisition sequence, and according to semantic relationships, multi-source heterogeneous data of the same sequence are searched, and the found data of the same sequence are horizontally correlated to obtain the data of the same sequence.

[0050] S302: Sequentially correlate the synchronously correlated data according to the data acquisition time sequence to obtain sequentially correlated data.

[0051] Specifically, according to the order of data collection time, the time-series related data are sequentially associated to obtain vertical data association based on time sequence, resulting in sequentially associated data.

[0052] S303: Perform time-series modeling on simultaneous and sequentially correlated data to obtain a data fusion model for power energy scheduling of production equipment.

[0053] Specifically, based on the simultaneous correlation data in horizontal association and the sequential correlation data in vertical association, the data of the current production unit is processed by time series modeling to construct a data fusion model corresponding to the current production unit. The historical and real-time data characteristics of the current production unit are analyzed through the data fusion model to provide data support for the power energy dispatch of the current production unit.

[0054] S40: Obtain the power consumption data corresponding to each stage of agricultural production, and input it into the data fusion model for power energy data fusion analysis and power energy allocation processing to obtain power energy monitoring data of the agricultural park.

[0055] Specifically, such as Figure 5 As shown, step S40 includes: S401: Obtain production site data under the current production unit, analyze the corresponding agricultural production stage based on the production site data, and collect the corresponding power energy consumption data.

[0056] Specifically, the system acquires production site data for the current production unit, including site environmental parameters, crop and livestock growth status parameters, and equipment operating parameters. Using the preset growth plan of the current production unit as a reference, it analyzes the corresponding agricultural production stage and collects power energy consumption data of the operating equipment corresponding to the current agricultural production stage, such as the power energy usage of each piece of equipment and the power energy allocation required for the next production stage.

[0057] The use of electrical energy in this embodiment includes the use of purchased electricity and the use of built-in photovoltaic power generation. This embodiment further calculates the park's carbon emissions based on the amount of purchased electricity and the amount of photovoltaic power generated, helping to assess the carbon emission level of the agricultural park during its energy use process.

[0058] S402: Input the power consumption data into the data fusion model, associate the data of all operating equipment under the current production unit, and track the operating status of each operating equipment according to the running sequence.

[0059] Specifically, power consumption data is input into the data fusion model. Through the data fusion relationship in the data fusion model, all operating equipment under the current production unit is associated with data, and the operating status of each operating equipment is tracked according to the running sequence. Based on the operating status tracking results, it is determined whether there are any abnormalities in power consumption, which helps to monitor the operating status of each operating equipment.

[0060] S403: Based on the equipment operation status analysis results, perform power energy allocation processing on the corresponding operating equipment to obtain power energy monitoring data that conforms to the agricultural production progress under the current production unit.

[0061] Specifically, based on the analysis results of equipment operation status, power energy is allocated to the corresponding operating equipment. For example, in conjunction with the current agricultural production progress, it is analyzed whether the current growth environment of crops or livestock meets the current growth requirements. If not, the operation status of the corresponding equipment is optimized by allocating power energy, such as oxygenation, cooling, feeding, irrigation, and temperature and humidity regulation. This ensures that the use of power energy meets the needs of the current agricultural production progress and helps to monitor the use of power energy by equipment and whether the allocation meets the needs of the current production progress through power energy monitoring data.

[0062] Specifically, such as Figure 6 As shown, the power energy monitoring method in this embodiment further includes: S50: Obtain the resource usage status and resource allocation ratio of all production units in the agricultural park, and in conjunction with the production plan of each production unit, assess whether the resource allocation ratio of the next production stage meets the production needs.

[0063] Specifically, the resource usage status and resource allocation ratio of all production units in the agricultural park are obtained, including the power usage status of each production unit and its proportion in the total power consumption of the park. Combined with the production plan of each production unit, the resource allocation ratio of the next production stage is evaluated to see if it meets the production needs. If the current production plan cannot be completed on time and in the required quantity, it needs to be supplemented on the basis of the next production plan, which may result in the resource allocation ratio of the next production stage not meeting the production needs.

[0064] This embodiment also includes generating a detailed power-side energy consumption report by calculating the proportion of different power sources, such as purchased electricity and photovoltaic power generation. The consumption data for purchased electricity and photovoltaic power generation will be statistically analyzed according to multiple time dimensions, including hours, days, months, and years, helping users clearly understand energy usage in different time periods and the substitution effect of photovoltaic power generation for purchased electricity. This provides an effective basis for decision-making regarding energy procurement and power generation system optimization. Load-side data analysis covers the main production units of the agricultural park. The system classifies and monitors electricity consumption according to different scenarios, accurately recording the electricity consumption and power load of different equipment in each production unit. For each load scenario, the system combines purchased electricity consumption and local grid carbon emission factors to calculate the corresponding carbon emissions, generating carbon emission analysis data to support the monitoring and management of carbon emissions in the park.

[0065] S60: Based on the production demand assessment results, dynamically adjust the resource allocation plan for all production units in the next production stage, optimize the overall power energy allocation plan of the agricultural park, and obtain coordinated operation data of the park.

[0066] Specifically, based on the production demand assessment results, the resource allocation plan for all production units in the next production stage will be dynamically adjusted. Additional resources will be allocated to units lagging behind in production progress, while resources will be appropriately reduced for units that have completed production ahead of schedule. Furthermore, the overall power energy allocation scheme of the agricultural park will be adjusted and optimized based on the adjusted resource allocation plan, so that the power energy of the entire park can be coordinated among all production units.

[0067] In actual operation, the embodiments of this application can dynamically optimize the equipment scheduling strategy according to the actual operating conditions of different production units. Through comprehensive analysis of equipment energy consumption level, operating status and external environment data, it can ensure that all types of equipment are always maintained at a better energy efficiency level, and can flexibly adjust the equipment operation mode according to the specific needs of each production unit to adapt to actual production needs.

[0068] In one embodiment, a winery in the agricultural product processing stage is used as an example for illustration. The monitoring of agricultural product processing in this embodiment is as follows: Figure 9As shown, a complete production process in a winery typically encompasses multiple steps, including fermentation, distillation, filtration, and packaging, involving a large number of different types of electrical equipment. Specifically, the agricultural park management center utilizes various sensors and intelligent monitoring devices deployed at key production stages in the winery to collect, dynamically track, and centrally manage the operational status and energy consumption of critical electrical equipment such as fermentation tanks, distillation equipment, filters, and packaging lines. Based on the actual workload and production demands of the equipment, the center rationally schedules equipment usage to achieve a balance between energy consumption and production efficiency. Specifically, the park management center monitors the fermentation tanks in real time, tracking their temperature, humidity, and workload to ensure stable environmental conditions during fermentation. Simultaneously, it dynamically adjusts equipment power consumption according to different stages of fermentation to ensure optimal fermentation efficiency, thereby achieving intelligent monitoring and refined scheduling of the entire energy consumption process in the winery.

[0069] Specifically, the operation of the three-dimensional transparent film packaging machine is monitored, its power consumption and operating frequency are recorded in real time, and the operating rhythm of the packaging machine is dynamically adjusted according to production needs to ensure efficient operation of the equipment and reduce energy waste.

[0070] Specifically, the crusher and multi-functional mixer are key equipment. The system collects their power consumption and operating status in real time to ensure stability under high load and avoid shutdown or energy consumption surge due to overload.

[0071] Specifically, the auxiliary equipment includes wine pumps, lighting equipment, fans, and radiators. The system comprehensively monitors the power usage of these devices and adjusts the lighting brightness, fan speed, and radiator operating status in real time, supporting the normal operation of the main equipment while effectively controlling overall energy consumption.

[0072] In one embodiment, an aquaponics greenhouse is used as an example for illustration. The monitoring of farmed fish in this embodiment is as follows: Figure 10 As shown, crop planting monitoring is as follows Figure 11 As shown, an aquaponics greenhouse is an ecological system that organically combines aquaculture and crop cultivation. To ensure the stability of the greenhouse environment and the healthy growth of fish and plants, it relies on various electrical devices to maintain various operating parameters.

[0073] In this embodiment, within the aquaponics greenhouse, the agricultural park management center can conduct real-time monitoring and management of various key electrical equipment used for crop growth, such as supplemental lighting, temperature control devices, ventilation systems, and automatic irrigation equipment, as well as water pumps, aeration equipment, feeding equipment, and circulating filtration units used for aquaculture. The system relies on environmental sensors distributed throughout the greenhouse to acquire real-time data on temperature, humidity, light intensity, carbon dioxide concentration, and key water quality indicators such as water temperature, dissolved oxygen, and pH value. This comprehensive understanding of the greenhouse's core environmental data, combined with the collected parameters, dynamically adjusts the operating status and modes of relevant electrical equipment to ensure the stable and efficient operation of the greenhouse's ecological environment.

[0074] Specifically, by monitoring the operation of key equipment such as supplemental lighting, wet curtains, fans, aerators, feeders, circulating water pumps, and intelligent water and fertilizer integrated machines, and dynamically adjusting the operating modes of the equipment according to the growth stages of crops and farmed fish and environmental conditions, the stability of the growth environment for crops and farmed fish and the optimization of equipment energy consumption are ensured.

[0075] Specifically, regarding supplemental lighting, the system monitors the operation of supplemental lighting in the greenhouse in real time. Based on the crop growth cycle, external light intensity, and current power load, it dynamically adjusts the brightness and usage time of the supplemental lighting to ensure that crops receive necessary supplementation when light is insufficient, while keeping energy consumption to a minimum.

[0076] Specifically, the intelligent water and fertilizer integrated machine is responsible for monitoring the operation of the irrigation equipment, including water pump power consumption and irrigation frequency. Based on greenhouse soil moisture, air humidity, and crop water requirements, the system automatically adjusts the start and stop of the irrigation equipment to achieve precise irrigation and avoid water waste.

[0077] Specifically, regarding the wet curtains and fans, the system monitors the wet curtains and fans inside the greenhouse in real time. Based on the temperature difference between inside and outside and the air circulation, it dynamically adjusts the fan speed and the working mode of the radiator to ensure that the greenhouse temperature is maintained within a suitable range.

[0078] Specifically, the water quality monitoring instrument collects key water quality parameters such as pool temperature, pH value, dissolved oxygen concentration, and salinity in real time. The system dynamically adjusts the operation of relevant equipment based on this data to ensure the health of fish and crops.

[0079] Specifically, circulating water pumps and aerators are used to maintain water circulation and oxygen content. The system automatically adjusts its operating frequency based on water quality monitoring data to ensure a stable water environment and reasonable power consumption.

[0080] Specifically, the feeding system automatically controls the feeding frequency and amount based on the fish's growth stage and water quality, promoting healthy fish growth while avoiding overfeeding that could lead to resource waste and water pollution.

[0081] In this embodiment, during actual implementation, the data visualization of the entire park is used for comprehensive management and monitoring of the park's data. Specifically, the power supply-side visualization information includes detailed data on purchased electricity and photovoltaic power generation, helping users to fully understand the structural proportion of the park's energy sources and intuitively analyze the energy contribution of purchased electricity and self-generated electricity, so as to optimize energy procurement and usage plans. The load-side visualization display covers environmental parameters, equipment operating status, energy consumption, and corresponding carbon emission data in diverse scenarios such as agricultural product processing, planting production, and livestock breeding. The park management center uses visualization terminals to intuitively present the overall operation information of the park in the form of charts and values, providing users with a comprehensive and clear reference for energy management and carbon emission status.

[0082] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0083] In one embodiment, a power energy monitoring system based on multi-source agricultural information interaction is provided, which corresponds one-to-one with the power energy monitoring method based on multi-source agricultural information interaction in the above embodiments. For example... Figure 12 As shown, this power energy monitoring system based on multi-source agricultural information interaction includes a framework construction module, a semantic analysis module, a model construction module, and a resource coordination module. Detailed descriptions of each functional module are as follows: The framework construction module is used to obtain equipment call data under each production unit in the agricultural park, and build a cloud-edge collaborative framework according to the preset agricultural production plan based on the equipment call data. The semantic analysis module is used to collect multi-source heterogeneous data during the production process of agricultural parks and perform data preprocessing. Combined with the cloud-edge collaboration framework, semantic association analysis is performed on the preprocessed multi-source heterogeneous data to obtain the semantic association relationship between heterogeneous data. The model building module is used to perform time-series modeling based on the data acquisition time sequence and semantic association of multi-source heterogeneous data to obtain a data fusion model for power energy scheduling of production equipment; The resource coordination module is used to acquire power energy consumption data corresponding to each stage of agricultural production, and input it into the data fusion model for power energy data fusion analysis and power energy allocation processing to obtain power energy monitoring data of the agricultural park.

[0084] Preferably, the resource coordination module specifically includes: The on-site data acquisition submodule is used to acquire on-site production data under the current production unit, analyze the corresponding agricultural production stage based on the on-site production data, and collect the corresponding power energy consumption data. The equipment status tracking submodule is used to input power consumption data into the data fusion model, associate data with all operating equipment under the current production unit, and track the operating status of each operating equipment according to the running sequence. The resource monitoring submodule is used to allocate power energy to the corresponding operating equipment based on the analysis results of the equipment operation status, and obtain power energy monitoring data that conforms to the agricultural production progress under the current production unit.

[0085] Preferably, the model building module specifically includes: The time-series data association submodule is used to obtain the data acquisition time sequence corresponding to multi-source heterogeneous data, and combine the semantic association relationship to associate the multi-source heterogeneous data with the data acquisition time sequence to obtain the time-series associated data. The sequential data association submodule is used to sequentially associate synchronously associated data according to the data acquisition time sequence to obtain sequentially associated data; The model building submodule is used to perform time-series modeling on simultaneous and sequentially correlated data to obtain a data fusion model for power energy scheduling of production equipment.

[0086] Preferably, the semantic analysis module specifically includes: The data preprocessing submodule is used to collect multi-source heterogeneous data in the production process of agricultural parks in real time, and to perform data format conversion processing on the multi-source heterogeneous data in combination with the preset data processing mechanism to obtain multi-source preprocessed data in the same data format. The semantic association submodule is used to perform semantic similarity analysis on multi-source preprocessed data based on the cloud-edge collaboration framework, and to perform semantic association processing on multi-source preprocessed data that reaches the preset semantic similarity, so as to obtain the semantic association relationship between heterogeneous data.

[0087] Preferably, the framework building modules specifically include: The permission allocation submodule is used to obtain the equipment call data under each production unit of the agricultural park, and to perform edge node permission allocation processing for each device according to the preset agricultural production plan of the current production unit. The framework construction submodule is used to build a cloud-edge collaboration framework that matches the preset agricultural production plan, based on the edge node permission allocation results and the data transmission relationship between each device and the agricultural park management center node.

[0088] Preferably, the power energy monitoring method also includes: The resource allocation assessment module is used to obtain the resource usage status and resource allocation ratio of all production units in the agricultural park, and, in conjunction with the production plan of each production unit, assess whether the resource allocation ratio of the next production stage meets the production needs. The park coordination module is used to dynamically adjust the resource allocation plan for all production units in the next production stage based on the production demand assessment results, optimize the overall power energy allocation plan of the agricultural park, and obtain park coordination operation data.

[0089] Specific limitations regarding the power energy monitoring system based on multi-source agricultural information interaction can be found in the limitations of the power energy monitoring method based on multi-source agricultural information interaction mentioned above, and will not be repeated here. Each module in the aforementioned power energy monitoring system based on multi-source agricultural information interaction can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0090] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 13 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores power energy monitoring data during the agricultural park's production process. The network interface communicates with external terminals via a network. When executed by the processor, the computer program implements a power energy monitoring method based on multi-source agricultural information interaction.

[0091] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a power energy monitoring method based on multi-source agricultural information interaction.

[0092] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0093] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0094] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0095] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A power energy monitoring method based on multi-source agricultural information interaction, characterized in that, include: Obtain equipment call data for each production unit in the agricultural park, and construct a cloud-edge collaborative framework based on the equipment call data and a preset agricultural production plan; Collect multi-source heterogeneous data from the production process in the agricultural park and perform data preprocessing. Combined with the cloud-edge collaboration framework, perform semantic association analysis on the preprocessed multi-source heterogeneous data to obtain the semantic association relationship between the heterogeneous data. Based on the data acquisition time sequence of the multi-source heterogeneous data and the semantic association relationship, time sequence modeling processing is performed to obtain a data fusion model for power energy scheduling of production equipment; The power consumption data corresponding to each stage of agricultural production is obtained and input into the data fusion model for power energy data fusion analysis and power energy allocation processing to obtain power energy monitoring data of the agricultural park.

2. The power energy monitoring method based on multi-source agricultural information interaction according to claim 1, characterized in that, The process of acquiring electricity consumption data corresponding to each stage of agricultural production and inputting it into the data fusion model for electricity data fusion analysis and electricity allocation processing to obtain electricity monitoring data for the agricultural park specifically includes: Acquire production site data under the current production unit, analyze the corresponding agricultural production stage based on the production site data, and collect the corresponding power energy consumption data; The power consumption data is input into the data fusion model to associate data with all operating equipment under the current production unit and track the operating status of each operating equipment according to the running sequence. Based on the equipment operation status analysis results, power energy allocation is carried out on the corresponding operating equipment to obtain power energy monitoring data that conforms to the agricultural production progress under the current production unit.

3. The power energy monitoring method based on multi-source agricultural information interaction according to claim 1, characterized in that, The step of performing time-series modeling based on the data acquisition time sequence and semantic relationships of the multi-source heterogeneous data to obtain a data fusion model for power energy scheduling of production equipment specifically includes: Obtain the data acquisition time sequence corresponding to the multi-source heterogeneous data, and combine it with the semantic association relationship to perform simultaneous data association of the multi-source heterogeneous data according to the data acquisition time sequence to obtain simultaneous associated data; The simultaneous associated data are sequentially associated according to the data acquisition time sequence to obtain sequentially associated data; The simultaneous correlation data and the sequential correlation data are processed by time series modeling to obtain a data fusion model for power energy scheduling of production equipment.

4. The power energy monitoring method based on multi-source agricultural information interaction according to claim 1, characterized in that, The process involves collecting multi-source heterogeneous data from the agricultural park's production process and preprocessing it. Then, using the cloud-edge collaboration framework, semantic association analysis is performed on the preprocessed multi-source heterogeneous data to obtain the semantic relationships between the heterogeneous data. Specifically, this includes: Multi-source heterogeneous data from the agricultural park's production process are collected in real time, and the data format of the multi-source heterogeneous data is converted using a preset data processing mechanism to obtain multi-source preprocessed data in the same data format. Based on the cloud-edge collaboration framework, semantic similarity analysis is performed on the multi-source preprocessed data, and semantic association processing is performed on the multi-source preprocessed data that reaches the preset semantic similarity to obtain the semantic association relationship between heterogeneous data.

5. The power energy monitoring method based on multi-source agricultural information interaction according to claim 1, characterized in that, The process of acquiring equipment call data for each production unit in the agricultural park and constructing a cloud-edge collaborative framework based on the equipment call data according to a preset agricultural production plan specifically includes: Obtain equipment call data for each production unit in the agricultural park, and assign edge node permissions to each device according to the preset agricultural production plan of the current production unit; Based on the edge node permission allocation results and the data transmission relationship between each device and the agricultural park management center node, a cloud-edge collaboration framework matching the preset agricultural production plan is constructed.

6. The power energy monitoring method based on multi-source agricultural information interaction according to claim 1, characterized in that, The power energy monitoring method also includes: Obtain the resource usage status and resource allocation ratio of all production units in the agricultural park, and in conjunction with the production plan of each production unit, assess whether the resource allocation ratio of the next production stage meets the production needs. Based on the production demand assessment results, the resource allocation plan for all production units in the next production stage is dynamically adjusted, and the overall power energy allocation plan of the agricultural park is optimized to obtain coordinated operation data of the park.

7. A power energy monitoring system based on multi-source agricultural information interaction, characterized in that, The power energy monitoring method based on multi-source agricultural information interaction according to any one of claims 1 to 6, the system comprising: The framework construction module is used to obtain equipment call data under each production unit in the agricultural park, and to construct a cloud-edge collaborative framework according to the preset agricultural production plan based on the equipment call data. The semantic analysis module is used to collect multi-source heterogeneous data during the production process of agricultural parks and perform data preprocessing. Combined with the cloud-edge collaborative framework, semantic association analysis is performed on the preprocessed multi-source heterogeneous data to obtain the semantic association relationship between heterogeneous data. The model building module is used to perform time-series modeling processing based on the data acquisition time sequence and semantic association of the multi-source heterogeneous data to obtain a data fusion model for power energy scheduling of production equipment; The resource coordination module is used to acquire the power energy consumption data corresponding to each stage of agricultural production, and input it into the data fusion model for power energy data fusion analysis and power energy allocation processing to obtain power energy monitoring data of the agricultural park.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the power energy monitoring method based on multi-source agricultural information interaction as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the power energy monitoring method based on multi-source agricultural information interaction as described in any one of claims 1 to 6.

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