Multi-scene equipment monitoring management system for smart park

Abstract

The invention provides a multi-scene equipment monitoring management system for a smart park, and the system comprises a data obtaining unit which is used for obtaining warehouse image data and road image data of each agricultural product warehouse, and carrying out the real-time monitoring of meteorological data; the warehouse management unit divides the agricultural product warehouse into a dynamic control warehouse and a constant temperature control warehouse according to the warehouse image data; the parameter analysis unit is used for carrying out adaptive temperature range analysis according to agricultural product categories stored in the dynamic control warehouse, carrying out compensation parameter analysis by combining the adaptive temperature range with meteorological data, and dynamically establishing a compensation parameter library according to an analysis result; the temperature management unit is used for analyzing each dynamic control warehouse according to the road image data to obtain a frequent time period and a normal time period of warehouse in and out, and carrying out control analysis on the warehouse temperature in combination with meteorological data; and the parameter input unit is used for performing temperature difference compensation for each time period according to the control analysis result in combination with the difference compensation parameter library.

Description

Technical Field

[0001] This invention relates to an equipment monitoring and management system, and more particularly to a multi-scenario equipment monitoring and management system for smart parks. Background Technology

[0002] This section provides only background information relevant to this disclosure and is not necessarily prior art.

[0003] In the field of agricultural product storage in smart industrial parks, temperature control technology plays a crucial role in ensuring the quality of agricultural products and extending their shelf life. It mainly uses traditional temperature control equipment, such as air conditioners and evaporative coolers, to regulate the warehouse temperature in order to create a suitable storage environment for agricultural products.

[0004] Currently, most temperature control for agricultural product storage operates in relatively fixed storage environments. When the outside temperature is suitable, the temperature control equipment may continue to run. For example, in spring and autumn, when the outdoor temperature is close to the suitable storage temperature for agricultural products, the equipment still cools or heats according to the preset fixed mode, resulting in a large waste of energy. Moreover, static temperature control causes excessive temperature differences when products leave the warehouse and come into contact with the outside temperature, or excessive temperature differences when products enter the warehouse and come into contact with the outside temperature. Excessive temperature differences can easily cause the surface of agricultural products to rot.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] Purpose of the invention: The technical problem to be solved by the present invention is to provide a multi-scenario equipment monitoring and management system for smart parks, which addresses the shortcomings of the existing technology.

[0007] To address the aforementioned technical problems, this invention discloses a multi-scenario equipment monitoring and management system for smart parks, comprising:

[0008] The system comprises a data acquisition unit, a warehouse management unit, a parameter analysis unit, a temperature management unit, and a parameter input unit; among which,

[0009] The data acquisition unit is used to acquire warehouse image data and road image data for each agricultural product warehouse, and at the same time monitor meteorological data in real time.

[0010] The warehouse management unit divides the agricultural product warehouse into a dynamically controlled warehouse and a temperature-controlled warehouse based on warehouse image data.

[0011] The parameter analysis unit performs an adaptation temperature range analysis based on the types of agricultural products stored in the dynamically controlled warehouse, combines the adaptation temperature range with meteorological data to perform a supplementary parameter analysis, and dynamically establishes a supplementary parameter library based on the analysis results.

[0012] The temperature management unit analyzes road image data for each dynamically controlled warehouse to obtain the frequent and normal periods of entry and exit, and combines meteorological data from different periods to control and analyze the warehouse temperature.

[0013] The parameter input unit performs temperature compensation for each time period based on the analysis results of the temperature management unit and the compensation parameter library.

[0014] Furthermore, the data acquisition unit includes:

[0015] Image acquisition module and meteorological monitoring module; among which,

[0016] The image acquisition module is used to acquire warehouse image data of each agricultural product warehouse through the warehouse monitoring device, and at the same time acquire road image data of the smart park roads through the road monitoring device.

[0017] The meteorological monitoring module is used to extract real-time data from meteorological websites, filter the extracted data according to the location of the smart park, and obtain the meteorological data corresponding to the smart park.

[0018] Furthermore, the warehouse management unit includes:

[0019] The product analysis module and the warehouse partitioning module; among them...

[0020] The product analysis module sets a sensitivity threshold and classifies agricultural products into dynamic products and constant-temperature products based on the temperature sensitivity values ​​of different agricultural products.

[0021] The warehouse division module identifies the stored product categories in agricultural product warehouses based on warehouse image data, and then divides the warehouses according to these categories, classifying agricultural product warehouses into dynamically controlled warehouses and temperature-controlled warehouses. The specific method is as follows:

[0022] A temperature-sensitive value greater than the sensitivity threshold indicates a constant-temperature product.

[0023] The temperature sensitivity value is less than the sensitivity threshold, indicating a dynamic product.

[0024] Furthermore, the parameter analysis unit includes:

[0025] Temperature analysis module and parameter generation module; among them,

[0026] The temperature analysis module uses the temperature range that the types of agricultural products stored in the dynamic control warehouse are suitable for as the corresponding suitable temperature range of the dynamic control warehouse.

[0027] The parameter generation module calculates the compensation parameters based on the adaptive temperature range and meteorological data corresponding to the dynamically controlled warehouse, and obtains the compensation parameters for adjusting the warehouse temperature to the adaptive temperature range under different meteorological data. The compensation parameters are then summarized to establish a compensation parameter library.

[0028] Furthermore, the temperature management unit includes:

[0029] Frequent period analysis module and control analysis module; among them,

[0030] The frequent time period analysis module extracts the historical driving routes and historical driving time periods of transport vehicles in the smart park based on road image data, calculates the traffic flow data of each dynamically controlled warehouse in each time period, and divides the frequent time periods and normal time periods based on the traffic flow data;

[0031] The control analysis module calculates the time interval between the current time period and the frequent time period, and obtains the number of normal time periods between the current time period and the frequent time period.

[0032] Set the maximum temperature variation value that each agricultural product can withstand in adjacent time periods based on the category of agricultural product;

[0033] Extract meteorological data corresponding to the normal period, and calculate the time required to adjust the warehouse temperature from the center value to the edge value of the suitable temperature range using temperature control equipment based on the meteorological data.

[0034] Temperature control analysis is performed based on the quantity and time required during normal periods to obtain temperature control strategies, and warehouse temperatures are adjusted according to these strategies.

[0035] Furthermore, the temperature control strategy includes:

[0036] When transitioning from a high-frequency period to a normal period, the warehouse temperature is adjusted from the edge of the suitable temperature range to the center value; conversely, when transitioning from a normal period to a high-frequency period, the warehouse temperature is adjusted from the center of the suitable temperature range to the edge value.

[0037] When the temperature in the meteorological data is greater than the suitable temperature range, the edge value of the suitable temperature range is taken upwards;

[0038] If the temperature in the meteorological data is lower than the suitable temperature range, then the edge value of the suitable temperature range is taken downwards.

[0039] Furthermore, the parameter input unit, based on the adjusted warehouse temperature value and the supplementary parameter library after adjustment according to the temperature control strategy by the control analysis module, obtains the supplementary parameters corresponding to the temperature value reached in each normal period, and sends the supplementary parameters to the temperature control equipment to control the temperature of the agricultural product warehouse.

[0040] Furthermore, the calculation employs the time required for the temperature control equipment to fluctuate the warehouse temperature from the center value to the edge value of the suitable temperature range, including:

[0041] Let T be the meteorological data for the time interval corresponding to the normal period. weather The temperature range for dynamic control of the warehouse is [T]. min ,T max The center value of the applicable temperature range is T. center ;

[0042] Edge values ​​are determined based on meteorological data, and the single temperature fluctuation range ΔT is determined. floot The details are as follows:

[0043] If T weather >T max Then the edge value is T. maxr ;

[0044] ΔT floot =T max -T center

[0045] If T we <T min Then the edge value is T. min ;

[0046] ΔT floot =T center -T min

[0047] The time required is calculated as follows:

[0048]

[0049] Among them, t need Where K is the required time, P is the warehouse insulation coefficient, η is the temperature control equipment power coefficient, and η is the temperature change safety factor.

[0050] Beneficial effects:

[0051] 1. This invention addresses the challenges of dynamically controlled warehouses by leveraging meteorological data to achieve energy-efficient temperature control. The parameter analysis unit calculates supplementary parameters based on meteorological data and the temperature range suitable for agricultural products within the warehouse. The temperature management unit controls the warehouse temperature according to historical periods of frequent inbound and outbound activity and meteorological data. During transitions between frequent and normal periods, the warehouse temperature is rationally adjusted to switch between the edge and center values ​​of the suitable temperature range. When the meteorological temperature is suitable, natural conditions are utilized to reduce the operating time and energy consumption of the temperature control equipment, thereby lowering operating costs.

[0052] 2. This invention categorizes agricultural products based on their temperature sensitivity and develops precise temperature control strategies for different types of warehouses. A parameter analysis unit determines the suitable temperature range for each dynamically controlled warehouse, while a temperature management unit precisely controls the warehouse temperature by combining meteorological data and inbound / outbound time periods. This prevents product quality damage due to excessive temperature differences during inbound and outbound processes. For temperature-controlled warehouses, a static control method ensures stable temperature, preventing spoilage of agricultural products due to temperature fluctuations, thus guaranteeing the quality and freshness of agricultural products and reducing losses.

[0053] 3. This invention achieves reasonable division of agricultural product warehouses through the analysis of warehouse image data by the warehouse management unit. The product analysis module classifies agricultural products according to their temperature sensitivity values ​​and thresholds, and the warehouse division module divides the warehouses into dynamically controlled warehouses and temperature-controlled warehouses accordingly, thereby improving the utilization efficiency of storage space and facilitating centralized management and maintenance of different types of agricultural products. Attached Figure Description

[0054] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0055] Figure 1 This is a schematic diagram of the overall structure of the present invention.

[0056] In the diagram: 10 is the data acquisition unit; 20 is the warehouse management unit; 30 is the parameter analysis unit; 40 is the temperature management unit; and 50 is the parameter input unit. Detailed Implementation

[0057] This invention provides a multi-scenario equipment monitoring and management system for smart parks, including a data acquisition unit, a warehouse management unit, a parameter analysis unit, a temperature management unit, and a parameter input unit;

[0058] The data acquisition unit is used to acquire warehouse image data of each agricultural product warehouse in the smart park and road image data within the park, while simultaneously monitoring meteorological data in real time.

[0059] The warehouse management unit is used to divide agricultural product warehouses into dynamically controlled warehouses and temperature-controlled warehouses based on warehouse image data.

[0060] The parameter analysis unit is used to analyze the temperature range of agricultural products stored in the dynamically controlled warehouse, and to combine the temperature range with meteorological data to perform supplementary parameter analysis, and to dynamically establish a supplementary parameter library based on the analysis results.

[0061] The temperature management unit is used to analyze the frequent periods of historical entry and exit for each dynamically controlled warehouse based on road image data, and to control and analyze the warehouse temperature by combining the current period with the meteorological data of the frequent periods and the corresponding periods.

[0062] The parameter input unit is used to input supplementary parameters for each time period based on the analysis results of the temperature management unit and the supplementary parameter library.

[0063] As a further improvement to this technical solution, the smart park in the data acquisition unit consists of multiple agricultural product warehouses, and a data connection is established with the central management terminal of the smart park to manage the temperature control equipment of the agricultural product warehouses.

[0064] As a further improvement to this technical solution, the data acquisition unit includes an image acquisition module and a meteorological monitoring module;

[0065] The image acquisition module is used to acquire warehouse image data of each agricultural product warehouse through the warehouse monitoring device, and at the same time acquire road image data of the smart park roads through the road monitoring device.

[0066] The meteorological monitoring module is used to extract real-time data from meteorological websites, filter the extracted data according to the location of the smart park, and thus obtain the meteorological data corresponding to the smart park.

[0067] As a further improvement to this technical solution, the warehouse management unit includes a product analysis module and a warehouse partitioning module;

[0068] The product analysis module is used to perform temperature-sensitive numerical analysis on different agricultural products, and at the same time set a sensitivity threshold. Based on the temperature-sensitive numerical values ​​and the sensitivity threshold, agricultural products are divided into dynamic products and constant-temperature products.

[0069] A temperature-sensitive value greater than the sensitivity threshold indicates a constant-temperature product.

[0070] The temperature sensitivity value is greater than the sensitivity threshold, indicating a dynamic product.

[0071] The warehouse division module is used to analyze the stored categories of agricultural product warehouses based on warehouse image data, and divide the warehouses according to the analysis results, classifying agricultural product warehouses into dynamic control warehouses and constant temperature control warehouses.

[0072] As a further improvement to this technical solution, the parameter analysis unit includes a temperature analysis module and a parameter generation module;

[0073] The temperature analysis module is used to extract the types of agricultural products stored in the dynamically controlled warehouse for temperature range analysis, and obtain the temperature range corresponding to each dynamically controlled warehouse.

[0074] The parameter generation module is used to perform differential parameter analysis by combining the suitable temperature range with meteorological data. By analyzing the data, it obtains the differential parameters for adjusting the warehouse temperature to the suitable temperature range under different meteorological data. Then, it summarizes the obtained differential parameters to establish a differential parameter library.

[0075] As a further improvement to this technical solution, the parameter analysis unit, temperature management unit, and parameter input unit are only adjusted for the dynamically controlled warehouse, enabling the dynamically controlled warehouse to use meteorological data to make energy-saving changes to the warehouse temperature.

[0076] For temperature-controlled warehouses, temperature control equipment is used for static control to limit temperature changes.

[0077] As a further improvement to this technical solution, the temperature management unit includes a frequent period analysis module and a control analysis module;

[0078] The frequent time period analysis module is used to extract the historical driving routes and historical driving time periods of historical transport vehicles in the smart park based on road image data. Then, it extracts the historical transport vehicles, historical driving routes, and historical driving time periods corresponding to the entry and exit of each dynamic control warehouse for traffic flow analysis, obtains the traffic flow data of each dynamic control warehouse in each time period, and divides the time period into frequent time periods and normal time periods based on the traffic flow data.

[0079] The control analysis module is used to calculate the difference between the current time period and the frequent time period to obtain the number of normal time periods between the current time period and the frequent time period.

[0080] Based on the category of agricultural products, temperature change tolerance analysis is conducted to obtain the maximum temperature change value that each agricultural product can withstand in adjacent time periods.

[0081] The meteorological data corresponding to the normal period quantity is extracted. Then, the extracted meteorological data is combined with the center and edge values ​​of the suitable temperature range and the temperature change value of agricultural products to analyze the time required for temperature fluctuation. The time required to fluctuate the warehouse temperature from the center value to the edge value of the suitable temperature range by using temperature control equipment to ensure the quality of agricultural products is obtained.

[0082] Then, the number of normal time periods is combined with the required time for temperature control analysis, and temperature values ​​are matched according to the order of magnitude of normal time periods, so that a corresponding temperature value is matched for each normal time period.

[0083] As a further improvement to this technical solution, when the control and analysis module performs temperature control analysis, it will float the warehouse temperature from the edge value to the center value when transitioning from a frequent period to a normal period, and will float the warehouse temperature from the center value to the edge value when transitioning from a normal period to a frequent period.

[0084] If the meteorological temperature is higher than the suitable temperature range, then the edge value of the suitable temperature range shall be taken upwards.

[0085] If the meteorological temperature is lower than the suitable temperature range, then the edge value of the suitable temperature range will be taken.

[0086] As a further improvement to this technical solution, the parameter input unit analyzes the temperature values ​​matched by the control analysis module for each normal period in conjunction with the differential parameter library to obtain the differential parameters corresponding to the temperature values ​​reached in each normal period. Then, the differential parameters are input and sent to the temperature control device, so that the temperature control device can control the temperature of the agricultural product warehouse according to the differential parameters.

[0087] Example:

[0088] like Figure 1 As shown, this embodiment provides a multi-scenario equipment monitoring and management system for smart parks, including a data acquisition unit 10, a warehouse management unit 20, a parameter analysis unit 30, a temperature management unit 40, and a parameter input unit 50;

[0089] The data acquisition unit 10 is used to acquire warehouse image data of each agricultural product warehouse in the smart park and road image data within the park, while simultaneously monitoring meteorological data in real time.

[0090] The smart park in data acquisition unit 10 consists of multiple agricultural product warehouses and establishes a data connection with the central management terminal of the smart park to manage the temperature control equipment of the agricultural product warehouses.

[0091] The data acquisition unit 10 includes an image acquisition module and a meteorological monitoring module;

[0092] The image acquisition module is used to acquire warehouse image data of each agricultural product warehouse through the warehouse monitoring device, and at the same time acquire road image data of the smart park roads through the road monitoring device;

[0093] The meteorological monitoring module is used to extract real-time data from meteorological websites (China Weather Network, China Meteorological Administration API), and then filter the extracted data according to the location of the smart park to obtain the meteorological data corresponding to the smart park.

[0094] Warehouse management unit 20 is used to divide agricultural product warehouses into dynamically controlled warehouses and temperature-controlled warehouses based on warehouse image data;

[0095] Warehouse management unit 20 includes a product analysis module and a warehouse partitioning module;

[0096] The product analysis module is used to perform temperature-sensitive numerical analysis on different agricultural products, and to set sensitivity thresholds. Based on the temperature-sensitive values ​​and sensitivity thresholds, agricultural products are divided into dynamic products and constant-temperature products.

[0097] A temperature-sensitive value greater than the sensitivity threshold indicates a constant-temperature product.

[0098] If the temperature sensitivity value is greater than the sensitivity threshold, it is a dynamic product. The specific steps are as follows:

[0099] Determine the temperature-sensitive numerical calculation indicators: Identify the key factors for temperature-sensitive numerical analysis of agricultural products, including but not limited to the characteristics of the agricultural products themselves (type, maturity, moisture content, etc.), storage time requirements, and quality change indicators (such as freshness, nutrient loss rate, spoilage rate, etc.).

[0100] Data collection and experimental measurement: Samples of the same type of agricultural products are stored in different temperature environments, and a storage period is set. At each time point, the quality change data of the agricultural products (such as weight loss rate, color change value, number of microorganisms, etc.) are recorded.

[0101] Temperature-sensitive value calculation: Based on the quality change data, the temperature-sensitive value is calculated using a specific algorithm. For example, the quality change rate can be used as the temperature-sensitive value. For each temperature level, the quality change rate is calculated. Assuming that the quality change index changes linearly with time, the slope can be calculated using linear regression as the change rate.

[0102] If it is nonlinear, the rate of change can be calculated by curve fitting and differentiation;

[0103] Setting Sensitive Thresholds: Based on common storage requirements and industry standards for agricultural products, a preliminary range of sensitive thresholds is determined. At the same time, historical storage data and experimental results can be combined to adjust and optimize the preliminary thresholds, and finally determine the sensitive thresholds.

[0104] Agricultural product classification: The calculated temperature sensitivity values ​​of each agricultural product are compared with the sensitivity thresholds. Based on the comparison results, agricultural products are classified into dynamic products and constant temperature products.

[0105] The warehouse division module is used to analyze the stored categories of agricultural product warehouses based on warehouse image data, and divide the warehouses according to the analysis results, classifying agricultural product warehouses into dynamic control warehouses and constant temperature control warehouses.

[0106] The warehouse image data is read and the image is denoised. Gaussian filtering is used to remove noise. The target detection algorithm is used to identify agricultural products in the image. For each identified agricultural product, its temperature sensitivity type is queried (by querying the previously established agricultural product temperature sensitivity type database).

[0107] The parameter analysis unit 30 is used to analyze the temperature range of agricultural products stored in the dynamically controlled warehouse, and at the same time, it combines the temperature range with meteorological data to perform supplementary parameter analysis, and dynamically establishes a supplementary parameter library based on the analysis results.

[0108] The parameter analysis unit 30 includes a temperature analysis module and a parameter generation module;

[0109] The temperature analysis module is used to extract the types of agricultural products stored in the dynamically controlled warehouse for temperature range analysis, and obtain the temperature range corresponding to each dynamically controlled warehouse.

[0110] To query the corresponding suitable temperature range for each agricultural product category, we can obtain it from the agricultural product characteristic database. Then, we can determine the suitable temperature range of the dynamically controlled warehouse as the intersection of the suitable temperature ranges of all stored agricultural product categories, thus obtaining the suitable temperature range of the warehouse.

[0111] The parameter generation module is used to perform differential parameter analysis by combining the suitable temperature range with meteorological data. Through analysis, it obtains differential parameters for adjusting the warehouse temperature to the suitable temperature range under different meteorological data. Then, it summarizes the obtained differential parameters to establish a differential parameter library. The specific steps are as follows:

[0112] Calculate temperature compensation: For different meteorological conditions (such as meteorological temperature being higher or lower than the suitable temperature range), calculate the required compensation parameters based on the difference between meteorological data and the suitable temperature range. Calculate the compensation parameters based on different meteorological conditions and temperature change ranges (such as seasonal changes, daytime temperature differences, etc.) to ensure that the warehouse temperature can be maintained within the suitable temperature range under temperature control equipment.

[0113] Summary of Compensation Parameters: All calculated compensation parameters are summarized and optimized according to actual conditions to form a compensation parameter library. This parameter library can be used for real-time adjustment and calibration of temperature control equipment. The formula is as follows:

[0114] When the ambient temperature is greater than the maximum value of the suitable temperature range:

[0115] ΔT lower =T weather -T max ;

[0116] When the ambient temperature is lower than the minimum value of the suitable temperature range:

[0117] ΔT higher =T min -weather;

[0118] Among them, T max and T min To accommodate the maximum and minimum temperatures within the range, T weather For meteorological temperature, ΔT lower This indicates the compensation parameter ΔT for reducing the temperature value. higher This indicates the parameter that compensates for the need to increase the temperature value.

[0119] The parameter analysis unit 30, temperature management unit 40, and parameter input unit 50 only adjust the dynamic control warehouse, enabling the dynamic control warehouse to use meteorological data to make energy-saving changes to the warehouse temperature.

[0120] For temperature-controlled warehouses, temperature control equipment is used for static control to limit temperature changes.

[0121] The temperature management unit 40 is used to analyze the frequent periods of historical entry and exit for each dynamic control warehouse based on road image data, and to control and analyze the warehouse temperature by combining the current period with the meteorological data of the frequent periods and the corresponding periods.

[0122] Temperature management unit 40 includes a frequent period analysis module and a control analysis module;

[0123] The frequent time period analysis module is used to extract the historical driving routes and historical driving time periods of transport vehicles in the smart park based on road image data. Then, it extracts the historical transport vehicles, historical driving routes, and historical driving time periods corresponding to the entry and exit of each dynamically controlled warehouse for traffic flow analysis. It obtains the traffic flow data of each dynamically controlled warehouse in each time period and divides the time period into frequent time periods and normal time periods based on the traffic flow data. The specific steps are as follows:

[0124] Extract historical transport vehicle driving data: Based on road image data or traffic monitoring data, extract the driving trajectory data for each transport vehicle, including the vehicle's timestamp and location coordinates. From this data, filter out the historical driving routes and driving time periods for each vehicle. Each driving trajectory should include the origin, destination, driving route, and driving time period information;

[0125] Extract inbound and outbound data of dynamically controlled warehouses: Based on the historical warehouse inbound and outbound records (such as inbound and outbound time, vehicle number, etc.), extract the historical inbound and outbound data of each dynamically controlled warehouse, determine the inbound and outbound time of each transport vehicle, and associate it with the corresponding warehouse.

[0126] Link historical transport vehicle data with warehouse entry and exit data: Associate the historical driving routes of each transport vehicle with its entry and exit events within a specific time period to determine the path, time period, and corresponding warehouse of each transport vehicle during the entry and exit process;

[0127] Traffic flow data extraction: By analyzing historical driving routes and historical driving time periods, traffic flow data for each dynamically controlled warehouse is calculated for each time period. Traffic flow data refers to the number of transport vehicles entering or leaving a warehouse within a specific time period, as shown in the following formula:

[0128]

[0129] Among them, C w For the warehouse traffic flow, t a and t b For a specific time period, N is the number of transport vehicles, and t in For the time of entry into the warehouse, t out Let k be the outbound time, and k be an indicator function, representing the time period [t]. a ,t b The value is 1 when entering or leaving the warehouse, and 0 otherwise.

[0130] Time period segmentation: Based on traffic flow data, segment the traffic flow characteristics of each warehouse in each time period, distinguish between peak time periods (frequent time periods) and off-peak time periods (normal time periods). By statistically analyzing the traffic flow in different time periods, determine which time periods belong to frequent time periods and which belong to normal time periods. For example, a traffic flow threshold can be set, and time periods with traffic flow exceeding the threshold are considered frequent time periods.

[0131] Traffic flow analysis results summary: Summarize the traffic flow data and frequent time period divisions for each warehouse at different time periods, and create a traffic flow analysis report for subsequent analysis and decision-making.

[0132] The control analysis module is used to calculate the difference between the current time period and the frequent time period to obtain the number of normal time periods between the current time period and the frequent time period.

[0133] By comparing the differences between the current time period and frequent time periods, we can obtain the normal time period (statistically the time interval between the current time period and frequent time periods) of the distance between the current time period and frequent time periods.

[0134] Based on the category of agricultural products, temperature change tolerance analysis is conducted to obtain the maximum temperature change value that each agricultural product can withstand in adjacent time periods.

[0135] Based on the different categories of agricultural products, the maximum temperature variation that each category can withstand within adjacent time periods is obtained. This temperature variation value is usually assessed based on the type of agricultural product, storage requirements, and environmental conditions, as shown in the following formula:

[0136] ΔT P =min(ΔT) max ,ΔTenv(P));

[0137] Where, ΔT P The maximum temperature change value, ΔT max ΔTenv(P) represents the maximum temperature variation that each agricultural product can withstand within adjacent time periods, and ΔTenv(P) represents the impact of environmental temperature changes on the agricultural product.

[0138] The meteorological data corresponding to the normal period quantity is extracted. Then, the extracted meteorological data is combined with the center and edge values ​​of the suitable temperature range and the temperature change value of agricultural products to analyze the time required for temperature fluctuation. The time required to fluctuate the warehouse temperature from the center value to the edge value of the suitable temperature range by using temperature control equipment to ensure the quality of agricultural products is obtained.

[0139] Extract meteorological data corresponding to the normal time period. This data may include information such as temperature and humidity. Analyze warehouse temperature changes based on the meteorological data, assess the temperature fluctuation range, and calculate the time required for the warehouse temperature to fluctuate from the center to the edge of the suitable temperature range based on the meteorological data and temperature change tolerance. The rate of temperature change is related to the warehouse's insulation and equipment efficiency. Consider these factors to estimate the required time, as follows:

[0140] Meteorological data for the time interval corresponding to the normal period are used as T. weather The temperature range for dynamic control of the warehouse is [T]. min T max The center value of the applicable temperature range is T. center ;

[0141] Edge values ​​are determined based on meteorological data, and the single temperature fluctuation range ΔT is determined. floot ;

[0142] If T weather >T max Then the edge value is T. max ;

[0143] ΔT floot =T maxr -T center ;

[0144] If T weather <T min Then the edge value is T. min ;

[0145] ΔT floot =T center -T min ;

[0146] The formula for calculating the required time is:

[0147]

[0148] Among them, t need Where ΔT is the required time, K is the warehouse insulation coefficient, P is the power coefficient of the temperature control equipment, and η is the temperature change safety factor (valued between 0.8 and 1.0). P The smaller the value, the smaller the value of η. K and P are preset parameters of the system, which are determined based on the characteristics of the warehouse building and the equipment model. For example, the K value is small for warehouses with good insulation, and the P value is large for warehouses with high equipment power.

[0149] Then, temperature control analysis is performed by combining the number of normal periods with the required time, and temperature values ​​are matched according to the order of magnitude of normal periods, so that a corresponding temperature value is matched for each normal period.

[0150] Temperature control analysis was conducted based on the normal production volume and required time. This analysis aimed to ensure that warehouse temperature was adjusted without affecting the quality of agricultural products.

[0151] When performing temperature control analysis, the control analysis module will float the warehouse temperature from the edge value to the center value when transitioning from a frequent period to a normal period, and will float the warehouse temperature from the center value to the edge value when transitioning from a normal period to a frequent period.

[0152] If the meteorological temperature is higher than the suitable temperature range, then the edge value of the suitable temperature range shall be taken upwards.

[0153] If the meteorological temperature is lower than the suitable temperature range, then the edge value of the suitable temperature range will be taken.

[0154] When the number of normal time periods is insufficient to support the warehouse temperature to move from the edge value to the center value and then back to the edge value, the center time point of the normal time period is selected as the temperature control inflection point. When the warehouse problem reaches the temperature control inflection point during the adjustment process towards the center value, the warehouse problem is immediately adjusted back towards the edge value.

[0155] The parameter input unit 50 is used to input the compensation parameters for each time period based on the analysis results of the temperature management unit 40 and the compensation parameter library.

[0156] The parameter input unit 50 analyzes the temperature values ​​matched for each normal period by the control analysis module in conjunction with the differential parameter library to obtain the differential parameters corresponding to the temperature values ​​reached in each normal period. Then, the differential parameters are input and sent to the temperature control equipment, enabling the temperature control equipment to control the temperature of the agricultural product warehouse based on the differential parameters. The specific steps are as follows:

[0157] Normal period temperature matching analysis: Based on the number of each normal period, combined with meteorological data and temperature control analysis, the temperature value matched for each normal period is determined. These temperature values ​​are based on a comprehensive result of meteorological temperature, the temperature range that agricultural products are adapted to, and temperature control strategies.

[0158] Compensation parameter library analysis: By analyzing the compensation parameter library, the corresponding compensation parameters are obtained based on the temperature values ​​of each normal period. The compensation parameters can include temperature fine-tuning, adjustment range, control strategy, etc., which are used to correct the temperature control behavior of equipment and system.

[0159] Inputting the compensation parameters to the temperature control equipment: The acquired compensation parameters are input to the temperature control equipment. The compensation parameters provide the temperature control equipment with the necessary adjustment information to ensure that the temperature control system adjusts the warehouse temperature according to the current environmental conditions and the needs of agricultural products. Then, the temperature control equipment adjusts the temperature of the agricultural product warehouse according to the received compensation parameters to ensure that the warehouse temperature is always maintained within the range suitable for agricultural product storage and to avoid exceeding the temperature fluctuation range that agricultural products can withstand.

[0160] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding a multi-scenario device monitoring and management system for smart parks, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0161] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MCU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0162] This invention provides a concept and method for a multi-scenario device monitoring and management system for smart parks. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A multi-scenario equipment monitoring and management system for smart parks, characterized in that, include: The system comprises a data acquisition unit (10), a warehouse management unit (20), a parameter analysis unit (30), a temperature management unit (40), and a parameter input unit (50); among which, The data acquisition unit (10) is used to acquire warehouse image data and road image data of each agricultural product warehouse, and at the same time monitor meteorological data in real time. The warehouse management unit (20) divides the agricultural product warehouse into a dynamic control warehouse and a constant temperature control warehouse based on the warehouse image data; The parameter analysis unit (30) performs an adaptation temperature range analysis based on the types of agricultural products stored in the dynamic control warehouse, combines the adaptation temperature range with meteorological data to perform a supplementary parameter analysis, and dynamically establishes a supplementary parameter library based on the analysis results. The temperature management unit (40) analyzes the road image data for each dynamically controlled warehouse to obtain the frequent and normal periods of entry and exit, and combines the meteorological data of different periods to control and analyze the warehouse temperature. The parameter input unit (50) performs temperature compensation for each time period based on the analysis results of the temperature management unit (40) and the compensation parameter library.

2. The multi-scenario equipment monitoring and management system for smart parks according to claim 1, characterized in that, The data acquisition unit (10) includes: Image acquisition module and meteorological monitoring module; among which, The image acquisition module is used to acquire warehouse image data of each agricultural product warehouse through the warehouse monitoring device, and at the same time acquire road image data of the smart park roads through the road monitoring device. The meteorological monitoring module is used to extract real-time data from meteorological websites, filter the extracted data according to the location of the smart park, and obtain the meteorological data corresponding to the smart park.

3. The multi-scenario equipment monitoring and management system for smart parks according to claim 2, characterized in that, The warehouse management unit (20) includes: The product analysis module and the warehouse partitioning module; among them... The product analysis module sets a sensitivity threshold and classifies agricultural products into dynamic products and constant-temperature products based on the temperature sensitivity values ​​of different agricultural products. The warehouse division module identifies the stored product categories in agricultural product warehouses based on warehouse image data, and divides the warehouses according to the stored product categories, classifying agricultural product warehouses into dynamic control warehouses and constant temperature control warehouses.

4. A multi-scenario equipment monitoring and management system for smart parks according to claim 3, characterized in that, The specific method for classifying agricultural products into dynamic products and constant-temperature products is as follows: A temperature-sensitive value greater than the sensitivity threshold indicates a constant-temperature product. The temperature sensitivity value is less than the sensitivity threshold, indicating a dynamic product.

5. A multi-scenario equipment monitoring and management system for smart parks according to claim 4, characterized in that, The parameter analysis unit (30) includes: Temperature analysis module and parameter generation module; among them, The temperature analysis module uses the temperature range that the types of agricultural products stored in the dynamic control warehouse are suitable for as the corresponding suitable temperature range of the dynamic control warehouse. The parameter generation module calculates the compensation parameters based on the adaptive temperature range and meteorological data corresponding to the dynamically controlled warehouse, and obtains the compensation parameters for adjusting the warehouse temperature to the adaptive temperature range under different meteorological data. The compensation parameters are then summarized to establish a compensation parameter library.

6. A multi-scenario equipment monitoring and management system for smart parks according to claim 5, characterized in that, The temperature management unit (40) includes: Frequent period analysis module and control analysis module; among them, The frequent time period analysis module extracts the historical driving routes and historical driving time periods of transport vehicles in the smart park based on road image data, calculates the traffic flow data of each dynamically controlled warehouse in each time period, and divides the frequent time periods and normal time periods based on the traffic flow data; The control analysis module calculates the time interval between the current time period and the frequent time period, and obtains the number of normal time periods between the current time period and the frequent time period. Set the maximum temperature variation value that each agricultural product can withstand in adjacent time periods based on the category of agricultural product; Extract meteorological data corresponding to the normal period, and calculate the time required to adjust the warehouse temperature from the center value to the edge value of the suitable temperature range using temperature control equipment based on the meteorological data. Temperature control analysis is performed based on the quantity and time required during normal periods to obtain temperature control strategies, and warehouse temperatures are adjusted according to these strategies.

7. A multi-scenario equipment monitoring and management system for smart parks according to claim 6, characterized in that, The temperature control strategy includes: When transitioning from a frequent period to a normal period, adjust the warehouse temperature from the edge of the suitable temperature range to the center value; conversely, when transitioning from a normal period to a frequent period, adjust the warehouse temperature from the center of the suitable temperature range to the edge value.

8. A multi-scenario equipment monitoring and management system for smart parks according to claim 7, characterized in that, The temperature control strategy also includes: When the temperature in the meteorological data is greater than the suitable temperature range, the edge value of the suitable temperature range is taken upwards; If the temperature in the meteorological data is lower than the suitable temperature range, then the edge value of the suitable temperature range is taken downwards.

9. A multi-scenario equipment monitoring and management system for smart parks according to claim 8, characterized in that, The parameter input unit (50) obtains the supplementary parameters corresponding to the temperature value reached in each normal period based on the temperature value adjusted by the control analysis module according to the temperature control strategy and the supplementary parameter library, and sends the supplementary parameters to the temperature control equipment to control the temperature of the agricultural product warehouse.

10. A multi-scenario equipment monitoring and management system for smart parks according to claim 9, characterized in that, The calculation, which uses temperature control equipment, includes the time required for the warehouse temperature to fluctuate from the center to the edge of the suitable temperature range, including: Let T be the meteorological data for the time interval corresponding to the normal period. weather The temperature range for dynamic control of the warehouse is [T]. min ,T max The center value of the applicable temperature range is T. center ; Edge values ​​are determined based on meteorological data, and the single temperature fluctuation range ΔT is determined. floot The details are as follows: If T weather >T max Then the edge value is T. maxr ; ΔT floot =T max -T center If T weather <T min Then the edge value is T. min ; ΔT floot =T center -t min The time required is calculated as follows: Among them, T need Where K is the required time, P is the warehouse insulation coefficient, η is the temperature control equipment power coefficient, and η is the temperature change safety factor.

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