Edible mushroom drying room temperature control method and system based on cloud platform

Abstract

The invention provides an edible mushroom drying chamber temperature control method and system based on a cloud platform. The method comprises the following steps: collecting multi-point temperature data in an edible mushroom drying chamber; according to the multi-point temperature data, the balance degree index of a temperature field in the current edible mushroom drying chamber is determined, and when the balance degree index is higher than a preset balance degree threshold value, a first-stage temperature control instruction and a second-stage temperature control instruction are generated; when the first-stage temperature control instruction is generated, the global temperature adjusting quantity is determined according to the multi-point temperature data and the target temperature of the current drying process stage, and when the second-stage temperature control instruction is generated, the local temperature adjusting quantity is determined according to the spatial distribution characteristics of the multi-point temperature data; and according to the global temperature regulating variable and the local temperature regulating variable, determining a multi-stage regulating command of the temperature in the edible mushroom drying chamber, and performing temperature grading regulation based on the multi-stage regulating command of the temperature. According to the technical scheme provided by the invention, the unbalanced temperature field can be accurately controlled in the edible mushroom drying chamber.

Description

Technical Field

[0001] This application relates to the field of temperature control technology, and more specifically, to a method and system for temperature control of edible fungus drying room based on a cloud platform. Background Technology

[0002] Temperature control is a core supporting technology in industrial production, public services, and high-end manufacturing. Its core objective is to stabilize the temperature of the target object within a set range, avoiding equipment failure, product quality deviation, or system performance degradation caused by temperature fluctuations. Early temperature control relied on manual adjustment, which had low precision and slow response, making it difficult to meet the needs of complex scenarios. With the development of automatic control theory, temperature control technology has gradually upgraded to a closed-loop system based on sensors, controllers, and actuators, realizing the leap from analog control to digital control. Today, temperature control technology is widely used in scenarios such as heat dissipation of electronic equipment, temperature control of chemical reaction vessels, thermal management of new energy batteries, and medical cold chain.

[0003] In existing temperature control systems, the system uses temperature sensors to detect the temperature of the controlled object in real time, transmits the collected temperature values ​​to the controller, compares them with the set target value, calculates the temperature deviation, and the controller calculates the temperature deviation according to a preset control algorithm to output a precise control signal. Finally, the control signal drives the actuator to perform temperature control. However, in the temperature control of edible fungus drying rooms based on cloud platforms, the drying room is affected by the airflow organization characteristics and the distribution of edible fungus loading, resulting in an uneven temperature gradient field. Traditional single-point temperature control strategies can only ensure that the temperature at the monitoring point conforms to the process curve, but cannot sense and compensate for the temperature differences in local spatial dimensions. This unevenness of the temperature field makes it difficult to control the consistency of edible fungus quality. Therefore, how to accurately control the uneven temperature field in edible fungus drying rooms has become a challenge for the industry. Summary of the Invention

[0004] This application provides a cloud platform-based method and system for temperature control in edible mushroom drying rooms, which can accurately control uneven temperature fields in edible mushroom drying rooms.

[0005] In a first aspect, this application provides a method for temperature control of an edible fungus drying room based on a cloud platform, comprising the following steps: Collect multi-point temperature data inside the edible fungus drying chamber and upload the multi-point temperature data to the cloud platform; After receiving the multi-point temperature data, the cloud platform determines the uniformity index of the temperature field in the current edible fungus drying room based on the multi-point temperature data. When the uniformity index is higher than the preset uniformity threshold, the first priority target is to track the preset drying process temperature curve, and a first-level temperature control command is generated. The second priority target is to optimize the uniformity of the temperature field in the edible fungus drying room, and a second-level temperature control command is generated. When generating the first-level temperature control command, the global temperature adjustment amount for adjusting the overall temperature of the room is determined based on the thermal state of the edible fungus drying room in the multi-point temperature data and the target temperature of the current drying process stage. When generating the second-level temperature control command, the local temperature adjustment amount for compensating the local temperature of the room is determined based on the spatial distribution characteristics of the multi-point temperature data. The cloud platform determines multi-level temperature control commands for the edible fungus drying chamber based on the global temperature adjustment and the local temperature adjustment, and sends the multi-level temperature control commands to the temperature control mechanism of the edible fungus drying chamber for graded temperature control.

[0006] In some embodiments, after receiving the multi-point temperature data, the cloud platform determines the uniformity index of the current temperature field in the edible fungus drying room based on the multi-point temperature data, specifically including: After receiving the multi-point temperature data, the cloud platform extracts the temperature value of each sampling point from the multi-point temperature data; Calculate the average temperature of the current temperature field in the edible fungus drying room based on the temperature values ​​of each sampling point. Calculate the deviation between the temperature value at each sampling point and the average temperature; The uniformity index of the temperature field in the current edible fungus drying room is determined based on all deviations.

[0007] In some embodiments, when the uniformity index is higher than a preset uniformity threshold, a primary temperature control command is generated with tracking the preset drying process temperature curve as the primary priority objective, and a secondary temperature control command is generated with optimizing the uniformity of the temperature field in the edible fungus drying chamber as the secondary priority objective. Specifically, the secondary temperature control command includes: When the balance index is higher than the preset balance threshold, the multi-level target control logic is activated. Based on the multi-level target control logic, tracking the preset drying process temperature curve is taken as the first-level priority target, and a first-level temperature control command is generated from the first-level priority target. At the same time, optimizing the uniformity of the temperature field in the edible fungus drying room is taken as the second-level priority target, and a second-level temperature control command is generated from the second-level priority target.

[0008] In some embodiments, when generating the primary temperature control command, determining the global temperature adjustment amount for adjusting the overall temperature of the drying chamber based on the thermal state corresponding to the edible fungus drying chamber in the multi-point temperature data and the target temperature of the current drying process stage specifically includes: When generating the first-level temperature control command, the overall average temperature of the edible fungus drying room is calculated based on multi-point temperature data as the corresponding thermal state of the edible fungus drying room. Retrieve the preset drying process temperature curve, and determine the target temperature corresponding to the current drying process stage based on the drying process temperature curve. The difference between the overall average temperature corresponding to the thermal state and the target temperature of the current drying process stage is calculated to obtain the global temperature deviation. Based on the global temperature deviation, a proportional-integral-derivative control algorithm is used to calculate the global temperature regulation amount used to adjust the overall indoor temperature.

[0009] In some embodiments, when generating the secondary temperature control command, determining the local temperature adjustment amount for compensating for local indoor temperature based on the spatial distribution characteristics of the multi-point temperature data specifically includes: When generating the secondary temperature control command, based on the spatial distribution characteristics of the multi-point temperature data, local high-temperature areas and local low-temperature areas in the temperature field of the edible fungus drying room are identified. The deviation between the overall temperature of the local high-temperature zone and the local low-temperature zone and the overall average temperature of the edible fungus drying room is calculated to obtain the local temperature deviation corresponding to each local area, wherein the local area includes the local high-temperature zone and the local low-temperature zone. The local temperature adjustment amount used to compensate for the local indoor temperature is determined based on the local temperature deviation corresponding to each local area.

[0010] In some embodiments, the cloud platform determines the multi-level control instructions for the temperature of the edible fungus drying chamber based on the global temperature adjustment and the local temperature adjustment, specifically including: The execution priority of the global temperature regulation is determined to be higher than that of the local temperature regulation. Based on the global temperature adjustment, basic control parameters are generated for controlling the main temperature control unit of the edible fungus drying chamber. Based on the local temperature adjustment amount and the distribution of the auxiliary temperature control units corresponding to the local area, compensation and control parameters for the local area are generated. The basic control parameters and the compensation control parameters are coupled and verified, and based on the coupling verification results, the basic control parameters and the compensation control parameters are integrated to form a multi-level control command for the temperature of the edible fungus drying room.

[0011] In some embodiments, temperature data at multiple points inside the edible fungus drying chamber are collected using temperature sensors.

[0012] Secondly, this application provides a cloud-based temperature control system for an edible fungus drying room, comprising: The data acquisition module is used to collect multi-point temperature data inside the edible fungus drying chamber and upload the multi-point temperature data to the cloud platform. The processing module is used to determine the uniformity index of the temperature field in the current edible fungus drying room based on the multi-point temperature data after the cloud platform receives the multi-point temperature data. When the uniformity index is higher than the preset uniformity threshold, a first-level temperature control command is generated with tracking the preset drying process temperature curve as the first priority target, and a second-level temperature control command is generated with optimizing the uniformity of the temperature field in the edible fungus drying room as the second priority target. The processing module is also used to determine the global temperature adjustment amount for adjusting the overall temperature of the room based on the thermal state of the edible fungus drying room in the multi-point temperature data and the target temperature of the current drying process stage when generating the first-level temperature control command, and to determine the local temperature adjustment amount for compensating the local temperature of the room based on the spatial distribution characteristics of the multi-point temperature data when generating the second-level temperature control command. The execution module is used by the cloud platform to determine the multi-level temperature control command of the edible fungus drying room based on the global temperature adjustment amount and the local temperature adjustment amount, and to send the multi-level temperature control command to the temperature control mechanism of the edible fungus drying room for graded temperature control.

[0013] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described cloud-based method for controlling the temperature of an edible fungus drying room.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described cloud-based method for controlling the temperature of an edible mushroom drying room.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The cloud-based method and system for temperature control in edible mushroom drying rooms provided in this application first collects multi-point temperature data inside the drying room and uploads the multi-point temperature data to the cloud platform. Second, after receiving the multi-point temperature data, the cloud platform determines the uniformity index of the current temperature field inside the drying room based on the multi-point temperature data. When the uniformity index is higher than a preset uniformity threshold, a first-level temperature control command is generated with tracking the preset drying process temperature curve as the primary priority objective, and a second-level temperature control command is generated with optimizing the uniformity of the temperature field inside the drying room as the secondary priority objective. Then, in the generation... When issuing the first-level temperature control command, a global temperature adjustment amount is determined based on the thermal state of the edible fungus drying room in the multi-point temperature data and the target temperature of the current drying process stage. When generating the second-level temperature control command, a local temperature adjustment amount is determined based on the spatial distribution characteristics of the multi-point temperature data to compensate for local temperatures within the room. Finally, the cloud platform determines a multi-level temperature control command for the edible fungus drying room based on the global and local temperature adjustment amounts, and sends the multi-level temperature control command to the temperature control mechanism of the edible fungus drying room for graded temperature control.

[0016] Therefore, this application demonstrates that it can precisely control the uneven temperature field in the edible mushroom drying chamber. First, it collects multi-point temperature data inside the drying chamber and uploads it to a cloud platform. This multi-point distributed acquisition comprehensively covers all areas of the drying chamber, avoiding the limitations of single-point sampling and providing complete raw data for temperature field analysis. Simultaneously, the cloud platform enables centralized and efficient data processing. Second, the cloud platform determines a uniformity index based on the multi-point temperature data and generates tiered control commands when the uniformity index exceeds a preset threshold. The first-level temperature control command prioritizes the accurate tracking of the drying process temperature curve to ensure that the dehydration rate and nutrient retention of the edible mushrooms meet requirements. The second-level temperature control command specifically optimizes the temperature field uniformity, avoiding excessive local temperature differences caused by a single control target, thus achieving dynamic adaptive control of the temperature field state. Then, it generates the first and second-level temperature... When issuing temperature control commands, global and local temperature adjustment amounts are determined separately. The global temperature adjustment amount stabilizes the overall temperature of the drying chamber to meet the requirements of the current process stage, providing a stable foundation for the drying process. The local temperature adjustment amount can accurately eliminate local temperature deviations caused by spatial structure, equipment layout, etc., solving the problem of uneven heating of the same batch of edible fungi. This achieves hierarchical control of global and local temperatures, avoiding the problem that traditional single-point temperature control strategies can only ensure that the temperature of the monitoring point conforms to the process curve, but cannot sense and compensate for temperature differences in local spatial dimensions, making it difficult to control the consistency of edible fungi quality. Finally, the cloud platform integrates the global and local temperature adjustment amounts to form multi-level control commands and issues them for execution, which can effectively and accurately control the uneven temperature field in the edible fungi drying chamber, ensuring the quality of edible fungi. In summary, the technical solution provided in this application can accurately control the uneven temperature field in the edible fungi drying chamber. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a cloud-based temperature control method for edible fungus drying rooms, according to some embodiments of this application. Figure 2 This is an exemplary flowchart illustrating the determination of balance index according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of the global temperature regulation amount according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a cloud-based temperature control system for an edible fungus drying room, as shown in some embodiments of this application. Figure 5 This is a schematic diagram of the structure of a computer device for implementing a cloud-based method for controlling the temperature of an edible fungus drying room, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a cloud-based temperature control method for edible mushroom drying rooms according to some embodiments of this application. The cloud-based temperature control method for edible mushroom drying rooms mainly includes the following steps: In step 101, multi-point temperature data inside the edible fungus drying chamber is collected and uploaded to the cloud platform.

[0020] In practice, temperature sensors can be used to collect multi-point temperature data inside the mushroom drying chamber. Specifically, temperature sensors are deployed at different sampling points in the mushroom drying chamber (including but not limited to the center, corners, near the heating source, and air duct inlets and outlets of the mushroom stacking area). The temperature values ​​of each sampling point are collected by the temperature sensors, and then the temperature values ​​collected from each sampling point are combined to obtain multi-point temperature data inside the mushroom drying chamber. At the same time, the collected multi-point temperature data is uploaded to the cloud platform in real time according to a preset communication protocol (such as MQTT or HTTP). The cloud platform refers to a remote intelligent decision-making and data processing platform built on a cloud computing architecture and designed for the temperature control scenario of mushroom drying.

[0021] It should be noted that the multi-point temperature data in this application refers to the set of temperature values ​​collected from different sampling points in the edible fungus drying room. By collecting multi-point temperature data, basic data support can be provided for subsequent temperature identification and control.

[0022] In step 102, after receiving the multi-point temperature data, the cloud platform determines the uniformity index of the temperature field in the current edible fungus drying room based on the multi-point temperature data. When the uniformity index is higher than the preset uniformity threshold, a first-level temperature control command is generated with tracking the preset drying process temperature curve as the first priority target, and a second-level temperature control command is generated with optimizing the uniformity of the temperature field in the edible fungus drying room as the second priority target.

[0023] In some embodiments, reference Figure 2 As shown in the figure, this is an exemplary flowchart for determining the uniformity index according to some embodiments of this application. In this embodiment, after the cloud platform receives the multi-point temperature data, the determination of the uniformity index of the current temperature field in the edible fungus drying room based on the multi-point temperature data can be achieved by the following steps: First, in step 1021, after receiving the multi-point temperature data, the cloud platform extracts the temperature value of each sampling point from the multi-point temperature data; Secondly, in step 1022, the average temperature of the current edible fungus drying chamber temperature field is calculated based on the temperature values ​​of each sampling point; Then, in step 1023, the deviation between the temperature value of each sampling point and the average temperature is calculated; Finally, in step 1024, the uniformity index of the temperature field in the current edible fungus drying chamber is determined based on all the deviations.

[0024] In specific implementation, firstly, after receiving the multi-point temperature data, the cloud platform extracts the temperature values ​​of each sampling point from the multi-point temperature data; secondly, it calculates the average temperature within the current temperature field of the edible fungus drying room based on the arithmetic mean of the temperature values ​​of each sampling point; then, it subtracts the average temperature from the temperature value of each sampling point to obtain the deviation value of each sampling point relative to the average temperature. The deviation value refers to the difference between the temperature value of a single sampling point and the average temperature within the temperature field of the edible fungus drying room, and is used to measure the degree of deviation of the temperature of a single sampling point relative to the overall temperature level of the drying room. A positive deviation indicates that the temperature of the sampling point is higher than the average temperature, and a negative deviation indicates that the temperature is lower than the average temperature; finally, it calculates the standard deviation of all deviations and uses this standard deviation as an indicator of the uniformity of the current temperature field in the edible fungus drying room. The standard deviation can more intuitively reflect the dispersion of the temperature of each sampling point. The smaller the standard deviation value, the smaller the deviation of the temperature of each sampling point from the average temperature, and the better the uniformity of the temperature field. Therefore, the standard deviation of all deviations can be used as an indicator of the uniformity of the current temperature field in the edible fungus drying room.

[0025] It should be noted that the uniformity index in this application refers to the dispersion index of the temperature field in the current edible fungus drying room. By determining the uniformity index, the temperature field status of the edible fungus drying room can be effectively and intuitively evaluated. The cloud platform can quickly determine the uniformity of the current temperature field by the magnitude of the uniformity index value. The smaller the uniformity index value, the smaller the deviation between the temperature of each sampling point and the average temperature, and the more uniform the overall temperature field distribution. Conversely, there are local high temperature or low temperature areas.

[0026] In some embodiments, when the uniformity index is higher than a preset uniformity threshold, a primary temperature control command is generated with tracking the preset drying process temperature curve as the primary priority objective, and optimizing the uniformity of the temperature field in the edible fungus drying chamber as the secondary priority objective. The generation of the secondary temperature control command is achieved through the following steps: When the balance index is higher than the preset balance threshold, the multi-level target control logic is activated. Based on the multi-level target control logic, tracking the preset drying process temperature curve is taken as the first-level priority target, and a first-level temperature control command is generated from the first-level priority target. At the same time, optimizing the uniformity of the temperature field in the edible fungus drying room is taken as the second-level priority target, and a second-level temperature control command is generated from the second-level priority target.

[0027] In specific implementation, when the uniformity index is higher than the preset uniformity threshold, a multi-level target control logic is activated. The multi-level target control logic refers to a control process framework pre-set in the cloud platform control program, which processes different temperature control requirements (specifically tracking the process temperature curve and optimizing the temperature field uniformity) according to the priority of the targets. This framework ensures that no logical conflicts occur when controlling multiple targets by clearly defining the target priorities and processing order. The uniformity threshold can be set according to actual needs or expert knowledge, and is not limited here. Secondly, based on the multi-level target control logic, tracking the preset drying process temperature curve is taken as the first-level priority target, and a first-level temperature control command is generated from the first-level priority target. At the same time, optimizing the uniformity of the temperature field in the edible fungus drying room is taken as the second-level priority target, and a second-level temperature control command is generated from the second-level priority target.

[0028] In specific implementation, when the balance index is less than or equal to the preset balance threshold, the temperature in the edible fungus drying chamber is controlled based on the preset drying process temperature curve. That is, the current temperature value is compared with the temperature value of the corresponding drying process temperature curve, and the temperature is increased (i.e., the temperature is increased when the current temperature value is less than the temperature value of the corresponding drying process temperature curve) or decreased (i.e., the temperature is decreased when the current temperature value is greater than the temperature value of the corresponding drying process temperature curve) according to the difference between the two. This will not be elaborated further here.

[0029] It should be noted that, in this application, the primary temperature control command refers to the temperature control signal used to track the preset drying process temperature curve; the secondary temperature control command refers to the temperature control signal used to optimize the uniformity of the temperature field in the edible fungus drying chamber. The determination of the primary and secondary temperature control commands stems from the dual requirements of the drying process for temperature: the overall temperature must strictly conform to the preset drying process temperature curve (the drying process temperature curve is the core prerequisite for ensuring the dehydration rate of edible fungi; if the overall temperature deviates from the curve, it will directly lead to insufficient drying, over-drying, or loss of effective components), while the local temperature must remain uniform (if the local temperature difference is too large, some edible fungi in the same batch will be over-dried while others fail to meet the moisture content standard, seriously affecting product consistency). Based on the logical relationship between foundation and optimization, hierarchical instructions are needed to avoid conflicting control objectives. The primary temperature control instruction prioritizes ensuring that the overall temperature of the drying chamber meets the preset process requirements, ensuring that the drying process always proceeds along the established process curve. It controls the drying rhythm and core quality indicators from a macro perspective, avoiding process failures caused by overall temperature inaccuracies. The secondary temperature control instruction, based on the primary instruction ensuring that the overall temperature meets the standard, specifically corrects local temperature deviations, eliminates temperature dead zones and local overheating areas in the drying chamber, and ensures that edible fungi in all locations within the chamber are in the same temperature environment. It improves the drying uniformity and quality consistency of the same batch of products from a micro perspective, thereby avoiding the problem that a single control instruction cannot take into account both the overall and local conditions, resulting in low control efficiency or poor effect.

[0030] In step 103, when generating the first-level temperature control command, a global temperature adjustment amount is determined based on the thermal state of the edible fungus drying room in the multi-point temperature data and the target temperature of the current drying process stage. When generating the second-level temperature control command, a local temperature adjustment amount is determined based on the spatial distribution characteristics of the multi-point temperature data to compensate for the local temperature in the room.

[0031] In some embodiments, reference Figure 3 As shown in the figure, this is an exemplary flowchart of determining the global temperature adjustment amount according to some embodiments of this application. In this embodiment, when generating the first-level temperature control command, the global temperature adjustment amount for adjusting the overall temperature of the room can be determined based on the thermal state corresponding to the edible fungus drying room in the multi-point temperature data and the target temperature of the current drying process stage. This can be achieved by the following steps: First, in step 1031, when generating the first-level temperature control command, the overall average temperature of the edible fungus drying room is calculated based on multi-point temperature data as the corresponding thermal state of the edible fungus drying room. Secondly, in step 1032, a preset drying process temperature curve is retrieved, and the target temperature corresponding to the current drying process stage is determined according to the drying process temperature curve. Then, in step 1033, the difference between the overall average temperature corresponding to the thermal state and the target temperature of the current drying process stage is calculated to obtain the global temperature deviation. Finally, in step 1034, based on the global temperature deviation, a proportional-integral-derivative control algorithm is used to calculate the global temperature adjustment amount for regulating the overall indoor temperature.

[0032] In specific implementation, firstly, when generating the first-level temperature control command, the overall average temperature of the edible fungus drying chamber is calculated based on multi-point temperature data to serve as the corresponding thermal state of the drying chamber. The thermal state refers to a state index that measures the overall temperature level inside the drying chamber, reflecting the current macroscopic temperature condition. Secondly, a preset drying process temperature curve is retrieved, and the target temperature corresponding to the current drying process stage is determined accordingly. This drying process temperature curve is a temperature-time correlation curve pre-defined and stored on a cloud platform based on the drying characteristics of specific edible fungus varieties. The drying process temperature curve is divided into different process stages according to the drying process, including but not limited to preheating, constant-rate dehydration, and de-rate dehydration. The temperature value corresponding to the drying process temperature curve is identified through the process stage identifier on the cloud platform as the target temperature corresponding to the current process stage. The target temperature refers to the standard temperature value set for the current drying process stage to ensure the quality of dried edible fungus. Then, the overall average temperature corresponding to the thermal state is subtracted from the target temperature of the current stage to obtain... The global temperature deviation is calculated as follows: a positive global temperature deviation indicates that the overall temperature is higher than the target temperature, while a negative global temperature deviation indicates that the temperature is lower than the target temperature. The global temperature deviation refers to the difference between the overall average temperature of the drying chamber and the target temperature of the current process stage, reflecting the degree to which the overall temperature deviates from the process requirements. Finally, based on the global temperature deviation, a proportional-integral-derivative (PID) control algorithm is used to calculate the global temperature adjustment amount for regulating the overall temperature of the chamber. Specifically, the proportional component of the PID control algorithm outputs an instantaneous adjustment component based on the absolute value of the global temperature deviation, the integral component outputs an adjustment component by calculating the cumulative value of the global temperature deviation over a period of time, and the derivative component outputs a predictive adjustment component based on the rate of change of the global temperature deviation (i.e., the trend of deviation increasing or decreasing over time). The adjustment components of the three components are then superimposed to obtain the global temperature adjustment amount for regulating the overall temperature of the chamber. When the global temperature deviation is negative, the obtained global temperature adjustment amount is the heating adjustment amount, and when the global temperature deviation is positive, the obtained global temperature adjustment amount is the cooling adjustment amount.

[0033] It should be noted that, in this application, the global temperature regulation amount refers to the control parameter used to drive the main temperature control unit of the edible fungus drying chamber to perform global temperature regulation. Determining the global temperature regulation amount can provide the main temperature control unit of the edible fungus drying chamber with a precise control basis, enabling it to specifically correct global temperature deviations, thereby ensuring that the overall temperature of the drying chamber always conforms to the target temperature of the current drying process stage. Specifically, the global temperature regulation amount is converted into executable equipment operating parameters by converting the comprehensive regulation component output by the PID control algorithm, directly guiding the main temperature control unit to perform global temperature regulation, thereby avoiding drying failure caused by global temperature deviation from the process curve.

[0034] In some embodiments, when generating the secondary temperature control command, determining the local temperature adjustment amount for compensating for local indoor temperature based on the spatial distribution characteristics of the multi-point temperature data is achieved through the following steps: When generating the secondary temperature control command, based on the spatial distribution characteristics of the multi-point temperature data, local high-temperature areas and local low-temperature areas in the temperature field of the edible fungus drying room are identified. The deviation between the overall temperature of the local high-temperature zone and the local low-temperature zone and the overall average temperature of the edible fungus drying room is calculated to obtain the local temperature deviation corresponding to each local area, wherein the local area includes the local high-temperature zone and the local low-temperature zone. The local temperature adjustment amount used to compensate for the local indoor temperature is determined based on the local temperature deviation corresponding to each local area.

[0035] In specific implementation, firstly, when generating secondary temperature control commands, local high-temperature zones and local low-temperature zones in the temperature field within the drying chamber are identified based on the spatial distribution characteristics of the multi-point temperature data. Specifically, the overall average temperature of the multi-point temperature data is calculated, and this overall average temperature is used as the spatial distribution characteristic of the multi-point temperature data. The temperature values ​​in the multi-point temperature data are then correlated one-to-one with their corresponding spatial coordinates to form discrete spatial coordinate-temperature data pairs. Based on all spatial coordinate-temperature data pairs, an existing inverse distance weighted interpolation algorithm is used to process the corresponding area of ​​the edible mushroom drying chamber, generating a temperature field distribution covering the entire space of the edible mushroom drying chamber. The temperature field distribution map is constructed by comparing the temperature values ​​at each point with the spatial distribution characteristics. Regions with continuous spatial distribution and temperatures exceeding the spatial distribution characteristics are designated as locally high-temperature zones, while regions with continuous spatial distribution and temperatures less than or equal to the spatial distribution characteristics are designated as locally low-temperature zones. Locally high-temperature zones refer to areas where the temperature inside the drying chamber is consistently higher than the overall average temperature, and locally low-temperature zones refer to areas where the temperature inside the drying chamber is consistently lower than the overall average temperature; both are specific manifestations of local temperature field imbalance. Next, the average temperature values ​​corresponding to the locally high-temperature and locally low-temperature zones are calculated as the overall temperature, and then the average temperature values ​​for the locally high-temperature zones are calculated. The deviation between the overall temperature of the local low-temperature zone and the overall average temperature of the edible fungus drying chamber is used to obtain the local temperature deviation for each local area. A positive deviation indicates that the area is a local high-temperature zone, quantifying its high temperature level, while a negative deviation indicates that the area is a local low-temperature zone, quantifying its low temperature level. The local temperature deviation refers to the difference between the average temperature of the local area and the overall average temperature of the chamber. This parameter directly quantifies the direction and degree of the local temperature deviation from the overall chamber temperature level and is the core basis for subsequent adjustment strategies. Finally, based on the local temperature deviation corresponding to each local area, a proportional-integral-derivative control algorithm is used to calculate the compensation for the local temperature deviation. The local temperature adjustment amount is calculated as follows: for each local area corresponding to the local temperature deviation, the proportional component of the proportional-integral-derivative control algorithm outputs an instantaneous adjustment component based on the absolute value of the local temperature deviation; the integral component calculates and outputs an adjustment component based on the cumulative value of the local temperature deviation over a period of time; and the derivative component outputs a predictive adjustment component based on the rate of change of the local temperature deviation (i.e., the trend of the deviation increasing or decreasing over time). The adjustment components of the three components are then superimposed to obtain the local temperature adjustment amount for compensating for the local temperature in the corresponding local area. The local temperature adjustment amount calculated for the local high-temperature area is the cooling adjustment parameter.

[0036] It should be noted that the local temperature regulation amount in this application refers to the parameter used to assist the temperature control unit in regulating the local temperature. Even after the overall temperature of the edible fungus drying room is achieved through global temperature regulation, local high-temperature or low-temperature areas will still occur due to differences in spatial structure (such as poor air circulation in corners or slow heat dissipation near the heating source), equipment layout characteristics (such as uneven heat coverage of the main control unit), or different material stacking densities. The global temperature regulation amount is for the overall temperature of the entire room and cannot be precisely applied to these local areas. This results in uneven heating of the same batch of edible fungi due to local temperature differences, leading to problems such as some parts being over-dried and some parts being incompletely dehydrated. Therefore, it is necessary to determine the local temperature regulation amount to solve this local temperature imbalance and avoid the situation where the overall temperature meets the standard but there are local quality defects. Therefore, determining the local temperature regulation amount can provide a precise quantitative control basis for the auxiliary temperature control unit in the local area.

[0037] In step 104, the cloud platform determines the multi-level temperature control command of the edible fungus drying room based on the global temperature adjustment amount and the local temperature adjustment amount, and sends the multi-level temperature control command to the temperature control mechanism of the edible fungus drying room for graded temperature control.

[0038] In some embodiments, the cloud platform determines the multi-level control command for the temperature of the edible fungus drying chamber based on the global temperature adjustment and the local temperature adjustment using the following steps: The execution priority of the global temperature regulation is determined to be higher than that of the local temperature regulation. Based on the global temperature adjustment, basic control parameters are generated for controlling the main temperature control unit of the edible fungus drying chamber. Based on the local temperature adjustment amount and the distribution of the auxiliary temperature control units corresponding to the local area, compensation and control parameters for the local area are generated. The basic control parameters and the compensation control parameters are coupled and verified, and based on the coupling verification results, the basic control parameters and the compensation control parameters are integrated to form a multi-level control command for the temperature of the edible fungus drying room.

[0039] In specific implementation, firstly, the cloud platform determines that the execution priority of global temperature regulation is higher than that of local temperature regulation through preset control priority rules. This control priority is based on the logical setting in the edible mushroom drying process that "achieving overall temperature is a prerequisite for local uniformity adjustment," ensuring that the overall temperature of the entire chamber meets process requirements when resources are limited or parameters conflict. The control priority rules refer to pre-set logical criteria used to regulate the execution order of different control parameters, avoiding command conflicts during multi-objective control. Secondly, basic control parameters for controlling the main temperature control unit of the edible mushroom drying chamber are generated based on the global temperature regulation, that is, the global temperature regulation is prioritized according to the main temperature control unit. The equipment characteristics of the temperature control unit (such as the main heater and the main circulating fan) are converted into parameters to generate basic control parameters for controlling the main temperature control unit of the edible fungus drying chamber. For example, when the global temperature adjustment corresponds to the heating adjustment, the global temperature adjustment is converted into the power increase value of the main heater; when the global temperature adjustment corresponds to the cooling adjustment, the global temperature adjustment is converted into the speed adjustment value of the main circulating fan. These basic control parameters are the operating parameters used to control the main temperature control unit to perform global temperature regulation, and are the core control basis for ensuring that the overall temperature of the entire chamber meets the standard.Then, based on the local temperature adjustment amount and the distribution of the corresponding auxiliary temperature control units in the local area, compensation control parameters for each local area are generated. This involves obtaining the auxiliary temperature control units (e.g., local heaters, zoned exhaust fans) corresponding to local high-temperature and local low-temperature zones. The local temperature adjustment amount is then converted into compensation control parameters adapted to the auxiliary temperature control units. For example, the local temperature adjustment amount for the local low-temperature zone is converted into the power setting value of the corresponding local heater, and the local temperature adjustment amount for the local high-temperature zone is converted into the airflow adjustment value of the corresponding exhaust fan. This generates compensation control parameters for each local area. These compensation control parameters are used to control the auxiliary temperature control units to regulate the local temperature, optimizing the local temperature distribution. Finally, the basic control parameters and compensation control parameters are coupled and verified. This process can employ parameter compatibility analysis, i.e., obtaining the respective range of action and temperature control rules of the basic control parameters (corresponding to the main temperature control unit, acting on the entire room) and the compensation control parameters (corresponding to the auxiliary temperature control unit, acting on local areas). For example, the main heater... The impact of heater power regulation on the average temperature of the entire chamber and the impact of local supplementary heaters on the temperature of specific areas are analyzed. Then, an existing thermal simulation model (e.g., finite element model or CFD fluid dynamics model, not limited here) is used to simulate the temperature field changes when both types of parameters are executed simultaneously. This identifies whether the temperature in the overlapping area (e.g., the main heating coverage area with local supplementary heating) exceeds the allowable fluctuation range (e.g., ±1℃). If it does, the supplementary control parameters are corrected according to the "global priority" principle (i.e., the supplementary control parameters are reduced according to the excess range) until the simulation shows that both the overall chamber and local temperatures are compliant, completing the coupling verification. The coupling verification refers to the process of verifying and correcting conflicts between the synergistic effect of the basic control parameters and the supplementary control parameters, aiming to ensure the consistency of multi-parameter control. Finally, when the coupling verification result is compliant, the basic control parameters and the supplementary control parameters are integrated into a multi-level control command for the temperature inside the edible fungus drying chamber according to priority (basic control parameters are executed first, followed by supplementary control parameters).

[0040] It should be noted that the multi-level control command in this application refers to a structured set of control commands that includes basic control parameters and compensation control parameters. The determination of the multi-level control command lies in integrating the basic control parameters corresponding to the global temperature adjustment and the compensation control parameters corresponding to the local temperature adjustment, so as to realize the hierarchical control of the overall and local temperature of the drying chamber. This ensures that the main temperature control unit stably maintains the overall temperature of the entire chamber in line with the preset drying process curve, and the auxiliary temperature control unit corrects the local high or low temperature deviations. The hierarchical control of both ensures that the temperature field meets the macroscopic process requirements and has local uniformity.

[0041] In some embodiments, the multi-level temperature control command is sent to the temperature control mechanism of the edible fungus drying room for graded temperature control, which is achieved by the following steps: The cloud platform sends multi-level temperature control commands to the temperature control mechanism of the edible fungus drying room; The temperature control mechanism receives and analyzes multi-level temperature control commands, separating the basic control parameters and the compensation control parameters. The main temperature control unit of the edible fungus drying chamber is driven to perform overall temperature regulation based on the aforementioned basic control parameters. Based on the compensation and control parameters, the auxiliary temperature control unit of the corresponding local area is driven to perform local temperature compensation.

[0042] In specific implementation, firstly, the cloud platform sends multi-level control commands to the temperature control mechanism of the edible mushroom drying room via an industrial-grade communication protocol. The temperature control mechanism, as the core control component at the field execution end of the edible mushroom drying room temperature control system, is responsible for connecting the cloud platform with various temperature control devices (i.e., the main temperature control unit and auxiliary temperature control units). Secondly, the temperature control mechanism receives the multi-level temperature control commands and analyzes them using existing analysis tools, separating the basic control parameters and compensation control parameters. Then, based on the basic control parameters, it drives the main temperature control unit of the edible mushroom drying room to perform overall temperature adjustment. If the main temperature control unit is an electric heater, the heating power is adjusted; if the main temperature control unit is an electric heater, the heating power is adjusted. The temperature control unit is the main circulating fan. Adjusting the fan speed is crucial. The main temperature control unit refers to the set of devices responsible for regulating the overall temperature of the edible mushroom drying chamber. Overall temperature regulation refers to the process of adjusting the operating parameters of the main temperature control unit to make the average temperature of the entire chamber match the target temperature. Finally, based on the local temperature control parameters, the auxiliary temperature control unit for the corresponding local area is driven to perform local temperature compensation. If the auxiliary temperature control unit is a local heater, the output power of the heater is adjusted; if the auxiliary temperature control unit is a zone exhaust fan, the airflow of the zone exhaust fan is adjusted. The auxiliary temperature control unit refers to the set of auxiliary devices responsible for regulating the temperature of a specific local area within the edible mushroom drying chamber.

[0043] It should be noted that, in this application, local temperature compensation refers to the process of eliminating the temperature deviation between a local area and the average temperature of the entire room by adjusting the operating parameters of the auxiliary temperature control unit.

[0044] In another aspect, in some embodiments, this application provides a cloud-based temperature control system for edible fungus drying rooms, with reference to... Figure 4 The figure is a schematic diagram of a cloud-based temperature control system for an edible mushroom drying room, according to some embodiments of this application. This cloud-based temperature control system includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below: The data acquisition module 201 in this application is mainly used to collect multi-point temperature data inside the edible fungus drying chamber and upload the multi-point temperature data to the cloud platform. Processing module 202 in this application is mainly used to determine the uniformity index of the temperature field in the current edible fungus drying room after the cloud platform receives the multi-point temperature data. When the uniformity index is higher than the preset uniformity threshold, a first-level temperature control command is generated with tracking the preset drying process temperature curve as the first priority target, and a second-level temperature control command is generated with optimizing the uniformity of the temperature field in the edible fungus drying room as the second priority target. The processing module 202 is further configured to, when generating the first-level temperature control command, determine the global temperature adjustment amount for adjusting the overall temperature of the room based on the thermal state corresponding to the edible fungus drying room in the multi-point temperature data and the target temperature of the current drying process stage; and when generating the second-level temperature control command, determine the local temperature adjustment amount for compensating the local temperature of the room based on the spatial distribution characteristics of the multi-point temperature data. The execution module 203 in this application is mainly used by the cloud platform to determine the multi-level control command of the temperature in the edible fungus drying room according to the global temperature adjustment amount and the local temperature adjustment amount, and to send the multi-level temperature control command to the temperature control mechanism of the edible fungus drying room for temperature graded control.

[0045] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described cloud-based method for controlling the temperature of an edible fungus drying room.

[0046] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device implementing a cloud-based temperature control method for edible mushroom drying rooms, according to some embodiments of this application. The cloud-based temperature control method for edible mushroom drying rooms described in the above embodiments can... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0047] The processor 301 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the cloud-based edible fungus drying room temperature control method described in this application.

[0048] The communication bus 302 can be used to transmit information between the aforementioned components.

[0049] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0050] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the temperature control method for the edible fungus drying room based on the cloud platform can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0051] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0052] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0053] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0054] In addition, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned cloud-based method for controlling the temperature of an edible fungus drying room.

[0055] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0056] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A cloud platform-based temperature control method for a mushroom drying chamber, characterized in that, The method comprises the following steps: Collecting multi-point temperature data in the drying room of edible fungi and uploading the multi-point temperature data to a cloud platform; After receiving the multi-point temperature data, the cloud platform determines an equilibrium index of the current temperature field in the drying room of edible fungi according to the multi-point temperature data, and when the equilibrium index is higher than a preset equilibrium threshold, generates a first temperature control instruction with tracking a preset drying process temperature curve as a first priority target, and generates a second temperature control instruction with optimizing the uniformity of the temperature field in the drying room of edible fungi as a second priority target; When generating the first temperature control instruction, a global temperature adjustment amount for adjusting the overall temperature in the room is determined according to the corresponding thermal state of the drying room of edible fungi in the multi-point temperature data and the target temperature of the current drying process stage, and when generating the second temperature control instruction, a local temperature adjustment amount for compensating for the local temperature in the room is determined according to the spatial distribution characteristics of the multi-point temperature data; The cloud platform determines a multi-level temperature regulation and control instruction of the temperature in the drying room of edible fungi according to the global temperature adjustment amount and the local temperature adjustment amount, and issues the temperature multi-level regulation and control instruction to the temperature control mechanism of the drying room of edible fungi for temperature multi-level regulation and control.

2. The method of claim 1, wherein, After receiving the multi-point temperature data, the cloud platform determines an equilibrium index of the current temperature field in the drying room of edible fungi according to the multi-point temperature data, and when the equilibrium index is higher than a preset equilibrium threshold, generates a first temperature control instruction with tracking a preset drying process temperature curve as a first priority target, and generates a second temperature control instruction with optimizing the uniformity of the temperature field in the drying room of edible fungi as a second priority target; After receiving the multi-point temperature data, the cloud platform extracts the temperature values of each sampling point from the multi-point temperature data; The average temperature of the current temperature field in the drying room of edible fungi is calculated according to the temperature values of each sampling point; The deviation of the temperature values of each sampling point from the average temperature is calculated; The equilibrium index of the current temperature field in the drying room of edible fungi is determined based on all the deviations.

3. The method of claim 1, wherein, When the equilibrium index is higher than the preset equilibrium threshold, a first temperature control instruction is generated with tracking a preset drying process temperature curve as a first priority target, and a second temperature control instruction is generated with optimizing the uniformity of the temperature field in the drying room of edible fungi as a second priority target, which specifically includes: When the equilibrium index is higher than the preset equilibrium threshold, a multi-level target control logic is started; Based on the multi-level target control logic, tracking the preset drying process temperature curve is taken as the first priority target, and a first temperature control instruction is generated from the first priority target, and optimizing the uniformity of the temperature field in the drying room of edible fungi is taken as the second priority target, and a second temperature control instruction is generated from the second priority target.

4. The method of claim 1, wherein, When generating the first temperature control instruction, a global temperature adjustment amount for adjusting the overall temperature in the room is determined according to the corresponding thermal state of the drying room of edible fungi in the multi-point temperature data and the target temperature of the current drying process stage, and when generating the second temperature control instruction, a local temperature adjustment amount for compensating for the local temperature in the room is determined according to the spatial distribution characteristics of the multi-point temperature data; When generating the first temperature control instruction, the overall average temperature of the drying room of edible fungi is calculated according to the multi-point temperature data as the corresponding thermal state of the drying room of edible fungi; The preset drying process temperature curve is retrieved, and the target temperature corresponding to the current drying process stage is determined according to the drying process temperature curve; a difference between the overall average temperature corresponding to the heat state and a target temperature of a current drying process stage is calculated to obtain a global temperature deviation; a global temperature adjustment amount for adjusting the overall temperature in the room is calculated based on the global temperature deviation using a proportional-integral-derivative control algorithm.

5. The method of claim 1, wherein, When generating the secondary temperature control instruction, the local temperature adjustment amount for compensating for the local temperature in the room is determined based on the spatial distribution characteristics of the multi-point temperature data, specifically including: When generating the secondary temperature control instruction, the local high-temperature area and the local low-temperature area in the edible mushroom drying room temperature field are identified based on the spatial distribution characteristics of the multi-point temperature data; deviations between the overall temperatures of the local high-temperature area and the local low-temperature area and the overall average temperature in the edible mushroom drying room are calculated to obtain local temperature deviations corresponding to each local area, the local area including the local high-temperature area and the local low-temperature area; a local temperature adjustment amount for compensating for the local temperature in the room is determined according to the local temperature deviations corresponding to each local area.

6. The method of claim 1, wherein, The cloud platform determines the multi-level regulation and control instruction of the temperature in the edible mushroom drying room according to the global temperature adjustment amount and the local temperature adjustment amount, specifically including: The execution priority of the global temperature adjustment amount is higher than that of the local temperature adjustment amount; a basic regulation and control parameter for controlling the main temperature control unit of the edible mushroom drying room is generated based on the global temperature adjustment amount; a compensation regulation and control parameter of the local area is generated according to the local temperature adjustment amount and the distribution of the auxiliary temperature control unit corresponding to the local area; The basic regulation and control parameter and the compensation regulation and control parameter are coupled and verified, and the basic regulation and control parameter and the compensation regulation and control parameter are integrated according to the coupling verification result to form the multi-level regulation and control instruction of the temperature in the edible mushroom drying room.

7. The method of claim 1, wherein, The multi-point temperature data inside the edible mushroom drying room is collected by a temperature sensor.

8. A cloud platform-based temperature control system for a mushroom drying chamber, characterized in that, The system includes: a collection module for collecting multi-point temperature data inside the edible mushroom drying room and uploading the multi-point temperature data to a cloud platform; a processing module for determining an equilibrium degree index of the current edible mushroom drying room temperature field according to the multi-point temperature data after the cloud platform receives the multi-point temperature data, generating a primary temperature control instruction when the equilibrium degree index is higher than a preset equilibrium degree threshold, and generating a secondary temperature control instruction when optimizing the uniformity of the edible mushroom drying room temperature field is a secondary priority target; The processing module is also used to determine a global temperature adjustment amount for adjusting the overall temperature in the room according to the heat state corresponding to the edible mushroom drying room in the multi-point temperature data and the target temperature of the current drying process stage when generating the primary temperature control instruction, and to determine a local temperature adjustment amount for compensating for the local temperature in the room through the spatial distribution characteristics of the multi-point temperature data when generating the secondary temperature control instruction. An execution module is configured to determine a multi-stage control instruction of the temperature in the mushroom drying chamber according to the global temperature adjustment amount and the local temperature adjustment amount, and send the multi-stage temperature control instruction to a temperature control mechanism of the mushroom drying chamber for temperature multi-stage control.

9. A computer device, comprising: The computer device comprises a memory and a processor, the memory stores a code, and the processor is configured to acquire the code and execute the cloud platform-based temperature control method of the mushroom drying chamber according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the cloud platform-based temperature control method of the mushroom drying chamber according to any one of claims 1 to 7.

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