A CO2 hydrate dissociation release control method and system based on multi-source information fusion

Abstract

The present application belongs to the technical field of product preservation, cold chain storage and intelligent control, and particularly relates to a CO2 hydrate decomposition release control method and system based on multi-source information fusion; multi-source sensing information of a target space is obtained; the current state phase of the object is dynamically identified according to the object state parameter; the corresponding target control parameter set is called from the strategy atlas; the real-time offset of the environmental state parameter relative to the target control parameter set is obtained; the independent release control instruction for the CO2 hydrate decomposition product is generated according to the real-time offset, and the rate or dose of the gas phase component, the cold component and the liquid phase component released to the target space is adjusted respectively. Through independent release control, feedforward compensation and predictive control, the present application realizes independent on-demand distribution of cold, CO2 gas and moisture, solves the multi-factor coupling interference problem, improves the preservation accuracy and intelligent level, prolongs the food preservation period and reduces the energy consumption.

Description

Technical Field

[0001] This invention belongs to the fields of food preservation, cold chain storage and intelligent control technology, specifically involving a method and system for controlling the decomposition and release of CO2 hydrates based on multi-source information fusion. Background Technology

[0002] Food preservation technology is a crucial research area in the food industry and cold chain logistics, directly impacting food shelf life, quality, and food safety. With the development of the fresh food industry and increasing consumer demand for fresh food, efficiently extending food shelf life, reducing spoilage, and achieving a green transformation of the supply chain have become key areas of technological development. Currently, mainstream food preservation technologies include refrigeration, freezing, modified atmosphere packaging, chemical preservation, biological preservation, and physical preservation methods. These technologies focus on controlling temperature, humidity, or oxygen / carbon dioxide concentration in the food storage environment to inhibit microbial growth and food respiration, thereby reducing the rate of spoilage.

[0003] However, the aforementioned existing technologies still have many shortcomings. Refrigeration and freezing preservation rely on continuous energy supply, resulting in high energy consumption, and typically only allow for single-dimensional temperature regulation, making it difficult to coordinate the control of humidity and gas concentration. Modified atmosphere storage suppresses food respiration by adjusting the proportion of surrounding gases, but it requires a high degree of airtightness in the storage environment and has high equipment costs. Chemical preservation may pose food safety risks, raising concerns about consumer health and the environment. Biological preservation relies on the action of bacterial strains or enzymes, and its preservation effect is not stable due to limitations in strain activity and environmental conditions. While physical preservation has environmental advantages, it cannot provide flexible preservation solutions for different types of food.

[0004] Furthermore, during the storage, transportation, and sales of fresh foods, the requirements for temperature, humidity, and gas concentration vary greatly depending on the type, packaging, and storage conditions. Leafy vegetables are sensitive to humidity; excessive water loss can lead to wilting. Berries are sensitive to CO2 concentration and microbial growth, requiring strict CO2 control to inhibit mold growth. Meat products have high requirements for temperature stability and an antibacterial environment. In mixed-category storage environments, multiple different foods coexist, making it difficult to simultaneously meet preservation needs, potentially compromising the quality of one category while harming another. Therefore, achieving intelligent, dynamic, and precise control, and providing automated preservation solutions tailored to the individual needs of different foods, has become a pressing technical challenge in the food preservation field. In recent years, some progress has been made in equipment and technologies related to food preservation. Cold storage facilities and refrigerated trucks, widely used in the cold chain logistics industry, primarily focus on constant temperature storage. However, cold chain equipment typically only allows for single-dimensional temperature regulation, making it difficult to respond in real-time to changes in humidity or gaseous environment requirements. Controlled atmosphere (CA) equipment is mostly designed for large, enclosed environments, resulting in complex systems and high costs, making it less suitable for scenarios requiring rapid response and precise control. Meanwhile, artificial intelligence (AI) and the Internet of Things (IoT) technologies are rapidly being integrated into the food preservation field, utilizing sensors to monitor parameters such as temperature, humidity, and CO2 concentration in real time and controlling them through feedback control algorithms. However, existing intelligent control solutions are mostly limited to single-parameter-centric control systems, failing to create a multi-factor-linked preservation environment and lacking dynamic learning and adaptive optimization capabilities, thus unable to adjust preservation strategies promptly when food types or product conditions change.

[0005] It is noteworthy that CO2 hydrate, as a novel functional energy storage and preservation medium, can simultaneously release cooling capacity, CO2 gas, and moisture through decomposition by lowering temperature or regulating pressure, bringing the possibility of achieving "three-effect synergistic" regulation with a single medium for food preservation applications. Existing research shows that the decomposition process of CO2 hydrate can provide the cooling capacity required for cold chains, the CO2 required for controlled atmosphere storage, and humidity control. However, current CO2 hydrate preservation technologies are mostly passively regulated, relying solely on single temperature and pressure conditions to trigger release, failing to efficiently match food preservation needs; lacking "decoupling" technology in the release process, the release ratio of cooling capacity, CO2 gas, and moisture is uncontrollable; limited system response speed, with lag and over-release issues reducing the stability of the preservation environment; furthermore, the lack of release management algorithms that adaptively match food types. Existing preservation equipment generally suffers from slow response speed and low release accuracy, making food susceptible to negative effects such as over-cooling, insufficient humidity, or abnormal CO2 concentration. Traditional preservation equipment also relies on manual parameter adjustment, which, facing frequently changing food types and storage conditions, is prone to affecting preservation effects due to improper adjustment or incorrect parameter settings.

[0006] Therefore, in order to provide automated preservation solutions for the personalized needs of different foods, and at the same time solve the technical problems such as the uncontrollable CO2 hydrate release process and insufficient precision of multi-factor synergistic regulation, it is urgent to design and develop a CO2 hydrate decomposition and release control method and system based on multi-source information fusion. Summary of the Invention

[0007] To overcome the shortcomings and difficulties of the existing technology, the purpose of this invention is to provide a method and system for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion; aiming to achieve intelligent, dynamic and precise control.

[0008] The first objective of this invention is to provide a CO2 hydrate decomposition and release control method based on multi-source information fusion; the second objective of this invention is to provide a CO2 hydrate decomposition and release control system based on multi-source information fusion.

[0009] The first objective of this invention is achieved by the method comprising: Create, generate, and acquire multi-source sensing information corresponding to a target space; wherein, the multi-source sensing information includes at least environmental state parameters and state parameters of objects contained within the target space; Based on the object's state parameters, the current state stage of the object is dynamically identified; and based on the state stage, the corresponding target control parameter set is called from a preset strategy map; wherein, the target control parameter set contains at least two mutually orthogonal environmental factor target values; The system constructs and acquires the real-time offset of the environmental state parameters relative to the target control parameter set; and based on the real-time offset, it creates and generates independent release control commands for CO2 hydrate decomposition products; wherein the decomposition products include at least gaseous components, cold components, and liquid components; the independent release control commands are used to adjust the rate or dose of each of the decomposition products released into the target space so that the environmental state parameters converge to the target control parameter set.

[0010] The second objective of this invention is achieved by the system used to implement the CO2 hydrate decomposition and release control method based on multi-source information fusion, the system comprising: A generation unit is created to generate and acquire multi-source sensing information corresponding to a target space; wherein the multi-source sensing information includes at least environmental state parameters and state parameters of objects contained within the target space; The dynamic identification unit dynamically identifies the current state stage of the object based on the object's state parameters; and calls the corresponding target control parameter set from a preset strategy map based on the state stage; wherein the target control parameter set contains at least two mutually orthogonal environmental factor target values. A generation unit is constructed to generate and acquire the real-time offset of the environmental state parameters relative to the target control parameter set; and based on the real-time offset, to create independent release control instructions for CO2 hydrate decomposition products; wherein the decomposition products include at least gaseous components, cold components, and liquid components; the independent release control instructions are used to adjust the rate or dose of each of the decomposition products released into the target space so that the environmental state parameters converge to the target control parameter set.

[0011] This invention acquires multi-source sensing information of the target space, including environmental state parameters and object state parameters; dynamically identifies the current state stage of the object based on the object state parameters; calls the corresponding target control parameter set from the strategy graph; obtains the real-time offset of the environmental state parameters relative to the target control parameter set; and generates independent release control commands for CO2 hydrate decomposition products in response to the real-time offset, adjusting the rate or dosage of gaseous components, cold components, and liquid components released into the target space to make the environmental state parameters converge to the target control parameter set. This invention achieves independent, on-demand allocation of cold energy, CO2 gas, and moisture through decoupled release control, feedforward compensation calculation, and predictive quality degradation control, solving the problem of multi-factor coupling interference, improving the accuracy and intelligence of food preservation, extending shelf life, and reducing energy consumption. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the steps of a CO2 hydrate decomposition and release control method based on multi-source information fusion in an embodiment of the present invention; Figure 2 This is a schematic diagram of the adaptive control process for food categories in an embodiment of the present invention; Figure 3 This is a schematic diagram of the three-level release control logic in an embodiment of the present invention; Figure 4 This is a schematic diagram of the carbon dioxide hydrate decomposition process in an embodiment of the present invention; Figure 5 This is a schematic diagram of the food category preservation database in an embodiment of the present invention; Figure 6 This is a schematic diagram of the food quality kinetic model in an embodiment of the present invention; Figure 7 This is a schematic diagram of the three-effect synergistic control process based on multi-parameter coupling compensation calculation in an embodiment of the present invention; Figure 8 This is a schematic diagram of a CO2 hydrate decomposition and release control system architecture based on multi-source information fusion in an embodiment of the present invention; Figure 9 This is a schematic diagram of the overall system flow structure in an embodiment of the present invention; In the diagram: 1-Refrigeration unit; 2-Carbon dioxide hydrate cold storage box; 3-Heat exchange coil; 4-Heat exchange coil outlet; 5-Heat exchange coil inlet; 6-Three-way valve; 7-Hydrate decomposition product outlet; 8-Carbon dioxide charging port; 9-Aqueous solution inlet; 10-Carbon dioxide hydrate cold storage material; 11-Carbon dioxide storage cylinder; 12-Aqueous solution supply component; 13-Waste liquid recovery device; 14-Induction feedback control console; 15-Visual screen; 16-Cold storage refrigerant inlet. 17-Refrigerant outlet for cold storage; 18-Fresh food storage; 19-Fruit storage; 20-Meat storage; 21-Beverage storage; 22-Vegetable storage; 23-Internal space and piping of cold storage; 24-Carbon dioxide inlet for cold storage; 25-Water inlet for cold storage; 26-Liquid flow regulator; 27-Gas flow regulator; 28-Liquid control valve; 29-Gas control valve; 30-Liquid separator; 31-Water and waste liquid; 32-Gas-liquid separator; 33-Solenoid valve. Detailed Implementation

[0014] To enable those skilled in the art to better understand the purpose, technical solution, and advantages of this application, the following detailed description is provided in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] Many specific details are set forth in the following description in order to provide a full understanding of the invention. Those skilled in the art can easily understand other advantages and effects of the invention from the content disclosed in this specification. However, the invention may also be implemented in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0016] As described in the background section, existing food preservation technologies are unable to adaptively match preservation parameters according to the dynamic needs of different food types and storage stages. The release ratio of cold energy, CO2 gas, and moisture during the decomposition and release of CO2 hydrates is uncontrollable and cannot be independently adjusted as needed. Temperature control lacks an effective compensation mechanism for the nonlinear coupling interference of humidity and CO2 solubility. The preservation strategy adopts a post-correction approach, which has problems such as delayed response, inability to actively intervene before quality deterioration, and conflicting preservation parameters when multiple food categories coexist.

[0017] In view of this, such as Figure 1 As shown in this embodiment of the invention, a method for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion is provided, the method comprising: S101. Create, generate, and acquire multi-source sensing information corresponding to the target space; wherein, the multi-source sensing information includes at least environmental state parameters and state parameters of objects contained in the target space; S102. Based on the object state parameters, dynamically identify the current state stage of the object; and based on the state stage, call the corresponding target control parameter set from the preset strategy map; wherein, the target control parameter set contains at least two mutually orthogonal environmental factor target values; S103. Construct, generate, and obtain the real-time offset of the environmental state parameters relative to the target control parameter set; and based on the real-time offset, create and generate independent release control commands for CO2 hydrate decomposition products; wherein the decomposition products include at least gaseous components, cold components, and liquid components; the independent release control commands are used to adjust the rate or dose of each of the decomposition products released into the target space so that the environmental state parameters converge to the target control parameter set.

[0018] In other words, the embodiments of the present invention collect environmental state parameters of the target space and state parameters of objects contained in the target space in real time through a multi-source sensing unit. The processing and control unit dynamically identifies the state stage of the object based on the state parameters, and then calls the target control parameter set that matches the state stage from the parameter map database. It also calculates the offset between the environmental state parameters and the target control parameter set in real time. In response to the offset, it generates independent release control commands for the three decomposition products of CO2 hydrate, and adjusts the rate or dose of each product released into the target space. The solution breaks through the limitations of traditional CO2 hydrate preservation technology, which involves the simultaneous release of cold energy, gas, and moisture in uncontrollable proportions, through a closed-loop logic of sensing, identification, matching, offset calculation, and independent control. It achieves independent decoupling control of three orthogonal environmental factors: temperature, humidity, and CO2 concentration. This allows the release rate of each decomposition product to be independently adjusted according to the real-time offset, enabling precise matching of the multi-dimensional and differentiated needs of different food categories and storage stages for preservation environments at any time. The solution ensures that environmental parameters continuously converge to the target control parameter set, solving the problem of insufficient adaptability of preservation parameters in existing technologies and improving the accuracy and flexibility of preservation environment control.

[0019] Based on the above embodiments, in another embodiment of the present invention, the step of dynamically identifying the current state stage of the object according to the object state parameters further includes: S1021. Generate and obtain the initial category identifier corresponding to the object; S1022. Based on the changing trend of the object's state parameters accumulated over time, determine whether the object is currently in the fresh period, mature period, decay period, or warning period; wherein, the object's state parameters include at least one of the following: weight change rate, image feature degradation index, or characteristic gas concentration gradient.

[0020] In other words, based on the above embodiments, the initial category identifier of the object is further obtained through image acquisition, barcode / QR code scanning, or manual selection. Then, based on the changing trend of the object's state parameters accumulated over time by the multi-source sensing unit (including at least one of weight change rate, image feature degradation index, or characteristic gas concentration gradient), the object is comprehensively determined to be in the freshness, maturity, decay, or warning stage. This scheme, by introducing time-dimensional state parameter accumulation analysis, achieves a leapfrog upgrade from static category identification and fixed parameter matching in traditional schemes to dynamic stage identification and adaptive parameter adjustment. Because the system can sense the physiological stage differences of the same food at different storage time points and actively adjust the target control parameters according to stage switching, the preservation strategy is no longer a fixed scheme but can dynamically change with the quality evolution trajectory of the food itself. This ensures that the preservation control always matches the actual state of the food, avoiding the drawbacks of over-adjustment or under-adjustment under a fixed strategy, and extending the preservation period.

[0021] In another embodiment of the invention, the step of creating an independent release control command for CO2 hydrate decomposition products further includes: S1031. Call the pre-stored quality degradation dynamics model; wherein, the model is used to characterize the nonlinear evolution relationship of the quality characteristic value of the object with time and environmental stress factors; S1032. Input the sequence of historical and current environmental state parameters into the quality degradation dynamics model, and predict the evolution trajectory of the quality characteristic values ​​within future time windows; S1033. When the evolutionary trajectory reaches a preset degradation threshold, the independent release control command is executed in advance, and the direction of the evolutionary trajectory is changed before the quality characteristic value exceeds the degradation threshold.

[0022] In other words, the embodiments of the present invention further specify that predictive control logic is used when generating independent release control commands, and a pre-stored quality degradation dynamic model corresponding to the food category and state stage is invoked. This model is used to characterize the nonlinear evolution relationship of the quality characteristic value of an object with time and environmental stress factors. The historical and current environmental state parameter sequences are input into the model to predict the evolution trajectory of the quality characteristic value within the future time window. When the evolution trajectory touches the preset deterioration threshold, the independent release control command is executed in advance. This solution represents a qualitative leap in preservation strategies, shifting from passively responding to deviations to proactively preventing deterioration. Traditional feedback control only initiates adjustments after environmental parameters exceed limits or quality deterioration has begun. In contrast, this invention can intervene in advance when quality deterioration is predicted to occur, moving the control timing from post-correction to pre-intervention. Since environmental regulation itself requires a certain response time, early intervention ensures that the regulatory effect is just about to exceed the quality threshold, achieving a precise match between the timing of regulation and the time window for quality deterioration. Especially for foods with rapid quality changes, such as fruits with climacteric respiration, it can proactively strengthen CO2 inhibition and cooling measures before the peak of respiration, delaying the aging process and extending the shelf life.

[0023] Based on the above embodiments, in another embodiment of the present invention, the method further includes a model self-correction step: S1041, recording the residual sequence between the predicted trajectory and the measured trajectory of the quality feature value; S1042, correcting the model parameters of the quality degradation dynamics model by means of a recursive parameter estimation method according to the residual sequence.

[0024] In other words, based on the above embodiments, a model self-correction mechanism is further introduced to record the residual sequence between the predicted trajectory and the measured trajectory of quality characteristic values. The model parameters of the quality degradation kinetic model are then corrected using a recursive parameter estimation method based on this residual sequence. This scheme enables the quality degradation kinetic model to have "online learning" capabilities, solving the model mismatch problem caused by individual differences in food from different batches, origins, and growth environments. Since the physiological characteristics, initial quality, and storage tolerance of the same type of food may vary significantly between different batches, a model with fixed parameters is difficult to achieve accurate prediction. This invention continuously records prediction deviations and recursively corrects model parameters, enabling the system to adaptively track the actual quality evolution of the current batch of food. The prediction accuracy increases with running time, and the control effect is optimized batch by batch, fundamentally solving the challenge to precise control brought about by the individual differences in biological materials.

[0025] In another embodiment of the present invention, the method further includes: S1051. Based on the temperature offset included in the real-time offset, and combined with the air saturated water vapor partial pressure ratio before and after cooling, calculate and generate the corresponding expected humidity offset vector. S1052. Based on the expected humidity offset vector, a compensation control command is generated for the liquid phase component; wherein the compensation control command is used to adjust the release flux of the liquid phase component in advance during or before the release of the cold energy component, so as to suppress the coupling disturbance of the cold energy release on the humidity parameter.

[0026] In other words, in this embodiment of the invention, a feedforward compensation mechanism is further introduced. When the system determines that the real-time offset includes a temperature offset, the expected humidity offset vector is calculated based on the ratio of the partial pressure of saturated water vapor in the air before and after cooling. Based on this vector, a compensation control command for the liquid phase component is generated in advance, adjusting the release flux of the liquid phase component during or before the release of the cold component. This scheme solves the nonlinear coupling problem of temperature adjustment inevitably disturbing humidity in traditional multi-factor control schemes. Unlike the feedback control mode in the prior art where deviations are generated first and then passively corrected, this invention completes the prediction of humidity disturbance and the calculation of compensation amount before the temperature adjustment action is implemented. This achieves the advance suppression of coupling interference rather than the post-correction, enabling the two dimensions of temperature control and humidity control to operate in coordination rather than mutually restricting each other. The temperature and humidity of the preservation environment can be stabilized within the target range simultaneously, avoiding the problem of microbial growth caused by excessive humidity due to cooling or accelerated food dehydration and wilting due to excessive humidity fluctuations caused by compensation lag. This improves the accuracy and stability of multi-factor collaborative control.

[0027] In another embodiment of the present invention, the method further includes: S1061, calculating the amount of dissolution and dissipation of the gaseous component in the liquid medium within the target space based on the Henry coefficient under the current temperature field and the partial pressure value of the gaseous component; S1062, creating an incremental release command for the gaseous component based on the amount of dissolution and dissipation; wherein the incremental release command is used to increase the actual release amount of the gaseous component by the amount of dissolution and dissipation based on the target release amount.

[0028] In other words, this invention further introduces a CO2 dissolution loss compensation mechanism. Based on the Henry's Law coefficient and CO2 partial pressure at the current temperature field, the amount of CO2 dissolved and dissipated in the liquid medium within the target space is calculated. Based on this dissolution loss, an incremental release command is generated for the gaseous components, actively increasing the actual CO2 release amount beyond the target release amount. This solution effectively solves the problem of implicit interference of temperature changes on CO2 concentration. In existing technologies, systems typically only adjust based on the deviation between the measured and target values ​​of gaseous CO2 concentration. However, a "falsely low" gaseous concentration caused by dissolution loss can trigger excessive release. Once the temperature stabilizes, the readjustment of the dissolution balance may again cause CO2 concentration to exceed the standard. This invention actively compensates for dissolution loss through a feedforward approach, ensuring that the gaseous CO2 concentration accurately converges to the target value under different temperature conditions. This avoids concentration oscillations and release overshoot caused by solubility changes, preventing the negative impact of high CO2 concentrations on the flavor and quality of some foods while maintaining the antibacterial effect.

[0029] In another embodiment of the present invention, in the step of creating an independent release control command for CO2 hydrate decomposition products, the following triggering modes are selected according to the modulus of the real-time offset: S1034, when the modulus exceeds a first preset threshold, a joint excitation mode is adopted to control the CO2 hydrate to accelerate phase change decomposition under heating and / or depressurization conditions, and to synchronously increase the release intensity of each decomposition product through the independent release control command; S1035, when the modulus does not exceed the first preset threshold and is only related to the offset of a single environmental factor, a single factor adjustment mode is adopted to generate the independent release control command only for the decomposition product corresponding to the single environmental factor.

[0030] In other words, in this embodiment of the invention, a differentiated triggering mode is selected to generate an independent release control command based on the magnitude of the real-time offset: when the magnitude of the offset exceeds the first preset threshold, a joint excitation mode is adopted to control the CO2 hydrate to accelerate phase change decomposition under heating and / or depressurization conditions, and the release intensity of each decomposition product is simultaneously increased through the independent release control command; when the magnitude of the offset does not exceed the first preset threshold and is only related to the offset of a single environmental factor, a single factor adjustment mode is adopted to generate an independent release control command only for the decomposition product corresponding to the single environmental factor. This graded triggering mechanism optimizes the system's energy efficiency strategy. Although the combined excitation mode consumes more energy, it can establish a stable preservation environment in the shortest time, preventing food from deteriorating rapidly due to drastic environmental fluctuations. In the single-factor adjustment mode, the system only activates a single release channel related to the offset factor, avoiding unnecessary release of cold energy, CO2, or moisture, and significantly reducing operating energy consumption. By adopting a coarse adjustment strategy of "rapid establishment and high energy consumption" when there is a large deviation, and a fine adjustment strategy of "precise maintenance and low energy consumption" when there is a small deviation, the optimal balance between response speed and operating energy consumption is achieved, improving the system's energy efficiency ratio while ensuring the preservation effect.

[0031] In another embodiment of the present invention, when multiple objects with different state stages are simultaneously contained in the target space, the step of creating an independent release control command for CO2 hydrate decomposition products further includes: S1036, constructing a corresponding comprehensive cost value function based on the target control parameter set corresponding to each object and a preset priority weight; S1037, substituting the current value of the environmental state parameter into the comprehensive cost value function to calculate the current comprehensive cost value, and determining whether the current comprehensive cost value is higher than a preset allowable cost value; wherein, if so, the parameters of the independent release control command are adjusted until the recalculated comprehensive cost value is not higher than the preset allowable cost value.

[0032] In other words, in this embodiment of the invention, the specific method for generating independent release control commands is further defined when multiple objects with different state stages are simultaneously contained in the target space. A comprehensive cost value function is constructed based on the target control parameter set corresponding to each object and the preset priority weight. The current value of the environmental state parameter is substituted into the function to calculate the current comprehensive cost value. It is determined whether the value is higher than the preset allowable cost value. If so, the parameters of the independent release control command are adjusted until the recalculated comprehensive cost value is not higher than the preset allowable cost value. This weighted optimization mechanism solves the technical problem of conflicting and unbalanced preservation parameters when multiple food categories coexist. Since different food categories have varying sensitivities and requirements for temperature, humidity, and CO2 concentration, a single environmental parameter cannot simultaneously meet the optimal needs of all categories. Simple compromises would result in none of the categories achieving ideal preservation conditions. This invention constructs a comprehensive cost-value function to weight and synthesize the needs of each category, enabling the final release strategy to achieve an optimal balance among their requirements. Higher-priority, sensitive categories are given higher weights to prioritize their preservation needs, while more tolerant categories are given lower weights, allowing for compromises within a certain range. Compared to the traditional method of manually setting parameters based on experience, this significantly reduces the complexity and risk of misoperation in mixed storage scenarios, achieving optimal overall preservation results when multiple food categories coexist.

[0033] Specifically, in the embodiments of the present invention, a three-effect graded release control technology for CO2 hydrate based on adaptive identification of food categories is provided. This technology aims to solve the problems of existing food preservation technologies, such as difficulty in automatically matching preservation parameters according to different food types, uncontrollable CO2 hydrate release, delayed preservation response, and parameter conflicts when multiple food categories coexist. Through innovative structural design and control methods, the unique characteristics of CO2 hydrate are fully utilized to achieve precise, efficient, and green food preservation environment regulation.

[0034] The present invention consists of the following modules, the functions and positional relationships of each module are clearly defined, as shown below: The CO2 hydrate module includes a storage tank for storing CO2 hydrates. The tank is made of low-temperature and high-pressure resistant materials and is equipped with a temperature controller and a pressure controller inside for precise regulation of decomposition conditions. The tank has three independent output interfaces, which are connected to the cold storage module, the gas storage module, and the liquid storage module respectively, to achieve decoupled release of CO2 gas, cold energy, and moisture.

[0035] The CO2 hydrate module is typically located outside the cold storage facility or in a dedicated machinery room. Its output interface is connected to the cold storage, gas storage, and liquid storage modules via pipelines. The release effect is maintained through insulation and pressure control devices. Utilizing the decomposition characteristics of CO2 hydrate, it simultaneously generates cooling energy, CO2 gas, and moisture, providing an energy source for the cold storage, gas storage, and liquid storage modules.

[0036] The triple-effect storage module includes a gas storage tank, a cold storage tank, and a liquid storage tank. The gas storage tank, connected to the CO2 hydrate module, collects and stores the CO2 gas released during decomposition. The tank is equipped with a gas uniform release pipe, a solenoid valve, and a gas control valve. The cold storage tank stores the cold energy generated during decomposition and delivers it to the preservation space via a heat exchange coil, a cold energy uniform release pipe, and a cold energy control valve. The liquid storage tank collects the moisture generated during hydrate decomposition and releases it to the preservation space via a water flow solenoid valve, a liquid control valve, and an atomizing device.

[0037] The gas storage tank is connected to the uniform release device in the preservation space via pipelines; the cold storage tank is connected to the cold energy release pipe and heat exchange coil; the liquid storage tank is connected to the atomizer via pipelines to uniformly release moisture into the preservation space. The separate storage and release of cold energy, gas, and moisture achieves precise control, avoiding waste or parameter instability issues caused by simultaneous release.

[0038] The release execution module includes multiple solenoid valves: a CO2 solenoid valve, a cooling solenoid valve, and a water flow solenoid valve, all equipped with state regulators to control the release intensity and cycle. It also features a uniform release device, including a gas release pipe, a cooling release pipe, and an atomizer. The release execution module is directly connected to the triple-effect storage module and the preservation space. It regulates the release status of various preservation factors based on real-time monitoring data, ensuring that temperature, humidity, and gas concentration are maintained within target ranges.

[0039] The multi-source sensing module includes multiple sensors such as a temperature sensor, humidity sensor, CO2 concentration sensor, O2 concentration sensor, pressure sensor, food weight sensor, and image acquisition unit. Installed inside the preservation space, it collects environmental data in real time; the data is transmitted to the intelligent control module for processing via a communication module. It provides real-time sensing of key parameters such as temperature, humidity, gas concentration, and pressure within the preservation space, offering data support for the control logic.

[0040] The food category identification module adds an intelligent storage stage determination function. It monitors the changes in the state of food during storage in real time through cameras, QR code / barcode scanners, manual selection interfaces, and multi-source sensing modules (such as weight sensors, image analysis, gas sensors, and microbial risk models). It intelligently determines the storage stage of the food (such as freshness, maturity, aging, spoilage warning stage, etc.) and dynamically adjusts the target values ​​and weights in the preservation parameter map according to the determined stage, thereby achieving an upgrade from "static category matching" to "dynamic stage adaptation".

[0041] The intelligent judgment function during storage is implemented by using weight sensors to monitor the water loss rate of food in real time. A water loss rate exceeding a threshold is used as a marker of entering the aging stage. Images of the food are periodically captured by cameras, and image recognition algorithms are used to analyze changes in color, shape, and surface gloss to determine freshness. Changes in CO2 and O2 concentrations reflect the respiration intensity of the food; changes in ethylene concentration can be used to determine fruit ripeness. Combining historical temperature and humidity data, a model predicts the risk of microbial growth and determines whether the food has entered the spoilage warning period.

[0042] Example of phase division: ; Specific implementation example: Adaptive preservation during the storage stage of berry-based foods (strawberries); Initial settings: Food category identification result: Strawberry (berry); Initial preservation parameter spectrum: Temperature: 0-4℃; Humidity: 85%-95%; CO2 concentration: 3%-5%; Control strategy is to first inhibit bacteria and control temperature to avoid excessive CO2 causing flavor changes.

[0043] Dynamic phase determination and parameter adjustment, Days 1-2 (freshness): The weight sensor shows a water loss rate of <3%, the image shows a bright red color, and the CO2 concentration is stable at 3.5%. The result is determined to be freshness. For parameter adjustment, maintain the initial spectrum, keep the CO2 concentration at 3%-4% to inhibit respiration, and control the temperature at 2℃.

[0044] On days 3-4 (ripening stage), the weight sensor showed a water loss rate of 7%, the image showed a slight darkening of the color, the CO2 concentration rose to 4.5%, and trace amounts of ethylene were detected. The result was determined to be the ripening stage (the fruit is in its peak respiration phase). For parameter adjustments, the target temperature was lowered from 2℃ to 1℃ to slow the respiration rate; the target CO2 concentration was increased from 3%-4% to 5%-6% to more strongly inhibit respiration. Humidity weighting was adjusted: the priority of humidity release was reduced (because excessive moisture during strawberry ripening easily leads to mold), and the humidity target was adjusted from 85%-95% to 80%-90%.

[0045] On days 5-6 (senescence stage), the water loss rate exceeded 12%, and images showed slight browning on some strawberry surfaces. CO2 concentration continued to rise to 6%, while O2 concentration decreased to 15%. The result was determined to be the senescence stage. For parameter adjustments, the target temperature was further lowered to 0℃; the target CO2 concentration was increased to 6%-8% (to inhibit mold growth); and the humidity target was lowered to 75%-85% (to reduce surface moisture and prevent mold growth). The release priority was prioritized for CO2 gas release, followed by cooling capacity, with humidity release becoming the lowest priority.

[0046] On day 7 (the spoilage warning period), the microbial risk model triggered an alert, with images showing mold spots on some strawberries and abnormal CO2 concentration fluctuations reaching 8%. The assessment concluded that the period was a spoilage warning. Emergency measures for parameter adjustments included: activating the combined temperature and pressure trigger mode to rapidly release high-concentration CO2 (8%-10%) and cooling (temperature dropping below 0℃); completely shutting off humidity release to prevent moisture from promoting mold growth; and notifying management personnel to handle or isolate the affected areas as soon as possible.

[0047] Installed in the receiving area, it can be integrated into the cold storage entrance or warehouse management system; the identification results are transmitted to the intelligent control module for matching preservation parameters. It is used to determine the food category and initiate the preservation parameter matching logic, providing the system with category information. The parameter database contains target parameters for different food categories, such as temperature range, humidity range, CO2 concentration range, allowable fluctuation range, maximum water loss rate, and release priority. The database supports real-time updates and parameter settings can be improved based on operational history. It provides reference for target parameters and release strategies for food preservation and interacts with the food identification module and intelligent control module.

[0048] The intelligent control module includes a central processing unit, a rule-based control algorithm module, a model-based predictive control module, and an AI-optimized control module. It is also equipped with a sensor feedback console and a visual screen, capable of calculating deviations based on real-time monitoring data and parameters, and outputting control commands. It calculates the deviation between the target value and the real-time value based on sensor data and parameter maps, generates release commands, and controls the operation of the storage and execution modules.

[0049] Specifically, when environmental parameters deviate significantly from target values, the intelligent control module triggers the CO2 hydrate module to rapidly decompose it through a combined heating and depressurization process, releasing a large amount of cold energy, CO2, and moisture, which simultaneously enters the storage module. This mode is suitable for situations where a stable preservation environment needs to be quickly established in the initial stage of food storage or after the door is opened.

[0050] When the CO2 concentration is low but the temperature and humidity are within the target range, the gas storage module is activated using a pressure-based independent control method. By adjusting the pressure inside the tank, the CO2 release rate is changed, thereby precisely controlling the gas concentration.

[0051] When the temperature deviates from the target range but the humidity and CO2 concentration are normal, the cooling capacity is released through the cold storage module to gradually adjust the ambient temperature back to the target range. This mode ensures the stability of temperature control and saves energy.

[0052] A calculation model for compensating for humidity changes caused by temperature drop is used to predict the trend of relative humidity changes after a temperature decrease due to the release of cooling energy from CO2 hydrates, and to calculate the required moisture compensation in advance to avoid humidity deviations during the cooling process. The model is expressed as calculating the change in relative humidity caused by the temperature drop based on air state parameters before and after the temperature change. (1) in, The relative humidity of the environment before cooling; To predict relative humidity after cooling; For temperature The pressure of saturated water vapor in the lower air; Target temperature The pressure of saturated water vapor in the lower air.

[0053] When predicting humidity > This reduces the amount of water released; when < This increases the amount of moisture released. This model allows for advance compensation for humidity changes during the cold energy release process.

[0054] The CO2 solubility change calculation model is used to solve the problem that the actual gas concentration cannot be accurately controlled due to the change in CO2 solubility under different temperature and pressure conditions.

[0055] The model can be represented as: (2) in, This represents the concentration of CO2 dissolved in the liquid phase. The Henry coefficient is a temperature-dependent factor. This is the partial pressure of CO2.

[0056] Since lower temperatures increase CO2 solubility, the system operates based on: (3) Calculate the actual concentration deviation.

[0057] when <0 indicates insufficient CO2 release, which increases the rate of hydrate decomposition; when A value >0 indicates an excess of CO2, which reduces the release rate.

[0058] A comprehensive model for the three-effect synergistic compensation calculation model: (4) in: This refers to the ambient temperature parameter. This refers to the relative humidity parameter. This refers to the CO2 concentration parameter. For food category parameters; The pressure parameter for hydrate release; To comprehensively release control quantities.

[0059] Further breakdown: (5) in: This is the temperature compensation amount; This is the humidity compensation amount; This is the amount of CO2 concentration compensation. , , , which are weighting coefficients determined based on a food category database.

[0060] For example, regarding strawberries: (6) For beef: (7) The intelligent system in this solution automatically adjusts the release priority of the three effects based on the type of food.

[0061] Preferably, CO2 hydrate is a cubic crystal structure formed by CO2 molecules and water molecules under low temperature and high pressure conditions. Its decomposition process involves the release of CO2 gas, cold energy, and water as the temperature increases or the pressure decreases; the decomposition rate is affected by temperature and pressure gradients, and the rate can be regulated by controlling the temperature and pressure.

[0062] CO2 concentration has an inhibitory effect on the respiration rate of food. For example, in an environment with appropriate CO2 levels, the respiration rate of leafy vegetables can be reduced, thereby slowing down spoilage. This invention achieves precise optimization of food respiration regulation through intelligent CO2 gas release.

[0063] Humidity is a crucial factor in food preservation, especially for leafy vegetables and other foods prone to water loss; a high-humidity environment can effectively inhibit wilting. This invention achieves dynamic humidity adjustment through graded moisture release.

[0064] Furthermore, this technical solution is based on the synergistic regulation of CO2 hydrate through multi-parameter coupled compensation calculations. This solution does not simply utilize the three functions of cold energy, CO2, and moisture released from CO2 hydrate decomposition, but rather establishes a dynamic coupling relationship between temperature changes, relative humidity changes, and CO2 gas dissolution / release behavior. A compensation calculation algorithm is used to synergistically predict and regulate the three-effect release process. During CO2 hydrate release, the system pre-calculates the changes in air saturated humidity, relative humidity increase, and CO2 concentration trend caused by temperature drops, based on the preservation requirements of the target food. Combined with a model of CO2 solubility changes under different temperature and pressure conditions, the system dynamically corrects the cold energy release, moisture compensation, and CO2 release amount, achieving precise matching of the three effects. Simultaneously, to address the nonlinear coupling relationship between temperature, humidity, and gas concentration in the actual environment, a multi-parameter compensation mechanism is introduced to avoid problems such as excessive humidity and CO2 concentration fluctuations caused by single-parameter adjustment.

[0065] This invention differs from traditional refrigeration, modified atmosphere storage, or single CO2 hydrate release technologies. This solution combines the thermodynamic changes during CO2 hydrate decomposition with a food preservation environment control model. By establishing a multi-parameter coupled calculation model of temperature changes, humidity migration, and CO2 concentration changes, it transforms the three-effect release process from passive release to active predictive control. Based on the target preservation parameters corresponding to the food category, it can pre-calculate the humidity compensation requirements caused by the cooling process and the gas concentration deviation caused by changes in CO2 solubility. Furthermore, it uses a compensation algorithm to dynamically optimize the release process, achieving a synergistic balance between the three effects of cooling, CO2, and moisture, thereby improving the precision of preservation environment control.

[0066] By combining image recognition, barcode / QR code scanning, inventory lists, and manual input, the system automatically identifies the type and condition (quantity, maturity, storage stage, etc.) of food entering the warehouse. Utilizing a food category identification module in conjunction with a preservation parameter database, the system automatically matches the target temperature, target humidity, and target CO2 concentration for different foods, and dynamically generates preservation strategies through an intelligent control module.

[0067] In specific application scenarios, it enables precise regulation of different foods such as leafy vegetables, berries, meats, and Chinese medicinal herbs to meet personalized needs.

[0068] For intelligent feedback and dynamic release control based on multi-source sensing, this solution employs a multi-source sensing module to collect real-time environmental and status information within the food storage space, including temperature, humidity, CO2 concentration, weight changes, door opening / closing status, flow rate, and internal pressure. Based on this real-time data, the system calculates the deviation between environmental parameters and target values, and dynamically generates a release strategy by considering the food's water loss sensitivity, microbial risk level, and respiration intensity. The solution constructs a three-effect synergistic release mechanism capable of independently adjusting temperature, humidity, and CO2 concentration to achieve precise control of the preservation environment. Simultaneously, based on a quality kinetic model, it predicts future trends and proactively intervenes before quality deterioration occurs, achieving adaptive control with rolling optimization.

[0069] Multi-source sensing fusion integrates various sensors such as temperature, humidity, CO2 concentration, weight, door status, flow rate, and pressure to comprehensively perceive the environmental and food conditions of the food space.

[0070] The three-effect synergy allows for independent adjustment of temperature, humidity, and CO2 concentration, avoiding the coupling interference caused by a single adjustment affecting multiple parameters in traditional solutions.

[0071] Dynamic strategy generation means that the release strategy is not a fixed value, but is calculated and generated in real time based on the characteristics of food categories (water loss sensitivity, microbial risk level, respiration intensity) and environmental deviations.

[0072] From post-mortem correction to pre-mortem intervention, based on the quality dynamics model to predict future quality deterioration trends, the decision-making basis has changed from "current measured value exceeds the standard" to "predicting that it will exceed the standard in the future," enabling proactive control before quality decline occurs and achieving advanced prevention.

[0073] In proactive predictive regulation based on quality kinetics models, a quality kinetics model is introduced. This model is pre-established to describe the mathematical relationships between food quality indicators (such as water loss rate, respiration rate, and microbial risk index) and environmental parameters and time. Different model parameters are used for different food categories and storage stages (freshness, maturity, and aging).

[0074] The system not only reads current sensor data but also combines it with historical data sequences, inputting them into a quality kinetics model. Using algorithms such as Kalman filtering or recurrent neural networks, it predicts the changing trends of food quality indicators over a future period (e.g., 2 hours). The predicted outputs include curves showing changes in water loss rate, CO2 concentration, and microbial risk index.

[0075] It also features proactive intervention decision-making, with the system setting quality deterioration warning thresholds (e.g., triggering aging intervention when water loss rate > 10%, triggering spoilage warning when microbial risk index > 0.8). If the prediction curve shows that the threshold will be exceeded at some point in the future, the system will initiate intervention strategies in advance, changing environmental parameters to bring the prediction curve back to a safe range.

[0076] For rolling optimization, the prediction is re-executed every prediction period (e.g., every 10 minutes), and the prediction curve is updated based on the new data to achieve rolling optimization control.

[0077] Specific implementation example: Active predictive control for strawberry preservation. Scenario setting: Strawberries are stored in a cold storage facility at a current temperature of 2°C, humidity of 90%, and CO2 concentration of 4%, and have been stored for 1 day.

[0078] In traditional feedback control, the system continuously monitors CO2 concentration and releases CO2 when the concentration reaches 5% or higher. However, by this time, the strawberries may have already entered their ripening stage, and their quality may begin to decline.

[0079] Step 1: Model Invocation. The system invokes the quality kinetics model of strawberries during their fresh period, including the respiration intensity model and the water loss model.

[0080] Step 2, Trend Prediction: Substitute current data (temperature 2℃, humidity 90%, CO2 concentration 4%) and historical data to predict the next 2 hours: CO2 concentration forecast: Currently at 4%, it will rise to 5.5% in 2 hours (exceeding the standard).

[0081] Microbial risk index prediction: currently 0.3, will rise to 0.6 in 2 hours (close to the warning threshold of 0.7).

[0082] Step 3: Proactive intervention. Although the current CO2 concentration of 4% is not yet above the standard, predictions indicate it will exceed the standard in two hours. Therefore, the system initiates intervention in advance: Actively release a small amount of CO2, increasing the CO2 concentration from 4% to 5% (reaching the target value ahead of schedule, inhibiting respiration). Slightly lower the temperature from 2℃ to 1.5℃ (delaying respiration and microbial growth).

[0083] Step 4: Effectiveness verification. One hour later, the sensor reported that the CO2 concentration had stabilized at 5%, and the microbial risk index had decreased from 0.6 to 0.5. Quality deterioration can be prevented.

[0084] The technology shifts from passively responding to deviations to proactively predicting and intervening, adjusting environmental parameters before quality declines to prevent disease. This extends the shelf life of strawberries by more than 30%.

[0085] Based on a dynamic quality status-based "three-effect synergy" multi-stage release strategy, this system identifies quality status and switches stages in real time by using multi-source sensing data (weight changes, CO2 concentration changes, time, etc.) and combining them with a quality dynamics model. During the fresh stage, water loss rate is low, respiration rate is low, and microbial risk is low. During the mature stage, respiration rate increases, and water loss rate begins to increase. During the aging stage, water loss rate accelerates, and microbial risk increases.

[0086] The system dynamically switches between the three-effect synergy release strategies based on the current quality status at different stages: ; The system achieves independent adjustment of three parameters by independently controlling the cooling coil (temperature), ultrasonic atomizer (humidity), and CO2 release valve (CO2 concentration), thus avoiding coupling interference.

[0087] Specific implementation example: Full-cycle regulation of strawberries from freshness to senescence.

[0088] Phase 1: Freshness period (days 1-3). Sensor data showed a temperature of 2°C, humidity of 92%, CO2 concentration of 3%, and water loss rate of 0.5% / day. The strategy was to maintain low temperature (2°C), high humidity (92%), and low CO2 (3%). The result was that the strawberries remained fresh and did not wilt due to water loss.

[0089] Phase 2: Maturation Stage (Days 4-6). Sensor data showed a temperature of 2°C, humidity of 92%, CO2 concentration of 3%, and a water loss rate of 0.8% / day, with an accelerated rate of CO2 concentration increase. The strategy was to automatically switch to the maturity stage strategy upon detecting increased respiration intensity: temperature was raised to 4°C (to promote ripening); humidity was reduced to 88% (to inhibit water loss); and CO2 concentration was increased to 5% (to inhibit respiration). The result was that the strawberries gradually ripened but showed no signs of spoilage.

[0090] Phase 3: Aging Period (Day 7), Sensor data: Temperature 4℃, Humidity 88%, CO2 concentration 5%, Water loss rate 1.2% / day, Microbial risk index 0.7. The strategy was to automatically switch to the aging period strategy when the system detected that the microbial risk was approaching the threshold: the temperature was immediately lowered to 0℃ (to inhibit microorganisms); humidity was increased to 95% (to prevent water loss); and CO2 concentration was increased to 7% (for strong antibacterial effect). The effect was to delay strawberry aging and extend shelf life by 2 days.

[0091] The strategy has shifted from a single, fixed approach to a multi-stage, dynamic switching approach, allowing for precise control based on the actual quality of the food, avoiding over- or under-adjustment; extending shelf life and reducing spoilage rates.

[0092] Multi-source sensing data-driven predictive-feedback hybrid control employs a hybrid architecture combining predictive control and feedback correction. Based on a quality dynamics model, it pre-calculates the optimal release strategy for a future period. Real-time sensor data serves as feedback, correcting the predictive model and compensating for model errors and environmental disturbances.

[0093] Self-learning and model updates: The system records the deviation between each prediction and the actual result, and uses machine learning algorithms (such as online learning and gradient descent) to continuously update the parameters of the quality dynamics model, making it increasingly accurate.

[0094] Specific Implementation Example: The system predicts that the CO2 concentration will rise to 5.5% after 2 hours, but in reality, it only rises to 5.0% after 1 hour (a significant deviation). The system records this deviation and uses this data to update the respiratory intensity model parameters (such as adjusting the respiratory rate constant) to make the next prediction more accurate. The technical effect is that the prediction accuracy gradually improves, the control effect becomes better and better, and it is adaptable to different batches of food and different growth environments.

[0095] Weighted optimization control for multi-category interaction: For mixed scenarios with multiple food products in the same cold storage, a weighted algorithm is used to optimize the overall target parameters. Based on the sensitivity thresholds and priority requirements of different foods for temperature, humidity, and CO2 concentration, a comprehensive preservation strategy that takes into account multiple categories is generated through intelligent weighting, avoiding conflicts in personalized needs.

[0096] This solution leverages industrial CO2 for resource utilization and green energy empowerment, using industrial by-product CO2 as raw material and employing CO2 hydrate technology to achieve green recycling of carbon resources. CO2 hydrate plays a dual role in the system as both a "preservation medium" and an "energy storage material," replacing traditional refrigerants and controlled atmosphere (CA) gas sources. This significantly reduces reliance on traditional refrigeration and CA equipment, decreases energy consumption, and achieves energy conservation and emission reduction, contributing to carbon neutrality goals. Specific features include: integrated dual functions: CO2 hydrate serves as both a preservation medium (releasing CO2 gas through decomposition for CA preservation) and an energy storage material (absorbing heat through decomposition for refrigeration), offering dual benefits and reducing equipment redundancy. Resource utilization of industrial by-product CO2: Utilizing industrial by-product CO2 from industries such as steel and chemicals as raw material, this solution transforms "waste" into a high-value preservation medium, achieving carbon resource recycling. Green and low-carbon: Compared to traditional refrigerants (such as Freon) and high-pressure cylinder CO2, this solution eliminates the risk of greenhouse gas leakage, has low energy consumption during generation, and reduces overall carbon emissions by more than 60%. Furthermore, the system has a high degree of integration, with a highly integrated system for the generation, storage, decomposition, and preservation and release of hydrates, forming an integrated closed loop of "production-storage-use".

[0097] A jet-type hydrate reactor is used to compress industrial byproduct CO2 to 2-5 MPa and pre-cool it to 1-5°C, while deionized water is atomized into micron-sized droplets through a microporous nozzle. Inside the reactor, the high-pressure CO2 and atomized water droplets mix at high speed, rapidly generating a CO2 hydrate slurry under low temperature (1-3°C) and high pressure (3-5 MPa) conditions (the rate is more than 10 times that of a traditional stirred tank reactor). After centrifugation, the hydrate slurry yields solid hydrate crystals, which are stored in an insulated, constant-pressure storage tank.

[0098] The adaptive "self-pressure maintaining" storage tank has its inner wall coated with a phase change material (PCM), whose phase change temperature matches the hydrate equilibrium temperature (e.g., -2°C). When a small amount of external heat seeps in, the PCM absorbs the heat, maintaining a constant internal temperature and preventing hydrate decomposition. A pressure compensation tank (containing inert gas or CO2) is connected to the top of the tank. When the slow decomposition of hydrates causes a pressure increase, gas is forced into the compensation tank; when the pressure decreases, the compensation tank automatically replenishes the gas, maintaining a stable internal pressure within the 2-3 MPa range. A cryogenic circulation pump is installed at the bottom of the tank, allowing the hydrate slurry to flow slowly at regular intervals (e.g., every hour) to prevent caking. The technical benefits include a 10-fold increase in hydrate formation rate, no need for external energy during the maintenance phase (passively maintaining for over 24 hours), and an 80% reduction in energy consumption.

[0099] Cooling capacity is utilized in stages and precisely distributed across multiple temperature zones. Based on the temperature at which cooling capacity is released during hydrate decomposition, the cooling capacity is divided into three levels: ; A tiered heat exchanger system is installed, with each exchanger corresponding to a specific cooling capacity level. An intelligent control module adjusts valve openings to proportionally distribute cooling capacity to different temperature zones. When demand increases in a particular temperature zone (e.g., a new batch of products arrives in the chilled meat section), the system prioritizes ensuring the supply of secondary cooling capacity to that zone. It then adjusts the release time of primary cooling capacity (e.g., accelerating the hydrate decomposition rate) to achieve a dynamic balance between supply and demand. When cooling capacity is sufficient, excess capacity is used for hydrate pre-cooling during the formation stage or stored in cold storage tanks (containing phase change materials) for peak demand. CO2 hydrates serve simultaneously as a preservation medium and energy storage material, reducing reliance on traditional refrigeration and controlled atmosphere equipment, lowering energy costs, and achieving energy conservation and emission reduction, thus contributing to the goal of carbon neutrality.

[0100] In this invention, CO2 hydrate is used as the sole preservation medium. During the release process, the cold energy, CO2 gas, and water are separated and released on demand through the gas storage module, cold storage module, and liquid storage module, respectively. Precise control of the cold energy solenoid valve, gas solenoid valve, and water volume solenoid valve, including the opening duration, release cycle, and release sequence, ensures that the cold energy, gas, and humidity are stably maintained within the target range. A graded release control logic is proposed to achieve a decoupled release mode with joint temperature and pressure triggering, pressure control, and temperature control.

[0101] Food categories and statuses are identified through image recognition, barcode / QR code scanning, inventory lists, or manual selection. Based on the identified food type, preservation parameter maps are retrieved, and target temperature, humidity, and CO2 concentration, as well as allowable fluctuation ranges and release priorities, are automatically matched. The parameter map library data is dynamically adjusted in conjunction with factors such as the food's respiration intensity, water loss rate, and microbial risk level, and real-time control is performed.

[0102] The multi-source sensing module collects information such as temperature, humidity, CO2 concentration, weight change, and door opening / closing status in real time, and integrates and provides real-time feedback on the data; it calculates parameter deviations based on real-time sensing data and generates dynamic release strategies in combination with food characteristics; the intelligent control module introduces rule-based control, model predictive control, and AI-optimized control algorithms to dynamically output graded control commands; and an active predictive control method based on a quality dynamics model calls a quality dynamics model that matches the food category and storage stage, predicts the changing trend of food quality indicators over a future period based on current sensor data and historical data, and initiates the release strategy in advance based on the prediction results.

[0103] For mixed-category storage scenarios, the main food categories and sensitive categories are determined based on image recognition results and the division of storage areas; a weighted algorithm is used to optimize the comprehensive target parameters according to the priority needs and threshold ranges of different foods; and the release strategy is optimized to ensure the stability and compatibility of the preservation environment for multiple food categories.

[0104] This invention proposes a three-stage, graded release control scheme for CO2 hydrate based on adaptive food category identification, utilizing gas, temperature, and humidity. Addressing the personalized preservation needs of various food categories, it provides an adaptive and intelligent control method. Existing food preservation equipment mainly relies on fixed parameters or simple manual adjustments, which struggles to meet the individualized requirements of different food categories for various preservation environment parameters (temperature, humidity, CO2 concentration, etc.). This invention introduces a food category identification module, combined with preservation parameter maps, to accurately identify the type, quantity, and state of food. Based on the specific preservation needs of different food categories, it automatically matches target preservation parameters and generates a graded release control scheme. For example, for leafy vegetables, humidity compensation prevents wilting due to water loss, while controlling a moderate CO2 concentration to inhibit respiration; for berries, precise temperature control and gas regulation prevent microbial growth and inhibit mold growth; for meat, the system prioritizes temperature stability and adjusts CO2 concentration in real time to inhibit bacteria. Through this adaptive matching, the preservation environment becomes more precise and scientific, significantly improving preservation effects and extending storage time.

[0105] In addition, the decoupled output of cooling capacity, CO2 gas and humidity is achieved, improving release accuracy and control flexibility. Unlike existing CO2 hydrate preservation technologies that are mostly passively regulated, this invention achieves the decoupled output of CO2 hydrate decomposition products by designing a gas storage module, a cold storage module and a liquid storage module, combined with a three-level release control logic. Cold energy is released separately, with the cold storage module and cold energy release pipe independently controlling the output rate to precisely maintain the target temperature. CO2 gas is released separately, with CO2 concentration controlled by the gas storage module and gas solenoid valve to prevent CO2 concentration from being too low or too high. Humidity is released separately, with humidity regulated by the liquid storage module and atomizing device to prevent food spoilage due to water loss or excessive humidity.

[0106] This decoupled control breaks the limitation of simultaneous release of CO2 hydrate decomposition products, effectively avoiding waste and imbalance, and improving system operating efficiency and preservation accuracy.

[0107] The solution also proposes a three-level release control logic, which significantly improves response speed and system energy efficiency. Existing preservation equipment typically uses a single triggering method to regulate preservation factors, resulting in slow response speed, large parameter fluctuations, and high energy consumption. This invention, by introducing a three-level release control logic, makes the release process of preservation factors more flexible and precise.

[0108] Level 1: Temperature and pressure combined control. When food is initially placed in storage, the door is opened, or the environment deviates significantly from the target parameters, the rapid decomposition of CO2 hydrates is triggered by heating, depressurizing, or a combination of temperature and pressure. This allows for the simultaneous and rapid replenishment of cold energy, CO2 concentration, and humidity, restoring the preservation environment as quickly as possible.

[0109] Level 2: Individual pressure control is used to regulate the CO2 release rate, prevent excessively high or low CO2 concentrations, and ensure gas balance in the food preservation environment.

[0110] Level 3: Individual temperature control, used for the release of minute amounts of cold energy and fine temperature adjustment, continuously maintaining the stability of the preservation environment while achieving operation with minimal energy consumption.

[0111] Through this strategy, the present invention can quickly respond to parameter deviations, reduce energy waste caused by combined temperature and pressure triggering, and significantly improve the system's energy efficiency ratio and preservation effect.

[0112] Efficient utilization of CO2 hydrates contributes to green preservation and carbon neutrality goals: This invention utilizes the room-temperature storage properties of CO2 hydrates, releasing cooling energy, CO2 gas, and moisture through a decomposition process. On one hand, this serves as a homologous preservation medium, significantly reducing reliance on traditional refrigeration equipment, controlled atmosphere devices, and humidification equipment. On the other hand, by digesting industrial byproduct CO2 to prepare hydrates, efficient reuse of CO2 resources is achieved, contributing to the development of green preservation and the realization of carbon neutrality goals. For example, all products of CO2 hydrates are effectively utilized, reducing the cost of traditional controlled atmosphere and humidification devices and improving the economic efficiency of system operation. CO2 recycling and reuse helps reduce carbon emissions, achieving energy conservation and emission reduction throughout the entire supply chain.

[0113] By improving shelf life, reducing food spoilage rates, and significantly enhancing economic and social benefits, this invention, through an adaptive intelligent preservation solution and efficient preservation parameter control, can significantly extend the shelf life of food, reduce food spoilage, and improve the economic efficiency of the entire industry chain. For example, in the preservation of leafy vegetables, compared with traditional refrigeration equipment, the system of this invention can extend the shelf life by approximately 50%-70%, reduce water loss by approximately 30%, and reduce the spoilage rate to below 3%; in the preservation of meat, this invention can extend the shelf life by approximately 40%-60%, while significantly reducing microbial growth rate and surface spoilage rate. This plays an important role in reducing food waste, ensuring food safety, and improving the economic returns of cold chain logistics.

[0114] To verify the technical advantages of the present invention, the following experiments and data tests were conducted: Shelf life testing of leafy vegetables and berries; test subjects: leafy vegetables (lettuce, bok choy) and berries (strawberries, blueberries); test method: stored in conventional refrigeration equipment and the equipment of this invention, respectively, and their shelf life, humidity fluctuation, CO2 concentration changes and final quality were measured. Results: The system of this invention can reduce the water loss rate of leafy vegetables to 3%-5% and extend the shelf life by more than 15 days; the microbial load of berries is reduced by about 40%, and fermentation caused by excessive CO2 concentration is avoided.

[0115] Energy consumption comparison: In mixed-category cold storage environments, compared with traditional cold chain equipment, the system of this invention, through a three-level release logic, reduces overall energy consumption by about 20%-30% while achieving the same preservation effect, especially showing significant energy-saving effects in multi-category storage needs.

[0116] In summary, this invention, through intelligent decomposition control of CO2 hydrates, adaptive food preservation parameter matching, and a three-stage release logic, not only achieves precise controlled atmosphere, temperature, and humidity control technically, but also provides a multifunctional, high-efficiency, and sustainable solution for food preservation. Its social, economic, and technical effects are summarized below: The social impact is that by extending the shelf life of food, reducing food waste, and ensuring food safety, it improves people's food supply chain and quality of life.

[0117] Economic benefits include reducing food spoilage rates and energy costs, and increasing the economic returns for businesses in the cold chain logistics and storage sectors.

[0118] The technology utilizes CO2 hydrates to achieve multi-factor synergistic regulation, improving the system's preservation accuracy, response speed, and energy efficiency ratio, thus driving the cold chain logistics industry towards intelligent, energy-saving, and green development.

[0119] This invention designs a food preservation system by combining CO2 hydrate as the core preservation medium, achieving synergistic regulation of controlled atmosphere, temperature, and humidity. Several specific embodiments are listed below to illustrate the content of this invention and the application of its technical concept.

[0120] Specific Implementation Example 1: Preservation of Leafy Vegetables Technical characteristics: Leafy vegetables are highly sensitive to humidity and are prone to wilting due to excessive water loss during storage. At the same time, they have a strong respiration rate and require a certain amount of CO2 concentration to inhibit respiration.

[0121] Main technical modules and components, CO2 hydrate module: Construct a hydrate storage tank to store CO2 hydrates under low temperature (e.g., 0-5℃) and high pressure (e.g., 4-10MPa) conditions; external temperature control heating device and pressure regulating device are connected to the tank to control the hydrate decomposition rate.

[0122] The three-effect storage module consists of: a gas storage module (gas produced by the decomposition of CO2 hydrates enters the gas storage tank and is injected into the preservation space via a solenoid valve; a gas uniformity release pipe is connected to the gas storage tank to ensure even gas distribution); a cold storage module (cold energy is stored through heat exchange coils and then introduced into the preservation space for temperature control); and a liquid storage module (moisture released during decomposition enters the liquid storage tank and is released into the preservation space using an atomizing device).

[0123] The multi-source sensing module includes a temperature sensor, a humidity sensor, a CO2 concentration sensor, and a food weight sensor, which collects food storage environment parameters in real time.

[0124] The food category identification module uses a camera to identify the food type as leafy vegetables, or obtains category information by scanning a QR code. The intelligent control module intelligently calculates temperature deviation, humidity deviation, and CO2 concentration deviation based on multi-source sensor data and preservation parameter maps, and generates control commands.

[0125] Specific control process: After leafy vegetables are stored in the cold storage, they are identified as leafy vegetables by camera or QR code; the intelligent control module sets the preservation target according to the parameter spectrum: temperature 0-5℃, humidity 90%-95%, CO2 concentration 3%-5%; when the humidity sensor detects that the humidity is lower than the target lower limit (90%), the intelligent control module drives the liquid storage module to turn on the atomizer to release water to replenish the humidity; when the CO2 concentration is lower than the target lower limit (3%), the intelligent control module drives the solenoid valve of the gas storage module to release CO2 and control the concentration to stabilize; all parameters are monitored in real time, and the release stops when the deviation range is within acceptable limits to avoid excessive release.

[0126] Static and dynamic relationships: Static relationship: Each module works independently according to its function. The CO2 hydrate module, gas storage module, cold storage module, and liquid storage module are connected to the preservation space through solenoid valves. The sensors in the sensing module collect data and transmit it to the intelligent control module to execute decision-making instructions. Dynamic relationship: Based on real-time feedback, the liquid storage module activates the atomizer to replenish humidity, the gas storage module opens the gas release pipe to increase the CO2 concentration, and the cold storage module regulates the temperature. Effects: Maintaining a high humidity environment for leafy vegetables to prevent wilting; inhibiting food respiration through CO2 to reduce spoilage; and dynamically adjusting preservation parameters through real-time monitoring and graded release logic to improve preservation effect.

[0127] Specific Implementation Example 2: Adaptive Preservation of Berry Foods (Strawberries); Implementation Process: Fresh strawberries were selected as the experimental subject and placed in an intelligent preservation environment equipped with a CO2 hydrate three-effect synergistic regulation system; The system first identifies the strawberry category through the food identification module and calls the food preservation parameter database to obtain the corresponding target preservation parameters for strawberries: target temperature range: 0~4℃; target relative humidity range: 90~95%; target CO2 concentration range: 3~5%; maximum allowable water loss rate: ≤3%; CO2 tolerance threshold: ≤8%. Based on the physiological characteristics of strawberries in the database, the system generates three-effect regulation weights: (8) Temperature and humidity regulation are given high priority to reduce the risk of strawberry wilting due to water loss. During the preservation process, the system monitors ambient temperature, humidity, and CO2 concentration in real time. When the system detects that the temperature drops from 4°C to 2°C due to the release of CO2 hydrates causing a cooling effect, the system predicts the following using a temperature drop and humidity compensation model: =93%; at the same time, the concentration deviation caused by the increase in CO2 dissolution due to the decrease in temperature was calculated by the CO2 solubility change model, and the CO2 release rate was adjusted.

[0128] The final environmental stability control was as follows: temperature: 2±0.5℃; humidity: 92±3%; CO2 concentration: 4±0.5%.

[0129] Tested after 10 days of continuous storage: ; The results show that the system can dynamically adjust the ratio of cold air, moisture and CO2 release according to the preservation needs of strawberries, thus achieving precise preservation.

[0130] Compared to traditional refrigeration conditions, a strawberry preservation experiment was conducted using ordinary refrigeration methods with controlled conditions: temperature: 4℃; humidity: approximately 85%; no CO2 control. After 10 days of storage: ; The results showed that the proposed method reduced water loss and spoilage of strawberries through the synergistic control of three parameters: temperature, humidity, and air.

[0131] Specific Implementation Example 3: Adaptive Preservation of Meat Products (Beef) Implementation Process: Fresh beef was selected as the experimental subject. After identifying the food category through the food identification module, the beef preservation parameter database was called: target temperature range 0~2℃; target humidity range 75~85%; target CO2 concentration range 20~40%; CO2 tolerance threshold ≤50%.

[0132] Since beef spoilage is primarily affected by microbial growth, the system automatically adjusts the weighting of the three-effect release: (9) Prioritizing temperature control and CO2 regulation, the system calculates humidity changes after cooling based on a temperature change prediction model to prevent surface microbial growth caused by high humidity. Simultaneously, it predicts CO2 concentration loss based on a CO2 solubility model and automatically increases CO2 release. The final control parameters are: temperature 1±0.5℃; humidity 80±3%; CO2 concentration 30±3%. Testing is performed after 15 days of storage. ; This demonstrates that the system can adjust the three-effect release strategy according to different food characteristics, rather than using fixed preservation parameters.

[0133] Compared to experiments using only CO2 modified atmosphere packaging for beef, this experiment employed traditional modified atmosphere packaging under the following conditions: CO2 concentration 30%; temperature 4℃; no cooling or moisture compensation. After 15 days: ; This demonstrates that simple CO2 control cannot solve the problems of temperature fluctuations and humidity changes, while this solution achieves comprehensive optimization through a three-effect synergistic compensation model.

[0134] In this solution, the electrical components and their connections are as follows: Sensing modules—temperature and humidity sensors and pressure sensors—are connected to the intelligent control module via a communication module. The CO2 hydrate storage tank's temperature control and heating device is connected to the control circuit to regulate the decomposition rate. The gas / cold / liquid storage modules operate independently, outputting to the cold storage via solenoid valves and pipelines. The intelligent control unit acquires parameter data through the communication module and controls the storage and release modules to execute preset parameters.

[0135] The above embodiments describe in detail the specific implementation process of storing leafy vegetables, meat, and mixed foods, covering static relationships (module layout and circuit connection), dynamic relationships (control process and data feedback) and their effects, providing highly operable technical support for solving the personalized preservation needs of different foods.

[0136] To achieve the objective of this invention, based on the above embodiments, in another embodiment of this application, such as... Figure 8 As shown, a CO2 hydrate decomposition and release control system based on multi-source information fusion is provided. The system is used to implement the CO2 hydrate decomposition and release control method based on multi-source information fusion. The system includes: A generation unit is created to generate and acquire multi-source sensing information corresponding to a target space; wherein the multi-source sensing information includes at least environmental state parameters and state parameters of objects contained within the target space; The dynamic identification unit dynamically identifies the current state stage of the object based on the object's state parameters; and calls the corresponding target control parameter set from a preset strategy map based on the state stage; wherein the target control parameter set contains at least two mutually orthogonal environmental factor target values. A generation unit is constructed to generate and acquire the real-time offset of the environmental state parameters relative to the target control parameter set; and based on the real-time offset, to create independent release control instructions for CO2 hydrate decomposition products; wherein the decomposition products include at least gaseous components, cold components, and liquid components; the independent release control instructions are used to adjust the rate or dose of each of the decomposition products released into the target space so that the environmental state parameters converge to the target control parameter set.

[0137] In other words, in this embodiment of the invention, a system corresponding to the above method is provided, the core components of which include a CO2 hydrate supply unit for providing CO2 hydrate as a common source of cooling, CO2 gas, and liquid water; a multi-effect storage and release unit coupled to the CO2 hydrate supply unit and including a gas storage component, a cold storage component, and a liquid storage component that operate decoupled from each other, for receiving and outputting the three decomposition products generated by the phase change decomposition of CO2 hydrate as needed; a multi-source sensing unit disposed in the target space, including a first type of sensing component for collecting environmental state parameters and a second type of sensing component for collecting object state parameters; and a processing and control unit communicatively connected to the multi-source sensing unit and the multi-effect storage and release unit for executing the aforementioned method. Through the above-mentioned modular architecture design at the system level, this invention implements the decoupled control logic at the method level in the form of physical components. The decoupled operation of the three components of gas storage, cold storage, and liquid storage enables the three decomposition products of cold energy, CO2, and water to achieve a process of separation, on-demand allocation, and independent output at the physical level, avoiding release interference caused by shared management and shared paths. The multi-dimensional sensing capability of the multi-source sensing unit provides a sufficient data foundation for precise control. The centralized decision-making of the processing and control unit ensures the coordinated consistency of the actions of each execution component. The overall system architecture is clear, the module functions are independent, and the communication connection is reliable, with good industrial feasibility and operational stability.

[0138] In another embodiment of this application, the dynamic identification unit further includes: The first generation module is used to generate and obtain the initial category identifier corresponding to the object; The first determination module is used to determine whether the object is currently in the fresh period, mature period, decay period or warning period based on the changing trend of the object state parameters accumulated over time; wherein, the object state parameters include at least one of the following: weight change rate, image feature degradation index or characteristic gas concentration gradient. And / or, the construction generation unit further includes: The calling module is used to call a pre-stored quality degradation dynamics model; wherein, the model is used to characterize the nonlinear evolution relationship of the quality characteristic values ​​of the object with time and environmental stress factors; The prediction module is used to input the historical and current environmental state parameter sequences into the quality degradation dynamics model and predict the evolution trajectory of the quality characteristic values ​​within future time windows. An execution module is used to execute the independent release control command in advance when the evolutionary trajectory reaches a preset degradation threshold, and to change the direction of the evolutionary trajectory before the quality characteristic value exceeds the degradation threshold. The recording module is used to record the residual sequence between the predicted trajectory and the measured trajectory of the quality feature value; The correction module is used to correct the model parameters of the quality degradation dynamics model based on the residual sequence using a recursive parameter estimation method. The second generation module is used to calculate and generate the corresponding expected humidity offset vector based on the temperature offset included in the real-time offset and the air saturated water vapor partial pressure ratio before and after cooling. The third generation module is used to create a compensation control command for the liquid phase component based on the expected humidity offset vector; wherein the compensation control command is used to adjust the release flux of the liquid phase component in advance during or before the release of the cold energy component, so as to suppress the coupling disturbance of the cold energy release on the humidity parameter. The fourth generation module is used to calculate the amount of dissolution and dissipation of the gas phase component in the liquid medium in the target space based on the Henry coefficient under the current temperature field and the partial pressure value of the gas phase component. The fifth generation module is used to create an incremental release instruction for the gas phase component based on the dissolved and dissipated amount; wherein the incremental release instruction is used to increase the actual release amount of the gas phase component by the dissolved and dissipated amount based on the target release amount; The control module is used to control the CO2 hydrate to accelerate phase change decomposition under heating and / or depressurization conditions by adopting a joint excitation mode when the modulus exceeds a first preset threshold, and to synchronously increase the release intensity of each decomposition product through the independent release control command. The sixth generation module is used to generate the independent release control command only for the decomposition products corresponding to the single environmental factor when the modulus does not exceed the first preset threshold and is only related to the offset of a single environmental factor, by adopting a single factor adjustment mode. The seventh generation module is used to construct and generate a corresponding comprehensive cost function based on the target control parameter set corresponding to each object and the preset priority weight. The eighth generation module is used to substitute the current value of the environmental state parameter into the comprehensive cost value function, calculate and generate the current comprehensive cost value, and determine whether the current comprehensive cost value is higher than the preset allowable cost value; wherein, if so, the parameters of the independent release control command are adjusted until the recalculated comprehensive cost value is not higher than the preset allowable cost value.

[0139] In other words, in this embodiment, the specific structure of the CO2 hydrate supply unit is further defined: a jet reactor is used to mix high-pressure CO2 gas flow with atomized water droplets under low temperature and high pressure conditions to generate CO2 hydrate slurry; an insulated pressure storage tank is connected to the jet reactor to contain CO2 hydrate, its inner wall is provided with a phase change cold storage material layer and its top is connected to a pressure balance chamber to passively maintain the stability of the temperature and pressure field inside the storage tank; the processing and control unit controls the temperature and pressure boundary conditions of the storage tank to trigger the controlled decomposition of CO2 hydrate and supply the decomposition products to the multi-effect storage and release unit. This supply unit structure realizes a closed-loop management of the entire chain, including efficient generation, stable storage, and controlled decomposition of CO2 hydrates. The efficient generation capacity of the jet reactor ensures that industrial by-product CO2 can be rapidly converted into solid hydrate form, improving the system's CO2 processing throughput and economic feasibility. The synergistic effect of the phase change material layer and pressure balance chamber of the insulated and pressure-maintaining storage tank enables the tank to passively maintain a stable internal temperature and pressure field without external energy input, avoiding unexpected decomposition of hydrates and loss of cold energy due to external heat intrusion, and significantly reducing energy consumption during the maintenance phase. The active regulation of the temperature and pressure boundary conditions of the storage tank by the processing and control unit allows the initiation, acceleration, and deceleration of hydrate decomposition to be precisely executed as needed, providing a stable and controllable source of decomposition products for the multi-effect storage and release unit, realizing a closed-loop process with precise control from the source of raw material supply.

[0140] This invention acquires multi-source sensing information of the target space, including environmental state parameters and object state parameters; dynamically identifies the current state stage of the object based on the object state parameters; calls the corresponding target control parameter set from the strategy graph; obtains the real-time offset of the environmental state parameters relative to the target control parameter set; and generates independent release control commands for CO2 hydrate decomposition products in response to the real-time offset, adjusting the rate or dosage of gaseous components, cold components, and liquid components released into the target space to make the environmental state parameters converge to the target control parameter set. This invention achieves independent, on-demand allocation of cold energy, CO2 gas, and moisture through decoupled release control, feedforward compensation calculation, and predictive quality degradation control, solving the problem of multi-factor coupling interference, improving the accuracy and intelligence of food preservation, extending shelf life, and reducing energy consumption.

[0141] In other words, the present invention achieves independent, on-demand allocation and coordinated precise regulation of cooling capacity, CO2 gas, and moisture through decoupled release control of CO2 hydrate decomposition products, multi-parameter feedforward compensation calculation, and predictive intervention based on a quality degradation kinetic model. This solves the problems of multi-factor coupling interference and passive response lag. It can dynamically match preservation strategies according to different food categories and storage stages, improve preservation accuracy and intelligence, effectively extend the food preservation period, reduce spoilage rate and overall energy consumption, and achieve carbon resource recycling by using industrial by-product CO2 as raw material.

[0142] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion, characterized in that, The method includes: Create, generate, and acquire multi-source sensing information corresponding to a target space; wherein, the multi-source sensing information includes at least environmental state parameters and state parameters of objects contained within the target space; Based on the object's state parameters, the current state stage of the object is dynamically identified; and based on the state stage, the corresponding target control parameter set is called from a preset strategy map; wherein, the target control parameter set contains at least two mutually orthogonal environmental factor target values; The system constructs and acquires the real-time offset of the environmental state parameters relative to the target control parameter set; and based on the real-time offset, it creates and generates independent release control commands for CO2 hydrate decomposition products; wherein the decomposition products include at least gaseous components, cold components, and liquid components; the independent release control commands are used to adjust the rate or dose of each of the decomposition products released into the target space.

2. The method for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion according to claim 1, characterized in that, The step of dynamically identifying the current state stage of the object based on the object's state parameters further includes: Generate and obtain the initial category identifier corresponding to the object; Based on the changing trend of the object's state parameters accumulated over time, it is determined whether the object is currently in the fresh period, mature period, decay period, or warning period; wherein, the object's state parameters include at least one of the following: weight change rate, image feature degradation index, or characteristic gas concentration gradient.

3. The method for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion according to claim 1, characterized in that, The creation of independent release control instructions for CO2 hydrate decomposition products also includes: Invoke a pre-stored quality degradation dynamics model; wherein the model is used to characterize the nonlinear evolution relationship of the quality characteristic values ​​of the object with time and environmental stress factors; The historical and current environmental state parameter sequences are input into the quality degradation dynamics model, and the evolution trajectory of the quality characteristic values ​​within future time windows is predicted. When the evolutionary trajectory reaches a preset degradation threshold, the independent release control command is executed in advance, and the direction of the evolutionary trajectory is changed before the quality characteristic value exceeds the degradation threshold.

4. The method for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion according to claim 3, characterized in that, The method also includes a model self-correction step: Record the residual sequence between the predicted trajectory and the measured trajectory of the quality feature value; Based on the residual sequence, the model parameters of the quality degradation dynamics model are corrected using a recursive parameter estimation method.

5. A method for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion according to claim 1, 2, or 3, characterized in that, The method further includes: Based on the temperature offset included in the real-time offset, and combined with the air saturated water vapor partial pressure ratio before and after cooling, the corresponding expected humidity offset vector is calculated and generated. Based on the expected humidity offset vector, a compensation control command is created for the liquid phase component; wherein the compensation control command is used to adjust the release flux of the liquid phase component in advance during or before the release of the cold component.

6. A method for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion according to claim 1, 2, or 3, characterized in that, The method further includes: Based on the Henry's law coefficient under the current temperature field and the partial pressure of the gaseous component, the amount of dissolution and dissipation of the gaseous component in the liquid medium within the target space is calculated. Based on the dissolved and dissipated amount, an incremental release command is generated for the gas phase component; wherein the incremental release command is used to increase the dissolved and dissipated amount by the actual release amount of the gas phase component based on the target release amount.

7. The method for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion according to claim 1, characterized in that, In the step of creating an independent release control command for CO2 hydrate decomposition products, one of the following trigger modes is selected based on the modulus of the real-time offset: When the modulus exceeds the first preset threshold, a joint excitation mode is adopted to control the CO2 hydrate to accelerate phase change decomposition under heating and / or depressurization conditions, and the release intensity of each decomposition product is synchronously increased through the independent release control command. When the modulus does not exceed the first preset threshold and is only related to the offset of a single environmental factor, a single-factor adjustment mode is adopted, and the independent release control command is generated only for the decomposition products corresponding to the single environmental factor.

8. The method for controlling the decomposition and release of CO2 hydrate based on multi-source information fusion according to claim 1, characterized in that, When multiple objects with different state stages are simultaneously contained within the target space, the step of creating and generating independent release control instructions for CO2 hydrate decomposition products also includes: Based on the target control parameter set corresponding to each object and the preset priority weight, a corresponding comprehensive cost function is constructed and generated; The current value of the environmental state parameter is substituted into the comprehensive cost value function to calculate the current comprehensive cost value, and it is determined whether the current comprehensive cost value is higher than the preset allowable cost value; if so, the parameters of the independent release control command are adjusted until the recalculated comprehensive cost value is not higher than the preset allowable cost value.

9. A CO2 hydrate decomposition and release control system based on multi-source information fusion, characterized in that, The system is used to implement the CO2 hydrate decomposition and release control method based on multi-source information fusion as described in any one of claims 1 to 8, the system comprising: A generation unit is created to generate and acquire multi-source sensing information corresponding to a target space; wherein the multi-source sensing information includes at least environmental state parameters and state parameters of objects contained within the target space; The dynamic identification unit dynamically identifies the current state stage of the object based on the object's state parameters; and calls the corresponding target control parameter set from a preset strategy map based on the state stage; wherein the target control parameter set contains at least two mutually orthogonal environmental factor target values. A generation unit is constructed to generate and acquire the real-time offset of the environmental state parameters relative to the target control parameter set; and based on the real-time offset, to create and generate independent release control instructions for CO2 hydrate decomposition products; wherein the decomposition products include at least gaseous components, cold components and liquid components; the independent release control instructions are used to adjust the rate or dose of each of the decomposition products released into the target space respectively.

10. A CO2 hydrate decomposition and release control system based on multi-source information fusion according to claim 9, characterized in that, The dynamic identification unit further includes: The first generation module is used to generate and obtain the initial category identifier corresponding to the object; The first determination module is used to determine whether the object is currently in the fresh period, mature period, decay period or warning period based on the changing trend of the object state parameters accumulated over time; wherein, the object state parameters include at least one of the following: weight change rate, image feature degradation index or characteristic gas concentration gradient. And / or, the construction generation unit further includes: The calling module is used to call a pre-stored quality degradation dynamics model; wherein, the model is used to characterize the nonlinear evolution relationship of the quality characteristic values ​​of the object with time and environmental stress factors; The prediction module is used to input the historical and current environmental state parameter sequences into the quality degradation dynamics model and predict the evolution trajectory of the quality characteristic values ​​within future time windows. An execution module is used to execute the independent release control command in advance when the evolutionary trajectory reaches a preset degradation threshold, and to change the direction of the evolutionary trajectory before the quality characteristic value exceeds the degradation threshold. The recording module is used to record the residual sequence between the predicted trajectory and the measured trajectory of the quality feature value; The correction module is used to correct the model parameters of the quality degradation dynamics model based on the residual sequence using a recursive parameter estimation method. The second generation module is used to calculate and generate the corresponding expected humidity offset vector based on the temperature offset included in the real-time offset and the air saturated water vapor partial pressure ratio before and after cooling. The third generation module is used to create a compensation control command for the liquid phase component based on the expected humidity offset vector; wherein the compensation control command is used to adjust the release flux of the liquid phase component in advance during or before the release of the cold component; The fourth generation module is used to calculate the amount of dissolution and dissipation of the gas phase component in the liquid medium in the target space based on the Henry coefficient under the current temperature field and the partial pressure value of the gas phase component. The fifth generation module is used to create an incremental release instruction for the gas phase component based on the dissolved and dissipated amount; wherein the incremental release instruction is used to increase the actual release amount of the gas phase component by the dissolved and dissipated amount based on the target release amount; The control module is used to control the CO2 hydrate to accelerate phase change decomposition under heating and / or depressurization conditions by adopting a joint excitation mode when the modulus exceeds a first preset threshold, and to synchronously increase the release intensity of each decomposition product through the independent release control command. The sixth generation module is used to generate the independent release control command only for the decomposition products corresponding to the single environmental factor when the modulus does not exceed the first preset threshold and is only related to the offset of a single environmental factor, by adopting a single factor adjustment mode. The seventh generation module is used to construct and generate a corresponding comprehensive cost function based on the target control parameter set corresponding to each object and the preset priority weight. The eighth generation module is used to substitute the current value of the environmental state parameter into the comprehensive cost value function, calculate and generate the current comprehensive cost value, and determine whether the current comprehensive cost value is higher than the preset allowable cost value; wherein, if so, the parameters of the independent release control command are adjusted until the recalculated comprehensive cost value is not higher than the preset allowable cost value.

Images