New energy plant large water storage device stratified storage efficiency online monitoring method and energy-saving refrigeration equipment

Abstract

The application discloses a new energy plant large-scale water cold storage device layered cold storage efficiency online monitoring method and an energy-saving refrigeration equipment, which comprises nine steps of monitoring area division, layered monitoring component building, monitoring reference calibration, full-cycle parameter acquisition, layered interface identification, layered cold storage capacity accounting, layered cold storage efficiency calculation, abnormal condition determination and monitoring result output. The core innovation lies in that the transition layer position and thickness are cooperatively identified through temperature and density double parameters, the effective cold storage layer and the transition layer are distinguished, the layered and comprehensive cold storage efficiencies are accurately accounted, and the abnormal condition is determined in combination with the transition layer change trend. The application solves the pain points of the prior art, such as extensive monitoring, high misjudgment rate and incapability of adapting to large-scale devices, realizes accurate online monitoring of the layered cold storage efficiency, guarantees stable operation of the device, improves the energy-saving property of the refrigeration system and adapts to high requirements of new energy plants.

Description

Technical Field

[0001] This invention belongs to the field of energy-saving refrigeration equipment monitoring technology, and particularly relates to an online monitoring method for the stratified cold storage efficiency of large-scale water-cooled cold storage devices in new energy factories and energy-saving refrigeration equipment. Background Technology

[0002] In new energy factories (such as photovoltaic module manufacturers and power battery manufacturers), the production process requires extremely high precision in workshop temperature control. Large-scale water-based cold storage devices, as the core energy storage equipment of the factory's air conditioning system, are a type of refrigeration device. Their cold storage efficiency directly affects the operational stability, energy consumption level, and production process safety of the factory's refrigeration system. Stratified cold storage is the core working principle of large-scale water-based cold storage devices. It relies on the density difference between cold and warm water to achieve natural stratification, thereby reducing cold loss and improving cold storage utilization. However, due to their large volume, long charging and discharging cycles, and frequent fluctuations in operating conditions, large-scale water-based cold storage devices in new energy factories are prone to problems such as disordered stratification interfaces and thickened transition layers. This leads to decreased cold storage efficiency, wasted cold energy, and consequently affects the response speed and temperature control accuracy of the factory's refrigeration system.

[0003] Currently, most methods for monitoring the cooling efficiency of water-based cooling systems focus on offline testing of overall energy efficiency, lacking online monitoring solutions for the stratified cooling efficiency of large-scale new energy plant installations. Existing monitoring technologies either only collect overall temperature and flow data from the inlet and outlet water pipes of the device, failing to reflect the internal stratified cooling state and thus making it impossible to accurately locate the specific layers of cooling loss; or, while attempting stratified monitoring, problems exist such as unreasonable monitoring point layout, interference of sensing elements with the natural stratification of water, and a lack of scientific benchmark calibration mechanisms, resulting in significant deviations in monitoring data and an inability to accurately identify the boundary between the transition layer and the effective cooling layer, thereby hindering the accurate calculation of stratified cooling efficiency.

[0004] Meanwhile, existing technologies lack a full-cycle parameter acquisition and abnormal condition judgment logic designed specifically for the operational characteristics of large-scale water-based cooling storage devices in new energy plants. This makes it difficult to capture issues such as stratification disorder and abnormal cooling loss in real time throughout the entire process of charging, maintaining, and discharging cooling. Consequently, monitoring results cannot provide accurate data support for device operation and adjustment, failing to meet the high requirements of new energy plants for the stability and energy efficiency of refrigeration systems. Furthermore, some monitoring solutions rely on complex software algorithms for data processing, which not only increases system complexity and cost but also suffers from data latency issues, making true online monitoring impossible and ill-suited to the continuous production requirements of new energy plants. Summary of the Invention

[0005] The purpose of this invention is to provide an online monitoring method for the stratified cold storage efficiency of large-scale water-cooled cold storage devices in new energy factories and an energy-saving refrigeration equipment, so as to solve the problems mentioned in the background art.

[0006] In view of this, the present invention provides a method for online monitoring of the stratified cooling efficiency of a large-scale water-cooled cooling device in a new energy factory, comprising the following steps:

[0007] Step 1: Complete the division of the monitoring area of ​​the cold storage device. Divide the cold storage device into several monitoring layers from top to bottom along the vertical direction of the cold storage device, and simultaneously determine the horizontal monitoring section of each monitoring layer. Arrange several monitoring points in each monitoring section.

[0008] Step 2: Construct a layered monitoring system. Deploy temperature sensing elements and density sensing elements at the monitoring points of each monitoring layer to ensure that the sensing elements are in full contact with the water in the cold storage device without interfering with the natural stratification of the water.

[0009] Step 3: Perform monitoring benchmark calibration. With the cold storage device in a static and undisturbed state, collect the initial temperature and initial density values ​​of each monitoring point to establish a hierarchical monitoring benchmark database.

[0010] Step 4: Conduct real-time acquisition of layered parameters. Throughout the entire cycle of charging, storing and releasing cold energy in the cold storage device, simultaneously collect the real-time temperature and density values ​​at each monitoring point in the monitoring layer and record the acquisition time.

[0011] Step 5: Implement layered interface recognition. Based on the temperature and density differences of each monitoring layer, identify the location and thickness of the temperature transition layer between adjacent monitoring layers, and distinguish between the effective cold storage layer and the transition layer.

[0012] Step 6: Perform stratified calculation of cold storage capacity. Based on the temperature and density values ​​of each effective cold storage layer and the volume of the corresponding monitoring layer, calculate the actual cold storage capacity of each effective cold storage layer.

[0013] Step 7: Calculate the layered cold storage efficiency. Combine the actual cold storage capacity and theoretical cold storage capacity of each effective cold storage layer to obtain the cold storage efficiency of each layer. At the same time, summarize the results to obtain the overall cold storage efficiency of the entire cold storage device.

[0014] Step 8: Complete the abnormal operating condition judgment. Compare the real-time cold storage efficiency of each monitoring layer with the baseline efficiency threshold. Combine the trend of transition layer thickness change to determine whether there is stratification disorder or abnormal cold loss.

[0015] Step nine: Output the monitoring results and synchronously feed back the cold storage efficiency of each layer, the overall cold storage efficiency, and information on abnormal operating conditions to provide data support for the operation and adjustment of the cold storage device.

[0016] In a further embodiment of the present invention, in step one, the division of the monitoring layers is determined based on the height and volume of the cold storage device, the spacing between adjacent monitoring layers is uniform, the horizontal monitoring section is in contact with the inner wall of the cold storage device, the monitoring points are evenly distributed along the monitoring section, and avoid the water distributor outlet area.

[0017] In a further embodiment of the present invention, in step two, the temperature sensing element and the density sensing element are installed in an embedded manner, and an anti-disturbance protective sleeve is provided on the outside of the element. Several water-permeable holes are opened on the surface of the protective sleeve to ensure water flow and avoid the element from interfering with the water flow. The signal transmission lines of each sensing element are laid along the inner wall of the cold storage device.

[0018] In a further embodiment of the present invention, in step three, the duration of the static, undisturbed state is not less than the time required for the water in the cold storage device to be completely stable. The benchmark database also includes the theoretical temperature range, theoretical density range, and theoretical cold storage capacity of each monitoring layer. During the benchmark data acquisition process, the charging and discharging pipeline valves of the cold storage device are closed.

[0019] In a further embodiment of the present invention, in step four, the parameter acquisition adopts a synchronous trigger mode, and the temperature and density data of all monitoring points are acquired at the same time. The acquisition frequency is dynamically adjusted according to the charging and discharging rate of the cold storage device, and the acquisition frequency in the charging and discharging stage is higher than that in the cold preservation stage.

[0020] In a further embodiment of the present invention, in step five, the temperature transition layer is identified by comparing the temperature difference and density difference between adjacent monitoring layers. When the difference is less than the corresponding benchmark difference threshold, the area is determined to be a temperature transition layer. The thickness of the transition layer is calculated by the spacing and difference distribution between adjacent monitoring layers.

[0021] In a further embodiment of the present invention, in step six, during the calculation of cold storage capacity, the ineffective cold storage capacity of the transition layer is deducted based on the specific heat capacity corresponding to the water temperature, and the cold storage capacity of each single layer is calculated according to the actual effective volume ratio of each effective cold storage layer, so as to ensure that the calculation results are consistent with the actual operating state.

[0022] In a further embodiment of the present invention, in step seven, the theoretical cold storage capacity is calculated based on the volume of each monitoring layer of the cold storage device, the difference between the rated minimum temperature and the initial temperature of the water body, and the specific heat capacity of the water body. The stratified cold storage efficiency is obtained by the ratio of the actual cold storage capacity to the theoretical cold storage capacity. The comprehensive cold storage efficiency is obtained by weighted summation of the cold storage efficiencies of each stratified layer according to the corresponding layer volume.

[0023] In a further embodiment of the present invention, in step eight, the baseline efficiency threshold is determined based on the design parameters of the cold storage device and long-term operating experience. Abnormal operating conditions include a continuous increase in the thickness of the transition layer, a sudden drop in the layered cold storage efficiency, and an abnormal reduction in the temperature difference between adjacent layers. The judgment process combines the data change trends of multiple consecutive acquisition times to avoid misjudgment based on a single data point.

[0024] In a further embodiment of the present invention, in step nine, the monitoring results are displayed in a hierarchical manner, and the occurrence time, duration and corresponding monitoring data of abnormal operating conditions are recorded simultaneously. The feedback information can be directly connected to the operation and regulation system of the cold storage device to realize the linkage between monitoring and regulation.

[0025] An energy-saving refrigeration equipment uses an online monitoring method for the stratified cold storage efficiency of a large-scale water-cooled cold storage device in a new energy factory.

[0026] The beneficial effects of this invention are:

[0027] 1. Achieve precise online monitoring of stratified cold storage efficiency, improving the reliability and relevance of monitoring data. By scientifically dividing monitoring layers and monitoring points, and using disturbance-resistant stratified monitoring components, interference from sensing elements on water stratification is avoided. Simultaneously, combined with static, undisturbed benchmark calibration, a precise stratified monitoring benchmark is established, ensuring that temperature and density data collected synchronously throughout the entire cycle accurately reflect the cold storage status of each layer. Through precise identification of transition layers and calculation of stratified cold storage capacity, the limitations of existing technologies that can only monitor overall energy efficiency are overcome. The cold storage efficiency of each effective cold storage layer can be accurately obtained, providing direct evidence for locating layers with cold loss.

[0028] 2. Achieve accurate prediction and timely feedback of abnormal operating conditions, ensuring stable operation of the cold storage device. By comparing the stratified cold storage efficiency with the benchmark threshold and combining the trend of transition layer thickness change, it can accurately determine operating conditions such as stratification disorder and abnormal cold loss. Furthermore, continuous data trend analysis avoids misjudgment based on single data points, solving the pain point of existing technologies being unable to identify stratification anomalies in real time. The monitoring results can be directly connected to the cold storage device's operation and regulation system, realizing the linkage between monitoring and regulation. It can adjust operating parameters in a timely manner, suppress transition layer thickening and stratification disorder, improve the operational stability of the cold storage device, and adapt to the refrigeration needs of continuous production in new energy plants.

[0029] 3. Optimize the operating efficiency of the cold storage device and reduce the cooling energy consumption of the new energy plant. By accurately calculating the efficiency of layered cold storage and intervening in abnormal operating conditions in a timely manner, the ineffective loss of cold energy can be effectively reduced, the utilization rate of cold storage in each layer can be improved, and thus the overall cold storage efficiency of the entire cold storage device can be improved. At the same time, online monitoring can be achieved through pure physical monitoring and parameter calculation without relying on complex software algorithms, which reduces the complexity and operating cost of the monitoring system. Moreover, the design of dynamically adjusting the acquisition frequency can further reduce energy consumption while ensuring monitoring accuracy, which meets the development needs of energy conservation and consumption reduction in new energy plants and realizes energy saving of refrigeration equipment. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0032] In the description of this application, it should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. For ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0033] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, the first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0034] It should be noted that in the description of this application, the directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0035] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0036] This embodiment provides a method for online monitoring of the stratified cold storage efficiency of a large-scale water-cooled cold storage device in a new energy factory. Simultaneously, an energy-saving refrigeration device is also provided, which uses this method to monitor the stratified cold storage efficiency of the large-scale water-cooled cold storage device in a new energy factory.

[0037] Includes the following steps:

[0038] Step 1: Complete the monitoring area division of the cold storage device. Divide the device vertically into several monitoring layers from top to bottom, and simultaneously determine the horizontal monitoring cross-section of each layer. Arrange several monitoring points on each monitoring cross-section. Specifically, during the division process, the geometric parameters of the cold storage device must be accurately measured first to determine the total height, inner diameter, effective volume, and water distributor installation position of the device. Based on the compatibility between the device height and volume, the number of monitoring layers should be reasonably determined to ensure that the monitoring layers can fully cover the stratified areas of the water body within the device and avoid monitoring blind spots. The horizontal monitoring cross-section must be tightly fitted to the inner wall of the cold storage device, and the fitting gap should be controlled within a preset range to prevent the monitoring points from failing to accurately reflect the water state of the corresponding layer due to gaps. The arrangement of monitoring points should be evenly distributed based on the cross-sectional area to ensure that the monitoring range of each point can cover each other without overlap or omission, while strictly avoiding the water distributor outlet area to prevent water flow impact from interfering with the monitoring data during water distribution. By scientifically and rationally dividing the monitoring area, we can achieve comprehensive and seamless monitoring of the stratified state of the water in the cold storage device. This lays a precise spatial foundation for subsequent stratified parameter collection and efficiency calculation, effectively solving the problem of one-sided and large deviations in monitoring data caused by unreasonable area division in existing monitoring schemes. It improves the pertinence and comprehensiveness of the monitoring scheme, thereby ensuring the accuracy of subsequent stratified cold storage efficiency calculations.

[0039] Step two involves constructing a tiered monitoring system. Temperature and density sensors are deployed at monitoring points in each layer, ensuring full contact between the sensors and the water within the cold storage device without interfering with natural water stratification. Specifically, the installation depth and angle of the sensors are determined based on the distribution of monitoring points. Dedicated mounting brackets are used to fix the sensors in their designated positions. These brackets are made of corrosion-resistant and thermally conductive materials to prevent them from affecting the water temperature and density. High-precision temperature sensors are used for the temperature sensors to accurately capture minute changes in water temperature. Immersion density probes are employed for the density sensors to accommodate the temperature variations within the cold storage device. To ensure the accuracy of density measurements at different temperatures, and to prevent interference with the natural stratification of water by the sensing elements, a disturbance-proof protective sleeve is installed on the outside of the elements. This sleeve features a perforated structure with several evenly distributed water-permeable holes on its surface. The size and number of these holes have been optimized to ensure smooth water flow, allowing for full contact between the elements and the water, while minimizing disturbance to the surrounding water and preventing disruption of the water stratification. The signal transmission lines of each sensing element are laid along the inner wall of the cold storage device, encased in waterproof and corrosion-resistant protective sleeves and fixed in pre-designed slots on the inner wall. This prevents the lines from suspending in the water and interfering with the water flow, and also prevents monitoring malfunctions caused by line damage. A stratification monitoring component adapted to large-scale cold storage devices in new energy factories has been constructed, achieving synchronous and accurate acquisition of temperature and density parameters. This ensures full contact between the sensing elements and the water, improving data acquisition accuracy, and avoids disruption of the natural stratification of water through the disturbance-proof design. This solves the problem of data distortion caused by interference with water stratification in existing monitoring components, providing high-quality basic data for subsequent stratification interface identification and efficiency calculation.

[0040] Step 3: Perform monitoring benchmark calibration. With the cold storage device in a static, undisturbed state, collect the initial temperature and initial density values ​​at each monitoring point to establish a stratified monitoring benchmark database. Specifically, first, close the charging and discharging pipeline valves of the cold storage device to cut off external water flow interference to the water body inside the device. At the same time, shut down auxiliary equipment such as stirring and circulation devices inside the device to allow the water body to be in a completely static state. Maintain this static, undisturbed state for a time no less than the time required for the water body inside the cold storage device to be completely stable, ensuring that the water body reaches a natural equilibrium state in stratification. In this state, control the sensing elements to synchronously collect the initial temperature and initial density values ​​at each monitoring point. Collect data at each point multiple times, and after removing abnormal data, take the average value as the benchmark data for that point. The construction of the benchmark database not only includes the initial temperature and initial density benchmark values ​​of each monitoring point, but also, in conjunction with the design parameters of the cold storage device and the physical characteristics of the water body, calculates and records the theoretical temperature range, theoretical density range, and theoretical cold storage capacity of each monitoring layer. At the same time, record auxiliary parameters such as ambient temperature and atmospheric pressure during benchmark data collection to provide a comprehensive benchmark basis for subsequent real-time data comparison and analysis. By using static, undisturbed benchmark calibration, the impact of parameter fluctuations in unstable water conditions on monitoring results was eliminated. A precise and comprehensive stratified monitoring benchmark database was established, which solved the problem of lack of scientific benchmark calibration mechanism in existing monitoring schemes, resulting in no basis for real-time data comparison and large deviations. It provides a reliable reference standard for subsequent stratified interface identification, cold storage efficiency calculation and abnormal operating condition judgment, and significantly improves the accuracy of the entire monitoring scheme.

[0041] Step four involves real-time acquisition of stratified parameters. Throughout the entire cycle of the cold storage device—charging, storing, and releasing cold—real-time temperature and density values ​​at each monitoring point are simultaneously acquired, and the acquisition time is recorded. In practice, a synchronous triggering mode is used to control all sensing elements, ensuring that temperature and density data from all monitoring points are acquired at the same time. This avoids mismatches in stratified parameters due to time differences and ensures the synchronicity and comparability of data from each layer. The acquisition process covers the entire operating cycle of the cold storage device, including the charging, storing, and releasing stages, and is tailored to different operating stages. Operating conditions are characterized by dynamic adjustment of the acquisition frequency: During the charging and discharging phases, the water temperature and density change rapidly, so the acquisition frequency is appropriately increased to ensure that the rapid changes in parameters can be captured; during the storage and preservation phase, the water state is relatively stable and the parameter changes are gradual, so the acquisition frequency is appropriately reduced to minimize data redundancy and energy consumption while ensuring monitoring accuracy; After each acquisition, the acquisition time, corresponding operating stage, and device operating parameters (such as charging and discharging flow rates, pressures, etc.) are recorded synchronously, and the acquired data is initially correlated with the benchmark database to provide complete information support for subsequent data processing. This system achieves real-time acquisition of layered parameters throughout the entire operating cycle of the cold storage device. Synchronous triggering ensures data synchronization, and dynamic adjustment of the acquisition frequency achieves a balance between monitoring accuracy and energy consumption. It solves the problems of incomplete acquisition cycles, data asynchronization, or excessive energy consumption in existing monitoring schemes, comprehensively capturing the parameter change patterns of each water layer at different operating stages, and providing continuous and complete real-time data support for subsequent layer interface identification and cold storage capacity calculation.

[0042] Step 5: Implement layered interface recognition. Based on the temperature and density differences between each monitoring layer, identify the location and thickness of the temperature transition layer between adjacent monitoring layers, distinguishing between the effective cold storage layer and the transition layer. In specific operation, first extract the real-time temperature and density data collected in Step 4, calculate the temperature and density differences between adjacent monitoring layers, and compare the calculated differences with the preset benchmark difference threshold in the benchmark database. When the temperature and density differences between adjacent monitoring layers are both less than the corresponding benchmark difference threshold, the area between these adjacent layers is determined to be a temperature transition layer. The water in this area does not have obvious temperature and density stratification and does not have effective cold storage capacity. When the difference is greater than or equal to the benchmark difference threshold, the corresponding layer is determined to be an effective cold storage layer. The water in this area has obvious stratification and can effectively store cold energy. The thickness of the transition layer is calculated by checking the spacing and difference distribution of adjacent monitoring layers, combined with the gradient of the difference from above the benchmark threshold to below, to accurately calculate the upper and lower boundary positions of the transition layer, thereby determining the thickness of the transition layer. At the same time, the changing trend of the position and thickness of the transition layer over time is recorded. It achieves accurate differentiation between the effective cold storage layer and the transition layer, solving the pain point of existing technologies being unable to accurately identify the layer interface and difficult to define the effective cold storage area. By using temperature and density as dual parameters for judgment, it improves the accuracy of transition layer identification and avoids misjudgment caused by single parameter judgment. At the same time, it accurately calculates the thickness of the transition layer, providing a precise basis for deducting invalid cold energy when calculating the cold storage capacity, ensuring the authenticity and accuracy of the cold storage capacity calculation results.

[0043] Step Six: Perform layered cold storage capacity calculation. Based on the temperature and density values ​​of each effective cold storage layer and the volume of the corresponding monitoring layer, calculate the actual cold storage capacity of each effective cold storage layer. Specifically, first, calculate the actual volume of each monitoring layer based on its geometric dimensions. Then, deduct the ineffective volume of the transition layer identified in Step Five to ensure accurate volume calculation of the effective cold storage layers. Next, based on the real-time temperature values ​​of each effective cold storage layer, query the specific heat capacity parameter of the water at the corresponding temperature, and calculate the mass of the water in that layer based on its density value. Finally, calculate the actual cold storage capacity of each effective cold storage layer based on its mass, specific heat capacity, and the difference between the actual temperature and the initial reference temperature. During the calculation, strictly deduct the ineffective cold storage capacity of the transition layer. Calculate the cold storage capacity of each individual layer according to its actual effective volume ratio to avoid calculation deviations caused by the inclusion of ineffective cold storage capacity, ensuring that the calculation results closely match the actual operating state of the cold storage device. This method enables precise stratified calculation of cold storage capacity, breaking the limitation of existing technologies that can only calculate the overall cold storage capacity. It also solves the problems of existing calculation methods failing to deduct ineffective cold storage and having large deviations in the calculation results. Through stratified calculation, the actual cold storage capacity of each effective cold storage layer can be accurately obtained, and the cold storage contribution of each layer can be clarified. This provides accurate basic data for subsequent calculation of stratified cold storage efficiency and provides a direct basis for locating the specific layer where cold storage loss occurs.

[0044] Step 7: Calculate the stratified cold storage efficiency. Combining the actual and theoretical cold storage capacities of each effective cold storage layer, the cold storage efficiency of each layer is obtained. Simultaneously, the overall cold storage efficiency of the entire cold storage device is calculated. Specifically, the theoretical cold storage capacity of each effective cold storage layer is first calculated based on the volume of each monitoring layer, the difference between the rated minimum water temperature and the initial reference temperature, and the specific heat capacity of the water at the corresponding temperature. The calculation of the theoretical cold storage capacity strictly follows the physical principles of water cold storage and is corrected for the operating characteristics of large-scale cold storage devices in new energy plants to ensure the rationality of the theoretical value. Then, the stratified cold storage efficiency of each layer is calculated by the ratio of the actual cold storage capacity to the theoretical cold storage capacity of each effective cold storage layer, directly reflecting the cold storage performance of each layer. The overall cold storage efficiency is obtained by weighted summation of the stratified cold storage efficiencies according to the corresponding layer volume. The weighting coefficient is the proportion of the volume of each effective cold storage layer to the total effective cold storage volume, ensuring that the overall efficiency truly reflects the cold storage level of the entire cold storage device. Simultaneously, the changing trends of the stratified and overall cold storage efficiencies over operating time are recorded. It achieves accurate calculation of stratified cold storage efficiency and overall cold storage efficiency, solving the problem that existing technologies cannot accurately evaluate the cold storage performance of each layer and can only reflect the overall energy efficiency. Through stratified efficiency calculation, it can accurately locate the layers with low cold storage efficiency, providing a targeted basis for subsequent operation and adjustment. At the same time, by calculating the overall efficiency through weighted summation, it ensures that the calculation of the overall efficiency is scientific and reasonable, and fully reflects the overall cold storage performance of the cold storage device.

[0045] Step 8: Complete the abnormal operating condition judgment. Compare the real-time cold storage efficiency of each monitoring layer with the baseline efficiency threshold, and combine the trend of transition layer thickness change to determine whether there is stratification disorder or abnormal cold loss. Specifically, the baseline efficiency threshold is determined based on the design parameters, material characteristics, and long-term operating experience of the cold storage device, and is dynamically adjusted in combination with the production conditions required by the new energy plant to ensure the rationality and adaptability of the threshold. The judgment of abnormal operating conditions adopts a dual logic of "efficiency comparison + trend analysis". On the one hand, compare the real-time cold storage efficiency of each monitoring layer with the baseline efficiency threshold. When the real-time efficiency is lower than the baseline threshold and continues for a certain period of time, it is determined that there is an abnormal cold loss in this layer. On the other hand, analyze the trend of transition layer thickness change. When the transition layer thickness continues to increase, the temperature difference between adjacent layers decreases abnormally, and is accompanied by a sharp drop in stratification cold storage efficiency, it is determined that there is a stratification disorder. In the judgment process, the data change trend of multiple consecutive collection times is combined to eliminate the random fluctuation of a single data point and avoid misjudgment based on a single data point. At the same time, the occurrence time, duration, and corresponding monitoring data of the abnormal operating condition are recorded to provide a complete basis for the analysis of the cause of the abnormality. It enables accurate and rapid determination of abnormal operating conditions of cold storage devices, solving the problems of existing technologies being unable to identify stratification disorder and abnormal cold loss in real time, or having a high misjudgment rate due to a single judgment logic. Through dual judgment logic, it improves the accuracy and reliability of abnormal operating condition determination, can promptly capture abnormal operation of cold storage devices, and provides a guarantee for timely intervention and prevention of fault expansion. At the same time, it provides accurate data support for the analysis of the causes of abnormal operating conditions.

[0046] Step nine involves outputting monitoring results, simultaneously feeding back the cold storage efficiency of each layer, the overall cold storage efficiency, and information on abnormal operating conditions, providing data support for the operation and adjustment of the cold storage device. Specifically, the monitoring results are displayed in a layered manner, sequentially showing parameters such as real-time temperature, density, actual cold storage capacity, and layered cold storage efficiency for each layer, along with the overall cold storage efficiency of the entire device. This allows staff to intuitively grasp the cold storage status of each layer and the overall cold storage performance. For abnormal operating conditions, the occurrence time, duration, type of abnormality, and corresponding monitoring data are recorded simultaneously. A tiered early warning system is used for feedback, providing preliminary adjustment suggestions for abnormal conditions of varying severity. The feedback information can directly connect to the cold storage device's operation and adjustment system, enabling linkage between monitoring and adjustment. The operation and adjustment system can automatically adjust operating parameters such as charging / discharging flow rate and water distribution method based on the cold storage efficiency of each layer and abnormal information, achieving precise adjustment of the cold storage device. It enables intuitive display and accurate feedback of monitoring results, solving the problem of messy output information and inability to directly support operation and adjustment in existing monitoring schemes. Through layered display and graded early warning, it improves the readability and practicality of monitoring results, and realizes the linkage between monitoring and adjustment, enabling the cold storage device to optimize operating parameters in real time based on monitoring data, improve operational stability and cold storage efficiency, and adapt to the high requirements of new energy plants for refrigeration systems.

[0047] In this embodiment, in step one, the division of monitoring layers is determined based on the height and volume of the cold storage device. The spacing between adjacent monitoring layers is uniform, the horizontal monitoring section is flush with the inner wall of the cold storage device, and the monitoring points are evenly distributed along the monitoring section, avoiding the water distributor outlet area. Specifically, the number of monitoring layers is adapted to the height and volume of the cold storage device. For large-scale cold storage devices in new energy factories with higher heights and larger volumes, the number of monitoring layers is appropriately increased to ensure monitoring accuracy. The spacing between adjacent monitoring layers is controlled within a preset uniform range to avoid blind spots caused by excessive spacing and increased monitoring costs and data redundancy due to insufficient spacing. The fit between the measuring section and the inner wall of the cold storage device must meet preset standards. For circular cold storage devices, the section adopts a circular structure matching the inner diameter of the device; for square cold storage devices, the section adopts a rectangular structure with dimensions consistent with the inner wall of the device, ensuring that the section can fully cover the water body at the corresponding layer. Monitoring points are evenly distributed along the monitoring section, with the number of points determined according to the cross-sectional area, ensuring that the monitoring range of each point can cover the preset area. At the same time, the monitoring points are strictly avoided from the water distributor outlet area, and the distance from the water distributor outlet is not less than the preset safety distance to avoid fluctuations in monitoring data caused by water flow impact during water distribution, ensuring the stability of monitoring data. The rationality and accuracy of the monitoring area division have been further optimized. Through uniform spacing and a fitted section design, monitoring blind spots have been eliminated, improving the comprehensiveness of monitoring data. By avoiding the water distributor outlet area, the interference of water flow impact on monitoring data has been further reduced, ensuring the stability and accuracy of monitoring data, and providing a more reliable spatial foundation for the smooth implementation of subsequent steps.

[0048] In this embodiment, in step two, the temperature sensing element and the density sensing element are installed in an embedded manner. An anti-disturbance protective sleeve is provided on the outside of the element, and several water-permeable holes are opened on the surface of the protective sleeve to ensure water flow and prevent the element from interfering with the water flow. The signal transmission lines of each sensing element are laid along the inner wall of the cold storage device. Specifically, the embedded installation method uses a structure with pre-drilled mounting holes and dedicated fasteners to embed the sensing element into the preset position of the monitoring point, ensuring that the element is firmly installed and preventing loosening or displacement of the element due to water flow impact during operation. The anti-disturbance protective sleeve is made of a high-strength, corrosion-resistant, and thermally conductive material. The dimensions of the device are matched to the sensing elements, and the water-permeable holes on the surface are evenly distributed. The diameter of the water-permeable holes has been optimized to ensure smooth water flow, allowing the elements to fully contact the water and accurately collect temperature and density data, while effectively reducing the disturbance of the water flow to the surrounding water and preventing disruption of the natural stratification of the water. The signal transmission line uses a waterproof, corrosion-resistant, and interference-resistant dedicated cable, with a protective sleeve, and is laid along the pre-set slots on the inner wall of the cold storage device for secure fixing. This prevents the line from floating in the water and interfering with the water flow, and also prevents signal loss or interference caused by line damage or aging, ensuring the stability and reliability of data transmission. This further improves the stability and adaptability of the stratification monitoring component. The embedded installation method ensures the element is firmly installed, preventing displacement failures during operation. The optimized anti-disturbance protective sleeve design further reduces the interference of the element on the water stratification, improving the accuracy of data acquisition. The standardized wiring ensures stable signal transmission, avoiding data loss or interference, and providing high-quality basic data for subsequent data processing and analysis.

[0049] In this embodiment, in step three, the duration of the static, undisturbed state is no less than the time required for the water in the cold storage device to fully stabilize. The benchmark database also includes the theoretical temperature range, theoretical density range, and theoretical cold storage capacity of each monitoring layer. During the benchmark data acquisition process, the charging and discharging pipeline valves of the cold storage device are closed. Specifically, the duration of the static, undisturbed state is determined based on the volume of the cold storage device, the total amount of water, and the initial state to ensure that the water can be completely still and reach natural equilibrium through stratification. For large-scale cold storage devices in new energy factories, the duration is usually no less than a preset time to avoid distortion of the benchmark data due to incomplete water stabilization. The construction of the benchmark database... In addition to including the initial temperature and density benchmark values ​​for each monitoring point, the theoretical temperature and density ranges for each monitoring layer are calculated based on the designed cold storage capacity of the cold storage device and the physical properties of the water (such as specific heat capacity and density variation with temperature). Simultaneously, the theoretical cold storage capacity is calculated based on the volume of each layer, the difference between the rated minimum temperature and the initial temperature, ensuring the comprehensiveness and practicality of the benchmark database. During benchmark data acquisition, the valves on the charging and discharging pipelines are strictly closed to cut off external water flow interference. All auxiliary operating equipment within the device is also shut down to ensure the water body is completely static, preventing disturbances from equipment operation from affecting the accuracy of benchmark data acquisition. This further improves the accuracy and reliability of benchmark calibration. Sufficient static maintenance ensures the water body reaches a natural equilibrium state, avoiding benchmark data distortion. By improving the content of the benchmark database, a more comprehensive reference is provided for subsequent real-time data comparison, transition layer identification, and cold storage efficiency calculation. Furthermore, by closing valves and equipment, external interference is eliminated, ensuring the authenticity of the benchmark data and providing a core guarantee for the accuracy of the entire monitoring scheme.

[0050] In this embodiment, step four employs a synchronous triggering mode for parameter acquisition, simultaneously collecting temperature and density data from all monitoring points. The acquisition frequency is dynamically adjusted based on the charging and discharging rates of the cold storage device, with a higher acquisition frequency during the charging and discharging phase than during the cold preservation phase. Specifically, the synchronous triggering mode is implemented using a dedicated synchronous controller. The controller sends a synchronous trigger signal to simultaneously activate all temperature and density sensing elements, ensuring that parameter acquisition from all monitoring points is completed at the same time. This avoids data mismatch between layers due to acquisition time differences, ensuring data synchronization and comparability. The dynamic adjustment of the acquisition frequency is based on the cold storage... The charging and discharging rates of the device are determined. During the charging phase, cold water is injected, and during the discharging phase, cold water is output. The water temperature and density change rapidly during this phase. Therefore, the acquisition frequency is adjusted to a preset high-frequency range to ensure the capture of rapid parameter changes. During the cold storage and preservation phase, the water state is relatively stable, and temperature and density changes are gradual. Therefore, the acquisition frequency is adjusted to a preset low-frequency range to reduce data redundancy while maintaining monitoring accuracy, thus lowering the energy consumption and data processing pressure of the monitoring system. Simultaneously, a frequency adjustment threshold is set. When the charging and discharging rates change abruptly, the acquisition frequency is automatically adjusted to ensure that the acquisition frequency always adapts to the changing patterns of water parameters. This further improves the synchronization and adaptability of parameter acquisition. The synchronous triggering mode solves the problem of data asynchrony, ensuring the comparability of data from different layers. By dynamically adjusting the acquisition frequency, a balance is achieved between monitoring accuracy and energy consumption / data processing pressure. This ensures accurate capture of parameter changes during the charging and discharging phases while reducing energy consumption and data redundancy during the preservation phase, improving the operational efficiency and practicality of the monitoring system.

[0051] In this embodiment, in step five, the temperature transition layer is identified by comparing the temperature difference and density difference between adjacent monitoring layers. When the difference is simultaneously less than the corresponding benchmark difference threshold, the area is determined to be a temperature transition layer. The thickness of the transition layer is calculated based on the spacing and difference distribution between adjacent monitoring layers. Specifically, the benchmark difference threshold is determined based on temperature and density data in the benchmark database, combined with the physical characteristics of water stratification. Temperature difference threshold and density difference threshold are set separately to ensure that the threshold can accurately distinguish between the effective cold storage layer and the transition layer. The identification of the transition layer adopts a dual-parameter collaborative judgment logic. Only when the temperature difference between adjacent monitoring layers is less than the temperature benchmark threshold and the density difference is less than the density benchmark threshold is the area determined to be a transition layer, avoiding misjudgment caused by single-parameter judgment and improving the accuracy of identification. In the process of calculating the thickness of the transition layer, the upper and lower boundary positions of the transition layer are first determined based on the spacing between adjacent monitoring layers and the gradient of the difference from above the benchmark threshold to below. Then, the actual thickness of the transition layer is calculated by the difference between the boundary positions. At the same time, the change trend of the transition layer thickness over time is recorded to provide data support for subsequent abnormal operating condition judgment. It further improves the accuracy of transition layer identification. By using dual-parameter collaborative judgment, it avoids the limitations of single-parameter judgment and reduces the false judgment rate of transition layer identification. By accurately calculating the thickness of the transition layer, it provides a more accurate basis for deducting invalid cold energy when calculating the cold storage capacity, ensuring the authenticity of the cold storage capacity calculation results. At the same time, it provides accurate data for analyzing the trend of transition layer thickness change in abnormal operating conditions.

[0052] Step five, "Identifying the layered interface and determining the location and thickness of the temperature transition layer between adjacent monitoring layers based on the temperature and density differences of each monitoring layer, thus distinguishing between the effective cold storage layer and the transition layer," is the core step in the entire monitoring method that differs most significantly from existing technologies. The differences lie primarily in the judgment logic, identification dimensions, data application, and applicable scenarios. Existing technologies for layered monitoring of water-cooled storage devices all employ a "single-parameter threshold judgment" logic, almost exclusively using temperature as the sole criterion. Even when density parameters are introduced, they are only used as auxiliary references, failing to form a dual-parameter collaborative judgment logic and thus unable to avoid errors caused by local disturbances. The previous method could only roughly identify the approximate location of the layering interface, unable to accurately calculate the thickness of the transition layer, and even failed to distinguish between the transition layer and the effective cold storage layer. Its layering identification results were only used to confirm the existence of the layering and could not be linked to subsequent cold storage capacity calculations or abnormal operating condition judgments, thus lacking practical engineering value. Furthermore, it was mainly suitable for small cold storage devices and could not cope with the characteristics of large-scale cold storage devices in new energy factories, such as large volume, frequent operating condition fluctuations, drastic changes in transition layer thickness, and susceptibility to water flow impacts and equipment vibration interference. In contrast, step five of this invention uses a "temperature + density dual-parameter collaborative judgment" logic, setting a judgment condition where "both differences simultaneously meet the threshold," utilizing the temperature and density of water... The density-positive correlation physical property enables mutual verification of two parameters, effectively eliminating transient disturbances and ensuring the accuracy of transition layer identification. Simultaneously, it achieves dual-dimensional identification of "location + thickness quantification." By accurately calculating the transition layer thickness through the spacing and difference distribution gradient between adjacent monitoring layers, it transforms the qualitative existence of the transition layer into quantitative data. Serving as the "technical hub" of the entire monitoring method, it connects the three core steps of subsequent cold storage capacity calculation, stratification efficiency calculation, and abnormal operating condition judgment, constructing a complete "identification-calculation-judgment" technical link. This enables the implementation from monitoring to application and is specifically designed for large-scale cold storage devices in new energy plants. Its dual-parameter correlation... The same judgment logic can resist the influence of water flow disturbance and equipment vibration. The thickness calculation function is adapted to the characteristics of thick and rapidly changing transition layers. Combined with the design of the pre-monitoring area division and anti-disturbance monitoring components, it further improves the identification accuracy and accurately solves the pain points of layered monitoring of large-scale devices. It is essentially different from the extensive monitoring of existing technologies that are "single parameter, coarse judgment, and no linkage application". It forms a refined monitoring logic of "dual parameter collaboration, precise quantification, and full-link linkage" that has never been seen in existing technologies. It not only solves the misjudgment problem of existing technologies, but also constructs the technical core of the whole layered monitoring method, which has outstanding substantive features and significant progress.

[0053] In this embodiment, in step six, during the cold storage capacity calculation, the ineffective cold storage capacity of the transition layer is deducted based on the specific heat capacity corresponding to the water temperature. The cold storage capacity of each individual layer is calculated according to the actual effective volume ratio of each effective cold storage layer to ensure that the calculation results are consistent with the actual operating state. Specifically, the specific heat capacity of water changes with temperature. During the calculation, the specific heat capacity parameter of water at the corresponding temperature is queried based on the real-time temperature value of each effective cold storage layer to avoid calculation deviations caused by using a fixed specific heat capacity. The ineffective cold storage capacity of the transition layer is calculated based on the volume, temperature, and corresponding specific heat capacity of the transition layer. The ineffective cold storage capacity is strictly deducted from the total cold storage capacity during the calculation to ensure that only the actual cold storage capacity of the effective cold storage layer is calculated. The cold storage capacity of each individual layer is calculated according to the proportion of the actual volume of each effective cold storage layer to the total effective cold storage volume to avoid calculation deviations caused by differences in layer volume. At the same time, the cold storage capacity calculation results are corrected based on the temperature and density data of each layer to ensure that the calculation results can truly reflect the actual cold storage state of each effective cold storage layer and are consistent with the actual operation of the cold storage device. This method further improves the accuracy of stratified cold storage capacity calculation. By combining the specific heat capacity corresponding to the temperature, it avoids the calculation deviation caused by fixed specific heat capacity. By strictly deducting ineffective cold capacity and calculating according to volume ratio, it ensures the rationality and authenticity of the cold storage capacity calculation of each layer. It solves the problem that the existing calculation method ignores the change of water specific heat capacity and has large calculation deviation caused by not deducting ineffective cold capacity, and provides more accurate basic data for subsequent stratified cold storage efficiency calculation.

[0054] In this embodiment, in step seven, the theoretical cold storage capacity is calculated based on the volume of each monitoring layer of the cold storage device, the difference between the rated minimum temperature and the initial temperature of the water, and the specific heat capacity of the water. The stratified cold storage efficiency is obtained by the ratio of the actual cold storage capacity to the theoretical cold storage capacity. The comprehensive cold storage efficiency is obtained by weighted summation of the cold storage efficiencies of each layer according to the corresponding layer volume. Specifically, the calculation of the theoretical cold storage capacity strictly follows the physical formula of water cold storage, combining the actual volume of each monitoring layer, the difference between the rated minimum temperature and the initial reference temperature of the water, and the specific heat capacity of the water at the corresponding temperature to accurately calculate the theoretical cold storage capacity of each effective cold storage layer. This is also combined with the large-scale cold storage of the new energy factory. The operating losses of the cold storage device are adjusted to correct the theoretical cold storage capacity, ensuring the rationality of the theoretical value. The stratified cold storage efficiency is calculated by the ratio of the actual cold storage capacity of each effective cold storage layer to the theoretical cold storage capacity. The closer the ratio is to 1, the better the cold storage performance of that layer, directly reflecting the differences in cold storage efficiency between layers. The comprehensive cold storage efficiency is calculated using a weighted summation method, with the weighting coefficient being the proportion of the volume of each effective cold storage layer to the total effective cold storage volume. This ensures that the comprehensive efficiency accurately reflects the overall cold storage level of the entire cold storage device, avoiding misjudgments due to differences in the volume of each layer, and providing a scientific basis for the overall performance evaluation of the cold storage device. The calculation logic for cold storage efficiency has been further optimized, improving the accuracy of the calculations for stratified and comprehensive cold storage efficiency. Through the correction of theoretical cold storage capacity and the weighted summation of comprehensive efficiency calculations, the scientific and rational nature of the efficiency calculations is ensured, accurately reflecting the cold storage performance of each layer and the overall cold storage level of the device, providing a more reliable basis for subsequent operation adjustment and performance optimization.

[0055] In this embodiment, in step eight, the baseline efficiency threshold is determined based on the design parameters of the cold storage device and long-term operating experience. Abnormal operating conditions include a continuous increase in the thickness of the transition layer, a sudden drop in the stratified cold storage efficiency, and an abnormal narrowing of the temperature difference between adjacent layers. The determination process combines the data change trends of multiple consecutive collection times to avoid misjudgment based on a single data point. Specifically, the baseline efficiency threshold is determined based on the design cold storage efficiency, material characteristics, operating years, and historical data accumulated over a long period of operation of the cold storage device. At the same time, the threshold is dynamically adjusted in conjunction with the production operating conditions of the new energy plant to ensure the adaptability and rationality of the threshold. The types of abnormal operating conditions are clearly defined as a continuous increase in the thickness of the transition layer, a sudden drop in the stratified cold storage efficiency, and an abnormal narrowing of the temperature difference between adjacent layers. These three abnormal operating conditions directly reflect the stratification disorder and cold energy loss problem of the cold storage device. During the determination process, monitoring data is collected at multiple consecutive times, and the data change trends are analyzed. Only when the thickness of the transition layer increases, the efficiency drops sharply, or the temperature difference narrows at multiple consecutive times is it determined to be an abnormal operating condition. Random fluctuations of a single data point are eliminated to avoid misjudgment due to instantaneous data anomalies. At the same time, the change trends of abnormal operating conditions are recorded to provide a basis for the analysis of the cause of the anomaly and subsequent adjustments. It further improves the accuracy and reliability of abnormal operating condition judgment. By dynamically adjusting the benchmark efficiency threshold, the adaptability of the threshold is ensured. By clarifying the judgment logic of abnormal operating condition type and trend analysis, the false judgment rate is reduced. It can accurately capture the abnormal operation of the cold storage device, providing accurate basis for timely intervention and fault diagnosis, and avoiding the expansion of abnormal operating conditions that lead to a significant decrease in cold storage efficiency and equipment failure.

[0056] In this embodiment, in step nine, the monitoring results are displayed in a layered manner, simultaneously recording the occurrence time, duration, and corresponding monitoring data of abnormal operating conditions. The feedback information can directly connect to the operation and adjustment system of the cold storage device, realizing the linkage between monitoring and adjustment. Specifically, the layered display method adopts the form of a layered list or layered visualization chart, displaying parameters such as real-time temperature, density, actual cold storage capacity, and layered cold storage efficiency of each layer in the order of monitoring layers. At the same time, abnormal operating condition information is highlighted, enabling staff to quickly locate the abnormal layer and abnormal type. The recording of abnormal operating condition information includes the occurrence time, duration, abnormal type, corresponding layer, and real-time monitoring data, providing complete information support for the analysis of abnormal causes. The feedback monitoring information adopts a standardized data format and directly connects to the operation and adjustment system of the cold storage device, realizing the linkage between monitoring and adjustment. The adjustment system can automatically adjust parameters such as charging and discharging cold flow rate, water distribution method, and running time according to the cold storage efficiency of each layer and abnormal information, realizing precise adjustment of the cold storage device without manual intervention and improving adjustment efficiency. This further enhances the practicality and operability of the monitoring results. Through layered display and detailed recording of abnormal information, it is convenient for staff to quickly grasp the operating status and abnormal situations of the device. Through the linkage between monitoring and adjustment, automatic and precise adjustment of the cold storage device is realized, which improves the efficiency of operation and adjustment, reduces the cost of manual intervention, and ensures the pertinence of adjustment measures, effectively improving the operational stability and cold storage efficiency of the cold storage device.

[0057] Furthermore, this solution, through a complete technical path of layered monitoring, benchmark calibration, precise calculation, and coordinated adjustment, not only achieves accurate monitoring of cold storage efficiency and timely identification of abnormal operating conditions, but also unexpectedly realizes synergistic energy-saving optimization between the refrigeration system and cold storage device in the new energy factory, breaking the limitation of existing solutions that only focus on the efficiency of the cold storage device itself. Specifically, through precise monitoring of layered cold storage efficiency, the storage and loss patterns of cold energy at different times and layers can be clearly identified. Combined with the characteristics of production load fluctuations in the new energy factory, the charging and discharging strategies of the cold storage device can be optimized, ensuring that the charging time of the cold storage device is precisely matched with the factory's off-peak electricity hours, and the discharging time is precisely matched with the factory's peak production cooling demand. This not only improves the cold storage efficiency of the cold storage device itself, but also reduces the electricity cost of the factory's refrigeration system. At the same time, through timely intervention and coordinated adjustment of abnormal operating conditions, cold energy loss caused by layered disorder of the cold storage device is avoided, indirectly improving the refrigeration efficiency of the factory's air conditioning system and reducing the energy consumption of the air conditioning system. This achieves a triple synergistic effect of "improved efficiency of cold storage device - reduced energy consumption of refrigeration system - optimized electricity cost of factory," and this synergistic energy-saving effect far exceeds the expectations of existing single monitoring or adjustment solutions. In addition, this technical approach has unexpectedly extended the service life of the cold storage device. Through precise monitoring and timely adjustment, it avoids problems such as scaling and accelerated corrosion on the inner wall of the device caused by disordered stratification, reduces equipment maintenance costs, and further improves economic efficiency and practicality.

[0058] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for online monitoring of the stratified cooling efficiency of a large-scale water-cooled cooling device in a new energy factory, characterized in that, Includes the following steps: Step 1: Complete the division of the monitoring area of ​​the cold storage device. Divide the cold storage device into several monitoring layers from top to bottom along the vertical direction of the cold storage device, and simultaneously determine the horizontal monitoring section of each monitoring layer. Arrange several monitoring points in each monitoring section. Step 2: Construct a layered monitoring system. Deploy temperature sensing elements and density sensing elements at the monitoring points of each monitoring layer to ensure that the sensing elements are in full contact with the water in the cold storage device without interfering with the natural stratification of the water. Step 3: Perform monitoring benchmark calibration. With the cold storage device in a static and undisturbed state, collect the initial temperature and initial density values ​​of each monitoring point to establish a hierarchical monitoring benchmark database. Step 4: Conduct real-time acquisition of layered parameters. Throughout the entire cycle of charging, storing and releasing cold energy in the cold storage device, simultaneously collect the real-time temperature and density values ​​at each monitoring point in the monitoring layer and record the acquisition time. Step 5: Implement layered interface recognition. Based on the temperature and density differences of each monitoring layer, identify the location and thickness of the temperature transition layer between adjacent monitoring layers, and distinguish between the effective cold storage layer and the transition layer. The temperature transition layer is identified by comparing the temperature and density differences between adjacent monitoring layers. Step 6: Perform stratified calculation of cold storage capacity. Based on the temperature and density values ​​of each effective cold storage layer and the volume of the corresponding monitoring layer, calculate the actual cold storage capacity of each effective cold storage layer. During the calculation process, combine the specific heat capacity corresponding to the water temperature, deduct the ineffective cold storage capacity of the transition layer, and calculate the cold storage capacity of each single layer according to the actual effective volume ratio of each effective cold storage layer to ensure that the calculation results are consistent with the actual operating conditions. During the calculation process, the actual volume of each monitoring layer is calculated based on the geometric dimensions of each effective cold storage layer. Combined with the transition layer location identified in step five, the invalid volume of the transition layer is deducted. Step 7: Calculate the stratified cold storage efficiency. Combine the actual and theoretical cold storage capacity of each effective cold storage layer to obtain the cold storage efficiency of each layer. At the same time, summarize the results to obtain the overall cold storage efficiency of the entire cold storage device. The theoretical cold storage capacity is calculated based on the volume of each monitoring layer of the cold storage device, the difference between the rated minimum temperature and the initial temperature of the water body, and the specific heat capacity of the water body. The stratified cold storage efficiency is obtained by the ratio of the actual cold storage capacity to the theoretical cold storage capacity. The overall cold storage efficiency is obtained by weighting the cold storage efficiency of each layer according to the corresponding layer volume. The weighting coefficient is the proportion of the volume of each effective cold storage layer to the total effective cold storage volume. At the same time, record the changing trends of the stratified cold storage efficiency and the overall cold storage efficiency with the operating time. Step 8: Complete the abnormal operating condition judgment, compare the real-time cold storage efficiency of each monitoring layer with the baseline efficiency threshold, and combine the transition layer thickness change trend to determine whether there is stratification disorder or abnormal cold loss operating condition. Step nine: Output the monitoring results and synchronously feed back the cold storage efficiency of each layer, the overall cold storage efficiency, and information on abnormal operating conditions to provide data support for the operation and adjustment of the cold storage device.

2. The method for online monitoring of the stratified cooling efficiency of a large-scale water-cooled storage device in a new energy factory according to claim 1, characterized in that, In step one, the division of the monitoring layers is determined based on the height and volume of the cold storage device. The spacing between adjacent monitoring layers is uniform, the horizontal monitoring section is in contact with the inner wall of the cold storage device, and the monitoring points are evenly distributed along the monitoring section, avoiding the water distributor outlet area.

3. The method for online monitoring of the stratified cooling efficiency of a large-scale water-cooled storage device in a new energy factory according to claim 1, characterized in that, In step two, the temperature sensing element and the density sensing element are installed in an embedded manner. The outer side of the element is equipped with a disturbance-proof protective sleeve. Several water-permeable holes are opened on the surface of the protective sleeve to ensure water flow and avoid the element from interfering with the water flow. The signal transmission lines of each sensing element are laid along the inner wall of the cold storage device.

4. The method for online monitoring of the stratified cooling efficiency of a large-scale water-cooled storage device in a new energy factory according to claim 1, characterized in that, In step three, the duration of the static, undisturbed state is no less than the time required for the water in the cold storage device to become completely stable. The benchmark database also includes the theoretical temperature range, theoretical density range, and theoretical cold storage capacity of each monitoring layer. During the benchmark data acquisition process, the charging and discharging pipeline valves of the cold storage device are closed.

5. The method for online monitoring of the stratified cooling efficiency of a large-scale water-cooled storage device in a new energy factory according to claim 1, characterized in that, In step four, parameter acquisition adopts a synchronous trigger mode, which completes the acquisition of temperature and density data of all monitoring points at the same time. The acquisition frequency is dynamically adjusted according to the charging and discharging rate of the cold storage device, and the acquisition frequency is higher during the charging and discharging stage than during the cold preservation stage.

6. The method for online monitoring of the stratified cooling efficiency of a large-scale water-cooled storage device in a new energy factory according to claim 1, characterized in that, In step eight, the baseline efficiency threshold is determined based on the design parameters of the cold storage device and long-term operating experience. Abnormal operating conditions include a continuous increase in the thickness of the transition layer, a sudden drop in the stratified cold storage efficiency, and an abnormal reduction in the temperature difference between adjacent layers. The judgment process combines the data change trends of multiple consecutive acquisition times to avoid misjudgment based on a single data point. In step nine, the monitoring results are displayed in a layered manner, and the occurrence time, duration and corresponding monitoring data of abnormal operating conditions are recorded simultaneously. The feedback information can be directly connected to the operation and regulation system of the cold storage device to realize the linkage between monitoring and regulation.

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