An intelligent storage shelf loading and counterweight system

Abstract

The present invention discloses an intelligent storage shelf loading and balancing system, which relates to the technical field of loading and balancing, and includes a monitoring center, a data acquisition module, a data analysis module, a region division module, a load-bearing assessment module and a control alarm module. The technical key points are: firstly, an analysis is performed around temperature, humidity and pressure to generate a comprehensive environmental coefficient, and interval comparison is performed to generate low, medium and high risk signals, and further analysis is performed based on the medium and high risk signals. By firstly performing a deep analysis on the collected shelf information, a first load-bearing interval, a second load-bearing interval and a third load-bearing interval are allocated, and at the same time, a number of monitoring nodes are set based on the divided load-bearing intervals to load and balance goods. In this process, the monitoring process is converted into a three-dimensional spatial analysis, and by obtaining the deviation ratio, the unreasonableness of the loading and balancing is determined, and an alarm or control signal is issued.

Description

Technical Field

[0001] The present invention relates to the technical field of loading and counterweighting, and in particular to an intelligent storage shelf loading and counterweighting system. Background Art

[0002] With the gradual popularization and development of intelligent warehousing systems, modern warehouse management is moving towards a more efficient and intelligent direction; however, in actual applications, environmental factors have a significant impact on the loading and counterweight system of storage shelves, which is directly related to the stability, safety and operating efficiency of the shelves; specifically, changes in environmental factors, such as temperature and humidity, will have many effects on the performance and performance of traditional shelf counterweight systems;

[0003] The traditional method of loading storage shelves relies on manual experience and weighting according to the weight and size of the goods. This is not only inefficient, but it is also often difficult to accurately grasp the center of gravity distribution of each shelf and is prone to deviations. To a certain extent, the shelf will tilt or even collapse due to unstable center of gravity, posing a safety hazard. At the same time, during the process of loading and weighting, it is impossible to make dynamic adjustments based on the actual load of goods, and there are certain lags and errors, which reduces the efficiency and flexibility of loading and weighting.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to propose an intelligent storage shelf loading and weighting system, which first generates a comprehensive environmental coefficient by analyzing the temperature, humidity and pressure, and compares the intervals to generate low, medium and high risk signals, and further analyzes based on the medium and high risk signals. The collected shelf information is first deeply analyzed to allocate the first load-bearing interval, the second load-bearing interval and the third load-bearing interval. At the same time, a number of monitoring nodes are set based on the divided load-bearing intervals to load and weight the goods. In this process, the monitoring process is converted into a three-dimensional spatial analysis, and the unreasonableness of the loading and weighting is determined by obtaining the deviation ratio, and an alarm or control signal is issued, thereby solving the problems raised in the background technology.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: an intelligent storage shelf loading and counterweighting system, including a monitoring center, a data acquisition module, a data analysis module, an area division module, a load-bearing evaluation module and a control alarm module;

[0007] When the monitoring center receives the supervision instruction, the data collection module immediately collects real-time environmental data, shelf information and goods information;

[0008] The data analysis module includes a first analysis unit and a second analysis unit; the first analysis unit performs in-depth analysis on environmental data and generates a comprehensive environmental coefficient to determine whether the loading and counterweighting process is safe and stable; it also generates high-risk signals, medium-risk signals, and low-risk signals; when the second analysis unit receives a low-risk signal, it does not perform any processing; on the contrary, when it receives a medium-risk signal or a high-risk signal, it immediately performs a load-bearing analysis on the shelf information and matches the corresponding load-bearing interval;

[0009] The area division module uses the midpoint of the storage shelf as the origin and the effective radius of the storage shelf to delineate the shelf monitoring area, establish a three-dimensional feature space, set up several monitoring nodes based on the divided load-bearing interval, and load the acquired cargo information;

[0010] The load-bearing assessment module performs preprocessing analysis based on the cargo information after loading, calculates the weight index based on the preprocessing results, performs deviation analysis, and marks the deviation ratio before and after counterweighting in the three-dimensional feature space. When the deviation ratio exceeds the set standard ratio range, an overweight warning signal is generated; otherwise, a counterweight control signal is generated;

[0011] The control alarm module receives the overweight warning signal and the counterweight control signal. When the overweight warning signal is received, the corresponding monitoring node is obtained to perform the cargo weight reduction operation; when the counterweight control signal is received, the corresponding monitoring node is obtained to perform the cargo weight increase operation.

[0012] Furthermore, the environmental data includes temperature, humidity and pressure values, and the process of in-depth analysis of the environmental data is as follows:

[0013] First, the environmental data is analyzed in a formulaic manner to obtain the comprehensive environmental coefficient;

[0014] The formula is: ;

[0015] In the formula, represents the comprehensive environmental factor, Indicates the temperature value, Indicates humidity value. Indicates the pressure value. , as well as are weight coefficients, and , as well as All are greater than 0;

[0016] In this embodiment:

[0017] In a storage environment, especially in a low or high temperature environment, the materials of the shelves (especially metal materials) may expand or contract. The thermal expansion and contraction of materials caused by temperature changes may cause the shelf structure to deform, thereby affecting the overall stability of the shelf; high temperature may also cause some counterweight materials to oxidize, age or chemically react, reducing the counterweight effect;

[0018] Humidity changes in the warehouse environment will have a direct impact on the materials of the storage racks and counterweight systems. Especially in a humid environment, metal counterweights are prone to corrosion and rust, causing counterweight failure or damage. Plastic or composite materials may also expand or deform in a humid environment, causing deviations in the originally precisely designed counterweight distribution and affecting the stability of the rack.

[0019] Changes in air pressure, especially in high altitude areas, may affect the overall stability of the smart storage system; in a low-pressure environment, the air density is low, which may affect the design of certain counterweights based on the principle of air buoyancy, or indirectly affect the stability of the loaded goods;

[0020] By analyzing the temperature, humidity and air pressure, we can determine the stability of the storage environment and refine the subsequent analysis process;

[0021] Then set the first comparison interval, the second comparison interval and the third comparison interval of the comprehensive environmental coefficient, and compare and analyze the comprehensive environmental coefficient with the first comparison interval, the second comparison interval and the third comparison interval respectively:

[0022] When the comprehensive environmental coefficient is within the first comparison interval, a low risk signal is generated;

[0023] When the comprehensive environmental coefficient is within the second comparison interval, a medium risk signal is generated;

[0024] When the comprehensive environmental coefficient is within the third comparison interval, a high-risk signal is generated;

[0025] The first comparison interval is smaller than the second comparison interval and the second comparison interval is smaller than the third comparison interval.

[0026] Furthermore, the shelf information includes the shelf load-bearing pressure and the cargo displacement direction, and the process of load-bearing analysis of the shelf information is as follows:

[0027] Select N0 shelves, mark any shelf as i, measure the shelf load-bearing pressure and cargo moving direction through pressure sensors and displacement sensors respectively; decompose the cargo moving direction as follows:

[0028] Taking the vertical axis as the baseline, the angle between the moving direction of the goods on shelf i and the baseline is marked as the moving inclination angle; combining the shelf load-bearing pressure with the moving inclination angle, a formula is set to output the shelf load-bearing assessment coefficient;

[0029] The formula is: ;

[0030] In the formula, Indicates the load-bearing assessment coefficient of the shelf, represents the conversion coefficient, Indicates the load-bearing pressure of the shelf. Indicates the moving inclination angle;

[0031] The first threshold interval, the second threshold interval and the third threshold interval of the load-bearing assessment coefficient are set, and the load-bearing assessment coefficient is compared and analyzed with the first threshold interval, the second threshold interval and the third threshold interval respectively to divide the first load-bearing interval, the second load-bearing interval and the third load-bearing interval.

[0032] Furthermore, the specific process of establishing the three-dimensional feature space is as follows:

[0033] The shelf monitoring area is delineated with the effective radius R, taking the midpoint of the storage shelf as the circle point; a three-dimensional coordinate system is established, and the directions perpendicular to each other in the horizontal plane where the storage shelf is located are defined as the X-axis and Y-axis directions, and the Z-axis is defined as being perpendicular to the surface of the storage shelf and pointing upward, to obtain a three-dimensional feature space;

[0034] Furthermore, the cargo information includes cargo volume and cargo weight, and the preprocessing process is: dimensionless processing of cargo volume and cargo weight, and setting a formula to calculate the cargo weight index, the formula based on which is:

[0035] ;

[0036] In the formula, represents the weight index, represents the volume of cargo after dimensionless processing, represents the dimensionless weight of the cargo, Represents the weight coefficient.

[0037] Furthermore, the process of deviation analysis is:

[0038] Obtain the real-time weight index at several monitoring nodes, and call the corresponding standard weight threshold under the load-bearing range, calculate the difference between the weight index and the corresponding standard weight threshold, set the gradient range of the difference ±wc%, when the difference is within the gradient range ±wc%, mark it as no deviation; otherwise, mark it as deviation;

[0039] Count the monitoring nodes with deviations and mark the deviation ratios in the three-dimensional feature space, including high deviation ratios, heavy deviation ratios, and low deviation ratios. When the different deviation ratios exceed the set standard ratio range, an overweight warning signal is generated; otherwise, a counterweight control signal is generated.

[0040] At the same time, the high deviation ratio, medium deviation ratio and low deviation ratio are marked as key factors, and corresponding weight control coefficients are assigned to the key factors to perform weight gain or weight loss operations.

[0041] Furthermore, the model on which the weight gain or weight loss operation is based is:

[0042] When operating for weight gain, the formula is: ;

[0043] When operating for weight loss, the formula used is: ;

[0044] In the formula, To add or subtract weight to the cargo. As the basic cargo weight, It represents the difference between the deviation proportion and the standard deviation proportion. Indicates the weight control coefficient.

[0045] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0046] The present invention includes a monitoring center, a data acquisition module, a data analysis module, a region division module, a load-bearing assessment module and a control alarm module. First, an analysis is performed around temperature, humidity and pressure to generate a comprehensive environmental coefficient, and interval comparison is performed to generate low, medium and high risk signals. Further analysis is performed based on the medium and high risk signals. By first performing an in-depth analysis on the collected shelf information, a first load-bearing interval, a second load-bearing interval and a third load-bearing interval are allocated. At the same time, a number of monitoring nodes are set based on the divided load-bearing intervals to load and balance the goods. In this process, the monitoring process is converted into a three-dimensional spatial analysis. By obtaining the deviation ratio, the unreasonableness of the loading and balancing is determined, and an alarm or control signal is issued to achieve dynamic adjustment. To a certain extent, the error is reduced and the efficiency of the loading and balancing is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A module schematic diagram of an intelligent storage shelf loading and counterweight system of the present invention is shown. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Embodiment: The present invention proposes an intelligent storage shelf loading and counterweighting system, including a monitoring center, a data acquisition module, a data analysis module, an area division module, a load-bearing assessment module, and a control alarm module;

[0050] Below is a description and explanation of each module:

[0051] When the monitoring center receives the supervision instruction, the data collection module immediately collects real-time environmental data, shelf information and goods information;

[0052] Among them, environmental data includes temperature, humidity and pressure values; shelf information includes shelf load-bearing pressure and cargo displacement direction; cargo information includes cargo volume and cargo weight;

[0053] The data analysis module includes a first analysis unit and a second analysis unit;

[0054] The first analysis unit performs in-depth analysis on the environmental data and generates a comprehensive environmental coefficient to determine whether the loading and counterweighting process is safe and stable; it also generates high-risk signals, medium-risk signals, and low-risk signals;

[0055] The process of in-depth analysis of environmental data is:

[0056] First, the environmental data is analyzed in a formulaic manner to obtain the comprehensive environmental coefficient;

[0057] The formula is: ;

[0058] In the formula, represents the comprehensive environmental factor, Indicates the temperature value, Indicates humidity value. Indicates the pressure value. , as well as are weight coefficients, and , as well as All are greater than 0;

[0059] Then set the first comparison interval, the second comparison interval and the third comparison interval of the comprehensive environmental coefficient, and compare and analyze the comprehensive environmental coefficient with the first comparison interval, the second comparison interval and the third comparison interval respectively:

[0060] When the comprehensive environmental coefficient is within the first comparison interval, a low risk signal is generated, and it is determined that the environment has little impact on the cargo loading and weighting process;

[0061] When the comprehensive environmental coefficient is within the second comparison interval, a medium risk signal is generated, and it is determined that the environment has a general impact on the cargo loading and weighting process;

[0062] When the comprehensive environmental coefficient is within the third comparison interval, a high-risk signal is generated, and it is determined that the environment has a serious impact on the cargo loading and weighting process;

[0063] Wherein, the first comparison interval is smaller than the second comparison interval and the second comparison interval is smaller than the third comparison interval;

[0064] When the second analysis unit receives a low-risk signal, it does not perform any processing; on the contrary, when it receives a medium-risk signal or a high-risk signal, it immediately performs a load-bearing analysis on the shelf information and matches the corresponding load-bearing range;

[0065] The process of load-bearing analysis of shelf information is as follows:

[0066] Select N0 shelves, mark any shelf as i, measure the shelf load-bearing pressure and cargo moving direction through pressure sensors and displacement sensors respectively; decompose the cargo moving direction as follows:

[0067] Taking the vertical axis as the baseline, the angle between the moving direction of the goods on shelf i and the baseline is marked as the moving inclination angle; combining the shelf load-bearing pressure with the moving inclination angle, a formula is set to output the shelf load-bearing assessment coefficient;

[0068] The formula is: ;

[0069] In the formula, Indicates the load-bearing assessment coefficient of the shelf, It represents the conversion coefficient, which is related to the risk signal received and is obtained through a large amount of data analysis. The conversion coefficient under high risk signal is often greater than the conversion coefficient under medium risk signal. Indicates the load-bearing pressure of the shelf. represents the moving inclination angle, and ∈(0,90°), when the moving angle is larger, The larger the value of;

[0070] The first threshold interval, the second threshold interval and the third threshold interval of the load-bearing evaluation coefficient are set, and the load-bearing evaluation coefficient is compared and analyzed with the first threshold interval, the second threshold interval and the third threshold interval respectively:

[0071] When the load-bearing evaluation coefficient is within the first threshold interval, it is marked as the first load-bearing interval;

[0072] When the load-bearing evaluation coefficient is within the second threshold interval, it is marked as the second load-bearing interval;

[0073] When the load-bearing assessment coefficient is within the third threshold interval, it is marked as the third load-bearing interval;

[0074] Wherein, the first threshold interval is smaller than the second threshold interval is smaller than the third threshold interval;

[0075] The area division module uses the midpoint of the storage shelf as the origin and the effective radius of the storage shelf to delineate the shelf monitoring area, establish a three-dimensional feature space, set up several monitoring nodes based on the divided load-bearing interval, and load the acquired cargo information;

[0076] The specific process of establishing a three-dimensional feature space is:

[0077] The shelf monitoring area is delineated with the effective radius R, taking the midpoint of the storage shelf as the circle point; a three-dimensional coordinate system is established, and the directions perpendicular to each other in the horizontal plane where the storage shelf is located are defined as the X-axis and Y-axis directions, and the Z-axis is defined as being perpendicular to the surface of the storage shelf and pointing upward, to obtain a three-dimensional feature space;

[0078] The load-bearing assessment module performs preprocessing analysis based on the cargo information after loading, calculates the weight index based on the preprocessing results, performs deviation analysis, and marks the deviation ratio before and after counterweighting in the three-dimensional feature space. When the deviation ratio exceeds the set standard ratio range, an overweight warning signal is generated; otherwise, a counterweight control signal is generated;

[0079] The pre-processing process is: dimensionless processing of cargo volume and cargo weight, and setting a formula to calculate the cargo weight index, based on the formula:

[0080] ;

[0081] In the formula, represents the weight index, represents the volume of cargo after dimensionless processing, represents the dimensionless weight of the cargo, represents the weight coefficient;

[0082] The process of deviation analysis is:

[0083] Obtain the real-time weight index at several monitoring nodes, and call the corresponding standard weight threshold under the load-bearing range, calculate the difference between the weight index and the corresponding standard weight threshold, set the gradient range of the difference ±wc%, when the difference is within the gradient range ±wc%, mark it as no deviation; otherwise, mark it as deviation;

[0084] Count the monitoring nodes with deviations and mark the deviation ratios in the three-dimensional feature space, including high deviation ratio, medium deviation ratio and low deviation ratio;

[0085] It should be noted that the definition is based on historical data, difference data is collected, the average value and standard deviation corresponding to the difference data are obtained, high deviation data is defined as data greater than the sum of the average value and 2 times the standard deviation, medium deviation data is defined as data less than the sum of the average value and 2 times the standard deviation and greater than the sum of the standard deviation of the average value and 1, and low deviation data is defined as data less than the sum of the standard deviation of the average value and 1;

[0086] In the three-dimensional feature space, the number of monitoring nodes under high deviation data, medium deviation data and low deviation data is counted, and the high deviation ratio, medium deviation ratio and low deviation ratio are divided, and color marking is performed in the three-dimensional feature space. For example, red is used to mark the high deviation ratio, yellow is used to mark the medium deviation ratio, and green is used to mark the low deviation ratio, so as to facilitate professionals to perform visual analysis and subsequent analysis and monitoring, and perform corresponding processing on the monitoring nodes;

[0087] When the proportion of different deviations exceeds the set standard proportion range, an overweight warning signal is generated; otherwise, a counterweight control signal is generated;

[0088] At the same time, the high deviation ratio, medium deviation ratio and low deviation ratio are marked as key factors, and the corresponding weight control coefficients are assigned to the key factors to perform weight increase or weight reduction operations;

[0089] The model on which weight gain or weight loss operations are based is:

[0090] When operating for weight gain, the formula is: ;

[0091] When operating for weight loss, the formula used is: ;

[0092] In the formula, To add or subtract weight to the cargo. As the basic cargo weight, It represents the difference between the deviation proportion and the standard deviation proportion. It represents the weight control coefficient, and the larger the difference is, the larger the weight control coefficient is.

[0093] In summary of the above technical solutions: The present invention generates a comprehensive environmental coefficient by analyzing temperature, humidity and pressure, and compares the intervals to generate low, medium and high risk signals, determines the severity of the storage environment, and further analyzes based on the medium and high risk signals. By first deeply analyzing the collected shelf information, the first load-bearing interval, the second load-bearing interval and the third load-bearing interval are allocated, thereby ensuring the accuracy and precision of the data to a certain extent. At the same time, a number of monitoring nodes are set based on the divided load-bearing intervals to load and balance the goods. In this process, the monitoring process is converted into a three-dimensional spatial analysis. By obtaining the deviation ratio, the unreasonableness of the loading and balancing is determined, and an alarm or control signal is issued to achieve intelligent and precise loading and balancing.

[0094] The setting of the threshold and interval size is for the convenience of comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value;

[0095] Those skilled in the art can appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by a device or a combination of computer software and electronic hardware; whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution;

[0096] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent storage shelf loading and counterweight system, characterized in that: It includes monitoring center, data acquisition module, data analysis module, area division module, load-bearing assessment module and control alarm module; When the monitoring center receives the supervision instruction, the data collection module immediately collects real-time environmental data, shelf information and goods information; The data analysis module includes a first analysis unit and a second analysis unit; the first analysis unit performs in-depth analysis on the environmental data to generate a comprehensive environmental coefficient to determine whether the loading and counterweighting process is safe and stable; and at the same time generates a high-risk signal, a medium-risk signal, and a low-risk signal; When the second analysis unit receives a low-risk signal, it does not perform any processing; On the contrary, when a medium-risk signal or a high-risk signal is received, the load-bearing analysis of the shelf information is immediately performed to match the corresponding load-bearing range; The area division module uses the midpoint of the storage shelf as the origin and the effective radius of the storage shelf to delineate the shelf monitoring area, establish a three-dimensional feature space, set up several monitoring nodes based on the divided load-bearing interval, and load the acquired cargo information; The load-bearing assessment module performs preprocessing analysis based on the cargo information after loading, calculates the weight index based on the preprocessing results, performs deviation analysis, and marks the deviation ratio before and after counterweighting in the three-dimensional feature space. When the deviation ratio exceeds the set standard ratio range, an overweight warning signal is generated; otherwise, a counterweight control signal is generated; Among them, the cargo information includes cargo volume and cargo weight. The preprocessing process is: dimensionless processing of cargo volume and cargo weight, and setting a formula to calculate the cargo weight index, the formula is based on: ； In the formula, represents the weight index, represents the volume of cargo after dimensionless processing, represents the dimensionless weight of the cargo, represents the weight coefficient; The control alarm module receives the overweight warning signal and the counterweight control signal. When the overweight warning signal is received, the corresponding monitoring node is obtained to perform the cargo weight reduction operation; when the counterweight control signal is received, the corresponding monitoring node is obtained to perform the cargo weight increase operation.

2. According to claim 1, the intelligent storage shelf loading and counterweight system is characterized by: Environmental data includes temperature, humidity and pressure values. The process of in-depth analysis of environmental data is as follows: First, the environmental data is analyzed in a formulaic manner to obtain the comprehensive environmental coefficient; The formula is: ; In the formula, represents the comprehensive environmental factor, Indicates the temperature value, Indicates humidity value. Indicates the pressure value. , as well as are weight coefficients, and , as well as All are greater than 0; Then set the first comparison interval, the second comparison interval and the third comparison interval of the comprehensive environmental coefficient, and compare and analyze the comprehensive environmental coefficient with the first comparison interval, the second comparison interval and the third comparison interval respectively: When the comprehensive environmental coefficient is within the first comparison interval, a low risk signal is generated; When the comprehensive environmental coefficient is within the second comparison interval, a medium risk signal is generated; When the comprehensive environmental coefficient is within the third comparison interval, a high-risk signal is generated; The first comparison interval is smaller than the second comparison interval and the second comparison interval is smaller than the third comparison interval.

3. The intelligent storage shelf loading and counterweight system according to claim 1 is characterized in that: The shelf information includes the shelf load-bearing pressure and the cargo displacement direction. The process of load-bearing analysis of the shelf information is as follows: Select N0 shelves, mark any shelf as i, measure the shelf load-bearing pressure and cargo moving direction through pressure sensors and displacement sensors respectively; decompose the cargo moving direction as follows: Taking the vertical axis as the baseline, the angle between the moving direction of the goods on shelf i and the baseline is marked as the moving inclination angle; combining the shelf load-bearing pressure with the moving inclination angle, a formula is set to output the shelf load-bearing assessment coefficient; The formula is: ; In the formula, Indicates the load-bearing assessment coefficient of the shelf, It represents the conversion coefficient, and the conversion coefficient is related to the received risk signal. It is obtained through a large amount of data analysis. The conversion coefficient under high risk signal is greater than the conversion coefficient under medium risk signal. Indicates the load-bearing pressure of the shelf. Indicates the moving inclination angle; The first threshold interval, the second threshold interval and the third threshold interval of the load-bearing assessment coefficient are set, and the load-bearing assessment coefficient is compared and analyzed with the first threshold interval, the second threshold interval and the third threshold interval respectively to divide the first load-bearing interval, the second load-bearing interval and the third load-bearing interval.

4. The intelligent storage shelf loading and counterweight system according to claim 1 is characterized by: The specific process of establishing a three-dimensional feature space is: The shelf monitoring area is delineated with the midpoint of the storage shelf as the circle and the effective radius R; a three-dimensional coordinate system is established, and the directions perpendicular to each other in the horizontal plane of the storage shelf are defined as the X-axis and Y-axis directions, and the Z-axis is defined as perpendicular to the surface of the storage shelf and pointing upward to obtain a three-dimensional feature space.

5. The intelligent storage shelf loading and counterweight system according to claim 1 is characterized by: The process of deviation analysis is: Obtain the real-time weight index at several monitoring nodes, and call the corresponding standard weight threshold under the load-bearing range, calculate the difference between the weight index and the corresponding standard weight threshold, set the gradient range of the difference ±wc%, when the difference is within the gradient range ±wc%, mark it as no deviation; otherwise, mark it as deviation; Count the monitoring nodes with deviations and mark the deviation ratios in the three-dimensional feature space, including high deviation ratios, heavy deviation ratios, and low deviation ratios. When the different deviation ratios exceed the set standard ratio range, an overweight warning signal is generated; otherwise, a counterweight control signal is generated. At the same time, the high deviation ratio, medium deviation ratio and low deviation ratio are marked as key factors, and corresponding weight control coefficients are assigned to the key factors to perform weight gain or weight loss operations.

6. The intelligent storage shelf loading and counterweight system according to claim 5, characterized in that: The model on which weight gain or weight loss operations are based is: When operating for weight gain, the formula is: ; When it comes to weight loss operations, the formula used is: ; In the formula, To add or subtract weight to the cargo. As the basic cargo weight, It represents the difference between the deviation proportion and the standard deviation proportion. Indicates the weight control coefficient.

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