High-rise intensive storage shelf stability monitoring system based on stress data acquisition

Abstract

The invention discloses a high-rise intensive storage shelf stability monitoring system based on stress data acquisition, which relates to the field of storage shelves and comprises a node load acquisition module, a node deformation quantity calculation module, a node deformation quantity prediction module and a deformation early warning module. The method comprises the following steps: acquiring a load value borne by a to-be-monitored node of a storage shelf and a deformation quantity data sequence changing along with time at the node when a loaded cargo load is G, constructing a grey prediction model according to the acquired data sequence, and calculating a deformation quantity prediction value of the load value borne by the to-be-monitored node; the allowable maximum deformation quantity of the to-be-monitored node of the storage shelf is set, the deformation quantity data sequence is updated in real time according to the obtained predicted value of the deformation quantity of the to-be-monitored node, the alarm strategy is formulated according to the sequence updating condition, and prediction can be carried out according to the change trend of the deformation quantity at the node. And formulating an early warning strategy in advance according to an accumulated action process analysis result of the goods shelf nodes under the load action.

Description

Technical Field

[0001] The present invention relates to the field of storage shelves, and in particular to a high-rise intensive storage shelf stability monitoring system based on force data collection. Background Art

[0002] With rapid socioeconomic development and the surge in logistics and warehousing demand, the concept of dense warehousing has emerged. Currently, research in dense warehousing technology focuses on three key areas: manual forklift-based dense warehousing, "shuttle technology + RF + rail-mounted racking" warehousing, and active integrated storage and distribution. Efficient warehousing systems can effectively increase material flow, reduce storage and transportation costs, and achieve rational control and management of various production resources, thereby improving the production efficiency of logistics and warehousing companies.

[0003] Dense storage shelf technology is divided into two types according to the movement mode of goods: passive and active. Passive dense storage shelf technology means that the goods are stationary on the shelf, and forklifts or stacking equipment are used to penetrate into the dense shelf aisles for storage and retrieval operations.

[0004] At present, the commonly used passive racking system is composed of several vertical column structures and several layers of beams connected to the columns. It is an H-dimensional beam-column structure between the two structural force system models of rigid frame and smart frame. In the existing technology, the stability monitoring of the rack is generally carried out by monitoring the instantaneous deformation or load value of the rack as a whole or at important nodes. When the instantaneous deformation or load value exceeds the safety value at the time of design, an alarm is issued. During the actual use of the rack, the structural nodes of the rack will produce different degrees of deformation under the action of static load, and the deformation process can be regarded as the cumulative effect of the load on the rack node in the time variable. Therefore, monitoring the instantaneous deformation or load value cannot reflect the change trend of the deformation over time. To this end, we propose a high-rise intensive storage rack stability monitoring system based on force data collection. Summary of the Invention

[0005] The main purpose of the present invention is to provide a high-rise intensive storage shelf stability monitoring system based on force data collection, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0007] A high-rise intensive warehouse shelf stability monitoring system based on force data collection includes the following steps:

[0008] S1: Get the load value G of the i-th node to be monitored when the loaded cargo load is G i , where G i Less than the design strength value of its node;

[0009] S2: Get the load G received by the node to be monitored i When , the deformation variable data sequence at the node changes with time in, It is represented by the load value G received by the i-th node to be monitored. i The deformation at the nth moment when , and Among them, the shape variable The method for obtaining includes the following steps:

[0010] S21: Construct a three-dimensional coordinate system for the space where the storage shelf is located, set the reference point coordinate value to (0,0,0), and obtain the initial coordinate value of the i-th node to be monitored (x i0 ,y i0 ,z i0 );

[0011] S21: Calculate the load G of the node to be monitored according to the formula i The initial distance value d0 between the time and the reference point is calculated as follows:

[0012]

[0013] S23: Obtain the load G received by the node to be monitored i When the distance d between the node and the reference point at the nth moment is n ;

[0014] S23: Calculate the distance value d n The difference Δd from the initial distance value d0 n , Δd n =|d n -d0|, to obtain the difference Δd n As the load G received by the i-th monitored node i When the deformation at the nth moment

[0015] S3: According to the acquired data sequence A grey prediction model is constructed, wherein the expression of the grey prediction model is:

[0016]

[0017] Where, It is represented by the load value G received by the i-th node to be monitored. i When , it is the accumulated value of the first to t-th data points in the shape variable data sequence; is the initial cumulative value of the deformation; t=1,2,...,n; a is the development coefficient; u is the gray action;

[0018] S4: Calculate the load value G received by the i-th node to be monitored according to the prediction model i The predicted value of the deformation variable The calculation formula is:

[0019]

[0020] Where, It is represented by the load value G received by the i-th node to be monitored. i The predicted value of the shape variable at the kth moment, k = 1, 2, ..., n + m; m is the number of future time steps predicted;

[0021] S5: Set the maximum allowable deformation s of the i-th node to be monitored on the storage shelf imax , update the sequence in real time according to the predicted value of the node shape variable to be monitored And according to the sequence Update the alarm strategy, specifically:

[0022] like middle, make Then after issuing an alarm, return to S1;

[0023] like middle, Both No alarm is issued and the process returns to S1.

[0024] The system includes a node load acquisition module, a node deformation calculation module, a node deformation prediction module, and a deformation early warning module;

[0025] The node load acquisition module is used to obtain the load value G received by the i-th node to be monitored when the loaded cargo load is G. i ;

[0026] The node deformation calculation module is used to obtain the load G that the node to be monitored is subjected to. i The deformation variable data of the node changing with time is obtained, and the deformation variable data sequence of the i-th node to be monitored at the n-th moment is constructed based on the acquired deformation variable data;

[0027] The node shape variable prediction module is used to obtain the data sequence Construct a grey prediction model and calculate the load value G of the i-th node to be monitored through the prediction model. i The predicted value of the deformation variable

[0028] The deformation warning module is used to set the maximum deformation value s allowed for the i-th node to be monitored on the storage shelf. imax , update the sequence in real time according to the predicted value of the node shape variable to be monitored And according to the sequence Update the situation and formulate alert strategies.

[0029] The present invention has the following beneficial effects:

[0030] Compared with the prior art, the technical solution of the present invention obtains the load value G received by the i-th node to be monitored when the loaded cargo load is G. i And the deformation data sequence of the nodes changing with time According to the acquired data series Construct a grey prediction model to calculate the load value G of the i-th node to be monitored i The predicted value of the deformation variable Set the maximum allowable deformation s of the i-th node to be monitored on the storage shelf imax , update the sequence in real time according to the predicted value of the node shape variable to be monitored And according to the sequence The alarm strategy can be formulated based on the update situation, and the changing trend of the deformation at the node can be predicted, so as to formulate an early warning strategy in advance according to the analysis results of the cumulative action process of the shelf node under the action of load. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of the workflow of the high-rise intensive storage shelf stability monitoring system based on force data collection of the present invention;

[0032] Figure 2 This is a structural block diagram of a high-rise intensive storage shelf stability monitoring system based on force data collection according to the present invention;

[0033] Figure 3 This is a schematic diagram of the main structure of the existing shelf. DETAILED DESCRIPTION

[0034] The present invention will be further described below in conjunction with specific embodiments. The accompanying drawings are for illustrative purposes only and represent only schematic diagrams rather than actual drawings. They should not be understood as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0035] Example 1

[0036] like Figure 1-3 As shown, the specific implementation process of the technical solution of the present invention includes the following steps:

[0037] Step 1: Obtain the load value G of the i-th node to be monitored when the loaded cargo load is G. i , where G i Less than the design strength value of its node;

[0038] It should be noted that if Figure 3 As shown in the schematic diagram of the existing shelf main structure, the node to be monitored can be selected from the location where the shelf has the maximum force value, such as the center point of the shelf column cross section, or the location of the intermediate connection node. The force condition of the shelf node is analyzed. In addition to the influence of its own structural factors, its force value is also affected by factors such as the size of the cargo load (G value) and the location of the cargo. In this embodiment, a structural model of the shelf to be monitored can be constructed and a finite element analysis can be performed on the structural model to simulate the load value distribution at the node, thereby obtaining the static load at each node of the storage shelf when the loaded cargo load is G. In the actual implementation process, stress sensors can also be set at the nodes to collect the actual load values.

[0039] Step 2: Obtain the load G received by the node to be monitored i When , the deformation variable data sequence at the node changes with time in, It is represented by the load value G received by the i-th node to be monitored. i The deformation at the nth moment when , and Among them, the shape variable The method for obtaining includes the following steps:

[0040] S21: Construct a three-dimensional coordinate system for the space where the storage shelf is located, set the reference point coordinate value to (0,0,0), and obtain the initial coordinate value of the i-th node to be monitored (x i0 ,y i0 ,z i0 );

[0041] S21: Calculate the load G of the node to be monitored according to the formula i The initial distance value d0 between the time and the reference point is calculated as follows:

[0042]

[0043] S23: Obtain the load G received by the node to be monitored i When the distance d between the node and the reference point at the nth moment is n ;

[0044] S23: Calculate the distance value d n The difference Δd from the initial distance value d0 n, Δd n =|d n -d0|, to obtain the difference Δd n As the load G received by the i-th monitored node i When the deformation at the nth moment

[0045] It should be noted that when calculating the deformation at the node, the reference point can be a point on the storage shelf or any position in space, but it should remain stable in the three-dimensional coordinate system of space, and its coordinate value should not be affected by the cargo load;

[0046] Step 3: Based on the acquired data sequence A grey prediction model is constructed, wherein the expression of the grey prediction model is:

[0047]

[0048] Where, It is represented by the load value G received by the i-th node to be monitored. i When , it is the accumulated value of the first to t-th data points in the shape variable data sequence; is the initial cumulative value of the deformation; t=1,2,...,n; a is the development coefficient; u is the gray action;

[0049] The steps of constructing the grey prediction model include:

[0050] S31: Obtain the load G received by the node to be monitored i When , the deformation variable data sequence at the node changes with time

[0051] S32: Perform an accumulation operation on the original data in the sequence to obtain another new sequence, which is:

[0052]

[0053] In this new sequence, It represents the cumulative value from the first data point to the t-th data point;

[0054] S33: Describe the accumulated data sequence using a first-order linear differential equation, where the equation is expressed as:

[0055]

[0056] S34: Integrate the equation in step S33 to obtain the expression of the grey prediction model:

[0057]

[0058] S35: Solve a and u through regression analysis or least squares method to obtain the actual expression of the grey prediction model;

[0059] It should be noted that when the number of nodes to be monitored exceeds one, each shelf node corresponds to an expression of a prediction model;

[0060] Step 4: Calculate the load value G of the i-th node to be monitored based on the prediction model i The predicted value of the deformation variable The calculation formula is:

[0061]

[0062] Where, It is represented by the load value G received by the i-th node to be monitored. i The predicted value of the shape variable at the kth moment, k = 1, 2, ..., n + m; m is the number of future time steps predicted;

[0063] Step 5: Set the maximum allowable deformation s of the i-th node to be monitored on the storage shelf imax , update the sequence in real time according to the predicted value of the node shape variable to be monitored The new sequence is expressed as: According to the sequence Update the alarm strategy, specifically:

[0064] Case 1:

[0065] like middle, make After issuing an alarm, return to step 1;

[0066] Case 2:

[0067] like middle, Both No alarm is issued and the process returns to step 1.

[0068] It should be noted that for case 1, it means that within the predicted future time step m, there is a moment when the deformation value at the node is greater than or equal to the maximum deformation value s. imax , it means that the deformation variable at the node to be detected will exceed the maximum deformation variable s in the future. imax There is a possibility of safety hazards; for case 2, it means that at all moments in the predicted future time step m, the value of the node deformation variable is less than the maximum deformation variable s imax, it means that the deformation variables at the node to be detected will be in a safe state for a period of time in the future. Through the above scheme, it is possible to predict the changing trend of the deformation variables at the node, so as to formulate an early warning strategy in advance according to the analysis results of the cumulative action process of the shelf node under the action of load.

[0069] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-rise intensive warehouse shelf stability monitoring system based on force data collection is characterized by: The implementation process of the system includes the following steps: S1: Get the load value G of the i-th node to be monitored when the loaded cargo load is G i ; S2: Get the load G received by the node to be monitored i When , the deformation variable data sequence at the node changes with time in, It is represented by the load value G received by the i-th node to be monitored. i The deformation at the nth moment when , and S3: According to the acquired data sequence A grey prediction model is constructed, wherein the expression of the grey prediction model is: Where, It is represented by the load value G received by the i-th node to be monitored. i When , it is the accumulated value of the first to t-th data points in the shape variable data sequence; is the initial cumulative value of the deformation; t=1,2,...,n; a is the development coefficient; u is the gray action; S4: Calculate the load value G received by the i-th node to be monitored according to the prediction model i The predicted value of the deformation variable S5: Set the maximum allowable deformation s of the i-th node to be monitored on the storage shelf imax , update the sequence Δs in real time according to the predicted value of the node shape variable to be monitored i (0) , and according to the sequence Δs i (0) Update the situation and formulate alert strategies.

2. The high-rise intensive storage shelf stability monitoring system based on force data collection according to claim 1 is characterized in that: Shape variable prediction value The calculation formula is: Where, It is represented by the load value G received by the i-th node to be monitored. i The predicted value of the shape variable at the kth moment, k = 1, 2, ..., n + m; m is the number of future time steps predicted.

3. The high-rise intensive storage shelf stability monitoring system based on force data collection according to claim 1 is characterized in that: The load value G received by the node to be monitored i The deformation at the nth moment The method for obtaining includes the following steps: S21: Construct a three-dimensional coordinate system for the space where the storage shelf is located, set the reference point coordinate value to (0,0,0), and obtain the initial coordinate value of the i-th node to be monitored (x i0 ,y i0 ,z i0 ); S21: Calculate the load G of the node to be monitored according to the formula i The initial distance value d0 between the time and the reference point is calculated as follows: S23: Obtain the load G received by the node to be monitored i When the distance d between the node and the reference point at the nth moment is n ; S23: Calculate the distance value d n The difference Δd from the initial distance value d0 n , Δd n =|d n -d0|, to obtain the difference Δd n As the load G received by the i-th monitored node i When the deformation at the nth moment 4. The high-rise intensive storage shelf stability monitoring system based on force data collection according to claim 1 is characterized in that: The maximum warning load value G that the i-th node to be monitored can bear imax Less than its design strength value.

5. The high-rise intensive storage shelf stability monitoring system based on force data collection according to claim 1 is characterized in that: The specific alarm strategy is: like middle, make Then after issuing an alarm, return to S1; like middle, Both No alarm is issued and the process returns to S1.

6. The high-rise intensive storage shelf stability monitoring system based on force data collection according to claim 1 is characterized in that: The system includes a node load acquisition module, a node deformation calculation module, a node deformation prediction module, and a deformation early warning module; The node load acquisition module is used to obtain the load value G received by the i-th node to be monitored when the loaded cargo load is G. i ; The node deformation calculation module is used to obtain the load G that the node to be monitored is subjected to. i The deformation variable data of the node changes with time, and the deformation variable data sequence of the i-th node to be monitored at the n-th moment is constructed based on the acquired deformation variable data. in, The node shape variable prediction module is used to obtain the data sequence Construct a grey prediction model and calculate the load value G of the i-th node to be monitored through the prediction model. i The predicted value of the deformation variable Wherein, the expression of the grey prediction model is: Where, It is represented by the load value G received by the i-th node to be monitored. i When , it is the accumulated value of the first to t-th data points in the shape variable data sequence; is the initial cumulative value of the deformation variable; t=1,2,…,n; a is the development coefficient; u is the gray action; the deformation variable prediction value The calculation formula is: Where, It is represented by the load value G received by the i-th node to be monitored. i The predicted value of the shape variable at the kth moment, k = 1, 2, ..., n + m; m is the number of future time steps predicted; The deformation warning module is used to set the maximum deformation value s allowed for the i-th node to be monitored on the storage shelf. imax , update the sequence in real time according to the predicted value of the node shape variable to be monitored And according to the sequence Update the alarm strategy, specifically: like middle, make Then issue an alarm; like middle, Both No alarm is issued.

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