Intelligent warehouse information collection method and system

Abstract

The present invention relates to the field of intelligent warehousing technology, and specifically discloses an intelligent warehousing information collection method and system, the method comprising obtaining the cargo quantity according to a sensor installed at a storage space at regular intervals; arranging the cargo quantity according to time information to obtain a cargo quantity array, and synchronously calculating the autocorrelation coefficient and the lag time difference of the cargo quantity data; comparing the autocorrelation coefficient and the lag time difference of each storage space to obtain the similarity between any two storage spaces; for any storage space, determining the image acquisition frequency according to the similarity between it and all other storage spaces. The present invention periodically analyzes the cargo change information of each storage space, determines the cargo change similarity between each storage space and other storage spaces according to the periodic analysis results, evaluates the importance of each storage space, and then adjusts the data acquisition frequency, optimizing the traditional fixed acquisition frequency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehousing technology, and in particular to an intelligent warehousing information collection method and system. Background Art

[0002] Intelligent warehousing refers to the deployment of various sensors, RFID tags, drones, robots and other intelligent hardware devices to comprehensively and accurately collect real-time information about goods, inventory status and environmental parameters in the warehouse, thereby achieving accurate prediction of inventory levels, rapid positioning of goods, and optimal planning of picking routes.

[0003] In the existing warehouse information collection scheme, each storage space is actually analyzed separately, and each storage space collects data at an independent information collection frequency, which puts a lot of pressure on data transmission. In fact, for warehousing scenarios, goods almost never change once in a long time. The actual data collection actually contains a lot of invalid data. How to optimize the existing data collection process is the technical problem that the technical solution of the present invention aims to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent warehouse information collection method and system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent warehouse information collection method, the method comprising:

[0007] The amount of goods is obtained regularly according to sensors installed in the storage space; the obtained amount of goods contains time information; the sensors in the storage space have the same operating frequency;

[0008] Arrange the cargo quantities according to the time information to obtain a cargo quantity array, and simultaneously calculate the autocorrelation coefficient and lag time difference of the cargo quantity data; the lag time difference is related to the sequence number difference of the elements in the array and the period when the cargo quantity is obtained regularly;

[0009] Compare the autocorrelation coefficients and lag time differences of each storage space to obtain the similarity between any two storage spaces;

[0010] For any storage space, the image acquisition frequency is determined based on the similarity between it and all other storage spaces.

[0011] Furthermore, the steps of arranging the cargo quantity according to the time information to obtain a cargo quantity array and synchronously calculating the autocorrelation coefficient and the lag time difference of the cargo quantity data include:

[0012] Arrange the cargo quantity according to the time information to obtain a cargo quantity array;

[0013] Starting from the end of the cargo quantity array, obtain the preset number of cargo quantities in reverse order as the array to be analyzed;

[0014] Create a sequence of sequence differences; the first term, step length, and last term of the sequence of sequence differences are all preset values;

[0015] For each serial number difference in the serial number difference series, the autocorrelation coefficient of the cargo quantity array is calculated once;

[0016] The maximum value of all calculated autocorrelation coefficients is selected as the final autocorrelation coefficient;

[0017] Read the serial number difference corresponding to the final autocorrelation coefficient, read the period when the cargo quantity is obtained regularly, and multiply the serial number difference by the period to obtain the lag time difference.

[0018] Furthermore, the process of calculating the autocorrelation coefficient includes:

[0019]

[0020] Where ρ(k) represents the autocorrelation coefficient when the sequence number difference is k, Cov(k) represents the autocovariance when the sequence number difference is k, and Var(k) represents the variance of the array to be analyzed when the sequence number difference is k; t represents the kth element in the array to be analyzed, μ is the mean of each element in the array to be analyzed; x t+k Represents the t+kth element in the array to be analyzed; N represents the total number of elements in the array to be analyzed.

[0021] Furthermore, the step of comparing the autocorrelation coefficients and lag time differences of the storage spaces to obtain the similarity between any two storage spaces includes:

[0022] The distance range for receiving staff to enter;

[0023] For any storage space, take it as the center and query the storage spaces within the distance range in turn, and pair them with the storage space at the center;

[0024] For each pair of storage spaces, read the autocorrelation coefficients and their lag time difference between the two storage spaces;

[0025] Compare the correlation coefficient and its lag time difference to calculate the similarity;

[0026] Among them, the similarity between any two storage spaces is calculated only once.

[0027] Furthermore, the step of determining the image acquisition frequency for any storage space based on the similarity between it and all other storage spaces includes:

[0028] For any storage space, read the similarity between it and all other storage spaces;

[0029] Calculating the mean of the read similarities, and determining the image acquisition frequency according to the mean;

[0030] Among them, the relationship between image acquisition frequency and mean is:

[0031] Where f represents the final image acquisition frequency, f0 represents the preset reference frequency, E represents the mean similarity corresponding to a certain storage space, E0 is the preset mean threshold; α and c are both preset parameters.

[0032] Furthermore, the step of regularly obtaining the amount of goods according to the sensor installed in the storage space includes:

[0033] Read the image of the storage space regularly;

[0034] Performing contour recognition on the image, locating the cargo, and simultaneously calculating the cargo volume;

[0035] Accuracy in determining cargo volume based on cargo volume;

[0036] When the accuracy is less than a preset accuracy threshold, an inspection request directed to the storage space is generated.

[0037] The technical solution of the present invention also provides an intelligent warehouse information collection system, which includes:

[0038] A cargo quantity acquisition module is used to obtain the cargo quantity according to the sensors installed in the storage space at regular intervals; the obtained cargo quantity contains time information; the sensors in the storage space have the same operating frequency;

[0039] A correlation determination module is used to arrange the cargo quantities according to the time information to obtain a cargo quantity array, and simultaneously calculate the autocorrelation coefficient and lag time difference of the cargo quantity data; the lag time difference is related to the sequence number difference of the elements in the array and the period when the cargo quantities are regularly obtained;

[0040] The similarity calculation module is used to compare the autocorrelation coefficients and lag time differences of each storage space to obtain the similarity between any two storage spaces;

[0041] The image acquisition module is used to determine the image acquisition frequency for any storage space based on the similarity between it and all other storage spaces.

[0042] Furthermore, the relevance determination module includes:

[0043] A cargo quantity arrangement unit is used to arrange the cargo quantity according to the time information to obtain a cargo quantity array;

[0044] A data interception unit is used to obtain a preset number of cargo quantities starting from the end of the cargo quantity array in reverse order as an array to be analyzed;

[0045] A sequence creation unit, configured to create a sequence of sequence differences; the first term, step length, and last term of the sequence of sequence differences are all preset values;

[0046] A calculation execution unit, for calculating an autocorrelation coefficient of the cargo quantity array once for each serial number difference in the serial number difference series;

[0047] The maximum value selection unit is used to select the maximum value of all calculated autocorrelation coefficients as the final autocorrelation coefficient;

[0048] The time difference calculation unit is used to read the serial number difference corresponding to the final autocorrelation coefficient, read the period when the cargo quantity is obtained regularly, and multiply the serial number difference by the period to obtain the lag time difference.

[0049] Furthermore, the similarity calculation module includes:

[0050] A distance range receiving unit is used to receive the distance range entered by the staff;

[0051] The pairing unit is used to query the storage spaces within the distance range of any storage space with it as the center, and pair them with the storage space at the center;

[0052] A parameter reading unit, used for reading the autocorrelation coefficients and lag time difference of the two storage spaces for each pair of storage spaces;

[0053] The parameter comparison unit is used to compare the correlation coefficient and its lag time difference to calculate the similarity;

[0054] Among them, the similarity between any two storage spaces is calculated only once.

[0055] Furthermore, the image acquisition module includes:

[0056] The similarity reading unit is used to read the similarity between any storage space and all other storage spaces;

[0057] a frequency determination unit, configured to calculate a mean of the read similarities and determine an image acquisition frequency based on the mean;

[0058] Among them, the relationship between image acquisition frequency and mean is:

[0059] Where f represents the final image acquisition frequency, f0 represents the preset reference frequency, E represents the mean similarity corresponding to a certain storage space, E0 is the preset mean threshold; α and c are both preset parameters.

[0060] Compared with the existing technology, the beneficial effects of the present invention are: the present invention periodically analyzes the cargo change information of each storage space, determines the similarity of cargo changes between each storage space and other storage spaces based on the periodic analysis results, evaluates the importance of each storage space, and then adjusts the data collection frequency, thereby optimizing the traditional fixed collection frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0062] Figure 1 This is a flowchart of the intelligent warehouse information collection method.

[0063] Figure 2 This is a structural diagram of the intelligent warehousing information collection system. DETAILED DESCRIPTION

[0064] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] Figure 1 The flowchart of the intelligent warehouse information collection method is as follows. In an embodiment of the present invention, an intelligent warehouse information collection method is provided, and the method includes:

[0066] Step S100: obtaining the cargo quantity at regular intervals according to sensors installed in the storage space; the obtained cargo quantity contains time information; the sensors in the storage space have the same operating frequency;

[0067] Storage spaces are generally warehouses, and sensors are generally pressure sensors used to obtain the amount of goods; the amount of goods is used to represent the amount of goods; for some more intelligent environments, a recording module will be set at the entrance of each storage space, and the entry and exit of goods will need to be recorded (equivalent to filing). At this time, even if sensors are not installed, the amount of goods in each storage space can be obtained.

[0068] It should be noted that the process of obtaining the cargo quantity is a timed process, for example, once every five minutes; in order to make the data more regular, this application assumes that all storage spaces have the same working time period, that is, the cargo quantity in all storage spaces is obtained once every period of time;

[0069] Step S200: Arrange the cargo quantities according to the time information to obtain a cargo quantity array, and simultaneously calculate the autocorrelation coefficient and lag time difference of the cargo quantity data; the lag time difference is related to the sequence number difference of the elements in the array and the period when the cargo quantities are regularly obtained;

[0070] Arrange the cargo volume in chronological order to obtain a cargo volume array. Perform autocorrelation analysis on the cargo volume array to determine whether the cargo volume is repeated. It actually evaluates the periodic characteristics. Among them, the autocorrelation coefficient represents the probability of periodicity, and the lag time difference is the corresponding period.

[0071] It should be noted that when calculating the cargo quantity array, the relationship between the elements of each sequence number is calculated. Only by multiplying it by the cargo quantity acquisition period can the data in time format, that is, the lag time difference, be obtained.

[0072] Step S300: Compare the autocorrelation coefficients and lag time differences of each storage space to obtain the similarity between any two storage spaces;

[0073] After the autocorrelation coefficient and its lag time difference of each storage space are determined, they are used as the characteristics of each storage space and compared pairwise to calculate the similarity between any two storage spaces.

[0074] Step S400: for any storage space, determine the image acquisition frequency based on the similarity between it and all other storage spaces;

[0075] After the similarity calculation is completed, for any storage space, the similarity between it and all other storage spaces is read, the similarity mean is calculated, and the image acquisition frequency is determined based on the similarity mean.

[0076] In one example of the technical solution of the present invention, the final output is to adjust the image acquisition frequency in each storage space, obtain actual visual information with the help of cameras installed in each storage space, and further identify it by the staff; when there are many storage spaces, the technical solution of this application can greatly simplify the number of storage spaces that require manual identification, that is, the staff only need to manually identify some of the more important storage spaces.

[0077] Regarding step S200: the steps of arranging the cargo quantities according to the time information to obtain a cargo quantity array and synchronously calculating the autocorrelation coefficient and the lag time difference of the cargo quantity data include:

[0078] Arrange the cargo quantity according to the time information to obtain a cargo quantity array;

[0079] Starting from the end of the cargo quantity array, obtain the preset number of cargo quantities in reverse order as the array to be analyzed;

[0080] Create a sequence of sequence differences; the first term, step length, and last term of the sequence of sequence differences are all preset values;

[0081] For each serial number difference in the serial number difference series, the autocorrelation coefficient of the cargo quantity array is calculated once;

[0082] The maximum value of all calculated autocorrelation coefficients is selected as the final autocorrelation coefficient;

[0083] Read the serial number difference corresponding to the final autocorrelation coefficient, read the period when the cargo quantity is obtained regularly, and multiply the serial number difference by the period to obtain the lag time difference.

[0084] In one example of the technical solution of the present invention, a specific description of the autocorrelation analysis process is given. The cargo quantities are arranged in chronological order to obtain a cargo quantity array. However, for some cargo quantities that are relatively old, there is actually no analytical significance. Therefore, this application performs data screening on the cargo quantity array. First, a range is determined, such as ten days. The data within ten days is intercepted from the cargo quantity array, which is called the array to be analyzed.

[0085] After obtaining the array to be analyzed, multiple serial number differences are determined. This application uses a gradually increasing method to determine the serial number differences, that is, a serial number difference series; for each serial number difference, the autocorrelation coefficient is calculated once; the actual effect of the serial number difference is that, assuming the serial number difference is five, each data is compared with the fifth data before it, and the comparisons are carried out in sequence to determine the autocorrelation coefficient; the final result is that each serial number difference corresponds to an autocorrelation coefficient.

[0086] Specifically, the process of calculating the autocorrelation coefficient includes:

[0087]

[0088] Where ρ(k) represents the autocorrelation coefficient when the sequence number difference is k, Cov(k) represents the autocovariance when the sequence number difference is k, and Var(k) represents the variance of the array to be analyzed when the sequence number difference is k; t represents the kth element in the array to be analyzed, μ is the mean of each element in the array to be analyzed; x t+k Represents the t+kth element in the array to be analyzed; N represents the total number of elements in the array to be analyzed.

[0089] The calculation result of the autocorrelation coefficient means that if ρ(k) is close to 1, it means that there is a high positive correlation between the data at lag k; if ρ(k) is close to 1, it means that there is almost no correlation at lag k; if ρ(k) is close to -1, it means that there is a strong negative correlation between the data at lag k. In the technical solution of the present invention, whether it is close to 1 or close to -1, it is considered to be correlated; selecting the maximum value of the autocorrelation coefficient is actually selecting the maximum absolute value of the autocorrelation coefficient.

[0090] Regarding step S300: the step of comparing the autocorrelation coefficients and lag time differences of each storage space to obtain the similarity between any two storage spaces includes:

[0091] The distance range for receiving staff to enter;

[0092] For any storage space, take it as the center and query the storage spaces within the distance range in turn, and pair them with the storage space at the center;

[0093] For each pair of storage spaces, read the autocorrelation coefficients and their lag time difference between the two storage spaces;

[0094] Compare the correlation coefficient and its lag time difference to calculate the similarity;

[0095] Among them, the similarity between any two storage spaces is calculated only once.

[0096] The distance range input by the staff is received. The distance range is a numerical value that can be understood as a radius. For any storage space, a range is determined with it as the center and the distance range as the radius. The storage spaces within the query range are paired with each other to calculate the similarity. The process of calculating the similarity is to compare the autocorrelation coefficients and the lag time difference of the two storage spaces, that is, the similarity is the dependent variable, and the autocorrelation coefficients and the lag time difference of the two storage spaces are the independent variables.

[0097] It is worth mentioning that the similarity between A and B is the same as the similarity between B and A. Therefore, before calculating the similarity, you can check in the historical calculation records whether the calculation has been completed. If the calculation is completed, there is no need to repeat the calculation; this also means that the calculated similarity needs to be recorded and stored in real time.

[0098] Regarding step S400: the step of determining the image acquisition frequency for any storage space based on the similarity between it and all other storage spaces includes:

[0099] For any storage space, read the similarity between it and all other storage spaces;

[0100] Calculating the mean of the read similarities, and determining the image acquisition frequency according to the mean;

[0101] In one embodiment of the technical solution of the present invention, the process of determining the image acquisition frequency is specifically described. For any storage space, the similarity between it and all other storage spaces is read, the average of the read similarities is calculated, and the image acquisition frequency is determined based on the average. The relationship between the image acquisition frequency and the average is:

[0102] Where f represents the final image acquisition frequency, f0 represents the preset reference frequency, E represents the mean similarity corresponding to a certain storage space, E0 is the preset mean threshold; α and c are both preset parameters.

[0103] The physical meaning of the above relationship is that the closer the mean similarity is to the preset threshold, the smaller the corresponding image acquisition frequency should be. The further away from the preset threshold, the more it means that the mean similarity is too small or too large. If it is too small, it means that the storage space is relatively special, and if it is too large, it means that the storage space is more universal. Both require a higher frequency.

[0104] Regarding step S100: the step of regularly obtaining the amount of goods according to the sensor installed in the storage space includes:

[0105] Read the image of the storage space regularly;

[0106] Performing contour recognition on the image, locating the cargo, and simultaneously calculating the cargo volume;

[0107] Accuracy in determining cargo volume based on cargo volume;

[0108] When the accuracy is less than a preset accuracy threshold, an inspection request directed to the storage space is generated.

[0109] In an example of the technical solution of the present invention, step S100 is supplemented. The function of the sensor is to obtain the quantity of goods. On the basis of already having an image acquisition device, the image acquisition device can also be used as a way to obtain the quantity of goods. It regularly reads the image of the storage space, performs contour recognition on the image, locates the goods, and synchronously calculates the volume of the goods. The quantity of goods can be determined based on the volume of the goods, but it is not very accurate. In the technical solution of the present invention, it is not necessary to calculate the precise quantity of goods through the volume of the goods. Instead, it is judged whether the calculation process of the quantity of goods is accurate based on the volume of the goods, thereby introducing a self-correction function. This self-correction actually detects two aspects, one is the sensor, and the other is the camera. As long as their results do not match, there may be problems between the sensor and the camera.

[0110] Figure 2The following is a structural block diagram of an intelligent warehouse information collection system. The technical solution of the present invention also provides an intelligent warehouse information collection system. The system 10 includes:

[0111] The cargo quantity acquisition module 11 is used to obtain the cargo quantity according to the sensors installed in the storage space at regular intervals; the obtained cargo quantity contains time information; the sensors in the storage space have the same operating frequency;

[0112] The correlation determination module 12 is used to arrange the cargo quantities according to the time information to obtain a cargo quantity array, and simultaneously calculate the autocorrelation coefficient and lag time difference of the cargo quantity data; the lag time difference is related to the sequence number difference of the elements in the array and the period when the cargo quantities are regularly obtained;

[0113] The similarity calculation module 13 is used to compare the autocorrelation coefficients and lag time differences of each storage space to obtain the similarity between any two storage spaces;

[0114] The image acquisition module 14 is used to determine the image acquisition frequency for any storage space according to the similarity between it and all other storage spaces.

[0115] Furthermore, the relevance determination module 12 includes:

[0116] A cargo quantity arrangement unit is used to arrange the cargo quantity according to the time information to obtain a cargo quantity array;

[0117] A data interception unit is used to obtain a preset number of cargo quantities starting from the end of the cargo quantity array in reverse order as an array to be analyzed;

[0118] A sequence creation unit, configured to create a sequence of sequence differences; the first term, step length, and last term of the sequence of sequence differences are all preset values;

[0119] A calculation execution unit, for calculating an autocorrelation coefficient of the cargo quantity array once for each serial number difference in the serial number difference series;

[0120] The maximum value selection unit is used to select the maximum value of all calculated autocorrelation coefficients as the final autocorrelation coefficient;

[0121] The time difference calculation unit is used to read the serial number difference corresponding to the final autocorrelation coefficient, read the period when the cargo quantity is obtained regularly, and multiply the serial number difference by the period to obtain the lag time difference.

[0122] Specifically, the similarity calculation module 13 includes:

[0123] A distance range receiving unit is used to receive the distance range entered by the staff;

[0124] The pairing unit is used to query the storage spaces within the distance range of any storage space with it as the center, and pair them with the storage space at the center;

[0125] A parameter reading unit, used for reading the autocorrelation coefficients and lag time difference of the two storage spaces for each pair of storage spaces;

[0126] The parameter comparison unit is used to compare the correlation coefficient and its lag time difference to calculate the similarity;

[0127] Among them, the similarity between any two storage spaces is calculated only once.

[0128] Furthermore, the image acquisition module 14 includes:

[0129] The similarity reading unit is used to read the similarity between any storage space and all other storage spaces;

[0130] a frequency determination unit, configured to calculate a mean of the read similarities and determine an image acquisition frequency based on the mean;

[0131] Among them, the relationship between image acquisition frequency and mean is:

[0132] Where f represents the final image acquisition frequency, f0 represents the preset reference frequency, E represents the mean similarity corresponding to a certain storage space, E0 is the preset mean threshold; α and c are both preset parameters.

[0133] The functions that can be achieved by the above-mentioned intelligent warehouse information collection method are all completed by computer equipment, and the computer equipment includes one or more processors and one or more memories, and at least one program code is stored in the one or more memories. The program code is loaded and executed by the one or more processors to realize the functions of the intelligent warehouse information collection method.

[0134] The processor retrieves instructions from the memory one by one, analyzes the instructions, and then performs corresponding operations according to the instructions, generating a series of control commands, so that the various parts of the computer can automatically, continuously and coordinately operate, forming an organic whole, realizing program input, data input, calculation and output of results. The arithmetic operations or logical operations generated in this process are all completed by the operator; the memory includes a read-only memory (ROM), which is used to store computer programs, and a protection device is provided on the outside of the memory.

[0135] For example, a computer program may be divided into one or more modules, one or more of which are stored in a memory and executed by a processor to implement the present invention. One or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device.

[0136] Those skilled in the art will understand that the description of the above-mentioned service device is merely an example and does not constitute a limitation on the terminal device. It may include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, etc.

[0137] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the terminal device and connects various parts of the entire user terminal using various interfaces and lines.

[0138] The memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and accessing data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required for a function (such as an information collection template display function and a product information release function); the data storage area can store data created based on the use of the berth status display system (such as product information collection templates corresponding to different product types and product information required to be released by different product providers). In addition, the memory can include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0139] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the modules / units in the above-mentioned embodiment system, and can also be completed by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium, and the computer program, when executed by the processor, can implement the functions of the above-mentioned various system embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0140] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0141] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent warehouse information collection method, characterized in that: The method comprises: The amount of goods is obtained regularly according to sensors installed in the storage space; the obtained amount of goods contains time information; the working frequencies of the sensors in the storage space are the same; Arrange the cargo quantity according to the time information to obtain a cargo quantity array, and synchronously calculate the autocorrelation coefficient of the cargo quantity data and its lag time difference; the lag time difference is related to the sequence number difference of the elements in the array and the period when the cargo quantity is obtained regularly; Compare the autocorrelation coefficients and lag time differences of each storage space to obtain the similarity between any two storage spaces; For any storage space, the image acquisition frequency is determined according to the similarity between it and all other storage spaces; The steps of arranging the cargo quantity according to the time information to obtain a cargo quantity array and synchronously calculating the autocorrelation coefficient and the lag time difference of the cargo quantity data include: Arrange the cargo quantity according to the time information to obtain a cargo quantity array; Starting from the end of the cargo quantity array, obtain a preset number of cargo quantities in reverse order as the array to be analyzed; Create a sequence of sequence differences; the first term, step length and last term of the sequence of sequence differences are all preset values; For each serial number difference in the serial number difference series, the autocorrelation coefficient is calculated once for the cargo quantity array; Select the maximum value of all calculated autocorrelation coefficients as the final autocorrelation coefficient; Read the serial number difference corresponding to the final autocorrelation coefficient, read the period when the cargo quantity is obtained regularly, and multiply the serial number difference by the period to obtain the lag time difference; The process of calculating the autocorrelation coefficient includes: ； ； ； In the formula, The serial number difference is The autocorrelation coefficient of The serial number difference is Autocovariance, The serial number difference is The variance of the array to be analyzed; Indicates the first elements, is the mean of each element in the array to be analyzed; Indicates the first elements; Indicates the total number of elements in the array to be analyzed; The step of determining the image acquisition frequency for any storage space according to the similarity between it and all other storage spaces comprises: For any storage space, read the similarity between it and all other storage spaces; Calculating the mean of the read similarities, and determining the image acquisition frequency according to the mean; Among them, the relationship between image acquisition frequency and mean is: ; In the formula, represents the final image acquisition frequency, Indicates the preset reference frequency, Represents the mean similarity value corresponding to a storage space, is the preset mean threshold; and These are preset parameters.

2. The intelligent warehouse information collection method according to claim 1 is characterized in that: The step of comparing the autocorrelation coefficients and lag time differences of each storage space to obtain the similarity between any two storage spaces includes: The distance range for receiving staff to enter; For any storage space, take it as the center, query the storage spaces within the distance range in turn, and pair them with the storage space at the center; For each pair of storage spaces, read the autocorrelation coefficients and their lag time difference between the two storage spaces; Compare the correlation coefficient and its lag time difference to calculate the similarity; Among them, the similarity between any two storage spaces is calculated only once.

3. The intelligent warehouse information collection method according to claim 1 is characterized in that: The step of obtaining the amount of goods at regular intervals according to the sensor installed in the storage space comprises: Read the image of storage space regularly; Performing contour recognition on the image, locating the cargo, and simultaneously calculating the cargo volume; The accuracy of determining cargo quantity based on cargo volume; When the accuracy is less than a preset accuracy threshold, an inspection request directed to the storage space is generated.

4. An intelligent warehouse information collection system, characterized in that: The system comprises: A cargo quantity acquisition module is used to obtain the cargo quantity according to the sensors installed in the storage space at regular intervals; the obtained cargo quantity contains time information; the working frequencies of the sensors in the storage space are the same; A correlation judgment module is used to arrange the cargo quantity according to the time information, obtain the cargo quantity array, and synchronously calculate the autocorrelation coefficient of the cargo quantity data and its lag time difference; the lag time difference is related to the sequence number difference of the elements in the array and the period when the cargo quantity is obtained regularly; The similarity calculation module is used to compare the autocorrelation coefficients and lag time differences of each storage space to obtain the similarity between any two storage spaces; An image acquisition module is used to determine the image acquisition frequency for any storage space according to its similarity with all other storage spaces; The correlation determination module comprises: A cargo quantity arrangement unit, used for arranging the cargo quantity according to the time information to obtain a cargo quantity array; A data interception unit is used to obtain a preset number of cargo quantities in reverse order starting from the end of the cargo quantity array as an array to be analyzed; A sequence creation unit, used to create a sequence of sequence differences; the first term, step length and last term of the sequence of sequence differences are all preset values; A calculation execution unit, for calculating an autocorrelation coefficient of the cargo quantity array once for each serial number difference in the serial number difference series; A maximum value selection unit is used to select the maximum value of all calculated autocorrelation coefficients as the final autocorrelation coefficient; The time difference calculation unit is used to read the serial number difference corresponding to the final autocorrelation coefficient, read the period when the cargo quantity is obtained regularly, and multiply the serial number difference by the period to obtain the lag time difference; The process of calculating the autocorrelation coefficient includes: ； ； ； In the formula, The serial number difference is The autocorrelation coefficient of The serial number difference is Autocovariance, The serial number difference is The variance of the array to be analyzed; Indicates the first elements, is the mean of each element in the array to be analyzed; Indicates the first elements; Indicates the total number of elements in the array to be analyzed; The image acquisition module comprises: A similarity reading unit is used to read the similarity between any storage space and all other storage spaces; A frequency determination unit, used to calculate the mean of the read similarities, and determine the image acquisition frequency according to the mean; Among them, the relationship between image acquisition frequency and mean is: ; In the formula, represents the final image acquisition frequency, Indicates the preset reference frequency, Represents the mean similarity value corresponding to a storage space, is the preset mean threshold; and These are preset parameters.

5. The intelligent warehouse information collection system according to claim 4 is characterized in that: The similarity calculation module comprises: A distance range receiving unit is used to receive the distance range entered by the staff; The pairing unit is used to query the storage spaces within the distance range of any storage space with it as the center, and pair them with the storage space at the center; A parameter reading unit, used for reading the autocorrelation coefficient and the lag time difference of the two storage spaces for each pair of storage spaces; A parameter comparison unit is used to compare the correlation coefficient and its lag time difference and calculate the similarity; Among them, the similarity between any two storage spaces is calculated only once.

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