A digital automated warehouse intelligent management and control method and system

Abstract

The present invention discloses a digital three-dimensional warehousing intelligent management and control method and system, which belongs to the field of warehousing management, wherein the method includes: obtaining multi-point warehousing environment monitoring time series information; loading cargo types, calibrating environmental condition expected values; performing similar point clustering on the monitoring time series information, generating multiple cluster points, performing central trend analysis, generating multiple groups of centralized environmental condition characteristic values, performing deviation analysis with environmental condition expected values, and generating multiple environmental deviation coefficients; extracting clusters greater than or equal to the environmental deviation coefficient threshold, setting cargo sampling points for cargo sampling, and performing warehousing management. This application solves the technical problem that the prior art relies on periodic spot checks to manage environmentally sensitive goods, resulting in a high probability of error in warehousing management and low management accuracy, and achieves the technical effect of accurately setting spot checks on environmentally sensitive goods by constructing an environmental deviation coefficient, reducing management errors, and improving management accuracy.

Description

Technical Field

[0001] The present invention relates to the field of warehouse management, and in particular to a digital three-dimensional warehouse intelligent management and control method and system. Background Art

[0002] Stereoscopic warehousing technology has been widely used in the fields of commodity trade and logistics warehousing. In actual application, in order to improve warehousing efficiency, it is necessary to accurately control the storage environment of stereoscopic warehouses. However, when storing environmentally sensitive goods, the existing stereoscopic warehousing management generally adopts the method of periodic sampling of stored environmentally sensitive goods, which is easy to cause inadequate inspection, thus failing to effectively prevent quality problems of environmentally sensitive goods, resulting in a high probability of management errors and low management accuracy. Summary of the invention

[0003] The present application provides a digital three-dimensional warehousing intelligent management and control method and system, aiming to solve the technical problem that the existing technology relies on periodic spot checks to manage environmentally sensitive goods, resulting in a high probability of errors in warehouse management and low management accuracy.

[0004] In view of the above problems, the present application provides a digital three-dimensional warehousing intelligent management and control method and system.

[0005] The first aspect disclosed in the present application provides a digital three-dimensional warehouse intelligent management and control method, the method comprising: when the first timing of the first timer of the first area meets the first preset period, obtaining the multi-point warehouse environment monitoring time series information of the first area, wherein the time series of the multi-point warehouse environment monitoring time series information is consistent with the first timing; loading the cargo type of the first area, and calibrating the expected value of the environmental condition based on the benchmark environment calibration table; performing similar point clustering on the multi-point warehouse environment monitoring time series information to generate multiple cluster points; traversing the multiple cluster points to perform central trend analysis to generate multiple groups of centralized environmental condition feature values; traversing the multiple groups of centralized environmental condition feature values, performing deviation analysis with the expected value of the environmental condition, and generating multiple environmental deviation coefficients; extracting a cluster whose environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold from the multiple environmental deviation coefficients, setting a cargo sampling point to perform cargo sampling, and obtaining the cargo sampling result; and performing warehouse management according to the cargo sampling result.

[0006] Another aspect disclosed in the present application provides a digital three-dimensional warehousing intelligent management and control system, the system comprising: a monitoring information acquisition module, used to obtain multi-point storage environment monitoring time series information of the first area when the first timing of the first timer of the first area meets the first preset period, wherein the timing of the multi-point storage environment monitoring time series information is consistent with the first timing; an expected value calibration module, used to load the cargo type of the first area, and calibrate the expected value of the environmental condition based on the benchmark environment calibration table; a similar point clustering module, used to perform similar point clustering on the multi-point storage environment monitoring time series information to generate multiple cluster points; a central tendency analysis module, used to traverse multiple cluster points to perform central tendency analysis to generate multiple groups of concentrated environmental condition characteristic values; a deviation analysis module, used to traverse multiple groups of concentrated environmental condition characteristic values, perform deviation analysis with the expected value of the environmental condition, and generate multiple environmental deviation coefficients; a cargo sampling result module, used to extract a cluster whose environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold from multiple environmental deviation coefficients, set cargo sampling points to perform cargo sampling, and obtain cargo sampling results; a warehouse management module, used to perform warehouse management according to the cargo sampling results.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By obtaining the time series information of multi-point storage environment monitoring in the first area, digital monitoring of the three-dimensional storage area environment is realized; the cargo type in the first area is loaded, and the expected value of the environmental condition is calibrated based on the benchmark environmental calibration table to realize customized management of the environmental requirements of different cargo types; similar points are clustered on the time series information of multi-point storage environment monitoring to generate multiple clusters of points, and environmental abnormal areas are identified in a targeted manner; multiple clusters of points are traversed to perform centralized trend analysis to generate multiple groups of centralized environmental condition characteristic values ​​to achieve accurate judgment of environmental abnormal areas; multiple groups of centralized environmental condition characteristic values ​​are traversed to perform deviation analysis with the expected value of the environmental condition to generate multiple environmental abnormal areas. Deviation coefficient, realize the digital association between environmental monitoring and quality prediction; extract clusters whose environmental deviation coefficients are greater than or equal to the environmental deviation coefficient threshold from multiple environmental deviation coefficients, set cargo sampling points for cargo sampling, obtain cargo sampling results, and realize accurate locking of quality risk areas due to environmental anomalies; the technical solution of warehousing management based on the results of cargo sampling solves the technical problem that the existing technology relies on periodic sampling to manage environmentally sensitive goods, resulting in a high error probability in warehousing management and low management accuracy, and achieves the technical effect of accurately setting sampling for environmentally sensitive goods by constructing an environmental deviation coefficient, reducing management errors, and improving management accuracy.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of a digital three-dimensional warehousing intelligent management and control method is provided for the embodiment of the present application;

[0011] Figure 2 A schematic diagram of a process for obtaining multiple environmental deviation coefficients in a digital three-dimensional warehouse intelligent management and control method is provided for an embodiment of the present application;

[0012] Figure 3 A structural schematic diagram of a digital three-dimensional warehouse intelligent management and control system is provided for an embodiment of the present application.

[0013] Explanation of the reference numerals: monitoring information acquisition module 11, expected value calibration module 12, similar point clustering module 13, central trend analysis module 14, deviation analysis module 15, goods sampling result module 16, warehouse management module 17. DETAILED DESCRIPTION

[0014] The overall idea of ​​the technical solution provided by this application is as follows:

[0015] The embodiment of the present application provides a digital three-dimensional warehousing intelligent management and control method and system. First, realize digital monitoring of the three-dimensional warehousing area environment, and obtain the storage environment time series information of multiple points in the area. Then, according to the different regional cargo types and their environmental sensitivity characteristics, establish a benchmark environment calibration table, and calibrate the expected values ​​of environmental conditions in each area in real time to achieve customized and precise management of environmental requirements of different cargo types. Further, perform cluster analysis on the storage environment time series information to identify regional clusters with abnormal environmental quality. Then, through the deviation analysis of the characteristic values ​​of the abnormal environmental areas and the expected values ​​of the environmental conditions, generate multiple environmental deviation coefficients to achieve accurate correlation and positioning of environmental monitoring and cargo quality hazards. Finally, conduct random inspections on the cargo in the abnormal area whose environmental deviation coefficient is greater than the threshold to obtain the cargo random inspection results, and perform warehousing management based on the cargo random inspection results.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0017] Embodiment 1

[0018] like Figure 1 As shown, the embodiment of the present application provides a digital three-dimensional warehouse intelligent management and control method, the method comprising:

[0019] When the first timing of the first timer of the first area meets the first preset period, the multi-point storage environment monitoring time sequence information of the first area is obtained, wherein the time sequence of the multi-point storage environment monitoring time sequence information is consistent with the first timing;

[0020] In an embodiment of the present application, the first area is an area for storing goods in a digital stereoscopic warehouse. In order to implement effective management and control of the goods in the first area, storage environment monitoring data of multiple points in the first area are obtained. Therefore, multi-point sensors are set in the first area for continuous monitoring of storage environment conditions such as temperature and humidity. At the same time, in order to obtain regular and regular monitoring data for analysis and judgment, a first timer is set in the first area, which is used to time the time of environmental monitoring. When the first timing of the first timer meets the pre-set first preset period, the first timing of the first timer is used as the time axis to obtain the storage environment monitoring timing information generated by the multi-point sensors in the first area. In this way, the multi-point storage environment monitoring information is obtained periodically through the timing of the first timer, which is conducive to the subsequent analysis and judgment of the data, and realizes the storage management and control of environmentally sensitive goods.

[0021] Load the cargo type of the first area, and calibrate the expected value of the environmental condition based on the reference environmental calibration table;

[0022] Furthermore, the embodiments of the present application include:

[0023] When the type of goods entering the first area has an environmentally sensitive tag, positive sample sampling is performed based on the type of goods entering the first area to obtain storage environment condition record data;

[0024] Extracting a first storage environment condition attribute set of the first storage environment condition record data, extracting a second storage environment condition attribute set of the second storage environment condition record data, until extracting an Nth storage environment condition attribute set of the Nth storage environment condition record data;

[0025] According to the first storage environment condition attribute set, the second storage environment condition attribute set, and up to the Nth storage environment condition attribute set, the first environment attribute support, the second environment attribute support, and up to the Mth environment attribute support are counted, wherein the environment attribute support is equal to the ratio of the number of environment attribute triggering times to the N value;

[0026] According to the first environmental attribute support, the second environmental attribute support, and up to the Mth environmental attribute support, extract environmental attribute types whose attribute support is greater than or equal to an attribute support threshold, and set them as a sensitive environmental attribute set;

[0027] Based on the storage environment condition record data, the sensitive environment attribute set is frequently calibrated to generate the expected value of the environment condition, which is associated with the type of goods entering the first area and stored in the benchmark environment calibration table.

[0028] Furthermore, the embodiments of the present application include:

[0029] Extracting a first sensitive environmental attribute from the sensitive environmental attribute set, and matching the first sensitive environmental attribute record value set from the storage environmental condition record data;

[0030] Sorting the first sensitive environment attribute record value set in ascending order to generate a first sensitive environment attribute record value set sorting result;

[0031] Calculate the interquartile range of the sorting result of the first sensitive environmental attribute record value set;

[0032] An expected interval of a first sensitive environmental attribute is constructed according to the quartile range, added into the expected value of the environmental condition, and stored in the benchmark environmental calibration table in association with the type of goods entering the first area.

[0033] In a preferred embodiment, first, determine whether the type of goods entering the warehouse in the first area has a label for environmental sensitivity, such as "environmentally sensitive", "low temperature" and other labels. If the type of goods entering the warehouse in the first area has an environmentally sensitive label, then based on the type of goods entering the warehouse in the first area, extract historical storage instances in which the environmental conditions do not affect the quality of the goods from the storage management system and its storage records as positive samples. Then, for each extracted positive sample instance, according to its storage location information, query and collect the environmental condition numerical data set of the storage location during the period when the positive sample was entered into the warehouse, as the storage environment condition record data. Subsequently, traverse the storage environment condition record data, extract each storage environment condition record, parse each storage environment condition record data, extract the corresponding storage environment condition attribute value, and obtain the first storage environment condition attribute set, the second storage environment condition attribute set, and the Nth storage environment condition attribute set in turn. Next, for all the extracted storage environment condition attribute sets, that is, the first storage environment condition attribute set to the Nth storage environment condition attribute set, traverse and analyze the number of occurrences of each environmental attribute. Then, count the number of times each environmental attribute appears in all N records, that is, calculate the support of the environmental condition attribute, reflecting the importance of the attribute as a storage environmental condition, thereby obtaining the first environmental attribute support, the second environmental attribute support, and the Mth environmental attribute support. Subsequently, determine in turn whether the first environmental attribute support, the second environmental attribute support, and the Mth environmental attribute support are greater than or equal to the attribute support threshold preset according to expert experience. If so, extract the environmental attribute; otherwise, discard it. Then, integrate all the screened environmental attributes to construct a set of sensitive environmental attributes. By setting a threshold to filter the support of environmental attributes, sensitive environmental attributes that have a significant impact on storage environmental conditions can be effectively extracted, providing a set of attributes for key analysis for subsequent environmental calibration.

[0034] Then, the sensitive environmental attribute set obtained by traversing the screening is extracted one by one, and a sensitive environmental attribute is extracted in turn as the first sensitive environmental attribute. Then, according to the first sensitive environmental attribute, all the record data containing the first sensitive environmental attribute are retrieved and matched from the storage environment condition record, and the attribute value corresponding to the first sensitive environmental attribute is extracted from it to form the first sensitive environmental attribute record value set. After that, for the extracted record value set of the first sensitive environmental attribute, all the record values ​​contained in it are sorted in order from small to large, and the sorting operation of the record value set is implemented to generate the sorting result of the first sensitive environmental attribute record value set. Subsequently, the interquartile range of the sorting result of the first sensitive environmental attribute record value set is calculated, that is, the total number N of elements in the attribute record value set is first determined, and then two elements with sequence numbers N / 4 and 3N / 4 in the sorting result are found, which are recorded as the first quartile and the third quartile respectively, and the difference between the third quartile and the first quartile is calculated to obtain the quartile range. Then, take the first quartile in the sorting result, subtract 1.5 times the quartile range as the lower limit of the interval, take the third quartile in the sorting result, add 1.5 times the quartile range as the upper limit of the interval, and form the expected interval of the first sensitive environmental attribute. Then, add the expected interval of the first sensitive environmental attribute to the expected value of the environmental condition, and store it in association with the type of goods entering the first area. By processing all the sensitive environmental attributes in the sensitive environmental attribute set, the benchmark environmental calibration table is obtained.

[0035] Performing similar point clustering on the multi-point storage environment monitoring time series information to generate multiple cluster points;

[0036] Furthermore, the embodiments of the present application include:

[0037] Extracting the first-point storage environment monitoring time series information and the second-point storage environment monitoring time series information from the multi-point storage environment monitoring time series information;

[0038] A similar point clustering function is constructed, and similar point clustering is performed on the multi-point storage environment monitoring time series information to generate the multi-cluster points.

[0039] Furthermore, the embodiments of the present application include:

[0040] Construct the first similarity evaluation function:

[0041]

[0042] Among them, SIM j Characterizes the similarity coefficient of environmental conditions of the jth time series, x 1ji The i-th attribute monitoring value of the j-th time series representing the storage environment monitoring time series information of the first point, x 2ji The i-th attribute monitoring value of the j-th time series representing the storage environment monitoring time series information of the second point, di0 represents the preset deviation threshold of the i-th attribute, Q represents the total number of environmental condition attributes, and count is the counting function;

[0043] Construct the second similarity evaluation function:

[0044]

[0045] Among them, SIM2 represents the overall similarity coefficient of environmental conditions, d0 represents the overall environmental deviation distance threshold of a single time series, and L represents the total number of time series;

[0046] A first similarity threshold and a second similarity threshold having an AND relationship are configured, and similar point clustering is performed in combination with the first similarity evaluation function and the second similarity evaluation function to generate the multiple cluster points.

[0047] In a preferred embodiment, after obtaining the multi-point warehouse environment monitoring timing information of the first area, the warehouse environment monitoring timing information of any two points is extracted therefrom, for example, the point numbered 1 and the point numbered 2, which constitute the first point warehouse environment monitoring timing information and the second point warehouse environment monitoring timing information, respectively.

[0048] At the same time, a similar point clustering function is constructed, which includes a first similarity evaluation function and a second similarity evaluation function, which are used to evaluate the similarity of the environmental condition time series. Among them, the first similarity evaluation function is:

[0049]

[0050] The first similarity evaluation function is used to evaluate the similarity of two environmental attribute values ​​of two-point storage environment monitoring time series information in a single time series segment. j Characterizes the similarity coefficient of environmental conditions of the jth time series, x 1ji The i-th attribute monitoring value of the j-th time series representing the storage environment monitoring time series information of the first point, x 2ji The i-th attribute monitoring value of the j-th time series representing the storage environment monitoring time series information of the second point, d i0 The preset deviation threshold of the i-th attribute is represented, Q represents the total number of environmental condition attributes, and count is a counting function. By calculating the absolute value of the difference between the two environmental attribute monitoring values ​​in a given time series segment, if it is less than or equal to the preset deviation threshold of the corresponding attribute, it is counted. The ratio of the counted number to the total number of attributes Q is the similarity of the environmental conditions of the two points in the time series segment.

[0051] Among them, the second similarity evaluation function is:

[0052]

[0053] The second similarity evaluation function is used to evaluate the similarity of environmental conditions in the entire time series of storage environment monitoring at two points. Among them, SIM2 represents the overall similarity coefficient of environmental conditions, d0 represents the overall environmental deviation distance threshold of a single time series, and L represents the total number of time series. Calculate the square root of the sum of the mean square errors of the two environmental attributes in each time series segment, determine whether it is less than d0, and count the number of time series segments that are less than or equal to the overall environmental deviation distance threshold requirement. The proportion of the total number of segments L is the similarity of the overall time series environmental conditions of the two points.

[0054] Then, a first similarity threshold of the first similarity evaluation function value is set; and a second similarity threshold of the second similarity evaluation function value is set at the same time. Subsequently, the first point storage environment monitoring time series information and the second point storage environment monitoring time series information extracted are brought into the above-mentioned first similarity evaluation function and the second similarity evaluation function to calculate the similarity function values ​​respectively, and the calculation result is SIM j and SIM2. Next, determine SIM j Is SIM greater than or equal to the first similarity threshold, and determines whether SIM2 is greater than or equal to the second similarity threshold. j Only when SIM1 and SIM2 meet the corresponding threshold requirements at the same time, the environmental conditions of the two points are considered to be highly similar, and the two points are recorded as one category and stored in a point cluster. Otherwise, the environmental conditions of the two points are considered to be dissimilar. By performing similar point clustering on any two points of multi-point storage environment monitoring time series information, multi-cluster points are obtained.

[0055] Traversing the multiple cluster points to perform central tendency analysis and generate multiple groups of central environmental condition characteristic values;

[0056] In an embodiment of the present application, multiple clusters of points are generated, wherein the environmental conditions of the points in each cluster are approximately the same. Then, a cluster of points is traversed and selected, and the storage environment monitoring time series information of the cluster points is extracted. Next, for each environmental attribute, the attribute values ​​of the cluster points in each monitoring time series segment are counted, and the change trend of the value of each attribute is analyzed. Finally, the median and average values ​​of the characteristic values ​​of each environmental attribute in the cluster point during the entire monitoring period are obtained to constitute the concentrated environmental condition characteristic values ​​of the cluster point. By analogy, all cluster points are traversed and analyzed and processed, and finally multiple groups of concentrated environmental condition characteristic values ​​storing the environmental characteristics of each cluster point are generated.

[0057] Traversing the multiple groups of concentrated environmental condition characteristic values, performing deviation analysis with the environmental condition expected values, and generating multiple environmental deviation coefficients;

[0058] Furthermore, the embodiments of the present application include:

[0059] Extracting a first group of concentrated environmental condition feature values ​​from the multiple groups of concentrated environmental condition feature values;

[0060] Wherein, the first group of concentrated environmental condition characteristic values ​​includes the first attribute characteristic value time series value, the second attribute characteristic value time series value to the Qth attribute characteristic value time series value;

[0061] The environmental condition expected value includes a first attribute expected value, a second attribute expected value, and up to a Qth attribute expected value;

[0062] Extracting a first deviation frequency ratio by comparing the first attribute characteristic value time series value with the first attribute expected value;

[0063] Until the Qth deviation frequency ratio is extracted according to the comparison between the Qth attribute characteristic value time series value and the Qth attribute expected value;

[0064] The average value of the first deviation frequency ratio, the second deviation frequency ratio and up to the Qth deviation frequency ratio is calculated and set as a first cluster environment deviation coefficient, which is added to the plurality of environment deviation coefficients.

[0065] Further, such as Figure 2 As shown, the embodiment of the present application includes:

[0066] Based on the coefficient of variation method, weighting is performed on the first attribute, the second attribute, and up to the Qth attribute to generate the first weight, the second weight, and up to the Qth weight;

[0067] According to the first weight, the second weight up to the Qth weight, weighted mean analysis is performed on the first deviation frequency ratio, the second deviation frequency ratio up to the Qth deviation frequency ratio to generate the first cluster environment deviation coefficient, which is added to the multiple environment deviation coefficients.

[0068] In a preferred embodiment, first, any group of concentrated environmental condition characteristic values ​​is selected from the generated multiple groups of concentrated environmental condition characteristic values, for example, the first group is extracted. Then, the first group of concentrated environmental condition characteristic values ​​is parsed, which contains characteristic statistical values ​​of multiple environmental attributes. For example, the characteristic value of the temperature attribute includes the minimum temperature value, the maximum temperature value, the average temperature value, the median temperature value, etc.; the humidity attribute also includes a corresponding set of characteristic statistical values, etc., and these characteristic values ​​are arranged according to the environmental attributes to form the first attribute characteristic value time series value, the second attribute characteristic value time series value and even the Qth attribute characteristic value time series value, where Q is the total number of environmental attributes. Subsequently, the constructed reference environment calibration table is obtained, which contains a group of environmental condition expected values ​​corresponding to the type of goods entering the warehouse in the first area, and the environmental condition expected values ​​are parsed, which contains the same number of expected value intervals as the total number of environmental attributes Q. For example, the temperature attribute corresponds to the expected temperature interval, the humidity attribute corresponds to the expected humidity interval, etc., and these interval values ​​are arranged in the order of environmental attributes to form the first attribute expected value, the second attribute expected value and even the Qth attribute expected value.

[0069] Next, compare the time series value of the first attribute feature value with the corresponding expected value of the first attribute to determine whether the time series value of the first attribute feature value is within the expected value of the first attribute, count the number of values ​​outside the interval, and calculate their proportion of the total sample size as the first deviation frequency ratio. Similarly, extract the time series value of the second attribute feature value until the Qth attribute feature value time series value, and extract the second attribute expected value until the Qth attribute expected value. For each attribute, take out the feature value in its feature value time series in turn to determine whether it is within the corresponding attribute expected interval. Similarly, count the number of values ​​outside the interval and calculate the second deviation frequency ratio until the Qth deviation frequency ratio.

[0070] Afterwards, for the deviation frequency ratio result data set obtained from the first attribute to the Qth attribute, the coefficient of variation method is used to calculate the weight coefficient of each attribute accordingly. Among them, the coefficient of variation reflects the discrete degree of the attribute value. Then, the first weight of the first attribute, the second weight of the second attribute, and so on until the Qth weight of the Qth attribute are generated accordingly. Next, the first deviation frequency ratio, the second deviation frequency ratio to the Qth deviation frequency ratio are taken out in sequence. And the weights are taken out accordingly, and weighted calculation is performed, that is, a weighted mean is generated, and the joint influence of the deviation rate index and weight of each attribute is considered at the same time, to avoid the biased influence of excessive fluctuations in some attribute values. The weighted mean calculated by the calculation constitutes the first cluster environmental deviation coefficient of the first area. Finally, the obtained first cluster environmental deviation coefficient is added to multiple environmental deviation coefficients.

[0071] Extracting a cluster whose environmental deviation coefficient is greater than or equal to an environmental deviation coefficient threshold from the multiple environmental deviation coefficients, setting a cargo sampling inspection point to perform cargo sampling inspection, and obtaining a cargo sampling inspection result;

[0072] In an embodiment of the present application, first, multiple environmental deviation coefficients are traversed to obtain an environmental deviation coefficient, and its value is determined to be greater than or equal to a preset environmental deviation coefficient threshold, which reflects the limit of deviation allowed by environmental conditions. Then, the points where the environmental deviation coefficient is greater than or equal to the threshold are extracted and determined as sampling points. Subsequently, actual cargo sampling operations are performed on the determined sampling points to obtain cargo sampling results, providing a basis for subsequent warehouse management decisions.

[0073] Carry out warehouse management according to the results of the random inspection of goods.

[0074] In the implementation of this application, after obtaining the results of the random inspection of goods, it is determined whether the random inspection results show that the goods have deteriorated. If the sampled goods are confirmed to have deteriorated, the status of the batch of goods in the warehouse management is updated, including marking the deterioration mark, changing the storage location, etc. Finally, subsequent disposal measures are proposed for the deteriorated goods, such as return, scrapping, etc., to achieve accurate warehouse management, thereby reducing warehouse management errors.

[0075] In summary, the digital three-dimensional warehouse intelligent management and control method provided by the embodiment of the present application has the following technical effects:

[0076] When the first timing of the first timer in the first area meets the first preset period, the multi-point storage environment monitoring time series information of the first area is obtained, laying a data foundation for subsequent analysis.

[0077] Load the cargo type of the first area, calibrate the expected value of the environmental condition based on the benchmark environmental calibration table, and realize customized fine management of environmental requirements. Perform clustering of similar points on the time series information of multi-point storage environment monitoring to generate multiple clusters of points to accurately locate abnormalities. Traverse multiple clusters of points to perform central trend analysis, generate multiple sets of centralized environmental condition characteristic values, and accurately determine the deviation of regional environmental quality. Traverse multiple sets of centralized environmental condition characteristic values, perform deviation analysis with the expected value of environmental conditions, generate multiple environmental deviation coefficients, and quantitatively determine the degree of environmental deviation. Extract clusters whose environmental deviation coefficients are greater than or equal to the environmental deviation coefficient threshold from multiple environmental deviation coefficients, set cargo sampling points for cargo sampling, obtain cargo sampling results, and perform storage management based on cargo sampling results, so as to accurately set sampling for environmentally sensitive goods, reduce management errors, and improve management accuracy.

[0078] Embodiment 2

[0079] Based on the same inventive concept as the digital stereoscopic warehousing intelligent management and control method in the aforementioned embodiment, Figure 3 As shown, the embodiment of the present application provides a digital three-dimensional warehouse intelligent management and control system, which includes:

[0080] The monitoring information acquisition module 11 is used to obtain the multi-point storage environment monitoring timing information of the first area when the first timing of the first timer of the first area meets the first preset period, wherein the timing of the multi-point storage environment monitoring timing information is consistent with the first timing;

[0081] An expected value calibration module 12 is used to load the first area cargo type and calibrate the expected value of the environmental condition based on the reference environmental calibration table;

[0082] A similar point clustering module 13 is used to perform similar point clustering on the multi-point storage environment monitoring time series information to generate multiple cluster points;

[0083] A central tendency analysis module 14 is used to traverse the multiple cluster points to perform central tendency analysis and generate multiple groups of central environmental condition characteristic values;

[0084] The deviation analysis module 15 is used to traverse the multiple sets of concentrated environmental condition characteristic values, perform deviation analysis with the environmental condition expected values, and generate multiple environmental deviation coefficients;

[0085] A cargo sampling inspection result module 16 is used to extract a cluster whose environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold from the multiple environmental deviation coefficients, set cargo sampling inspection points to perform cargo sampling inspection, and obtain cargo sampling inspection results;

[0086] The warehouse management module 17 is used to perform warehouse management according to the goods sampling inspection results.

[0087] Furthermore, the expected value calibration module 12 includes the following execution steps:

[0088] When the type of goods entering the first area has an environmentally sensitive tag, positive sample sampling is performed based on the type of goods entering the first area to obtain storage environment condition record data;

[0089] Extracting a first storage environment condition attribute set of the first storage environment condition record data, extracting a second storage environment condition attribute set of the second storage environment condition record data, until extracting an Nth storage environment condition attribute set of the Nth storage environment condition record data;

[0090] According to the first storage environment condition attribute set, the second storage environment condition attribute set, and up to the Nth storage environment condition attribute set, the first environment attribute support, the second environment attribute support, and up to the Mth environment attribute support are counted, wherein the environment attribute support is equal to the ratio of the number of environment attribute triggering times to the N value;

[0091] According to the first environmental attribute support, the second environmental attribute support, and up to the Mth environmental attribute support, extract environmental attribute types whose attribute support is greater than or equal to an attribute support threshold, and set them as a sensitive environmental attribute set;

[0092] Based on the storage environment condition record data, the sensitive environment attribute set is frequently calibrated to generate the expected value of the environment condition, which is associated with the type of goods entering the first area and stored in the benchmark environment calibration table.

[0093] Furthermore, the expected value calibration module 12 also includes the following execution steps:

[0094] Extracting a first sensitive environmental attribute from the sensitive environmental attribute set, and matching the first sensitive environmental attribute record value set from the storage environmental condition record data;

[0095] Sorting the first sensitive environment attribute record value set in ascending order to generate a first sensitive environment attribute record value set sorting result;

[0096] Calculate the interquartile range of the sorting result of the first sensitive environmental attribute record value set;

[0097] An expected interval of a first sensitive environmental attribute is constructed according to the quartile range, added into the expected value of the environmental condition, and stored in the benchmark environmental calibration table in association with the type of goods entering the first area.

[0098] Furthermore, the similar point clustering module 13 includes the following execution steps:

[0099] Extracting the first-point storage environment monitoring time series information and the second-point storage environment monitoring time series information from the multi-point storage environment monitoring time series information;

[0100] A similar point clustering function is constructed, and similar point clustering is performed on the multi-point storage environment monitoring time series information to generate the multi-cluster points.

[0101] Furthermore, the similar point clustering module 13 further includes the following execution steps:

[0102] Construct the first similarity evaluation function:

[0103]

[0104] Among them, SIM j Characterizes the similarity coefficient of environmental conditions of the jth time series, x 1ji The i-th attribute monitoring value of the j-th time series representing the storage environment monitoring time series information of the first point, x 2ji The i-th attribute monitoring value of the j-th time series representing the storage environment monitoring time series information of the second point, d i0 represents the preset deviation threshold of the i-th attribute, Q represents the total number of environmental condition attributes, and count is the counting function;

[0105] Construct the second similarity evaluation function:

[0106]

[0107] Among them, SIM2 represents the overall similarity coefficient of environmental conditions, d0 represents the overall environmental deviation distance threshold of a single time series, and L represents the total number of time series;

[0108] A first similarity threshold and a second similarity threshold having an AND relationship are configured, and similar point clustering is performed in combination with the first similarity evaluation function and the second similarity evaluation function to generate the multiple cluster points.

[0109] Furthermore, the deviation analysis module 15 includes the following execution steps:

[0110] Extracting a first group of concentrated environmental condition feature values ​​from the multiple groups of concentrated environmental condition feature values;

[0111] Wherein, the first group of concentrated environmental condition characteristic values ​​includes the first attribute characteristic value time series value, the second attribute characteristic value time series value to the Qth attribute characteristic value time series value;

[0112] The environmental condition expected value includes a first attribute expected value, a second attribute expected value, and up to a Qth attribute expected value;

[0113] Extracting a first deviation frequency ratio by comparing the first attribute characteristic value time series value with the first attribute expected value;

[0114] Until the Qth deviation frequency ratio is extracted according to the comparison between the Qth attribute characteristic value time series value and the Qth attribute expected value;

[0115] The average value of the first deviation frequency ratio, the second deviation frequency ratio and up to the Qth deviation frequency ratio is calculated and set as a first cluster environment deviation coefficient, which is added to the plurality of environment deviation coefficients.

[0116] Furthermore, the deviation analysis module 15 also includes the following execution steps:

[0117] Based on the coefficient of variation method, weighting is performed on the first attribute, the second attribute, and up to the Qth attribute to generate the first weight, the second weight, and up to the Qth weight;

[0118] According to the first weight, the second weight up to the Qth weight, weighted mean analysis is performed on the first deviation frequency ratio, the second deviation frequency ratio up to the Qth deviation frequency ratio to generate the first cluster environment deviation coefficient, which is added to the multiple environment deviation coefficients.

[0119] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0120] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A digital three-dimensional warehousing intelligent management and control method, characterized in that: include: When the first timing of the first timer of the first area meets the first preset period, the multi-point storage environment monitoring time sequence information of the first area is obtained, wherein the time sequence of the multi-point storage environment monitoring time sequence information is consistent with the first timing; Load the cargo type of the first area, and calibrate the expected value of the environmental condition based on the reference environmental calibration table; Performing similar point clustering on the multi-point storage environment monitoring time series information to generate multiple cluster points; Traversing the multiple cluster points to perform central tendency analysis and generate multiple groups of central environmental condition characteristic values; Traversing the multiple groups of concentrated environmental condition characteristic values, performing deviation analysis with the environmental condition expected values, and generating multiple environmental deviation coefficients; Extracting a cluster whose environmental deviation coefficient is greater than or equal to an environmental deviation coefficient threshold from the multiple environmental deviation coefficients, setting a cargo sampling inspection point to perform cargo sampling inspection, and obtaining a cargo sampling inspection result; Carry out warehouse management according to the results of the random inspection of the goods; The first area cargo type is loaded, and the expected value of the environmental condition is calibrated based on the reference environmental calibration table, which includes: When the type of goods entering the first area has an environmentally sensitive tag, positive sample sampling is performed based on the type of goods entering the first area to obtain storage environment condition record data; Extracting a first storage environment condition attribute set of the first storage environment condition record data, extracting a second storage environment condition attribute set of the second storage environment condition record data, until extracting an Nth storage environment condition attribute set of the Nth storage environment condition record data; According to the first storage environment condition attribute set, the second storage environment condition attribute set, and up to the Nth storage environment condition attribute set, the first environment attribute support, the second environment attribute support, and up to the Mth environment attribute support are counted, wherein the environment attribute support is equal to the ratio of the number of environment attribute triggering times to the N value; According to the first environmental attribute support, the second environmental attribute support, and up to the Mth environmental attribute support, extract environmental attribute types whose attribute support is greater than or equal to an attribute support threshold, and set them as a sensitive environmental attribute set; Based on the storage environment condition record data, the sensitive environment attribute set is frequently calibrated to generate the expected value of the environment condition, and the expected value is associated with the type of goods entering the first area and stored in the reference environment calibration table; The multiple sets of concentrated environmental condition characteristic values ​​are traversed, and deviation analysis is performed with the environmental condition expected values ​​to generate multiple environmental deviation coefficients, including: Extracting a first group of concentrated environmental condition feature values ​​from the multiple groups of concentrated environmental condition feature values; Wherein, the first group of concentrated environmental condition characteristic values ​​includes the first attribute characteristic value time series value, the second attribute characteristic value time series value to the Qth attribute characteristic value time series value; The environmental condition expected value includes a first attribute expected value, a second attribute expected value, and up to a Qth attribute expected value; Extracting a first deviation frequency ratio by comparing the first attribute characteristic value time series value with the first attribute expected value; Until the Qth deviation frequency ratio is extracted according to the comparison between the Qth attribute characteristic value time series value and the Qth attribute expected value; Counting the average of the first deviation frequency ratio, the second deviation frequency ratio and up to the Qth deviation frequency ratio, setting it as a first cluster environment deviation coefficient, and adding it to the multiple environment deviation coefficients; The first deviation frequency ratio, the second deviation frequency ratio and the average value of the Qth deviation frequency ratio are counted and set as the first cluster environment deviation coefficient, and added into the multiple environment deviation coefficients, including: Based on the coefficient of variation method, weighting is performed on the first attribute, the second attribute, and up to the Qth attribute to generate the first weight, the second weight, and up to the Qth weight; According to the first weight, the second weight up to the Qth weight, weighted mean analysis is performed on the first deviation frequency ratio, the second deviation frequency ratio up to the Qth deviation frequency ratio to generate the first cluster environment deviation coefficient, which is added to the multiple environment deviation coefficients.

2. The method according to claim 1, characterized in that Based on the storage environment condition record data, the sensitive environment attribute set is frequently calibrated to generate the expected value of the environment condition, and the expected value is associated with the type of goods entering the first area and stored in the reference environment calibration table, including: Extracting a first sensitive environmental attribute from the sensitive environmental attribute set, and matching the first sensitive environmental attribute record value set from the storage environmental condition record data; Sorting the first sensitive environment attribute record value set in ascending order to generate a first sensitive environment attribute record value set sorting result; Calculate the interquartile range of the sorting result of the first sensitive environmental attribute record value set; An expected interval of a first sensitive environmental attribute is constructed according to the quartile range, added into the expected value of the environmental condition, and stored in the benchmark environmental calibration table in association with the type of goods entering the first area.

3. The method according to claim 1, characterized in that Performing similar point clustering on the multi-point storage environment monitoring time series information to generate multiple cluster points, including: Extracting the first-point storage environment monitoring time series information and the second-point storage environment monitoring time series information from the multi-point storage environment monitoring time series information; A similar point clustering function is constructed, and similar point clustering is performed on the multi-point storage environment monitoring time series information to generate the multi-cluster points.

4. The method according to claim 3, characterized in that Constructing a similar point clustering function, performing similar point clustering on the multi-point storage environment monitoring time series information, and generating the multi-cluster points, including: Construct the first similarity evaluation function: Among them, SIM j Characterizes the similarity coefficient of environmental conditions of the jth time series, x 1ji The i-th attribute monitoring value of the j-th time series representing the storage environment monitoring time series information of the first point, x 2ji The i-th attribute monitoring value of the j-th time series representing the storage environment monitoring time series information of the second point, d i0 represents the preset deviation threshold of the i-th attribute, Q represents the total number of environmental condition attributes, and count is the counting function; Construct the second similarity evaluation function: Among them, SIM2 represents the overall similarity coefficient of environmental conditions, d0 represents the overall environmental deviation distance threshold of a single time series, and L represents the total number of time series; A first similarity threshold and a second similarity threshold having an AND relationship are configured, and similar point clustering is performed in combination with the first similarity evaluation function and the second similarity evaluation function to generate the multiple cluster points.

5. A digital three-dimensional warehousing intelligent management and control system, characterized in that: A digital three-dimensional warehousing intelligent management and control method for implementing any one of claims 1 to 4, comprising: A monitoring information acquisition module, wherein the monitoring information acquisition module is used to obtain multi-point storage environment monitoring timing information of the first area when the first timing of the first timer of the first area meets the first preset period, wherein the timing of the multi-point storage environment monitoring timing information is consistent with the first timing; An expected value calibration module, the expected value calibration module is used to load the first area cargo type and calibrate the expected value of the environmental condition based on the reference environmental calibration table; A similar point clustering module, which is used to perform similar point clustering on the multi-point storage environment monitoring time series information to generate multiple cluster points; A central tendency analysis module, the central tendency analysis module is used to traverse the multiple cluster points to perform central tendency analysis and generate multiple groups of central environmental condition characteristic values; A deviation analysis module, the deviation analysis module is used to traverse the multiple groups of concentrated environmental condition characteristic values, perform deviation analysis with the environmental condition expected values, and generate multiple environmental deviation coefficients; A cargo sampling inspection result module, the cargo sampling inspection result module is used to extract a cluster whose environmental deviation coefficient is greater than or equal to an environmental deviation coefficient threshold from the multiple environmental deviation coefficients, set cargo sampling inspection points to perform cargo sampling inspection, and obtain cargo sampling inspection results; A warehouse management module is used to perform warehouse management according to the goods sampling inspection results.

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