A warehouse intelligent monitoring method and system

Abstract

The present application provides a method for intelligent warehouse monitoring, including: obtaining warehouse commodity data, retrieving preset warehouse operation parameters according to the data category of the commodity data; generating a warehouse monitoring model according to the warehouse operation parameters, retrieving the monitoring equipment operation authority based on the monitoring model, and executing the monitoring equipment control program; obtaining the monitoring data of the monitoring equipment, inputting the monitoring data into a preset monitoring analysis model and outputting the monitoring results; generating a warehouse control instruction according to the monitoring results, and controlling the correction of the warehouse environment parameters. The present application technology can automatically perform warehouse monitoring and adjustment by inputting commodity data when the commodity enters the warehouse, ensuring that the cost is also reduced when the commodity loss is reduced.

Description

Technical Field

[0001] The present application seeks to protect a storage monitoring technology, and in particular, relates to an intelligent storage monitoring method. The present application also relates to an intelligent storage monitoring system. Background Art

[0002] Warehousing and logistics are the basic pillars of economic operation. With the rise of the Internet and the development of e-commerce, the importance of warehousing and logistics has become increasingly apparent.

[0003] The convenience of e-commerce provides consumers with more choices and better services. However, some special commodities are limited by their own characteristics in e-commerce, and their costs are high, or they cannot enter the field of e-commerce, especially commodities that require special temperature and humidity, which suffer huge losses in warehousing and logistics.

[0004] Therefore, cold chain logistics has become a hot industry. However, due to the limitations of multiple unpredictable factors, effective testing and monitoring cannot be carried out in a warehouse environment, or it requires high costs to implement. In this case, although the loss of goods is reduced, the cost reduction is not obvious. Summary of the invention

[0005] In order to solve one or more problems raised in the above background technology, the present application proposes a warehouse intelligent monitoring method. The present application also relates to a warehouse intelligent monitoring system.

[0006] The present application provides a warehouse intelligent monitoring method, comprising:

[0007] Acquire warehouse commodity data, and retrieve preset warehouse operation parameters according to the data category of the commodity data;

[0008] Generate a warehouse monitoring model according to the warehouse operation parameters, retrieve the monitoring equipment operation authority based on the monitoring model, and execute the monitoring equipment control program;

[0009] Acquire monitoring data of the monitoring device, input the monitoring data into a preset monitoring analysis model and output monitoring results;

[0010] A storage control instruction is generated according to the monitoring result to control the modification of the storage environment parameters.

[0011] Optionally, the data categories include: environmental data categories and data level categories.

[0012] Optionally, the inspection model includes monitoring items and monitoring data thresholds.

[0013] Optionally, the inspection and analysis model includes: a pre-trained convolutional neural network model for data analysis.

[0014] Optionally, it also includes issuing an alarm message according to preset alarm rules.

[0015] The present application also provides a warehouse intelligent monitoring system, including:

[0016] A setting module is used to obtain warehouse commodity data and retrieve preset warehouse operation parameters according to the data category of the commodity data;

[0017] An analysis module, used to generate a storage monitoring model according to the storage operation parameters, retrieve the monitoring equipment operation authority based on the monitoring model, and execute the monitoring equipment control program;

[0018] A calculation module, used for acquiring monitoring data of the monitoring device, inputting the monitoring data into a preset monitoring analysis model and outputting monitoring results;

[0019] The execution module is used to generate a storage control instruction according to the monitoring result to control the modification of the storage environment parameters.

[0020] Optionally, the data categories include: environmental data categories and data level categories.

[0021] Optionally, the inspection model includes monitoring items and monitoring data thresholds.

[0022] Optionally, the inspection and analysis model includes: a pre-trained convolutional neural network model for data analysis.

[0023] Optionally, it also includes issuing an alarm message according to preset alarm rules.

[0024] The advantages of this application compared to the prior art are:

[0025] The present application provides a method for intelligent warehouse monitoring, including: obtaining warehouse commodity data, retrieving preset warehouse operation parameters according to the data category of the commodity data; generating a warehouse monitoring model according to the warehouse operation parameters, retrieving the monitoring equipment operation authority based on the monitoring model, and executing the monitoring equipment control program; obtaining the monitoring data of the monitoring equipment, inputting the monitoring data into a preset monitoring analysis model and outputting the monitoring results; generating a warehouse control instruction according to the monitoring results, and controlling the correction of the warehouse environment parameters. The present application technology can automatically perform warehouse monitoring and adjustment by inputting commodity data when the commodity enters the warehouse, ensuring that the cost is also reduced when the commodity loss is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the intelligent warehouse monitoring process in this application.

[0027] Figure 2 It is a flow chart of the execution of the monitoring device control program in this application.

[0028] Figure 3 It is a schematic diagram of the intelligent warehouse monitoring system in this application. DETAILED DESCRIPTION

[0029] The following contents are all examples of specific implementation processes provided for describing in detail the technical solutions to be protected by this application. However, this application may also be implemented in other ways different from the descriptions herein. Persons skilled in the art may, under the guidance of the concepts of this application, adopt different technical means to implement this application. Therefore, this application is not limited to the specific embodiments below.

[0030] The present application provides a method for intelligent warehouse monitoring, including: obtaining warehouse commodity data, retrieving preset warehouse operation parameters according to the data category of the commodity data; generating a warehouse monitoring model according to the warehouse operation parameters, retrieving the monitoring equipment operation authority based on the monitoring model, and executing the monitoring equipment control program; obtaining the monitoring data of the monitoring equipment, inputting the monitoring data into a preset monitoring analysis model and outputting the monitoring results; generating a warehouse control instruction according to the monitoring results, and controlling the correction of the warehouse environment parameters. The present application technology can automatically perform warehouse monitoring and adjustment by inputting commodity data when the commodity enters the warehouse, ensuring that the cost is also reduced when the commodity loss is reduced.

[0031] Figure 1 It is a schematic diagram of the intelligent warehouse monitoring process in this application.

[0032] Please refer to Figure 1 As shown, S101 obtains warehouse commodity data, and retrieves preset warehouse operation parameters according to the data category of the commodity data.

[0033] The commodity data shown includes storage requirement parameters of the commodity, which include temperature, humidity, light and other requirements. For example, the storage requirement parameters of a commodity are temperature ≤ 0°C, humidity less than 4%, and light blocking.

[0034] Obtain commodity parameters, read the storage requirement parameters from the commodity parameters, and classify the storage requirement parameters to obtain data categories.

[0035] In the present application, the categories include environmental data categories such as temperature category, humidity category and illumination category, and data level category of each environmental data category. Specifically, the differentiation of the data level categories includes combining a temperature interval in the temperature category with a humidity interval in the humidity category and a illumination interval in an illumination category to generate a data category, and each of the data categories has a determined temperature interval, humidity interval and illumination interval.

[0036] Then, according to the storage requirement data in the commodity data, the data category is determined according to the storage requirement data, and the storage operation parameters are determined according to the data category.

[0037] The storage operation parameters are determined according to the storage requirement data. The storage requirement data determines the storage environment of the goods. The environmental parameters that need to be adjusted for the storage, namely, the storage operation parameters, are determined according to the storage environment.

[0038] Specifically, the environmental parameters can be mapped and associated with the warehouse operation parameters, so as to determine the warehouse operation parameters based on the environmental parameters. Another method is to detect the parameters of the storage environment, such as temperature, humidity and light intensity, so as to judge the difference between the current storage environment and the environmental parameters of commodity storage, so as to determine the storage environment parameters.

[0039] Please refer to Figure 1 As shown, S102 generates a warehouse monitoring model according to the warehouse operation parameters, retrieves the monitoring equipment operation authority based on the monitoring model, and executes the monitoring equipment control program.

[0040] The inspection model includes: monitoring items and monitoring data thresholds. The inspection items refer to items of each parameter in the storage operation parameters, and the data monitoring threshold is determined according to the storage requirement data.

[0041] In this application, the generation of the warehouse monitoring model first requires determining the monitoring items and calling the corresponding monitoring equipment based on the monitoring items. The monitoring equipment can be an integrated monitoring equipment or a split monitoring equipment. The integrated inspection equipment refers to equipment that inputs different sensors into the same processing control device, and the split monitoring equipment refers to the sum of different monitoring equipment set according to different monitoring types.

[0042] Preferably, the monitoring model is pre-configured and stored, and there may be multiple monitoring models, each of which determines the monitoring items and monitoring data thresholds.

[0043] A monitoring model is generated based on the warehouse operation parameters, and then the operation authority of the corresponding monitoring equipment is retrieved according to the monitoring items in the monitoring model, and the monitoring equipment is started to execute the monitoring equipment control program.

[0044] In the present application, the monitoring equipment control program may perform the following control steps.

[0045] like Figure 2 As shown, S201 obtains monitoring data;

[0046] S202 compares the monitoring data with the monitoring data threshold;

[0047] In the present application, the monitoring data are all provided with a unique number, which may be a number, a letter, or other custom symbols, and the number corresponds to each of the monitoring items.

[0048] The numbering in this application is selected based on the comparison result, and the selection can be performed using the following expression:

[0049]

[0050] Wherein, the final value of X is S or 0, S is the number, d is the number coefficient, and A i -B i is the comparison result, the A i is the monitoring data, the B i is the monitoring data threshold. When the monitoring data does not exceed the threshold, d takes the value of 0, otherwise it takes the value of 1.

[0051] Based on the selected numbers, a number table is generated, and then the G=A i -B i Corresponding one to one with the above-mentioned numbers.

[0052] S203 performs control selection according to the comparison result.

[0053] Preferably, there are two control options, one of which includes, when the monitoring data is within the range specified by the monitoring data threshold, reducing the frequency of obtaining monitoring data, and the other is increasing the frequency of obtaining monitoring data.

[0054] The parameter to be modified is determined according to the number, and then the size of the parameter modification is determined according to G, and the modification is performed.

[0055] Please refer to Figure 1 As shown, S103 obtains the monitoring data of the monitoring device, inputs the monitoring data into a preset monitoring analysis model and outputs the monitoring results.

[0056] The monitoring and analysis module can analyze the internal relationship of the data based on the monitoring data, so as to obtain accurate monitoring results.

[0057] Specifically, the monitoring and analysis model includes conditional judgment execution and result analysis execution.

[0058] The conditional judgment execution refers to detecting and analyzing the influencing factors of each monitoring item, and obtaining the analysis results and estimating the monitoring results.

[0059] Taking temperature as an example, the factors affecting the temperature in the warehouse include: external ambient temperature, refrigeration efficiency of the refrigerator, insulation conditions and sealing conditions.

[0060] Furthermore, according to the preset inspection process, the external ambient temperature, refrigeration efficiency of the refrigerator, insulation conditions and sealing conditions are checked in turn. If there are no problems with the above inspection items, the monitoring results are actual monitoring results. Otherwise, maintenance is performed according to the inspection items with problems in the inspection results, and re-inspection is performed.

[0061] The result analysis execution includes determining further execution according to the inspection items, including issuing a prompt or alarm after detecting problems with one or more inspection items, and setting the monitoring result as the final monitoring result after detecting that no problems occur in all inspection items.

[0062] A preferred real-time example includes parameterizing the inspection results of the inspection items, and then inputting the inspection results and monitoring results into a pre-trained convolutional neural network model to obtain a final monitoring result. The final monitoring result is the monitoring result after removing the influence of problems in other inspection items.

[0063] Please refer to Figure 1 As shown, S104 generates a storage control instruction according to the monitoring result to control the modification of the storage environment parameters.

[0064] After the monitoring results are obtained, a storage control instruction is generated based on the monitoring results, and the storage control instruction includes controlling refrigeration, controlling humidity, controlling light, etc., and further does not include controlling air pressure, etc. The control of refrigeration, controlling humidity, and controlling light are provided by corresponding working equipment. After the storage control instruction is input into the working equipment, the power of the working equipment is controlled to achieve the control.

[0065] The present application also provides an intelligent warehouse monitoring system, including: a setting module 301, an analysis module 302, a calculation module 303 and an execution module 304.

[0066] Figure 3 It is a schematic diagram of the intelligent warehouse monitoring system in this application.

[0067] Please refer to Figure 3 As shown, the setting module 301 is used to obtain warehouse commodity data and retrieve preset warehouse operation parameters according to the data category of the commodity data.

[0068] The commodity data shown includes storage requirement parameters of the commodity, which include temperature, humidity, light and other requirements. For example, the storage requirement parameters of a commodity are temperature ≤ 0°C, humidity less than 4%, and light blocking.

[0069] Obtain commodity parameters, read the storage requirement parameters from the commodity parameters, and classify the storage requirement parameters to obtain data categories.

[0070] In the present application, the categories include environmental data categories such as temperature category, humidity category and illumination category, and data level category of each environmental data category. Specifically, the differentiation of the data level categories includes combining a temperature interval in the temperature category with a humidity interval in the humidity category and a illumination interval in an illumination category to generate a data category, and each of the data categories has a determined temperature interval, humidity interval and illumination interval.

[0071] Then, according to the storage requirement data in the commodity data, the data category is determined according to the storage requirement data, and the storage operation parameters are determined according to the data category.

[0072] The storage operation parameters are determined according to the storage requirement data. The storage requirement data determines the storage environment of the goods. The environmental parameters that need to be adjusted for the storage, namely, the storage operation parameters, are determined according to the storage environment.

[0073] Specifically, the environmental parameters can be mapped and associated with the warehouse operation parameters, so as to determine the warehouse operation parameters based on the environmental parameters. Another method is to detect the parameters of the storage environment, such as temperature, humidity and light intensity, so as to judge the difference between the current storage environment and the environmental parameters of commodity storage, so as to determine the storage environment parameters.

[0074] Please refer to Figure 3 As shown, the analysis module 302 is used to generate a warehouse monitoring model according to the warehouse operation parameters, retrieve the monitoring equipment operation authority based on the monitoring model, and execute the monitoring equipment control program.

[0075] The inspection model includes: monitoring items and monitoring data thresholds. The inspection items refer to items of each parameter in the storage operation parameters, and the data monitoring threshold is determined according to the storage requirement data.

[0076] In this application, the generation of the warehouse monitoring model first requires determining the monitoring items and calling the corresponding monitoring equipment based on the monitoring items. The monitoring equipment can be an integrated monitoring equipment or a split monitoring equipment. The integrated inspection equipment refers to equipment that inputs different sensors into the same processing control device, and the split monitoring equipment refers to the sum of different monitoring equipment set according to different monitoring types.

[0077] Preferably, the monitoring model is pre-configured and stored, and there may be multiple monitoring models, each of which determines the monitoring items and monitoring data thresholds.

[0078] A monitoring model is generated based on the warehouse operation parameters, and then the operation authority of the corresponding monitoring equipment is retrieved according to the monitoring items in the monitoring model, and the monitoring equipment is started to execute the monitoring equipment control program.

[0079] In the present application, the monitoring equipment control program may perform the following control steps.

[0080] like Figure 2 As shown, S201 obtains monitoring data;

[0081] S202 compares the monitoring data with the monitoring data threshold;

[0082] In the present application, the monitoring data are all provided with a unique number, which may be a number, a letter, or other custom symbols, and the number corresponds to each of the monitoring items.

[0083] The numbering in this application is selected based on the comparison result, and the selection can be performed using the following expression:

[0084]

[0085] Wherein, the final value of X is S or 0, S is the number, d is the number coefficient, and A i -B i is the comparison result, the A i is the monitoring data, the B i is the monitoring data threshold. When the monitoring data does not exceed the threshold, d takes the value of 0, otherwise it takes the value of 1.

[0086] Based on the selected numbers, a number table is generated, and then the G=A i -B i Corresponding one to one with the above-mentioned numbers.

[0087] S203 performs control selection according to the comparison result.

[0088] Preferably, there are two control options, one of which includes, when the monitoring data is within the range specified by the monitoring data threshold, reducing the frequency of obtaining monitoring data, and the other is increasing the frequency of obtaining monitoring data.

[0089] The parameter to be modified is determined according to the number, and then the size of the parameter modification is determined according to G, and the modification is performed.

[0090] Please refer to Figure 3 As shown, the calculation module 303 is used to obtain the monitoring data of the monitoring device, input the monitoring data into a preset monitoring analysis model and output the monitoring results.

[0091] The monitoring and analysis module can analyze the internal relationship of the data based on the monitoring data, so as to obtain accurate monitoring results.

[0092] Specifically, the monitoring and analysis model includes conditional judgment execution and result analysis execution.

[0093] The conditional judgment execution refers to detecting and analyzing the influencing factors of each monitoring item, and obtaining the analysis results and estimating the monitoring results.

[0094] Taking temperature as an example, the factors affecting the temperature in the warehouse include: external ambient temperature, refrigeration efficiency of the refrigerator, insulation conditions and sealing conditions.

[0095] Furthermore, according to the preset inspection process, the external ambient temperature, refrigeration efficiency of the refrigerator, insulation conditions and sealing conditions are checked in turn. If there are no problems with the above inspection items, the monitoring results are actual monitoring results. Otherwise, maintenance is performed according to the inspection items with problems in the inspection results, and re-inspection is performed.

[0096] The result analysis execution includes determining further execution according to the inspection items, including issuing a prompt or alarm after detecting problems with one or more inspection items, and setting the monitoring result as the final monitoring result after detecting that no problems occur in all inspection items.

[0097] A preferred real-time example includes parameterizing the inspection results of the inspection items, and then inputting the inspection results and monitoring results into a pre-trained convolutional neural network model to obtain a final monitoring result. The final monitoring result is the monitoring result after removing the influence of problems in other inspection items.

[0098] Please refer to Figure 3 As shown, the execution module 304 is used to generate a storage control instruction according to the monitoring result to control the modification of the storage environment parameters.

[0099] After the monitoring results are obtained, a storage control instruction is generated based on the monitoring results, and the storage control instruction includes controlling refrigeration, controlling humidity, controlling light, etc., and further does not include controlling air pressure, etc. The control of refrigeration, controlling humidity, and controlling light are provided by corresponding working equipment. After the storage control instruction is input into the working equipment, the power of the working equipment is controlled to achieve the control.

Claims

1. A warehouse intelligent monitoring method, characterized in that: include: Acquire warehouse commodity data, and retrieve preset warehouse operation parameters according to the data category of the commodity data; Obtain commodity parameters, read storage requirement parameters from commodity parameters, and classify the storage requirement parameters to obtain data categories; storage operation parameters are determined according to storage requirement data, which determine the storage environment of commodities, and determine environmental parameters that need to be adjusted for storage according to the storage environment, namely, storage operation parameters; Generate a warehouse monitoring model according to the warehouse operation parameters, retrieve the monitoring equipment operation authority based on the monitoring model, and execute the monitoring equipment control program; The storage monitoring model includes: monitoring items and monitoring data thresholds; monitoring items refer to items of each parameter in the storage operation parameters, and the monitoring data thresholds are determined according to the storage requirement data; The monitoring equipment control program performs the following control steps: S201 obtains monitoring data; S202 compares the monitoring data with the monitoring data threshold; the monitoring data is provided with a unique number, which corresponds to each monitoring item; the number is selected according to the comparison result, as shown in the following expression: Among them, the final value of X is S or 0, S is the number, d is the number coefficient, is the comparison result, It is monitoring data. is the threshold of the monitoring data. When the monitoring data does not exceed the threshold, d takes the value of 0, otherwise it takes the value of 1. Based on the numbers selected above, a number table is generated, and then One-to-one correspondence with the number; S203 performs control selection according to the comparison result; the control selection includes: when the monitoring data is within the range specified by the monitoring data threshold, the monitoring data acquisition frequency is reduced, otherwise the monitoring data acquisition frequency is increased; the parameter to be modified is determined according to the number, and then the size of the parameter modification is determined according to G, and the modification is performed; Acquire monitoring data of the monitoring device, input the monitoring data into a preset monitoring analysis model and output monitoring results; The monitoring and analysis model includes conditional judgment execution and result analysis execution; conditional judgment execution refers to checking and analyzing the influencing factors of each monitoring item, and obtaining analysis results and estimated monitoring results; result analysis execution includes determining further execution according to the inspection items, including issuing prompts or alarms after checking that one or more inspection items have problems, and setting the monitoring results as the final monitoring results after checking that all inspection items have no problems; parameterizing the inspection results of the inspection items, and then inputting the inspection results and monitoring results into the pre-trained convolutional neural network model to obtain the final monitoring results; A storage control instruction is generated according to the monitoring result to control the modification of the storage operation parameters.

2. The intelligent warehouse monitoring method according to claim 1 is characterized in that: The data categories include: environmental data categories and data level categories.

3. The intelligent warehouse monitoring method according to claim 1 is characterized in that: It also includes sending out alarm information according to preset alarm rules.

4. An intelligent warehouse monitoring system, characterized in that: include: A setting module is used to obtain warehouse commodity data and retrieve preset warehouse operation parameters according to the data category of the commodity data; Obtain commodity parameters, read storage requirement parameters from commodity parameters, and classify the storage requirement parameters to obtain data categories; storage operation parameters are determined according to storage requirement data, which determine the storage environment of commodities, and determine environmental parameters that need to be adjusted for storage according to the storage environment, namely, storage operation parameters; An analysis module, used to generate a storage monitoring model according to the storage operation parameters, retrieve the monitoring equipment operation authority based on the monitoring model, and execute the monitoring equipment control program; The storage monitoring model includes: monitoring items and monitoring data thresholds; monitoring items refer to items of each parameter in the storage operation parameters, and the monitoring data thresholds are determined according to the storage requirement data; The monitoring equipment control program performs the following control steps: S201 obtains monitoring data; S202 compares the monitoring data with the monitoring data threshold; the monitoring data is provided with a unique number, which corresponds to each monitoring item; the number is selected according to the comparison result, as shown in the following expression: Among them, the final value of X is S or 0, S is the number, d is the number coefficient, is the comparison result, It is monitoring data. is the threshold of the monitoring data. When the monitoring data does not exceed the threshold, d takes the value of 0, otherwise it takes the value of 1. Based on the numbers selected above, a number table is generated, and then One-to-one correspondence with the number; S203 performs control selection according to the comparison result; the control selection includes: when the monitoring data is within the range specified by the monitoring data threshold, the monitoring data acquisition frequency is reduced, otherwise the monitoring data acquisition frequency is increased; the parameter to be modified is determined according to the number, and then the size of the parameter modification is determined according to G, and the modification is performed; A calculation module, used for acquiring monitoring data of the monitoring device, inputting the monitoring data into a preset monitoring analysis model and outputting monitoring results; The monitoring and analysis model includes conditional judgment execution and result analysis execution; conditional judgment execution refers to checking and analyzing the influencing factors of each monitoring item, and obtaining analysis results and estimated monitoring results; result analysis execution includes determining further execution according to the inspection items, including issuing prompts or alarms after checking that one or more inspection items have problems, and setting the monitoring results as the final monitoring results after checking that all inspection items have no problems; parameterizing the inspection results of the inspection items, and then inputting the inspection results and monitoring results into the pre-trained convolutional neural network model to obtain the final monitoring results; The execution module is used to generate a storage control instruction according to the monitoring result to control the modification of the storage operation parameters.

5. The intelligent warehouse monitoring system according to claim 4 is characterized in that: The data categories include: environmental data categories and data level categories.

6. The intelligent warehouse monitoring system according to claim 4 is characterized in that: It also includes sending out alarm information according to preset alarm rules.

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