A safety monitoring system for warehouse logistics production

Abstract

The application relates to the technical field of safety monitoring of warehouse logistics production, in particular to a safety monitoring system for warehouse logistics production, which comprises a data acquisition module, a monitoring focus analysis module, an equipment adjustment analysis module, an abnormal monitoring analysis module, a warning and execution module and a data storage module. The warehouse enrichment information is obtained by analyzing the warehouse flow data, the equipment state data is obtained by analyzing the equipment information parameters, the equipment adjustment instruction information is obtained by analyzing the equipment state data and the warehouse enrichment information, the equipment adjustment is monitored according to the equipment adjustment instruction information, the utilization of the monitoring resources is increased, and the convenience is improved; the warehouse monitoring information is obtained by analyzing the warehouse environment data and the cargo state data, the safety information of the warehouse can be obtained through the warehouse monitoring information, timely warning can be facilitated, and the safety of the warehouse is ensured.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology for warehousing and logistics production, and in particular to a safety monitoring system for warehousing and logistics production. Background Technology

[0002] In warehousing production safety, relying on advanced technologies such as big data, cloud computing, and artificial intelligence, intelligent monitoring and management of the entire warehousing and logistics process has been achieved. These technologies integrate various sensors, cameras, and other monitoring and surveillance equipment to collect and transmit data on the warehousing environment and cargo status in real time, providing precise information support for warehouse managers. At the same time, combined with big data analysis and artificial intelligence algorithms, the system can autonomously detect anomalies, issue risk warnings, and make intelligent decisions, thereby significantly improving the safety and efficiency of warehousing and logistics operations.

[0003] In warehousing and logistics production, the amount of goods stored in the warehouse is constantly changing, so the areas where goods are concentrated in the warehouse are constantly changing. Existing security monitoring systems collect data through monitoring equipment, but the angle of the monitoring equipment is adjusted to cover the entire warehouse and cannot automatically adjust the monitoring focus according to the concentration points and value of the stored goods, making it inconvenient to use. Summary of the Invention

[0004] This invention provides a safety monitoring system for warehousing and logistics production, which solves the aforementioned technical problems existing in the prior art.

[0005] The first aspect of the present invention provides a safety monitoring system for warehousing and logistics production, including a data acquisition module, a monitoring focus analysis module, an equipment adjustment analysis module, an anomaly monitoring analysis module, an early warning and execution module, and a data storage module.

[0006] The data acquisition module is used to collect equipment information parameters from the monitoring equipment and obtain warehousing information parameters from the monitoring equipment, and send the equipment information parameters and warehousing information parameters to the data storage module. The equipment information parameters include equipment location information and equipment characteristic information; the warehousing information parameters include warehousing flow data, warehousing environment data and cargo status data.

[0007] The monitoring and analysis module is used to acquire warehouse information parameters, identify the warehouse information parameters to obtain warehouse flow data, analyze the warehouse flow data to obtain warehouse enrichment information, and send the warehouse enrichment information to the equipment adjustment and analysis module.

[0008] As a further improvement to the present invention, the warehouse flow data is analyzed, and the specific analysis method is as follows:

[0009] Warehouse flow data includes storage information, cargo location information, and cargo value data. Storage information is identified to obtain the original cargo storage volume and the corresponding cargo change volume per unit time for each monitoring area corresponding to each warehouse. When the cargo change volume per unit time is positive, the original cargo storage volume and the cargo change volume are added together to calculate the current cargo storage volume; conversely, when the cargo change volume is negative, the original cargo storage volume is subtracted from the cargo change volume. The storage volume of each monitoring area corresponding to each warehouse is divided into multiple storage volume intervals, each interval corresponding to a storage volume impact value. The current cargo storage volume of each monitoring area is matched with multiple storage volume intervals to obtain the corresponding storage volume impact value. The storage volume impact values ​​of each monitoring area are summed to obtain the total storage volume impact value for each warehouse.

[0010] Using the location information of each cargo in each monitoring area as a reference point, the distance value between each cargo and the cargo location information of adjacent cargo in each direction is compared with the reference point of each cargo. The average value of the distance values ​​in each direction is calculated and recorded as the distance reference value. The distance reference value is compared with a set threshold. When the distance reference value is greater than the set threshold, the corresponding distance reference value is marked as a high discrete reference value. The total number of cargoes and the number of high discrete reference values ​​are counted. The number of high discrete reference values ​​is compared with the total number of cargoes to obtain the discrete cargo ratio.

[0011] The system identifies the value data of each cargo to obtain its characteristic information and value. It also acquires image information and stored information for each cargo. The image information of each cargo is then identified to obtain its image feature information. This image feature information is compared with the stored feature information of each cargo. When the image feature information and the stored feature information are different, an anomaly information for the corresponding cargo is generated. Conversely, when the image feature information and the stored feature information are the same, the image feature information corresponding to the cargo is matched with the cargo feature information of each cargo to obtain the value of each cargo. Finally, the cargo values ​​of each cargo in each monitoring area are summed to obtain the total value of the cargo in each monitoring area.

[0012] An ellipse is constructed using the total impact value of storage and the total value of goods as the major and minor axes of an ellipse. A circle is then constructed using the discrete goods percentage as the radius of a circle. The centers of the ellipse and the circle are aligned, and the area of ​​the ellipse minus the circle is calculated. This area is then marked as the goods enrichment value. The goods enrichment value is divided into multiple enrichment value intervals, each corresponding to a degree of enrichment information. The goods enrichment value of each monitoring area is matched with multiple enrichment value intervals to obtain the corresponding degree of enrichment information. Finally, the degree of enrichment information of each warehouse corresponding to each monitoring area is integrated to obtain the corresponding warehousing enrichment information.

[0013] The equipment adjustment and analysis module is used to acquire equipment information parameters and analyze them to obtain equipment status data. It receives and analyzes the equipment status data and the enriched information to obtain equipment adjustment command information, and then sends the equipment adjustment command information to the early warning execution module.

[0014] As a further improvement of the present invention, the equipment information parameters are analyzed, and the specific analysis method is as follows:

[0015] Equipment location and characteristic information are obtained by identifying equipment information parameters. The characteristic information includes the viewing angle range, sampling frequency, and sampling accuracy of each monitoring device. Based on the location and viewing angle range of each device, the corresponding monitoring area that each device can cover is determined. The optimal viewing angle range for each device is determined based on its sampling accuracy. Viewing angle ranges exceeding the optimal range are marked as attenuated viewing angle ranges. A set sampling accuracy threshold is obtained. Based on the viewing angle distance corresponding to the attenuated viewing angle range, the corresponding sampling accuracy for different viewing angle distances is obtained. The sampling accuracy for different viewing angle distances is compared with the sampling accuracy threshold. Viewing angle distances below the threshold are marked as the effective sampling viewing angle range of the monitoring device. The optimal and effective sampling viewing angle ranges of each monitoring device are combined to form the monitoring range information of each device. Multiple frequency intervals are set for the sampling frequency of each monitoring device, each frequency interval corresponding to a sampling output level. The sampling frequency of each monitoring device is matched with multiple frequency intervals to obtain the corresponding sampling output level. The monitoring range information and sampling output level information of each monitoring device are used as the corresponding device status data.

[0016] As a further improvement to the present invention, the equipment status data and warehouse enrichment information are analyzed, and the specific analysis method is as follows:

[0017] Based on the enrichment information of the warehouse, the enrichment level information of each monitoring area is obtained. The enrichment level information of each monitoring area is identified to obtain the cargo status of each monitoring area. When the enrichment level information is zero, the monitoring demand information is generated as "no demand". When the monitoring demand information is "no demand", the monitoring range information and sampling output level information of the corresponding monitoring equipment are obtained for the corresponding monitoring area. The monitoring range information of each monitoring equipment in the monitoring area is identified. When the monitoring range of a monitoring equipment is only one monitoring area, the equipment adjustment command for that monitoring equipment is to adjust the sampling output level to the lowest level. When the monitoring range of a monitoring equipment is multiple monitoring areas, the equipment adjustment command for that monitoring equipment is to adjust the viewing angle range and move the optimal viewing angle range to the other monitoring areas.

[0018] When the enrichment level information is not zero, the number of monitoring areas corresponding to the monitoring range information of the corresponding monitoring device is obtained. When the monitoring range corresponding to the monitoring device is only one monitoring area, the sampling output level of the monitoring device is matched with the enrichment level information to obtain the sampling output level corresponding to the enrichment level information of the monitoring device. Then the device adjustment command for the monitoring device is to adjust the sampling output level to the corresponding level. When the monitoring range corresponding to the monitoring device is multiple monitoring areas, the enrichment level of each monitoring area is compared to obtain the enrichment level ratio of each monitoring area. Then the adjustment command for the corresponding monitoring device is to allocate the best viewing angle to the corresponding monitoring area according to the enrichment level ratio.

[0019] The anomaly monitoring and analysis module is used to acquire warehousing information parameters and identify them to obtain warehousing environment data and cargo status data. It analyzes the warehousing environment data and cargo status data to obtain warehousing monitoring information. When the warehousing monitoring information is abnormal, it generates corresponding anomaly monitoring information and sends the anomaly monitoring information to the early warning and execution module.

[0020] As a further improvement to the present invention, the warehousing environment data and cargo status data are analyzed, and the specific analysis method is as follows:

[0021] The storage environment data includes temperature and humidity data. When the temperature data exceeds the set threshold, the portion of the temperature data exceeding the threshold is marked as a temperature overflow value. Similarly, the humidity overflow value is obtained based on the humidity data. The temperature overflow value and the humidity overflow value are calculated and summed to obtain the total value of temperature and humidity impact.

[0022] The cargo status data includes cargo placement parameters and storage time. The placement parameters of each cargo shelf in the warehouse are identified to determine the deviation direction of each cargo. The number of cargo in each deviation direction is counted to obtain the total offset in each direction. The total offset in each direction is compared with the total number of cargo to obtain the cargo offset ratio in each direction. The cargo offset ratio in each direction is compared with a set threshold. When the cargo offset ratio is greater than the set threshold, the corresponding cargo offset ratio is recorded as an abnormal offset ratio. The entry time of each cargo is marked and recorded to obtain the storage time of each cargo. The corresponding shelf life of each cargo is obtained, and the storage time of each cargo is compared with the shelf life to obtain the storage time percentage.

[0023] The total impact of temperature and humidity, the ratio of abnormal deviations, and the proportion of storage time are normalized and their values ​​are taken according to the formula. The warehouse monitoring impact value CK is obtained; where ST, PY and TM represent the total impact value of temperature and humidity, the abnormal offset ratio and the storage time ratio, respectively; p1, p2 and p3 are preset weighting factors with values ​​of 2.43, 3.81 and 1.76, respectively; when the warehouse monitoring impact value is greater than the set monitoring impact threshold, the corresponding warehouse monitoring information is generated as abnormal.

[0024] The early warning and execution module is used to receive equipment adjustment instructions and abnormal monitoring information, and to perform corresponding early warnings and operations based on these instructions and information.

[0025] The data storage module is used to store equipment information parameters and warehouse information parameters, as well as warehouse enrichment information, equipment adjustment instruction information, and warehouse monitoring information.

[0026] The technical solution provided by this invention has the following advantages compared with the prior art:

[0027] 1. This invention obtains warehouse enrichment information by analyzing warehouse flow data, obtains equipment status data by analyzing equipment information parameters, and then obtains equipment adjustment command information by analyzing equipment status data and warehouse enrichment information. Finally, it adjusts the monitoring equipment according to the equipment adjustment command information, thereby increasing the utilization of monitoring resources and increasing the convenience of use.

[0028] 2. This invention obtains warehousing environment data and cargo status data by identifying warehousing information parameters, and obtains warehousing monitoring information by analyzing the warehousing environment data and cargo status data. The warehousing monitoring information can be used to obtain warehousing safety information, so as to make timely early warnings and ensure warehousing safety. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the principle of the present invention. Detailed Implementation

[0030] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings and specific embodiments.

[0031] For ease of understanding, the specific process of the embodiments of the present invention will be described below, such as... Figure 1 As shown in the figure, one embodiment of a safety monitoring system for warehousing and logistics production in this invention includes: a data acquisition module, a monitoring focus analysis module, an equipment adjustment analysis module, an anomaly monitoring analysis module, an early warning and execution module, and a data storage module.

[0032] The data acquisition module collects equipment information parameters from the monitoring equipment and obtains warehousing information parameters from the monitoring equipment. It then sends the equipment information parameters and warehousing information parameters to the data storage module. The equipment information parameters include equipment location information and equipment characteristic information. The warehousing information parameters include warehousing flow data, warehousing environment data, and cargo status data.

[0033] The monitoring and analysis module acquires warehouse information parameters and identifies them to obtain warehouse flow data. It then analyzes the warehouse flow data to obtain warehouse enrichment information and sends the enrichment information to the equipment adjustment and analysis module.

[0034] The specific analysis method for warehouse transaction data is as follows:

[0035] Warehouse flow data includes storage information, cargo location information, and cargo value data. Storage information is identified to obtain the original cargo storage volume and the corresponding cargo change volume per unit time for each monitoring area corresponding to each warehouse. When the cargo change volume per unit time is positive, the original cargo storage volume and the cargo change volume are added together to calculate the current cargo storage volume; conversely, when the cargo change volume is negative, the original cargo storage volume is subtracted from the cargo change volume. The storage volume of each monitoring area corresponding to each warehouse is divided into multiple storage volume intervals, each interval corresponding to a storage volume impact value. The current cargo storage volume of each monitoring area is matched with multiple storage volume intervals to obtain the corresponding storage volume impact value. The storage volume impact values ​​of each monitoring area are summed to obtain the total storage volume impact value for each warehouse.

[0036] Using the location information of each cargo in each monitoring area as a reference point, the distance value between each cargo and the cargo location information of adjacent cargo in each direction is compared with the reference point of each cargo. The average value of the distance values ​​in each direction is calculated and recorded as the distance reference value. The distance reference value is compared with a set threshold. When the distance reference value is greater than the set threshold, the corresponding distance reference value is marked as a high discrete reference value. The total number of cargoes and the number of high discrete reference values ​​are counted. The number of high discrete reference values ​​is compared with the total number of cargoes to obtain the discrete cargo ratio.

[0037] The system identifies the value data of each cargo to obtain its characteristic information and value. It also acquires image information and stored information for each cargo. The image information of each cargo is then identified to obtain its image feature information. This image feature information is compared with the stored feature information of each cargo. When the image feature information and the stored feature information are different, an anomaly information for the corresponding cargo is generated. Conversely, when the image feature information and the stored feature information are the same, the image feature information corresponding to the cargo is matched with the cargo feature information of each cargo to obtain the value of each cargo. Finally, the cargo values ​​of each cargo in each monitoring area are summed to obtain the total value of the cargo in each monitoring area.

[0038] An ellipse is constructed using the total impact value of storage and the total value of goods as the major and minor axes of an ellipse. A circle is then constructed using the discrete goods percentage as the radius of a circle. The centers of the ellipse and the circle are aligned, and the area of ​​the ellipse minus the circle is calculated. This area is then marked as the goods enrichment value. The goods enrichment value is divided into multiple enrichment value intervals, each corresponding to a degree of enrichment information. The goods enrichment value of each monitoring area is matched with multiple enrichment value intervals to obtain the corresponding degree of enrichment information. Finally, the degree of enrichment information of each warehouse corresponding to each monitoring area is integrated to obtain the corresponding warehousing enrichment information.

[0039] The equipment adjustment and analysis module acquires equipment information parameters and analyzes them to obtain equipment status data. It receives and analyzes the equipment status data and the enriched information to obtain equipment adjustment command information, and then sends the equipment adjustment command information to the early warning execution module.

[0040] The equipment information parameters are analyzed, and the specific analysis method is as follows:

[0041] Equipment location and characteristic information are obtained by identifying equipment information parameters. The characteristic information includes the viewing angle range, sampling frequency, and sampling accuracy of each monitoring device. Based on the location and viewing angle range of each device, the corresponding monitoring area that each device can cover is determined. The optimal viewing angle range for each device is determined based on its sampling accuracy. Viewing angle ranges exceeding the optimal range are marked as attenuated viewing angle ranges. A set sampling accuracy threshold is obtained. Based on the viewing angle distance corresponding to the attenuated viewing angle range, the corresponding sampling accuracy for different viewing angle distances is obtained. The sampling accuracy for different viewing angle distances is compared with the sampling accuracy threshold. Viewing angle distances below the threshold are marked as the effective sampling viewing angle range of the monitoring device. The optimal and effective sampling viewing angle ranges of each monitoring device are combined to form the monitoring range information of each device. Multiple frequency intervals are set for the sampling frequency of each monitoring device, each frequency interval corresponding to a sampling output level. The sampling frequency of each monitoring device is matched with multiple frequency intervals to obtain the corresponding sampling output level. The monitoring range information and sampling output level information of each monitoring device are used as the corresponding device status data.

[0042] The analysis of equipment status data and warehouse enrichment information is conducted using the following specific methods:

[0043] Based on the enrichment information of the warehouse, the enrichment level information of each monitoring area is obtained. The enrichment level information of each monitoring area is identified to obtain the cargo status of each monitoring area. When the enrichment level information is zero, the monitoring demand information is generated as "no demand". When the monitoring demand information is "no demand", the monitoring range information and sampling output level information of the corresponding monitoring equipment are obtained for the corresponding monitoring area. The monitoring range information of each monitoring equipment in the monitoring area is identified. When the monitoring range of a monitoring equipment is only one monitoring area, the equipment adjustment command for that monitoring equipment is to adjust the sampling output level to the lowest level. When the monitoring range of a monitoring equipment is multiple monitoring areas, the equipment adjustment command for that monitoring equipment is to adjust the viewing angle range and move the optimal viewing angle range to the other monitoring areas.

[0044] When the enrichment level information is not zero, the number of monitoring areas corresponding to the monitoring range information of the corresponding monitoring device is obtained. When the monitoring range corresponding to the monitoring device is only one monitoring area, the sampling output level of the monitoring device is matched with the enrichment level information to obtain the sampling output level corresponding to the enrichment level information of the monitoring device. Then the device adjustment command for the monitoring device is to adjust the sampling output level to the corresponding level. When the monitoring range corresponding to the monitoring device is multiple monitoring areas, the enrichment level of each monitoring area is compared to obtain the enrichment level ratio of each monitoring area. Then the adjustment command for the corresponding monitoring device is to allocate the best viewing angle to the corresponding monitoring area according to the enrichment level ratio.

[0045] The anomaly monitoring and analysis module acquires warehousing information parameters and identifies them to obtain warehousing environment data and cargo status data. It then analyzes these data to obtain warehousing monitoring information. When the warehousing monitoring information is abnormal, the module generates corresponding anomaly monitoring information and sends it to the early warning and execution module.

[0046] The analysis of warehousing environment data and cargo status data is conducted using the following methods:

[0047] The storage environment data includes temperature and humidity data. When the temperature data exceeds the set threshold, the portion of the temperature data exceeding the threshold is marked as a temperature overflow value. Similarly, the humidity overflow value is obtained based on the humidity data. The temperature overflow value and the humidity overflow value are calculated and summed to obtain the total value of temperature and humidity impact.

[0048] The cargo status data includes cargo placement parameters and storage time. The placement parameters of each cargo shelf in the warehouse are identified to determine the deviation direction of each cargo. The number of cargo in each deviation direction is counted to obtain the total offset in each direction. The total offset in each direction is compared with the total number of cargo to obtain the cargo offset ratio in each direction. The cargo offset ratio in each direction is compared with a set threshold. When the cargo offset ratio is greater than the set threshold, the corresponding cargo offset ratio is recorded as an abnormal offset ratio. The entry time of each cargo is marked and recorded to obtain the storage time of each cargo. The corresponding shelf life of each cargo is obtained, and the storage time of each cargo is compared with the shelf life to obtain the storage time percentage.

[0049] The total impact of temperature and humidity, the ratio of abnormal deviations, and the proportion of storage time are normalized and their values ​​are taken according to the formula. The warehouse monitoring impact value CK is obtained; where ST, PY, and TM represent the total impact value of temperature and humidity, the abnormal offset ratio, and the storage time percentage, respectively; p1, p2, and p3 are preset weighting factors with customizable values ​​of 2.43, 3.81, and 1.76, respectively; when the warehouse monitoring impact value is greater than the set monitoring impact threshold, the corresponding warehouse monitoring information is generated as abnormal.

[0050] The early warning and execution module receives equipment adjustment instructions and anomaly monitoring information, and performs corresponding early warnings and operations based on these instructions and information.

[0051] The data storage module stores device information parameters and warehouse information parameters, and is used to store warehouse enrichment information, equipment adjustment command information, and warehouse monitoring information.

[0052] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; those skilled in the art can modify or make equivalent substitutions to the technical solutions described in the foregoing embodiments; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A safety monitoring system for warehouse logistics production, comprising a data acquisition module and a data storage module, characterized in that, Also comprising: A monitoring focus analysis module for obtaining warehouse information parameters and identifying the warehouse information parameters to obtain warehouse flow data, analyzing the warehouse flow data to obtain warehouse enrichment information, and sending the warehouse enrichment information to the equipment adjustment analysis module; An equipment adjustment analysis module for obtaining equipment information parameters and analyzing the equipment information parameters to obtain equipment state data, receiving the warehouse enrichment information and analyzing the equipment state data and the warehouse enrichment information to obtain equipment adjustment instruction information, and sending the equipment adjustment instruction information to the early warning execution module; Analyzing the equipment information parameters, the specific analysis method being: Obtaining equipment location information and equipment characteristic information by identifying the equipment information parameters; the equipment characteristic information including the viewing angle range, sampling frequency and sampling accuracy of each monitoring device, obtaining the monitoring area that each monitoring device can cover according to the location information and viewing angle range of each monitoring device, and obtaining the best viewing angle range of each monitoring device according to the sampling accuracy of each monitoring device, marking the viewing angle range that exceeds the best viewing angle range as a decay viewing angle range, obtaining a set sampling accuracy threshold, obtaining the sampling accuracy corresponding to different viewing angle distances based on the viewing angle distance corresponding to the decay viewing angle range, and comparing the sampling accuracy corresponding to different viewing angle distances with the sampling accuracy threshold, marking the viewing angle distance that is lower than the sampling accuracy threshold as the effective sampling viewing angle range of the monitoring device, and comprehensively constituting the monitoring range information of each monitoring device from the best viewing angle range and the effective sampling viewing angle range of the monitoring device; setting multiple frequency intervals for the sampling frequency of each monitoring device, each frequency interval corresponding to a sampling output level, matching the sampling frequency of each monitoring device with the multiple frequency intervals to obtain the corresponding sampling output level; and taking the monitoring range information and the sampling output level information of each monitoring device as the corresponding equipment state data; Analyzing the equipment state data and the warehouse enrichment information, the specific analysis method being: Obtaining the enrichment degree information of each monitoring area in the warehouse according to the warehouse enrichment information, identifying the enrichment degree information of each monitoring area to obtain the cargo state of each monitoring area, generating monitoring demand information as no demand when the enrichment degree information is zero, obtaining the monitoring range information and the sampling output level information of the corresponding monitoring device corresponding to the equipment state data of the corresponding monitoring area when the monitoring demand information is no demand, identifying the monitoring range information of each monitoring device of the monitoring area, and generating the equipment adjustment instruction of the corresponding monitoring device as adjusting the sampling output level to the lowest when the monitoring range corresponding to the monitoring device only has one monitoring area; Generating the equipment adjustment instruction of the corresponding monitoring device as adjusting the best viewing angle range to the remaining monitoring areas when the monitoring range corresponding to the monitoring device corresponds to multiple monitoring areas. When the enrichment degree information is not zero, the number of monitoring areas corresponding to the monitoring range information of the corresponding monitoring device is obtained, when the monitoring range corresponding to the monitoring device has only one monitoring area, the sampling output level of the monitoring device is matched with the enrichment degree information to obtain the sampling output level corresponding to the enrichment degree information of the monitoring device, and then the device adjustment instruction corresponding to the monitoring device is to adjust the sampling output level to the corresponding level; when the monitoring range corresponding to the monitoring device corresponds to multiple monitoring areas, the enrichment degrees of the monitoring areas are compared to obtain the proportion of the enrichment degrees of the monitoring areas, and then the adjustment instruction of the corresponding monitoring device is to distribute the best viewing angle to the corresponding monitoring area according to the proportion of the enrichment degrees; The abnormal monitoring analysis module is used for obtaining warehouse information parameters and identifying the warehouse information parameters to obtain warehouse environment data and cargo state data, analyzing the warehouse environment data and the cargo state data to obtain warehouse monitoring information, and sending the abnormal monitoring information to the early warning and execution module when the warehouse monitoring information is abnormal. The warehouse environment data and the cargo state data are analyzed, and the specific analysis method is as follows: The warehouse environment data includes temperature and humidity data, when the temperature data is greater than the set threshold, the part of the temperature data exceeding the threshold is marked as a temperature overflow value, and similarly, the humidity overflow value is obtained according to the humidity data; the temperature overflow value and the humidity overflow value are calculated and summed to obtain the total value of temperature and humidity influence; The cargo state data includes cargo placement parameters and storage time; the cargo placement parameters of each cargo storage rack in the warehouse are identified to obtain the deviation direction of each cargo, the number of cargos in each deviation direction is counted to obtain the total number of deviations in each direction, the total number of deviations in each direction is compared with the total number of cargos to obtain the cargo deviation ratio in each direction, and the cargo deviation ratio in each direction is compared with the set threshold value, when the cargo deviation ratio is greater than the set threshold value, the corresponding cargo deviation ratio is recorded as an abnormal deviation ratio; the storage time of each cargo is marked and recorded to obtain the storage time of each cargo, the shelf life of each cargo is obtained, and the storage time of each cargo is compared with the shelf life to obtain the storage time proportion; The total value of temperature and humidity influence, the abnormal offset ratio and the storage time ratio are normalized and the values are taken, and the formula is obtained. The warehouse monitoring influence value CK is obtained; wherein ST, PY and TM represent the total value of temperature and humidity influence, the abnormal offset ratio and the storage time ratio respectively; p1, p2 and p3 are all preset weight factors; when the warehouse monitoring influence value is greater than the set monitoring influence threshold value, the corresponding warehouse monitoring information is generated as an anomaly. The early warning and execution module is used for receiving device adjustment instruction information and abnormal monitoring information, and performing corresponding early warning and operation execution according to the device adjustment instruction information and the abnormal monitoring information.

2. The safety monitoring system for warehouse logistics production of claim 1, wherein, It also includes a data acquisition module, which is used for acquiring device information parameters of the monitoring device and obtaining warehouse information parameters through the monitoring device, and sending the device information parameters and the warehouse information parameters to the data storage module; the device information parameters include device location information and device characteristic information; the warehouse information parameters include warehouse flow data, warehouse environment data and cargo state data. 3.The safety monitoring system for warehouse logistics production of claim 1, wherein, The warehouse flow data is analyzed, and the specific analysis method is as follows: The warehouse flow data includes stock information, cargo location information and cargo value data; the stock information is identified to obtain the original stock of the cargo in each monitoring area corresponding to each warehouse and the corresponding cargo change amount per unit time, when the corresponding cargo change amount per unit time is positive, the original stock of the cargo is added to the cargo change amount to obtain the current stock of the cargo; otherwise, when the cargo change amount is negative, the original stock of the cargo is subtracted from the cargo change amount; the stock of each monitoring area corresponding to each warehouse is divided into a plurality of stock intervals, each stock interval corresponds to a stock influence value, the current stock of each monitoring area is matched with the plurality of stock intervals to obtain the corresponding stock influence value, and the stock influence values of the monitoring areas are added to obtain the total stock influence value corresponding to each warehouse; The cargo location information of each cargo in each monitoring area is taken as a reference point, and the reference point of each cargo is compared with the cargo location information of the adjacent cargo in each direction to obtain the distance value of each cargo and the adjacent cargo in each direction, the distance values in each direction are calculated to obtain an average value, which is denoted as a distance reference value, the distance reference value is compared with a set threshold value, when the distance reference value is greater than the set threshold value, the corresponding distance reference value is marked as a high dispersion reference value, the total number of cargos and the number of high dispersion reference values are counted, and the number of high dispersion reference values is compared with the total number of cargos to obtain a dispersion cargo proportion value; The cargo value data is identified to obtain cargo feature information and cargo value, the image information and storage information of each cargo are obtained, the image information of each cargo is identified to obtain image feature information of each cargo, and the image feature information is compared with the pre-stored storage feature information of each cargo in the storage information, when the image feature information is different from the storage feature information, abnormal information of the corresponding cargo is generated, otherwise, when the image feature information is the same as the storage feature information, the image feature information of the cargo is matched with the cargo feature information of each cargo to obtain the cargo value of each cargo, and the cargo values of the cargos corresponding to each monitoring area are added to obtain the total cargo value of each monitoring area; The values of the total stock influence value and the total cargo value are taken as the major axis and the minor axis of an ellipse to construct the ellipse, the value of the dispersion cargo proportion value is taken as the radius of a circle to construct the circle, the centers of the ellipse and the circle are overlapped, the area of the part of the ellipse subtracted from the circle is calculated, and the value of the area is marked as a cargo enrichment value, the cargo enrichment value is divided into a plurality of enrichment value intervals, each enrichment value interval corresponds to an enrichment degree information, the cargo enrichment value of each monitoring area is matched with the plurality of enrichment value intervals to obtain the corresponding enrichment degree information, and the enrichment degree information of each monitoring area corresponding to each warehouse is comprehensively obtained to obtain corresponding warehouse enrichment information.

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