Warehouse intelligent management system and method based on cloud computing

Abstract

The invention discloses a warehouse intelligent management system and method based on cloud computing, and relates to the technical field of warehouse intelligent management, and the system comprises a data collection module, an influence analysis module, an analysis set establishment module, a comparison analysis module and an early warning module. And the data acquisition module is used for acquiring information, including physical information and system information, during operation of the temperature sensor in the medicine storage, and transmitting the physical information and the system information to the influence analysis module after acquisition. According to the invention, the influence condition of the detection precision of the temperature sensor for medicine storage is monitored, and when the detection precision of the temperature sensor is abnormal, a worker is prompted to check the temperature sensor in time, so that the curative effect and the treatment effect of a medicine are guaranteed, and the treatment effect and the safety of a patient are further guaranteed; and intelligent and efficient management of medicine storage is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse intelligent management, and in particular to a warehouse intelligent management system and method based on cloud computing. Background Art

[0002] A warehouse is a general term for buildings and places for keeping and storing items. It is composed of storage, transportation facilities (such as cranes, elevators, slides, etc.), transportation pipelines and equipment entering and leaving the warehouse, and management rooms. It can be divided into warehouses for storing solid items, liquid items, gaseous items and powdered items according to the form of the stored items. According to the nature of the stored items, it can be divided into warehouses for storing raw materials, finished products and finished products. According to the building form, it can be divided into multi-storey warehouses and cylindrical warehouses.

[0003] Pharmaceutical warehousing refers to warehouses used to store and distribute pharmaceutical products. Due to the particularity of pharmaceutical products, the management and requirements of pharmaceutical warehousing temperature are extremely strict, especially when storing sensitive products such as medicines and vaccines. Inappropriate temperature may cause drug degradation, loss of activity or deterioration, thereby affecting the efficacy and safety of the drugs.

[0004] Pharmaceutical warehouses in the prior art are usually equipped with temperature control systems, which monitor the temperature inside the warehouse through temperature sensors and maintain the temperature range required for pharmaceutical warehouses through the temperature control system. The temperature control system can automatically adjust according to the preset temperature threshold to ensure that the temperature inside the warehouse is always within an acceptable range.

[0005] The existing technology has the following deficiencies: However, most of the existing pharmaceutical warehouses are unable to understand the detection accuracy of the temperature sensor. When the detection accuracy of the temperature sensor used in the pharmaceutical warehouse is abnormal, it may cause inaccurate temperature monitoring in the warehouse or fail to grasp the actual temperature situation in time. If the temperature exceeds the stable range of the drug, the drug may degrade, lose activity or deteriorate, which may cause the drug's efficacy to decrease or lose its therapeutic effect, affecting the patient's treatment effect and safety, making it inconvenient to efficiently manage the pharmaceutical warehouse.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a cloud computing-based warehouse intelligent management system and method. The present invention monitors the impact of the detection accuracy of the temperature sensor used for pharmaceutical warehousing, and can promptly discover when the detection accuracy of the temperature sensor is abnormal, thereby ensuring that the temperature in the warehouse is accurately monitored and the actual temperature in the warehouse is timely grasped, effectively preventing the drugs in the warehouse from being degraded, losing activity, and deteriorating due to inaccurate temperature detection, thereby ensuring the efficacy and treatment effect of the drugs, and then ensuring the treatment effect and safety of patients, and realizing intelligent and efficient management of pharmaceutical warehousing to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a cloud computing-based warehouse intelligent management system, comprising a data acquisition module, an impact analysis module, an analysis set establishment module, a comparison analysis module and an early warning module;

[0009] The data collection module collects the information of the temperature sensor in the pharmaceutical warehouse during operation, including physical information and system information. After the collection, the physical information and system information are transmitted to the impact analysis module;

[0010] The impact analysis module establishes a data analysis model based on the physical information and system information of the temperature sensor during operation, generates an impact coefficient, and transmits the impact coefficient to the analysis set establishment module;

[0011] An analysis set establishment module establishes an analysis set with a number of influence coefficients generated when the temperature sensor is running, compares and analyzes the influence coefficients in the analysis set with pre-set influence coefficients, generates influence evaluation coefficients, and transmits the influence evaluation coefficients to the comparison analysis module;

[0012] The comparison analysis module analyzes the impact evaluation coefficient generated by several impact coefficients in the analysis set with the pre-set reference threshold of the impact evaluation coefficient, generates an impact signal, and transmits the impact signal to the early warning module, through which the early warning module generates or does not generate an early warning prompt.

[0013] Preferably, the physical information of the temperature sensor during operation includes a linearity fluctuation coefficient and an input voltage abnormal drift coefficient. After collection, the data acquisition module calibrates the linearity fluctuation coefficient and the input voltage abnormal drift coefficient as XX, respectively. y and PY y , the system information includes the data transmission stability coefficient. After collection, the data acquisition module calibrates the data transmission stability coefficient as WD y .

[0014] Preferably, the logic for obtaining the linearity fluctuation coefficient is as follows:

[0015] S1. Obtain the signal output value and the corresponding temperature value of the signal at different times within the time T, and calibrate the signal output value and the corresponding temperature value of the signal as Xin respectively. x and Tem x , x represents the number of the signal output value and the temperature value corresponding to the signal collected within T time, x = 1, 2, 3, 4, ..., c, c is a positive integer;

[0016] S2, through the Xin at different times in T time x and Tem x Get the linear ratio at different times within time T and calibrate the linear ratio as Xbz x , linear ratio Xbz x The expression obtained is Xbz x =Xin x / Tem x ;

[0017] S3. Find the linear ratio Xbz at different times within T time. x The standard deviation of is calibrated as E. The calculation formula of standard deviation E is:

[0018] ,

[0019] in, is the linear ratio Xbz at different times within T time x The average value of is obtained as:

[0020]

[0021] S4, through the linear ratio Xbz at different times in time T x The linearity fluctuation coefficient is obtained by the standard deviation E of y =E.

[0022] Preferably, the logic for obtaining the abnormal input voltage drift coefficient is as follows:

[0023] S1. Obtain the optimal operating voltage range of the temperature sensor and calibrate the optimal operating voltage range as V bes min ~V bes max ;

[0024] S2. Obtain the input voltage value of the temperature sensor at different times within the time T, and calibrate the input voltage value as V sr ;

[0025] S3, will not be in the optimal operating voltage range V bes min ~V besmax Input voltage value between V sr calibrated as V sr j , j indicates that it is not in the optimal operating voltage range V bes min ~V bes max Input voltage value between V sr , j = 1, 2, 3, 4, ..., h, where h is a positive integer;

[0026] S4. Calculate the input voltage abnormal drift coefficient. The calculation expression is: In the formula, V bes min and V bes max The average value of

[0027] Preferably, the logic of obtaining the data transmission stability coefficient is as follows:

[0028] S1. Obtain the data transmission rate of the temperature sensor at different time periods within T time, and calibrate the data transmission rate as V l k , k represents the number of the data transmission rate in different time periods within the time T, k = 1, 2, 3, 4, ..., n, n is a positive integer;

[0029] S2. Calculate the data transmission rate V of the temperature sensor at different time periods within T time. l k The standard deviation of is calibrated as Q. The calculation formula of standard deviation Q is:

[0030] ,

[0031] in, is the data transmission rate V at different time periods within T time l k The average value of is obtained as:

[0032] S3, data transmission rate V at different time periods within T time l k The standard deviation Q of the data transmission is used to obtain the data transmission stability coefficient, and the expression obtained is: WD y =Q.

[0033] Preferably, the impact analysis module obtains the linearity fluctuation coefficient XX y , Input voltage abnormal drift coefficient PY yAnd the data transmission stability factor WD y After that, a data analysis model is established to generate the influence coefficient YX m , based on the formula:

[0034] ,

[0035] Where, f1, f2, and f3 are linear fluctuation coefficients XX respectively. y , Input voltage abnormal drift coefficient PY y And the data transmission stability factor WD y The preset proportional coefficient, and f1, f2, and f3 are all greater than 0.

[0036] Preferably, the analysis set establishment module establishes an analysis set with several influence coefficients generated when the temperature sensor is running, and calibrates the data set as Z, then Z={YX m}={YX1, YX2, YX3,…,YX G}, m represents the number of the influence coefficient in the analysis set, m = 1, 2, 3, 4, ..., G, G is a positive integer;

[0037] The influence coefficient is compared with the preset influence coefficient reference threshold, and the influence coefficient greater than the influence coefficient reference threshold is calibrated as YX u , u represents the number of the influence coefficient greater than the reference threshold of the influence coefficient, u = 1, 2, 3, 4, ..., g, g is a positive integer, and the influence evaluation coefficient is generated by the influence coefficient and the reference threshold of the influence coefficient. The calculation expression is: Where PJ represents the impact evaluation coefficient generated by analyzing several impact coefficients in the set when the temperature sensor is running, and Yz is the reference threshold of the impact coefficient.

[0038] Preferably, the comparison and analysis module analyzes the impact evaluation coefficient generated by several impact coefficients in the analysis set with a pre-set reference threshold of the impact evaluation coefficient. If the impact evaluation coefficient is greater than or equal to the reference threshold of the impact evaluation coefficient, a high impact signal is generated by the comparison and analysis module, and the signal is transmitted to the early warning module, and an early warning prompt is issued by the early warning module, and the early warning prompt is transmitted to the mobile monitoring terminal, and the staff is prompted to check the temperature sensor through the mobile monitoring terminal. If the impact evaluation coefficient is less than the reference threshold of the impact evaluation coefficient, a low impact signal is generated by the comparison and analysis module, and the signal is transmitted to the early warning module, and the early warning prompt is not issued by the early warning module.

[0039] A cloud computing-based warehouse intelligent management method comprises the following steps:

[0040] Collect information about the temperature sensors in pharmaceutical warehouses during operation, including physical and system information;

[0041] The physical information and system information of the temperature sensor during operation are used to establish a data analysis model to generate an influence coefficient;

[0042] Establish an analysis set of several influence coefficients generated when the temperature sensor is running, and compare and analyze the influence coefficients in the analysis set with the pre-set influence coefficients to generate an influence evaluation coefficient;

[0043] An impact evaluation coefficient generated by a number of impact coefficients in the analysis set is analyzed with a preset reference threshold of the impact evaluation coefficient to generate an impact signal, and an early warning prompt is generated or not generated for the impact signal.

[0044] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0045] The present invention monitors the influence of the detection accuracy of the temperature sensor used for medicine storage, and can timely discover when the detection accuracy of the temperature sensor is abnormal, thereby ensuring accurate monitoring of the temperature in the warehouse and timely grasping the real actual temperature in the warehouse, effectively preventing the medicines in the warehouse from being degraded, losing activity, and deteriorating due to inaccurate temperature detection, thereby ensuring the efficacy and treatment effect of the medicines, and further ensuring the treatment effect and safety of the patients, and realizing intelligent and efficient management of medicine storage;

[0046] The present invention establishes an analysis set for several influence coefficients generated when the temperature sensor is running, and conducts a comprehensive analysis on the influence coefficients in the analysis set to determine whether to generate an early warning prompt. This can effectively prevent the accidental triggering of early warning prompts, improve the accuracy of analyzing abnormal conditions of temperature sensor detection accuracy, and further improve the trust of the mobile monitoring terminal in the early warning of the operating status of the temperature sensor, thereby ensuring the stable and efficient operation of the temperature sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1 The module diagram of a cloud computing-based warehouse intelligent management system and method of the present invention is shown in FIG.

[0049] Figure 2 The present invention is a flowchart of a cloud computing-based warehouse intelligent management system and method. DETAILED DESCRIPTION

[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0051] The present invention provides Figure 1 A cloud computing-based warehouse intelligent management system is shown, comprising a data collection module, an impact analysis module, an analysis set establishment module, a comparison analysis module and an early warning module;

[0052] The data collection module collects the information of the temperature sensor in the pharmaceutical warehouse during operation, including physical information and system information. After the collection, the physical information and system information are transmitted to the impact analysis module;

[0053] The physical information of the temperature sensor during operation includes the linearity fluctuation coefficient and the input voltage abnormal drift coefficient. After collection, the data acquisition module calibrates the linearity fluctuation coefficient and the input voltage abnormal drift coefficient as XX respectively. y and PY y ;

[0054] The temperature sensor has a linear response characteristic, that is, the output signal is linearly related to the output temperature. However, if the linearity of the temperature sensor fluctuates abnormally, it will have the following serious effects on the detection accuracy of the temperature sensor:

[0055] Inaccurate temperature measurement: Large linearity fluctuations mean that there is a large deviation or nonlinear relationship between the sensor output signal and the actual temperature, which will lead to inaccurate temperature measurement results, resulting in a large error between the measured temperature value and the actual temperature;

[0056] Reliability issues: The reliability of the temperature sensor is the key to ensuring its long-term stability and accuracy. If the linearity of the output signal and the output temperature fluctuates greatly, the reliability of the sensor will be affected, which may cause the temperature sensor to work unstably or fail, thus failing to provide reliable temperature monitoring;

[0057] Reduced data credibility: The output data of the temperature sensor is used for temperature monitoring and control, which is particularly important for pharmaceutical warehousing. If the linearity of the sensor fluctuates greatly, the monitored temperature data will become unreliable. Unreliable temperature data may lead to wrong decisions and actions, and it is impossible to accurately judge temperature anomalies or problems.

[0058] Therefore, by monitoring the linear relationship between the temperature sensor output signal and the output temperature, abnormal linearity of the temperature sensor can be discovered in time;

[0059] The logic for obtaining the linearity fluctuation coefficient is as follows:

[0060] S1. Obtain the signal output value and the corresponding temperature value of the signal at different times within the time T, and calibrate the signal output value and the corresponding temperature value of the signal as Xin respectively. x and Tem x , x represents the number of the signal output value and the temperature value corresponding to the signal collected within T time, x = 1, 2, 3, 4, ..., c, c is a positive integer;

[0061] It should be noted that special data acquisition equipment, such as a data collector or a data logger, is connected to the temperature sensor. These devices can read the output signal of the sensor and convert it into a temperature value, so that the signal output value of the temperature sensor and the temperature value corresponding to the signal can be obtained through the data acquisition equipment;

[0062] S2, through the Xin at different times in T time x and Tem x Get the linear ratio at different times within time T and calibrate the linear ratio as Xbz x , linear ratio Xbz x The expression obtained is: Xbz x =Xin x / Tem x ;

[0063] S3. Find the linear ratio Xbz at different times within T time. x The standard deviation of is calibrated as E. The calculation formula of standard deviation E is:

[0064] ,

[0065] in, is the linear ratio Xbz at different times within T time x The average value of is obtained as:

[0066]

[0067] S4, through the linear ratio Xbz at different times in time T x The linearity fluctuation coefficient is obtained by the standard deviation E of y =E;

[0068] From the expression, we can see that the smaller the standard deviation E of the linear ratio at different times in time T, that is, the linearity fluctuation coefficient XX y The smaller the performance value, the smaller the fluctuation of the linear ratio, indicating that the detection accuracy of the temperature sensor is less affected during use, and the larger the standard deviation E of the linear ratio at different times within the time T, that is, the linearity fluctuation coefficient Xbzx The larger the performance value is, the greater the fluctuation of the linear ratio is, indicating that the detection accuracy of the temperature sensor is more affected during use;

[0069] When the input voltage of the temperature sensor in the pharmaceutical warehouse is abnormal, it may cause the following serious impacts on the detection accuracy of the temperature sensor:

[0070] Increased error: The detection accuracy of a temperature sensor is usually related to the stability and accuracy of its input voltage. If the input voltage is abnormal, such as deviating from the standard voltage value or fluctuating greatly, the temperature value output by the sensor will be erroneous, which may cause a large difference between the actual temperature and the sensor measurement value, affecting the accuracy of the temperature.

[0071] Loss of accuracy: The working principle and accuracy of temperature sensors usually rely on stable input voltage. If the input voltage is abnormal, the sensor may not work properly or its internal calibration and compensation mechanism may fail, resulting in reduced detection accuracy. The temperature value output by the sensor may drift, become unstable or inaccurate.

[0072] Failure or damage: Abnormal input voltage may have adverse effects on the circuits and components of the temperature sensor, causing the sensor to malfunction or be damaged, which may cause the sensor to not work properly and provide accurate temperature measurements;

[0073] Therefore, by monitoring the input voltage of the temperature sensor, abnormal input voltage of the temperature sensor can be discovered in time;

[0074] The logic for obtaining the input voltage abnormal drift coefficient is as follows:

[0075] S1. Obtain the optimal operating voltage range of the temperature sensor and calibrate the optimal operating voltage range as V bes min ~V bes max ;

[0076] It should be noted that temperature sensors usually have corresponding data sheets or technical manuals, which contain detailed information about the operating parameters of the temperature sensor, including voltage requirements. By consulting the data sheets provided by the temperature sensor manufacturer, accurate information about the optimal operating voltage range can be obtained;

[0077] S2. Obtain the input voltage value of the temperature sensor at different times within the time T, and calibrate the input voltage value as V sr ;

[0078] It should be noted that the input voltage value of the temperature sensor can be obtained by connecting the data acquisition device to the circuit of the temperature sensor. The data acquisition module is a device specially designed to collect and transmit sensor data. It can be connected to the input circuit of the temperature sensor and provide interface and communication functions to read and record the input voltage value in real time. Common data acquisition modules include modules based on analog-to-digital converters (ADCs) or data acquisition cards with digital input channels.

[0079] S3, will not be in the optimal operating voltage range V bes min ~V bes max Input voltage value between V sr calibrated as V sr j , j indicates that it is not in the optimal operating voltage range V bes min ~V bes max Input voltage value between V sr , j = 1, 2, 3, 4, ..., h, where h is a positive integer;

[0080] S4. Calculate the input voltage abnormal drift coefficient. The calculation expression is: In the formula, V bes min and V bes max The average value of

[0081] It can be seen from the calculated expression that the larger the input voltage abnormal drift coefficient when the temperature sensor is running within the T time, the greater the impact on the detection accuracy of the temperature sensor during use; the smaller the input voltage abnormal drift coefficient when the temperature sensor is running within the T time, the smaller the impact on the detection accuracy of the temperature sensor during use;

[0082] System information includes the data transmission stability coefficient. After collection, the data acquisition module calibrates the data transmission stability coefficient as WD y ;

[0083] When the data transmission rate stability of the temperature sensor in the pharmaceutical warehouse is poor, it may cause the following serious impacts on the detection accuracy of the temperature sensor:

[0084] Data delay: Unstable data transmission rate may cause sensor data delay. Delay means that there is a certain time difference between the detected temperature value and the actual temperature. In pharmaceutical warehousing, real-time temperature changes are very important for the storage and preservation of medicines. Therefore, data delay may make it impossible to detect and respond to temperature fluctuations in time, thus affecting the quality and safety of medicines.

[0085] Data inaccuracy: The instability of data transmission rate may lead to data packet loss, duplication or erroneous transmission, which may lead to inaccurate sensor data, that is, the output temperature value deviates from the actual temperature value. Inaccurate temperature data may lead to wrong decisions and operations, which in turn affects the control of the storage environment and the quality of drugs.

[0086] Data inconsistency: Unstable data transmission rates may lead to data inconsistency, that is, inconsistent or incomplete data acquired at different time points, which makes it difficult to analyze and compare temperature data and may lead to an inability to accurately assess trends and patterns of temperature changes. In pharmaceutical warehousing, accurate temperature trend analysis is essential for monitoring and managing drug storage conditions;

[0087] Data loss risk: Unstable data transmission rates increase the risk of data loss. If sensor data is lost, temperature information cannot be obtained during the data loss period, making it impossible to detect and respond to temperature anomalies in a timely manner. This may result in failure to meet drug storage requirements and may have an adverse impact on drug quality and safety.

[0088] Therefore, by monitoring the data transmission rate of the temperature sensor, it is possible to promptly discover the deterioration of the stability of the data transmission rate of the temperature sensor;

[0089] The logic for obtaining the data transmission stability coefficient is as follows:

[0090] S 1. Obtain the data transmission rate of the temperature sensor in different time periods within T time (the time in the time period can be equal or unequal), and calibrate the data transmission rate as V l k , k represents the number of the data transmission rate in different time periods within the time T, k = 1, 2, 3, 4, ..., n, n is a positive integer;

[0091] It should be noted that a data logger is a device specifically used to record sensor data. It can be connected to a temperature sensor and record the output signal of the sensor. The data logger has a real-time data transmission function. By connecting the data logger to a computer or monitoring system, sensor data and transmission rate information can be obtained in real time.

[0092] S2. Calculate the data transmission rate V of the temperature sensor at different time periods within T time. lk The standard deviation of is calibrated as Q. The calculation formula of standard deviation Q is:

[0093]

[0094] in, is the data transmission rate V at different time periods within T time l k The average value of is obtained as:

[0095] S3, data transmission rate V at different time periods within T time l k The standard deviation Q of the data transmission is used to obtain the data transmission stability coefficient, and the expression obtained is: WD y =Q;

[0096] From the expression, we can know that the data transmission rate V in different periods of time T l k The smaller the standard deviation Q is, the greater the data transmission stability coefficient WD is. y The smaller the performance value, the smaller the fluctuation of the data transmission rate during data transmission, indicating that the detection accuracy of the temperature sensor is less affected during use. The data transmission rate V at different time periods within T time l k The larger the standard deviation Q, the greater the data transmission stability coefficient WD. y The larger the performance value is, the greater the fluctuation of the data transmission rate is during data transmission, indicating that the detection accuracy of the temperature sensor is more affected during use;

[0097] The impact analysis module establishes a data analysis model based on the physical information and system information of the temperature sensor during operation, generates an impact coefficient, and transmits the impact coefficient to the analysis set establishment module;

[0098] The impact analysis module obtains the linearity fluctuation coefficient XX y , Input voltage abnormal drift coefficient PY y And the data transmission stability factor WD y After that, a data analysis model is established to generate the influence coefficient YX m , based on the formula:

[0099] ,

[0100] Where, f1, f2, and f3 are linear fluctuation coefficients XX respectively. y , Input voltage abnormal drift coefficient PY y And the data transmission stability factor WD yThe preset proportional coefficient, and f1, f2, and f3 are all greater than 0;

[0101] It can be seen from the calculation formula that the larger the linear fluctuation coefficient of the temperature sensor during operation within the T time, the larger the input voltage abnormal drift coefficient, and the larger the data transmission stability coefficient, that is, the influence coefficient YX m The larger the performance value, the greater the impact on the detection accuracy of the temperature sensor during use. The smaller the linear fluctuation coefficient of the temperature sensor during operation within T time, the smaller the input voltage abnormal drift coefficient, and the smaller the data transmission stability coefficient, that is, the influence coefficient YX m The smaller the performance value is, the less the detection accuracy of the temperature sensor is affected during use;

[0102] An analysis set establishment module establishes an analysis set with a number of influence coefficients generated when the temperature sensor is running, compares and analyzes the influence coefficients in the analysis set with pre-set influence coefficients, generates influence evaluation coefficients, and transmits the influence evaluation coefficients to the comparison analysis module;

[0103] The analysis set establishment module establishes an analysis set with several influence coefficients generated by the temperature sensor during operation, and calibrates the data set as Z, then Z = {YX m}={YX1, YX2, YX3,…,YX G}, m represents the number of the influence coefficient in the analysis set, m = 1, 2, 3, 4, ..., G, G is a positive integer;

[0104] The influence coefficient is compared with the preset influence coefficient reference threshold, and the influence coefficient greater than the influence coefficient reference threshold is calibrated as YX u , u represents the number of the influence coefficient greater than the reference threshold of the influence coefficient, u = 1, 2, 3, 4, ..., g, g is a positive integer, and the influence evaluation coefficient is generated by the influence coefficient and the reference threshold of the influence coefficient. The calculation expression is: Where PJ represents the impact evaluation coefficient generated by analyzing several impact coefficients in the set when the temperature sensor is running, and Yz is the reference threshold of the impact coefficient;

[0105] It can be seen from the calculated expression that the larger the performance value of the impact evaluation coefficient generated in the analysis set, the greater the impact on the detection accuracy of the temperature sensor during use, and the smaller the performance value of the impact evaluation coefficient generated in the analysis set, the smaller the impact on the detection accuracy of the temperature sensor during use;

[0106] The comparison analysis module analyzes the impact evaluation coefficient generated by several impact coefficients in the analysis set with the preset reference threshold of the impact evaluation coefficient, generates an impact signal, and transmits the impact signal to the early warning module, through which the early warning module generates or does not generate an early warning prompt;

[0107] The comparison and analysis module analyzes the impact evaluation coefficient generated by several impact coefficients in the analysis set with the preset reference threshold of the impact evaluation coefficient. If the impact evaluation coefficient is greater than or equal to the reference threshold of the impact evaluation coefficient, a high impact signal is generated through the comparison and analysis module, and the signal is transmitted to the early warning module, and an early warning prompt is issued through the early warning module, and the early warning prompt is transmitted to the mobile monitoring terminal, and the staff is prompted through the mobile monitoring terminal that the detection accuracy of the temperature sensor has been greatly affected and needs to be checked in time to promptly discover that the temperature sensor may have problems with affected detection accuracy. If the impact evaluation coefficient is less than the reference threshold of the impact evaluation coefficient, a low impact signal is generated through the comparison and analysis module, and the signal is transmitted to the early warning module, and no early warning prompt is issued through the early warning module;

[0108] The present invention monitors the influence of the detection accuracy of the temperature sensor used for medicine storage, and can timely discover when the detection accuracy of the temperature sensor is abnormal, prompting the staff to timely check the temperature sensor, ensuring that the temperature in the warehouse is accurately monitored and the real actual temperature in the warehouse is timely grasped, effectively preventing the medicines in the warehouse from being degraded, losing activity, and deteriorating due to inaccurate temperature detection, ensuring the efficacy and treatment effect of the medicines, and then ensuring the treatment effect and safety of patients, and realizing intelligent and efficient management of medicine storage;

[0109] The present invention establishes an analysis set for several influence coefficients generated when the temperature sensor is running, and conducts a comprehensive analysis on the influence coefficients in the analysis set to determine whether to generate an early warning prompt. This can effectively prevent the accidental triggering of early warning prompts, improve the accuracy of analyzing abnormal conditions of temperature sensor detection accuracy, and further improve the trust of the mobile monitoring terminal in the early warning of the operating status of the temperature sensor, thereby ensuring the stable and efficient operation of the temperature sensor.

[0110] The present invention provides Figure 2 A cloud computing-based warehouse intelligent management method is shown, comprising the following steps:

[0111] Collect information about the temperature sensors in pharmaceutical warehouses during operation, including physical and system information;

[0112] The physical information and system information of the temperature sensor during operation are used to establish a data analysis model to generate an influence coefficient;

[0113] Establish an analysis set of several influence coefficients generated when the temperature sensor is running, and compare and analyze the influence coefficients in the analysis set with the pre-set influence coefficients to generate an influence evaluation coefficient;

[0114] Analyze the impact evaluation coefficient generated by several impact coefficients in the analysis set with the preset reference threshold of the impact evaluation coefficient to generate an impact signal, and generate or not generate an early warning prompt for the impact signal;

[0115] A cloud computing-based warehouse intelligent management method provided in an embodiment of the present invention is implemented through the above-mentioned cloud computing-based warehouse intelligent management system. The specific method and process of a cloud computing-based warehouse intelligent management method are detailed in the embodiment of the above-mentioned cloud computing-based warehouse intelligent management system, which will not be repeated here.

[0116] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0117] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0118] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0119] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0120] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0121] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0123] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0124] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A cloud computing-based warehouse intelligent management system, characterized in that: It includes data collection module, impact analysis module, analysis set establishment module, comparison analysis module and early warning module; The data collection module collects the information of the temperature sensor in the pharmaceutical warehouse during operation, including physical information and system information. After the collection, the physical information and system information are transmitted to the impact analysis module; The impact analysis module establishes a data analysis model based on the physical information and system information of the temperature sensor during operation, generates an impact coefficient, and transmits the impact coefficient to the analysis set establishment module; An analysis set establishment module establishes an analysis set with a number of influence coefficients generated when the temperature sensor is running, compares and analyzes the influence coefficients in the analysis set with pre-set influence coefficients, generates influence evaluation coefficients, and transmits the influence evaluation coefficients to the comparison analysis module; The comparison analysis module analyzes the impact evaluation coefficient generated by several impact coefficients in the analysis set with the pre-set reference threshold of the impact evaluation coefficient, generates an impact signal, and transmits the impact signal to the early warning module, through which the early warning module generates or does not generate an early warning prompt.

2. The cloud computing-based warehouse intelligent management system according to claim 1 is characterized in that: The physical information of the temperature sensor during operation includes the linearity fluctuation coefficient and the input voltage abnormal drift coefficient. After collection, the data acquisition module calibrates the linearity fluctuation coefficient and the input voltage abnormal drift coefficient as XX respectively. y and PY y , the system information includes the data transmission stability coefficient. After collection, the data acquisition module calibrates the data transmission stability coefficient as WD y .

3. The cloud computing-based warehouse intelligent management system according to claim 1 is characterized in that: The logic for obtaining the linearity fluctuation coefficient is as follows: S1. Obtain the signal output value and the corresponding temperature value of the signal at different times within the time T, and calibrate the signal output value and the corresponding temperature value of the signal as Xin respectively. x and Tem x , x represents the number of the signal output value and the temperature value corresponding to the signal collected within T time, x = 1, 2, 3, 4, ..., c, c is a positive integer; S2, through the Xin at different times in T time x and Tem x Get the linear ratio at different times within time T and calibrate the linear ratio as Xbz x , linear ratio Xbz x The expression obtained is: Xbz x =Xin x / Tem x ; S3. Find the linear ratio Xbz at different times within T time. x The standard deviation of is calibrated as E. The calculation formula of standard deviation E is: ， in, is the linear ratio Xbz at different times within T time x The average value of is obtained as: S4, through the linear ratio Xbz at different times in time T x The linearity fluctuation coefficient is obtained by the standard deviation E of y =E.

4. The cloud computing-based warehouse intelligent management system according to claim 3 is characterized in that: The logic for obtaining the input voltage abnormal drift coefficient is as follows: S1. Obtain the optimal operating voltage range of the temperature sensor and calibrate the optimal operating voltage range as V bes min ~V bes max ; S2. Obtain the input voltage value of the temperature sensor at different times within the time T, and calibrate the input voltage value as V sr ; S3, will not be in the optimal operating voltage range V bes min ~V bes max Input voltage value between V sr calibrated as V sr j , j indicates that it is not in the optimal operating voltage range V bes min ~V bes max Input voltage value between V sr , j = 1, 2, 3, 4, ..., h, where h is a positive integer; S4. Calculate the input voltage abnormal drift coefficient. The calculation expression is: In the formula, V bes min and V bes max The average value of 5. The cloud computing-based warehouse intelligent management system according to claim 4 is characterized in that: The logic for obtaining the data transmission stability coefficient is as follows: S1. Obtain the data transmission rate of the temperature sensor at different time periods within T time, and calibrate the data transmission rate as V l k , k represents the number of the data transmission rate in different time periods within the time T, k = 1, 2, 3, 4, ..., n, n is a positive integer; S2. Calculate the data transmission rate V of the temperature sensor at different time periods within T time. l k The standard deviation of is calibrated as Q. The calculation formula of standard deviation Q is: ， in, is the data transmission rate V at different time periods within T time l k The average value of is obtained as: S3, data transmission rate V at different time periods within T time l k The standard deviation Q of the data transmission is used to obtain the data transmission stability coefficient, and the expression obtained is: WD y =Q.

6. The cloud computing-based warehouse intelligent management system according to claim 5, characterized in that: The impact analysis module obtains the linearity fluctuation coefficient XX y , Input voltage abnormal drift coefficient PY y And the data transmission stability factor WD y After that, a data analysis model is established to generate the influence coefficient YX m , based on the formula: ， Where, f1, f2, and f3 are linear fluctuation coefficients XX respectively. y , Input voltage abnormal drift coefficient PY y And the data transmission stability factor WD y The preset proportional coefficient, and f1, f2, and f3 are all greater than 0.

7. The cloud computing-based warehouse intelligent management system according to claim 6, characterized in that: The analysis set establishment module establishes an analysis set with several influence coefficients generated by the temperature sensor during operation, and calibrates the data set as Z, then Z = {YX m }={YX1, YX2, YX3,…,YX G }, m represents the number of the influence coefficient in the analysis set, m = 1, 2, 3, 4, ..., G, G is a positive integer; The influence coefficient is compared with the preset influence coefficient reference threshold, and the influence coefficient greater than the influence coefficient reference threshold is calibrated as YX u , u represents the number of the influence coefficient greater than the reference threshold of the influence coefficient, u = 1, 2, 3, 4, ..., g, g is a positive integer, and the influence evaluation coefficient is generated by the influence coefficient and the reference threshold of the influence coefficient. The calculation expression is: Where PJ represents the impact evaluation coefficient generated by analyzing several impact coefficients in the set when the temperature sensor is running, and Yz is the reference threshold of the impact coefficient.

8. The cloud computing-based warehouse intelligent management system according to claim 7, characterized in that: The comparison and analysis module analyzes the impact evaluation coefficient generated by several impact coefficients in the analysis set with the pre-set reference threshold of the impact evaluation coefficient. If the impact evaluation coefficient is greater than or equal to the reference threshold of the impact evaluation coefficient, a high impact signal is generated through the comparison and analysis module, and the signal is transmitted to the early warning module. An early warning prompt is issued through the early warning module, and the early warning prompt is transmitted to the mobile monitoring terminal. The staff is prompted to check the temperature sensor through the mobile monitoring terminal. If the impact evaluation coefficient is less than the reference threshold of the impact evaluation coefficient, a low impact signal is generated through the comparison and analysis module, and the signal is transmitted to the early warning module, and no early warning prompt is issued through the early warning module.

9. A cloud computing-based warehouse intelligent management method, implemented by a cloud computing-based warehouse intelligent management system according to any one of claims 1 to 8, characterized in that: The following steps are involved: Collect information about the temperature sensors in pharmaceutical warehouses during operation, including physical and system information; The physical information and system information of the temperature sensor during operation are used to establish a data analysis model to generate an influence coefficient; Establish an analysis set of several influence coefficients generated when the temperature sensor is running, and compare and analyze the influence coefficients in the analysis set with the pre-set influence coefficients to generate an influence evaluation coefficient; An impact evaluation coefficient generated by a number of impact coefficients in the analysis set is analyzed with a preset reference threshold of the impact evaluation coefficient to generate an impact signal, and an early warning prompt is generated or not generated for the impact signal.

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