A data anomaly monitoring method and system for a disinfection supply platform

Abstract

The present application discloses a data anomaly monitoring method and system for a disinfection supply platform, which relates to the field of data processing. The data anomaly monitoring method for a disinfection supply platform includes: receiving a return request, pre-processing the return request, and obtaining data to be disinfected; analyzing the data to be disinfected, obtaining the current disinfection strategy and sending it; receiving real-time disinfection data collected when executing the current disinfection strategy, analyzing the real-time disinfection data, and generating a disinfection result; if the disinfection result is normal, using the returned object after completing the disinfection task as a storage object, obtaining the current storage strategy of the storage object and sending it; obtaining the stored real-time data collected after completing the storage according to the current storage strategy, analyzing the stored real-time data, generating a storage anomaly result and sending it. The present application can flexibly adapt to the actual needs of different medical tasks, different medical devices and / or medical equipment, and ensure the high quality and stability of cleaning and disinfection.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data anomaly monitoring method and system for a disinfection supply platform. Background Art

[0002] A disinfection supply center is a place in medical institutions, pharmaceutical factories or other related fields for storing and managing disinfected medical supplies, equipment or other materials. Disinfection supply refers to the work carried out by the disinfection supply center (department) in the hospital that is responsible for the cleaning, disinfection, sterilization of all reusable diagnostic and treatment instruments, instruments and items in various departments, as well as the supply of sterile items. Different types of medical instruments, medical supplies and medical equipment used in different medical tasks have different disinfection requirements and storage conditions. In the regional disinfection supply monitoring scenario, faced with the need to handle a large number of disinfection supply instruments and / or equipment, how to improve the accuracy of disinfection, improve the quality of disinfection, and provide timely warnings and traceability of abnormal situations to avoid the spread of contamination has become a core issue in improving the level of regional disinfection supply monitoring. Traditional regional disinfection and supply monitoring usually relies on manual operations, and has low comprehensive and accurate identification of disinfection and supply instruments and / or equipment. It is impossible to quickly and accurately construct disinfection strategies and storage strategies suitable for different medical tasks, different medical instruments and / or medical equipment. It is difficult to ensure the high quality and stability of cleaning and disinfection, and it cannot flexibly adapt to the actual needs of different medical tasks, different medical instruments and / or medical equipment. It is impossible to accurately analyze and trace abnormal situations that occur during the entire supply and return process of medical instruments and / or medical equipment. It is difficult to issue alarms for abnormal situations in a timely manner and it is difficult to avoid the spread of contamination.

[0003] Therefore, it is necessary to provide a new data anomaly monitoring method and system for the disinfection supply platform to solve the above problems. Summary of the Invention

[0004] The purpose of this application is to provide a data anomaly monitoring method and system for a disinfection supply platform, which can monitor and trace the request and return of medical devices and / or medical equipment; can flexibly adapt to the actual needs of different medical tasks, different medical devices and / or medical equipment, ensure the high quality and stability of cleaning and disinfection, and can promptly issue alarms for abnormal situations, thereby avoiding the spread of contamination.

[0005] To achieve the above-mentioned purpose, the present application provides a data anomaly monitoring method for a disinfection supply platform, comprising the following steps: S1: receiving a return request, pre-processing the return request, and obtaining data to be disinfected; wherein the return request includes: a supply code, a return account, a return time, and at least one return data, each return data corresponds to a return object, and each return data includes at least: an identifier, a category code, and a return process data; the return process data includes: recycling route data and recycling equipment data, and the return process data also includes: recycling video data and / or recycling image data; the data to be disinfected includes: supply item data and multiple return object data; S2: analyzing the data to be disinfected, obtaining the current disinfection strategy, and sending it, wherein the current disinfection strategy includes at least: a disinfection strategy for each data to be disinfected, and each disinfection strategy includes: multiple disinfection nodes, the execution order of each disinfection node, and the identifier of the execution device corresponding to each disinfection node, And the disinfection conditions of each disinfection node in the disinfection process; S3: Receive the real-time disinfection data collected when executing the current disinfection strategy, analyze the real-time disinfection data, generate a disinfection result, if the real-time disinfection data is consistent with the disinfection conditions, the generated disinfection result is normal, execute S4; if the real-time disinfection data is inconsistent with the disinfection conditions, the generated disinfection result is abnormal, and the disinfection result is sent; S4: Use the returned object after completing the disinfection task as the storage object, obtain the current storage strategy of the storage object, and send it, where the current storage strategy includes: current storage location code and current storage condition; S5: Obtain the storage real-time data collected after storage is completed according to the current storage strategy, analyze the storage real-time data, generate a storage abnormality result and send it; if the storage real-time data is consistent with the current storage condition, the generated storage abnormality result is normal, if the storage real-time data is inconsistent with the current storage condition, the generated storage abnormality result is abnormal.

[0006] As above, the sub-steps of pre-processing the return request and obtaining the data to be disinfected are as follows: S11: traverse the supply information in the cache unit according to the supply code in the return request, and determine that the supply information whose supply code is consistent with the supply code in the return request is the target supply information; S12: classify and screen the multiple supply objects in the target supply information, and determine that the non-consumable supply objects are the objects to be disinfected and verified; S13: use the total amount of objects to be disinfected and verified and the identifier of each object to be disinfected and verified as the first verification data, verify the total amount of the returned data and the identifier in the returned data through the first verification data, and generate a first verification result. If the generated first verification result is If the verification result is normal, execute S12; if the generated first verification result is abnormal, generate and send a first alarm message; S14: use the return process prediction data of each object to be disinfected and verified in the target supply information as the return process verification data, verify the return process data of the return data through the return process verification data, and generate a second verification result. If the generated second verification result is normal, execute S15; if the generated second verification result is abnormal, generate and send a second alarm message; S15: use the identification code and category code of each returned object as the return object data, and use the supply item data and all return object data in the target supply information as the data to be disinfected.

[0007] As above, wherein the total amount of returned data and the identifier in the returned data are verified by the first verification data, and the sub-steps of generating the first verification result are as follows: S131: The total amount of the returned data is verified using the total amount of the objects to be disinfected and verified, and a quantity verification result is generated. If the total amount of the objects to be disinfected and verified is equal to the total amount of the returned data, the generated quantity verification result is normal, and S132 is executed; if the total amount of the objects to be disinfected and verified is not equal to the total amount of the returned data, the generated quantity verification result is abnormal, and the quantity verification result is used as the first verification result; S132: The identifier of the returned data is verified using the identifier of the object to be disinfected and verified, and an identification verification result is generated. If the identifier of the object to be disinfected and verified corresponds one-to-one with the identifier in the returned data, the generated identification verification result is normal, and the identification verification result is used as the first verification result; if the identifier of one or more objects to be disinfected and verified is different from the identifier in the returned data, the generated identification verification result is abnormal, and the identification verification result is used as the first verification result.

[0008] As above, the return process data of the return data is verified by the return process verification data, and the sub-steps of generating the second verification result are as follows: S141: Obtain the actual return time according to the supply time and the return time, and use the recovery prediction time in the return process verification data to judge the actual return time, and generate a time judgment result. If the actual return time is less than or equal to the recovery prediction time, the generated time judgment result is normal, and S142 is executed; if the actual return time is greater than the recovery prediction time, the generated time judgment result is abnormal, and the time judgment result is used as the second verification result; S142: Use the recovery route prediction data in the return process verification data to judge the recovery route data, and generate a route judgment result. If the recovery route prediction data is consistent with the recovery route data, the generated route judgment result is normal, and S143 is executed; if the recovery route prediction data is inconsistent with the recovery route data, the generated route judgment result is abnormal, and The route judgment result is used as the second verification result; S143: The recycling equipment equipment data in the return process verification data is used to judge the recycling equipment data to generate an equipment judgment result. If the category code in the recycling equipment data is consistent with the recycling equipment equipment data, the generated equipment judgment result is normal, and S144 is executed; if the category code in the recycling equipment data is inconsistent with the recycling equipment equipment data, the generated equipment judgment result is abnormal, and the equipment judgment result is used as the second verification result; S144: The recycling video standard data and / or recycling image standard data in the return process verification data are used to analyze the recycling video data and / or recycling image data to obtain an operation similarity value, and the operation similarity value is judged by a preset operation similarity threshold to generate a second verification result; if the operation similarity value is greater than or equal to the operation similarity threshold, the generated second verification result is normal; if the operation similarity value is less than the operation similarity threshold, the generated second verification result is abnormal.

[0009] As above, the sub-steps of analyzing the data to be disinfected and obtaining the current disinfection strategy are as follows: S21: Generate a first analysis sequence number for each returned object data in a random order, and the first analysis sequence number increases sequentially; S22: Take the returned object data with the smallest first analysis sequence number as the current analysis item, obtain the disinfection strategy of the current analysis item, and execute S23; S23: Use the total number of returned object data to judge the first analysis sequence number of the current analysis item. If the first analysis sequence number of the current analysis item is less than the total number of returned object data, eliminate the first analysis sequence number of the current analysis item and execute S22; if the first analysis sequence number of the current analysis item is equal to the total number of returned object data, eliminate the first analysis sequence number of the current analysis item, and use all disinfection strategies as the current disinfection strategy.

[0010] As above, the sub-steps for obtaining the disinfection strategy of the current analysis item are as follows: S221: Traverse the disinfection strategy database according to the category code in the current analysis item, and use the disinfection strategy data packet with the same category code as the category code in the current analysis item as the target disinfection strategy data packet; S222: Extract features from the supply item data in the current analysis item to obtain a supply item feature set; S223: Use the supply item feature set to analyze the medical task feature set of each sub-disinfection strategy in the target disinfection strategy data packet to obtain a task similarity value; S224: Use the preset task similarity threshold to analyze the task similarity value. If all task similarity values ​​are less than the task similarity threshold, generate a third alarm message and send it; if there are one or more task similarity values ​​greater than or equal to the task similarity threshold, use the sub-disinfection strategy corresponding to the maximum value of all task similarity values ​​as the target sub-disinfection strategy, wherein the target The sub-disinfection strategy includes at least: multiple disinfection nodes, the execution order of each disinfection node, the category code of the execution device corresponding to each disinfection node, and the disinfection conditions of each disinfection node in the disinfection process; S225: traverse the execution device information library according to the category code of the execution device in the target sub-disinfection strategy, determine that the sub-information data packet corresponding to the disinfection supply platform that sends the return request is the target sub-information data packet, and determine that the execution device information in the target sub-information data packet that is consistent with the category code of the execution device is the target execution device information; S226: obtain the current execution efficiency of the execution device corresponding to the identifier of each execution device in the target execution device information; S227: take the execution device corresponding to the identifier of the execution device corresponding to the maximum value of all current execution efficiencies as the current execution device, and correspond the current execution device to the corresponding disinfection node in the target sub-disinfection strategy one by one, so as to obtain the disinfection strategy of the current analysis item.

[0011] As above, the expression of current execution efficiency is as follows: Among them, Pzx i The current execution efficiency of the execution device corresponding to the identifier of the i-th execution device in the target execution device information; The maximum number of disinfection tasks that can be completed by the execution device corresponding to the identifier of the i-th execution device in unit time; Drw i is the actual total number of disinfection tasks waiting to be processed by the execution device corresponding to the identifier of the i-th execution device at the current time node.

[0012] As described above, after the storage of the storage object is completed according to the current storage policy, the supply information in the cached supply information that is consistent with the supply code of this return request is deleted.

[0013] As described above, the sub-steps of obtaining the current storage policy of the storage object are as follows: S41: traversing the storage information according to the category code of the storage object, determining that the storage information cache file corresponding to the disinfection supply platform that sent the return request is the target storage information cache file, and determining that the storage information data packet in the target storage information cache file that is consistent with the category code of the storage object is the target storage information data packet; S42: obtaining the storability probability of each storage information data in the target storage information data packet;

[0014] S43: Take the storage information data corresponding to the maximum value of all storable probabilities as the target storage information data; S44: Take all idle storage locations in the target storage information data as selection objects, randomly select a storage location from the selection objects as the current storage location, and obtain the storage location code of the current storage location; S45: Take the storage location code of the current storage location as the current storage location code, take the storage condition in the target storage information data as the current storage condition, and take the current storage location code and the current storage condition as the current storage strategy.

[0015] The present application also provides a data anomaly monitoring system for a disinfection supply platform, comprising: at least one disinfection supply platform and a data anomaly monitoring center; wherein the disinfection supply platform: is used to send a return request; receives and executes the current disinfection strategy; collects and sends real-time disinfection data when executing the current disinfection strategy; receives and executes the current storage strategy; collects and sends real-time storage data after storage is completed according to the current storage strategy; receives storage anomaly results; the data anomaly monitoring center: is used to execute the above-mentioned data anomaly monitoring method for the disinfection supply platform.

[0016] The beneficial effects achieved by this application are as follows:

[0017] (1) The data anomaly monitoring method and system of the disinfection supply platform of the present application can monitor and trace the request and return of medical devices and / or medical equipment (i.e., supplies) based on supply information and return requests.

[0018] (2) The data anomaly monitoring method and system of the disinfection supply platform of the present application can comprehensively consider the abnormal situations caused by factors such as the wrong return and / or missed return of medical devices and / or medical equipment, irregular return operations of medical devices and / or medical equipment, abnormal return time and abnormal return path during the entire supply and return process of medical devices and / or medical equipment, and quickly and accurately construct disinfection strategies and storage strategies suitable for different medical tasks, different medical devices and / or medical equipment, which can ensure the high quality and stability of cleaning and disinfection, and can flexibly adapt to the actual needs of different medical tasks, different medical devices and / or medical equipment.

[0019] (3) The data anomaly monitoring method and system of the disinfection supply platform of the present application can accurately analyze and trace abnormal situations that occur during the entire supply and return process of medical devices and / or medical equipment, and can promptly issue an alarm for abnormal situations, thereby avoiding the spread of contamination. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0021] Figure 1 A schematic diagram of the structure of an embodiment of a data anomaly monitoring system for a disinfection supply platform;

[0022] Figure 2 The present invention is a flowchart of an embodiment of a method for monitoring data anomalies on a disinfection supply platform. DETAILED DESCRIPTION

[0023] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] like Figure 1 As shown, the present application provides a data anomaly monitoring system for a disinfection supply platform, including: at least one disinfection supply platform 110 and a data anomaly monitoring center 120.

[0025] Among them, the disinfection supply platform 110 is used to send a return request; receive and execute the current disinfection strategy; collect and send real-time disinfection data when executing the current disinfection strategy; receive and execute the current storage strategy; collect and send real-time storage data after storage is completed according to the current storage strategy; receive storage exception results.

[0026] Specifically, one disinfection supply center corresponds to one disinfection supply platform 110 .

[0027] The data anomaly monitoring center 120 is used to execute the data anomaly monitoring method of the disinfection supply platform described below.

[0028] Furthermore, the data anomaly monitoring center 120 includes at least: a pre-processing unit, a strategy building unit, a first analysis unit, a second analysis unit, a cache unit, and a storage unit.

[0029] Among them, the preprocessing unit: receives the return request, preprocesses the return request, obtains the data to be disinfected, and sends the data to be disinfected to the strategy construction unit.

[0030] Strategy building unit: Analyzes the data to be disinfected, obtains the current disinfection strategy, and sends it; obtains the current storage strategy of the storage object and sends it.

[0031] The first analysis unit: receives the real-time disinfection data collected when executing the current disinfection strategy, analyzes the real-time disinfection data, and generates a disinfection result. If the real-time disinfection data is consistent with the disinfection conditions, the generated disinfection result is normal, and the returned object after completing the disinfection task is used as the storage object, and the storage object is sent to the strategy construction unit; if the real-time disinfection data is inconsistent with the disinfection conditions, the generated disinfection result is abnormal, and the disinfection result is sent.

[0032] The second analysis unit is used to obtain the real-time storage data collected after the storage is completed according to the current storage policy, analyze the real-time storage data, generate a storage abnormality result, and send it.

[0033] Cache unit: used to cache provisioning information and storage information.

[0034] Among them, each supply information includes: supply code, supply time, supply item data, collection account, multiple supply objects, and return process prediction data for each supply object; among them, one supply object corresponds to an identifier and a category code, the identifier of each supply object is different, and the category code of supply objects with the same item category is the same.

[0035] Specifically, the supply code is a task sequence randomly generated when the disinfection supply platform provides corresponding medical devices and / or medical equipment for a medical task. The supply code can quickly and accurately associate the return data with the corresponding supply information.

[0036] The supply time is the time point when the disinfection supply platform provides the corresponding medical instruments and / or medical equipment for a medical task.

[0037] Supply project data refers to the specific category and content of the medical task in which the supplied medical devices and / or medical equipment will be used. Different medical tasks generate different contamination factors.

[0038] Collection account: The account of the person who obtains the medical devices and / or medical equipment required for this medical task from the disinfection supply platform, to facilitate the traceability of the medical devices and / or medical equipment.

[0039] The supply objects are medical instruments and / or medical equipment provided from the disinfection supply platform to the corresponding medical tasks. The consumption categories of the supply objects are: non-consumable and consumable. The non-consumable supply objects are medical instruments and / or medical equipment that can be recycled, such as: scalpels, surgical scissors, hemostatic forceps, tweezers, cataract phacoemulsification handles for ophthalmology, dental drills for dentistry, flexible endoscopes such as gastroscopes and colonoscopes, rigid endoscopes such as laparoscopes, surgical retractors and instrument trays, etc. The consumable supply objects are medical instruments and / or medical equipment that cannot be recycled, such as: disposable scalpels, disposable syringes, disposable infusion sets, disposable medical masks, disposable medical gloves, disposable disinfectant wipes, disposable suction tubes and disposable suture materials, etc.

[0040] An identifier is a unique identifier used to identify a medical device and / or medical equipment. Each medical device and / or medical equipment has a different identifier.

[0041] Furthermore, the return process prediction data includes at least: predicted recovery time, predicted recovery route data, recovery equipment configuration data, recovery video standard data and / or recovery image standard data;

[0042] The expression of the predicted recycling time is:

[0043] Tysc=Tjs-Tgy+Δtwc k ;

[0044] Among them, Tysc is the predicted recycling time; Tjs is the end time of the reported supply project data; Tgy is the supply time of the disinfection supply platform; Δtwc k is the allowable error time of the kth category supply project data. Tjs-Tgy is the interval between the supply time of the disinfection supply platform and the end time of the reported supply project data.

[0045] Specifically, because the actual operation duration of medical tasks is affected by multiple factors and can vary for the same type of medical task, the predicted recovery duration is set to the longest estimated recovery duration based on the supply time and the end time of the supply item data. Recovering the returned items within the predicted recovery duration can better prevent the spread of contamination. The allowable error duration varies for each type of supply item data, and the specific value of the allowable error duration is set based on actual experience and / or actual conditions.

[0046] The recycling route prediction data is a predicted route constructed based on the actual usage location of the medical devices and / or medical equipment and the actual recycling location of the medical devices and / or medical equipment. The predicted route is the optimal route that can avoid the spread of contamination.

[0047] The recycling equipment configuration data is the category code of the professional recycling equipment that complies with regulations and is used to recycle and load corresponding medical devices and / or medical equipment.

[0048] Standard recycling video data and / or standard recycling image data are standard recycling process videos and / or images of medical devices and / or equipment with consistent category codes for the same or similar supply items (i.e., medical tasks). Standard recycling video data and / or standard recycling image data can be pre-recorded or generated using existing software.

[0049] Furthermore, when the Data Anomaly Monitoring Center receives a new supply request from the disinfection supply platform, it constructs and stores new supply information in the cached supply information based on the new supply request. After executing the current return request, the Data Anomaly Monitoring Center deletes the supply information in the cached supply information that matches the supply code of the current return request.

[0050] Among them, the storage information at least includes: multiple storage information cache files, one storage information cache file corresponds to a disinfection supply platform, each storage information cache file includes: multiple storage information data packets, each storage information data packet corresponds to a category code of a storage object, each storage information data packet includes: multiple storage information data, each storage information data includes: multiple storage location codes, storage conditions and the current storage status of the storage location corresponding to each storage location code, wherein the current storage status is a storage status or an idle status; one storage location corresponds to one storage location code.

[0051] Furthermore, the storage information is updated according to the addition, deletion and / or modification of the category code of the storage object of each disinfection supply platform, the addition, deletion and / or modification of the storage location, and the real-time change of the current storage status.

[0052] Storage unit: used to store the disinfection strategy database and execution equipment information database.

[0053] Among them, the disinfection strategy database includes multiple disinfection strategy data packets, one disinfection strategy data packet corresponds to a category code, each disinfection strategy data packet includes at least: multiple sub-disinfection strategies, one sub-disinfection strategy corresponds to a medical task feature set, and the sub-disinfection strategy includes at least: multiple disinfection nodes, the execution order of each disinfection node, the category code of the execution equipment corresponding to each disinfection node, and the disinfection conditions of each disinfection node in the disinfection process.

[0054] The specific types of disinfection factors included in the disinfection conditions are set according to actual conditions, such as temperature factor, humidity factor, and time factor. The specific value of each disinfection factor is set according to the operating specifications or operating standards required for the corresponding medical task.

[0055] Execution devices of the same category have the same category code, and execution devices of different categories have different category codes.

[0056] The execution device information library includes: multiple sub-information data packets, each sub-information data packet corresponds to a disinfection supply platform, each disinfection supply platform includes: multiple execution device information, each execution device information corresponds to a category code of an execution device, and each execution device information includes: multiple execution device identifiers. Each execution device identifier is different.

[0057] Furthermore, when the update conditions are met, the disinfection policy database and / or the execution device information database are updated.

[0058] Specifically, the update conditions include at least: reaching a preset update time node, and data being added, modified and / or deleted.

[0059] like Figure 2 As shown, the present application provides a data anomaly monitoring method for a disinfection supply platform, comprising the following steps:

[0060] S1: Receive a return request, pre-process the return request, and obtain the data to be disinfected; wherein, the return request includes: supply code, return account number, return time and at least one return data, each return data corresponds to a return object, and each return data includes at least: identifier, category code and return process data; return process data includes: recycling route data and recycling equipment data, return process data also includes: recycling video data and / or recycling image data; data to be disinfected includes: supply item data and multiple return object data.

[0061] Specifically, the return object is the medical device and / or medical equipment that needs to be disinfected and stored and returned to the disinfection supply platform after completing the corresponding medical task. One return object corresponds to one identifier, and the identifier of each return object is different.

[0062] Return process data is collected data about the actual recycling process of the returned object. This data includes recycling route data and recycling equipment data. It also includes recycling video data and / or recycling image data. For example, this data may include video or images of the returned object being collected in accordance with required recycling specifications and placed in the appropriate recycling vehicle or container, as well as data on the path taken by the recycling vehicle or container to return the returned object to the recycling location.

[0063] Furthermore, the sub-steps of pre-processing the return request and obtaining the data to be disinfected are as follows:

[0064] S11: Traverse the supply information in the cache unit according to the supply code in the return request, and determine that the supply information whose supply code is consistent with the supply code in the return request is the target supply information.

[0065] Specifically, the supply codes of the supply information cached in the cache unit are different.

[0066] S12: Classify and screen the multiple supply objects in the target supply information, and determine non-consumable supply objects as objects to be disinfected and checked.

[0067] Specifically, the consumption category of the supply object is queried according to the identifier of the supply object, wherein the consumption category includes: non-consumable type and consumable type.

[0068] S13: The total amount of objects to be disinfected and verified and the identifier of each object to be disinfected and verified are used as the first verification data. The total amount of returned data and the identifiers in the returned data are verified using the first verification data, and a first verification result is generated. If the generated first verification result is normal, execute S12; if the generated first verification result is abnormal, generate and send a first alarm message.

[0069] Specifically, after the data anomaly monitoring center generates a first alarm message, it sends the first alarm message to the disinfection supply platform, which then traces the returned object based on the first alarm message. The first alarm message includes at least: the alarm time, the anomaly reason, the supply code, the return account number, and the collection account number.

[0070] Furthermore, the sub-steps of verifying the total number of returned data and the identifiers in the returned data using the first verification data and generating a first verification result are as follows:

[0071] S131: Use the total amount of objects to be disinfected and verified to verify the total amount of returned data, and generate a quantity verification result. If the total amount of objects to be disinfected and verified is equal to the total amount of returned data, the generated quantity verification result is normal, and S132 is executed; if the total amount of objects to be disinfected and verified is not equal to the total amount of returned data, the generated quantity verification result is abnormal, and the quantity verification result is used as the first verification result.

[0072] Specifically, if the total amount of objects to be disinfected and verified is not equal to the total amount of returned data, it means that there is an abnormality in the returned objects in the return request, and there may be a situation where a returned object is omitted or there are extra returned objects.

[0073] S132: Use the identifier of the object to be disinfected and checked to check the identifier of the returned data to generate an identification verification result. If the identifier of the object to be disinfected and checked corresponds one-to-one with the identifier in the returned data, the generated identification verification result is normal, and the identification verification result is used as the first verification result; if the identifier of one or more objects to be disinfected and checked is different from the identifier in the returned data, the generated identification verification result is abnormal, and the identification verification result is used as the first verification result.

[0074] S14: Use the return process prediction data of each object to be disinfected and verified in the target supply information as the return process verification data, verify the return process data of the return data through the return process verification data, and generate a second verification result. If the generated second verification result is normal, execute S15; if the generated second verification result is abnormal, generate a second alarm information and send it.

[0075] Specifically, after the data anomaly monitoring center generates a second alert, it sends it to the disinfection supply platform. Based on this second alert, the disinfection supply platform traces the areas and equipment that may have caused contamination during the return process. The second alert includes at least the alarm time, anomaly cause, supply code, return account number, collection account number, and return process data.

[0076] Furthermore, the sub-steps of verifying the return process data of the returned data by returning the process verification data and generating a second verification result are as follows:

[0077] S141: Obtain the actual return duration based on the supply time and return time, use the recovery prediction duration in the return process verification data to judge the actual return duration, and generate a duration judgment result. If the actual return duration is less than or equal to the recovery prediction duration, the generated duration judgment result is normal, and execute S142; if the actual return duration is greater than the recovery prediction duration, the generated duration judgment result is abnormal, and the duration judgment result is used as the second verification result.

[0078] S142: Use the recycling route prediction data in the return process verification data to judge the recycling route data and generate a route judgment result. If the recycling route prediction data is consistent with the recycling route data, the generated route judgment result is normal and execute S143; if the recycling route prediction data is inconsistent with the recycling route data, the generated route judgment result is abnormal and the route judgment result is used as the second verification result.

[0079] S143: Use the recycling equipment configuration data in the return process verification data to judge the recycling equipment data and generate an equipment judgment result. If the category code in the recycling equipment data is consistent with the recycling equipment configuration data, the generated equipment judgment result is normal and S144 is executed; if the category code in the recycling equipment data is inconsistent with the recycling equipment configuration data, the generated equipment judgment result is abnormal and the equipment judgment result is used as the second verification result.

[0080] S144: Analyze the recovered video data and / or recovered image data using the recovered video standard data and / or recovered image standard data in the return process verification data to obtain an operation similarity value, and judge the operation similarity value using a preset operation similarity threshold to generate a second verification result; if the operation similarity value is greater than or equal to the operation similarity threshold, the generated second verification result is normal; if the operation similarity value is less than the operation similarity threshold, the generated second verification result is abnormal.

[0081] Specifically, a pre-trained neural network model or artificial intelligence model analyzes the standard recycling video data and / or standard recycling image data, as well as the recycling video data and / or recycling image data in the return process verification data, to obtain an operation similarity value. The higher the operation similarity value, the more consistent the operation process of the return object is with the standard recycling operation process.

[0082] S15: The identification code and category code of each returned object are used as returned object data, and the supply item data in the target supply information and all returned object data are used as data to be disinfected.

[0083] S2: Analyze the data to be disinfected, obtain the current disinfection strategy, and send it, where the current disinfection strategy at least includes: a disinfection strategy for each data to be disinfected, each disinfection strategy includes: multiple disinfection nodes, the execution order of each disinfection node, the identifier of the execution device corresponding to each disinfection node, and the disinfection conditions of each disinfection node in the disinfection process.

[0084] Furthermore, the disinfection data is analyzed and the sub-steps to obtain the current disinfection strategy are as follows:

[0085] S21: Generate a first analysis sequence number for each return object data in a random order, and the first analysis sequence number increases in sequence.

[0086] Specifically, the first analysis sequence number of the return object data with the first random order is 1, the first analysis sequence number of the return object data with the second random order is 2, ..., and the first analysis sequence number of the return object data with the Nth random order is N.

[0087] S22: Take the returned object data with the smallest first analysis sequence number as the current analysis item, obtain the disinfection strategy of the current analysis item, and execute S23.

[0088] Furthermore, the sub-steps for obtaining the disinfection strategy for the current analysis item are as follows:

[0089] S221: Traverse the disinfection policy database according to the category code in the current analysis item, and use the disinfection policy data packet with the same category code as the category code in the current analysis item as the target disinfection policy data packet.

[0090] S222: Extract features from the supply item data in the current analysis item to obtain a supply item feature set.

[0091] Specifically, feature extraction is performed on the supply item data in the current analysis item through existing technology or a pre-trained feature extraction model to obtain a supply item feature set.

[0092] The supply item feature set is a set consisting of multiple supply item features that can clearly and accurately indicate the specific content of the medical task.

[0093] S223: Analyze the medical task feature set of each sub-disinfection strategy in the target disinfection strategy data package using the supply item feature set to obtain a task similarity value.

[0094] Specifically, the medical task feature set is a set consisting of multiple medical task features that can clearly and accurately indicate the specific content of the medical task.

[0095] Furthermore, the expression of task similarity value is as follows:

[0096]

[0097] Among them, Rxs r is the task similarity value between the supply project feature set and the medical task feature set of the r-th sub-disinfection strategy in the target disinfection strategy data package, r∈[1,R], R is the total number of medical task feature sets of the sub-disinfection strategy in the target disinfection strategy data package; The wth medical task feature in the medical task feature set and the vth medical task feature set of the rth sub-disinfection strategy r The correlation value between the medical task features, v r ∈[1,V r ],V r is the total number of medical task features in the medical task feature set of the rth sub-disinfection strategy, w∈[1,W], and W is the total number of supply item features in the supply item feature set.

[0098] Specifically, the correlation value between supply item features and medical task features can be determined using existing technologies or pre-trained analysis models. The larger the correlation value, the greater the similarity and correlation between the supply item features and the medical task features. The larger the task similarity value, the more consistent the supply item feature set and the medical task feature set.

[0099] S224: Analyze the task similarity values ​​using a preset task similarity threshold. If all task similarity values ​​are less than the task similarity threshold, generate and send a third alarm message. If there are one or more task similarity values ​​greater than or equal to the task similarity threshold, use the sub-disinfection strategy corresponding to the maximum value of all task similarity values ​​as the target sub-disinfection strategy, wherein the target sub-disinfection strategy includes at least: multiple disinfection nodes, the execution order of each disinfection node, the category code of the execution device corresponding to each disinfection node, and the disinfection conditions of each disinfection node in the disinfection process.

[0100] Specifically, after the data anomaly monitoring center generates the third alarm information, it sends the third alarm information to the disinfection supply platform, and the disinfection supply platform manually sets the disinfection strategy of the current analysis item according to the third alarm information.

[0101] S225: Traverse the execution device information library according to the category code of the execution device in the target sub-disinfection strategy, determine that the sub-information data packet corresponding to the disinfection supply platform that sends the return request is the target sub-information data packet, and determine that the execution device information in the target sub-information data packet that is consistent with the category code of the execution device is the target execution device information.

[0102] S226: Acquire the current execution efficiency of the execution device corresponding to the identifier of each execution device in the target execution device information.

[0103] Furthermore, the expression of the current execution efficiency is as follows:

[0104]

[0105] Among them, Pzx i The current execution efficiency of the execution device corresponding to the identifier of the i-th execution device in the target execution device information; The maximum number of disinfection tasks that can be completed by the execution device corresponding to the identifier of the i-th execution device in unit time; Drw i is the actual total number of disinfection tasks waiting to be processed by the execution device corresponding to the identifier of the i-th execution device at the current time node.

[0106] S227: The execution device corresponding to the identifier of the execution device corresponding to the maximum value of all current execution efficiencies is taken as the current execution device, and the current execution device is matched one-to-one with the corresponding disinfection node in the target sub-disinfection strategy to obtain the disinfection strategy of the current analysis item.

[0107] Specifically, after the current execution device is mapped one-to-one to the corresponding disinfection node in the target sub-disinfection strategy, each disinfection strategy includes: multiple disinfection nodes, the execution order of each disinfection node, the identifier of the execution device corresponding to each disinfection node, and the disinfection conditions for each disinfection node in the disinfection process. The disinfection conditions for each disinfection node are the current execution data of the execution device.

[0108] S23: Use the total number of returned object data to judge the first analysis sequence number of the current analysis item. If the first analysis sequence number of the current analysis item is less than the total number of returned object data, the first analysis sequence number of the current analysis item is eliminated and S22 is executed; if the first analysis sequence number of the current analysis item is equal to the total number of returned object data, the first analysis sequence number of the current analysis item is eliminated and all disinfection strategies are used as the current disinfection strategy.

[0109] S3: Receive the real-time disinfection data collected when executing the current disinfection strategy, analyze the real-time disinfection data, and generate a disinfection result. If the real-time disinfection data is consistent with the disinfection conditions, the generated disinfection result is normal and S4 is executed; if the real-time disinfection data is inconsistent with the disinfection conditions, the generated disinfection result is abnormal and the disinfection result is sent.

[0110] Specifically, when the disinfection result is abnormal, the data anomaly monitoring center generates a fourth alarm message and sends the fourth alarm message to the disinfection supply platform. The disinfection supply platform traces and reprocesses the disinfection object with an abnormal disinfection result (i.e., the returned object after completing the disinfection task) according to the fourth alarm message, for example: re-execute the disinfection task.

[0111] S4: The returned object after the disinfection task is completed is used as a storage object, and the current storage policy of the storage object is obtained and sent. The current storage policy includes: a current storage location code and a current storage condition.

[0112] Furthermore, the sub-steps for obtaining the current storage policy of the storage object are as follows:

[0113] S41: Traverse the storage information according to the category code of the storage object, determine that the storage information cache file corresponding to the disinfection supply platform that sends the return request is the target storage information cache file, and determine that the storage information data packet consistent with the category code of the storage object in the target storage information cache file is the target storage information data packet.

[0114] S42: Obtain the storability probability of each storage information data in the target storage information data packet.

[0115] Furthermore, the expression of the storable probability is as follows:

[0116]

[0117] Among them, Cgl j Wzs is the storable probability of the jth storage information data in the target storage information data packet; j Ycc is the total number of storage locations for the jth stored information data; j is the total number of storage locations in the jth storage information data that are in the storage state.

[0118] S43: The storage information data corresponding to the maximum value among all storable probabilities is used as the target storage information data.

[0119] S44: All storage locations in the target storage information data that are in an idle state are taken as selection objects, a storage location is randomly selected from the selection objects as the current storage location, and a storage location code of the current storage location is obtained.

[0120] S45: The storage position code of the current storage position is used as the current storage position code, the storage condition in the target storage information data is used as the current storage condition, and the current storage position code and the current storage condition are used as the current storage strategy.

[0121] Furthermore, after completing the storage of the storage object according to the current storage policy, the cache unit deletes the supply information that is consistent with the supply code of this return request.

[0122] S5: Obtain the stored real-time data collected after storage is completed according to the current storage policy, analyze the stored real-time data, generate a storage exception result and send it; if the stored real-time data is consistent with the current storage conditions, the generated storage exception result is normal; if the stored real-time data is inconsistent with the current storage conditions, the generated storage exception result is abnormal.

[0123] Furthermore, when the storage abnormality result is abnormal, the data abnormality monitoring center generates a fifth alarm message and sends the fifth alarm message to the disinfection supply platform. The disinfection supply platform traces and reprocesses the storage area where the storage abnormality result is abnormal and the storage objects stored in the storage area according to the fifth alarm message, for example: inspecting and adjusting the storage area, and re-disinfecting the storage objects.

[0124] If the storage abnormality result is normal, the real-time storage data is analyzed using the critical storage conditions to generate a critical result. If the real-time storage data is consistent with the critical storage conditions, the critical result is critical, and the probability of an abnormality in the storage area exceeds a preset value. A sixth alarm message is generated and sent to the disinfection supply platform, which then inspects or adjusts the storage area based on the sixth alarm message. If the real-time storage data is inconsistent with the critical storage conditions, the critical result is non-critical, and the probability of an abnormality in the storage area is within the normal range.

[0125] Furthermore, the disinfection supply platform collects, stores, and transmits real-time data according to a preset collection frequency. The specific value of the collection frequency is set according to actual conditions.

[0126] Furthermore, the data anomaly monitoring center obtains the current usage fluctuation degree of the consumable supply objects of the corresponding disinfection supply center according to the corresponding preset acquisition conditions, uses the preset usage fluctuation degree threshold to judge the current usage fluctuation degree, generates and sends the usage anomaly result. If the current usage fluctuation degree is greater than or equal to the usage fluctuation degree threshold, the generated usage anomaly result is abnormal, and the seventh alarm information is generated and sent to the disinfection supply center. The disinfection supply center traces and verifies the consumable supply objects with abnormal usage according to the seventh alarm information; if the current usage fluctuation degree is less than the usage fluctuation degree threshold, the generated usage anomaly result is normal.

[0127] The preset acquisition conditions include at least: acquisition interval duration and usage acquisition duration.

[0128] Specifically, there is an acquisition interval between the time node when the data anomaly monitoring center last obtained the current usage fluctuation degree of the consumable supply objects of the disinfection supply center and the time node when the data anomaly monitoring center next obtains the current usage fluctuation degree of the consumable supply objects of the disinfection supply center.

[0129] The usage acquisition duration refers to the duration during which the Data Anomaly Monitoring Center acquires the current usage fluctuation of the consumable supply items of the disinfection supply center. The usage acquisition duration is less than or equal to the acquisition interval duration. The usage acquisition duration can be a seconds, a minutes, a hours, a day, a week, a month, a quarter, or a year, preferably a day in this application. When the usage acquisition duration is a day, the usage acquisition duration is the number of days for the usage acquisition.

[0130] Furthermore, the expression of the degree of usage fluctuation is:

[0131]

[0132] Among them, By his the usage fluctuation degree of the h-th type of consumption-type supply object; f is the number of days for obtaining the usage; Eyc is the preset abnormality threshold; G h (q) is the normalized abnormality of the usage of the h-th type of consumption-type supply object on the qth day.

[0133] Specifically, the specific values ​​of the usage fluctuation threshold and the abnormality threshold are set according to the actual situation. h (q).

[0134] The beneficial effects achieved by this application are as follows:

[0135] (1) The data anomaly monitoring method and system of the disinfection supply platform of the present application can monitor and trace the request and return of medical devices and / or medical equipment (i.e., supplies) based on supply information and return requests.

[0136] (2) The data anomaly monitoring method and system of the disinfection supply platform of the present application can comprehensively consider the abnormal situations caused by factors such as the wrong return and / or missed return of medical devices and / or medical equipment, irregular return operations of medical devices and / or medical equipment, abnormal return time and abnormal return path during the entire supply and return process of medical devices and / or medical equipment, and quickly and accurately construct disinfection strategies and storage strategies suitable for different medical tasks, different medical devices and / or medical equipment, which can ensure the high quality and stability of cleaning and disinfection, and can flexibly adapt to the actual needs of different medical tasks, different medical devices and / or medical equipment.

[0137] (3) The data anomaly monitoring method and system of the disinfection supply platform of the present application can accurately analyze and trace abnormal situations that occur during the entire supply and return process of medical devices and / or medical equipment, and can promptly issue an alarm for abnormal situations, thereby avoiding the spread of contamination.

[0138] Although preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the underlying inventive concepts. Therefore, the scope of protection of this application is intended to include the preferred embodiments and all changes and modifications that fall within the scope of this application. Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if such changes and modifications of this application fall within the scope of protection of this application and its equivalents, then this application is intended to include such changes and modifications.

Claims

1. A data anomaly monitoring method for a disinfection supply platform, characterized in that: The steps include: S1: Receive a return request, pre-process the return request, and obtain data to be disinfected; wherein the return request includes: a supply code, a return account number, a return time, and at least one return data; each return data corresponds to a return object, and each return data includes at least: an identifier, a category code, and return process data; the return process data includes: recycling route data and recycling equipment data, and the return process data also includes: recycling video data and / or recycling image data; the data to be disinfected includes: supply item data and multiple return object data; S2: Analyze the data to be disinfected, obtain the current disinfection strategy, and send it. The current disinfection strategy at least includes: a disinfection strategy for each data to be disinfected, each disinfection strategy includes: multiple disinfection nodes, the execution order of each disinfection node, the identifier of the execution device corresponding to each disinfection node, and the disinfection conditions of each disinfection node in the disinfection process; S3: Receive the real-time disinfection data collected when executing the current disinfection strategy, analyze the real-time disinfection data, and generate a disinfection result. If the real-time disinfection data is consistent with the disinfection conditions, the generated disinfection result is normal, and S4 is executed; if the real-time disinfection data is inconsistent with the disinfection conditions, the generated disinfection result is abnormal, and the disinfection result is sent; S4: The returned object after the disinfection task is completed is used as a storage object, and the current storage policy of the storage object is obtained and sent. The current storage policy includes: a current storage location code and a current storage condition; S5: Acquire the stored real-time data collected after storage is completed according to the current storage policy, analyze the stored real-time data, generate a storage anomaly result, and send it; if the stored real-time data is consistent with the current storage conditions, the generated storage anomaly result is normal; if the stored real-time data is inconsistent with the current storage conditions, the generated storage anomaly result is abnormal; The sub-steps for pre-processing the return request and obtaining the data to be disinfected are as follows: S11: traverse the supply information in the cache unit according to the supply code in the return request, and determine the supply information whose supply code is consistent with the supply code in the return request as the target supply information; S12: Classify and screen multiple supply objects in the target supply information, and determine non-consumable supply objects as objects to be disinfected and checked; S13: Using the total number of objects to be disinfected and the identifier of each object to be disinfected as first verification data, verifying the total number of returned data and the identifiers in the returned data using the first verification data, and generating a first verification result. If the generated first verification result is normal, execute S12; if the generated first verification result is abnormal, generate and send a first alarm message; S14: Using the return process prediction data of each object to be disinfected and verified in the target supply information as return process verification data, verifying the return process data of the return data using the return process verification data, and generating a second verification result. If the generated second verification result is normal, execute S15; if the generated second verification result is abnormal, generate and send a second alarm message; S15: The identification code and category code of each returned object are used as returned object data, and the supply item data in the target supply information and all returned object data are used as data to be disinfected.

2. The data anomaly monitoring method for the disinfection supply platform according to claim 1 is characterized in that: The sub-steps of verifying the total number of returned data and the identifiers in the returned data using the first verification data and generating the first verification result are as follows: S131: The total amount of the returned data is verified using the total amount of the objects to be disinfected and verified, and a quantity verification result is generated. If the total amount of the objects to be disinfected and verified is equal to the total amount of the returned data, the generated quantity verification result is normal, and S132 is executed; If the total amount of objects to be disinfected and verified is not equal to the total amount of the returned data, the generated quantity verification result is abnormal, and the quantity verification result is used as the first verification result; S132: Using the identifier of the object to be disinfected and verified, the identifier of the returned data is verified to generate an identifier verification result. If the identifier of the object to be disinfected and verified corresponds one-to-one with the identifier in the returned data, the generated identifier verification result is normal, and the identifier verification result is used as the first verification result. If the identifier of one or more objects to be disinfected and checked is different from the identifier in the returned data, the generated identification verification result is abnormal, and the identification verification result is used as the first verification result.

3. The data anomaly monitoring method for the disinfection supply platform according to claim 2, characterized in that: The sub-steps for verifying the return process data of the returned data by using the return process verification data and generating the second verification result are as follows: S141: Obtain the actual return duration based on the supply time and return time, and use the predicted recovery duration in the return process verification data to determine the actual return duration. Generate a duration determination result. If the actual return duration is less than or equal to the predicted recovery duration, the generated duration determination result is normal, and S142 is executed. If the actual return duration is longer than the predicted recovery duration, the generated duration judgment result is abnormal and the duration judgment result is used as the second verification result; S142: The recycling route data is judged using the recycling route prediction data in the return process verification data to generate a route judgment result. If the recycling route prediction data is consistent with the recycling route data, the generated route judgment result is normal, and S143 is executed. If the recycling route prediction data is inconsistent with the recycling route data, the generated route judgment result is abnormal, and the route judgment result is used as the second verification result. S143: Using the recycling equipment configuration data in the return process verification data, the recycling equipment data is judged to generate an equipment judgment result. If the category code in the recycling equipment data is consistent with the recycling equipment configuration data, the generated equipment judgment result is normal, and S144 is executed; If the category code in the recycling equipment data is inconsistent with the recycling equipment configuration data, the generated equipment judgment result is abnormal, and the equipment judgment result is used as the second verification result; S144: Analyze the recovered video data and / or recovered image data using the recovered video standard data and / or recovered image standard data in the returned process verification data to obtain an operation similarity value, and judge the operation similarity value using a preset operation similarity threshold to generate a second verification result; If the operation similarity value is greater than or equal to the operation similarity threshold, the generated second verification result is normal; if the operation similarity value is less than the operation similarity threshold, the generated second verification result is abnormal.

4. The data anomaly monitoring method for the disinfection supply platform according to claim 3 is characterized in that: The sub-steps for analyzing the disinfection data and obtaining the current disinfection strategy are as follows: S21: Generate a first analysis sequence number for each returned object data in a random order, and the first analysis sequence number increases in sequence; S22: taking the returned object data with the smallest first analysis sequence number as the current analysis item, obtaining the disinfection strategy of the current analysis item, and executing S23; S23: Use the total number of returned object data to judge the first analysis sequence number of the current analysis item. If the first analysis sequence number of the current analysis item is less than the total number of returned object data, the first analysis sequence number of the current analysis item is eliminated and S22 is executed; if the first analysis sequence number of the current analysis item is equal to the total number of returned object data, the first analysis sequence number of the current analysis item is eliminated and all disinfection strategies are used as the current disinfection strategy.

5. The data anomaly monitoring method for the disinfection supply platform according to claim 4 is characterized in that: The sub-steps to obtain the disinfection strategy for the current analysis item are as follows: S221: Traverse the disinfection policy database according to the category code in the current analysis item, and use the disinfection policy data packet with the same category code as the category code in the current analysis item as the target disinfection policy data packet; S222: Extracting features from the supply item data in the current analysis item to obtain a supply item feature set; S223: Analyze the medical task feature set of each sub-disinfection strategy in the target disinfection strategy data package using the supply item feature set to obtain a task similarity value; S224: Analyze the task similarity values ​​using a preset task similarity threshold. If all task similarity values ​​are less than the task similarity threshold, generate and send a third alarm message. If one or more task similarity values ​​are greater than or equal to the task similarity threshold, use the sub-disinfection strategy corresponding to the maximum value among all task similarity values ​​as the target sub-disinfection strategy, wherein the target sub-disinfection strategy includes at least: multiple disinfection nodes, the execution order of each disinfection node, the category code of the execution device corresponding to each disinfection node, and the disinfection conditions of each disinfection node in the disinfection process. S225: Traverse the execution device information database according to the category code of the execution device in the target sub-disinfection strategy, determine that the sub-information data packet corresponding to the disinfection supply platform that sent the return request is the target sub-information data packet, and determine that the execution device information in the target sub-information data packet that is consistent with the category code of the execution device is the target execution device information; S226: Obtain the current execution efficiency of the execution device corresponding to the identifier of each execution device in the target execution device information; S227: The execution device corresponding to the identifier of the execution device corresponding to the maximum value of all current execution efficiencies is taken as the current execution device, and the current execution device is matched one-to-one with the corresponding disinfection node in the target sub-disinfection strategy to obtain the disinfection strategy of the current analysis item.

6. The data anomaly monitoring method for the disinfection supply platform according to claim 5, characterized in that: The expression of current execution efficiency is as follows: Among them, Pzx i The current execution efficiency of the execution device corresponding to the identifier of the i-th execution device in the target execution device information; The maximum number of disinfection tasks that can be completed by the execution device corresponding to the identifier of the i-th execution device in unit time; Drw i is the actual total number of disinfection tasks waiting to be processed by the execution device corresponding to the identifier of the i-th execution device at the current time node.

7. The data anomaly monitoring method for a disinfection supply platform according to claim 5, characterized in that: After completing the storage of the storage object according to the current storage policy, delete the supply information in the cached supply information that is consistent with the supply code of this return request.

8. The data anomaly monitoring method for a disinfection supply platform according to claim 5, characterized in that: The sub-steps to obtain the current storage policy of a storage object are as follows: S41: Traversing the storage information according to the category code of the storage object, determining that the storage information cache file corresponding to the disinfection supply platform that sent the return request is the target storage information cache file, and determining that the storage information data packet in the target storage information cache file that is consistent with the category code of the storage object is the target storage information data packet; S42: Obtaining the storability probability of each storage information data in the target storage information data packet; S43: taking the storage information data corresponding to the maximum value among all storable probabilities as the target storage information data; S44: All storage locations in the target storage information data that are in an idle state are selected as selection objects, a storage location is randomly selected from the selection objects as the current storage location, and a storage location code of the current storage location is obtained; S45: The storage position code of the current storage position is used as the current storage position code, the storage condition in the target storage information data is used as the current storage condition, and the current storage position code and the current storage condition are used as the current storage strategy.

9. A data anomaly monitoring system for a disinfection supply platform, characterized in that: include: At least one disinfection supply platform and data anomaly monitoring center; Among them, the disinfection supply platform is used to: send return requests; receive and execute the current disinfection strategy; collect and send real-time disinfection data when executing the current disinfection strategy; receive and execute the current storage strategy; collect and send real-time storage data after storage is completed according to the current storage strategy; receive storage exception results; Data anomaly monitoring center: used to execute the data anomaly monitoring method of the disinfection supply platform described in any one of claims 1-8.

Images