Data exception monitoring method and system for disinfection supply platform

Abstract

The invention discloses a data exception monitoring method and system for a disinfection supply platform, and relates to the field of data processing.The data exception monitoring method for the disinfection supply platform comprises the steps that a return request is received, the return request is preprocessed, and to-be-disinfected data is obtained; analyzing the to-be-disinfected data to obtain a current disinfection strategy and sending the current disinfection strategy; receiving disinfection real-time data collected when the current disinfection strategy is executed, and analyzing the disinfection real-time data to generate a disinfection result; if the disinfection result is normal, taking the return object after the disinfection task is completed as a storage object, and obtaining and sending a current storage strategy of the storage object; and acquiring storage real-time data acquired after storage is completed according to the current storage strategy, analyzing the storage real-time data, generating a storage abnormal result, and sending the storage abnormal result. The device can flexibly adapt to actual requirements of different medical tasks, different medical instruments and / or medical equipment, and high quality and stability of cleaning and disinfection are guaranteed.

Description

Technical Field

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

[0002] The disinfection supply center is a place for storing and managing disinfected medical supplies, equipment or other materials in medical institutions, pharmaceutical factories or other related fields. Disinfection supply refers to the work carried out by the disinfection supply center (department) in the hospital, which undertakes the cleaning, disinfection, sterilization of all reusable diagnostic and treatment instruments, utensils and articles in each department, as well as the supply of sterile articles. The disinfection requirements and storage conditions of different categories of medical devices, medical supplies and medical equipment used in different medical tasks are different. In the regional disinfection supply monitoring scenario, in the face of the processing requirements of a large number of disinfection supply instruments and / or equipment, how to improve the accuracy of disinfection, improve the disinfection quality, and timely warn and trace abnormal situations, so as to avoid the spread of pollution has become the core problem of improving the regional disinfection supply monitoring level. Traditional regional disinfection supply monitoring usually relies on manual operation, with low overall accuracy in identifying disinfection supply instruments and / or equipment, and it is impossible to quickly and accurately construct disinfection strategies and storage strategies suitable for different medical tasks, different medical devices 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 devices 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 devices and / or medical equipment, difficult to timely alarm abnormal situations, and also difficult to avoid the spread of pollution.

[0003] Therefore, there is an urgent need to provide a brand-new method and system for monitoring data anomalies in a disinfection supply platform to solve the above problems. Summary of the Invention

[0004] The purpose of the present application is to provide a method and system for monitoring data anomalies in a disinfection supply platform, which can monitor and trace the requisition 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 timely alarm abnormal situations, thereby avoiding the spread of pollution.

[0005] To achieve the above object, the present application provides a method for monitoring data anomalies in a disinfection supply platform, including the following steps: S1: Receive a return request, preprocess the return request to obtain data to be disinfected; wherein, the return request at least includes: supply code, return account, return time, and at least one return data, each return data corresponds to a return object, and each return data at least includes: identifier, category code, and return process data; the return process data at least includes: collection route data, collection equipment data, collection video data, and / or collection image data; the data to be disinfected includes: supply item data and multiple return object data; S2: Analyze the data to be disinfected to obtain the current disinfection strategy and send it, wherein the current disinfection strategy at least includes: the disinfection strategy for each data to be disinfected, and 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; S3: Receive the real-time disinfection data collected when executing the current disinfection strategy, analyze the real-time disinfection data to 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: Use the return object after completing the disinfection task as a storage object, obtain the current storage strategy of the storage object and send it, wherein the current storage strategy includes: the current storage location code and the current storage conditions; S5: Obtain the real-time storage data collected after storing according to the current storage strategy, analyze the real-time storage data to generate a storage anomaly result and send it; if the real-time storage data is consistent with the current storage conditions, the generated storage anomaly result is normal, and if the real-time storage data is inconsistent with the current storage conditions, the generated storage anomaly result is abnormal.

[0006] As described above, the sub-steps for preprocessing the return request to obtain 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 with the supply code consistent with the supply code in the return request is the target supply information; S12: Classify and screen 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 the 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 number of return data and the identifiers in the return data through the first verification data, and generate the first verification result. If the generated first verification result is normal, execute S12; if the generated first verification result is abnormal, generate the first alarm information and send it; 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 the second verification result. If the generated second verification result is normal, execute S15; if the generated second verification result is abnormal, generate the second alarm information and send it; S15: Use the identification code and category code of each return object as the return object data, and use the supply item data in the target supply information and all the return object data as the data to be disinfected.

[0007] As described above, the sub-steps for verifying the total number of return data and the identifiers in the return data through the first verification data and generating the first verification result are as follows: S131: Verify the total amount of the return data by using the total amount of the objects to be disinfected and verified, and generate a quantity verification result. If the total amount of the objects to be disinfected and verified is equal to the total amount of the return data, the generated quantity verification result is normal, and execute S132; if the total amount of the objects to be disinfected and verified is not equal to the total amount of the return data, the generated quantity verification result is abnormal, and use the quantity verification result as the first verification result; S132: Verify the identifiers of the return data by using the identifiers of the objects to be disinfected and verified, and generate an identification verification result. If the identifiers of the objects to be disinfected and verified correspond one by one to the identifiers in the return data, the generated identification verification result is normal, and use the identification verification result as the first verification result; if the identifiers of one or more objects to be disinfected and verified are different from the identifiers in the return data, the generated identification verification result is abnormal, and use the identification verification result as the first verification result.

[0008] As described above, among them, the sub-steps of verifying the return process data of the return data by using the return process verification data and generating a second verification result are as follows: S141: Obtain the actual return duration according to the supply time and the return time, and 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 S142 is executed; 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; 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: Use the recovery equipment allocation data in the return process verification data to judge the recovery equipment data, and generate an equipment judgment result. If the category code in the recovery equipment data is consistent with the recovery equipment allocation data, the generated equipment judgment result is normal, and S144 is executed; if the category code in the recovery equipment data is inconsistent with the recovery equipment allocation data, the generated equipment judgment result is abnormal, and the equipment judgment result is used as the second verification result; S144: Analyze the recovery video data and / or the recovery image data by using the recovery video standard data and / or the recovery image standard data in the return process verification data to obtain an operation similarity value, and judge the operation similarity value by 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, and if the operation similarity value is less than the operation similarity threshold, the generated second verification result is abnormal.

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

[0010] As described 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 category code consistent with the category code in the current analysis item as the target disinfection strategy data packet; S222: Extract the features of 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 packet respectively by using the supply item feature set to obtain task similarity values; S224: Analyze the task similarity values by using a preset task similarity threshold. If all the task similarity values are less than the task similarity threshold, generate and send a third alarm message; if there is 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 among all the task similarity values as the target sub-disinfection strategy, where the target sub-disinfection strategy at least includes: 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 the sub-information data packet corresponding to the disinfection supply platform that sends the return request as the target sub-information data packet, and determine the execution device information consistent with the category code of the execution device in the target sub-information data packet as 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: Use the execution device corresponding to the identifier with the maximum value among all the 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 described above, the expression of the current execution efficiency is as follows: Where Pzx i is the current execution efficiency of the execution device corresponding to the identifier of the i-th execution device in the target execution device information; is the maximum value of the total number of disinfection tasks that the execution device corresponding to the identifier of the i-th execution device can complete per 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 storing the storage object according to the current storage strategy, delete the supply information in the cache supply information that is consistent with the supply code of this return request.

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

[0014] S43: Use the storage information data corresponding to the maximum value among all the storage probabilities as the target storage information data; S44: Use all the storage positions in the target storage information data that are in the idle state as the selection objects, randomly select a storage position from the selection objects as the current storage position, and obtain the storage position code of the current storage position; S45: Use the storage position code of the current storage position as the current storage position code, use the storage conditions in the target storage information data as the current storage conditions, and use the current storage position code and the current storage conditions as the current storage policy.

[0015] This application also provides a data anomaly monitoring system for a disinfection supply platform, including: at least one disinfection supply platform and a data anomaly monitoring center; wherein, the disinfection supply platform: is used to send a return request; receive and execute the current disinfection policy; collect the real-time disinfection data when executing the current disinfection policy, and send it; receive and execute the current storage policy; collect the real-time storage data after storage according to the current storage policy, and send it; receive the storage anomaly result; the data anomaly monitoring center: is used to execute the data anomaly monitoring method of the above-mentioned 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 this application can monitor and trace the requisition and return of medical devices and / or medical equipment (i.e., supplies) according to the supply information and return requests.

[0018] (2) The data anomaly monitoring method and system of the disinfection supply platform of this application can comprehensively consider the abnormal situations caused by factors such as mis-returning and / or missing the return of medical devices and / or medical equipment, non-standard 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 alarm abnormal situations, thereby avoiding the spread of contamination. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 1 FIG. is a schematic structural diagram of an embodiment of the data anomaly monitoring system for the disinfection supply platform;

[0022] Figure 2 FIG. is a flowchart of an embodiment of the data anomaly monitoring method for the disinfection supply platform. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0024] As Figure 1 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 the real-time disinfection data when executing the current disinfection strategy and send it; receive and execute the current storage strategy; collect the real-time storage data after storage according to the current storage strategy and send it; receive the storage anomaly result.

[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 for the disinfection supply platform described below.

[0028] Furthermore, the data anomaly monitoring center 120 at least includes: a preprocessing unit, a strategy construction 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 to obtain the data to be disinfected, and sends the data to be disinfected to the policy construction unit.

[0030] The policy construction unit: analyzes the data to be disinfected to obtain the current disinfection policy and sends it; obtains the current storage policy of the storage object and sends it.

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

[0032] The second analysis unit: obtains the real-time storage data collected after storage according to the current storage policy, analyzes the real-time storage data, generates and sends a storage anomaly result.

[0033] The cache unit: is used to cache supply information and storage information.

[0034] Among them, each supply information includes: supply code, supply time, supply item data, receiving 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 identifiers of each supply object are different, and the category codes of supply objects with the same item category are the same.

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

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

[0037] The supply item data is the specific category and specific content of the medical task that will use the supplied medical devices and / or medical equipment. Different medical tasks generate different pollution factors.

[0038] The receiving 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, which is convenient for tracing the medical devices and / or medical equipment.

[0039] The supply targets are medical devices and / or medical equipment provided from the disinfection supply platform to corresponding medical tasks. Consumption categories of the supply targets: non-consumable and consumable. Non-consumable supply targets are medical devices and / or medical equipment that can be recycled, such as: scalpel, surgical scissors, hemostatic forceps, tweezers, phacoemulsification handle for ophthalmology, dental drill for stomatology, flexible endoscopes such as gastroscope and colonoscope, rigid endoscopes such as laparoscope, surgical retractor, instrument tray, etc. Consumable supply targets are medical devices and / or medical equipment that cannot be recycled, such as: disposable scalpel, disposable syringe, disposable infusion set, disposable medical mask, disposable medical glove, disposable disinfection wet wipe, disposable suction tube, and disposable suture material, etc.

[0040] An identifier is a unique identification used to identify medical devices and / or medical equipment. The identifiers of each medical device and / or medical equipment are different.

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

[0042] Among them, the expression of the predicted recycling duration is:

[0043] Tysc = Tjs - Tgy + Δtwc k ;

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

[0045] Specifically, since the actual operation duration of medical tasks will be affected by various factors and there are differences in the actual operation duration of the same medical task, therefore, the predicted recycling duration is set to the longest predicted recycling duration based on the supply time and the end time of the supply item data. Completing the recycling of the return target within the predicted recycling duration can better avoid the spread of contamination. The allowable error duration of each type of supply item data is different, and the specific value of the allowable error duration is set according to actual experience and / or actual situation.

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

[0047] The recycling equipment is equipped with category codes that comply with regulations for professional recycling equipment used to recycle and load corresponding medical devices and / or medical equipment.

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

[0049] Furthermore, when the data anomaly monitoring center receives a new supply request sent by the disinfection supply platform, the data anomaly monitoring center constructs and stores new supply information in the cached supply information according to the new supply request. After completing the current return request, the data anomaly monitoring center deletes the supply information in the cached supply information that is consistent with the supply code of this return request.

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

[0051] Furthermore, update the stored information according to the addition, deletion, and / or modification of the category codes of the storage objects of each disinfection supply platform, the addition, deletion, and / or modification of the storage locations, and the real-time changes in the current storage status.

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

[0053] Among them, the disinfection strategy database includes multiple disinfection strategy data packets, one disinfection strategy data packet corresponds to one category code, and each disinfection strategy data packet at least includes: multiple sub-disinfection strategies, one sub-disinfection strategy corresponds to a set of medical task characteristics, and the sub-disinfection strategy at least includes: 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.

[0054] The specific types of disinfection factors included in the disinfection conditions are set according to the actual situation. For example: temperature factor, humidity factor, duration factor, etc. The specific values of each disinfection factor are set according to the operation specifications or operation standards required for the corresponding medical tasks.

[0055] The category codes of the execution devices in the same category of execution devices are the same, and the category codes of the execution devices in different categories of execution devices are different.

[0056] Among them, the execution device information library includes: a plurality of sub-information data packets, one sub-information data packet corresponds to one disinfection supply platform, each disinfection supply platform includes: a plurality of execution device information, one execution device information corresponds to the category code of one execution device, and each execution device information includes: a plurality of identifiers of the execution devices. The identifiers of each execution device are all different.

[0057] Further, when the update condition is satisfied, the disinfection policy database and / or the execution device information library are updated.

[0058] Specifically, the update condition at least includes: reaching a preset update time node, and new data appears, is modified and / or deleted.

[0059] As Figure 2 shown, the present application provides a method for monitoring data anomalies of a disinfection supply platform, including the following steps:

[0060] S1: Receive a return request, preprocess the return request to obtain data to be disinfected; among them, the return request at least 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 at least includes: an identifier, a category code, and return process data; the return process data at least includes: recovery route data, recovery device data, recovery video data, and / or recovery image data; the data to be disinfected includes: supply item data and a plurality of return object data.

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

[0062] The return process data is the data of the actual recovery process of the return object collected, and the return process data at least includes: recovery route data, recovery device data, recovery video data, and / or recovery image data. For example: the video or image of returning the return object to the corresponding recovery vehicle or recovery container according to the required recovery specifications, and the path data of the recovery vehicle or recovery container returning the return object to the recovery location.

[0063] Further, the sub-steps of preprocessing the return request to obtain 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 the supply information with the supply code consistent with the supply code in the return request as the target supply information.

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

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

[0067] Specifically, query the consumption category to which the supply object belongs according to the identifier of the supply object, where the consumption category includes: non-consumable and consumable.

[0068] S13: Use the total quantity of the 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 number of the return data and the identifiers in the return data through the first verification data, and generate a first verification result. If the generated first verification result is normal, execute S12; if the generated first verification result is abnormal, generate a first alarm message and send it.

[0069] Specifically, after the data anomaly monitoring center generates the first alarm message, it sends the first alarm message to the disinfection supply platform, and the disinfection supply platform traces the return objects according to the first alarm message. Among them, the first alarm message at least includes: alarm time, reason for anomaly, supply code, return account, and receiving account.

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

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

[0072] Specifically, if the total quantity of the objects to be disinfected and verified is not equal to the total quantity of the return data, it means that there is an anomaly in the return objects in the return request, and there may be a situation of missing return objects or extra return objects.

[0073] S132: Verify the identifier of the returned data using the identifier of the object to be verified for disinfection, and generate an identifier verification result. If the identifier of the object to be verified for disinfection corresponds one by 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 one or more identifiers of the objects to be verified for disinfection are different from the identifier in the returned data, the generated identifier verification result is abnormal, and the identifier verification result is used as the first verification result.

[0074] S14: Use the return process prediction data of each object to be verified for disinfection in the target supply information as the return process verification data, verify the return process data of the returned 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 message and send it.

[0075] Specifically, after the data anomaly monitoring center generates the second alarm message, it sends the second alarm message to the disinfection supply platform, and the disinfection supply platform traces the areas and equipment that may cause pollution during the return process of the return object according to the second alarm message. Among them, the second alarm message at least includes: alarm time, reason for anomaly, supply code, return account, collection account, and return process data.

[0076] Further, the sub-steps of verifying the return process data of the returned data through the return process verification data and generating the second verification result are as follows:

[0077] S141: Obtain the actual return duration based on the supply time and the return time, use the predicted recovery 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 predicted recovery duration, the generated duration judgment result is normal, and execute S142; if the actual return duration is greater than the predicted recovery duration, the generated duration judgment result is abnormal, and the duration judgment result is used as the second verification result.

[0078] S142: Use the predicted recovery route data in the return process verification data to judge the recovery route data, and generate a route judgment result. If the predicted recovery route data is consistent with the recovery route data, the generated route judgment result is normal, and execute S143; if the predicted recovery route 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.

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

[0080] S144: Analyze the recycling video data and / or recycling image data using the recycling video standard data and / or recycling image standard data in the return process verification data to obtain an operation similarity value, and judge the operation similarity value through 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, and if the operation similarity value is less than the operation similarity threshold, the generated second verification result is abnormal.

[0081] Specifically, analyze the recycling video standard data and / or recycling image standard data and recycling video data and / or recycling image data in the return process verification data through a pre-trained neural network model or artificial intelligence model to obtain an operation similarity value. The higher the operation similarity value, the more the operation process of the current return object conforms to the standard recycling operation process.

[0082] S15: Use the identification code and category code of each return object as return object data, and use the supply item data in the target supply information and all return object data as data to be disinfected.

[0083] S2: Analyze the data to be disinfected to obtain the current disinfection strategy and send it. Among them, the current disinfection strategy at least includes: the disinfection strategy for each data to be disinfected, and 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] Further, the sub-steps for analyzing the data to be disinfected to obtain the current disinfection strategy are as follows:

[0085] S21: Generate a first analysis serial number for each return object data in a random order, and the first analysis serial numbers increase sequentially.

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

[0087] S22: Use the return object data with the smallest first analysis serial number as the current analysis item, obtain the disinfection strategy for 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 strategy database according to the category code in the current analysis item, and use the disinfection strategy data packet with the category code consistent with the category code in the current analysis item as the target disinfection strategy 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, use existing technology or a pre-trained feature extraction model to extract features from the supply item data in the current analysis item to obtain a supply item feature set.

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

[0093] S223: Use the supply item feature set to analyze each medical task feature set in the target disinfection strategy data packet to obtain a task similarity value.

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

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

[0096]

[0097] Where, Rxs r is the task similarity value between the supply item feature set and the medical task feature set of the r-th sub-disinfection strategy in the target disinfection strategy data packet, r ∈ [1, R], and R is the total number of medical task feature sets of the sub-disinfection strategies in the target disinfection strategy data packet; is the association value between the w-th medical task feature in the medical task feature set and the v r -th medical task feature in the medical task feature set of the r-th sub-disinfection strategy, v r ∈ [1, V r , V r is the total number of medical task features in the medical task feature set of the r-th 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 the supply item characteristics and the medical task characteristics can be achieved by using the existing technology or a pre-trained analysis model. The larger the correlation value, the greater the similarity and relevance between the supply item characteristics and the medical task characteristics. The larger the task similarity value, the more consistent the supply item characteristic set and the medical task characteristic set are.

[0099] S224: Analyze the task similarity value using a preset task similarity threshold. If all the task similarity values are less than the task similarity threshold, generate a third alarm message and send it; if there is 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 among all the task similarity values as the target sub-disinfection strategy, where the target sub-disinfection strategy at least includes: 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 message, it sends the third alarm message to the disinfection supply platform, and the disinfection supply platform manually sets the disinfection strategy for the current analysis item according to the third alarm message.

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

[0102] S226: Obtain 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] where Pzx i is the current execution efficiency of the execution device corresponding to the identifier of the i-th execution device in the target execution device information; is the maximum value of the total number of disinfection tasks that the execution device corresponding to the identifier of the i-th execution device can complete per 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: Use the execution device corresponding to the identifier of the execution device with the maximum value among all current execution efficiencies as the current execution device, and map the current execution device to the corresponding disinfection nodes in the target sub-disinfection policy one by one, so as to obtain the disinfection policy for the current analysis item.

[0107] Specifically, after the current execution device is mapped to the corresponding disinfection nodes in the target sub-disinfection policy one by one, each disinfection policy 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. The disinfection condition of each disinfection node is the current execution data of the execution device.

[0108] S23: Use the total number of returned object data to judge the first analysis serial number of the current analysis item. If the first analysis serial number of the current analysis item is less than the total number of returned object data, then remove the first analysis serial number of the current analysis item and execute S22; if the first analysis serial number of the current analysis item is equal to the total number of returned object data, then remove the first analysis serial number of the current analysis item and use all the disinfection policies as the current disinfection policy.

[0109] S3: Receive the real-time disinfection data collected when executing the current disinfection policy, 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 (i.e., the returned object after completing the disinfection task) with an abnormal disinfection result according to the fourth alarm message, for example: re-execute the disinfection task.

[0111] S4: Use the returned object after completing the disinfection task as the storage object, obtain the current storage policy of the storage object, and send it, where the current storage policy includes: the current storage location code and the current storage conditions.

[0112] Further, 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 the target storage information cache file corresponding to the disinfection supply platform that sends the return request, and determine the target storage information data packet in the target storage information cache file that is consistent with the category code of the storage object as the target storage information data packet.

[0114] S42: Obtain the storable 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 is the storable probability of the j-th storage information data in the target storage information data packet; Wzs j is the total number of storage positions of the j-th storage information data; Ycc j is the total number of storage positions in the storage state in the j-th storage information data.

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

[0119] S44: Take all the storage positions in the target storage information data that are in the idle state as selection objects, randomly select one storage position from the selection objects as the current storage position, and obtain the storage position code of the current storage position.

[0120] S45: Take the storage position code of the current storage position as the current storage position code, take the storage conditions in the target storage information data as the current storage conditions, and take the current storage position code and the current storage conditions as the current storage strategy.

[0121] Furthermore, after storing the storage object according to the current storage strategy, the cache unit deletes the supply information that is the same as the supply code of this return request.

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

[0123] Furthermore, when the storage anomaly result is abnormal, the data anomaly 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 with the abnormal storage anomaly result and the storage objects stored in the storage area according to the fifth alarm message. For example: repair and adjust the storage area, and re-disinfect the storage objects.

[0124] When the stored abnormal result is normal, the critical storage condition is used to analyze the stored real-time data to generate a critical result. If the stored real-time data is consistent with the critical storage condition, and the probability of an abnormality in the storage area exceeds the preset value, a sixth alarm message is generated and sent to the disinfection supply platform. The disinfection supply platform performs maintenance or adjustment on the storage area according to the sixth alarm message. If the stored real-time data is inconsistent with the critical storage condition, 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 and sends the stored real-time data according to a preset collection frequency. The specific value of the collection frequency is set according to the actual situation.

[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, judges the current usage fluctuation degree by using a preset usage fluctuation degree threshold, generates a usage anomaly result and sends it. If the current usage fluctuation degree is greater than or equal to the usage fluctuation degree threshold, the generated usage anomaly result is abnormal, a seventh alarm message is generated and sent to the disinfection supply center, and the disinfection supply center traces and verifies the consumable supply objects with abnormal usage degree according to the seventh alarm message; if the current usage fluctuation degree is less than the usage fluctuation degree threshold, the generated usage anomaly result is normal.

[0127] Among them, the preset acquisition conditions at least include: acquisition interval duration and usage acquisition duration.

[0128] Specifically, there is an interval of an acquisition interval duration between the acquisition 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 acquisition 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 when the data anomaly monitoring center obtains the current usage fluctuation degree of the consumable supply objects of the disinfection supply center each time. 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 days, a weeks, a months, a quarters or a years. This application preferably uses a days. When the usage acquisition duration is a days, the usage acquisition duration is the usage acquisition days.

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

[0131]

[0132] Among them, Byl hThe degree of usage fluctuation of the supply object of the hth type of consumable; f is the number of days to obtain usage; Eyc is the preset abnormal degree threshold; G h (q) is the normalized abnormal degree of the usage of the supply object of the hth type of consumable on the qth day.

[0133] Specifically, the specific values of the usage fluctuation degree threshold and the abnormal degree threshold are set according to the actual situation. G can be obtained through the prior art 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 this application can monitor and trace the requisition and return of medical devices and / or medical equipment (i.e., supplies) according to supply information and return requests.

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

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

[0138] Although the preferred embodiments of this application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the protection scope of this application is intended to be interpreted to include the preferred embodiments and all changes and modifications that fall within the scope of this application. Obviously, those skilled in the art can make various changes and variations to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the protection of this application and its equivalent technologies, this application also intends to include these modifications and variations.

Claims

1. A data anomaly monitoring method for a disinfection supply platform, characterized in that: The steps include: S1: receiving a return request, preprocessing the return request, and obtaining data to be disinfected; wherein the return request at least 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 at least includes: an identifier, a category code, and return process data; the return process data at least includes: recycling route data, recycling equipment data, 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, wherein 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 condition 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: taking the returned object after completing the disinfection task as the storage object, obtaining the current storage strategy of the storage object, and sending it, wherein the current storage strategy includes: the current storage location code and the 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 and send the storage exception result; if the storage real-time data is consistent with the current storage conditions, the generated storage exception result is normal; if the storage real-time data is inconsistent with the current storage conditions, the generated storage exception result is abnormal.

2. The data anomaly monitoring method for the disinfection supply platform according to claim 1 is characterized in that: The sub-steps of preprocessing 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: The total amount of objects to be disinfected and the identifier of each object to be disinfected are used as the first verification data, and the total amount of returned data and the identifier in the returned data are verified by the first verification data, and a first verification result is generated. If the generated first verification result is normal, S12 is executed; if the generated first verification result is abnormal, a first alarm message is generated and sent; S14: using the return process prediction data of each object to be disinfected and checked in the target supply information as the return process verification data, verifying the return process data of the return data through 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.

3. The data anomaly monitoring method for the disinfection supply platform according to claim 2 is characterized in that: The sub-steps of verifying the total number of returned data and the identifier in the returned data by using the first verification data and generating the first verification result are as follows: S131: Use the total amount of the objects to be disinfected and verified to verify the total amount of the returned data, and generate a quantity verification result. 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 returned data, the generated quantity verification result is abnormal, and the quantity verification result is used as the first verification result; S132: Use the identifier of the object to be disinfected and verified to verify the identifier of the returned data, and 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.

4. The data anomaly monitoring method for the disinfection supply platform according to claim 3 is characterized in that: The sub-steps of verifying the return process data of the returned data by returning the process verification data and 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 predicted recovery 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 predicted recovery time, the generated time judgment result is normal, and S142 is executed; If the actual return time is longer than the predicted recovery time, the generated time judgment result is abnormal, and the time judgment result is used as the second verification result; S142: using the recycling route prediction data in the return process verification data to judge the recycling route data, generating 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 to judge the recycling equipment data, generating 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 the recovered image data using the recovered video standard data and / or the 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.

5. The data anomaly monitoring method for the disinfection supply platform according to claim 4, characterized in that: The sub-steps for analyzing the data to be disinfected and obtaining the current disinfection strategy are as follows: 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; 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 number of the current analysis item. If the first analysis number of the current analysis item is less than the total number of returned object data, the first analysis number of the current analysis item is eliminated and S22 is executed; if the first analysis number of the current analysis item is equal to the total number of returned object data, the first analysis number of the current analysis item is eliminated and all disinfection strategies are used as the current disinfection strategies.

6. The data anomaly monitoring method for the disinfection supply platform according to claim 5, characterized in that: The sub-steps to obtain the disinfection strategy for 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 category code consistent with the category code in the current analysis item as the target disinfection strategy data packet; S222: extracting features from the supply item data in the current analysis item to obtain a supply item feature set; S223: using the supply item feature set to analyze the medical task feature set of each sub-disinfection strategy in the target disinfection strategy data package to obtain a task similarity value; S224: Analyze the task similarity values ​​using a preset task similarity threshold. If all the 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 condition 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 sends the return request is the target sub-information data packet, and determine that the execution device information consistent with the category code of the execution device in the target sub-information data packet is the target execution device information; S226: Acquire 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 by one with the corresponding disinfection node in the target sub-disinfection strategy, so as to obtain the disinfection strategy of the current analysis item.

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

8. The data anomaly monitoring method for a disinfection supply platform according to claim 6, 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.

9. The data anomaly monitoring method for a disinfection supply platform according to claim 6, 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 sends the return request is the target storage information cache file, and determining 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; 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: taking all storage locations in the target storage information data that are in an idle state as selection objects, randomly selecting a storage location from the selection objects as the current storage location, and obtaining a storage location code of the current storage location; 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.

10. 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 a return request; receive and execute the current disinfection strategy; collect and send the real-time disinfection data when executing the current disinfection strategy; receive and execute the current storage strategy; collect and send the real-time storage data after storage according to the current storage strategy; receive storage abnormality results; Data anomaly monitoring center: used to execute the data anomaly monitoring method of the disinfection supply platform described in any one of claims 1-9.

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