Community mutual aid endowment service information management method, system and medium

Abstract

This invention provides a method, system, and medium for managing information on community-based mutual-aid elderly care services. The method includes: receiving elderly care service records submitted by users; performing basic information verification on the records to obtain basic information verification results; comparing the hash fingerprint of each proof image in the proof image set with the hash fingerprint of historical proof images to generate image duplication identification results; generating high-frequency upload identification results based on the frequency of elderly care service records uploaded by the current user within a preset time period; generating a comprehensive risk assessment result based on the basic information verification results, image duplication identification results, and high-frequency upload identification results; outputting the basic information verification results, image duplication identification results, high-frequency upload identification results, and comprehensive risk assessment results to the administrator terminal; and upon receiving confirmation information, determining the elderly care service record to be valid and identifying it as a reliable record. This solution can improve the management reliability and continuous operation capability of community-based mutual-aid elderly care services.

Description

Technical Field

[0001] This invention relates to the field of smart elderly care technology, and more specifically to a method, system and medium for managing information on community mutual assistance elderly care services. Background Technology

[0002] As my country's population continues to age, community-based home care and mutual assistance for the elderly have gradually become an indispensable and important part of my country's elderly care service system. Traditional professional elderly care services rely heavily on professional institutions, full-time personnel, and continuous financial investment, resulting in high operating costs and relatively limited service coverage, making it difficult to fully meet the dispersed, immediate, and frequent daily care needs of the elderly. However, within communities, there exists a large number of spontaneously formed neighborly mutual assistance behaviors for the elderly, providing a natural social foundation for filling this service gap.

[0003] However, most current neighborhood assistance for the elderly occurs sporadically, lacks unified recording and management, and is difficult to accumulate into a continuously usable service resource.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] The present invention is proposed in view of the above-mentioned problems. According to one aspect of the present invention, a method for managing information on community mutual assistance elderly care services is provided, comprising: The system receives elderly care service records submitted by users, which include at least the service type, service duration range, service location, collection of supporting images, and description of the service behavior. The basic information verification of the elderly care service records is performed to obtain the basic information verification result. The basic information verification includes at least one of data integrity verification, format legality verification, basic consistency verification and image quality verification. The hash fingerprint of each proof image in the proof image set is compared with the hash fingerprint of the historical proof images to generate a duplicate image identification result. The historical proof images and the proof image set are submitted by the same user, have the same service type, and have the same service location. The time difference between the submission time of the historical proof images and the submission time of the proof image set is less than a preset time difference threshold. Based on the frequency with which the user uploads records of elderly care services within a preset time period, a high-frequency upload identification result is generated; Based on the basic information verification results, the image duplicate recognition results, and the high-frequency upload recognition results, a comprehensive risk assessment result is generated. The comprehensive risk assessment result is used to indicate the risk status of the current received elderly care service record. The system outputs the basic information verification result, the image duplicate recognition result, the high-frequency upload recognition result, and the comprehensive risk assessment result to the administrator terminal. Upon receiving confirmation from the administrator terminal, the system determines that the elderly assistance service record is valid and identifies it as a reliable record for counting.

[0006] For example, generating a high-frequency upload identification result based on the frequency with which the user uploads elderly care service records within a preset time period includes: The first frequency of the user uploading elderly care service records within a first preset time period and the second frequency of the user uploading elderly care service records within a second preset time period are counted. If the first frequency is greater than the first frequency threshold, or the second frequency is greater than the second frequency threshold, the high-frequency upload recognition result is determined to be abnormal; otherwise, the high-frequency upload recognition result is determined to be normal.

[0007] For example, comparing the hash fingerprint of each proof image in the proof image set with the hash fingerprint of historical proof images includes: For any proof image in the set of proof images Based on the hash fingerprint of each image, calculate the Hamming distance between the proof image and each of the historical proof images. If the Hamming distance between the proof image and any of the historical proof images does not exceed a preset Hamming distance threshold, the proof image is determined to be a similarity anomalous image. If the number of similar abnormal images in the proof image set exceeds the abnormal number threshold, the image duplication identification result is determined to be an image duplication; otherwise, the image duplication identification result is determined to be an image non-duplication. Preferably, for any proof image in the set of proof images, the hash fingerprint of the proof image is obtained in the following way: Perform a discrete cosine transform on the preprocessed proof image to extract the low-frequency coefficient region of the preset size in the upper left corner; A binary perceptual hash fingerprint is generated based on the relationship between the low-frequency coefficient of each pixel in the low-frequency coefficient region and the average low-frequency coefficient of the low-frequency coefficient region.

[0008] For example, the basic information verification includes image quality verification; the basic information verification of the elderly care service record includes: For any proof image in the set of proof images, The proof image is preprocessed, and the preprocessing includes at least grayscale processing, size normalization, and noise reduction. Calculate the Laplace variance of the proof image to obtain the image's sharpness; If the clarity of the proof image is less than the clarity threshold, the proof image is determined to be a blurry image; Calculate the mean of the Laplacian variance of each proof image in the proof image set to obtain the average sharpness of the proof image set; If the average sharpness is less than the sharpness threshold or the proportion of blurry images in the proof image set exceeds a preset ratio, the image quality verification result is determined to be insufficient; otherwise, the image quality verification result is determined to be good. The basic information verification result includes the image quality verification result; And / or, The basic information verification includes format validity verification. The basic information verification of the elderly care service record includes: The format validity check result is determined to be valid only if the elderly care service record meets the following requirements: the text information of the service location includes a valid point description; the text length of the service behavior description is within a preset text length range; the service duration range belongs to a standard duration set; the format of each image in the proof image set belongs to a preset format set, the size of each image does not exceed a preset image size, and the resolution of each image is not lower than a preset resolution threshold.

[0009] For example, the basic information verification includes basic consistency verification; the basic information verification of the elderly care service record includes: Determine whether the service duration interval is within the duration range corresponding to the service type to obtain a duration interval matching result. If the service duration interval is within the duration range corresponding to the service type, the duration interval matching result is a match; otherwise, the duration interval matching result is a mismatch. Determine whether the standard location corresponding to the service location is in a preset location set to obtain a location coverage result. If the standard location corresponding to the service location is in the preset location set, the location coverage result is coverage; otherwise, the location coverage result is no coverage. A basic consistency score is calculated based at least on the time interval matching results and the location coverage results; If the basic consistency score is greater than or equal to the consistency threshold, the basic consistency result is determined to be of normal basic consistency; otherwise, the basic consistency result is determined to be of abnormal basic consistency. The basic information verification result includes the basic consistency result; Preferably, before determining whether the standard location corresponding to the service location is in a preset location set, the method further includes: The text information of the service locations is standardized. Calculate the cosine similarity between the text information of the service location and each standard location in the standard location dictionary; The standard location with the highest cosine similarity to the service location is determined as the candidate standard location; When the cosine similarity between the candidate standard location and the service location is greater than a first similarity threshold, the standard location corresponding to the service location is determined as the candidate standard location; Preferably, the elderly care service record also includes the service occurrence time, and the basic information verification of the elderly care service record further includes: Calculate the time difference between the upload time of the elderly assistance service record and the time when the service occurred; When the time difference is within a preset time difference range, the service time consistency result is determined to be consistent; otherwise, the service time consistency result is determined to be inconsistent. The calculation of a basic consistency score, based at least on the time interval matching results and the location coverage results, includes: The basic consistency score is calculated based on the time interval matching results, the location coverage results, and the service time consistency results.

[0010] For example, the basic information verification includes data integrity verification, and the basic information verification of the elderly care service record includes: The integrity of each key information item in the elderly care service record is verified to obtain the individual integrity result of each key information item. The key information items include service type, service duration range, service location, set of supporting images, and description of service behavior. Based on the individual integrity results of each of the key information items, the data integrity result is determined, and the basic information verification result includes the data integrity result; Specifically, for the service type, the service type is considered complete if it is not empty; for the service duration interval, the service duration interval is considered complete if it is not empty; for the service location, the service location is considered complete if the text length of the service location is greater than a first preset length; for the proof image set, the proof image set is considered complete if the number of images in the proof image set is greater than a minimum number threshold; and for the service behavior description, the service behavior description is considered complete if the text length of the service behavior description is greater than a second preset length. Preferably, determining the data integrity result based on the individual integrity results of each of the key information items includes: The individual integrity results of each key information item are binarized, and the weighted sum of the individual integrity results of each key information item is calculated to obtain a data integrity score. If the data integrity score is greater than or equal to the integrity threshold, the data integrity result is determined to be complete; otherwise, the data integrity result is determined to be incomplete.

[0011] Exemplarily, the method further includes: Based on the administrator's feedback on any one of the following results: the basic information verification result, the image duplicate recognition result, and the high-frequency upload recognition result, the threshold used to determine the result is adjusted; the feedback information is any one of the following: threshold too strict, threshold too lenient, and judgment appropriate; For any of the basic information verification results, the image duplicate recognition results, and the high-frequency upload recognition results, if the proportion of identical feedback information received within a preset time exceeds a proportion threshold, the threshold used to determine the result is adjusted according to the feedback information. Specifically, when the feedback information indicates that the threshold is too strict, the threshold in this operation is adjusted in the lenient direction by a preset step size; when the feedback information indicates that the threshold is too lenient, the threshold in this operation is adjusted in the strict direction by a preset step size; when the feedback information indicates that the threshold is appropriate, the threshold is kept unchanged. Preferably, after adjusting the threshold in the operation used to determine the result, the method further includes: Apply boundary constraints to the adjusted threshold so that the adjusted threshold is within the threshold range corresponding to that threshold.

[0012] Exemplarily, the method further includes: When the submitted elderly assistance service record is valid, the single elderly assistance incentive value is calculated based on the preset basic service score, the type weighting value corresponding to the service type of the elderly assistance service record, the duration weighting value corresponding to the service duration range of the elderly assistance service record, the continuous participation weighting value corresponding to the number of valid elderly assistance services provided by the user within the target duration, and the risk deduction value corresponding to the comprehensive risk assessment result of the elderly assistance service record. Preferably, the method further includes: The total elderly care incentive value for the user within the preset period is calculated by accumulating the incentive value for each individual instance of elderly care received by the user within the preset period. Based on each user's total elder care incentive value, perform at least one of the following operations: For users in the same region, they are sorted from largest to smallest according to their total elderly assistance incentive value to generate an elderly assistance ranking list within the preset period; For any user, generate a virtual honor badge corresponding to the range of incentive values ​​that the user's total elderly care incentive value falls within; For any user, a list of redeemable items is generated based on the user's total incentive value for helping the elderly. The redemption points for each item in the list are less than the user's total incentive value for helping the elderly. Preferably, the method further includes: Calculate the administrator's incentive value based on the administrator's promotion behavior quantification value and review behavior quantification value; The quantitative values ​​for promotional behavior include the number of effective promoters; the quantitative values ​​for review behavior include the number of effective reviews, the timeliness of review, the consistency rate of review, and the deduction value for abnormal reviews. The administrator is used to review the corresponding elderly care service records based on the basic information verification results, the image duplicate recognition results, the high-frequency upload recognition results, and the comprehensive risk assessment results.

[0013] According to another aspect of the present invention, a community mutual assistance elderly care service information management system is provided. The system includes a central platform and a user terminal, wherein the central platform is communicatively connected to the user terminal, and the central platform is used to implement the above-described method.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program / instructions that, when executed by a processor, implement the method described above.

[0015] In the aforementioned technical solution, by receiving each elderly care service record and integrating multiple identification and verification results, a comprehensive risk level assessment of the service record is completed. Finally, various verification and risk assessment results are output intuitively. On one hand, this approach establishes a unified elderly care service record mechanism, enabling the procedural recording and standardized retention of elderly care behaviors, thus solving the problem of difficult structured record-keeping of elderly care behaviors in existing technologies. On the other hand, by combining multiple verifications, automated risk control rule processing of elderly care behavior records can be achieved, improving the accuracy of elderly care fact verification, reliable counting, and anomaly identification. This provides more accurate quantitative information for elderly care service records, assisting managers in accurately controlling the actual situation of mutual-aid elderly care services, providing solid data support for the refined and scientific management of community mutual-aid elderly care services, and helping the community mutual-aid elderly care mechanism achieve stable and long-term operation. Furthermore, this solution combines automatic detection with administrator confirmation information to determine the validity of the record. This method, combined with manual judgment, avoids the misjudgment problem caused by single-algorithm judgment, balancing strict risk control and business flexibility, further improving the management reliability and continuous operation capability of community mutual-aid elderly care services.

[0016] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0018] Figure 1 A schematic flowchart illustrating a community mutual aid elderly care service information management method according to an embodiment of the present invention is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0020] As described above, current neighborhood elderly care activities are mostly scattered and lack unified recording and management, making it difficult to accumulate them into a sustainable and usable service resource. Specifically, on the one hand, existing community elderly care services rely heavily on manual registration, WeChat group feedback, or verbal confirmation. Different communities and different staff members have inconsistent registration standards, resulting in inconsistent recorded content. Subsequent statistical analysis easily leads to omissions, errors, duplicates, and inability to verify data, making it difficult to form a continuously accumulated and reusable data foundation. This makes it impossible to automatically extract, verify, and aggregate elderly care records through procedural means, and hinders the structured recording of elderly care activities. On the other hand, current judgments on elderly care activity records still rely heavily on subjective judgment. There is a lack of procedural verification mechanisms for the relationships between images, locations, times, durations, and historical behaviors, resulting in insufficient authenticity and credibility of the records and increasing the difficulty of subsequent review. In short, there is currently a lack of a unified method for recording and managing scattered community mutual aid elderly care activities. In view of this, the present invention provides a method, system, and medium for managing information on community mutual-aid elderly care services. This method can automatically and quantitatively assess the overall reliability of each uploaded community mutual-aid elderly care service record, thereby providing accurate and objective evaluation criteria for the validity review of each elderly care service record. Furthermore, it provides solid data support for the refined and scientific management of community mutual-aid elderly care services, helping the community mutual-aid elderly care mechanism achieve stable and long-term operation. The method, system, and medium are described in detail below.

[0021] According to one aspect of the present invention, a method for managing information on community mutual-aid elderly care services is provided. Figure 1 A schematic flowchart illustrating a community mutual-aid elderly care service information management method according to an embodiment of the present invention is shown. Figure 1 As shown, the method includes the following steps: S110, S120, S130, S140, S150, S160 and S170.

[0022] In step S110, the elderly care service record submitted by the user is received. The elderly care service record includes at least the service type, service duration range, service location, a set of supporting pictures, and a description of the service behavior.

[0023] In this document, users can upload their service records using terminal devices (such as mobile phones and computers) after participating in community-based mutual assistance elderly care services. In some embodiments, users can pre-register an account on the mutual assistance elderly care service platform via a mobile terminal and select one or more levels from their district / county, street / township, and community to establish a correspondence between the user and the service area, facilitating community-based management. The platform can create a user profile for each user and register their community affiliation information. This information is used for subsequent management activities such as identifying elderly care users, community administrator review, ranking lists, and incentive statistics. Users in this document include, but are not limited to, community residents, volunteers, family members of the elderly, and mutual assistance service providers.

[0024] In this example, the elderly care service record should include at least the service type, service duration range, service location, a collection of supporting images, and a description of the service behavior.

[0025] The service types may include, but are not limited to: food delivery, cleaning and tidying, personal care, dressing and mobility assistance, health rehabilitation, companionship and visits, errand running, medical accompaniment, and other elderly assistance. In practice, the service type options can be preset, allowing users to choose a service type from these preset options.

[0026] The service duration range refers to the total duration of this elderly assistance service. This service duration range can be set by the user directly inputting a specific time, or it can be a preset option. In some embodiments, different duration ranges can be preset as options. For example, a fixed duration option set can be provided on the upload interface: ,in, It means within 15 minutes. It means 15 to 30 minutes. Indicates 30 to 60 minutes. This indicates a duration of 60 minutes or more. Users can select one of these as the service duration range field.

[0027] Both service location and service behavior description can be filled in by the user through direct text input. The service location can be a location description provided by the user. The service behavior description is used to help administrators understand the elderly assistance scenario.

[0028] The evidence image set is important proof that the elderly assistance has occurred. Users can add images by uploading them, and each set can include at least one supporting image. The evidence image set can be represented as: .

[0029] Elderly care service records may also include the submitting user identifier, the user's community identifier, the recipient's identifier, and the record source terminal identifier. These identifiers can be automatically generated when a user uploads an elderly care service record. For example, they can be linked to the user's account on their terminal device. The recipient identifier can include the recipient's name, number, and type, which can be filled in by the user or automatically generated based on the service location entered by the user; details are omitted here.

[0030] Elderly care service records can also include the service date. This field is optional. If the user does not fill in this field, the system will not automatically invalidate the record due to its absence. This reduces the burden on the front-end data entry and avoids false or inaccurate input caused by mandatory field completion.

[0031] In step S120, basic information verification is performed on the elderly care service records to obtain the basic information verification results. The basic information verification includes at least one of data integrity verification, format legality verification, basic consistency verification, and image quality verification.

[0032] After obtaining the elderly care service records, basic information verification can be performed on the information in the records to obtain the basic information verification results. In this example, basic information verification includes at least one of data integrity verification, format validity verification, basic consistency verification, and image quality verification. Correspondingly, the basic information verification results can include the results corresponding to each of the above verifications. These results can be represented in a binary form. For example, the results of data integrity verification (i.e., data integrity results) include information complete (1) and information incomplete (0). The results of format validity verification (i.e., format validity verification results) include format valid (1) and format invalid (0). The results of basic consistency verification (i.e., basic consistency results) include basic consistency normal (1) and basic consistency abnormal (0). The results of image quality verification (i.e., image quality verification results) include image quality good (1) and image quality insufficient (0).

[0033] In step S130, the hash fingerprint of each proof image in the proof image set is compared with the hash fingerprint of the historical proof images to generate a duplicate image identification result. The historical proof images and the proof image set have the same submitting user, the same corresponding service type, and the same corresponding service location, and the time difference between the submission time of the historical proof images and the submission time of the proof image set is less than a preset time difference threshold.

[0034] The preset time difference threshold can be set as needed, such as 24h, 48h, etc. In this example solution, each set of evidence images obtained can be partitioned using a keyBy partitioning method based on a combination of "user identifier + service type + service location code + time bucket". This ensures that elderly care activities performed by the same user in similar time periods, under the same service type, and near the same location are processed in the same logical partition.

[0035] In community-based mutual-aid elderly care scenarios, duplicate uploads do not necessarily mean identical original images. Users may have cropped, compressed, or adjusted the brightness of the same image, or taken nearly identical photos consecutively. Relying solely on filename or pixel-by-pixel comparison is insufficient to reliably identify such duplicates. Therefore, this example introduces perceptual hash fingerprinting to effectively prevent the same elderly care event from being split, submitted multiple times, and scored repeatedly. The hash fingerprint is used to identify whether the currently submitted proof image is a duplicate of historical proof images, thus more accurately determining the image duplication result.

[0036] In step S140, a high-frequency upload identification result is generated based on the frequency with which the current user uploads elderly care service records within a preset time period.

[0037] In community-based mutual-aid elderly care scenarios, abnormal behavior in submitting elderly care records is often not detectable by looking at a single record, but rather requires observation over a period of time. Normal elderly care behavior generally follows a natural rhythm, and the frequency of submissions is relatively dispersed. Abnormal elderly care behavior, on the other hand, may involve multiple uploads within a relatively short time window (i.e., a preset duration). Therefore, the risk of currently submitted elderly care service records can be assessed based on the current user's upload frequency.

[0038] In step S150, based on the basic information verification results, image duplicate recognition results, and high-frequency upload recognition results, a comprehensive risk assessment result is generated. The comprehensive risk assessment result is used to indicate the risk status of the currently received elderly care service record.

[0039] The comprehensive risk assessment results are positively correlated with the risk status of the current elderly care service record, and can reflect the risk status more intuitively.

[0040] In step S160, the basic information verification results, image duplicate recognition results, high-frequency upload recognition results, and comprehensive risk assessment results are output to the administrator terminal.

[0041] In step S170, upon receiving confirmation information from the administrator terminal, the elderly assistance service record is determined to be valid, and the elderly assistance service record is identified as a reliable count.

[0042] The above results can be output to the administrator's terminal, allowing the administrator to assess the authenticity and quality of the elderly care service records. The administrator can view supplementary review information comprised of basic information verification results, image duplication detection results, high-frequency upload detection results, and comprehensive risk assessment results, and then quickly review the elderly care service records against these results. The administrator can then proceed with the following actions based on the review outcome: approval (at which point the administrator can send a confirmation message), rejection, request for supplementary explanation, or transfer for further review. Once a record is approved, it is recognized as a valid elderly care service event and recorded as a reliable event. Reliable recording must adhere to at least the following rules: 1. Each independent, authentic, and verified elderly care service event will be counted as one valid assistance instance; 2. Even if multiple photos of the scene are submitted for the same event, it will only be counted once; 3. Only one similar record submitted by the same user within the same time window is allowed to be recorded. 4. Rejection records will not be counted, scored, or included in the leaderboard; 5. Records that are pending further explanation or review will not be counted.

[0043] Through this process, elderly assistance records must first undergo risk control screening, then proceed to manual review, and only after passing the review can the record be recorded. Thus, the platform establishes a processing mechanism of "risk control first, review second, and recording only after approval."

[0044] The aforementioned technical solution receives each elderly care service record and integrates multiple identification and verification results to determine the comprehensive risk level of the service record. It then intuitively outputs various verification and risk assessment results. On one hand, this approach establishes a unified elderly care service record mechanism, enabling the procedural recording and standardized retention of elderly care behaviors, thus solving the problem of structured record-keeping of elderly care behaviors in existing technologies. On the other hand, by combining multiple verifications, it enables automated risk control rule processing of elderly care behavior records, improving the accuracy of fact verification, reliable counting, and anomaly identification. This provides more accurate quantitative information for elderly care service records, assisting managers in accurately controlling the actual situation of mutual-aid elderly care services and providing solid data support for the refined and scientific management of community mutual-aid elderly care services, thus helping the community mutual-aid elderly care mechanism achieve stable and long-term operation. Furthermore, this solution combines automatic detection with administrator confirmation information to determine the validity of the record. This combination of automatic detection and manual judgment avoids misjudgment problems caused by single-algorithm judgment, balancing strict risk control with business flexibility, further improving the management reliability and continuous operation capability of community mutual-aid elderly care services.

[0045] For example, the basic information verification includes data integrity verification. Step S120, which verifies the basic information of the elderly care service record, may specifically include the following steps S121 and S122.

[0046] In step S121, the integrity of each key information item in the elderly care service record is verified to obtain the individual integrity result of each key information item. The key information items include service type, service duration range, service location, set of supporting images, and description of service behavior.

[0047] In step S122, the data integrity result is determined based on the individual integrity result of each key information item. The basic information verification result includes the data integrity result.

[0048] Specifically, for service type, the service type is considered complete if it is not empty; for service duration range, the service duration range is considered complete if it is not empty; for service location, the service location is considered complete if the text length of the service location is greater than a first preset length; for proof image set, the proof image set is considered complete if the number of images in the proof image set is greater than a minimum number threshold; for service behavior description, the service behavior description is considered complete if the text length of the service behavior description is greater than a second preset length. The first preset length, second preset length, and minimum number threshold can be selected as needed. For example, the first preset length can be 1, the second preset length can be 15, and the minimum number threshold can be 2. Additionally, to ensure upload performance and prevent resource overload, a maximum number threshold can be set to limit the number of images; this maximum number threshold can be 5.

[0049] In this example scheme, service type(s), service duration range(d), and service location(s) can be specified. l raw The service behavior description (m) and image set (I) are checked item by item, and a field validity indicator is generated. , , , , The value is 1 if the corresponding field exists and meets the minimum requirement, otherwise it is 0. This applies when the service type has been selected (i.e., is not empty). When the original text of the service location is not empty and reaches the minimum length. The service behavior description must be at least 15 characters long. When the service duration range has been selected ; Prove that the number of images in the image set is between 2 and 5. .

[0050] In some embodiments, determining the data integrity result based on the individual integrity results of each key information item includes: binarizing the individual integrity results of each key information item and calculating a weighted sum of the individual integrity results of each key information item to obtain a data integrity score; if the data integrity score is greater than or equal to an integrity threshold, the data integrity result is determined to be information complete; otherwise, the data integrity result is determined to be information incomplete. The integrity threshold can be selected as needed, for example, 0.8. After obtaining the binarized form of each individual integrity result (i.e., the field validity indicator mentioned above), a weighted sum of each item can be calculated, and the weight values ​​of each item can be selected as needed. In a specific embodiment, the data integrity score can be calculated using the following formula: ;when When the data integrity result is determined to be incomplete, the data integrity result is... =1; otherwise, =0. In this case, a flag can be added and written to the replenishment material queue, and a notification message can be sent to promptly remind the user to replenish materials. This can reduce communication costs and improve user experience.

[0051] The aforementioned technical solution conducts standardized basic information verification on elderly care service records. It first verifies the completeness of each key information item, including service type, service duration, service location, supporting image collection, and service behavior description, accurately obtaining the completeness status of each item. Then, it integrates all individual verification results to uniformly determine the overall data integrity, accurately assessing data integrity by determining the completeness of each key information item. Furthermore, this solution focuses only on key information dimensions during integrity assessment. This design fully considers the unique characteristics of community-based mutual-aid elderly care scenarios: the sources of elderly care service records are highly dispersed. On the one hand, many volunteers participating in elderly care services are older and generally lack proficiency in operating digital devices, making it difficult to submit complex structured data completely. On the other hand, many service records are hastily uploaded after the service ends, easily resulting in missing information or non-standard formatting. Based on this, the solution only lists service type, service location, service description, service duration, and supporting image materials as mandatory key information for assessment, appropriately reducing the integrity requirements for non-core data. This differentiated assessment strategy effectively reduces the user's operational burden, avoids dampening their enthusiasm for participation due to cumbersome reporting requirements, and ensures the integrity of the core evidence chain required for service authenticity verification, thus greatly improving the efficiency and operability of the assessment work.

[0052] For example, the basic information verification includes format validity verification. Step S120, which verifies the basic information of the elderly care service record, may include the following steps S123: the format validity verification result is determined to be valid if the elderly care service record meets at least the following requirements: the text information of the service location includes a valid point description; the text length of the service behavior description is within a preset text length range; the service duration range belongs to the standard duration set; the format of each image in the proof image set belongs to the preset format set, the size of each image does not exceed the preset image size, and the resolution of each image is not lower than the preset resolution threshold.

[0053] In this example solution, the validity of the format is considered. In community-based mutual assistance for elderly care, issues such as inconsistent image formats, excessively short descriptions, and arbitrary durations are common when submitting elderly care service records. Therefore, the validity of the format is considered as one of the criteria for evaluating the effectiveness of elderly care service records.

[0054] In one specific embodiment, the text information of the service location includes a valid point description (which can be determined using a large language model). The text length of the service behavior description should be within a preset text length range [15, 200]. The service duration range satisfies... Each image file is limited to a preset format set (JPG, JPEG, PNG, or WEBP). The size of a single image file cannot exceed the preset image size of 10MB. The image resolution must not be lower than the preset resolution threshold. At this point, the format validity check result can be considered as valid. =0, otherwise, =1.

[0055] In some embodiments, when the format is invalid, a prompt message can be output to the user's terminal device to remind the user to make timely corrections and improvements. After the user makes the corrections, the record can be re-evaluated based on the corrected record, which will not be elaborated further.

[0056] The above scheme uses format legality as one of the bases for evaluating the validity of elderly care service records. By outputting the format legality results, on the one hand, it can improve the accuracy of the evaluation of elderly care service records, and on the other hand, it can provide auditors with clear format legality information, making it easier for auditors to determine the reasons for the records being unqualified.

[0057] For example, the basic information verification includes basic consistency verification; step S120, which verifies the basic information of the elderly care service record, may include the following steps S123, S124, S125 and S126.

[0058] In step S123, it is determined whether the service duration interval is within the duration range corresponding to the service type to obtain the duration interval matching result. If the service duration interval is within the duration range corresponding to the service type, the duration interval matching result is a match; otherwise, the duration interval matching result is a mismatch.

[0059] In step S124, it is determined whether the standard location corresponding to the service location is in the preset location set to obtain the location coverage result. If the standard location corresponding to the service location is in the preset location set, the location coverage result is coverage; otherwise, the location coverage result is not coverage.

[0060] In step S125, a basic consistency score is calculated based at least on the time interval matching results and the location coverage results.

[0061] In step S126, if the basic consistency score is greater than or equal to the consistency threshold, the basic consistency result is determined to be of normal basic consistency; otherwise, the basic consistency result is determined to be of abnormal basic consistency. The basic information verification result includes the basic consistency result. The consistency threshold can be selected as needed, for example, it can be 0.8. When the basic consistency result is of abnormal basic consistency, it can be recorded as a basic consistency result. =1, otherwise, =0.

[0062] In this example, the duration ranges for different service types can be pre-defined. In this case, it can be determined whether the service duration intervals in the elderly care service records are consistent with the typical duration ranges corresponding to the service types. It can also be determined whether the standard locations corresponding to the service locations are within a preset set of locations (which can be pre-defined based on the jurisdiction). This assesses the basic consistency of the data.

[0063] The above-mentioned scheme verifies the matching between the service duration range and the standard duration range of the corresponding service type, and the coverage of the service location with the preset set of compliant locations. It calculates a basic consistency score based on at least these two verification results, and then determines whether the basic consistency is in a normal or abnormal state by combining it with the set consistency threshold. This can quickly identify abnormal service data with inconsistent duration and location information, and effectively improve the accuracy and standardization of the review of elderly care service record information.

[0064] For example, before determining whether the standard location corresponding to the service location is in the preset location set, the method further includes: standardizing the text information of the service location; calculating the cosine similarity between the text information of the service location and each standard location in the standard location dictionary; determining the standard location with the largest cosine similarity to the service location as a candidate standard location; and determining the standard location corresponding to the service location as a candidate standard location when the cosine similarity between the candidate standard location and the service location is greater than a first similarity threshold.

[0065] For example, firstly, the text information of the service location. Perform standardization processing and map to standard location representations. Standardization processes include removing leading and trailing whitespace, standardizing full-width and half-width characters, standardizing common abbreviations, removing redundant punctuation, standardizing building and door number expressions, standardizing administrative division names, and replacing synonymous place words. The standardized place text is denoted as: .

[0066] And maintain a standard location dictionary in advance: , of which each This represents a standard location representation. This representation can be a standard address, community name, building unit representation, standard POI name, or administrative division code.

[0067] right and each candidate standard location Construct TF-IDF feature vectors for each vector and calculate their cosine similarity: .

[0068] Select the candidate standard locations with the highest similarity. .

[0069] when At that time, the location entered by the user can be mapped to a standard location. .

[0070] When the maximum similarity is less than the first similarity threshold of 0.80, the location can be marked as a location awaiting manual confirmation, prompting the administrator to confirm. Upon receiving confirmation from the administrator, the corresponding standard location will be determined.

[0071] Research has revealed that in community-based mutual-aid elderly care scenarios, location descriptions are often not standard addresses but rather colloquial expressions. Users might fill in phrases like "Aunt Zhang's house," "near the south gate," "near the health station," or "the elderly person's home in Unit 1 of Building 3." Different people may also describe the same location differently. Therefore, this solution considers introducing location standardization and text similarity matching to address the issue of numerous variations in location descriptions in community elderly care records, which may actually refer to the same place. By mapping colloquial locations to standardized location expressions as much as possible, the workload of administrators manually verifying each entry is reduced, thus improving review efficiency.

[0072] For example, the elderly care service record also includes the service occurrence time. The basic information verification of the elderly care service record also includes: calculating the time difference between the upload time of the elderly care service record and the service occurrence time; if the time difference is within a preset time difference range, the service time consistency result is determined to be consistent; otherwise, the service time consistency result is inconsistent.

[0073] A basic consistency score should be calculated based on at least the time interval matching results and the location coverage results, including: calculating the basic consistency score based on the time interval matching results, the location coverage results, and the service time consistency results.

[0074] In this example, we consider examining the difference between the upload time and the service occurrence time: ;in, Indicates the upload time; This indicates the time the service occurred. If the time difference is within the range of [0, 48h], then the service time consistency result can be considered consistent.

[0075] The results of duration interval matching, location coverage, and service time consistency can all be represented in binary form, i.e., when the duration interval matching result is a match, =1, otherwise =0. Location coverage result is when covered. =1, otherwise =0. When the service time consistency result is consistent. =1, otherwise =0.

[0076] The basic consistency score can be determined using the following formula: ; when When this happens, the system marks the record as having a basic consistency anomaly. The above-mentioned solution can rely on the service occurrence time information contained in the elderly care service records to complete the basic information verification of the elderly care service records. By accurately calculating the time difference between the service record upload time and the actual service occurrence time, and combining it with the preset time difference judgment standard for automatic comparison and analysis, it can quickly and accurately obtain the verification result of whether the service time is consistent. It can effectively identify abnormal data that does not match the service time entered and the actual service time, improve the accuracy and efficiency of the elderly care service record information review, ensure the authenticity and standardization of elderly care service related data, and help to achieve standardized supervision and scientific data management of the elderly care service process.

[0077] For example, the basic information verification includes image quality verification; step S120, which verifies the basic information of the elderly care service record, may also include the following steps S127, S128 and S129.

[0078] In step S127, for any proof image in the proof image set, the proof image is preprocessed, including at least grayscale processing, size normalization, and noise reduction; the Laplacian variance of the proof image is calculated to obtain the sharpness of the proof image; when the sharpness of the proof image is less than the sharpness threshold, the proof image is determined to be a blurry image.

[0079] In step S128, the mean of the Laplacian variance of each proof image in the proof image set is calculated to obtain the average sharpness of the proof image set.

[0080] In step S129, if the average sharpness is less than the sharpness threshold or the proportion of blurry images in the proof image set exceeds a preset ratio, the image quality verification result is determined to be insufficient; otherwise, the image quality verification result is determined to be good. The basic information verification result includes the image quality verification result.

[0081] For example, after obtaining the set of proof images, the image processing component can be called to perform reading, decoding, grayscale conversion, scale normalization, and feature extraction on the uploaded images.

[0082] Specifically, for each image The system can first read the image data and complete the format decoding. If reading fails, decoding fails, file header is abnormal, file size is zero, or pixel matrix is ​​empty, the image is marked as invalid. ).

[0083] For successfully read color images Convert it to grayscale image : .

[0084] After grayscale conversion, the image is scale-normalized. Let the original image size be... The target size is fixed as The system first calculates the scaling factor: Then calculate the scaled size. The system centers the scaled grayscale image. In the target canvas, the remaining area is filled using edge copying and patching, while simultaneously generating an effective area mask. .

[0085] After normalization is completed, an application can be made to the image ( A Gaussian smoothing kernel is applied to suppress compressed noise and isolated noise points. Sharpness feature extraction is then performed, applying a Gaussian smoothing kernel to the grayscale image. The Laplace operator is used to obtain the second-order response image. and in the effective area Internal calculation of Laplace variance: .in, This represents the mean of the Laplace response within the effective region.

[0086] In this embodiment, the clarity threshold for blur determination can be 120. This is because in community-based mutual assistance elderly care scenarios, community elderly care photos often suffer from issues such as shaky hands, dim lighting, rushed shooting angles, and unstable operation by the elderly. If the threshold is set too high, many still recognizable normal elderly care photos may be misjudged as blurry; if the threshold is set too low, many low-quality photos that cannot be verified will be allowed. Therefore, 120 is used as the initial determination value that balances recognizability and scene tolerance. When the image is deemed blurry, set the following settings: Otherwise, set .

[0087] For upload records containing multiple images, calculate the average image clarity metric for the image set: .

[0088] when Alternatively, if the percentage of blurry images is not less than a preset ratio of 0.50, the image quality verification result can be determined as insufficient image quality. ,otherwise, .

[0089] Research has found that in community-based mutual-aid elderly care scenarios, on-site photos are the most direct supporting evidence, but also the most prone to quality fluctuations. This is because the photographers may be elderly people, ordinary residents, or temporary caregivers, and the shooting environments may include hallways, elevator entrances, the elderly person's home, community health stations, or outdoor locations at night. Therefore, determining whether these photos still possess basic verification value is crucial for subsequent successful review. If the images are severely blurry, undecipherable, or highly repetitive, it significantly weakens the credibility of the elderly care assistance provided. In light of this, this solution considers introducing image quality verification and using the image quality verification results as one of the factors in subsequent risk assessment, thereby more accurately assessing the risk of currently submitted elderly care service records. Furthermore, considering that submitted records may contain more than one image, this solution considers evaluating the image quality verification results from the perspective of overall average clarity or the proportion of blurry images, which helps to further improve the accuracy of the assessment.

[0090] For example, step S130, comparing the hash fingerprint of each proof image in the proof image set with the hash fingerprint of the historical proof images, may include the following steps S131 and S132.

[0091] In step S131, for any proof image in the proof image set, the Hamming distance between the proof image and each historical proof image is calculated based on the hash fingerprint of each image; if the Hamming distance between the proof image and any historical proof image does not exceed the preset Hamming distance threshold, the proof image is determined to be a similarity anomalous image.

[0092] In step S132, if the number of similar abnormal images in the image set exceeds the abnormality threshold, the image duplication identification result is determined to be an image duplication; otherwise, the image duplication identification result is determined to be an image non-duplication. The abnormality threshold can be 2.

[0093] In one specific embodiment, a time window with a preset time difference threshold length can be used to store the hash fingerprints of historical proof images. This time window sets a 48-hour elimination policy for the historical fingerprint status, causing image fingerprints, upload timestamps, and associated features that exceed this time period to automatically become invalid, thereby controlling the scale of the status and ensuring that similar image determination mainly targets short-term, highly correlated upload behaviors.

[0094] Upon receiving the set of proof images, for each proof image in the current set, a perceptual hash fingerprint is generated, and within the same "user identifier + service type + standard location code + time bucket" partition, the Hamming distance is calculated one by one with the hash fingerprints in the historical window. .

[0095] when At 6 o'clock, the system determines that the current image is a duplicate or highly similar to a historical image and sets an anomaly flag. Otherwise, set .

[0096] After comparing individual images, the comparison results for all images within the same upload record are summarized. When two or more images in the same upload record are determined to be duplicates or highly similar to historical images, the image duplication result is confirmed as an image duplication. ;otherwise, .

[0097] The aforementioned scheme accurately calculates the Hamming distance between the proof image and historical proof images by comparing their hash fingerprints. Based on a preset distance threshold, it quickly determines whether a single image is a similar abnormal image. Combined with an abnormal image quantity threshold, it completes the overall image duplication identification. This method efficiently and accurately identifies duplicate proof images, significantly improving the accuracy of duplicate upload detection and providing a reliable basis for subsequent comprehensive risk assessment. Furthermore, this scheme only uses the fingerprints of images whose submission time difference is less than a preset time difference threshold as the basis for duplication judgment. This is because duplication issues in community elderly care often occur in short-term repeated submissions of the same event, rather than normal similar services spanning long periods. This time frame is beneficial for identifying short-term duplicate submissions while also considering processing timeliness. For example, a user might upload multiple images of the same accompanying medical visit within a day, or split the same visit into several separate records for submission. By saving fingerprint summaries in a short window, the identification focus can be placed on the stages most likely to involve score manipulation and duplicate entries.

[0098] For example, for any proof image in the proof image set, the hash fingerprint of the proof image is obtained as follows: A discrete cosine transform is performed on the preprocessed proof image to extract a low-frequency coefficient region of a preset size in the upper left corner; a binary-aware hash fingerprint is generated based on the relationship between the low-frequency coefficients of each pixel in the low-frequency coefficient region and the average low-frequency coefficient of the region. The preprocessing method has been described in detail above and will not be repeated here.

[0099] For example, firstly, a discrete cosine transform is performed on the normalized grayscale image to extract the top left corner (…). The low-frequency coefficient region is identified, and a 64-bit binary perceptual hash fingerprint is generated based on the relationship between the low-frequency coefficients and the mean. Then, the fingerprints of each image ( ), Clarity score ( ), upload timestamp and record identifier write enhancement event ( Simultaneously, historical image fingerprint summaries from the last 48 hours are stored in StateBackend. Specifically, MapState stores the mapping relationship between record identifiers and image features, ListState stores the fingerprint sequence, clarity score, and timestamp of images uploaded within the last 48 hours under the current key-value partition, and ValueState stores the risk summary corresponding to the current key-value pair.

[0100] The above scheme can quickly and accurately generate a hash fingerprint for each image, thus providing an accurate basis for the subsequent determination of duplicate images.

[0101] For example, step S140, which generates a high-frequency upload identification result based on the frequency of the current user uploading elderly care service records within a preset time period, may include the following steps S141 and S142.

[0102] In step S141, the first frequency of the current user uploading elderly care service records within the first preset time period and the second frequency of the current user uploading elderly care service records within the second preset time period are counted.

[0103] In step S142, if the first frequency is greater than the first frequency threshold or the second frequency is greater than the second frequency threshold, the high-frequency upload recognition result is determined to be abnormal; otherwise, the high-frequency upload recognition result is determined to be normal.

[0104] The first and second preset durations can be selected as needed; for example, the first preset duration can be 3 hours, and the second preset duration can be 24 hours. The first frequency threshold can be 4, and the second frequency threshold can be 8. In a specific embodiment, two time windows of different lengths can be used to detect short-term high-frequency upload behavior: the short window uses a 3-hour window with a 30-minute sliding step; the day window uses a 24-hour window with a 1-hour sliding step. The 3-hour short window is mainly used to identify concentrated upload behavior that deviates significantly from the normal elderly care rhythm in a short period of time, making it easier to detect anomalies and issue warnings as early as possible. The 24-hour day window is mainly used to identify the cumulative upload density of a user within a natural service cycle, preventing the same user from circumventing short window detection by lengthening the time interval. The window length is defined as the same user at time t. The number of uploads within the specified range is: ;when At that time, it was determined that the user had a short-term high-frequency upload anomaly, and the high-frequency upload identification result was obtained. .otherwise, .

[0105] The above scheme can accurately identify short-term high-frequency upload behavior, thereby enabling a more accurate assessment of the risks associated with high-frequency uploads and improving the accuracy of subsequent comprehensive risk assessment results.

[0106] For example, step S150, which generates a comprehensive risk assessment result based on the basic information verification result, the image duplicate recognition result, and the high-frequency upload recognition result, may include the following steps S151 and S152.

[0107] In step S151, the basic information verification results, image duplicate recognition results, and high-frequency upload recognition results are binarized. In this example, the value corresponding to negative results is set to 1, and the value corresponding to positive results is set to 0. The negative result field with a value of 1 is designated as an anomaly field, and the system can generate an anomaly reason based on the original verification item corresponding to the anomaly field. For example, the correspondence between anomaly fields and anomaly reasons can be shown in Table 1 below.

[0108] Table 1. Comparison of Abnormal Fields and Abnormal Causes

[0109] In step S152, the weighted sum of the binarized basic information verification result, the image duplicate recognition result, and the high-frequency upload recognition result is calculated to obtain the comprehensive risk assessment result.

[0110] The weight values ​​for the verification results of various basic information, the image duplicate recognition results, and the high-frequency upload recognition results can be selected as needed. In a specific embodiment, the comprehensive risk assessment result is calculated using the following formula: .

[0111] In community-based mutual-aid elderly care settings, community assistance records naturally exhibit a degree of non-standardization. For example, location descriptions may be inaccurate, images may be slightly blurry, and dates may not be precisely recorded. Therefore, instead of rejecting an entire record based on a single anomaly, a risk classification approach is adopted by integrating multiple anomaly signals. This allows for the screening out of truly high-risk records that require priority review, rather than excluding all imperfect records. This approach aligns better with the governance requirements of community-based mutual-aid elderly care: "tolerating normal fluctuations while focusing on identifying obvious anomalies."

[0112] For example, the method further includes: when the comprehensive risk assessment result is less than a first risk threshold, determining that the current elderly care service record is normal and adding a "no obvious abnormality" mark; when the comprehensive risk assessment result is greater than or equal to the first risk threshold and less than a second risk threshold, determining that the current elderly care service record has low risk, adding a "general concern record" mark and generating a risk alert; when the comprehensive risk assessment result is greater than or equal to the second risk threshold and less than a third risk threshold, determining that the current elderly care service record has medium risk and adding a medium-risk record mark; when the comprehensive risk assessment result is greater than or equal to the third risk threshold, determining that the current elderly care service record has high risk and adding a high-risk record mark. Each risk threshold can be selected as needed.

[0113] In a specific embodiment, the graded processing scheme for the comprehensive risk assessment result R is as follows: when When the record shows no obvious abnormalities, the system can mark the elderly care service record as having no obvious abnormalities. At this time, the system can directly display the basic verification results, image quality results, and risk score results to the administrator for quick review.

[0114] when When this happens, the elderly care service record is marked as a general concern record, and a risk alert is generated. At this time, the system can display the abnormal fields (the fields corresponding to negative results) and the corresponding reasons for the abnormality on the administrator's terminal, which the administrator can then review in conjunction with the alert information.

[0115] when When this happens, the elderly care service record will be marked as a medium-risk record, and a priority review notice will be generated. Administrators can choose to approve, reject, or request users to provide supplementary pictures, descriptions, location, or time information.

[0116] when When this happens, the system marks the record as a high-risk record and generates a proposed rejection result. Administrators can conduct a focused review of the record and, based on the review results, choose to approve, reject, or request supplementary materials.

[0117] For any of the above-mentioned risk levels of elderly care service records, the system will not perform reliable counts or calculate incentive values ​​before the administrator approves the record. After the administrator approves the record, the system will calculate the incentive value for each instance of elderly care service based on the service type, service duration range, effective number of elderly care services, and the risk deduction value corresponding to the comprehensive risk assessment result.

[0118] In practical scenarios, records of elderly care services can be sorted according to risk indicators. The lower the risk, the higher the ranking. This allows administrators to quickly process low-risk elderly care service records, improving processing efficiency.

[0119] For example, the method further includes: adjusting the threshold used to determine the result based on feedback from the administrator regarding any one of the following results: basic information verification result, image duplicate recognition result, and high-frequency upload recognition result; the feedback information is any one of threshold being too strict, threshold being too lenient, or judgment being appropriate; for any one of the following results, if the proportion of identical feedback information received within a preset time exceeds a proportion threshold, the threshold used to determine the result is adjusted according to the feedback information; wherein, when the feedback information is threshold being too strict, the threshold in the operation is adjusted in the lenient direction by a preset step size; when the feedback information is threshold being too lenient, the threshold in the operation is adjusted in the strict direction by a preset step size; when the feedback information is judgment being appropriate, the threshold remains unchanged. The preset time can be selected as needed, for example, it can be 7 days.

[0120] In this example, the administrator can review the basic information verification results, image duplicate recognition results, and frequently uploaded recognition results, and submit feedback. If, for any type of result, the proportion of identical feedback received within a preset time exceeds a threshold, the threshold can be adjusted according to the feedback to improve scenario adaptability. For example, for image duplicate recognition results, if the number of "threshold too strict" feedback messages received within 7 days exceeds 50% of the total number of feedback messages within 7 days, the corresponding threshold can be adjusted to be more lenient.

[0121] In this example, the corresponding judgment result is not directly modified based on the feedback information. Instead, the threshold of the corresponding rule is adjusted. When a rule receives multiple "threshold too strict" feedbacks, the threshold of that rule is slightly adjusted towards leniency. When a rule receives multiple "threshold too lenient" feedbacks, the threshold of that rule is slightly adjusted towards strictness. When the feedback is "judgment appropriate," the current threshold remains unchanged.

[0122] Threshold update can be represented as: .in, This indicates the single adjustment step size. When the feedback is "threshold too strict", Take the preset adjustment value in the lenient direction; when the feedback is "threshold too loose", Take the preset adjustment value for the strict direction; when the feedback is "appropriate", .

[0123] In some embodiments, after adjusting the threshold in the operation used to determine the result, the method further includes: applying a boundary constraint to the adjusted threshold so that the adjusted threshold is within the threshold interval corresponding to the threshold.

[0124] In this embodiment, for each adjustable threshold The system has its allowable adjustment range preset. After adjusting the threshold, boundary constraints can be applied to obtain the actual effective threshold. .

[0125] For example, the sharpness threshold The allowed range can be set to [80, 150]; the preset Hamming distance threshold is... The allowable range can be set to [6, 12]; frequency threshold The allowed range can be set to [3, 10].

[0126] Research has revealed that community-based mutual assistance in elderly care exhibits strong regional characteristics. Different communities may differ in mobile device capabilities, user age structure, shooting habits, service types, and review standards. Using a completely fixed threshold for an extended period can easily lead to two problems: either the rules are too strict, filtering out a large number of genuine elderly care records; or the rules are too lenient, allowing duplicate and low-quality records to enter the scoring process. Therefore, in this example solution, to prevent misjudgments or omissions due to a fixed threshold across different communities, time periods, and upload scenarios, an entry point for threshold feedback is provided to administrators. Based on the feedback, the threshold is dynamically adjusted within the corresponding range, thereby improving the applicability and implementation of the rules.

[0127] For example, the method further includes: when the currently submitted elderly assistance service record is valid, calculating the single elderly assistance incentive value based on the preset basic service score, the type weighting value corresponding to the service type of the elderly assistance service record, the duration weighting value corresponding to the service duration range of the elderly assistance service record, the continuous participation weighting value corresponding to the number of valid elderly assistance services provided by the user within the target duration, and the risk deduction value corresponding to the comprehensive risk assessment result of the elderly assistance service record.

[0128] In this example, upon receiving confirmation from the administrator, the submitted elderly assistance service record is deemed valid. Subsequently, the incentive value for this assistance action can be calculated. In a specific embodiment, the incentive value for a single assistance action can be calculated using the following formula: ; in, The basic service score reflects the fundamental value of a valid service record and can be set based on experience. This represents the type weighting value, used to distinguish different elderly care behaviors. Specifically, the type weighting value corresponding to different service types can be preset, and the current type weighting value can be obtained by looking up the table. This represents the duration-weighted value, used to reflect the level of service investment. Similar to the type-weighted value, different duration-weighted values ​​can be preset for different duration intervals, and the current duration-weighted value can be determined by looking up a table. This represents the continuous participation weighting value, used to encourage continuous participation. The continuous participation weighting value corresponding to different effective number of elderly care services within a target duration can be preset, and the current continuous participation weighting value can be determined by looking up a table. This represents the risk deduction value, used to constrain behaviors such as high-frequency rejections and abnormal repetitions. In practical scenarios, risk deduction values ​​can be set for different intervals of comprehensive risk assessment results, and then the risk deduction value can be determined based on the interval in which the comprehensive risk assessment result of the current elderly care service record is located.

[0129] The aforementioned base scores and weighted values ​​can be set by the platform according to a preset rule table, and can be adjusted based on community operation, service guidance direction, and management requirements.

[0130] As described above, incentive value calculation is only performed when the elderly assistance service record is valid. In some embodiments, a user submits three similar records consecutively within a short period, and all three records use highly similar images. Through hash fingerprint comparison and frequency statistics, image duplication identification results and high-frequency upload identification results can be obtained. After reviewing the data on the review end, the administrator can determine that the three records correspond to the same event based on the image duplication identification results and high-frequency upload identification results. Ultimately, only one record is allowed to pass, and the others are rejected for "duplicate upload". The system only scores and records the number of times the approved record is recorded, thereby preventing score manipulation.

[0131] For example, the method further includes: accumulating each individual elderly assistance incentive value obtained by the user within a preset period to obtain the user's total elderly assistance incentive value within the preset period; and performing at least one of the following operations based on each user's total elderly assistance incentive value: for users in the same region, sorting them from largest to smallest according to their total elderly assistance incentive value to generate an elderly assistance ranking list within the preset period; for any user, generating a virtual honor badge corresponding to the incentive value range in which the user's total elderly assistance incentive value falls; and for any user, generating a list of redeemable items based on the user's total elderly assistance incentive value, wherein the redemption points corresponding to each item in the list are less than the user's total elderly assistance incentive value. The incentive value for a single instance of helping the elderly can be used as points for that instance. By summing the points over a preset period, the total incentive value for helping the elderly can be obtained. This total incentive value is used for updating points accounts, counting the number of times an individual helps, ranking on leaderboards, awarding honors, and determining eligibility for redemption benefits, thereby achieving a good incentive effect.

[0132] In one specific embodiment, a user registers and binds their community. After completing a medical assistance service, the user uploads photos of the scene via a mini-program, fills in the service duration range and a brief description, and submits a record of the elderly assistance service. Upon receiving the record, basic information verification, duplicate image identification, and high-frequency upload identification are performed, and a comprehensive risk assessment result is calculated. All results and the record are output to the administrator's terminal. After reviewing and deeming the record authentic and valid, the community administrator approves it. Upon receiving confirmation from the administrator, the record is recorded as a valid assistance instance; corresponding points are awarded to the user based on the calculated single-action incentive value; the community ranking, street / township ranking, and district / county ranking are updated; the service record is written to the historical transaction record; and it is determined whether a certain honor or redemption threshold has been reached. In this way, ordinary users can receive immediate feedback and continuous incentives from genuine elderly assistance activities.

[0133] For example, the method further includes: calculating the administrator incentive value based on the administrator's promotion behavior quantification value and review behavior quantification value; wherein, the promotion behavior quantification value includes the number of effective promoters; the review behavior quantification value includes the number of effective reviews, review timeliness rate, review consistency rate, and abnormal review deduction value; wherein, the administrator is used to review the corresponding elderly care service records based on the basic information verification results, image duplicate recognition results, high-frequency upload recognition results, and comprehensive risk assessment results.

[0134] In one specific embodiment, the administrator incentive value is calculated in the following way: ; in, The effective promotion number refers to the number of users who, within a preset statistical period, were promoted, guided to register, or assigned to the management area of ​​the administrator, and thus formed the first effective elderly care service record. This indicates the number of valid reviews, referring to the number of records that the administrator has completed review and processing within the preset statistical period; The score indicates the timeliness of the review, which is determined by the percentage of records in which the administrator completes the review within the preset review time limit; The score indicates the consistency rate of the review, which is determined based on the degree of consistency between the administrator's original review results and subsequent review results. This represents the deduction value for abnormal reviews, which is determined based on abnormal review behaviors such as review timeout, obviously abnormally high pass rate, obviously abnormally high rejection rate, excessively high review overturn rate, or failure to process high-risk records according to rules. and This indicates a preset weight, used to adjust the relative proportion of promotional and review contributions in the administrator's incentive value. Through the above steps, administrators can be transformed from simple backend reviewers into organizers and maintainers with incentivized feedback, thereby helping to improve administrator motivation and enhance the overall efficiency and effectiveness of community elderly care services.

[0135] In one specific implementation, a community administrator is responsible for promoting and reviewing records on the community platform. During the statistical period, the administrator guides several residents to complete registration, with some of them creating their first valid service record. The administrator also reviews a large number of pending records with a high timeliness and consistency rate. At the end of the statistical period, the administrator's incentive value is automatically calculated and added to the administrator ranking and reward pool. In this way, the administrator is no longer just passively fulfilling review responsibilities, but rather acts as an important organizer of the platform's operation, receiving quantifiable and feedback-based incentive results.

[0136] Research has revealed that while some elderly care assistance programs currently available primarily target middle-aged and elderly users, many seniors with specific needs struggle to use these programs effectively, resulting in generally low platform activity and participation. Furthermore, the sustainable operation of community-based mutual aid elderly care requires not only the participation of ordinary residents but also the involvement of organizations such as mutual aid service providers, community administrators, and social workers to handle promotion, verification, and order maintenance. Existing solutions mostly only incentivize users, failing to incorporate these organizations into a comprehensive incentive system. This leads to a reliance on administrative intervention and a lack of sustained internal motivation. Therefore, this proposal constructs a two-way incentive mechanism that encourages universal participation, covers both ordinary users and organizations, and balances material and non-material incentives. It calculates incentive values ​​for users and quantifies the work of administrators, thereby helping to enhance enthusiasm and motivation for participation in community-based mutual aid elderly care.

[0137] In some embodiments, leaderboard data can be updated based on incentive values. Leaderboards may include, but are not limited to, community leaderboards, street / township leaderboards, and district / county leaderboards. Leaderboard participants can be both ordinary users and administrators. Leaderboard ranking criteria include at least cumulative effective assistance counts, cumulative points, periodic activity level, continuous participation days, and administrator incentive values. Leaderboards support switching between weekly, monthly, and yearly rankings. The platform automatically includes users or administrators in the corresponding leaderboards based on their regional affiliation. This step enables the display and dissemination of achievements in assisting the elderly, thereby providing positive incentives for such actions.

[0138] In some schemes, the total incentive value for helping the elderly can be considered as the user's total points. Different points can be set for redeeming different items based on the actual availability of goods. When a regular user's points reach a preset threshold, the platform allows them to initiate a redemption request. The platform then processes this request by verifying the user's points balance, redemption eligibility, and rights inventory, deducting points, and writing a redemption log. Simultaneously, the platform can award users with "Elderly Assistance Star" badges, certificates of honor, prominent rankings, or priority access to special events based on their number of assistance attempts, points earned, and ranking on the leaderboard. For administrators, the platform can also configure corresponding honors and rewards based on their incentive values. This allows the reliable recording results and two-way incentives to be further translated into concrete benefits, forming a complete closed loop from recording, review, and participation to incentive fulfillment, thereby enhancing the incentive effect.

[0139] In one specific implementation, user and administrator results can be simultaneously projected onto community, street / township, and district / county rankings. For example, a user ranking highly in their community will be included in the street / township monthly ranking; an administrator excelling in timely review rate and effective promotion numbers this month will be included in the street / township administrator ranking. The system will synchronously display the ranking results on both the front-end and back-end statistics pages, creating visible positive examples. This not only enhances individual participation but also strengthens healthy competition between communities.

[0140] In related technologies, mutual-aid elderly care services often remain at a partial stage, with administrators manually recording users' elderly care service information. There is a lack of effective connection between recording, verification, recording, incentives, rankings, redemption, and feedback, making it difficult to form a unified data flow and service loop, and also difficult to support the continuous operation and dynamic optimization of community mutual-aid elderly care services. To address this issue, this solution integrates all stages—record submission, risk control processing, manual verification, reliable recording, two-way incentives, ranking updates, and rights feedback—to form a complete operational loop. Specifically, this solution includes two types of participants. The first type is the user. Users can be both active service providers and participants, mainly including mutual aid service providers, volunteers, community residents, and elderly family members. After completing elderly care activities, users submit service records through the terminal. After verification, the system awards points and provides rankings, honors, or other feedback. The second type is the administrator. Administrators are the organizers and verifiers of the service, mainly including community staff, professional social workers, or other management personnel. Administrators are responsible for promoting the platform, verifying records, and maintaining operational order. The administrator's promotional activities, verification activities, and verification quality are also included in the incentive system. The two types of entities are interconnected through the user end, management end, and data processing center, forming a complete operational chain of "behavior-recording-review-incentive-feedback". This model does not rely on direct government order assignment, nor does it rely entirely on market transactions. Instead, it continuously motivates community residents and organizational nodes to participate through a two-way incentive mechanism, thereby forming a sustainable community mutual assistance elderly care service mechanism.

[0141] According to another aspect of the present invention, a community-based mutual-aid elderly care service information management system is provided. The system includes a central platform and user terminals, with the central platform communicatively connected to the user terminals. The central platform is used to implement the methods described above. The system may also include an administrator terminal, which is communicatively connected to the central platform.

[0142] According to another aspect of the present invention, a computer-readable storage medium is also provided. The storage medium stores a computer program / instructions that, when executed by a processor, implement the method described above. The storage medium may, for example, include a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0143] Those skilled in the art will readily understand the implementation structure, working principle, and beneficial effects of the system and computer-readable storage medium by reading the above methods. For the sake of brevity, further details will not be elaborated upon here.

[0144] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0145] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0146] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0147] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0148] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, this approach should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0149] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or elements of any method or apparatus so disclosed may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0150] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0151] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules in the system according to embodiments of the present invention. The present invention can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing some or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0152] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0153] The above description is merely a specific embodiment of the present invention or an explanation of that embodiment. The scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for managing information on community-based mutual-aid elderly care services, characterized in that, include: The system receives elderly care service records submitted by users, which include at least the service type, service duration range, service location, collection of supporting images, and description of the service behavior. The basic information verification of the elderly care service records is performed to obtain the basic information verification result. The basic information verification includes at least one of data integrity verification, format legality verification, basic consistency verification and image quality verification. The hash fingerprint of each proof image in the proof image set is compared with the hash fingerprint of the historical proof images to generate a duplicate image identification result. The historical proof images and the proof image set are submitted by the same user, have the same service type, and have the same service location. The time difference between the submission time of the historical proof images and the submission time of the proof image set is less than a preset time difference threshold. Based on the frequency with which the user uploads records of elderly care services within a preset time period, a high-frequency upload identification result is generated; Based on the basic information verification results, the image duplicate recognition results, and the high-frequency upload recognition results, a comprehensive risk assessment result is generated. The comprehensive risk assessment result is used to indicate the risk status of the current received elderly care service record. The system outputs the basic information verification result, the image duplicate recognition result, the high-frequency upload recognition result, and the comprehensive risk assessment result to the administrator terminal. Upon receiving confirmation from the administrator terminal, the system determines that the elderly assistance service record is valid and identifies it as a reliable record for counting.

2. The method according to claim 1, characterized in that, The step of generating a high-frequency upload identification result based on the frequency with which the user uploads elderly care service records within a preset time period includes: The first frequency of the user uploading elderly care service records within a first preset time period and the second frequency of the user uploading elderly care service records within a second preset time period are counted. If the first frequency is greater than the first frequency threshold, or the second frequency is greater than the second frequency threshold, the high-frequency upload recognition result is determined to be abnormal; otherwise, the high-frequency upload recognition result is determined to be normal.

3. The method according to claim 1, characterized in that, The comparison of the hash fingerprint of each proof image in the proof image set with the hash fingerprint of historical proof images includes: For any proof image in the set of proof images Based on the hash fingerprint of each image, calculate the Hamming distance between the proof image and each of the historical proof images. If the Hamming distance between the proof image and any of the historical proof images does not exceed a preset Hamming distance threshold, the proof image is determined to be a similarity anomalous image. If the number of similar abnormal images in the proof image set exceeds the abnormal number threshold, the image duplication identification result is determined to be an image duplication; otherwise, the image duplication identification result is determined to be an image non-duplication. Preferably, for any proof image in the set of proof images, the hash fingerprint of the proof image is obtained in the following way: Perform a discrete cosine transform on the preprocessed proof image to extract the low-frequency coefficient region of the preset size in the upper left corner; A binary perceptual hash fingerprint is generated based on the relationship between the low-frequency coefficient of each pixel in the low-frequency coefficient region and the average low-frequency coefficient of the low-frequency coefficient region.

4. The method according to claim 1, characterized in that, The basic information verification includes image quality verification; the basic information verification of the elderly assistance service records includes: For any proof image in the set of proof images, The proof image is preprocessed, and the preprocessing includes at least grayscale processing, size normalization, and noise reduction. Calculate the Laplace variance of the proof image to obtain the image's sharpness; If the clarity of the proof image is less than the clarity threshold, the proof image is determined to be a blurry image; Calculate the mean of the Laplacian variance of each proof image in the proof image set to obtain the average sharpness of the proof image set; If the average sharpness is less than the sharpness threshold or the proportion of blurry images in the proof image set exceeds a preset ratio, the image quality verification result is determined to be insufficient; otherwise, the image quality verification result is determined to be good. The basic information verification result includes the image quality verification result; And / or, The basic information verification includes format validity verification. The basic information verification of the elderly care service record includes: The format validity check result is determined to be valid only if the elderly care service record meets the following requirements: the text information of the service location includes a valid point description; the text length of the service behavior description is within a preset text length range; the service duration range belongs to a standard duration set; the format of each image in the proof image set belongs to a preset format set, the size of each image does not exceed a preset image size, and the resolution of each image is not lower than a preset resolution threshold.

5. The method according to claim 1, characterized in that, The basic information verification includes basic consistency verification; the basic information verification of the elderly care service records includes: Determine whether the service duration interval is within the duration range corresponding to the service type to obtain a duration interval matching result. If the service duration interval is within the duration range corresponding to the service type, the duration interval matching result is a match; otherwise, the duration interval matching result is a mismatch. Determine whether the standard location corresponding to the service location is in a preset location set to obtain a location coverage result. If the standard location corresponding to the service location is in the preset location set, the location coverage result is coverage; otherwise, the location coverage result is no coverage. A basic consistency score is calculated based at least on the time interval matching results and the location coverage results; If the basic consistency score is greater than or equal to the consistency threshold, the basic consistency result is determined to be of normal basic consistency; otherwise, the basic consistency result is determined to be of abnormal basic consistency. The basic information verification result includes the basic consistency result; Preferably, before determining whether the standard location corresponding to the service location is in a preset location set, the method further includes: The text information of the service locations is standardized. Calculate the cosine similarity between the text information of the service location and each standard location in the standard location dictionary; The standard location with the highest cosine similarity to the service location is determined as the candidate standard location; When the cosine similarity between the candidate standard location and the service location is greater than a first similarity threshold, the standard location corresponding to the service location is determined as the candidate standard location; Preferably, the elderly care service record also includes the service occurrence time, and the basic information verification of the elderly care service record further includes: Calculate the time difference between the upload time of the elderly assistance service record and the time when the service occurred; When the time difference is within a preset time difference range, the service time consistency result is determined to be consistent; otherwise, the service time consistency result is determined to be inconsistent. The calculation of a basic consistency score, based at least on the time interval matching results and the location coverage results, includes: The basic consistency score is calculated based on the time interval matching results, the location coverage results, and the service time consistency results.

6. The method according to claim 1, characterized in that, The basic information verification includes data integrity verification. The basic information verification of the elderly care service records includes: The integrity of each key information item in the elderly care service record is verified to obtain the individual integrity result of each key information item. The key information items include service type, service duration range, service location, set of supporting images, and description of service behavior. Based on the individual integrity results of each of the key information items, the data integrity result is determined, and the basic information verification result includes the data integrity result; Specifically, for the service type, the service type is considered complete if it is not empty; for the service duration interval, the service duration interval is considered complete if it is not empty; for the service location, the service location is considered complete if the text length of the service location is greater than a first preset length; for the proof image set, the proof image set is considered complete if the number of images in the proof image set is greater than a minimum number threshold; and for the service behavior description, the service behavior description is considered complete if the text length of the service behavior description is greater than a second preset length. Preferably, determining the data integrity result based on the individual integrity results of each of the key information items includes: The individual integrity results of each key information item are binarized, and the weighted sum of the individual integrity results of each key information item is calculated to obtain a data integrity score. If the data integrity score is greater than or equal to the integrity threshold, the data integrity result is determined to be complete; otherwise, the data integrity result is determined to be incomplete.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Based on the administrator's feedback on any one of the following results: the basic information verification result, the image duplicate recognition result, and the high-frequency upload recognition result, the threshold used to determine the result is adjusted; the feedback information is any one of the following: threshold too strict, threshold too lenient, and judgment appropriate; For any of the basic information verification results, the image duplicate recognition results, and the high-frequency upload recognition results, if the proportion of identical feedback information received within a preset time exceeds a proportion threshold, the threshold used to determine the result is adjusted according to the feedback information. Specifically, when the feedback information indicates that the threshold is too strict, the threshold in this operation is adjusted in the lenient direction by a preset step size; when the feedback information indicates that the threshold is too lenient, the threshold in this operation is adjusted in the strict direction by a preset step size; when the feedback information indicates that the threshold is appropriate, the threshold is kept unchanged. Preferably, after adjusting the threshold in the operation used to determine the result, the method further includes: Apply boundary constraints to the adjusted threshold so that the adjusted threshold is within the threshold range corresponding to that threshold.

8. The method according to any one of claims 1-6, characterized in that, The method further includes: When the submitted elderly assistance service record is valid, the single elderly assistance incentive value is calculated based on the preset basic service score, the type weighting value corresponding to the service type of the elderly assistance service record, the duration weighting value corresponding to the service duration range of the elderly assistance service record, the continuous participation weighting value corresponding to the number of valid elderly assistance services provided by the user within the target duration, and the risk deduction value corresponding to the comprehensive risk assessment result of the elderly assistance service record. Preferably, the method further includes: The total elderly care incentive value for the user within the preset period is calculated by accumulating the incentive value for each individual instance of elderly care received by the user within the preset period. Based on each user's total elder care incentive value, perform at least one of the following operations: For users in the same region, they are sorted from largest to smallest according to their total elderly assistance incentive value to generate an elderly assistance ranking list within the preset period; For any user, generate a virtual honor badge corresponding to the range of incentive values ​​that the user's total elderly care incentive value falls within; For any user, a list of redeemable items is generated based on the user's total incentive value for helping the elderly. The redemption points for each item in the list are less than the user's total incentive value for helping the elderly. Preferably, the method further includes: Calculate the administrator's incentive value based on the administrator's promotion behavior quantification value and review behavior quantification value; The quantitative values ​​for promotional behavior include the number of effective promoters; the quantitative values ​​for review behavior include the number of effective reviews, the timeliness of review, the consistency rate of review, and the deduction value for abnormal reviews. The administrator is used to review the corresponding elderly care service records based on the basic information verification results, the image duplicate recognition results, the high-frequency upload recognition results, and the comprehensive risk assessment results.

9. A community-based mutual-aid elderly care service information management system, characterized in that, It includes a central platform and a user terminal, wherein the central platform is communicatively connected to the user terminal, and the central platform is used to implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The system stores a computer program / instructions that, when executed by a processor, implement the method as described in any one of claims 1-8.

Images