A cross-organizational material sharing method and system

Abstract

The application provides a cross-organizational material sharing method and system, and relates to the technical field of data processing.The method comprises the following steps: obtaining idle material data of each member enterprise to form a material data set; reading available state information of target material in the material data set according to a material requisition request of a demand member enterprise, and calculating a priority weight of the material requisition request based on an emergency degree of the material requisition request, an approval chain length and a historical occupation number; dynamically locking the target material, adjusting the state of the target material from the available state to the locked state, and only allowing a request with the highest priority weight to maintain the lock; continuously monitoring the requisition and approval progress of the locked material, judging whether the locked state should be released in advance; and adjusting the state of the target material from the locked state to the requisitioned state when the approval process is completed and delivery is confirmed through logistics information.The application improves the autonomy and accuracy of cross-organizational material sharing.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for cross-organizational material sharing. Background Art

[0002] Existing cross-organizational material sharing platforms generally use a centralized data entry and automated matching mechanism. Member companies enter their respective idle material information into a shared pool, and demand-side companies can query and initiate applications on the platform. To prevent the same material from being applied for by multiple parties at the same time, some systems use a "material lock" mechanism. That is, once an application is submitted, the relevant material will be marked as temporarily unavailable in the subsequent process, and other companies cannot apply for it again. However, some platforms, for the sake of process simplicity, do not set up a strict locking mechanism, allowing the same material to be applied for by multiple parties at the same time, and manual screening and allocation will be carried out after the approval process is completed.

[0003] For example, if multiple subsidiaries within a group participate in emergency allocation, if the platform doesn't have a locking mechanism, multiple companies can repeatedly apply for the same batch of critical supplies in a very short period of time, only to discover that the supplies are insufficient at the end of the approval process. This can lead to some companies' needs not being met in a timely manner, and even delays in emergency response due to misjudgments. Conversely, if a strict locking mechanism is implemented, once an application is submitted, the supplies will be locked for a long time. If there are delays in subsequent approval or circulation links, the target supplies will continue to be "occupied", and other companies will be unable to submit requests again, resulting in long-term unavailable supplies and affecting actual supply efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide a cross-organization material sharing method and system, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] In a first aspect, a method for sharing materials across organizations is provided, the method comprising:

[0007] Obtain idle material data of each member enterprise and generate unique material identification, storage location, current quantity and historical flow information to form a material data set;

[0008] Based on the material transfer request from the demanding member enterprise, the availability status information of the target material in the material data set is read. Based on the urgency of the material transfer request, the length of the approval chain, and the number of historical occupancy times, the priority weight of this material transfer request is calculated to obtain the priority parameter data.

[0009] According to the priority parameter data, the target material is dynamically locked, the locking time is recorded, the state of the target material is adjusted from the available state to the locked state, only the material allocation request with the highest priority weight is allowed to maintain the lock, and the rest of the requests are rejected and a lock failure prompt is returned;

[0010] The allocation approval progress of the locked material is continuously monitored to determine whether the locked state should be released in advance. When the approval is not completed within the preset period and there is a higher priority weight of the material allocation request, the release process is triggered, the state of the target material is restored from the locked state to the available state, and the flow information is updated to the material data set;

[0011] When the approval process is completed and the delivery is confirmed by the logistics information, the state of the target material is adjusted from the locked state to the allocated state, and all parameters of this allocation are written into the historical flow information.

[0012] Preferably, based on the urgency of the material allocation request, the length of the approval chain and the historical occupation times, the priority weight of the material allocation request is calculated to obtain the priority parameter data, including:

[0013] According to the material allocation request, the demand completion time limit is extracted, which is compared with the preset standard completion time limit, and the urgency is quantified as an urgency index according to the proportion of the completion time limit reduction;

[0014] Analyze the complete list of approval nodes required by the material allocation request, count the total number of approval nodes, and compare it with the preset number of approval node benchmarks, and quantify the length of the approval chain as an approval chain length index according to the increase or decrease of the number of approval nodes;

[0015] Retrieve the historical flow information of the target material, count the sum of the allocation times and the locking times within the preset period, and quantify it as a historical occupation times index;

[0016] The urgency index, the approval chain length index and the historical occupation times index are weighted and summed by a preset weighting coefficient to calculate the priority weight to form the priority parameter data and bind it with the corresponding material allocation request.

[0017] Preferably, according to the priority parameter data, the target material is dynamically locked, the locking time is recorded, the state of the target material is adjusted from the available state to the locked state, only the material allocation request with the highest priority weight is allowed to maintain the lock, and the rest of the requests are rejected and a lock failure prompt is returned, including:

[0018] For all target materials in the available state, retrieve the priority parameter data of the corresponding material allocation request, and sort them in descending order of priority weight to obtain a material demand priority data set;

[0019] According to the material demand priority data set, the material allocation request with the highest priority weight is assigned a lock qualification, the state of the corresponding target material is adjusted from the available state to the locked state, and the material allocation request number, lock start time and priority parameter of this lock are recorded in the lock record field of the material data set;

[0020] In the material demand priority data set, the remaining material allocation requests except the one with the highest priority weight are pushed to the demand member enterprise with a lock failure prompt, and the material allocation request is not accepted.

[0021] Preferably, the allocation approval progress of the locked material is continuously monitored to determine whether the locked state should be released in advance. When the approval is not completed within the preset period and there is a material allocation request with a higher priority weight, the release process is triggered, the state of the target material is restored to the available state from the locked state, and the flow information is updated to the material data set, including:

[0022] For all target materials in the locked state, the approval node progress of the associated material allocation request is periodically collected at a preset time interval, and the cumulative processing time since the lock time is counted;

[0023] When the cumulative processing time of the approval node progress is greater than the preset maximum approval period, and it is detected that the priority weight of the newly submitted material allocation request of the target material is higher than the priority weight of the current associated material allocation request, the release process is triggered, and the following steps are performed:

[0024] The state of the target material is restored to the available state from the locked state, and the execution time, reason, related approval node information and replaced material allocation request number of this release operation are written into the historical flow information of the target material;

[0025] When the state of the target material is restored to the available state, the available target material is dynamically locked again according to the priority parameter data of all current pending material allocation requests.

[0026] Preferably, according to the material allocation request, the demand completion time limit is extracted, which is compared with the preset standard completion time limit, and the urgency is quantified as an emergency index according to the proportion of the completion time limit reduction, including:

[0027] According to the material allocation request, the demand completion time limit is extracted, which is subtracted from the preset standard completion time limit to obtain a time difference;

[0028] When the time difference is negative, the time difference is matched according to the preset classification time interval to obtain an emergency value;

[0029] When the time difference is negative and the time difference is within a preset extreme value range, the urgency value of the material requisition request is increased to the highest level of urgency value, and the material requisition request is marked as a priority response request;

[0030] When the time difference is non-negative, the material requisition request is assigned a preset basic urgency value;

[0031] The final urgency value is used as an urgency indicator of the corresponding material requisition request and is bound to it.

[0032] Preferably, the entire list of approval nodes required for analyzing the material requisition request is analyzed, the total number of approval nodes is counted, and it is compared with a preset approval node quantity benchmark value. According to the magnitude of the increase or decrease in the number of approval nodes, the length of the approval chain is quantified as an approval chain length indicator, including:

[0033] According to the material requisition request, the approval process associated with it is analyzed, the approval nodes that need to be completed in sequence are obtained, and the corresponding post weight parameters and historical approval time of each approval node are retrieved to form an approval node dataset;

[0034] According to the approval node dataset, the difference between the total number of approval nodes and the preset benchmark number of approval nodes is calculated, and the corresponding weight coefficient is set through the post weight parameters and historical approval time of each approval node. The total number of approval nodes is weighted and corrected to generate an approval chain length indicator, which is associated with the material requisition request;

[0035] When there is a special link in the approval chain, a first weight correction coefficient is set according to the special link to adjust the approval chain length indicator to obtain a first adjusted approval chain length indicator. The special link includes multi-level countersigning, parallel approval, and automatic approval;

[0036] When it is detected that the number of abnormal situations of the approval node in the historical data reaches a preset abnormal number, a timeout warning is set for the approval node, and a second weight correction coefficient is set according to the first adjusted approval chain length indicator to obtain a second adjusted approval chain length indicator.

[0037] Preferably, the historical flow information of the target material is retrieved, the sum of the number of requisitions and the number of lockings within a preset period is counted, and it is quantified as a historical occupation frequency indicator, including:

[0038] The historical flow information of the target material within a preset period is retrieved, the historical flow information is analyzed, the total number of requisitions and the total number of lockings of the target material are counted, and the two values are added together as a historical occupation basic number;

[0039] According to historical flow information, the frequency of abnormal situations is counted, and the abnormal situations are weighted according to the preset abnormal weight to obtain an abnormal correction value;

[0040] The historical occupation total number is obtained by adding the historical occupation basis number and the abnormal correction value, and the historical occupation total number is classified into corresponding historical occupation numbers according to the preset grading threshold.

[0041] The corresponding historical occupation number is used as the historical occupation number index of the current material allocation request of the target material, and is bound with the allocation request.

[0042] Preferably, the emergency index, the approval chain length index and the historical occupation number index are weighted and summed by a preset weighting coefficient to calculate a priority weight to form a priority parameter data, including:

[0043] The emergency index, the approval chain length index and the historical occupation number index are respectively set with a basic weight coefficient;

[0044] The emergency index, the approval chain length index and the historical occupation number index are respectively multiplied by the corresponding basic weight coefficient to obtain the respective weighted results;

[0045] The three weighted results are added to obtain a priority weight value, and the priority weight value and the corresponding emergency index, the approval chain length index and the historical occupation number index are used as the priority parameter data.

[0046] Preferably, the corresponding weight coefficient is set by the post weight parameter and the historical approval time consumption of each approval node, including:

[0047] According to the post weight parameter corresponding to each approval node, the post category information of each approval node is extracted, and the post weight value of the approval node is obtained according to the preset post weight table;

[0048] According to the historical approval time consumption corresponding to each approval node, the average processing time consumption is calculated, and the average processing time consumption is compared with the preset standard approval time to obtain an approval time difference;

[0049] When the approval time difference is a non-negative number, the post weight value of the approval node is linearly improved according to the proportion, and when the approval time difference is a negative number, the post weight value of the approval node is linearly reduced according to the proportion, to generate a corrected post weight value which is used as the weight coefficient of the approval node.

[0050] In a second aspect, a cross-organization material sharing system, the system comprises:

[0051] A data collection module is configured to acquire idle material data of each member enterprise, and generate a unique material identifier, a storage location, a current quantity and historical flow information of the idle material, thereby forming a material data set;

[0052] A request processing module is configured to read available state information of target material in the material data set according to a material requisition of a demand member enterprise, and calculate a priority weight of the material requisition based on an emergency degree of the material requisition, a length of an approval chain and a historical occupation number, thereby obtaining priority parameter data;

[0053] A lock control module is configured to dynamically lock the target material according to the priority parameter data, record a lock time, adjust a state of the target material from an available state to a locked state, and only allow a material requisition with the highest priority weight to maintain the lock, and reject and return a lock failure prompt to other requisitions;

[0054] A progress monitoring module is configured to continuously monitor a requisition and approval progress of the locked material, judge whether the locked state should be released in advance, trigger a release process when the approval is not completed within a preset period and there is a material requisition with a higher priority weight, restore the state of the target material from the locked state to the available state, and update flow information to the material data set;

[0055] A state changing module is configured to adjust the state of the target material from the locked state to a requisitioned state when the approval process is completed and delivery is confirmed by logistics information, and write all parameters of the requisition into historical flow information.

[0056] The above scheme of the present application at least has the following beneficial effects:

[0057] By uniquely identifying and standardizing management of idle materials of each member enterprise, unified collection and dynamic updating of material information can be realized, and accuracy and traceability of data are ensured. In the process of material allocation, a priority parameter data computer mechanism based on multiple factors such as emergency degree, approval chain length and historical occupation times is introduced, so that the platform can scientifically judge the urgency and rationality of various allocation requests, and automatically realize intelligent sorting and resource allocation. Compared with the traditional "material locking" or completely no locking mechanism, the application can implement dynamic locking of target materials, real-time monitoring of approval progress of the rest of the material allocation request, and timely judgment of whether to release the locking state in advance in combination with the priority of the new request, effectively preventing the long-term occupation of materials due to approval lag or the waste of resources due to disorderly competition. For example, when multiple subsidiaries of a group simultaneously initiate emergency allocation applications for critical materials, the rest of the material allocation request can dynamically give the highest priority to the request with the highest emergency, the shortest approval chain and the lowest historical occupation rate, and lock the resources in priority; if the approval is delayed and there is a new request with higher priority, the rest of the material allocation request can release the materials proactively, ensuring rapid response and maximum satisfaction of actual demand, thereby significantly improving the flow efficiency, resource utilization rate and emergency response capability of the cross-organization material sharing platform, and effectively overcoming the specific problems such as manual screening errors, long-term resource freezing and delayed allocation response in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 is a flow chart of a cross-organization material sharing method provided by an embodiment of the application. DETAILED DESCRIPTION

[0059] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0060] As Figure 1 shown, an embodiment of the application proposes a cross-organization material sharing method, which comprises:

[0061] Obtaining idle material data of each member enterprise, and generating material unique identification, storage location, current quantity and historical flow information thereof to form a material data set;

[0062] According to the material allocation request of the demand member enterprise, reading the available state information of the target material in the material data set, and calculating the priority weight of the material allocation request based on the emergency degree, the approval chain length and the historical occupation times of the material allocation request to obtain priority parameter data;

[0063] According to the priority parameter data, the target material is dynamically locked, the locking time is recorded, and the state of the target material is adjusted from the available state to the locked state, only the material allocation request with the highest priority weight is allowed to maintain the lock, and the rest is rejected and a lock failure prompt is returned;

[0064] The allocation approval progress of the locked material is continuously monitored, and it is judged whether the locked state should be released in advance, when the approval is not completed within the preset period and there is a higher priority weight of the material allocation request, the release process is triggered, the state of the target material is restored from the locked state to the available state, and the flow information is updated to the material data set;

[0065] When the approval process is completed and the delivery is confirmed by the logistics information, the state of the target material is adjusted from the locked state to the allocated state, and all parameters of this allocation are written into the historical flow information.

[0066] In the embodiment of the application, the idle material data of each member enterprise can be obtained and standardized to form a complete material data set, and a unique identifier, storage location, current quantity and historical flow information are generated for each material, which significantly improves the traceability and fine management level of cross-organization materials. Depending on the dynamic priority parameter data calculation, different member enterprises can accurately evaluate the importance of the allocation request according to the actual allocation demand, combined with the emergency degree, the length of the approval chain and the historical number of times of occupation and other multi-dimensional factors, to realize intelligent sorting and automatic competitive allocation between material allocation requests. Further, by monitoring the allocation approval progress of the locked material in real time and judging the release condition, the material resources can be released in time according to the actual business pressure, and the flow efficiency of idle materials between organizations is improved. When the approval process is completed and the target material is confirmed by the logistics information, all allocation parameters are written into the historical flow information, which is convenient for subsequent business backtracking and allocation performance analysis. For example, a member enterprise urgently allocates a batch of key components during the peak period, the system can automatically assign it a higher priority weight, so that it can obtain resources preferentially, and effectively cope with the sudden business demand.

[0067] The historical flow information specifically includes:

[0068] For each target material, the system continuously records all the events related to the material in its life cycle, such as allocation, locking, release, delivery, etc., forming a time-sequenced data set. Each time the material state changes, the system automatically generates a flow record entry, which at least contains the event occurrence time, event type (such as allocation application, locking, release, allocated, etc.), associated allocation request number, unique identification of the target material, post information of the operation, actual operator information (if any), allocation quantity, allocation unit, approval node information, approval link state, logistics information (such as delivery time, arrival time, logistics number) and corresponding priority parameter data, etc.

[0069] In the flow information, the system also marks the abnormal situations that occur, such as approval timeout, allocation not completed, locking released in advance, etc., and records the detailed reasons for the occurrence of the abnormality, the processing results and the impact range. Each flow record is archived according to the preset data structure, which is convenient for subsequent query, statistics, traceability and analysis. For example, when a target material is allocated multiple times or appears multiple times of locking and releasing, the system can automatically count the number of historical allocations, the number of lockings and the frequency of related abnormalities, further providing data support for dynamic adjustment of priority parameters and assignment of historical occupation frequency indicators.

[0070] Among them, all the parameters of this allocation include:

[0071] For the archiving link after the completion of the current material allocation request, the system automatically organizes and records all the core data directly related to this allocation process as "all parameters of this allocation". These parameters at least include the unique identification of the target material of this allocation, the allocation request number, the information of the demand member enterprise, the allocation application time, the demand completion time limit, the allocation quantity, the allocation unit, all the approval node names involved in the approval process and their approval results, the actual approval time consumption of each node, the start and end time of the locking state, the locking duration, the priority parameter data (including the urgency indicator, the approval chain length indicator, the historical occupation frequency indicator and their weight values), the allocation result (such as success, failure or interruption), the delivery confirmation logistics information (such as logistics number, actual delivery time, receipt confirmation information) and the abnormal situations that occur in the allocation process and the processing results.

[0072] For the content involving priority parameter data, the system automatically performs weighted summation according to the urgency of the requisition, the length of the approval chain and the historical occupation times, and archives the weighted result as the priority weight of the current requisition. All the above parameters are uniformly archived by the system according to the data structure, facilitating the whole-process tracing and analysis of the requisition business process, and supporting subsequent management, audit, performance evaluation and other applications. For example, if the current requisition involves multiple approval nodes and the approval of a certain node is overdue, the time consumption and abnormal situation of the related node will be archived together as the parameters of the current requisition, and can be used for dynamic correction of the priority parameters of the next requisition.

[0073] In a preferred embodiment of the present application, based on the urgency of the material requisition, the length of the approval chain and the historical occupation times, the priority weight of the current material requisition is calculated to obtain the priority parameter data, including:

[0074] According to the material requisition, the demand completion time limit is extracted and compared with the preset standard completion time limit, and the urgency is quantified as an urgency index according to the proportion of the completion time limit reduction;

[0075] The total number of approval nodes required by the material requisition is analyzed and counted, and compared with the preset number of approval nodes, and the length of the approval chain is quantified as an approval chain length index according to the amplitude of the increase or decrease of the number of approval nodes;

[0076] The historical flow information of the target material is searched, and the sum of the requisition times and the locking times in the preset period is counted and quantified as a historical occupation times index;

[0077] The urgency index, the approval chain length index and the historical occupation times index are weighted and summed by a preset weighting coefficient to calculate the priority weight, form the priority parameter data, and bind it with the corresponding material requisition.

[0078] In the embodiment of the present application, by comparing the demand completion time limit of the material requisition with the preset standard completion time limit, the urgency can be quantified to form an urgency index, so that the system can automatically identify the urgency of different requisitions. The length of the approval chain is converted into an approval chain length index by comparing the difference between the total number of approval nodes and the reference value, combined with the complexity of the approval chain, so as to reflect the influence of the approval process on the requisition efficiency. The historical occupation times are accurately reflected by the comprehensive statistics of the requisition times and the locking times, reflecting the historical flow activity and availability of the material. The three indexes are weighted and summed to generate priority parameter data, making the requisition process more scientific and objective. For example, if a requisition has a very high urgency, but the approval process is short and the historical occupation frequency of the material is low, the system can still automatically give the request a high priority based on the weighted result, realizing multi-dimensional dynamic optimization of resources.

[0079] In a preferred embodiment of the present invention, target materials are dynamically locked based on priority parameter data, the locking time is recorded, and the status of the target materials is adjusted from available to locked. Only material transfer requests with the highest priority weight are allowed to remain locked, while other requests are rejected and a lock failure prompt is returned, including:

[0080] For all target materials in the available state, retrieve the priority parameter data of their corresponding material allocation requests and sort them from high to low according to the priority weight to obtain the material demand priority data set;

[0081] Based on the material demand priority dataset, the material transfer request with the highest priority is assigned a lock qualification, the corresponding target material status is adjusted from available to locked, and the locked material transfer request number, lock start time, and priority parameters are simultaneously recorded in the lock record field of the material dataset.

[0082] In the material demand priority data set, for all material allocation requests except the one with the highest priority weight, a notification indicating that the request cannot be locked will be pushed to the demanding member enterprise, and the material allocation request will not be accepted.

[0083] In an embodiment of the present invention, the system can automatically retrieve and sort the priority parameter data of each transfer request for all target materials in available states, and form a material demand priority data set in real time. This mechanism ensures that each locking operation has the best decision-making basis, and the material transfer request with the highest priority weight can obtain exclusive locking qualifications. At the same time, the locking operation is numbered, the start time and the priority parameters are fully recorded to facilitate subsequent tracing. For transfer requests that fail to obtain locking qualifications, the system will automatically push a prompt that it cannot be locked, effectively avoiding repeated applications and waste of resources. For example, within a large group, multiple branches apply for the same batch of equipment at the same time. The system will give priority to applicants who are in urgent need, have a short process, and have less historical occupancy. The remaining branches will receive feedback immediately, effectively improving the transparency and fairness of the cross-organizational material sharing process.

[0084] In a preferred embodiment of the present invention, the progress of the allocation approval of locked materials is continuously monitored to determine whether the locked status should be released in advance. When the approval is not completed within the preset period and there is a material allocation request with a higher priority weight, the release process is triggered to restore the target material status from the locked state to the available state, and the flow information is updated in the material data set, including:

[0085] For all target materials in the locked state, the approval node progress of their related material transfer requests is collected regularly at preset time intervals, and the cumulative processing time since the locking moment is calculated;

[0086] When the cumulative processing duration of the approval node progress is greater than the preset maximum approval period, and it is detected that there is a new submitted material allocation request with a priority weight higher than the priority weight of the current associated material allocation request, a release process is triggered, and the following steps are executed:

[0087] The state of the target material is restored from the locked state to the available state, and the execution time, reason, related approval node information, and the replaced material allocation request number of the current release operation are written into the historical flow information of the target material;

[0088] After the state of the target material is restored to the available state, the target material in the available state is dynamically locked again according to the priority parameter data of all the pending material allocation requests.

[0089] In the embodiment of the application, by continuously monitoring the allocation approval progress of the locked material and periodically collecting the progress data of the related approval node at a preset time interval, the real-time flow state of the locked material can be accurately mastered. By counting the cumulative processing duration since the locking time and comparing it with the preset maximum approval period, the system can automatically trigger the release process when it finds that the approval is overdue and there is a higher priority allocation request, and restore the material state from locked to available, and record the details of the current release operation to the historical flow information. This mechanism ensures that the material will not be occupied for a long time due to the blocking of the approval process, so that the resources can be dynamically allocated according to the actual priority. For example, when resources are scarce, if the approval of the locked material is overdue and there is a more urgent new request, the system can release the resources in time and reallocate them, greatly improving the utilization efficiency and allocation response speed of the resources.

[0090] The preset maximum approval period specifically includes:

[0091] The platform system sets a maximum allowed approval time length as the preset maximum approval period according to the business management requirements and industry actual experience. The period can be flexibly configured by the system administrator according to different types of materials, the complexity of the approval process, historical flow data and other factors. Usually, the maximum approval period is set in hours or days, for example, 24 hours for ordinary materials and 4 hours for emergency materials.

[0092] The system automatically records the locking start time when the allocation approval process is started, and compares it with the actual approval time of each approval node in real time to count the cumulative approval time since the locking start. If the cumulative approval time of the current allocation approval process is greater than or equal to the preset maximum approval period, the system will automatically determine that the allocation request is overdue, which is the trigger condition for the release process.

[0093] In practical applications, the administrator can dynamically adjust the longest approval period parameters of different material categories and different approval processes according to the operation situation, so as to adapt to the platform operation and enterprise emergency demand. For example, a shorter longest approval period can be set for important production materials to ensure the allocation efficiency and prevent the long-term occupation of materials due to delayed approval.

[0094] In the release operation, the execution time, the reason, the related approval node information and the replaced material allocation request number are written into the historical flow information of the target material, and the historical flow information specifically includes:

[0095] When the release process is triggered automatically or manually, the system immediately generates a new flow record for archiving the detailed information of the release operation. The record includes the following contents:

[0096] Firstly, the system obtains and records the actual execution time of the release operation as the timestamp of the flow record; secondly, the system automatically generates a release reason description according to the trigger condition of the release process, for example, due to "approval timeout release" or "detection of higher priority allocation request release". Subsequently, the system synchronously records all the approval node information associated with the release time point, including the approval node name, the actual approval state of each node, the list of completed and incomplete nodes, the approval time consumption of each node and the like.

[0097] In addition, if the release operation occurs in the case of intervention of a higher priority allocation request, the system will automatically search and record the original allocation request number (i.e. the replaced material allocation request number) to realize the traceability of the allocation process. All the above information will be written into the historical flow information table of the target material according to a unified data structure, so as to facilitate the subsequent query, statistics and analysis of the release link.

[0098] For example, when an allocation request is released due to approval period timeout, the system will record the accurate time point of the release operation, the direct reason "approval timeout", the processing state of each node of the approval process at this time, and the unique number of the allocation request. These information not only helps to optimize the subsequent process, but also provides reliable data support for platform managers to analyze the approval process bottleneck and the allocation failure reason.

[0099] In a preferred embodiment of the present application, according to the material allocation request, the demand completion time limit is extracted, compared with the preset standard completion time limit, and the emergency degree is quantified as an emergency index according to the proportion of the completion time limit reduction, including:

[0100] According to the material allocation request, the demand completion time limit is extracted, compared with the preset standard completion time limit, and the time difference is obtained;

[0101] When the time difference is negative, the time difference is matched according to the preset hierarchical time interval, and an emergency value is obtained;

[0102] When the time difference is negative and the time difference is in the preset extreme value range, the emergency value of the material allocation request is promoted to the highest level of emergency value, and the material allocation request is marked as a material allocation request that needs to be responded to in priority;

[0103] When the time difference is non-negative, the material allocation request is assigned a preset basic emergency value;

[0104] The final emergency value is used as an emergency index corresponding to the material allocation request and is bound to it.

[0105] In the embodiment of the application, by calculating the difference between the demand completion time limit and the standard completion time limit in the material allocation request, and mapping the time difference to different hierarchical time intervals, the corresponding emergency value of the allocation request can be automatically quantified and assigned. For requests in the extreme emergency interval, the system can further promote the emergency value and mark it as a priority response object, so as to ensure that critical business requests can obtain the highest priority in the allocation sorting. For ordinary requests, a basic emergency value is assigned to realize differentiated management of the allocation process. For example, if a production line equipment temporarily fails, the completion time limit set by the related material allocation request is much shorter than the regular standard, and the system identifies it as a high emergency request, ensuring that the material is prioritized and reducing production losses.

[0106] The preset standard completion time limit specifically includes:

[0107] The system sets the standard completion time limit of the material allocation process according to the platform historical allocation data, the type of materials, the business needs of the enterprise and the industry practices. The time limit is the reasonable length of time required for the allocation request from submission to expected completion, usually in hours or days. For example, for ordinary production materials, the standard completion time limit can be set to 48 hours; for emergency materials, the standard completion time limit can be set to 12 hours.

[0108] The standard completion time limit is set by the system administrator in the configuration interface and can be configured separately for different categories of materials. After the allocation request is initiated, the system automatically obtains the demand completion time limit in the request and compares it with the standard completion time limit of the corresponding category to evaluate the emergency degree and sorting priority of the current allocation. The administrator can dynamically adjust the standard completion time limit according to the actual business changes of the enterprise to match the needs of different scenarios such as production, sales or emergency response.

[0109] The preset hierarchical time interval specifically includes:

[0110] The system divides several time intervals according to the difference between the demand completion time limit and the standard completion time limit, which is used to quantify the urgency index. Each time interval represents a certain level of urgency. For example, the interval with a negative time difference can be set as "high urgency", the interval with a time difference of zero as "regular", and the interval with a positive time difference as "relaxed"; it can also be further refined as:

[0111] The interval with a time difference less than -24 hours is "extremely high urgency",

[0112] The interval with a time difference between -24 hours and -12 hours is "high urgency",

[0113] The interval with a time difference between -12 hours and 0 is "medium urgency",

[0114] The interval with a time difference equal to or greater than 0 is "basic urgency".

[0115] The system assigns different urgency values to the allocation request according to the time interval, and realizes the quantitative grading processing of the urgency level. The administrator can flexibly adjust the value boundary and the number of intervals according to the actual business situation and historical data.

[0116] The preset extreme value range includes:

[0117] The platform system sets one or more extreme value ranges for the time difference between the demand completion time limit and the standard completion time limit, which is used to identify extremely urgent allocation requests. When the time difference is less than a certain preset extreme lower limit (such as -48 hours), the system determines that the allocation request is in the extreme urgency interval. In this interval, the system assigns the highest level of urgency value to the allocation request and automatically marks it as "need to respond urgently", so that it has the highest priority in subsequent sorting and allocation.

[0118] This extreme value range can be set by the administrator according to the actual situation of enterprise emergency process, historical emergency event processing time, etc., and can be dynamically adjusted according to subsequent data optimization. For example, during special periods or peak production seasons, the extreme value range can be temporarily adjusted to adapt to high-frequency emergency allocation demands.

[0119] The preset basic urgency value includes:

[0120] The basic urgency value is the lowest level of urgency value set by the system for allocation requests without obvious urgency characteristics. Usually, the demand completion time limit is equal to or greater than the standard completion time limit, and the system will directly assign the basic urgency value to the request to represent the regularity of its business demand. The basic urgency value is set by the system administrator according to the allocation priority scoring system to ensure the scientific and reasonable allocation sorting.

[0121] In the subsequent priority weight calculation, the basic emergency degree value is used as an emergency degree index to participate in weighting, ensuring that regular allocation requests will not occupy resources due to misjudgment, and providing a stable sorting baseline for the allocation system. Administrators can regularly adjust the basic emergency degree value according to actual allocation results and feedback to make it more consistent with enterprise resource allocation strategies and production and operation rules.

[0122] In a preferred embodiment of the present application, the list of all approval nodes required by the analysis material allocation request is analyzed, the total number of approval nodes is counted, and it is compared with the preset approval node quantity reference value. According to the amplitude of the increase or decrease of the number of approval nodes, the length of the approval chain is quantified as an approval chain length index, including:

[0123] According to the material allocation request, the approval process associated with it is analyzed, the approval nodes that need to be completed in sequence are obtained, and the corresponding post weight parameters and historical approval time of each approval node are retrieved to form an approval node data set;

[0124] According to the approval node data set, the difference between the total number of approval nodes and the preset reference number of approval nodes is calculated, and the corresponding weight coefficient is set through the corresponding post weight parameters and historical approval time of each approval node. The total number of approval nodes is weighted and corrected to generate an approval chain length index, which is associated with the material allocation request;

[0125] When there is a special link in the approval chain, a first weight correction coefficient is set according to the special link to adjust the approval chain length index to obtain a first adjusted approval chain length index. The special link includes multi-level signing, parallel approval, and automatic approval.

[0126] When it is detected that the number of abnormal situations of the approval node in the historical data reaches the preset number of exceptions, a timeout warning is set for the approval node, and a second weight correction coefficient is set according to the first adjusted approval chain length index to obtain a second adjusted approval chain length index.

[0127] In the embodiments of the present application, the approval process associated with the material allocation request can be analyzed in depth, and the approval node dataset is established by extracting the post weight parameter and historical approval time consumption of each approval node. The system dynamically corrects the approval chain length index according to the difference between the total number of approval nodes and the reference number, and the weight parameter and time consumption of each node, and comprehensively reflects the complexity of the approval process. When there is a special link (such as multi-level signing, parallel approval or automatic approval) in the approval chain, the weight correction coefficient is further adjusted to adjust the approval chain length index; if the historical approval node is abnormal or delayed, the system automatically sets a timeout warning and implements secondary weight adjustment. This dynamic adjustment mechanism can accurately reflect the actual approval efficiency and process bottleneck, and promote more scientific and efficient resource allocation. For example, when the approval chain is long or a certain link is often delayed, the allocation system automatically lowers the priority of the related request, effectively avoiding long-term occupation and circulation congestion of materials.

[0128] The post weight parameter specifically includes:

[0129] According to the internal management system and the material allocation approval process of the enterprise, the system presets a weight score for each category according to the post category corresponding to different approval nodes (such as ordinary employees, supervisors, department managers, senior management personnel, etc.). The score reflects the decision-making influence and process complexity of different posts in the approval chain. For example, the weight of ordinary employees can be set to 1, the weight of department managers can be set to 2, and the weight of senior management personnel can be set to 3.

[0130] During the analysis of the allocation process, the system automatically identifies the post category to which each approval node belongs and finds the corresponding post weight parameter. Then, the system uses the post weight parameters of each node for weighted calculation of the approval chain length index. The higher the weight parameter value, the greater the influence of the node on the overall complexity of the approval chain. The administrator can adjust the post category and its weight parameter according to the organizational structure to dynamically reflect the actual management and business process changes of the enterprise.

[0131] The first weight correction coefficient is set according to the special link to adjust the approval chain length index, specifically including:

[0132] In the approval process, the system automatically identifies special process links such as multi-level signing, parallel approval, and automatic approval. For each type of special link, the system pre-configures a first weight correction coefficient. For example, multi-level signing is usually more complex than sequential single-node approval, and its first weight correction coefficient can be set to 1.5; parallel approval can be set to 1.3, and automatic approval can be set to 0.7 to reflect its simplicity.

[0133] When the special link is included in the approval chain, the system multiplies the first weight correction coefficient of the link with the current approval chain length index or adjusts it in a preset weighting manner to obtain the initially adjusted approval chain length index. In this way, the complexity of the actual process and the approval time consumption can be truly reflected, and the subsequent priority calculation is more reasonable. The administrator can periodically optimize and adjust the correction coefficient settings of various special links according to actual business experience and process analysis.

[0134] The preset abnormal number of times specifically includes:

[0135] When monitoring and analyzing the historical operation of the approval node, the system sets an abnormal number of times threshold to determine the cumulative number of times that a certain approval node is abnormal (such as approval delay, process interruption, node not completed on time, etc.) within a specified statistical period. The threshold is set by the administrator according to historical process data, business sensitivity, and actual enterprise needs. For example, the preset abnormal number of times can be set to 3, that is, when a certain approval node appears abnormal situation cumulatively reaches or exceeds 3 times within a continuous statistical period, the system automatically determines that the node is a high-risk or delay-prone node.

[0136] When the abnormal number of times of a certain approval node reaches the preset threshold, the system will automatically trigger the subsequent weight correction or warning mechanism to prevent process bottlenecks from affecting overall allocation efficiency. The administrator can dynamically adjust the threshold of the preset abnormal number of times according to the system operation and allocation business feedback, so that it is more suitable for enterprise management goals.

[0137] The preset second weight correction coefficient specifically includes:

[0138] When the abnormal number of times of a certain approval node reaches or exceeds the preset abnormal number of times threshold, the system will automatically call the corresponding second weight correction coefficient to further correct the initially adjusted approval chain length index. The coefficient is generally greater than 1, used to significantly increase the influence of abnormal nodes on the complexity of the approval chain. For example, the second weight correction coefficient can be set to 1.2, indicating that when there is a high-risk node, the overall approval chain length index will be further adjusted, thereby reducing the overall priority of the related allocation request.

[0139] In the weight correction process, the system first calculates the preliminary index according to the post weight parameter and the first weight correction coefficient, and then applies the second weight correction coefficient for secondary adjustment according to the presence of abnormal nodes. In this way, the substantial influence of historical abnormal situations on the priority of the allocation process can be dynamically reflected. The administrator can periodically evaluate and adjust the second weight correction coefficient according to actual process analysis and abnormal node data, to improve the adaptability and risk prevention and control ability of the system.

[0140] In a preferred embodiment of the present application, the historical flow information of the target material is retrieved, the sum of the number of allocations and the number of lockings in a preset period is counted, and the sum is quantified as a historical occupation frequency index, which includes:

[0141] The historical flow information of the target material in a preset period is retrieved, the historical flow information is analyzed, the total number of allocations and the total number of lockings of the target material are counted, and the two values are added together as a historical occupation basic number;

[0142] According to the historical flow information, the frequency of abnormal situations is counted, and the abnormal situations are weighted according to a preset abnormal weight to obtain an abnormal correction value;

[0143] The historical occupation basic number and the abnormal correction value are added together to obtain a historical occupation total number, and the historical occupation total number is classified into a corresponding historical occupation frequency according to a preset threshold;

[0144] The corresponding historical occupation frequency is used as the historical occupation frequency index of the current material allocation request of the target material, and is bound to the allocation request.

[0145] In an embodiment of the present application, by retrieving and analyzing the historical flow information of the target material in a preset period, the total number of allocations and the total number of lockings of the target material in the period can be counted comprehensively, and the two values are added together as a historical occupation basic number. Further, the system can also identify abnormal situations in the historical flow information, such as abnormal termination in the allocation process, abnormal release of the locking state, etc., and weight these abnormal situations according to a preset abnormal weight to obtain an abnormal correction value. By adding the historical occupation basic number and the abnormal correction value, the system can obtain a historical occupation total number reflecting the actual flow risk and activity. Subsequently, the system classifies the historical occupation total number according to a preset threshold, converts it into a corresponding historical occupation frequency, and binds it as the historical occupation frequency index of the current material allocation request of the target material. After such processing, the historical flow and abnormal conditions can objectively affect the priority of the allocation request, improving the scientificity of resource allocation and the rationality of dynamic scheduling. For example, for a certain type of material that is frequently allocated and has multiple abnormal releases, the system will automatically increase its historical occupation frequency index, thereby reducing the priority of subsequent allocation requests, preventing repeated invalid occupation of resources, and improving the overall efficiency and stability of cross-organizational material sharing.

[0146] According to the historical flow information, the frequency of abnormal situations is counted, and the abnormal situations are weighted according to a preset abnormal weight to obtain an abnormal correction value, which specifically includes:

[0147] The system automatically retrieves the historical flow information of the target material within a specified historical statistical period, traverses and classifies all flow records, and identifies abnormal situations including allocation not completed, lock released in advance, and approval interrupted. For each type of abnormal situation, the system classifies and counts the frequency of occurrence of each type of abnormal situation. For example, there are 3 times of allocation not completed, 2 times of lock released in advance, and 1 time of approval interrupted in a certain period.

[0148] Subsequently, the system sets the corresponding abnormal weight for different types of abnormal situations in advance, reflecting the actual impact of this type of abnormality on resource occupation or allocation efficiency. For example, the weight of allocation not completed is 2, the weight of lock released in advance is 1.5, and the weight of approval interrupted is 2.5.

[0149] In the weighted calculation, the system multiplies the frequency of occurrence of each type of abnormal situation with the corresponding abnormal weight one by one, and the sum of all weighted results is the abnormal correction value of the target material in the historical statistical period.

[0150] In this way, high-risk and high-impact abnormal situations can occupy a larger proportion in priority evaluation, thereby reflecting the availability risk of the target material in actual allocation. For example, if a material has multiple allocation not completed, the final abnormal correction value will be higher, resulting in an increase in the historical occupation times index, thereby reducing the priority of subsequent allocation of the material.

[0151] Among them, according to the preset grading threshold, the total number of historical occupations is classified into corresponding historical occupation times, which specifically includes:

[0152] The system administrator sets several grading thresholds for the total number of historical occupations according to the actual operation data and management needs of the platform, and divides the total number into different occupation grades. For example, the grading threshold can be set as: 0-5 for low occupation, 6-10 for medium occupation, 11-20 for high occupation, and 20 or more for extremely high occupation.

[0153] When the system calculates the total number of historical occupations (i.e. the sum of allocation and lock times, plus the abnormal correction value) of a certain target material in a statistical period, the system automatically compares the grading threshold interval to classify the value into the corresponding historical occupation times grade. Each grade corresponds to a specific historical occupation times index value for subsequent priority parameter calculation.

[0154] This classification process helps to quantitatively classify historical occupation, allowing materials with different activity levels and risk levels to be scientifically distinguished. For example, a material has a total number of historical occupations of 13, which is automatically classified as a high occupation grade by the system, and a higher historical occupation times index is assigned in the priority calculation, improving the rationality and accuracy of the allocation ranking.

[0155] The administrator can dynamically optimize the grading threshold according to the enterprise operation changes and resource utilization feedback to ensure that the grading standard adapts to the actual business development needs.

[0156] In a preferred embodiment of the application, the emergency index, the approval chain length index and the historical occupation frequency index are weighted and summed by a preset weighting coefficient to calculate the priority weight to form the priority parameter data, which includes:

[0157] A basic weight coefficient is set for the emergency index, the approval chain length index and the historical occupation frequency index respectively;

[0158] The emergency index, the approval chain length index and the historical occupation frequency index are multiplied by their corresponding basic weight coefficients respectively to obtain their respective weighted results;

[0159] The three weighted results are added to obtain the priority weight value, and the corresponding emergency index, the approval chain length index and the historical occupation frequency index are taken as the priority parameter data.

[0160] In the embodiment of the application, the system sets a basic weight coefficient for the emergency index, the approval chain length index and the historical occupation frequency index respectively, and adds the three indexes multiplied by their respective weight coefficients to obtain the final priority weight value. In this way, the allocation system can automatically realize the quantitative fusion of multi-dimensional indexes and form a unique and comparable priority parameter data for each material allocation request. The allocation request with a higher priority weight value can be processed first in the subsequent material allocation, dynamic locking and resource sorting links. This weighted sum method fully considers the business urgency, process complexity and historical occupation, making the allocation decision more scientific and targeted. For example, when facing multiple different allocation requests, the system can meet the requests that are urgent, have simple processes and are not frequently occupied by resources through weight distribution, realizing optimal allocation of resources.

[0161] The basic weight coefficients for the emergency index, the approval chain length index and the historical occupation frequency index are set respectively, specifically including:

[0162] In the priority weight calculation link, the system needs to consider the emergency degree, the approval process complexity and the historical occupation situation of the target material of the allocation request. In order to ensure that the contribution of the three types of indexes in the final priority weight meets the actual business needs, the system sets a basic weight coefficient for each type of index in advance. For example, the basic weight coefficient of the emergency index can be set to 0.5, the approval chain length index to 0.3, and the historical occupation frequency index to 0.2. The weight coefficient is set by the system administrator on the background configuration page and can be dynamically adjusted according to historical running data, allocation effect and enterprise dispatching preferences.

[0163] In the priority parameter data calculation, the system automatically multiplies each type of index with its corresponding basic weight coefficient, obtains the weighted result after aggregation, and thus forms the final priority weight value. This way ensures that the influence of each index on the allocation decision can be flexibly adjusted, improving the controllability and adaptability of the system. For example, when the enterprise operation pays more attention to efficiency response, the weight coefficient of the urgency index can be increased; when the enterprise pays more attention to resource flow fairness, the weight of the historical occupation frequency index can be appropriately increased.

[0164] In a preferred embodiment of the present application, the corresponding weight coefficient of each approval node is set by the corresponding post weight parameter and the historical approval time consumption of the approval node, including:

[0165] According to the post weight parameter corresponding to each approval node, the post category information of each approval node is extracted, and the post weight value of the approval node is obtained according to the preset post weight reference table;

[0166] According to the historical approval time consumption corresponding to each approval node, the average processing time consumption is calculated, and the average processing time consumption is compared with the preset standard approval time to obtain the approval time difference;

[0167] When the approval time difference is a non-negative number, the post weight value of the approval node is proportionally and linearly increased, and when the approval time difference is a negative number, the post weight value of the approval node is proportionally and linearly decreased, to generate a corrected post weight value which is used as the weight coefficient of the approval node.

[0168] In the embodiment of the present application, the corresponding weight coefficient of each approval node is set by the corresponding post weight parameter and the historical approval time consumption of the approval node, which can effectively reflect the actual influence of each node in the approval process on the overall flow efficiency. The system first obtains the post weight value according to the post category information of the approval node and the preset post weight reference, and then dynamically corrects the post weight value according to the comparison result of the historical approval time of each node and the standard approval time, to finally generate a corrected post weight coefficient. The nodes with longer time consumption or higher post weight in the approval process are automatically amplified in the priority parameter data, reflecting the approval bottleneck or key link. After such processing, when the allocation request encounters a complex approval process or a node is often delayed, the system will automatically reduce the priority of the related request, effectively preventing the decline of resource flow efficiency. For example, a certain allocation process involves multiple high-weight posts and the historical approval link is often overtime, so the process will present higher complexity and lower priority in the system calculation, thereby avoiding the occupation of materials by non-efficient process for a long time.

[0169] The preset post weight reference table specifically includes:

[0170] The system formulates a post weight table according to the organization structure and actual approval process of the enterprise, to reflect the decision-making influence and responsibility level of different posts in the approval process. The table takes post category as index, and assigns a unique weight value to each post category. For example, the weight of ordinary employee post is 1, the weight of section leader or supervisor is 2, the weight of department manager is 3, and the weight of vice general manager or general manager is 4.

[0171] The post weight table is set by the administrator in the background according to the management level, approval authority and historical process analysis experience, and can be adjusted according to actual business. When processing the allocation request, the system automatically identifies the post category corresponding to each approval node, finds the corresponding weight value from the table, and provides basic data for subsequent approval chain length and node complexity evaluation.

[0172] This ensures the objectivity and consistency of the approval chain length index and node weight correction process, and facilitates flexible management after future post changes and organizational structure adjustments.

[0173] The preset standard approval duration specifically includes:

[0174] The system sets the standard approval duration of each approval node according to different post categories, material types or historical business data. The standard approval duration refers to the reasonable time required by the system for a post to handle an approval task under normal circumstances, usually in hours.

[0175] For example, the standard approval duration of an ordinary employee node is 2 hours, that of a supervisor is 4 hours, and that of a manager is 8 hours. The standard approval duration is set by the administrator according to the actual operation efficiency and historical approval time of the enterprise, and can be updated periodically.

[0176] In the material allocation approval process, the system automatically compares the average processing time of the actual approval node with the standard approval duration of the corresponding post, to evaluate the node approval efficiency, and adjusts the post weight value accordingly. The setting of standard approval duration helps to quantify the normal work load of each node in the process, and provides a basic reference for identifying abnormal nodes and dynamically optimizing the process.

[0177] When the approval time difference is a non-negative number, the post weight value of the approval node is proportionally and linearly increased, and when the approval time difference is a negative number, the post weight value of the approval node is proportionally and linearly decreased, to generate a corrected post weight value, which is used as the weight coefficient of the approval node. Specifically, the method includes:

[0178] When analyzing the historical approval time consumption of each approval node, the system first calculates the difference between the actual average approval time consumption and the standard approval time length of the node (approval time difference). If the approval time difference is a non-negative number, it means that the actual processing efficiency of the node is low or there is a delay. The system will linearly increase the original post weight value, for example, if the standard time length is exceeded by 1 hour, the weight value will be increased by a certain percentage;

[0179] On the contrary, if the approval time difference is negative, it means that the approval efficiency of the node is better than the standard. The system will linearly decrease the original post weight value, for example, if the approval is completed 1 hour in advance, the post weight value will be reduced by a corresponding percentage.

[0180] After the above dynamic adjustment, the system will use the corrected post weight value as the final weight coefficient of the node to participate in the subsequent calculation of the approval chain length index and the overall allocation priority. This can not only reflect the efficiency changes of the process nodes in real time, but also guide the enterprise to focus on the approval bottlenecks and efficiency improvement points. For example, if a node often exceeds the time limit in the past, its post weight will be continuously increased, and the system will reduce the ranking of the allocation request containing this node in the process when calculating the priority, promoting process optimization.

[0181] Embodiments of the present application also provide a cross-organizational material sharing system, which comprises:

[0182] A data acquisition module is configured to obtain idle material data of each member enterprise, and generate a unique material identifier, a storage location, a current quantity, and historical flow information for the idle material data, thereby forming a material data set;

[0183] A request processing module is configured to read available state information of target material in the material data set according to a material allocation request of a demand member enterprise, and calculate a priority weight of the material allocation request based on an urgency of the material allocation request, a length of an approval chain, and a historical occupation number, thereby obtaining priority parameter data;

[0184] A lock control module is configured to dynamically lock the target material according to the priority parameter data, record a locking time, adjust a state of the target material from an available state to a locked state, and only allow a material allocation request with the highest priority weight to maintain the lock, while rejecting other requests and returning a lock failure prompt;

[0185] A progress monitoring module is configured to continuously monitor the allocation and approval progress of the locked material, determine whether the locked state should be released in advance, trigger a release process when the approval is not completed within a preset period and there is a material allocation request with a higher priority weight, restore the state of the target material from the locked state to the available state, and update the flow information to the material data set;

[0186] The state changing module is configured to change the state of the target material from the locked state to the allocated state when the approval process is completed and the delivery is confirmed by the logistics information, and write all parameters of the current allocation into the historical flow information.

[0187] It should be noted that the system is a system corresponding to the above method, and all the implementation manners in the above method embodiment are applicable to this embodiment and can also achieve the same technical effects.

[0188] Embodiments of the present application also provide a computing device, comprising a processor, a memory storing a computer program, the computer program being executed by the processor to perform the method as described above. All the implementation manners in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0189] Embodiments of the present application also provide a computer readable storage medium storing instructions, when the instructions are executed on a computer, the computer executes the method as described above. All the implementation manners in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0190] The above is the preferred embodiment of the present application, it should be noted that for those skilled in the art, without departing from the principles of the present application, can make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A cross-organizational asset sharing method, characterized by, The method comprises: Obtain the idle material data of each member enterprise, and generate the unique identification, storage location, current quantity and historical flow information of the material to form a material data set; According to the material allocation request of the demand member enterprise, read the available state information of the target material in the material data set, and calculate the priority weight of the material allocation request based on the emergency degree, approval chain length and historical occupation times of the material allocation request to obtain priority parameter data; According to the priority parameter data, the target material is dynamically locked, the locking time is recorded, and the state of the target material is adjusted from the available state to the locked state, only the material allocation request with the highest priority weight is allowed to maintain the lock, and the rest of the requests are rejected and a lock failure prompt is returned; Continuously monitor the allocation and approval progress of the locked material, judge whether the locked state should be released in advance, and when the approval is not completed within the preset period and there is a material allocation request with higher priority weight, trigger the release process, restore the state of the target material from the locked state to the available state, and update the flow information to the material data set; When the approval process is completed and the delivery is confirmed by the logistics information, the state of the target material is adjusted from the locked state to the allocated state, and all parameters of this allocation are written into the historical flow information.

2. The cross-organizational asset sharing method of claim 1, wherein, According to the emergency degree, approval chain length and historical occupation times of the material allocation request, the priority weight of the material allocation request is calculated to obtain priority parameter data, including: According to the material allocation request, the demand completion time limit is extracted, which is compared with the preset standard completion time limit, and the emergency degree is quantified as an emergency index according to the proportion of the shortened completion time limit; Analyze the complete list of approval nodes required by the material allocation request, count the total number of approval nodes, and compare it with the preset approval node quantity benchmark value, and quantify the approval chain length as an approval chain length index according to the amplitude of the increase and decrease of the number of approval nodes; Retrieve the historical flow information of the target material, count the sum of the allocation times and the locking times within the preset period, and quantify it as a historical occupation times index; The emergency index, the approval chain length index and the historical occupation times index are weighted and summed by a preset weighting coefficient to calculate the priority weight to form the priority parameter data and bind it with the corresponding material allocation request.

3. The cross-organizational asset sharing method of claim 1, wherein, According to the priority parameter data, the target material is dynamically locked, the locking time is recorded, and the state of the target material is adjusted from the available state to the locked state, only the material allocation request with the highest priority weight is allowed to maintain the lock, and the rest of the requests are rejected and a lock failure prompt is returned, including: For all target materials in the available state, retrieve the priority parameter data of the corresponding material allocation request, and sort them from high to low according to the priority weight to obtain a material demand priority data set; According to the material demand priority data set, the material allocation request with the highest priority weight is assigned a locking qualification, the state of the corresponding target material is adjusted from the available state to the locked state, and the material allocation request number, locking start time and priority parameter of this locking are recorded in the locking record field of the material data set. In the material demand priority data set, except for the highest priority weight of the material allocation request, the rest of the material allocation request is pushed to the demand member enterprise with a lock prompt, and the material allocation request is not accepted.

4. The cross-organizational asset sharing method of claim 1, wherein, Continuously monitor the progress of the allocation approval of the locked material, determine whether the lock state should be released in advance, and when the approval is not completed within the preset period and there is a higher priority weight of the material allocation request, trigger the release process to restore the target material state from the locked state to the available state, and update the flow information to the material data set, including: For all target materials in the locked state, periodically collect the approval node progress of the associated material allocation request at a preset time interval, and count the cumulative processing time since the lock time; When the cumulative processing time of the approval node progress is greater than the preset maximum approval period, and it is detected that the priority weight of the newly submitted material allocation request of the target material is higher than the priority weight of the current associated material allocation request, trigger the release process and perform the following steps: Restore the target material state from the locked state to the available state, and write the execution time, reason, related approval node information and replaced material allocation request number of this release operation into the historical flow information of the target material; When the state of the target material is restored to the available state, the available target material is dynamically locked again according to the priority parameter data of all pending material allocation requests.

5. The cross-organizational asset sharing method of claim 2, wherein, According to the material allocation request, extract the demand completion time limit, compare it with the preset standard completion time limit, and quantify the urgency according to the proportion of the completion time limit reduction, including: According to the material allocation request, subtract the demand completion time limit from the preset standard completion time limit to obtain the time difference; When the time difference is negative, match the time difference according to the preset classification time interval to obtain the emergency value; When the time difference is negative and the time difference is within the preset extreme value range, the emergency value of the material allocation request is increased to the highest level of emergency value, and is marked as a material allocation request that needs to be responded to in priority; When the time difference is non-negative, the material allocation request is assigned a preset basic emergency value; The final emergency value is used as the emergency index of the corresponding material allocation request and is bound to it.

6. The cross-organizational asset sharing method of claim 2, wherein, Analyze the complete list of approval nodes required by the material allocation request, count the total number of approval nodes, and compare it with the preset number of approval nodes baseline value, and according to the increase and decrease of the number of approval nodes, the length of the approval chain is quantified as the length of the approval chain index, including: According to the material allocation request, analyze its associated approval process, obtain the approval nodes that need to be completed in sequence, and retrieve the corresponding post weight parameters and historical approval time of each approval node to form an approval node data set; According to the approval node data set, calculate the difference between the total number of approval nodes and the preset baseline number of approval nodes, and set the weight coefficient of each approval node through the corresponding post weight parameters and historical approval time of each approval node, and weight correct the total number of approval nodes to generate the length of the approval chain index and associate it with the material allocation request; When there is a special link in the approval chain, a first weight correction coefficient is set according to the special link to adjust the length of the approval chain, and an initially adjusted length of the approval chain is obtained, wherein the special link includes multi-level approval, parallel approval and automatic approval; When the number of abnormal situations of the approval node in the historical data reaches a preset number of abnormalities, a timeout warning is set for the approval node, and a second weight correction coefficient is set to adjust the initially adjusted length of the approval chain, and a second adjusted length of the approval chain is obtained.

7. The cross-organizational asset sharing method of claim 2, wherein, The historical flow information of the target material is searched, the sum of the number of allocations and the number of locks in a preset period is counted, and the sum is quantified as a historical occupation number index, including: The historical flow information of the target material in the preset period is searched, the historical flow information is analyzed, the total number of allocations and the total number of locks of the target material are counted, and the two values are added together as a historical occupation base number; According to the historical flow information, the frequency of abnormal situations is counted, and the abnormal situations are weighted according to a preset abnormal weight to obtain an abnormal correction value; The historical occupation base number and the abnormal correction value are added together to obtain a total historical occupation number, and the total historical occupation number is classified into a corresponding historical occupation number according to a preset classification threshold; The corresponding historical occupation number is used as the historical occupation number index of the current material allocation request of the target material, and is bound to the allocation request.

8. The cross-organizational asset sharing method of claim 2, wherein, The emergency index, the length of the approval chain and the historical occupation number index are weighted and summed by a preset weighting coefficient to calculate a priority weight to form a priority parameter data, including: A base weight coefficient is set for the emergency index, the length of the approval chain and the historical occupation number index respectively; The emergency index, the length of the approval chain and the historical occupation number index are multiplied by their corresponding base weight coefficients respectively to obtain their respective weighted results; The three weighted results are added together to obtain a priority weight value, and the priority weight value is used as the priority parameter data together with the corresponding emergency index, the length of the approval chain and the historical occupation number index.

9. The cross-organizational asset sharing method of claim 6, wherein, A weight coefficient is set for each approval node according to the corresponding post weight parameter and historical approval time, including: According to the post weight parameter corresponding to each approval node, the post category information of each approval node is extracted, and the post weight value of the approval node is obtained according to a preset post weight table; According to the historical approval time corresponding to each approval node, an average processing time is calculated, and the average processing time is compared with a preset standard approval time to obtain an approval time difference; When the approval time difference is a non-negative number, the post weight value of the approval node is proportionally and linearly increased, and when the approval time difference is a negative number, the post weight value of the approval node is proportionally and linearly decreased to generate a corrected post weight value, which is used as the weight coefficient of the approval node.

10. A cross-organizational asset sharing system, characterized by, The system is applied to the method of any one of claims 1 to 9, and the system comprises: A data acquisition module is configured to acquire idle material data of each member enterprise, and generate a unique material identifier, a storage location, a current quantity and historical flow information of the material to form a material data set; The request processing module is configured to read available state information of target materials in the material data set according to a material requisition of a demand member enterprise, and calculate a priority weight of the material requisition based on an emergency degree of the material requisition, a length of an approval chain and a historical occupation number, to obtain priority parameter data; The lock control module is configured to dynamically lock the target materials according to the priority parameter data, record a lock time, adjust a state of the target materials from the available state to a locked state, and only allow a material requisition with the highest priority weight to maintain the lock, and reject and return a lock failure prompt to other requisitions; The progress monitoring module is configured to continuously monitor a requisition and approval progress of the locked materials, judge whether the locked state should be released in advance, trigger a release process when the approval is not completed within a preset period and there is a material requisition with a higher priority weight, restore the state of the target materials from the locked state to the available state, and update flow information to the material data set; The state changing module is configured to adjust the state of the target materials from the locked state to a requisitioned state when the approval process is completed and delivery is confirmed by logistics information, and write all parameters of the requisition into historical flow information.

Images